diff --git a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Ccie Torrent Achieve Your CCIE Goals with Expert Guidance and Support.md b/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Ccie Torrent Achieve Your CCIE Goals with Expert Guidance and Support.md
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-<h1>What is Ccie Torrent?</h1>
-<p>If you are looking for a way to prepare for the Cisco Certified Internetwork Expert (CCIE) certification exams, you might have heard of Ccie Torrent. But what is it exactly and how can it help you?</p>
-<p>Ccie Torrent is a collection of video courses, lab exercises, practice tests, and other resources that are designed to help you master the skills and knowledge required for the CCIE exams. These exams are among the most challenging and prestigious in the IT industry, covering topics such as routing and switching, service provider, security, wireless, data center, collaboration, and enterprise infrastructure.</p>
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-<p>By downloading Ccie Torrent, you can access high-quality and up-to-date materials that are created by experts and instructors who have years of experience in teaching and working with Cisco technologies. You can learn at your own pace, review the concepts as many times as you need, and practice with realistic scenarios that simulate the real exam environment. You can also save money and time by avoiding expensive and inconvenient classroom training.</p>
-<p>In this article, we will show you how to download Ccie Torrent, what are the best sources for it, and how to use it effectively. By following these steps, you will be able to boost your confidence and readiness for the CCIE exams.</p>
- <h2>How to download Ccie Torrent?</h2>
-<p>Before you can download Ccie Torrent, you need to have some requirements and precautions in mind. Here are some tips to ensure a smooth and safe downloading process:</p>
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-<li>You need to have a torrent client installed on your computer. A torrent client is a software that allows you to download files from other users who are sharing them on a peer-to-peer network. Some of the most popular torrent clients are uTorrent, BitTorrent, qBittorrent, Transmission, etc.</li>
-<li>You need to have enough disk space on your computer. Depending on the size and number of files you want to download, you might need several gigabytes or even terabytes of free space on your hard drive. Make sure you have enough space before you start downloading.</li>
-<li>You need to have a reliable and fast internet connection. Downloading large files can take a long time if your internet speed is slow or unstable. You might also experience interruptions or errors if your connection drops or fluctuates. To avoid these issues, use a wired connection instead of wireless, close any unnecessary programs or tabs that consume bandwidth, and avoid peak hours when the network is congested.</li>
-<li>You need to be careful about the sources you download from. Not all torrent websites are trustworthy or legal. Some of them might contain malware, viruses, spyware, or other harmful software that can damage your computer or compromise your privacy. Some of them might also violate copyright laws or ethical standards by distributing pirated or stolen content. To avoid these risks, only download from reputable and verified websites that offer legitimate and high-quality content.</li>
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- <h3>What are the best sources for Ccie Torrent?</h3>
-<p>There are many websites that offer Ccie Torrent, but not all of them are equally good. Some of them might have outdated or incomplete content, poor video quality, broken links, or low seeders (users who share files). To help you find the best sources for Ccie Torrent, we have compiled a list of four reliable and popular websites that offer high-quality content:</p>
- <h4>SolidTorrents</h4>
-<p>SolidTorrents is a torrent search engine that indexes millions of torrents from various sources. It has a simple and user-friendly interface that allows you to filter your results by category, size, date, seeders, leechers (users who download files), etc. You can also sort your results by relevance or popularity.</p>
-<p>One of the advantages of SolidTorrents is that it offers a lot of content related to CCIE Service Provider certification. You can find video courses, lab exercises, practice tests, diagrams, PDFs, etc., that cover topics such as MPLS VPNs (Virtual Private Networks), L2VPN (Layer 2 VPN), MPLS TE (Traffic Engineering), QoS (Quality of Service), Multicast VPNs (MVPN), etc.</p>
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-<p>To download Ccie Torrent from SolidTorrents:</p>
-<ol>
-<li>Go to <a href="https://solidtorrents.to/">https://solidtorrents.to/</a>.</li>
-<li>Type "Ccie" in the search box and press Enter.</li>
-<li>Browse through the results and select the one that matches your needs.</li>
-<li>Click on the "Download" button next to the result.</li>
-<li>Open the downloaded torrent file with your torrent client.</li>
-<li>Wait for the download to complete.</li>
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-<p>UPW.IO is a torrent hosting service that allows users to upload and download torrents without registration or login. It has a minimalist and modern design that makes it easy to use. It also has a fast and secure server that ensures a smooth downloading experience.</p>
-<h4>GitHub</h4>
-<p>GitHub is a platform that hosts and manages software development projects using Git, a version control system. It allows users to collaborate, share, and review code, as well as host and distribute software applications. It has a large and active community of developers and users who contribute to various open-source and proprietary projects.</p>
-<p>One of the advantages of GitHub is that it offers a lot of content related to CCIE Data Center certification. You can find repositories that contain scripts, tools, templates, guides, books, etc., that cover topics such as Cisco ACI (Application Centric Infrastructure), Cisco UCS (Unified Computing System), Cisco Nexus switches, Cisco MDS (Multilayer Director Switches), storage networking, network virtualization, etc.</p>
-<p>To download Ccie Torrent from GitHub:</p>
-<ol>
-<li>Go to <a href="https://github.com/">https://github.com/</a>.</li>
-<li>Type "Ccie" in the search box and press Enter.</li>
-<li>Browse through the results and select the one that matches your needs.</li>
-<li>Click on the "Code" button on the top right corner of the repository.</li>
-<li>Select "Download ZIP" from the dropdown menu.</li>
-<li>Extract the downloaded ZIP file to your desired location.</li>
-</ol>
- <h4>Kbits.Live</h4>
-<p>Kbits.Live is a website that offers online training courses for Cisco certifications. It has a team of experienced and certified instructors who teach live classes via Zoom. It also provides recorded videos, lab exercises, practice tests, study materials, and support forums for students.</p>
-<p>One of the advantages of Kbits.Live is that it offers a lot of content related to CCIE Routing and Switching certification. You can find courses that cover topics such as IP routing (RIP (Routing Information Protocol), EIGRP, OSPF, BGP), IP services (DHCP, NAT, NTP, etc.), IP multicast (IGMP (Internet Group Management Protocol), PIM (Protocol Independent Multicast), etc.), switching technologies (VLANs, STP, EtherChannel, etc.), network security (ACLs, VPNs, IPSec, etc.), network troubleshooting (ping (Packet Internet Groper), traceroute (Trace Route), debug (Debugging), etc.), etc.</p>
-<p>To download Ccie Torrent from Kbits.Live:</p>
-<ol>
-<li>Go to <a href="https://kbits.live/">https://kbits.live/</a>.</li>
-<li>Select the course that matches your needs.</li>
-<li>Click on the "Buy Now" button and complete the payment process.</li>
-<li>Access the course dashboard and download the videos and materials.</li>
-</ol>
- <h2>How to use Ccie Torrent?</h2>
-<p>After you have downloaded Ccie Torrent from one or more sources, you need to know how to use it effectively. Here are some steps to guide you:</p>
- <h3>How to install Ccie Torrent?</h3>
-<p>The installation process of Ccie Torrent depends on the type and format of the files you have downloaded. Some files might be ready to use without any installation, while others might require some additional steps. Here are some general instructions on how to install Ccie Torrent on different operating systems:</p>
- <h4>Windows</h4>
-<p>If you have downloaded video files in MP4 or AVI format, you can play them with any media player such as VLC or Windows Media Player. If you have downloaded PDF files or other documents, you can open them with any PDF reader such as Adobe Acrobat or Microsoft Edge. If you have downloaded ZIP or RAR files, you need to extract them with any compression software such as WinRAR or 7-Zip. If you have downloaded ISO files or other disk images, you need to mount them with any virtual drive software such as Daemon Tools or PowerISO. If you have downloaded EXE files or other executable files, you need to run them with administrator privileges and follow the installation wizard.</p>
- <h4>Linux</h4>
-<p>If you have downloaded video files in MP4 or AVI format, you can play them with any media player such as VLC or MPlayer. If you have downloaded PDF files or other documents, you can open them with any PDF reader such as Evince or Okular. If you have downloaded ZIP or RAR files, you need to extract them with any compression software such as unzip or unrar. If you have downloaded ISO files or other disk images, you need to mount them with any virtual drive software such as Furius ISO Mount or AcetoneISO. If you have downloaded BIN files or other executable files, you need to make them executable with chmod +x command and run them with sudo command.</p>
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-<h4>Mac</h4>
-<p>If you have downloaded video files in MP4 or AVI format, you can play them with any media player such as VLC or QuickTime Player. If you have downloaded PDF files or other documents, you can open them with any PDF reader such as Preview or Adobe Acrobat. If you have downloaded ZIP or RAR files, you need to extract them with any compression software such as The Unarchiver or Keka. If you have downloaded ISO files or other disk images, you need to mount them with any virtual drive software such as DAEMON Tools Lite or VirtualBox. If you have downloaded DMG files or other installer files, you need to double-click them and follow the installation wizard.</p>
- <h3>How to configure Ccie Torrent?</h3>
-<p>The configuration process of Ccie Torrent depends on the type and format of the files you have downloaded. Some files might be ready to use without any configuration, while others might require some additional steps. Here are some general instructions on how to configure Ccie Torrent for optimal performance and security:</p>
- <h4>General settings</h4>
-<p>Some of the general settings that you might want to adjust are:</p>
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-<li>Bandwidth: You can limit the upload and download speed of your torrent client to avoid consuming too much of your internet bandwidth. You can also set the maximum number of connections and peers that your torrent client can handle.</li>
-<li>Encryption: You can enable encryption on your torrent client to protect your data from being intercepted or modified by third parties. Encryption can also help you bypass some firewalls or ISP (Internet Service Provider) restrictions that might block or throttle your torrent traffic.</li>
-<li>Proxy: You can use a proxy server on your torrent client to hide your real IP address and location from other users and trackers. A proxy server can also help you access geo-restricted or censored content that might not be available in your region.</li>
-</ul>
- <h4>Advanced settings</h4>
-<p>Some of the advanced settings that you might want to adjust are:</p>
-<ul>
-<li>Port forwarding: You can enable port forwarding on your router or firewall to allow incoming connections from other users and trackers. Port forwarding can improve your download speed and availability, as well as increase your chances of finding more peers and seeders.</li>
-<li>DHT (Distributed Hash Table): You can enable DHT on your torrent client to find more peers and seeders without relying on trackers. DHT is a decentralized network that stores and distributes information about torrents and their availability.</li>
-<li>Peer exchange: You can enable peer exchange on your torrent client to exchange information about peers and seeders with other users who are downloading the same torrent. Peer exchange can help you find more sources and improve your download speed.</li>
-</ul>
- <h2>Conclusion</h2>
-<p>Ccie Torrent is a great way to prepare for the CCIE certification exams. It offers a lot of high-quality and up-to-date content that covers all the topics and skills required for the exams. By downloading Ccie Torrent from reliable and verified sources, you can access video courses, lab exercises, practice tests, and other resources that will help you master Cisco technologies and boost your confidence and readiness for the exams.</p>
-<p>If you want to download Ccie Torrent, you need to have a torrent client installed on your computer, enough disk space and internet bandwidth, and a careful selection of sources. You also need to know how to install and configure Ccie Torrent for optimal performance and security. By following these steps, you will be able to enjoy a smooth and safe downloading experience.</p>
-<p>We hope this article has helped you understand what is Ccie Torrent, how to download it, what are the best sources for it, and how to use it effectively. If you have any questions or feedback, please feel free to leave a comment below. Thank you for reading and happy learning!</p>
- <h2>Frequently Asked Questions</h2>
-<p>Here are some of the most common questions that people ask about Ccie Torrent:</p>
- <h4>What is CCIE?</h4>
-<p>CCIE stands for Cisco Certified Internetwork Expert. It is a certification program that validates the skills and knowledge of network engineers who can plan, design, implement, operate, troubleshoot, and optimize complex network infrastructures using Cisco technologies.</p>
- <h4>How many CCIE certifications are there?</h4>
-<p>There are eight CCIE certifications available: Routing and Switching, Service Provider, Security, Wireless, Data Center, Collaboration, Enterprise Infrastructure, and Enterprise Wireless.</p>
- <h4>How do I get CCIE certified?</h4>
-<p>To get CCIE certified, you need to pass two exams: a written exam that tests your theoretical knowledge of network concepts and technologies, and a lab exam that tests your practical skills in configuring and troubleshooting network scenarios using real equipment.</p>
- <h4>How much does CCIE certification cost?</h4>
-<p>The cost of CCIE certification varies depending on the exam location and currency. The written exam costs $450 USD per attempt, while the lab exam costs $1600 USD per attempt.</p>
- <h4>How long does CCIE certification last?</h4>
-<p>CCIE certification lasts for three years from the date of passing the lab exam. To maintain your certification status, you need to recertify before the expiration date by passing any current CCIE written exam or lab exam.</p>
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diff --git a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Download MS Project 2019 for Free The Best Project Management Software.md b/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Download MS Project 2019 for Free The Best Project Management Software.md
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diff --git a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Download Arthdal Chronicles Sub Indo Drama Korea Penuh Misteri dan Legenda - Drakorindo.md b/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Download Arthdal Chronicles Sub Indo Drama Korea Penuh Misteri dan Legenda - Drakorindo.md
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- <h3>The unique and captivating story of Arthdal Chronicles</h3>
-<p>Arthdal Chronicles is not your typical Korean drama. It is a rare example of a historical fantasy genre that explores the origins of civilization and humanity. It has a rich and complex plot that spans across different times and places, as well as different races and tribes. It also has a lot of twists and turns that will keep you on the edge of your seat. If you are looking for a refreshing and innovative story that will challenge your imagination and emotions, Arthdal Chronicles is the drama for you.</p>
- <h3>The stunning visuals and performances of Arthdal Chronicles</h3>
-<p>Another reason to watch Arthdal Chronicles is its amazing visuals and performances. The drama showcases some of the best cinematography, special effects, and costumes that create a realistic and immersive world of Arth. The drama also features some of the best actors in Korea, who deliver outstanding performances that portray their characters' personalities, emotions, and conflicts. You will be impressed by the quality and professionalism of the production and the cast of Arthdal Chronicles.</p>
- <h2>How to download Arthdal Chronicles sub Indo from Drakorindo?</h2>
-<p>Now that you know why you should watch Arthdal Chronicles sub Indo, you might wonder how to download it from Drakorindo, one of the most popular and reliable websites for Korean drama downloads. Here are the steps to follow:</p>
- <h3>What is Drakorindo?</h3>
-<p>Drakorindo is a website that provides free downloads of Korean dramas with various subtitles, including Indonesian. It has a large and updated collection of dramas from different genres and networks. It also has a simple and user-friendly interface that makes it easy to find and download your favorite dramas. You can access Drakorindo from any device, such as a computer, a smartphone, or a tablet.</p>
- <h3>The steps to download Arthdal Chronicles sub Indo from Drakorindo</h3>
-<p>To download Arthdal Chronicles sub Indo from Drakorindo, you need to follow these steps:</p>
-<ol>
-<li>Go to the official website of Drakorindo at <a href="">https://drakorindo.cc/</a>.</li>
-<li>Type "Arthdal Chronicles" in the search box and click on the magnifying glass icon.</li>
-<li>Select the episode you want to download from the list of results.</li>
-<li>Scroll down to the bottom of the page and click on the link that says "Download Subtitle Indonesia/English".</li>
-<li>Choose the subtitle language you prefer (Indonesian or English) and click on the download button.</li>
-<li>Save the subtitle file to your device.</li>
-<li>Go back to the previous page and click on the link that says "Download Episode".</li>
-<li>Choose the video quality you prefer (360p, 540p, or 720p) and click on the download button.</li>
-<li>Save the video file to your device.</li>
-<li>Open the video file with a media player that supports subtitles, such as VLC or MX Player.</li>
-<li>Load the subtitle file from your device and enjoy watching Arthdal Chronicles sub Indo.</li>
-</ol>
- <h3>The alternative ways to watch Arthdal Chronicles sub Indo online</h3>
-<p>If you don't want to download Arthdal Chronicles sub Indo from Drakorindo, you can also watch it online from other sources. Here are some of the alternative ways to watch Arthdal Chronicles sub Indo online:</p>
-<ul>
-<li>Netflix: You can stream Arthdal Chronicles sub Indo on Netflix, if you have a subscription. Netflix offers high-quality video and audio, as well as multiple subtitle options. You can also watch Arthdal Chronicles on Netflix offline by downloading it to your device.</li>
-<li>Viu: You can stream Arthdal Chronicles sub Indo on Viu, if you have an account. Viu is a streaming service that specializes in Asian content, including Korean dramas. You can watch Arthdal Chronicles on Viu for free with ads, or upgrade to a premium membership for ad-free viewing and more features.</li>
-<li>Kshowsubindo: You can stream Arthdal Chronicles sub Indo on Kshowsubindo, a website that provides free streaming of Korean shows with subtitles. You can watch Arthdal Chronicles on Kshowsubindo without registration or downloading, but you might encounter some pop-up ads and low-quality videos.</li>
-</ul>
- <h2>Conclusion</h2>
-<p>Arthdal Chronicles is a Korean drama that you should not miss if you love historical fantasy genres. It has a unique and captivating story, stunning visuals and performances, and a lot of benefits for watching it with subtitles in Indonesian. You can download Arthdal Chronicles sub Indo from Drakorindo easily and quickly, or watch it online from other sources. Either way, you will enjoy watching this epic and ambitious drama that will take you to a mythical land of Arth.</p>
- <h2>FAQs</h2>
-<p>Here are some of the frequently asked questions about Arthdal Chronicles sub Indo:</p>
-<ul>
-<li><b>Q: Is Arthdal Chronicles based on a true story?</b></li>
-<li>A: No, Arthdal Chronicles is not based on a true story. It is a fictional story that is loosely inspired by the legend of Dangun, the founder of Gojoseon, the first Korean kingdom.</li>
-<li><b>Q: Is Arthdal Chronicles finished or ongoing?</b></li <li>A: Arthdal Chronicles is finished, but it has a cliffhanger ending that suggests a possible continuation. The producers have stated that they have plans for a second season, but it has not been confirmed or announced yet.</li>
-<li><b>Q: Who is the main character of Arthdal Chronicles?</b></li>
-<li>A: There is no clear main character of Arthdal Chronicles, as the drama has multiple protagonists and antagonists who have their own stories and perspectives. However, some of the most prominent characters are Ta-gon, Eun-seom, Tan-ya, and Tae Al-ha.</li>
-<li><b>Q: How many episodes are there in Arthdal Chronicles?</b></li>
-<li>A: There are 18 episodes in Arthdal Chronicles, divided into three parts. Part 1 and Part 2 have six episodes each, while Part 3 has six episodes as well. Each episode is about 80 minutes long.</li>
-<li><b>Q: Where can I watch Arthdal Chronicles with English subtitles?</b></li>
-<li>A: You can watch Arthdal Chronicles with English subtitles on Netflix, Viu, or Drakorindo. You just need to select the English subtitle option when you stream or download the drama.</li>
-</ul></p> 197e85843d<br />
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diff --git a/spaces/1phancelerku/anime-remove-background/English Movies Download 2017 The Most Popular and Critically Acclaimed Films of the Year.md b/spaces/1phancelerku/anime-remove-background/English Movies Download 2017 The Most Popular and Critically Acclaimed Films of the Year.md
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+++ /dev/null
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-
-<h1>How to Download English Movies from 2017</h1>
-<p>If you are a movie lover, you might be interested in downloading some of the best English movies from 2017. Whether you want to watch them offline, save them for later, or share them with your friends, downloading movies can be a convenient and enjoyable way to enjoy cinema. However, you might also wonder how to download movies safely and legally, without risking your device or breaking the law. In this article, we will show you how to download English movies from 2017, what are the top 10 movies from that year, and what are the best practices for downloading movies online.</p>
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- <h2>Introduction</h2>
-<h3>Why download movies from 2017?</h3>
-<p>2017 was a great year for English movies, with many genres, themes, and styles represented. From action-packed superhero flicks to heartwarming animated features, from dark comedies to thrilling dramas, there was something for everyone in 2017. Some of the movies from that year were critically acclaimed, winning awards and nominations, while others were commercially successful, breaking box office records and becoming cult classics. Downloading movies from 2017 can allow you to revisit some of your favorites, discover new gems, or catch up on what you missed.</p>
- <h3>What are the best sources for downloading movies?</h3>
-<p>There are many sources for downloading movies online, but not all of them are reliable, safe, or legal. Some of the best sources for downloading movies are:</p>
-<ul>
-<li><strong>Streaming platforms:</strong> These are services that offer a library of movies and shows that you can watch online or download to your device. Some examples are Netflix, Amazon Prime Video, Hulu, Disney+, and HBO Max. These platforms usually require a subscription fee or a one-time payment to access their content.</li>
-<li><strong>Online archives:</strong> These are websites that offer free access to public domain or creative commons movies that are not protected by copyright. Some examples are Internet Archive, Open Culture, and Public Domain Torrents. These websites usually have older or independent movies that you can download legally.</li>
-<li><strong>Torrent sites:</strong> These are websites that use peer-to-peer technology to share files among users. Some examples are The Pirate Bay, RARBG, and 1337x. These websites usually have newer or popular movies that you can download illegally.</li>
-</ul>
- <h2>Top 10 English Movies from 2017</h2>
-<h3>Logan</h3>
-<p>Logan is a superhero movie based on the Marvel Comics character Wolverine. It is the tenth installment in the X-Men film series and the third and final film in the Wolverine trilogy. It stars Hugh Jackman as Logan/Wolverine, Patrick Stewart as Charles Xavier/Professor X, and Dafne Keen as Laura/X-23. The movie is set in a dystopian future where mutants are nearly extinct and Logan is an aging and weary loner who must protect a young mutant girl from a sinister organization. Logan is widely regarded as one of the best superhero movies of all time, praised for its dark tone, emotional depth, and powerful performances.</p>
- <h3>Coco</h3>
-<p>Coco is an animated movie produced by Pixar Animation Studios and distributed by Walt Disney Pictures. It is inspired by the Mexican holiday of Día de los Muertos (Day of the Dead). It features the voices of Anthony Gonzalez as Miguel Rivera, Gael García Bernal as Hector Rivera, Benjamin Bratt as Ernesto de la Cruz, and Alanna Ub <p>ach as Imelda Rivera. The movie follows Miguel, a young boy who dreams of becoming a musician, as he journeys into the Land of the Dead to find his great-great-grandfather and learn the truth about his family's history. Coco is widely regarded as one of the best animated movies of all time, praised for its colorful animation, musical score, cultural representation, and emotional impact.</p>
- <h3>Thor: Ragnarok</h3>
-<p>Thor: Ragnarok is a superhero movie based on the Marvel Comics character Thor. It is the third installment in the Thor film series and the seventeenth film in the Marvel Cinematic Universe. It stars Chris Hemsworth as Thor, Tom Hiddleston as Loki, Cate Blanchett as Hela, Mark Ruffalo as Bruce Banner/Hulk, and Tessa Thompson as Valkyrie. The movie follows Thor as he tries to stop Hela, the goddess of death, from destroying his home world of Asgard and triggering the prophesied Ragnarok, the end of all things. Thor: Ragnarok is widely regarded as one of the best superhero movies of all time, praised for its humor, action, visuals, and direction.</p>
- <h3>The Meyerowitz Stories</h3>
-<p>The Meyerowitz Stories is a comedy-drama movie written and directed by Noah Baumbach. It stars Adam Sandler, Ben Stiller, Dustin Hoffman, Elizabeth Marvel, and Emma Thompson. The movie follows the dysfunctional Meyerowitz family as they reunite to celebrate their father's artistic legacy and cope with his declining health. The Meyerowitz Stories is widely regarded as one of the best comedy-drama movies of all time, praised for its witty script, realistic characters, and poignant themes.</p>
- <h3>Wind River</h3>
-<p>Wind River is a neo-Western thriller movie written and directed by Taylor Sheridan. It stars Jeremy Renner as Cory Lambert, Elizabeth Olsen as Jane Banner, and Gil Birmingham as Martin Hanson. The movie follows Cory, a wildlife tracker and hunter, and Jane, an FBI agent, as they investigate the murder of a young Native American woman on the Wind River Indian Reservation in Wyoming. Wind River is widely regarded as one of the best thriller movies of all time, praised for its suspenseful plot, atmospheric setting, and social commentary.</p>
- <h3>Dunkirk</h3>
-<p>Dunkirk is a war movie written and directed by Christopher Nolan. It stars Fionn Whitehead, Tom Glynn-Carney, Jack Lowden, Harry Styles, Aneurin Barnard, James D'Arcy, Barry Keoghan, Kenneth Branagh, Cillian Murphy, Mark Rylance, and Tom Hardy. The movie depicts the Dunkirk evacuation of World War II from three perspectives: land, sea, and air. It shows the struggles and sacrifices of the Allied soldiers and civilians who were trapped on the beaches of Dunkirk and rescued by a flotilla of boats. Dunkirk is widely regarded as one of the best war movies of all time, praised for its immersive cinematography, sound design, editing, and storytelling.</p>
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- <h3>Get Out</h3>
-<p>Get Out is a horror movie written and directed by Jordan Peele. It stars Daniel Kaluuya as Chris Washington, Allison Williams as Rose Armitage, Lil Rel Howery as Rod Williams, Bradley Whitford as Dean Armitage, Caleb Landry Jones as Jeremy Armitage, Stephen Root as Jim Hudson, and Catherine Keener as Missy Armitage. The movie follows Chris, a young black man, who visits the family of his white girlfriend, Rose, and uncovers a horrifying secret involving their true intentions. Get Out is widely regarded as one of the best horror movies of all time, praised for its originality, satire, social critique, and performances.</p>
- <h3>The Shape of Water</h3>
-<p>The Shape of Water is a fantasy romance movie directed by Guillermo del Toro and written by del Toro and Vanessa Taylor. It stars Sally Hawkins as Elisa Esposito, Doug Jones as the Amphibian Man, Michael Shannon as Richard Strickland, Richard Jenkins as Giles, Octavia Spencer as Zelda Fuller, and Michael Stuhlbarg as Robert Hoffstetler. The movie is set in the Cold War era and follows Elisa, a mute janitor who works at a secret government facility, where she forms a bond with a mysterious aquatic creature that is held captive there. The Shape of Water is widely regarded as one of the best fantasy romance movies of all time, praised for its visual style, direction, score, and themes.</p>
- <h3>Lady Bird</h3>
-<p>Lady Bird is a coming-of-age comedy-drama movie written and directed by Greta Gerwig. It stars Saoirse Ronan as Christine "Lady Bird" McPherson, Laurie Metcalf as Marion McPherson, Tracy Letts as Larry McPherson, Lucas Hedges as Danny O'Neill, Timothée Chalamet as Kyle Scheible, Beanie Feldstein as Julianne "Julie" Steffans, and Lois Smith as Sister Sarah Joan. The movie follows Lady Bird, a rebellious and artistic teenager who navigates her senior year of high school in Sacramento, California in 2002. Lady Bird is widely regarded as one of the best coming-of-age movies of all time, praised for its humor, authenticity, and performances.</p>
- <h3>Three Billboards Outside Ebbing, Missouri</h3>
-<p>Three Billboards Outside Ebbing, Missouri is a black comedy-drama movie written and directed by Martin McDonagh. It stars Frances McDormand as Mildred Hayes, Woody Harrelson as Bill Willoughby, Sam Rockwell as Jason Dixon, John Hawkes as Charlie Hayes, and Peter Dinklage as James. The movie follows Mildred, a grieving mother who rents three billboards to call attention to the unsolved murder of her daughter and the lack of action by the local police. Three Billboards Outside Ebbing, Missouri is widely regarded as one of the best black comedy-drama movies of all time, praised for its sharp dialogue, dark humor, and performances.</p>
- <h2>How to Download Movies Safely and Legally</h2>
-<h3>Use a VPN service</h3>
-<p>A VPN (Virtual Private Network) is a service that encrypts your internet traffic and hides your IP address, making you anonymous and secure online. A VPN can help you download movies safely and legally by:</p>
-<ul>
-<li><strong>Bypassing geo-restrictions:</strong> A VPN can help you access streaming platforms or online archives that are not available in your region, such as Netflix US or BBC iPlayer.</li>
-<li><strong>Protecting your privacy:</strong> A VPN can help you avoid being tracked or monitored by your ISP (Internet Service Provider), government agencies, hackers, or advertisers.</li>
-<li><strong>Preventing legal issues:</strong> A VPN can help you avoid getting into trouble for downloading movies illegally from torrent sites, as it can mask your identity and location.</li>
-</ul>
-<p>Some of the best VPN services for downloading movies are ExpressVPN, NordVPN, Surfshark, and CyberGhost.</p>
- <h3>Choose a reputable site or platform</h3>
-<p>Another way to download movies safely and legally is to choose a reputable site or platform that offers high-quality and legal content. Some of the factors to consider when choosing a site or platform are:</p>
-<ul>
-<li><strong>Licensing and legality:</strong> Make sure that the site or platform has the rights to distribute the movies that you want to download, and that it complies with the laws of your country.</li>
-<li><strong>Selection and variety:</strong> Make sure that the site or platform has a large and diverse library of movies that suits your preferences and tastes.</li>
-<li><strong>Quality and compatibility:</strong> Make sure that the site or platform offers movies in high resolution and with good audio quality, and that they are compatible with your device and media player.</li>
-<li><strong>Customer service and support:</strong> Make sure that the site or platform has a reliable and responsive customer service and support team that can help you with any issues or questions.</li>
-</ul>
- <h3>Check the file format and size</h3>
-<p>A third way to download movies safely and legally is to check the file format and size before downloading them. Some of the factors to consider when checking the file format and size are:</p>
-<ul>
-<li><strong>File format:</strong> Make sure that the file format is compatible with your device and media player. Some of the most common file formats for movies are MP4, MKV, AVI, MOV, and WMV.</li>
-<li><strong>File size:</strong> Make sure that the file size is not too large or too small for the quality and duration of the movie. A larger file size usually means a higher quality, but it also takes longer to download and consumes more storage space. A smaller file size usually means a lower quality, but it also takes less time to download and consumes less storage space. A good rule of thumb is to aim for a file size of around 1 GB per hour of movie.</li>
-</ul>
- <h3>Scan the file for viruses or malware</h3>
-<p>A fourth way to download movies safely and legally is to scan the file for viruses or malware before opening or playing them. Some of the risks of downloading movies from untrusted sources are:</p>
-<ul>
-<li><strong>Viruses:</strong> These are malicious programs that can infect your device and damage your system, files, or data.</li>
-<li><strong>M <p>alware:</strong> These are malicious programs that can spy on your device and steal your personal information, such as passwords, credit card numbers, or identity.</li>
-<li><strong>Adware:</strong> These are malicious programs that can display unwanted ads on your device and redirect your browser to unwanted sites.</li>
-<li><strong>Ransomware:</strong> These are malicious programs that can lock your device or files and demand a ransom to unlock them.</li>
-</ul>
-<p>To avoid these risks, you should always scan the file for viruses or malware before opening or playing them. You can use a reliable antivirus or anti-malware software, such as Norton, McAfee, Kaspersky, or Malwarebytes, to scan the file and remove any threats.</p>
- <h2>Conclusion</h2>
-<p>Downloading English movies from 2017 can be a fun and rewarding way to enjoy some of the best cinema of that year. However, you should also be careful and responsible when downloading movies online, as there are many risks and challenges involved. By following the tips and steps in this article, you can download movies safely and legally, and avoid any problems or issues. Happy watching!</p>
- <h2>FAQs</h2>
-<p>Here are some of the frequently asked questions about downloading English movies from 2017:</p>
-<ol>
-<li><strong>What is the best streaming platform for downloading English movies from 2017?</strong></li>
-<p>The answer to this question depends on your preferences, budget, and availability. However, some of the most popular and reputable streaming platforms for downloading English movies from 2017 are Netflix, Amazon Prime Video, Hulu, Disney+, and HBO Max. These platforms offer a wide range of movies from different genres, styles, and countries, as well as original and exclusive content. They also offer high-quality and legal downloads, as well as other features such as offline viewing, multiple devices, parental controls, and subtitles.</p>
-<li><strong>What is the best online archive for downloading English movies from 2017?</strong></li>
-<p>The answer to this question depends on your interests, tastes, and curiosity. However, some of the most interesting and reliable online archives for downloading English movies from 2017 are Internet Archive, Open Culture, and Public Domain Torrents. These websites offer free access to public domain or creative commons movies that are not protected by copyright. They also offer a variety of movies from different eras, cultures, and genres, as well as rare and independent movies that you might not find elsewhere.</p>
-<li><strong>What is the best torrent site for downloading English movies from 2017?</strong></li>
-<p>The answer to this question depends on your risk tolerance, ethics, and legality. However, some of the most popular and notorious torrent sites for downloading English movies from 2017 are The Pirate Bay, RARBG, and 1337x. These websites offer a huge collection of movies from different sources, qualities, and languages, as well as fast and easy downloads. However, they also pose many dangers and challenges, such as viruses, malware, legal issues, and ethical dilemmas.</p>
-<li><strong>How can I download movies faster and easier?</strong></li>
-<p>There are some tips and tricks that can help you download movies faster and easier, such as:</p>
-<ul>
-<li><strong>Use a download manager:</strong> A download manager is a software that can help you manage, organize, and accelerate your downloads. Some examples are Internet Download Manager, Free Download Manager, and JDownloader.</li>
-<li><strong>Use a torrent client:</strong> A torrent client is a software that can help you download files from torrent sites. Some examples are BitTorrent, uTorrent, and qBittorrent.</li>
-<li><strong>Use a Wi-Fi connection:</strong> A Wi-Fi connection is usually faster and more stable than a mobile data connection. It can also help you save your data plan and avoid extra charges.</li>
-<li><strong>Choose the right time:</strong> The speed and availability of downloads can vary depending on the time of the day, the traffic of the site or platform, and the demand of the movie. It is usually better to download movies during off-peak hours, such as late at night or early in the morning.</li>
-</ul>
-<li><strong>How can I watch downloaded movies on my TV?</strong></li>
-<p>There are some ways that you can watch downloaded movies on your TV, such as:</p>
-<ul>
-<li><strong>Use a HDMI cable:</strong> A HDMI cable is a cable that can connect your device to your TV and transmit audio and video signals. You can use a HDMI cable to connect your laptop, tablet, or smartphone to your TV and play the downloaded movie on your device.</li>
-<li><strong>Use a streaming device:</strong> A streaming device is a device that can connect to your TV and stream content from the internet or your device. Some examples are Chromecast, Roku, Fire TV Stick, and Apple TV. You can use a streaming device to cast or mirror the downloaded movie from your device to your TV.</li>
-<li><strong>Use a USB drive:</strong> A USB drive is a device that can store data and plug into your TV or other devices. You can use a USB drive to transfer the downloaded movie from your device to your TV and play it using the TV's media player.</li>
-</ul>
-</ol></p> 401be4b1e0<br />
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diff --git a/spaces/232labs/VToonify/vtoonify/model/raft/core/extractor.py b/spaces/232labs/VToonify/vtoonify/model/raft/core/extractor.py
deleted file mode 100644
index 9a9c759d1243d4694e8656c2f6f8a37e53edd009..0000000000000000000000000000000000000000
--- a/spaces/232labs/VToonify/vtoonify/model/raft/core/extractor.py
+++ /dev/null
@@ -1,267 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-
-class ResidualBlock(nn.Module):
-    def __init__(self, in_planes, planes, norm_fn='group', stride=1):
-        super(ResidualBlock, self).__init__()
-  
-        self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, padding=1, stride=stride)
-        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, padding=1)
-        self.relu = nn.ReLU(inplace=True)
-
-        num_groups = planes // 8
-
-        if norm_fn == 'group':
-            self.norm1 = nn.GroupNorm(num_groups=num_groups, num_channels=planes)
-            self.norm2 = nn.GroupNorm(num_groups=num_groups, num_channels=planes)
-            if not stride == 1:
-                self.norm3 = nn.GroupNorm(num_groups=num_groups, num_channels=planes)
-        
-        elif norm_fn == 'batch':
-            self.norm1 = nn.BatchNorm2d(planes)
-            self.norm2 = nn.BatchNorm2d(planes)
-            if not stride == 1:
-                self.norm3 = nn.BatchNorm2d(planes)
-        
-        elif norm_fn == 'instance':
-            self.norm1 = nn.InstanceNorm2d(planes)
-            self.norm2 = nn.InstanceNorm2d(planes)
-            if not stride == 1:
-                self.norm3 = nn.InstanceNorm2d(planes)
-
-        elif norm_fn == 'none':
-            self.norm1 = nn.Sequential()
-            self.norm2 = nn.Sequential()
-            if not stride == 1:
-                self.norm3 = nn.Sequential()
-
-        if stride == 1:
-            self.downsample = None
-        
-        else:    
-            self.downsample = nn.Sequential(
-                nn.Conv2d(in_planes, planes, kernel_size=1, stride=stride), self.norm3)
-
-
-    def forward(self, x):
-        y = x
-        y = self.relu(self.norm1(self.conv1(y)))
-        y = self.relu(self.norm2(self.conv2(y)))
-
-        if self.downsample is not None:
-            x = self.downsample(x)
-
-        return self.relu(x+y)
-
-
-
-class BottleneckBlock(nn.Module):
-    def __init__(self, in_planes, planes, norm_fn='group', stride=1):
-        super(BottleneckBlock, self).__init__()
-  
-        self.conv1 = nn.Conv2d(in_planes, planes//4, kernel_size=1, padding=0)
-        self.conv2 = nn.Conv2d(planes//4, planes//4, kernel_size=3, padding=1, stride=stride)
-        self.conv3 = nn.Conv2d(planes//4, planes, kernel_size=1, padding=0)
-        self.relu = nn.ReLU(inplace=True)
-
-        num_groups = planes // 8
-
-        if norm_fn == 'group':
-            self.norm1 = nn.GroupNorm(num_groups=num_groups, num_channels=planes//4)
-            self.norm2 = nn.GroupNorm(num_groups=num_groups, num_channels=planes//4)
-            self.norm3 = nn.GroupNorm(num_groups=num_groups, num_channels=planes)
-            if not stride == 1:
-                self.norm4 = nn.GroupNorm(num_groups=num_groups, num_channels=planes)
-        
-        elif norm_fn == 'batch':
-            self.norm1 = nn.BatchNorm2d(planes//4)
-            self.norm2 = nn.BatchNorm2d(planes//4)
-            self.norm3 = nn.BatchNorm2d(planes)
-            if not stride == 1:
-                self.norm4 = nn.BatchNorm2d(planes)
-        
-        elif norm_fn == 'instance':
-            self.norm1 = nn.InstanceNorm2d(planes//4)
-            self.norm2 = nn.InstanceNorm2d(planes//4)
-            self.norm3 = nn.InstanceNorm2d(planes)
-            if not stride == 1:
-                self.norm4 = nn.InstanceNorm2d(planes)
-
-        elif norm_fn == 'none':
-            self.norm1 = nn.Sequential()
-            self.norm2 = nn.Sequential()
-            self.norm3 = nn.Sequential()
-            if not stride == 1:
-                self.norm4 = nn.Sequential()
-
-        if stride == 1:
-            self.downsample = None
-        
-        else:    
-            self.downsample = nn.Sequential(
-                nn.Conv2d(in_planes, planes, kernel_size=1, stride=stride), self.norm4)
-
-
-    def forward(self, x):
-        y = x
-        y = self.relu(self.norm1(self.conv1(y)))
-        y = self.relu(self.norm2(self.conv2(y)))
-        y = self.relu(self.norm3(self.conv3(y)))
-
-        if self.downsample is not None:
-            x = self.downsample(x)
-
-        return self.relu(x+y)
-
-class BasicEncoder(nn.Module):
-    def __init__(self, output_dim=128, norm_fn='batch', dropout=0.0):
-        super(BasicEncoder, self).__init__()
-        self.norm_fn = norm_fn
-
-        if self.norm_fn == 'group':
-            self.norm1 = nn.GroupNorm(num_groups=8, num_channels=64)
-            
-        elif self.norm_fn == 'batch':
-            self.norm1 = nn.BatchNorm2d(64)
-
-        elif self.norm_fn == 'instance':
-            self.norm1 = nn.InstanceNorm2d(64)
-
-        elif self.norm_fn == 'none':
-            self.norm1 = nn.Sequential()
-
-        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3)
-        self.relu1 = nn.ReLU(inplace=True)
-
-        self.in_planes = 64
-        self.layer1 = self._make_layer(64,  stride=1)
-        self.layer2 = self._make_layer(96, stride=2)
-        self.layer3 = self._make_layer(128, stride=2)
-
-        # output convolution
-        self.conv2 = nn.Conv2d(128, output_dim, kernel_size=1)
-
-        self.dropout = None
-        if dropout > 0:
-            self.dropout = nn.Dropout2d(p=dropout)
-
-        for m in self.modules():
-            if isinstance(m, nn.Conv2d):
-                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
-            elif isinstance(m, (nn.BatchNorm2d, nn.InstanceNorm2d, nn.GroupNorm)):
-                if m.weight is not None:
-                    nn.init.constant_(m.weight, 1)
-                if m.bias is not None:
-                    nn.init.constant_(m.bias, 0)
-
-    def _make_layer(self, dim, stride=1):
-        layer1 = ResidualBlock(self.in_planes, dim, self.norm_fn, stride=stride)
-        layer2 = ResidualBlock(dim, dim, self.norm_fn, stride=1)
-        layers = (layer1, layer2)
-        
-        self.in_planes = dim
-        return nn.Sequential(*layers)
-
-
-    def forward(self, x):
-
-        # if input is list, combine batch dimension
-        is_list = isinstance(x, tuple) or isinstance(x, list)
-        if is_list:
-            batch_dim = x[0].shape[0]
-            x = torch.cat(x, dim=0)
-
-        x = self.conv1(x)
-        x = self.norm1(x)
-        x = self.relu1(x)
-
-        x = self.layer1(x)
-        x = self.layer2(x)
-        x = self.layer3(x)
-
-        x = self.conv2(x)
-
-        if self.training and self.dropout is not None:
-            x = self.dropout(x)
-
-        if is_list:
-            x = torch.split(x, [batch_dim, batch_dim], dim=0)
-
-        return x
-
-
-class SmallEncoder(nn.Module):
-    def __init__(self, output_dim=128, norm_fn='batch', dropout=0.0):
-        super(SmallEncoder, self).__init__()
-        self.norm_fn = norm_fn
-
-        if self.norm_fn == 'group':
-            self.norm1 = nn.GroupNorm(num_groups=8, num_channels=32)
-            
-        elif self.norm_fn == 'batch':
-            self.norm1 = nn.BatchNorm2d(32)
-
-        elif self.norm_fn == 'instance':
-            self.norm1 = nn.InstanceNorm2d(32)
-
-        elif self.norm_fn == 'none':
-            self.norm1 = nn.Sequential()
-
-        self.conv1 = nn.Conv2d(3, 32, kernel_size=7, stride=2, padding=3)
-        self.relu1 = nn.ReLU(inplace=True)
-
-        self.in_planes = 32
-        self.layer1 = self._make_layer(32,  stride=1)
-        self.layer2 = self._make_layer(64, stride=2)
-        self.layer3 = self._make_layer(96, stride=2)
-
-        self.dropout = None
-        if dropout > 0:
-            self.dropout = nn.Dropout2d(p=dropout)
-        
-        self.conv2 = nn.Conv2d(96, output_dim, kernel_size=1)
-
-        for m in self.modules():
-            if isinstance(m, nn.Conv2d):
-                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
-            elif isinstance(m, (nn.BatchNorm2d, nn.InstanceNorm2d, nn.GroupNorm)):
-                if m.weight is not None:
-                    nn.init.constant_(m.weight, 1)
-                if m.bias is not None:
-                    nn.init.constant_(m.bias, 0)
-
-    def _make_layer(self, dim, stride=1):
-        layer1 = BottleneckBlock(self.in_planes, dim, self.norm_fn, stride=stride)
-        layer2 = BottleneckBlock(dim, dim, self.norm_fn, stride=1)
-        layers = (layer1, layer2)
-    
-        self.in_planes = dim
-        return nn.Sequential(*layers)
-
-
-    def forward(self, x):
-
-        # if input is list, combine batch dimension
-        is_list = isinstance(x, tuple) or isinstance(x, list)
-        if is_list:
-            batch_dim = x[0].shape[0]
-            x = torch.cat(x, dim=0)
-
-        x = self.conv1(x)
-        x = self.norm1(x)
-        x = self.relu1(x)
-
-        x = self.layer1(x)
-        x = self.layer2(x)
-        x = self.layer3(x)
-        x = self.conv2(x)
-
-        if self.training and self.dropout is not None:
-            x = self.dropout(x)
-
-        if is_list:
-            x = torch.split(x, [batch_dim, batch_dim], dim=0)
-
-        return x
diff --git a/spaces/4H17Joycelyn/text_generater/app.py b/spaces/4H17Joycelyn/text_generater/app.py
deleted file mode 100644
index 26292935892fc29d8ad978b0d3d25afe7cea6f63..0000000000000000000000000000000000000000
--- a/spaces/4H17Joycelyn/text_generater/app.py
+++ /dev/null
@@ -1,10 +0,0 @@
-import gradio as gr
-from transformers import pipeline
-
-generator = pipeline('text-generation', model='gpt2')
-
-def generate(text):
-    result=generator(text)
-    return result[0]['generated_text']
-
-gr.Interface(fn=generate, inputs=gr.inputs.Textbox(), outputs=gr.outputs.Textbox()).launch()
\ No newline at end of file
diff --git a/spaces/7hao/bingo/src/components/ui/codeblock.tsx b/spaces/7hao/bingo/src/components/ui/codeblock.tsx
deleted file mode 100644
index aabda4e3b59f4e36b6ab79feb19d8d18b70e881b..0000000000000000000000000000000000000000
--- a/spaces/7hao/bingo/src/components/ui/codeblock.tsx
+++ /dev/null
@@ -1,142 +0,0 @@
-'use client'
-
-import { FC, memo } from 'react'
-import { Prism as SyntaxHighlighter } from 'react-syntax-highlighter'
-import { coldarkDark } from 'react-syntax-highlighter/dist/cjs/styles/prism'
-
-import { useCopyToClipboard } from '@/lib/hooks/use-copy-to-clipboard'
-import { IconCheck, IconCopy, IconDownload } from '@/components/ui/icons'
-import { Button } from '@/components/ui/button'
-
-interface Props {
-  language: string
-  value: string
-}
-
-interface languageMap {
-  [key: string]: string | undefined
-}
-
-export const programmingLanguages: languageMap = {
-  javascript: '.js',
-  python: '.py',
-  java: '.java',
-  c: '.c',
-  cpp: '.cpp',
-  'c++': '.cpp',
-  'c#': '.cs',
-  ruby: '.rb',
-  php: '.php',
-  swift: '.swift',
-  'objective-c': '.m',
-  kotlin: '.kt',
-  typescript: '.ts',
-  go: '.go',
-  perl: '.pl',
-  rust: '.rs',
-  scala: '.scala',
-  haskell: '.hs',
-  lua: '.lua',
-  shell: '.sh',
-  sql: '.sql',
-  html: '.html',
-  css: '.css'
-  // add more file extensions here, make sure the key is same as language prop in CodeBlock.tsx component
-}
-
-export const generateRandomString = (length: number, lowercase = false) => {
-  const chars = 'ABCDEFGHJKLMNPQRSTUVWXY3456789' // excluding similar looking characters like Z, 2, I, 1, O, 0
-  let result = ''
-  for (let i = 0; i < length; i++) {
-    result += chars.charAt(Math.floor(Math.random() * chars.length))
-  }
-  return lowercase ? result.toLowerCase() : result
-}
-
-const CodeBlock: FC<Props> = memo(({ language, value }) => {
-  const { isCopied, copyToClipboard } = useCopyToClipboard({ timeout: 2000 })
-
-  const downloadAsFile = () => {
-    if (typeof window === 'undefined') {
-      return
-    }
-    const fileExtension = programmingLanguages[language] || '.file'
-    const suggestedFileName = `file-${generateRandomString(
-      3,
-      true
-    )}${fileExtension}`
-    const fileName = window.prompt('Enter file name' || '', suggestedFileName)
-
-    if (!fileName) {
-      // User pressed cancel on prompt.
-      return
-    }
-
-    const blob = new Blob([value], { type: 'text/plain' })
-    const url = URL.createObjectURL(blob)
-    const link = document.createElement('a')
-    link.download = fileName
-    link.href = url
-    link.style.display = 'none'
-    document.body.appendChild(link)
-    link.click()
-    document.body.removeChild(link)
-    URL.revokeObjectURL(url)
-  }
-
-  const onCopy = () => {
-    if (isCopied) return
-    copyToClipboard(value)
-  }
-
-  return (
-    <div className="codeblock relative w-full bg-zinc-950 font-sans">
-      <div className="flex w-full items-center justify-between bg-zinc-800 px-6 py-2 pr-4 text-zinc-100">
-        <span className="text-xs lowercase">{language}</span>
-        <div className="flex items-center space-x-1">
-          <Button
-            variant="ghost"
-            className="hover:bg-zinc-800 focus-visible:ring-1 focus-visible:ring-slate-700 focus-visible:ring-offset-0"
-            onClick={downloadAsFile}
-            size="icon"
-          >
-            <IconDownload />
-            <span className="sr-only">Download</span>
-          </Button>
-          <Button
-            variant="ghost"
-            size="icon"
-            className="text-xs hover:bg-zinc-800 focus-visible:ring-1 focus-visible:ring-slate-700 focus-visible:ring-offset-0"
-            onClick={onCopy}
-          >
-            {isCopied ? <IconCheck /> : <IconCopy />}
-            <span className="sr-only">Copy code</span>
-          </Button>
-        </div>
-      </div>
-      <SyntaxHighlighter
-        language={language}
-        style={coldarkDark}
-        PreTag="div"
-        showLineNumbers
-        customStyle={{
-          margin: 0,
-          width: '100%',
-          background: 'transparent',
-          padding: '1.5rem 1rem'
-        }}
-        codeTagProps={{
-          style: {
-            fontSize: '0.9rem',
-            fontFamily: 'var(--font-mono)'
-          }
-        }}
-      >
-        {value}
-      </SyntaxHighlighter>
-    </div>
-  )
-})
-CodeBlock.displayName = 'CodeBlock'
-
-export { CodeBlock }
diff --git a/spaces/AI-Hobbyist/Hoyo-RVC/infer_pack/modules/F0Predictor/DioF0Predictor.py b/spaces/AI-Hobbyist/Hoyo-RVC/infer_pack/modules/F0Predictor/DioF0Predictor.py
deleted file mode 100644
index eb60d8830714338448be009d1075e3594337db15..0000000000000000000000000000000000000000
--- a/spaces/AI-Hobbyist/Hoyo-RVC/infer_pack/modules/F0Predictor/DioF0Predictor.py
+++ /dev/null
@@ -1,90 +0,0 @@
-from infer_pack.modules.F0Predictor.F0Predictor import F0Predictor
-import pyworld
-import numpy as np
-
-
-class DioF0Predictor(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 resize_f0(self, x, target_len):
-        source = np.array(x)
-        source[source < 0.001] = np.nan
-        target = np.interp(
-            np.arange(0, len(source) * target_len, len(source)) / target_len,
-            np.arange(0, len(source)),
-            source,
-        )
-        res = np.nan_to_num(target)
-        return res
-
-    def compute_f0(self, wav, p_len=None):
-        if p_len is None:
-            p_len = wav.shape[0] // self.hop_length
-        f0, t = pyworld.dio(
-            wav.astype(np.double),
-            fs=self.sampling_rate,
-            f0_floor=self.f0_min,
-            f0_ceil=self.f0_max,
-            frame_period=1000 * self.hop_length / self.sampling_rate,
-        )
-        f0 = pyworld.stonemask(wav.astype(np.double), f0, t, self.sampling_rate)
-        for index, pitch in enumerate(f0):
-            f0[index] = round(pitch, 1)
-        return self.interpolate_f0(self.resize_f0(f0, p_len))[0]
-
-    def compute_f0_uv(self, wav, p_len=None):
-        if p_len is None:
-            p_len = wav.shape[0] // self.hop_length
-        f0, t = pyworld.dio(
-            wav.astype(np.double),
-            fs=self.sampling_rate,
-            f0_floor=self.f0_min,
-            f0_ceil=self.f0_max,
-            frame_period=1000 * self.hop_length / self.sampling_rate,
-        )
-        f0 = pyworld.stonemask(wav.astype(np.double), f0, t, self.sampling_rate)
-        for index, pitch in enumerate(f0):
-            f0[index] = round(pitch, 1)
-        return self.interpolate_f0(self.resize_f0(f0, p_len))
diff --git a/spaces/AchyuthGamer/OpenGPT-Chat-UI/src/lib/types/Message.ts b/spaces/AchyuthGamer/OpenGPT-Chat-UI/src/lib/types/Message.ts
deleted file mode 100644
index 61e517da14ff331f268b308c73293e3ef706dd5a..0000000000000000000000000000000000000000
--- a/spaces/AchyuthGamer/OpenGPT-Chat-UI/src/lib/types/Message.ts
+++ /dev/null
@@ -1,10 +0,0 @@
-import type { Timestamps } from "./Timestamps";
-
-export type Message = Partial<Timestamps> & {
-	from: "user" | "assistant";
-	id: ReturnType<typeof crypto.randomUUID>;
-	content: string;
-	webSearchId?: string;
-	score?: -1 | 0 | 1;
-	isCode: boolean;
-};
diff --git a/spaces/Adapter/T2I-Adapter/ldm/modules/encoders/adapter.py b/spaces/Adapter/T2I-Adapter/ldm/modules/encoders/adapter.py
deleted file mode 100644
index 0eef97edcaca1186835f32dc1b0c7bcb9c4bd3ec..0000000000000000000000000000000000000000
--- a/spaces/Adapter/T2I-Adapter/ldm/modules/encoders/adapter.py
+++ /dev/null
@@ -1,258 +0,0 @@
-import torch
-import torch.nn as nn
-from collections import OrderedDict
-
-
-def conv_nd(dims, *args, **kwargs):
-    """
-    Create a 1D, 2D, or 3D convolution module.
-    """
-    if dims == 1:
-        return nn.Conv1d(*args, **kwargs)
-    elif dims == 2:
-        return nn.Conv2d(*args, **kwargs)
-    elif dims == 3:
-        return nn.Conv3d(*args, **kwargs)
-    raise ValueError(f"unsupported dimensions: {dims}")
-
-
-def avg_pool_nd(dims, *args, **kwargs):
-    """
-    Create a 1D, 2D, or 3D average pooling module.
-    """
-    if dims == 1:
-        return nn.AvgPool1d(*args, **kwargs)
-    elif dims == 2:
-        return nn.AvgPool2d(*args, **kwargs)
-    elif dims == 3:
-        return nn.AvgPool3d(*args, **kwargs)
-    raise ValueError(f"unsupported dimensions: {dims}")
-
-
-class Downsample(nn.Module):
-    """
-    A downsampling layer with an optional convolution.
-    :param channels: channels in the inputs and outputs.
-    :param use_conv: a bool determining if a convolution is applied.
-    :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
-                 downsampling occurs in the inner-two dimensions.
-    """
-
-    def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1):
-        super().__init__()
-        self.channels = channels
-        self.out_channels = out_channels or channels
-        self.use_conv = use_conv
-        self.dims = dims
-        stride = 2 if dims != 3 else (1, 2, 2)
-        if use_conv:
-            self.op = conv_nd(
-                dims, self.channels, self.out_channels, 3, stride=stride, padding=padding
-            )
-        else:
-            assert self.channels == self.out_channels
-            self.op = avg_pool_nd(dims, kernel_size=stride, stride=stride)
-
-    def forward(self, x):
-        assert x.shape[1] == self.channels
-        return self.op(x)
-
-
-class ResnetBlock(nn.Module):
-    def __init__(self, in_c, out_c, down, ksize=3, sk=False, use_conv=True):
-        super().__init__()
-        ps = ksize // 2
-        if in_c != out_c or sk == False:
-            self.in_conv = nn.Conv2d(in_c, out_c, ksize, 1, ps)
-        else:
-            # print('n_in')
-            self.in_conv = None
-        self.block1 = nn.Conv2d(out_c, out_c, 3, 1, 1)
-        self.act = nn.ReLU()
-        self.block2 = nn.Conv2d(out_c, out_c, ksize, 1, ps)
-        if sk == False:
-            self.skep = nn.Conv2d(in_c, out_c, ksize, 1, ps)
-        else:
-            self.skep = None
-
-        self.down = down
-        if self.down == True:
-            self.down_opt = Downsample(in_c, use_conv=use_conv)
-
-    def forward(self, x):
-        if self.down == True:
-            x = self.down_opt(x)
-        if self.in_conv is not None:  # edit
-            x = self.in_conv(x)
-
-        h = self.block1(x)
-        h = self.act(h)
-        h = self.block2(h)
-        if self.skep is not None:
-            return h + self.skep(x)
-        else:
-            return h + x
-
-
-class Adapter(nn.Module):
-    def __init__(self, channels=[320, 640, 1280, 1280], nums_rb=3, cin=64, ksize=3, sk=False, use_conv=True):
-        super(Adapter, self).__init__()
-        self.unshuffle = nn.PixelUnshuffle(8)
-        self.channels = channels
-        self.nums_rb = nums_rb
-        self.body = []
-        for i in range(len(channels)):
-            for j in range(nums_rb):
-                if (i != 0) and (j == 0):
-                    self.body.append(
-                        ResnetBlock(channels[i - 1], channels[i], down=True, ksize=ksize, sk=sk, use_conv=use_conv))
-                else:
-                    self.body.append(
-                        ResnetBlock(channels[i], channels[i], down=False, ksize=ksize, sk=sk, use_conv=use_conv))
-        self.body = nn.ModuleList(self.body)
-        self.conv_in = nn.Conv2d(cin, channels[0], 3, 1, 1)
-
-    def forward(self, x):
-        # unshuffle
-        x = self.unshuffle(x)
-        # extract features
-        features = []
-        x = self.conv_in(x)
-        for i in range(len(self.channels)):
-            for j in range(self.nums_rb):
-                idx = i * self.nums_rb + j
-                x = self.body[idx](x)
-            features.append(x)
-
-        return features
-
-
-class LayerNorm(nn.LayerNorm):
-    """Subclass torch's LayerNorm to handle fp16."""
-
-    def forward(self, x: torch.Tensor):
-        orig_type = x.dtype
-        ret = super().forward(x.type(torch.float32))
-        return ret.type(orig_type)
-
-
-class QuickGELU(nn.Module):
-
-    def forward(self, x: torch.Tensor):
-        return x * torch.sigmoid(1.702 * x)
-
-
-class ResidualAttentionBlock(nn.Module):
-
-    def __init__(self, d_model: int, n_head: int, attn_mask: torch.Tensor = None):
-        super().__init__()
-
-        self.attn = nn.MultiheadAttention(d_model, n_head)
-        self.ln_1 = LayerNorm(d_model)
-        self.mlp = nn.Sequential(
-            OrderedDict([("c_fc", nn.Linear(d_model, d_model * 4)), ("gelu", QuickGELU()),
-                         ("c_proj", nn.Linear(d_model * 4, d_model))]))
-        self.ln_2 = LayerNorm(d_model)
-        self.attn_mask = attn_mask
-
-    def attention(self, x: torch.Tensor):
-        self.attn_mask = self.attn_mask.to(dtype=x.dtype, device=x.device) if self.attn_mask is not None else None
-        return self.attn(x, x, x, need_weights=False, attn_mask=self.attn_mask)[0]
-
-    def forward(self, x: torch.Tensor):
-        x = x + self.attention(self.ln_1(x))
-        x = x + self.mlp(self.ln_2(x))
-        return x
-
-
-class StyleAdapter(nn.Module):
-
-    def __init__(self, width=1024, context_dim=768, num_head=8, n_layes=3, num_token=4):
-        super().__init__()
-
-        scale = width ** -0.5
-        self.transformer_layes = nn.Sequential(*[ResidualAttentionBlock(width, num_head) for _ in range(n_layes)])
-        self.num_token = num_token
-        self.style_embedding = nn.Parameter(torch.randn(1, num_token, width) * scale)
-        self.ln_post = LayerNorm(width)
-        self.ln_pre = LayerNorm(width)
-        self.proj = nn.Parameter(scale * torch.randn(width, context_dim))
-
-    def forward(self, x):
-        # x shape [N, HW+1, C]
-        style_embedding = self.style_embedding + torch.zeros(
-            (x.shape[0], self.num_token, self.style_embedding.shape[-1]), device=x.device)
-        x = torch.cat([x, style_embedding], dim=1)
-        x = self.ln_pre(x)
-        x = x.permute(1, 0, 2)  # NLD -> LND
-        x = self.transformer_layes(x)
-        x = x.permute(1, 0, 2)  # LND -> NLD
-
-        x = self.ln_post(x[:, -self.num_token:, :])
-        x = x @ self.proj
-
-        return x
-
-
-class ResnetBlock_light(nn.Module):
-    def __init__(self, in_c):
-        super().__init__()
-        self.block1 = nn.Conv2d(in_c, in_c, 3, 1, 1)
-        self.act = nn.ReLU()
-        self.block2 = nn.Conv2d(in_c, in_c, 3, 1, 1)
-
-    def forward(self, x):
-        h = self.block1(x)
-        h = self.act(h)
-        h = self.block2(h)
-
-        return h + x
-
-
-class extractor(nn.Module):
-    def __init__(self, in_c, inter_c, out_c, nums_rb, down=False):
-        super().__init__()
-        self.in_conv = nn.Conv2d(in_c, inter_c, 1, 1, 0)
-        self.body = []
-        for _ in range(nums_rb):
-            self.body.append(ResnetBlock_light(inter_c))
-        self.body = nn.Sequential(*self.body)
-        self.out_conv = nn.Conv2d(inter_c, out_c, 1, 1, 0)
-        self.down = down
-        if self.down == True:
-            self.down_opt = Downsample(in_c, use_conv=False)
-
-    def forward(self, x):
-        if self.down == True:
-            x = self.down_opt(x)
-        x = self.in_conv(x)
-        x = self.body(x)
-        x = self.out_conv(x)
-
-        return x
-
-
-class Adapter_light(nn.Module):
-    def __init__(self, channels=[320, 640, 1280, 1280], nums_rb=3, cin=64):
-        super(Adapter_light, self).__init__()
-        self.unshuffle = nn.PixelUnshuffle(8)
-        self.channels = channels
-        self.nums_rb = nums_rb
-        self.body = []
-        for i in range(len(channels)):
-            if i == 0:
-                self.body.append(extractor(in_c=cin, inter_c=channels[i]//4, out_c=channels[i], nums_rb=nums_rb, down=False))
-            else:
-                self.body.append(extractor(in_c=channels[i-1], inter_c=channels[i]//4, out_c=channels[i], nums_rb=nums_rb, down=True))
-        self.body = nn.ModuleList(self.body)
-
-    def forward(self, x):
-        # unshuffle
-        x = self.unshuffle(x)
-        # extract features
-        features = []
-        for i in range(len(self.channels)):
-            x = self.body[i](x)
-            features.append(x)
-
-        return features
diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/bejeweled/methods/BoardMethods.js b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/bejeweled/methods/BoardMethods.js
deleted file mode 100644
index a4d91ec17c408da4a3ad5c1fd7f7c65b9d9d8e23..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/bejeweled/methods/BoardMethods.js
+++ /dev/null
@@ -1,41 +0,0 @@
-export default {
-    setBoardSize(width, height) {
-        this.board.setBoardWidth(width).setBoardHeight(height);
-        return this;
-    },
-
-    // Chess properties
-    getChessMoveTo(chess) {
-        return (chess) ? chess.rexMoveTo : undefined;
-    },
-
-    getChessTileZ() {
-        return this.board.chessTileZ;
-    },
-
-    worldXYToChess(worldX, worldY) {
-        return this.board.worldXYToChess(worldX, worldY);
-    },
-
-    tileXYToChess(tileX, tileY) {
-        return this.board.tileXYToChess(tileX, tileY);
-    },
-
-    getNeighborChessAtAngle(chess, angle) {
-        return this.board.getNeighborChessAtAngle(chess, angle);
-    },
-
-    getNeighborChessAtDirection(chess, direction) {
-        return this.board.getNeighborChessAtDirection(chess, direction);
-    },
-
-    // Expose board instance
-    getBoard() {
-        return this.board.board;
-    },
-
-    // Expose match instance
-    getMatch() {
-        return this.board.match;
-    }
-}
\ No newline at end of file
diff --git a/spaces/AiMimicry/sovits-models/inference/slicer.py b/spaces/AiMimicry/sovits-models/inference/slicer.py
deleted file mode 100644
index b05840bcf6bdced0b6e2adbecb1a1dd5b3dee462..0000000000000000000000000000000000000000
--- a/spaces/AiMimicry/sovits-models/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
diff --git a/spaces/AkitoP/umamusume_bert_vits2/README.md b/spaces/AkitoP/umamusume_bert_vits2/README.md
deleted file mode 100644
index fd92ae264c2b383621a8dead1231619a84187b15..0000000000000000000000000000000000000000
--- a/spaces/AkitoP/umamusume_bert_vits2/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Umamusume Bert Vits2
-emoji: 📊
-colorFrom: red
-colorTo: green
-sdk: gradio
-sdk_version: 3.47.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Al-Chan/Vits_League_of_Legends_Yuumi_TTS/voice_upload.py b/spaces/Al-Chan/Vits_League_of_Legends_Yuumi_TTS/voice_upload.py
deleted file mode 100644
index 5c825a933a7970e17e57c381b59a5fc4e06ea569..0000000000000000000000000000000000000000
--- a/spaces/Al-Chan/Vits_League_of_Legends_Yuumi_TTS/voice_upload.py
+++ /dev/null
@@ -1,28 +0,0 @@
-from google.colab import files
-import shutil
-import os
-import argparse
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--type", type=str, required=True, help="type of file to upload")
-    args = parser.parse_args()
-    file_type = args.type
-
-    basepath = os.getcwd()
-    uploaded = files.upload() # 上传文件
-    assert(file_type in ['zip', 'audio', 'video'])
-    if file_type == "zip":
-        upload_path = "./custom_character_voice/"
-        for filename in uploaded.keys():
-            #将上传的文件移动到指定的位置上
-            shutil.move(os.path.join(basepath, filename), os.path.join(upload_path, "custom_character_voice.zip"))
-    elif file_type == "audio":
-        upload_path = "./raw_audio/"
-        for filename in uploaded.keys():
-            #将上传的文件移动到指定的位置上
-            shutil.move(os.path.join(basepath, filename), os.path.join(upload_path, filename))
-    elif file_type == "video":
-        upload_path = "./video_data/"
-        for filename in uploaded.keys():
-            # 将上传的文件移动到指定的位置上
-            shutil.move(os.path.join(basepath, filename), os.path.join(upload_path, filename))
\ No newline at end of file
diff --git a/spaces/Alex123aaa/1234/app.py b/spaces/Alex123aaa/1234/app.py
deleted file mode 100644
index a2d60613a9f4f83ff82eaaf5d13d25484d3dd03c..0000000000000000000000000000000000000000
--- a/spaces/Alex123aaa/1234/app.py
+++ /dev/null
@@ -1,61 +0,0 @@
-import os
-os.system("pip uninstall -y gradio")
-os.system("pip install gradio==3.44.4")
-import gradio as gr
-from sklearn.datasets import fetch_california_housing
-from sklearn.model_selection import train_test_split
-from sklearn.linear_model import LinearRegression
-
-
-data = fetch_california_housing()
-X = data.data
-y = data.target
-
-
-X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
-
-
-X_train = X_train[:, :5]
-X_test = X_test[:, :5]
-
-model = LinearRegression()
-model.fit(X_train, y_train)
-
-
-def predict_housing_price(sqft, bedrooms, bathrooms, latitude, longitude):
-
-    if sqft is None or bedrooms is None or bathrooms is None or latitude is None or longitude is None:
-        return "Please provide all input values."
-
-
-    try:
-        sqft = float(sqft)
-        bedrooms = float(bedrooms)
-        bathrooms = float(bathrooms)
-        latitude = float(latitude)
-        longitude = float(longitude)
-    except ValueError:
-        return "Invalid input. Please provide numeric values."
-
-
-    input_features = [sqft, bedrooms, bathrooms, latitude, longitude]
-    predicted_price = model.predict([input_features])[0]
-
-
-    return f"Predicted Price: ${predicted_price:.2f}"
-
-input_components = [
-    gr.inputs.Slider(label="Sqft", minimum=0, maximum=5000),
-    gr.inputs.Slider(label="Bedrooms", minimum=0, maximum=10),
-    gr.inputs.Slider(label="Bathrooms", minimum=0, maximum=5),
-    gr.inputs.Slider(label="Latitude", minimum=32.5, maximum=35),
-    gr.inputs.Slider(label="Longitude", minimum=-125, maximum=-120)
-]
-
-iface = gr.Interface(
-    fn=predict_housing_price,
-    inputs=input_components,
-    outputs="text"  
-)
-
-iface.launch()
\ No newline at end of file
diff --git a/spaces/Alichuan/VITS-Umamusume-voice-synthesizer/text/shanghainese.py b/spaces/Alichuan/VITS-Umamusume-voice-synthesizer/text/shanghainese.py
deleted file mode 100644
index cb29c24a08d2e406e8399cf7bc9fe5cb43cb9c61..0000000000000000000000000000000000000000
--- a/spaces/Alichuan/VITS-Umamusume-voice-synthesizer/text/shanghainese.py
+++ /dev/null
@@ -1,64 +0,0 @@
-import re
-import cn2an
-import opencc
-
-
-converter = opencc.OpenCC('zaonhe')
-
-# List of (Latin alphabet, ipa) pairs:
-_latin_to_ipa = [(re.compile('%s' % x[0]), x[1]) for x in [
-    ('A', 'ᴇ'),
-    ('B', 'bi'),
-    ('C', 'si'),
-    ('D', 'di'),
-    ('E', 'i'),
-    ('F', 'ᴇf'),
-    ('G', 'dʑi'),
-    ('H', 'ᴇtɕʰ'),
-    ('I', 'ᴀi'),
-    ('J', 'dʑᴇ'),
-    ('K', 'kʰᴇ'),
-    ('L', 'ᴇl'),
-    ('M', 'ᴇm'),
-    ('N', 'ᴇn'),
-    ('O', 'o'),
-    ('P', 'pʰi'),
-    ('Q', 'kʰiu'),
-    ('R', 'ᴀl'),
-    ('S', 'ᴇs'),
-    ('T', 'tʰi'),
-    ('U', 'ɦiu'),
-    ('V', 'vi'),
-    ('W', 'dᴀbɤliu'),
-    ('X', 'ᴇks'),
-    ('Y', 'uᴀi'),
-    ('Z', 'zᴇ')
-]]
-
-
-def _number_to_shanghainese(num):
-    num = cn2an.an2cn(num).replace('一十','十').replace('二十', '廿').replace('二', '两')
-    return re.sub(r'((?:^|[^三四五六七八九])十|廿)两', r'\1二', num)
-
-
-def number_to_shanghainese(text):
-    return re.sub(r'\d+(?:\.?\d+)?', lambda x: _number_to_shanghainese(x.group()), text)
-
-
-def latin_to_ipa(text):
-    for regex, replacement in _latin_to_ipa:
-        text = re.sub(regex, replacement, text)
-    return text
-
-
-def shanghainese_to_ipa(text):
-    text = number_to_shanghainese(text.upper())
-    text = converter.convert(text).replace('-','').replace('$',' ')
-    text = re.sub(r'[A-Z]', lambda x: latin_to_ipa(x.group())+' ', text)
-    text = re.sub(r'[、；：]', '，', text)
-    text = re.sub(r'\s*，\s*', ', ', text)
-    text = re.sub(r'\s*。\s*', '. ', text)
-    text = re.sub(r'\s*？\s*', '? ', text)
-    text = re.sub(r'\s*！\s*', '! ', text)
-    text = re.sub(r'\s*$', '', text)
-    return text
diff --git a/spaces/Amite5h/EuroSAT_/README.md b/spaces/Amite5h/EuroSAT_/README.md
deleted file mode 100644
index 5af3c062c31de996681f0156e283d593630920be..0000000000000000000000000000000000000000
--- a/spaces/Amite5h/EuroSAT_/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: EuroSAT
-emoji: ⚡
-colorFrom: gray
-colorTo: purple
-sdk: streamlit
-sdk_version: 1.21.0
-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/Amon1/ChatGPTForAcadamic/functional.py b/spaces/Amon1/ChatGPTForAcadamic/functional.py
deleted file mode 100644
index eccc0ac251784f4611c60ae754194448fca2e9e8..0000000000000000000000000000000000000000
--- a/spaces/Amon1/ChatGPTForAcadamic/functional.py
+++ /dev/null
@@ -1,70 +0,0 @@
-# 'primary' 颜色对应 theme.py 中的 primary_hue
-# 'secondary' 颜色对应 theme.py 中的 neutral_hue
-# 'stop' 颜色对应 theme.py 中的 color_er
-# 默认按钮颜色是 secondary
-from toolbox import clear_line_break
-
-def get_functionals():
-    return {
-        "英语学术润色": {
-            # 前言
-            "Prefix":   r"Below is a paragraph from an academic paper. Polish the writing to meet the academic style, " +
-                        r"improve the spelling, grammar, clarity, concision and overall readability. When necessary, rewrite the whole sentence. " +
-                        r"Furthermore, list all modification and explain the reasons to do so in markdown table." + "\n\n",
-            # 后语 
-            "Suffix":   r"",
-            "Color":    r"secondary",    # 按钮颜色
-        },
-        "中文学术润色": {
-            "Prefix":   r"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，" + 
-                        r"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":   r"",
-        },
-        "查找语法错误": {
-            "Prefix":   r"Can you help me ensure that the grammar and the spelling is correct? " +
-                        r"Do not try to polish the text, if no mistake is found, tell me that this paragraph is good." +
-                        r"If you find grammar or spelling mistakes, please list mistakes you find in a two-column markdown table, " +
-                        r"put the original text the first column, " +
-                        r"put the corrected text in the second column and highlight the key words you fixed.""\n"
-                        r"Example:""\n"
-                        r"Paragraph: How is you? Do you knows what is it?""\n"
-                        r"| Original sentence | Corrected sentence |""\n"
-                        r"| :--- | :--- |""\n"
-                        r"| How **is** you? | How **are** you? |""\n"
-                        r"| Do you **knows** what **is** **it**? | Do you **know** what **it** **is** ? |""\n"
-                        r"Below is a paragraph from an academic paper. "
-                        r"You need to report all grammar and spelling mistakes as the example before."
-                        + "\n\n",
-            "Suffix":   r"",
-            "PreProcess": clear_line_break,    # 预处理：清除换行符
-        },
-        "中译英": {
-            "Prefix":   r"Please translate following sentence to English:" + "\n\n",
-            "Suffix":   r"",
-        },
-        "学术中英互译": {
-            "Prefix":   r"I want you to act as a scientific English-Chinese translator, " +
-                        r"I will provide you with some paragraphs in one language " +
-                        r"and your task is to accurately and academically translate the paragraphs only into the other language. " +
-                        r"Do not repeat the original provided paragraphs after translation. " +
-                        r"You should use artificial intelligence tools, " +
-                        r"such as natural language processing, and rhetorical knowledge " +
-                        r"and experience about effective writing techniques to reply. " +
-                        r"I'll give you my paragraphs as follows, tell me what language it is written in, and then translate:" + "\n\n",
-            "Suffix": "",
-            "Color": "secondary",
-        },
-        "英译中": {
-            "Prefix":   r"请翻译成中文：" + "\n\n",
-            "Suffix":   r"",
-        },
-        "找图片": {
-            "Prefix":   r"我需要你找一张网络图片。使用Unsplash API(https://source.unsplash.com/960x640/?<英语关键词>)获取图片URL，" +
-                        r"然后请使用Markdown格式封装，并且不要有反斜线，不要用代码块。现在，请按以下描述给我发送图片：" + "\n\n",
-            "Suffix":   r"",
-        },
-        "解释代码": {
-            "Prefix":   r"请解释以下代码：" + "\n```\n",
-            "Suffix":   "\n```\n",
-        },
-    }
diff --git a/spaces/Amrrs/DragGan-Inversion/PTI/__init__.py b/spaces/Amrrs/DragGan-Inversion/PTI/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/tests/pipelines/__init__.py b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/tests/pipelines/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/Andy1621/uniformer_image_detection/configs/free_anchor/retinanet_free_anchor_r50_fpn_1x_coco.py b/spaces/Andy1621/uniformer_image_detection/configs/free_anchor/retinanet_free_anchor_r50_fpn_1x_coco.py
deleted file mode 100644
index 28f983c29edd071b32a50f18ac7b3f5c1bfdda88..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_detection/configs/free_anchor/retinanet_free_anchor_r50_fpn_1x_coco.py
+++ /dev/null
@@ -1,22 +0,0 @@
-_base_ = '../retinanet/retinanet_r50_fpn_1x_coco.py'
-model = dict(
-    bbox_head=dict(
-        _delete_=True,
-        type='FreeAnchorRetinaHead',
-        num_classes=80,
-        in_channels=256,
-        stacked_convs=4,
-        feat_channels=256,
-        anchor_generator=dict(
-            type='AnchorGenerator',
-            octave_base_scale=4,
-            scales_per_octave=3,
-            ratios=[0.5, 1.0, 2.0],
-            strides=[8, 16, 32, 64, 128]),
-        bbox_coder=dict(
-            type='DeltaXYWHBBoxCoder',
-            target_means=[.0, .0, .0, .0],
-            target_stds=[0.1, 0.1, 0.2, 0.2]),
-        loss_bbox=dict(type='SmoothL1Loss', beta=0.11, loss_weight=0.75)))
-optimizer_config = dict(
-    _delete_=True, grad_clip=dict(max_norm=35, norm_type=2))
diff --git a/spaces/Andy1621/uniformer_image_detection/mmdet/core/bbox/samplers/sampling_result.py b/spaces/Andy1621/uniformer_image_detection/mmdet/core/bbox/samplers/sampling_result.py
deleted file mode 100644
index 419a8e39a3c307a7cd9cfd0565a20037ded0d646..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_detection/mmdet/core/bbox/samplers/sampling_result.py
+++ /dev/null
@@ -1,152 +0,0 @@
-import torch
-
-from mmdet.utils import util_mixins
-
-
-class SamplingResult(util_mixins.NiceRepr):
-    """Bbox sampling result.
-
-    Example:
-        >>> # xdoctest: +IGNORE_WANT
-        >>> from mmdet.core.bbox.samplers.sampling_result import *  # NOQA
-        >>> self = SamplingResult.random(rng=10)
-        >>> print(f'self = {self}')
-        self = <SamplingResult({
-            'neg_bboxes': torch.Size([12, 4]),
-            'neg_inds': tensor([ 0,  1,  2,  4,  5,  6,  7,  8,  9, 10, 11, 12]),
-            'num_gts': 4,
-            'pos_assigned_gt_inds': tensor([], dtype=torch.int64),
-            'pos_bboxes': torch.Size([0, 4]),
-            'pos_inds': tensor([], dtype=torch.int64),
-            'pos_is_gt': tensor([], dtype=torch.uint8)
-        })>
-    """
-
-    def __init__(self, pos_inds, neg_inds, bboxes, gt_bboxes, assign_result,
-                 gt_flags):
-        self.pos_inds = pos_inds
-        self.neg_inds = neg_inds
-        self.pos_bboxes = bboxes[pos_inds]
-        self.neg_bboxes = bboxes[neg_inds]
-        self.pos_is_gt = gt_flags[pos_inds]
-
-        self.num_gts = gt_bboxes.shape[0]
-        self.pos_assigned_gt_inds = assign_result.gt_inds[pos_inds] - 1
-
-        if gt_bboxes.numel() == 0:
-            # hack for index error case
-            assert self.pos_assigned_gt_inds.numel() == 0
-            self.pos_gt_bboxes = torch.empty_like(gt_bboxes).view(-1, 4)
-        else:
-            if len(gt_bboxes.shape) < 2:
-                gt_bboxes = gt_bboxes.view(-1, 4)
-
-            self.pos_gt_bboxes = gt_bboxes[self.pos_assigned_gt_inds, :]
-
-        if assign_result.labels is not None:
-            self.pos_gt_labels = assign_result.labels[pos_inds]
-        else:
-            self.pos_gt_labels = None
-
-    @property
-    def bboxes(self):
-        """torch.Tensor: concatenated positive and negative boxes"""
-        return torch.cat([self.pos_bboxes, self.neg_bboxes])
-
-    def to(self, device):
-        """Change the device of the data inplace.
-
-        Example:
-            >>> self = SamplingResult.random()
-            >>> print(f'self = {self.to(None)}')
-            >>> # xdoctest: +REQUIRES(--gpu)
-            >>> print(f'self = {self.to(0)}')
-        """
-        _dict = self.__dict__
-        for key, value in _dict.items():
-            if isinstance(value, torch.Tensor):
-                _dict[key] = value.to(device)
-        return self
-
-    def __nice__(self):
-        data = self.info.copy()
-        data['pos_bboxes'] = data.pop('pos_bboxes').shape
-        data['neg_bboxes'] = data.pop('neg_bboxes').shape
-        parts = [f"'{k}': {v!r}" for k, v in sorted(data.items())]
-        body = '    ' + ',\n    '.join(parts)
-        return '{\n' + body + '\n}'
-
-    @property
-    def info(self):
-        """Returns a dictionary of info about the object."""
-        return {
-            'pos_inds': self.pos_inds,
-            'neg_inds': self.neg_inds,
-            'pos_bboxes': self.pos_bboxes,
-            'neg_bboxes': self.neg_bboxes,
-            'pos_is_gt': self.pos_is_gt,
-            'num_gts': self.num_gts,
-            'pos_assigned_gt_inds': self.pos_assigned_gt_inds,
-        }
-
-    @classmethod
-    def random(cls, rng=None, **kwargs):
-        """
-        Args:
-            rng (None | int | numpy.random.RandomState): seed or state.
-            kwargs (keyword arguments):
-                - num_preds: number of predicted boxes
-                - num_gts: number of true boxes
-                - p_ignore (float): probability of a predicted box assinged to \
-                    an ignored truth.
-                - p_assigned (float): probability of a predicted box not being \
-                    assigned.
-                - p_use_label (float | bool): with labels or not.
-
-        Returns:
-            :obj:`SamplingResult`: Randomly generated sampling result.
-
-        Example:
-            >>> from mmdet.core.bbox.samplers.sampling_result import *  # NOQA
-            >>> self = SamplingResult.random()
-            >>> print(self.__dict__)
-        """
-        from mmdet.core.bbox.samplers.random_sampler import RandomSampler
-        from mmdet.core.bbox.assigners.assign_result import AssignResult
-        from mmdet.core.bbox import demodata
-        rng = demodata.ensure_rng(rng)
-
-        # make probabalistic?
-        num = 32
-        pos_fraction = 0.5
-        neg_pos_ub = -1
-
-        assign_result = AssignResult.random(rng=rng, **kwargs)
-
-        # Note we could just compute an assignment
-        bboxes = demodata.random_boxes(assign_result.num_preds, rng=rng)
-        gt_bboxes = demodata.random_boxes(assign_result.num_gts, rng=rng)
-
-        if rng.rand() > 0.2:
-            # sometimes algorithms squeeze their data, be robust to that
-            gt_bboxes = gt_bboxes.squeeze()
-            bboxes = bboxes.squeeze()
-
-        if assign_result.labels is None:
-            gt_labels = None
-        else:
-            gt_labels = None  # todo
-
-        if gt_labels is None:
-            add_gt_as_proposals = False
-        else:
-            add_gt_as_proposals = True  # make probabalistic?
-
-        sampler = RandomSampler(
-            num,
-            pos_fraction,
-            neg_pos_ub=neg_pos_ub,
-            add_gt_as_proposals=add_gt_as_proposals,
-            rng=rng)
-        self = sampler.sample(assign_result, bboxes, gt_bboxes, gt_labels)
-        return self
diff --git a/spaces/Andy1621/uniformer_image_segmentation/configs/fcn/fcn_r101-d8_769x769_40k_cityscapes.py b/spaces/Andy1621/uniformer_image_segmentation/configs/fcn/fcn_r101-d8_769x769_40k_cityscapes.py
deleted file mode 100644
index 6b4cc571294fa45b4442c2bfeb9fda13a14fc5c2..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_segmentation/configs/fcn/fcn_r101-d8_769x769_40k_cityscapes.py
+++ /dev/null
@@ -1,2 +0,0 @@
-_base_ = './fcn_r50-d8_769x769_40k_cityscapes.py'
-model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
diff --git a/spaces/AnishKumbhar/DogDiseasePredictor/app.py b/spaces/AnishKumbhar/DogDiseasePredictor/app.py
deleted file mode 100644
index de74067280d49b869258ce1176c9694ab16b5ef3..0000000000000000000000000000000000000000
--- a/spaces/AnishKumbhar/DogDiseasePredictor/app.py
+++ /dev/null
@@ -1,80 +0,0 @@
-import torch
-import fastapi
-import numpy as np
-from PIL import Image
-
-class TorchTensor(torch.Tensor):
-    pass
-
-class Prediction:
-    prediction: TorchTensor
-
-app = fastapi.FastAPI(docs_url="/")
-from transformers import ViTForImageClassification
-
-# Define the number of classes in your custom dataset
-num_classes = 20
-
-# Initialize the ViTForImageClassification model
-model = ViTForImageClassification.from_pretrained(
-    'google/vit-base-patch16-224-in21k',
-    num_labels=num_classes  # Specify the number of classes
-)
-
-class_names = [
-    "Acral Lick Dermatitis",
-    "Acute moist dermatitis",
-    "Canine atopic dermatitis",
-    "Cherry Eye",
-    "Ear infections",
-    "External Parasites",
-    "Folliculitis",
-    "Healthy",
-    "Leishmaniasis",
-    "Lupus",
-    "Nuclear sclerosis",
-    "Otitis externa",
-    "Pruritus",
-    "Pyoderma",
-    "Rabies",
-    "Ringworm",
-    "Sarcoptic Mange",
-    "Sebaceous adenitis",
-    "Seborrhea",
-    "Skin tumor"
-]
-
-model.load_state_dict(torch.load('best_model.pth', map_location='cpu'))
-# Define a function to preprocess the input image
-def preprocess_input(input: fastapi.UploadFile):
-    image = Image.open(input.file)
-    image = image.resize((224, 224)).convert("RGB")
-    input = np.array(image)
-    input = np.transpose(input, (2, 0, 1))
-    input = torch.from_numpy(input).float()
-    input = input.unsqueeze(0)
-    return input
-
-# Define an endpoint to make predictions
-@app.post("/predict")
-async def predict_endpoint(input:fastapi.UploadFile):
-    """Make a prediction on an image uploaded by the user."""
-
-    # Preprocess the input image
-    input = preprocess_input(input)
-
-    # Make a prediction
-    prediction = model(input)
-
-
-    logits = prediction.logits
-    num_top_predictions = 3
-    top_predictions = torch.topk(logits, k=num_top_predictions, dim=1)
-    top_indices = top_predictions.indices.squeeze().tolist()
-    top_probabilities = torch.softmax(top_predictions.values, dim=1).squeeze().tolist()
-
-    # Return the top N class indices and their probabilities in JSON format
-    response_data = [{"class_index": class_names[idx], "probability": prob} for idx, prob in zip(top_indices, top_probabilities)]
-    return {"predictions": response_data}
-
-    
diff --git a/spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/cnn/bricks/registry.py b/spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/cnn/bricks/registry.py
deleted file mode 100644
index 39eabc58db4b5954478a2ac1ab91cea5e45ab055..0000000000000000000000000000000000000000
--- a/spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/cnn/bricks/registry.py
+++ /dev/null
@@ -1,16 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from annotator.uniformer.mmcv.utils import Registry
-
-CONV_LAYERS = Registry('conv layer')
-NORM_LAYERS = Registry('norm layer')
-ACTIVATION_LAYERS = Registry('activation layer')
-PADDING_LAYERS = Registry('padding layer')
-UPSAMPLE_LAYERS = Registry('upsample layer')
-PLUGIN_LAYERS = Registry('plugin layer')
-
-DROPOUT_LAYERS = Registry('drop out layers')
-POSITIONAL_ENCODING = Registry('position encoding')
-ATTENTION = Registry('attention')
-FEEDFORWARD_NETWORK = Registry('feed-forward Network')
-TRANSFORMER_LAYER = Registry('transformerLayer')
-TRANSFORMER_LAYER_SEQUENCE = Registry('transformer-layers sequence')
diff --git a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/chardet/universaldetector.py b/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/chardet/universaldetector.py
deleted file mode 100644
index 30c441dc28ee327076a850b1d3c88a9a2c8f04f0..0000000000000000000000000000000000000000
--- a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/chardet/universaldetector.py
+++ /dev/null
@@ -1,362 +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 #########################
-"""
-Module containing the UniversalDetector detector class, which is the primary
-class a user of ``chardet`` should use.
-
-:author: Mark Pilgrim (initial port to Python)
-:author: Shy Shalom (original C code)
-:author: Dan Blanchard (major refactoring for 3.0)
-:author: Ian Cordasco
-"""
-
-
-import codecs
-import logging
-import re
-from typing import List, Optional, Union
-
-from .charsetgroupprober import CharSetGroupProber
-from .charsetprober import CharSetProber
-from .enums import InputState, LanguageFilter, ProbingState
-from .escprober import EscCharSetProber
-from .latin1prober import Latin1Prober
-from .macromanprober import MacRomanProber
-from .mbcsgroupprober import MBCSGroupProber
-from .resultdict import ResultDict
-from .sbcsgroupprober import SBCSGroupProber
-from .utf1632prober import UTF1632Prober
-
-
-class UniversalDetector:
-    """
-    The ``UniversalDetector`` class underlies the ``chardet.detect`` function
-    and coordinates all of the different charset probers.
-
-    To get a ``dict`` containing an encoding and its confidence, you can simply
-    run:
-
-    .. code::
-
-            u = UniversalDetector()
-            u.feed(some_bytes)
-            u.close()
-            detected = u.result
-
-    """
-
-    MINIMUM_THRESHOLD = 0.20
-    HIGH_BYTE_DETECTOR = re.compile(b"[\x80-\xFF]")
-    ESC_DETECTOR = re.compile(b"(\033|~{)")
-    WIN_BYTE_DETECTOR = re.compile(b"[\x80-\x9F]")
-    ISO_WIN_MAP = {
-        "iso-8859-1": "Windows-1252",
-        "iso-8859-2": "Windows-1250",
-        "iso-8859-5": "Windows-1251",
-        "iso-8859-6": "Windows-1256",
-        "iso-8859-7": "Windows-1253",
-        "iso-8859-8": "Windows-1255",
-        "iso-8859-9": "Windows-1254",
-        "iso-8859-13": "Windows-1257",
-    }
-    # Based on https://encoding.spec.whatwg.org/#names-and-labels
-    # but altered to match Python names for encodings and remove mappings
-    # that break tests.
-    LEGACY_MAP = {
-        "ascii": "Windows-1252",
-        "iso-8859-1": "Windows-1252",
-        "tis-620": "ISO-8859-11",
-        "iso-8859-9": "Windows-1254",
-        "gb2312": "GB18030",
-        "euc-kr": "CP949",
-        "utf-16le": "UTF-16",
-    }
-
-    def __init__(
-        self,
-        lang_filter: LanguageFilter = LanguageFilter.ALL,
-        should_rename_legacy: bool = False,
-    ) -> None:
-        self._esc_charset_prober: Optional[EscCharSetProber] = None
-        self._utf1632_prober: Optional[UTF1632Prober] = None
-        self._charset_probers: List[CharSetProber] = []
-        self.result: ResultDict = {
-            "encoding": None,
-            "confidence": 0.0,
-            "language": None,
-        }
-        self.done = False
-        self._got_data = False
-        self._input_state = InputState.PURE_ASCII
-        self._last_char = b""
-        self.lang_filter = lang_filter
-        self.logger = logging.getLogger(__name__)
-        self._has_win_bytes = False
-        self.should_rename_legacy = should_rename_legacy
-        self.reset()
-
-    @property
-    def input_state(self) -> int:
-        return self._input_state
-
-    @property
-    def has_win_bytes(self) -> bool:
-        return self._has_win_bytes
-
-    @property
-    def charset_probers(self) -> List[CharSetProber]:
-        return self._charset_probers
-
-    def reset(self) -> None:
-        """
-        Reset the UniversalDetector and all of its probers back to their
-        initial states.  This is called by ``__init__``, so you only need to
-        call this directly in between analyses of different documents.
-        """
-        self.result = {"encoding": None, "confidence": 0.0, "language": None}
-        self.done = False
-        self._got_data = False
-        self._has_win_bytes = False
-        self._input_state = InputState.PURE_ASCII
-        self._last_char = b""
-        if self._esc_charset_prober:
-            self._esc_charset_prober.reset()
-        if self._utf1632_prober:
-            self._utf1632_prober.reset()
-        for prober in self._charset_probers:
-            prober.reset()
-
-    def feed(self, byte_str: Union[bytes, bytearray]) -> None:
-        """
-        Takes a chunk of a document and feeds it through all of the relevant
-        charset probers.
-
-        After calling ``feed``, you can check the value of the ``done``
-        attribute to see if you need to continue feeding the
-        ``UniversalDetector`` more data, or if it has made a prediction
-        (in the ``result`` attribute).
-
-        .. note::
-           You should always call ``close`` when you're done feeding in your
-           document if ``done`` is not already ``True``.
-        """
-        if self.done:
-            return
-
-        if not byte_str:
-            return
-
-        if not isinstance(byte_str, bytearray):
-            byte_str = bytearray(byte_str)
-
-        # First check for known BOMs, since these are guaranteed to be correct
-        if not self._got_data:
-            # If the data starts with BOM, we know it is UTF
-            if byte_str.startswith(codecs.BOM_UTF8):
-                # EF BB BF  UTF-8 with BOM
-                self.result = {
-                    "encoding": "UTF-8-SIG",
-                    "confidence": 1.0,
-                    "language": "",
-                }
-            elif byte_str.startswith((codecs.BOM_UTF32_LE, codecs.BOM_UTF32_BE)):
-                # FF FE 00 00  UTF-32, little-endian BOM
-                # 00 00 FE FF  UTF-32, big-endian BOM
-                self.result = {"encoding": "UTF-32", "confidence": 1.0, "language": ""}
-            elif byte_str.startswith(b"\xFE\xFF\x00\x00"):
-                # FE FF 00 00  UCS-4, unusual octet order BOM (3412)
-                self.result = {
-                    # TODO: This encoding is not supported by Python. Should remove?
-                    "encoding": "X-ISO-10646-UCS-4-3412",
-                    "confidence": 1.0,
-                    "language": "",
-                }
-            elif byte_str.startswith(b"\x00\x00\xFF\xFE"):
-                # 00 00 FF FE  UCS-4, unusual octet order BOM (2143)
-                self.result = {
-                    # TODO: This encoding is not supported by Python. Should remove?
-                    "encoding": "X-ISO-10646-UCS-4-2143",
-                    "confidence": 1.0,
-                    "language": "",
-                }
-            elif byte_str.startswith((codecs.BOM_LE, codecs.BOM_BE)):
-                # FF FE  UTF-16, little endian BOM
-                # FE FF  UTF-16, big endian BOM
-                self.result = {"encoding": "UTF-16", "confidence": 1.0, "language": ""}
-
-            self._got_data = True
-            if self.result["encoding"] is not None:
-                self.done = True
-                return
-
-        # If none of those matched and we've only see ASCII so far, check
-        # for high bytes and escape sequences
-        if self._input_state == InputState.PURE_ASCII:
-            if self.HIGH_BYTE_DETECTOR.search(byte_str):
-                self._input_state = InputState.HIGH_BYTE
-            elif (
-                self._input_state == InputState.PURE_ASCII
-                and self.ESC_DETECTOR.search(self._last_char + byte_str)
-            ):
-                self._input_state = InputState.ESC_ASCII
-
-        self._last_char = byte_str[-1:]
-
-        # next we will look to see if it is appears to be either a UTF-16 or
-        # UTF-32 encoding
-        if not self._utf1632_prober:
-            self._utf1632_prober = UTF1632Prober()
-
-        if self._utf1632_prober.state == ProbingState.DETECTING:
-            if self._utf1632_prober.feed(byte_str) == ProbingState.FOUND_IT:
-                self.result = {
-                    "encoding": self._utf1632_prober.charset_name,
-                    "confidence": self._utf1632_prober.get_confidence(),
-                    "language": "",
-                }
-                self.done = True
-                return
-
-        # If we've seen escape sequences, use the EscCharSetProber, which
-        # uses a simple state machine to check for known escape sequences in
-        # HZ and ISO-2022 encodings, since those are the only encodings that
-        # use such sequences.
-        if self._input_state == InputState.ESC_ASCII:
-            if not self._esc_charset_prober:
-                self._esc_charset_prober = EscCharSetProber(self.lang_filter)
-            if self._esc_charset_prober.feed(byte_str) == ProbingState.FOUND_IT:
-                self.result = {
-                    "encoding": self._esc_charset_prober.charset_name,
-                    "confidence": self._esc_charset_prober.get_confidence(),
-                    "language": self._esc_charset_prober.language,
-                }
-                self.done = True
-        # If we've seen high bytes (i.e., those with values greater than 127),
-        # we need to do more complicated checks using all our multi-byte and
-        # single-byte probers that are left.  The single-byte probers
-        # use character bigram distributions to determine the encoding, whereas
-        # the multi-byte probers use a combination of character unigram and
-        # bigram distributions.
-        elif self._input_state == InputState.HIGH_BYTE:
-            if not self._charset_probers:
-                self._charset_probers = [MBCSGroupProber(self.lang_filter)]
-                # If we're checking non-CJK encodings, use single-byte prober
-                if self.lang_filter & LanguageFilter.NON_CJK:
-                    self._charset_probers.append(SBCSGroupProber())
-                self._charset_probers.append(Latin1Prober())
-                self._charset_probers.append(MacRomanProber())
-            for prober in self._charset_probers:
-                if prober.feed(byte_str) == ProbingState.FOUND_IT:
-                    self.result = {
-                        "encoding": prober.charset_name,
-                        "confidence": prober.get_confidence(),
-                        "language": prober.language,
-                    }
-                    self.done = True
-                    break
-            if self.WIN_BYTE_DETECTOR.search(byte_str):
-                self._has_win_bytes = True
-
-    def close(self) -> ResultDict:
-        """
-        Stop analyzing the current document and come up with a final
-        prediction.
-
-        :returns:  The ``result`` attribute, a ``dict`` with the keys
-                   `encoding`, `confidence`, and `language`.
-        """
-        # Don't bother with checks if we're already done
-        if self.done:
-            return self.result
-        self.done = True
-
-        if not self._got_data:
-            self.logger.debug("no data received!")
-
-        # Default to ASCII if it is all we've seen so far
-        elif self._input_state == InputState.PURE_ASCII:
-            self.result = {"encoding": "ascii", "confidence": 1.0, "language": ""}
-
-        # If we have seen non-ASCII, return the best that met MINIMUM_THRESHOLD
-        elif self._input_state == InputState.HIGH_BYTE:
-            prober_confidence = None
-            max_prober_confidence = 0.0
-            max_prober = None
-            for prober in self._charset_probers:
-                if not prober:
-                    continue
-                prober_confidence = prober.get_confidence()
-                if prober_confidence > max_prober_confidence:
-                    max_prober_confidence = prober_confidence
-                    max_prober = prober
-            if max_prober and (max_prober_confidence > self.MINIMUM_THRESHOLD):
-                charset_name = max_prober.charset_name
-                assert charset_name is not None
-                lower_charset_name = charset_name.lower()
-                confidence = max_prober.get_confidence()
-                # Use Windows encoding name instead of ISO-8859 if we saw any
-                # extra Windows-specific bytes
-                if lower_charset_name.startswith("iso-8859"):
-                    if self._has_win_bytes:
-                        charset_name = self.ISO_WIN_MAP.get(
-                            lower_charset_name, charset_name
-                        )
-                # Rename legacy encodings with superset encodings if asked
-                if self.should_rename_legacy:
-                    charset_name = self.LEGACY_MAP.get(
-                        (charset_name or "").lower(), charset_name
-                    )
-                self.result = {
-                    "encoding": charset_name,
-                    "confidence": confidence,
-                    "language": max_prober.language,
-                }
-
-        # Log all prober confidences if none met MINIMUM_THRESHOLD
-        if self.logger.getEffectiveLevel() <= logging.DEBUG:
-            if self.result["encoding"] is None:
-                self.logger.debug("no probers hit minimum threshold")
-                for group_prober in self._charset_probers:
-                    if not group_prober:
-                        continue
-                    if isinstance(group_prober, CharSetGroupProber):
-                        for prober in group_prober.probers:
-                            self.logger.debug(
-                                "%s %s confidence = %s",
-                                prober.charset_name,
-                                prober.language,
-                                prober.get_confidence(),
-                            )
-                    else:
-                        self.logger.debug(
-                            "%s %s confidence = %s",
-                            group_prober.charset_name,
-                            group_prober.language,
-                            group_prober.get_confidence(),
-                        )
-        return self.result
diff --git a/spaces/Awesimo/jojogan/e4e/training/ranger.py b/spaces/Awesimo/jojogan/e4e/training/ranger.py
deleted file mode 100644
index 3d63264dda6df0ee40cac143440f0b5f8977a9ad..0000000000000000000000000000000000000000
--- a/spaces/Awesimo/jojogan/e4e/training/ranger.py
+++ /dev/null
@@ -1,164 +0,0 @@
-# Ranger deep learning optimizer - RAdam + Lookahead + Gradient Centralization, combined into one optimizer.
-
-# https://github.com/lessw2020/Ranger-Deep-Learning-Optimizer
-# and/or
-# https://github.com/lessw2020/Best-Deep-Learning-Optimizers
-
-# Ranger has now been used to capture 12 records on the FastAI leaderboard.
-
-# This version = 20.4.11
-
-# Credits:
-# Gradient Centralization --> https://arxiv.org/abs/2004.01461v2 (a new optimization technique for DNNs), github:  https://github.com/Yonghongwei/Gradient-Centralization
-# RAdam -->  https://github.com/LiyuanLucasLiu/RAdam
-# Lookahead --> rewritten by lessw2020, but big thanks to Github @LonePatient and @RWightman for ideas from their code.
-# Lookahead paper --> MZhang,G Hinton  https://arxiv.org/abs/1907.08610
-
-# summary of changes:
-# 4/11/20 - add gradient centralization option.  Set new testing benchmark for accuracy with it, toggle with use_gc flag at init.
-# full code integration with all updates at param level instead of group, moves slow weights into state dict (from generic weights),
-# supports group learning rates (thanks @SHolderbach), fixes sporadic load from saved model issues.
-# changes 8/31/19 - fix references to *self*.N_sma_threshold;
-# changed eps to 1e-5 as better default than 1e-8.
-
-import math
-import torch
-from torch.optim.optimizer import Optimizer
-
-
-class Ranger(Optimizer):
-
-	def __init__(self, params, lr=1e-3,  # lr
-				 alpha=0.5, k=6, N_sma_threshhold=5,  # Ranger options
-				 betas=(.95, 0.999), eps=1e-5, weight_decay=0,  # Adam options
-				 use_gc=True, gc_conv_only=False
-				 # Gradient centralization on or off, applied to conv layers only or conv + fc layers
-				 ):
-
-		# parameter checks
-		if not 0.0 <= alpha <= 1.0:
-			raise ValueError(f'Invalid slow update rate: {alpha}')
-		if not 1 <= k:
-			raise ValueError(f'Invalid lookahead steps: {k}')
-		if not lr > 0:
-			raise ValueError(f'Invalid Learning Rate: {lr}')
-		if not eps > 0:
-			raise ValueError(f'Invalid eps: {eps}')
-
-		# parameter comments:
-		# beta1 (momentum) of .95 seems to work better than .90...
-		# N_sma_threshold of 5 seems better in testing than 4.
-		# In both cases, worth testing on your dataset (.90 vs .95, 4 vs 5) to make sure which works best for you.
-
-		# prep defaults and init torch.optim base
-		defaults = dict(lr=lr, alpha=alpha, k=k, step_counter=0, betas=betas, N_sma_threshhold=N_sma_threshhold,
-						eps=eps, weight_decay=weight_decay)
-		super().__init__(params, defaults)
-
-		# adjustable threshold
-		self.N_sma_threshhold = N_sma_threshhold
-
-		# look ahead params
-
-		self.alpha = alpha
-		self.k = k
-
-		# radam buffer for state
-		self.radam_buffer = [[None, None, None] for ind in range(10)]
-
-		# gc on or off
-		self.use_gc = use_gc
-
-		# level of gradient centralization
-		self.gc_gradient_threshold = 3 if gc_conv_only else 1
-
-	def __setstate__(self, state):
-		super(Ranger, self).__setstate__(state)
-
-	def step(self, closure=None):
-		loss = None
-
-		# Evaluate averages and grad, update param tensors
-		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('Ranger optimizer does not support sparse gradients')
-
-				p_data_fp32 = p.data.float()
-
-				state = self.state[p]  # get state dict for this param
-
-				if len(state) == 0:  # if first time to run...init dictionary with our desired entries
-					# if self.first_run_check==0:
-					# self.first_run_check=1
-					# print("Initializing slow buffer...should not see this at load from saved model!")
-					state['step'] = 0
-					state['exp_avg'] = torch.zeros_like(p_data_fp32)
-					state['exp_avg_sq'] = torch.zeros_like(p_data_fp32)
-
-					# look ahead weight storage now in state dict
-					state['slow_buffer'] = torch.empty_like(p.data)
-					state['slow_buffer'].copy_(p.data)
-
-				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)
-
-				# begin computations
-				exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
-				beta1, beta2 = group['betas']
-
-				# GC operation for Conv layers and FC layers
-				if grad.dim() > self.gc_gradient_threshold:
-					grad.add_(-grad.mean(dim=tuple(range(1, grad.dim())), keepdim=True))
-
-				state['step'] += 1
-
-				# compute variance mov avg
-				exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)
-				# compute mean moving avg
-				exp_avg.mul_(beta1).add_(1 - beta1, grad)
-
-				buffered = self.radam_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
-					if N_sma > self.N_sma_threshhold:
-						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'])
-					else:
-						step_size = 1.0 / (1 - beta1 ** state['step'])
-					buffered[2] = step_size
-
-				if group['weight_decay'] != 0:
-					p_data_fp32.add_(-group['weight_decay'] * group['lr'], p_data_fp32)
-
-				# apply lr
-				if N_sma > self.N_sma_threshhold:
-					denom = exp_avg_sq.sqrt().add_(group['eps'])
-					p_data_fp32.addcdiv_(-step_size * group['lr'], exp_avg, denom)
-				else:
-					p_data_fp32.add_(-step_size * group['lr'], exp_avg)
-
-				p.data.copy_(p_data_fp32)
-
-				# integrated look ahead...
-				# we do it at the param level instead of group level
-				if state['step'] % group['k'] == 0:
-					slow_p = state['slow_buffer']  # get access to slow param tensor
-					slow_p.add_(self.alpha, p.data - slow_p)  # (fast weights - slow weights) * alpha
-					p.data.copy_(slow_p)  # copy interpolated weights to RAdam param tensor
-
-		return loss
\ No newline at end of file
diff --git a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/tools/plain_train_net.py b/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/tools/plain_train_net.py
deleted file mode 100644
index 4851a8398e128bdce1986feccf0f1cca4a12f704..0000000000000000000000000000000000000000
--- a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/tools/plain_train_net.py
+++ /dev/null
@@ -1,223 +0,0 @@
-#!/usr/bin/env python
-# Copyright (c) Facebook, Inc. and its affiliates.
-"""
-Detectron2 training script with a plain training loop.
-
-This script reads a given config file and runs the training or evaluation.
-It is an entry point that is able to train standard models in detectron2.
-
-In order to let one script support training of many models,
-this script contains logic that are specific to these built-in models and therefore
-may not be suitable for your own project.
-For example, your research project perhaps only needs a single "evaluator".
-
-Therefore, we recommend you to use detectron2 as a library and take
-this file as an example of how to use the library.
-You may want to write your own script with your datasets and other customizations.
-
-Compared to "train_net.py", this script supports fewer default features.
-It also includes fewer abstraction, therefore is easier to add custom logic.
-"""
-
-import logging
-import os
-from collections import OrderedDict
-import torch
-from torch.nn.parallel import DistributedDataParallel
-
-import detectron2.utils.comm as comm
-from detectron2.checkpoint import DetectionCheckpointer, PeriodicCheckpointer
-from detectron2.config import get_cfg
-from detectron2.data import (
-    MetadataCatalog,
-    build_detection_test_loader,
-    build_detection_train_loader,
-)
-from detectron2.engine import default_argument_parser, default_setup, default_writers, launch
-from detectron2.evaluation import (
-    CityscapesInstanceEvaluator,
-    CityscapesSemSegEvaluator,
-    COCOEvaluator,
-    COCOPanopticEvaluator,
-    DatasetEvaluators,
-    LVISEvaluator,
-    PascalVOCDetectionEvaluator,
-    SemSegEvaluator,
-    inference_on_dataset,
-    print_csv_format,
-)
-from detectron2.modeling import build_model
-from detectron2.solver import build_lr_scheduler, build_optimizer
-from detectron2.utils.events import EventStorage
-
-logger = logging.getLogger("detectron2")
-
-
-def get_evaluator(cfg, dataset_name, output_folder=None):
-    """
-    Create evaluator(s) for a given dataset.
-    This uses the special metadata "evaluator_type" associated with each builtin dataset.
-    For your own dataset, you can simply create an evaluator manually in your
-    script and do not have to worry about the hacky if-else logic here.
-    """
-    if output_folder is None:
-        output_folder = os.path.join(cfg.OUTPUT_DIR, "inference")
-    evaluator_list = []
-    evaluator_type = MetadataCatalog.get(dataset_name).evaluator_type
-    if evaluator_type in ["sem_seg", "coco_panoptic_seg"]:
-        evaluator_list.append(
-            SemSegEvaluator(
-                dataset_name,
-                distributed=True,
-                output_dir=output_folder,
-            )
-        )
-    if evaluator_type in ["coco", "coco_panoptic_seg"]:
-        evaluator_list.append(COCOEvaluator(dataset_name, output_dir=output_folder))
-    if evaluator_type == "coco_panoptic_seg":
-        evaluator_list.append(COCOPanopticEvaluator(dataset_name, output_folder))
-    if evaluator_type == "cityscapes_instance":
-        assert (
-            torch.cuda.device_count() > comm.get_rank()
-        ), "CityscapesEvaluator currently do not work with multiple machines."
-        return CityscapesInstanceEvaluator(dataset_name)
-    if evaluator_type == "cityscapes_sem_seg":
-        assert (
-            torch.cuda.device_count() > comm.get_rank()
-        ), "CityscapesEvaluator currently do not work with multiple machines."
-        return CityscapesSemSegEvaluator(dataset_name)
-    if evaluator_type == "pascal_voc":
-        return PascalVOCDetectionEvaluator(dataset_name)
-    if evaluator_type == "lvis":
-        return LVISEvaluator(dataset_name, cfg, True, output_folder)
-    if len(evaluator_list) == 0:
-        raise NotImplementedError(
-            "no Evaluator for the dataset {} with the type {}".format(dataset_name, evaluator_type)
-        )
-    if len(evaluator_list) == 1:
-        return evaluator_list[0]
-    return DatasetEvaluators(evaluator_list)
-
-
-def do_test(cfg, model):
-    results = OrderedDict()
-    for dataset_name in cfg.DATASETS.TEST:
-        data_loader = build_detection_test_loader(cfg, dataset_name)
-        evaluator = get_evaluator(
-            cfg, dataset_name, os.path.join(cfg.OUTPUT_DIR, "inference", dataset_name)
-        )
-        results_i = inference_on_dataset(model, data_loader, evaluator)
-        results[dataset_name] = results_i
-        if comm.is_main_process():
-            logger.info("Evaluation results for {} in csv format:".format(dataset_name))
-            print_csv_format(results_i)
-    if len(results) == 1:
-        results = list(results.values())[0]
-    return results
-
-
-def do_train(cfg, model, resume=False):
-    model.train()
-    optimizer = build_optimizer(cfg, model)
-    scheduler = build_lr_scheduler(cfg, optimizer)
-
-    checkpointer = DetectionCheckpointer(
-        model, cfg.OUTPUT_DIR, optimizer=optimizer, scheduler=scheduler
-    )
-    start_iter = (
-        checkpointer.resume_or_load(cfg.MODEL.WEIGHTS, resume=resume).get("iteration", -1) + 1
-    )
-    max_iter = cfg.SOLVER.MAX_ITER
-
-    periodic_checkpointer = PeriodicCheckpointer(
-        checkpointer, cfg.SOLVER.CHECKPOINT_PERIOD, max_iter=max_iter
-    )
-
-    writers = default_writers(cfg.OUTPUT_DIR, max_iter) if comm.is_main_process() else []
-
-    # compared to "train_net.py", we do not support accurate timing and
-    # precise BN here, because they are not trivial to implement in a small training loop
-    data_loader = build_detection_train_loader(cfg)
-    logger.info("Starting training from iteration {}".format(start_iter))
-    with EventStorage(start_iter) as storage:
-        for data, iteration in zip(data_loader, range(start_iter, max_iter)):
-            storage.iter = iteration
-
-            loss_dict = model(data)
-            losses = sum(loss_dict.values())
-            assert torch.isfinite(losses).all(), loss_dict
-
-            loss_dict_reduced = {k: v.item() for k, v in comm.reduce_dict(loss_dict).items()}
-            losses_reduced = sum(loss for loss in loss_dict_reduced.values())
-            if comm.is_main_process():
-                storage.put_scalars(total_loss=losses_reduced, **loss_dict_reduced)
-
-            optimizer.zero_grad()
-            losses.backward()
-            optimizer.step()
-            storage.put_scalar("lr", optimizer.param_groups[0]["lr"], smoothing_hint=False)
-            scheduler.step()
-
-            if (
-                cfg.TEST.EVAL_PERIOD > 0
-                and (iteration + 1) % cfg.TEST.EVAL_PERIOD == 0
-                and iteration != max_iter - 1
-            ):
-                do_test(cfg, model)
-                # Compared to "train_net.py", the test results are not dumped to EventStorage
-                comm.synchronize()
-
-            if iteration - start_iter > 5 and (
-                (iteration + 1) % 20 == 0 or iteration == max_iter - 1
-            ):
-                for writer in writers:
-                    writer.write()
-            periodic_checkpointer.step(iteration)
-
-
-def setup(args):
-    """
-    Create configs and perform basic setups.
-    """
-    cfg = get_cfg()
-    cfg.merge_from_file(args.config_file)
-    cfg.merge_from_list(args.opts)
-    cfg.freeze()
-    default_setup(
-        cfg, args
-    )  # if you don't like any of the default setup, write your own setup code
-    return cfg
-
-
-def main(args):
-    cfg = setup(args)
-
-    model = build_model(cfg)
-    logger.info("Model:\n{}".format(model))
-    if args.eval_only:
-        DetectionCheckpointer(model, save_dir=cfg.OUTPUT_DIR).resume_or_load(
-            cfg.MODEL.WEIGHTS, resume=args.resume
-        )
-        return do_test(cfg, model)
-
-    distributed = comm.get_world_size() > 1
-    if distributed:
-        model = DistributedDataParallel(
-            model, device_ids=[comm.get_local_rank()], broadcast_buffers=False
-        )
-
-    do_train(cfg, model, resume=args.resume)
-    return do_test(cfg, model)
-
-
-if __name__ == "__main__":
-    args = default_argument_parser().parse_args()
-    print("Command Line Args:", args)
-    launch(
-        main,
-        args.num_gpus,
-        num_machines=args.num_machines,
-        machine_rank=args.machine_rank,
-        dist_url=args.dist_url,
-        args=(args,),
-    )
diff --git a/spaces/BaiyuS/Real-CUGAN-YZ/README.md b/spaces/BaiyuS/Real-CUGAN-YZ/README.md
deleted file mode 100644
index d673114edadba73e80f33a3c71bc0dbee8758cc8..0000000000000000000000000000000000000000
--- a/spaces/BaiyuS/Real-CUGAN-YZ/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/Bart92/RVC_HF/lib/uvr5_pack/utils.py b/spaces/Bart92/RVC_HF/lib/uvr5_pack/utils.py
deleted file mode 100644
index 0fafe8793b0d539fa58dd024342250b24b6187a9..0000000000000000000000000000000000000000
--- a/spaces/Bart92/RVC_HF/lib/uvr5_pack/utils.py
+++ /dev/null
@@ -1,120 +0,0 @@
-import torch
-import numpy as np
-from tqdm import tqdm
-import json
-
-
-def load_data(file_name: str = "./lib/uvr5_pack/name_params.json") -> dict:
-    with open(file_name, "r") as f:
-        data = json.load(f)
-
-    return data
-
-
-def make_padding(width, cropsize, offset):
-    left = offset
-    roi_size = cropsize - left * 2
-    if roi_size == 0:
-        roi_size = cropsize
-    right = roi_size - (width % roi_size) + left
-
-    return left, right, roi_size
-
-
-def inference(X_spec, device, model, aggressiveness, data):
-    """
-    data ： dic configs
-    """
-
-    def _execute(
-        X_mag_pad, roi_size, n_window, device, model, aggressiveness, is_half=True
-    ):
-        model.eval()
-        with torch.no_grad():
-            preds = []
-
-            iterations = [n_window]
-
-            total_iterations = sum(iterations)
-            for i in tqdm(range(n_window)):
-                start = i * roi_size
-                X_mag_window = X_mag_pad[
-                    None, :, :, start : start + data["window_size"]
-                ]
-                X_mag_window = torch.from_numpy(X_mag_window)
-                if is_half:
-                    X_mag_window = X_mag_window.half()
-                X_mag_window = X_mag_window.to(device)
-
-                pred = model.predict(X_mag_window, aggressiveness)
-
-                pred = pred.detach().cpu().numpy()
-                preds.append(pred[0])
-
-            pred = np.concatenate(preds, axis=2)
-        return pred
-
-    def preprocess(X_spec):
-        X_mag = np.abs(X_spec)
-        X_phase = np.angle(X_spec)
-
-        return X_mag, X_phase
-
-    X_mag, X_phase = preprocess(X_spec)
-
-    coef = X_mag.max()
-    X_mag_pre = X_mag / coef
-
-    n_frame = X_mag_pre.shape[2]
-    pad_l, pad_r, roi_size = make_padding(n_frame, data["window_size"], model.offset)
-    n_window = int(np.ceil(n_frame / roi_size))
-
-    X_mag_pad = np.pad(X_mag_pre, ((0, 0), (0, 0), (pad_l, pad_r)), mode="constant")
-
-    if list(model.state_dict().values())[0].dtype == torch.float16:
-        is_half = True
-    else:
-        is_half = False
-    pred = _execute(
-        X_mag_pad, roi_size, n_window, device, model, aggressiveness, is_half
-    )
-    pred = pred[:, :, :n_frame]
-
-    if data["tta"]:
-        pad_l += roi_size // 2
-        pad_r += roi_size // 2
-        n_window += 1
-
-        X_mag_pad = np.pad(X_mag_pre, ((0, 0), (0, 0), (pad_l, pad_r)), mode="constant")
-
-        pred_tta = _execute(
-            X_mag_pad, roi_size, n_window, device, model, aggressiveness, is_half
-        )
-        pred_tta = pred_tta[:, :, roi_size // 2 :]
-        pred_tta = pred_tta[:, :, :n_frame]
-
-        return (pred + pred_tta) * 0.5 * coef, X_mag, np.exp(1.0j * X_phase)
-    else:
-        return pred * coef, X_mag, np.exp(1.0j * X_phase)
-
-
-def _get_name_params(model_path, model_hash):
-    data = load_data()
-    flag = False
-    ModelName = model_path
-    for type in list(data):
-        for model in list(data[type][0]):
-            for i in range(len(data[type][0][model])):
-                if str(data[type][0][model][i]["hash_name"]) == model_hash:
-                    flag = True
-                elif str(data[type][0][model][i]["hash_name"]) in ModelName:
-                    flag = True
-
-                if flag:
-                    model_params_auto = data[type][0][model][i]["model_params"]
-                    param_name_auto = data[type][0][model][i]["param_name"]
-                    if type == "equivalent":
-                        return param_name_auto, model_params_auto
-                    else:
-                        flag = False
-    return param_name_auto, model_params_auto
diff --git a/spaces/Benson/text-generation/Examples/Adivina La Pelcula.md b/spaces/Benson/text-generation/Examples/Adivina La Pelcula.md
deleted file mode 100644
index e5b9d85e9473d2e4a03936217cbb5872c9a90450..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/Adivina La Pelcula.md	
+++ /dev/null
@@ -1,50 +0,0 @@
-<br />
-<h1>Adivina la película: Un juego divertido y desafiante para los amantes del cine</h1> <p> ¿Te gustan las películas? ¿Crees que puedes reconocer cualquier película de una sola escena, un cartel o un actor? Si es así, entonces deberías probar Guess the Movie, un juego divertido y desafiante que pone a prueba tu conocimiento y memoria cinematográfica. Adivina la película es un juego en el que tienes que adivinar el título de una película basado en diferentes pistas, como imágenes, sonidos, géneros, directores, actores, etc. Puedes jugarlo online o offline, solo o con amigos, y pasarlo en grande mientras aprendes nuevos hechos y curiosidades sobre las películas. </p>
-<h2>Adivina la película</h2><br /><p><b><b>Download Zip</b> &#10004;&#10004;&#10004; <a href="https://bltlly.com/2v6MXS">https://bltlly.com/2v6MXS</a></b></p><br /><br /> <h2>Cómo Jugar Adivina la Película</h2> <p>Las reglas de Adivina la Película son simples. Se te dará una pista sobre una película, como una imagen, un clip de sonido, un género, un director, un actor, etc. Tienes que adivinar el título de la película lo más rápido posible. Puede escribir su respuesta o elegir entre varias opciones. Dependiendo del tipo de pista y el nivel de dificultad, obtendrá más o menos puntos por cada suposición correcta. También puede omitir una pista si no la conoce o usar pistas si necesita ayuda. El juego termina cuando se le acaba el tiempo o las pistas. </p> <h3>Tipos de suposiciones</h3> <p>Hay diferentes tipos de suposiciones que puedes hacer en Adivina la película. Algunos de ellos son:</p> <ul> <li>Título: Tienes que adivinar el título completo de la película. </li> <li>Género: Tienes que adivinar el género o categoría de la película. </li> <li>Actor: Tienes que adivinar el nombre de un actor que protagonizó la película. </li> <li>Director: Tienes que adivinar el nombre del director que hizo la película. </li> <li>Año: Tienes que adivinar el año en que se estrenó la película. </li> <li>Cita: Tienes que adivinar qué película contiene una cita famosa. </li> </ul> <h3>Sistema de puntuación</h3> <p>El sistema de puntuación en Adivina la película depende de varios factores, como:</p> <ul> <li>El tipo de pista: Algunas pistas son más fáciles que otras y dan menos puntos. </li> <li El límite de tiempo: Cuanto más rápido adivines, más puntos obtendrás. </li>
-
-
-<li>El número de saltos: Cuantos más saltos uses, menos puntos obtendrás. </li>
-</ul>
-<p>El juego le mostrará su puntuación después de cada adivinanza y al final del juego. Puedes comparar tu puntuación con la de otros jugadores y ver lo bien que lo hiciste. </p>
-<h3>Consejos y trucos</h3>
-<p>Adivinar películas puede ser complicado, pero hay algunos consejos y trucos que pueden ayudarle a mejorar sus habilidades y velocidad. Estos son algunos de ellos:</p>
-<p></p>
-<ul>
-<li>Preste atención a los detalles: A veces, un pequeño detalle puede revelar la película, como un logotipo, un accesorio, un traje, una ubicación, etc.</li>
-<li>Usa tu memoria: Intenta recordar si has visto u oído de la película antes, y lo que recuerdas de ella. </li>
-<li>Usa tu lógica: Trata de deducir la película de las pistas, usando el sentido común y el razonamiento. </li>
-<li>Usa tus conocimientos: Trata de usar lo que sabes sobre películas, como géneros, directores, actores, premios, etc.</li>
-<li>Utilice su creatividad: Trate de pensar fuera de la caja y llegar a diferentes posibilidades. </li>
-</ul>
-<h2>Beneficios de jugar a adivinar la película</h2>
-<p>Jugar a adivinar la película no solo es divertido y desafiante, sino también beneficioso para el cerebro y la mente. Algunos de los beneficios son:</p>
-<ul>
-<li>Mejora tu memoria: al adivinar películas, activas tu memoria a largo y corto plazo, y fortaleces tu capacidad de recordar. </li>
-<li>Mejora tu conocimiento: adivinando películas, aprendes nuevos hechos y trivia sobre películas, como títulos, géneros, directores, actores, etc.</li>
-<li>Aumenta su creatividad: Al adivinar películas, estimula su imaginación y habilidades de pensamiento divergentes. </li>
-<li>Reduce el estrés: al adivinar películas, relajas tu mente y te diviertes. </li>
-<li>Aumenta la interacción social: adivinando películas, puedes jugar con amigos y familiares, y tener una buena conversación. </li>
-</ul>
-<h2>Ejemplos de adivinar los juegos de películas</h2>
-
-<h3>Moviedle</h3>
-<p>Moviedle es un juego en línea que te muestra una versión de un segundo de una película y te pide que adivines el título. Puedes elegir entre diferentes géneros y niveles de dificultad. También puedes crear tus propios Moviedles y compartirlos con otros. Moviedle es una forma rápida y divertida de probar tus habilidades de reconocimiento de películas. </p>
-<h3>Enmarcado</h3>
-<p>Framed es un juego online que te muestra seis fotogramas de una película y te pide que adivines el título. Puedes elegir entre diferentes categorías y niveles de dificultad. También puedes crear tus propios juegos enmarcados y compartirlos con otros. Enmarcado es una forma desafiante y adictiva de probar tus habilidades de observación de películas. </p>
-<h3>CineNerdle</h3>
-<p>CineNerdle es un juego en línea que te muestra una cuadrícula de fichas de un cartel de película y te pide que adivines el título. Puedes elegir entre diferentes géneros y niveles de dificultad. También puedes crear tus propios CineNerdles y compartirlos con otros. CineNerdle es una forma inteligente y divertida de poner a prueba tus conocimientos cinematográficos. </p>
-<h3>Charadas</h3>
-<p>Charades es un juego offline que consiste en representar un título de película sin hablar. Puedes jugar con dos o más personas, en equipos o individualmente. Puedes elegir entre diferentes géneros y niveles de dificultad. También puedes crear tus propias cartas de Charadas y usarlas en el juego. Charadas es una forma clásica y entretenida de poner a prueba tus habilidades de actuación cinematográfica. </p>
-<h2>Conclusión</h2>
-<p>Adivina la película es un juego divertido y desafiante que pone a prueba tu conocimiento de la película y la memoria. Es fácil jugar en línea o fuera de línea, solo o con amigos. Tiene muchos beneficios para el cerebro y la mente, como mejorar la memoria, el conocimiento, la creatividad, etc. También tiene muchos ejemplos de juegos que se basan en adivinanzas de películas, como Moviedle, Enmarcado, CineNerdle, Charadas, etc. Si te gustan las películas y quieres pasar un buen rato mientras aprendes nuevos hechos y trivia sobre ellas, ¡entonces deberías probar Guess the Movie hoy! </p>
-<h2>Preguntas frecuentes</h2>
-
- <ol>
- <li><b> ¿Qué es Adivina la película? </b><br/>Adivina la película es un juego donde tienes que adivinar el título de una película basado en diferentes pistas, como imágenes, sonidos, géneros, directores, actores, etc.</li>
-<li><b> ¿Cómo se juega Adivina la película? </b><br/>Puedes jugar Adivina la película en línea o fuera de línea, solo o con amigos. Se te dará una pista sobre una película, y tienes que adivinar el título lo más rápido posible. Puede escribir su respuesta o elegir entre varias opciones. También puede omitir una pista o usar sugerencias si necesita ayuda. </li>
-<li><b>¿Cuáles son los beneficios de jugar Adivina la película? </b><br/>Jugar a adivinar la película es beneficioso para el cerebro y la mente, ya que mejora la memoria, el conocimiento, la creatividad, la reducción del estrés y la interacción social. </li>
-<li><b> ¿Cuáles son algunos ejemplos de Adivina los juegos de películas? </b><br/>Algunos ejemplos de juegos en línea que se basan en adivinanzas son Moviedle, Framed, CineNerdle, etc. Algunos ejemplos de juegos en línea que se basan en adivinanzas son Charadas, Pictionary, etc.</li>
-<li><b> ¿Dónde puedo encontrar más información sobre Adivina la película? </b><br/>Puedes encontrar más información sobre Adivina la película en los siguientes sitios web: [Moviedle], [Framed], [CineNerdle], etc.</li>
-</ol></p> 64aa2da5cf<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/Benson/text-generation/Examples/Bloons Td 6 Descarga Uptodown.md b/spaces/Benson/text-generation/Examples/Bloons Td 6 Descarga Uptodown.md
deleted file mode 100644
index f4a042eb8747a71ba67d10903b9ee0efe58474b6..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/Bloons Td 6 Descarga Uptodown.md	
+++ /dev/null
@@ -1,90 +0,0 @@
-
-<h1>Bloons TD 6: Un juego de defensa de torre divertido y desafiante</h1>
-<p>Si usted está buscando un juego de torre de defensa que le mantendrá entretenido durante horas, es posible que desee echa un vistazo a Bloons TD 6. Esta es la última entrega de la popular serie Bloons, que ha existido desde 2007. En este juego, tienes que usar una variedad de torres de monos para hacer estallar globos (o bloons) que están tratando de invadir tu territorio. Suena simple, ¿verdad? Bueno, no del todo. Los bloons vienen en diferentes colores, formas y tamaños, cada uno con sus propias habilidades y resistencias. Algunos bloons pueden volar, algunos pueden camuflarse, algunos pueden regenerarse, algunos pueden dividirse en bloons más pequeños, y algunos pueden incluso proteger a otros bloons. Necesitarás usar estrategia, habilidad y creatividad para superar estos desafíos. </p>
-<h2>bloons td 6 descarga uptodown</h2><br /><p><b><b>Download</b> &#10026;&#10026;&#10026; <a href="https://bltlly.com/2v6Jiz">https://bltlly.com/2v6Jiz</a></b></p><br /><br />
-<p>Bloons TD 6 no es solo un simple juego de torre de defensa. Es un juego masivo de torre de defensa en 3D que ofrece muchas características y contenido para mantenerte involucrado. Puedes elegir entre diferentes modos, mapas, torres, héroes, mejoras, eventos, misiones, trofeos, desafíos, odisea y más. También puedes jugar con hasta otros tres jugadores en modo cooperativo, o competir con otros equipos en el modo territorio disputado. Si usted es un jugador casual o un fan hardcore, encontrará algo para disfrutar en Bloons TD 6.</p>
-<h2>¿Qué es Bloons TD 6?</h2>
-<p>Bloons TD 6 es un juego de torre de defensa desarrollado y publicado por Ninja Kiwi, una empresa con sede en Nueva Zelanda que se especializa en la creación de juegos divertidos y adictivos. El juego fue lanzado el 13 de junio de 2018 para dispositivos Android e iOS, y más tarde llevado a Steam para Windows y Macintosh. El juego es parte de la franquicia Bloons, que incluye otros juegos como Bloons Monkey City, Bloons Adventure Time TD, Bloons Super Monkey 2, y más. </p>
-
-<p>El modo de juego de Bloons TD 6 es similar a otros juegos de torre de defensa. Tienes una cantidad limitada de dinero para gastar en torres de monos, que puedes colocar en lugares designados en el mapa. Cada torre tiene un rango y un tipo de ataque, y puede apuntar a diferentes tipos de bloons. También puede actualizar sus torres para hacerlas más eficaces, pero esto cuesta más dinero. El objetivo es evitar que los bloons lleguen al final de la pista, donde reducirán sus vidas. Si pierdes todas tus vidas, pierdes el juego. </p>
-<p>Hay diferentes modos para elegir en Bloons TD 6, cada uno con sus propias reglas y desafíos. Algunos de los modos son:</p>
-<ul>
-<li>Estándar: El modo básico donde puedes elegir la dificultad y el mapa. </li>
-<li>Primary Only: Solo puedes usar torres de monos primarias, como monos dardos, monos boomerang, tiradores de bombas, tiradores de tachuelas, monos de hielo y artilleros de pegamento. </li>
-<li>Military Only: Solo puedes usar torres militares de monos, como monos francotiradores, monos submarinos, monos bucaneros, monos ases, pilotos de helicópteros y monos de mortero. </li>
-<li>Solo magia: Solo puedes usar torres de monos mágicos, como monos druidas, monos alquimistas, súper monos, monos ninja y monos hechiceros. </li>
-<li>Deflación: Empiezas con una cantidad fija de dinero y sin ingresos. Tienes que sobrevivir el mayor tiempo posible con lo que tienes. </li>
-<li>Apopalypse: Los bloons vienen en ondas más rápidas y más rápidas sin cualquier rotura. Usted tiene que sobrevivir tanto tiempo como sea posible. </li>
-<li>Impoppable: El modo más difícil donde los bloons son mucho más difíciles y las torres son más caras. Tienes que usar tu mejor estrategia y habilidades para ganar. </li>
-</ul>
-<h3>¿Cuáles son las principales características de Bloons TD 6?</h3>
-<h4>Gráficos 3D y mecánica de línea de visión</h4>
-
-<h4>23 torres de monos con 3 rutas de actualización cada una</h4>
-<p>Bloons TD 6 ofrece una gran variedad de torres de monos para elegir, cada una con sus propias fortalezas y debilidades. Hay cuatro categorías de torres: primarias, militares, mágicas y de apoyo. Cada torre tiene tres rutas de actualización que desbloquean diferentes habilidades y efectos. Por ejemplo, el mono dardo puede actualizar a un mono ballesta, un mono spike-o-pult, o un mono gigante. Puede mezclar y combinar dos rutas de actualización por torre, pero solo puede obtener una actualización de quinto nivel por ruta. Las mejoras de quinto nivel son muy potentes y caras, y pueden cambiar el juego drásticamente. </p>
-<h4>14 héroes con habilidades y personalidades únicas</h4>
-<p>Bloons TD 6 también presenta héroes, que son monos especiales que tienen habilidades y personalidades únicas. Los héroes suben de nivel automáticamente durante el juego, desbloqueando nuevas habilidades que pueden ayudarte de varias maneras. Por ejemplo, Quincy es un héroe arquero que puede disparar múltiples flechas a la vez, Gwendolin es un héroe mago de fuego que puede incendiar bloons, Striker Jones es un héroe experto en bombas que puede aturdir bloons de clase MOAB, y así sucesivamente. Solo puedes usar un héroe por juego, así que elige sabiamente. </p>
-<p></p>
-<h4>Actualizaciones regulares con nuevos contenidos y eventos</h4>
-<p>Bloons TD 6 se actualiza constantemente con nuevos contenidos y eventos por Ninja Kiwi. El juego añade nuevos mapas, modos, torres, héroes, pieles, misiones, trofeos, logros, desafíos, odisea, y más. El juego también cuenta con eventos de temporada, como Halloween, Navidad, Pascua y más, que ofrecen recompensas y desafíos especiales. También puedes participar en carreras diarias y semanales, donde puedes competir con otros jugadores para completar un mapa lo más rápido posible. Siempre hay algo nuevo y emocionante que hacer en Bloons TD 6.</p>
-<h2>¿Cómo descargar Bloons TD 6 desde uptodown? </h2>
-
-<ol>
-<li>Ir a la página <a href="">Bloons TD 6</a> en uptodown.com. </li>
-<li>Haga clic en el botón verde "Descargar" y espere a que el archivo APK se descargue. </li>
-<li>Una vez que la descarga se haya completado, abra el archivo APK y toque en "Instalar". Es posible que necesite habilitar la instalación de aplicaciones de fuentes desconocidas en la configuración de su dispositivo. </li>
-<li> Espere a que la instalación termine y luego inicie el juego desde el cajón de la aplicación o la pantalla de inicio. </li>
-<li>Disfruta de hacer estallar bloons! </li>
-</ol>
-<h2>¿Por qué descargar Bloons TD 6 desde uptodown? </h2>
-<p>Hay varias razones por las que es posible que desee descargar Bloons TD 6 de uptodown en lugar de otras fuentes, como Google Play Store o Steam. Algunos de ellos son:</p>
-<ul>
-<li>Puedes obtener el juego gratis, sin pagar dinero ni ver ningún anuncio. </li>
-<li> Puede obtener la última versión del juego, sin esperar actualizaciones o parches. </li>
-<li>Puedes acceder a todas las características y contenidos del juego, sin restricciones ni limitaciones. </li>
-<li>Puede jugar el juego sin conexión, sin necesidad de una conexión a Internet o una cuenta de Google. </li>
-</ul>
-<p>Sin embargo, también hay algunos inconvenientes de descargar Bloons TD 6 desde uptodown, como:</p>
-<ul>
-<li>Es posible que no pueda jugar en línea o en modo cooperativo con otros jugadores que hayan descargado el juego de otras fuentes. </li>
-<li>Es posible que no pueda sincronizar su progreso o logros con otros dispositivos o plataformas. </li>
-<li>Es posible que no pueda recibir apoyo oficial o comentarios de Ninja Kiwi en caso de cualquier problema o error. </li>
-<li>Puede arriesgarse a violar los términos y condiciones de Ninja Kiwi o Google Play Store descargando una versión no oficial del juego. </li>
-</ul>
-
-<p>Bloons TD 6 es un juego que requiere mucha estrategia y habilidad, especialmente en las dificultades y modos superiores. Aquí hay algunos consejos y trucos que pueden ayudarte a mejorar tu rendimiento y divertirte más en el juego:</p>
-<ul>
-<li>Usa las torres correctas para los bloons correctos. Los diferentes bloons tienen diferentes propiedades y resistencias, por lo que debe usar las torres apropiadas para lidiar con ellos. Por ejemplo, los bloons de camuflaje solo pueden ser vistos por torres que tienen detección de camuflaje, los bloons de plomo solo pueden ser reventados por torres que tienen ataques agudos o explosivos, y los bloons morados son inmunes a los ataques de magia y fuego. </li>
-<li>Usa el sistema de conocimiento del mono. El conocimiento del mono es una característica que te permite desbloquear mejoras permanentes y bonos para tus torres, héroes y poderes. Puedes ganar puntos de conocimiento de monos al subir de nivel tu cuenta, completar logros o participar en eventos. Puedes gastar estos puntos en varias ramas del árbol de conocimiento del mono, como primaria, militar, magia, apoyo, héroes y poderes. </li>
-<li>Usa el modo sandbox. El modo Sandbox es una función que te permite probar cualquier combinación de torres, mejoras, héroes, bloons y configuraciones en cualquier mapa. Puedes usar este modo para experimentar con diferentes estrategias, aprender cómo interactúan diferentes torres y bloons, o simplemente divertirte con dinero y vidas ilimitadas. </li>
-<li>Ver vídeos y guías de otros jugadores. Bloons TD 6 tiene una gran y activa comunidad de jugadores que comparten sus consejos, trucos, guías y vídeos en varias plataformas, como YouTube, Reddit, Discord, Steam y más. Puedes aprender mucho viendo cómo otros jugadores juegan el juego, especialmente en los modos y mapas más desafiantes. </li>
-
-</ul>
-<h2>Revisión de Bloons TD 6</h2>
-<p>Bloons TD 6 es un juego que personalmente disfruto mucho. Es un juego que combina el clásico género de defensa de la torre con gráficos coloridos, animaciones humorísticas y un juego adictivo. Es un juego que tiene mucho contenido y valor de repetición, gracias a sus actualizaciones y eventos regulares. Es un juego que atrae tanto a jugadores casuales como hardcore, gracias a sus múltiples modos y dificultades. </p>
-<p>Sin embargo, Bloons TD 6 no es un juego perfecto. Es un juego que puede volverse repetitivo y aburrido después de un tiempo, especialmente si juegas los mismos mapas y modos una y otra vez. Es un juego que puede ser frustrante e injusto a veces, especialmente en las dificultades y modos más altos donde los bloons son extremadamente difíciles y rápidos. Es un juego que puede resultar caro si quieres desbloquear todo rápidamente o utilizar elementos o características premium. </p>
-<p>En general, calificaría Bloons TD 6 como un 8/10. Es un juego que tiene sus defectos, pero también sus puntos fuertes. Es un juego que recomendaría a cualquiera que le gusten los juegos de torre de defensa o simplemente quiere divertirse haciendo estallar bloons. </p>
-<h2>Conclusión</h2>
-<p>Bloons TD 6 es un divertido y desafiante juego de torre de defensa que ofrece muchas características y contenido para mantenerte entretenido durante horas. Puedes elegir entre diferentes modos, mapas, torres, héroes, mejoras, eventos, misiones, trofeos, desafíos, odisea y más. También puedes jugar con hasta otros tres jugadores en modo cooperativo, o competir con otros equipos en el modo territorio disputado. Si usted es un jugador casual o un fan hardcore, encontrará algo para disfrutar en Bloons TD 6.</p>
-
-<p>Sin embargo, también debes ser consciente de los inconvenientes de descargar Bloons TD 6 desde uptodown, como no poder jugar en línea o modo cooperativo con otros jugadores que han descargado el juego de otras fuentes, no ser capaz de sincronizar su progreso o logros con otros dispositivos o plataformas, no ser capaz de recibir apoyo oficial o comentarios de Ninja Kiwi en caso de cualquier problema o error, y el riesgo de violar los términos y condiciones de Ninja Kiwi o Google Play Store mediante la descarga de una versión no oficial del juego. </p>
-<p>Por lo tanto, usted debe pesar los pros y los contras de la descarga de Bloons TD 6 de uptodown antes de tomar su decisión. También debes respetar los derechos y esfuerzos de Ninja Kiwi como desarrollador y editor del juego, y considerar apoyarlos comprando el juego de sus canales oficiales. </p>
-<p>Bloons TD 6 es un juego que requiere mucha estrategia y habilidad, especialmente en las dificultades y modos superiores. Puedes usar el sistema de conocimiento del mono para desbloquear mejoras y bonificaciones permanentes para tus torres, héroes y poderes. Puedes usar el modo sandbox para probar cualquier combinación de torres, mejoras, héroes, bloons y configuraciones en cualquier mapa. Puedes ver videos y guías de otros jugadores para aprender de sus consejos, trucos y estrategias. También puedes divertirte y ser creativo con las posibilidades del juego. </p>
-<p>Bloons TD 6 es un juego que personalmente disfruto mucho. Es un juego que combina el clásico género de defensa de la torre con gráficos coloridos, animaciones humorísticas y un juego adictivo. Es un juego que tiene mucho contenido y valor de repetición, gracias a sus actualizaciones y eventos regulares. Es un juego que atrae tanto a jugadores casuales como hardcore, gracias a sus múltiples modos y dificultades. </p>
-
-<h2>Preguntas frecuentes</h2>
-<p>Aquí hay algunas preguntas frecuentes sobre Bloons TD 6:</p>
-<ul>
-<li><b>Q: ¿Cuánto cuesta Bloons TD 6? </b></li>
-<li>A: Bloons TD 6 cuesta $4.99 en Google Play Store y Steam, pero se puede descargar de forma gratuita desde uptodown. </li>
-<li><b>Q: ¿Es Bloons TD 6 compatible con mi dispositivo? </b></li>
-<li>A: Bloons TD 6 requiere Android 5.0 o superior para dispositivos Android, iOS 11.0 o posterior para dispositivos iOS y Windows 7 o superior para dispositivos PC. </li>
-<li><b>Q: ¿Cómo puedo guardar mi progreso en Bloons TD 6?</b></li>
-<li>A: Bloons TD 6 guarda automáticamente tu progreso cada vez que completas un mapa o sales del juego. También puede guardar manualmente su progreso tocando el botón de menú y luego tocando el botón de guardar. </li>
-<li><b>Q: ¿Cómo puedo restaurar mi progreso en Bloons TD 6?</b></li>
-<li>A: Si has descargado el juego desde Google Play Store o Steam, puedes restaurar tu progreso iniciando sesión con tu cuenta de Google o Steam respectivamente. Si has descargado el juego desde uptodown, puedes restaurar tu progreso copiando tu archivo de guardado desde tu dispositivo antiguo a tu nuevo dispositivo. </li>
-<li><b>Q: ¿Cómo puedo contactar a Ninja Kiwi para apoyo o retroalimentación? </b></li>
-<li>A: Si has descargado el juego desde Google Play Store o Steam, puedes ponerte en contacto con Ninja Kiwi enviándole un correo electrónico a support@ninjakiwi.com o visitando su sitio web en https://ninjakiwi.com/support. Si has descargado el juego desde uptodown, usted no puede ser capaz de recibir apoyo oficial o comentarios de Ninja Kiwi.</li>
-</ul></p> 64aa2da5cf<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/chardet/universaldetector.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/chardet/universaldetector.py
deleted file mode 100644
index 30c441dc28ee327076a850b1d3c88a9a2c8f04f0..0000000000000000000000000000000000000000
--- a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/chardet/universaldetector.py
+++ /dev/null
@@ -1,362 +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 #########################
-"""
-Module containing the UniversalDetector detector class, which is the primary
-class a user of ``chardet`` should use.
-
-:author: Mark Pilgrim (initial port to Python)
-:author: Shy Shalom (original C code)
-:author: Dan Blanchard (major refactoring for 3.0)
-:author: Ian Cordasco
-"""
-
-
-import codecs
-import logging
-import re
-from typing import List, Optional, Union
-
-from .charsetgroupprober import CharSetGroupProber
-from .charsetprober import CharSetProber
-from .enums import InputState, LanguageFilter, ProbingState
-from .escprober import EscCharSetProber
-from .latin1prober import Latin1Prober
-from .macromanprober import MacRomanProber
-from .mbcsgroupprober import MBCSGroupProber
-from .resultdict import ResultDict
-from .sbcsgroupprober import SBCSGroupProber
-from .utf1632prober import UTF1632Prober
-
-
-class UniversalDetector:
-    """
-    The ``UniversalDetector`` class underlies the ``chardet.detect`` function
-    and coordinates all of the different charset probers.
-
-    To get a ``dict`` containing an encoding and its confidence, you can simply
-    run:
-
-    .. code::
-
-            u = UniversalDetector()
-            u.feed(some_bytes)
-            u.close()
-            detected = u.result
-
-    """
-
-    MINIMUM_THRESHOLD = 0.20
-    HIGH_BYTE_DETECTOR = re.compile(b"[\x80-\xFF]")
-    ESC_DETECTOR = re.compile(b"(\033|~{)")
-    WIN_BYTE_DETECTOR = re.compile(b"[\x80-\x9F]")
-    ISO_WIN_MAP = {
-        "iso-8859-1": "Windows-1252",
-        "iso-8859-2": "Windows-1250",
-        "iso-8859-5": "Windows-1251",
-        "iso-8859-6": "Windows-1256",
-        "iso-8859-7": "Windows-1253",
-        "iso-8859-8": "Windows-1255",
-        "iso-8859-9": "Windows-1254",
-        "iso-8859-13": "Windows-1257",
-    }
-    # Based on https://encoding.spec.whatwg.org/#names-and-labels
-    # but altered to match Python names for encodings and remove mappings
-    # that break tests.
-    LEGACY_MAP = {
-        "ascii": "Windows-1252",
-        "iso-8859-1": "Windows-1252",
-        "tis-620": "ISO-8859-11",
-        "iso-8859-9": "Windows-1254",
-        "gb2312": "GB18030",
-        "euc-kr": "CP949",
-        "utf-16le": "UTF-16",
-    }
-
-    def __init__(
-        self,
-        lang_filter: LanguageFilter = LanguageFilter.ALL,
-        should_rename_legacy: bool = False,
-    ) -> None:
-        self._esc_charset_prober: Optional[EscCharSetProber] = None
-        self._utf1632_prober: Optional[UTF1632Prober] = None
-        self._charset_probers: List[CharSetProber] = []
-        self.result: ResultDict = {
-            "encoding": None,
-            "confidence": 0.0,
-            "language": None,
-        }
-        self.done = False
-        self._got_data = False
-        self._input_state = InputState.PURE_ASCII
-        self._last_char = b""
-        self.lang_filter = lang_filter
-        self.logger = logging.getLogger(__name__)
-        self._has_win_bytes = False
-        self.should_rename_legacy = should_rename_legacy
-        self.reset()
-
-    @property
-    def input_state(self) -> int:
-        return self._input_state
-
-    @property
-    def has_win_bytes(self) -> bool:
-        return self._has_win_bytes
-
-    @property
-    def charset_probers(self) -> List[CharSetProber]:
-        return self._charset_probers
-
-    def reset(self) -> None:
-        """
-        Reset the UniversalDetector and all of its probers back to their
-        initial states.  This is called by ``__init__``, so you only need to
-        call this directly in between analyses of different documents.
-        """
-        self.result = {"encoding": None, "confidence": 0.0, "language": None}
-        self.done = False
-        self._got_data = False
-        self._has_win_bytes = False
-        self._input_state = InputState.PURE_ASCII
-        self._last_char = b""
-        if self._esc_charset_prober:
-            self._esc_charset_prober.reset()
-        if self._utf1632_prober:
-            self._utf1632_prober.reset()
-        for prober in self._charset_probers:
-            prober.reset()
-
-    def feed(self, byte_str: Union[bytes, bytearray]) -> None:
-        """
-        Takes a chunk of a document and feeds it through all of the relevant
-        charset probers.
-
-        After calling ``feed``, you can check the value of the ``done``
-        attribute to see if you need to continue feeding the
-        ``UniversalDetector`` more data, or if it has made a prediction
-        (in the ``result`` attribute).
-
-        .. note::
-           You should always call ``close`` when you're done feeding in your
-           document if ``done`` is not already ``True``.
-        """
-        if self.done:
-            return
-
-        if not byte_str:
-            return
-
-        if not isinstance(byte_str, bytearray):
-            byte_str = bytearray(byte_str)
-
-        # First check for known BOMs, since these are guaranteed to be correct
-        if not self._got_data:
-            # If the data starts with BOM, we know it is UTF
-            if byte_str.startswith(codecs.BOM_UTF8):
-                # EF BB BF  UTF-8 with BOM
-                self.result = {
-                    "encoding": "UTF-8-SIG",
-                    "confidence": 1.0,
-                    "language": "",
-                }
-            elif byte_str.startswith((codecs.BOM_UTF32_LE, codecs.BOM_UTF32_BE)):
-                # FF FE 00 00  UTF-32, little-endian BOM
-                # 00 00 FE FF  UTF-32, big-endian BOM
-                self.result = {"encoding": "UTF-32", "confidence": 1.0, "language": ""}
-            elif byte_str.startswith(b"\xFE\xFF\x00\x00"):
-                # FE FF 00 00  UCS-4, unusual octet order BOM (3412)
-                self.result = {
-                    # TODO: This encoding is not supported by Python. Should remove?
-                    "encoding": "X-ISO-10646-UCS-4-3412",
-                    "confidence": 1.0,
-                    "language": "",
-                }
-            elif byte_str.startswith(b"\x00\x00\xFF\xFE"):
-                # 00 00 FF FE  UCS-4, unusual octet order BOM (2143)
-                self.result = {
-                    # TODO: This encoding is not supported by Python. Should remove?
-                    "encoding": "X-ISO-10646-UCS-4-2143",
-                    "confidence": 1.0,
-                    "language": "",
-                }
-            elif byte_str.startswith((codecs.BOM_LE, codecs.BOM_BE)):
-                # FF FE  UTF-16, little endian BOM
-                # FE FF  UTF-16, big endian BOM
-                self.result = {"encoding": "UTF-16", "confidence": 1.0, "language": ""}
-
-            self._got_data = True
-            if self.result["encoding"] is not None:
-                self.done = True
-                return
-
-        # If none of those matched and we've only see ASCII so far, check
-        # for high bytes and escape sequences
-        if self._input_state == InputState.PURE_ASCII:
-            if self.HIGH_BYTE_DETECTOR.search(byte_str):
-                self._input_state = InputState.HIGH_BYTE
-            elif (
-                self._input_state == InputState.PURE_ASCII
-                and self.ESC_DETECTOR.search(self._last_char + byte_str)
-            ):
-                self._input_state = InputState.ESC_ASCII
-
-        self._last_char = byte_str[-1:]
-
-        # next we will look to see if it is appears to be either a UTF-16 or
-        # UTF-32 encoding
-        if not self._utf1632_prober:
-            self._utf1632_prober = UTF1632Prober()
-
-        if self._utf1632_prober.state == ProbingState.DETECTING:
-            if self._utf1632_prober.feed(byte_str) == ProbingState.FOUND_IT:
-                self.result = {
-                    "encoding": self._utf1632_prober.charset_name,
-                    "confidence": self._utf1632_prober.get_confidence(),
-                    "language": "",
-                }
-                self.done = True
-                return
-
-        # If we've seen escape sequences, use the EscCharSetProber, which
-        # uses a simple state machine to check for known escape sequences in
-        # HZ and ISO-2022 encodings, since those are the only encodings that
-        # use such sequences.
-        if self._input_state == InputState.ESC_ASCII:
-            if not self._esc_charset_prober:
-                self._esc_charset_prober = EscCharSetProber(self.lang_filter)
-            if self._esc_charset_prober.feed(byte_str) == ProbingState.FOUND_IT:
-                self.result = {
-                    "encoding": self._esc_charset_prober.charset_name,
-                    "confidence": self._esc_charset_prober.get_confidence(),
-                    "language": self._esc_charset_prober.language,
-                }
-                self.done = True
-        # If we've seen high bytes (i.e., those with values greater than 127),
-        # we need to do more complicated checks using all our multi-byte and
-        # single-byte probers that are left.  The single-byte probers
-        # use character bigram distributions to determine the encoding, whereas
-        # the multi-byte probers use a combination of character unigram and
-        # bigram distributions.
-        elif self._input_state == InputState.HIGH_BYTE:
-            if not self._charset_probers:
-                self._charset_probers = [MBCSGroupProber(self.lang_filter)]
-                # If we're checking non-CJK encodings, use single-byte prober
-                if self.lang_filter & LanguageFilter.NON_CJK:
-                    self._charset_probers.append(SBCSGroupProber())
-                self._charset_probers.append(Latin1Prober())
-                self._charset_probers.append(MacRomanProber())
-            for prober in self._charset_probers:
-                if prober.feed(byte_str) == ProbingState.FOUND_IT:
-                    self.result = {
-                        "encoding": prober.charset_name,
-                        "confidence": prober.get_confidence(),
-                        "language": prober.language,
-                    }
-                    self.done = True
-                    break
-            if self.WIN_BYTE_DETECTOR.search(byte_str):
-                self._has_win_bytes = True
-
-    def close(self) -> ResultDict:
-        """
-        Stop analyzing the current document and come up with a final
-        prediction.
-
-        :returns:  The ``result`` attribute, a ``dict`` with the keys
-                   `encoding`, `confidence`, and `language`.
-        """
-        # Don't bother with checks if we're already done
-        if self.done:
-            return self.result
-        self.done = True
-
-        if not self._got_data:
-            self.logger.debug("no data received!")
-
-        # Default to ASCII if it is all we've seen so far
-        elif self._input_state == InputState.PURE_ASCII:
-            self.result = {"encoding": "ascii", "confidence": 1.0, "language": ""}
-
-        # If we have seen non-ASCII, return the best that met MINIMUM_THRESHOLD
-        elif self._input_state == InputState.HIGH_BYTE:
-            prober_confidence = None
-            max_prober_confidence = 0.0
-            max_prober = None
-            for prober in self._charset_probers:
-                if not prober:
-                    continue
-                prober_confidence = prober.get_confidence()
-                if prober_confidence > max_prober_confidence:
-                    max_prober_confidence = prober_confidence
-                    max_prober = prober
-            if max_prober and (max_prober_confidence > self.MINIMUM_THRESHOLD):
-                charset_name = max_prober.charset_name
-                assert charset_name is not None
-                lower_charset_name = charset_name.lower()
-                confidence = max_prober.get_confidence()
-                # Use Windows encoding name instead of ISO-8859 if we saw any
-                # extra Windows-specific bytes
-                if lower_charset_name.startswith("iso-8859"):
-                    if self._has_win_bytes:
-                        charset_name = self.ISO_WIN_MAP.get(
-                            lower_charset_name, charset_name
-                        )
-                # Rename legacy encodings with superset encodings if asked
-                if self.should_rename_legacy:
-                    charset_name = self.LEGACY_MAP.get(
-                        (charset_name or "").lower(), charset_name
-                    )
-                self.result = {
-                    "encoding": charset_name,
-                    "confidence": confidence,
-                    "language": max_prober.language,
-                }
-
-        # Log all prober confidences if none met MINIMUM_THRESHOLD
-        if self.logger.getEffectiveLevel() <= logging.DEBUG:
-            if self.result["encoding"] is None:
-                self.logger.debug("no probers hit minimum threshold")
-                for group_prober in self._charset_probers:
-                    if not group_prober:
-                        continue
-                    if isinstance(group_prober, CharSetGroupProber):
-                        for prober in group_prober.probers:
-                            self.logger.debug(
-                                "%s %s confidence = %s",
-                                prober.charset_name,
-                                prober.language,
-                                prober.get_confidence(),
-                            )
-                    else:
-                        self.logger.debug(
-                            "%s %s confidence = %s",
-                            group_prober.charset_name,
-                            group_prober.language,
-                            group_prober.get_confidence(),
-                        )
-        return self.result
diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/pygments/cmdline.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/pygments/cmdline.py
deleted file mode 100644
index de73b06b4cfa3b68a25455148c7e086b32676e95..0000000000000000000000000000000000000000
--- a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/pygments/cmdline.py
+++ /dev/null
@@ -1,668 +0,0 @@
-"""
-    pygments.cmdline
-    ~~~~~~~~~~~~~~~~
-
-    Command line interface.
-
-    :copyright: Copyright 2006-2022 by the Pygments team, see AUTHORS.
-    :license: BSD, see LICENSE for details.
-"""
-
-import os
-import sys
-import shutil
-import argparse
-from textwrap import dedent
-
-from pip._vendor.pygments import __version__, highlight
-from pip._vendor.pygments.util import ClassNotFound, OptionError, docstring_headline, \
-    guess_decode, guess_decode_from_terminal, terminal_encoding, \
-    UnclosingTextIOWrapper
-from pip._vendor.pygments.lexers import get_all_lexers, get_lexer_by_name, guess_lexer, \
-    load_lexer_from_file, get_lexer_for_filename, find_lexer_class_for_filename
-from pip._vendor.pygments.lexers.special import TextLexer
-from pip._vendor.pygments.formatters.latex import LatexEmbeddedLexer, LatexFormatter
-from pip._vendor.pygments.formatters import get_all_formatters, get_formatter_by_name, \
-    load_formatter_from_file, get_formatter_for_filename, find_formatter_class
-from pip._vendor.pygments.formatters.terminal import TerminalFormatter
-from pip._vendor.pygments.formatters.terminal256 import Terminal256Formatter, TerminalTrueColorFormatter
-from pip._vendor.pygments.filters import get_all_filters, find_filter_class
-from pip._vendor.pygments.styles import get_all_styles, get_style_by_name
-
-
-def _parse_options(o_strs):
-    opts = {}
-    if not o_strs:
-        return opts
-    for o_str in o_strs:
-        if not o_str.strip():
-            continue
-        o_args = o_str.split(',')
-        for o_arg in o_args:
-            o_arg = o_arg.strip()
-            try:
-                o_key, o_val = o_arg.split('=', 1)
-                o_key = o_key.strip()
-                o_val = o_val.strip()
-            except ValueError:
-                opts[o_arg] = True
-            else:
-                opts[o_key] = o_val
-    return opts
-
-
-def _parse_filters(f_strs):
-    filters = []
-    if not f_strs:
-        return filters
-    for f_str in f_strs:
-        if ':' in f_str:
-            fname, fopts = f_str.split(':', 1)
-            filters.append((fname, _parse_options([fopts])))
-        else:
-            filters.append((f_str, {}))
-    return filters
-
-
-def _print_help(what, name):
-    try:
-        if what == 'lexer':
-            cls = get_lexer_by_name(name)
-            print("Help on the %s lexer:" % cls.name)
-            print(dedent(cls.__doc__))
-        elif what == 'formatter':
-            cls = find_formatter_class(name)
-            print("Help on the %s formatter:" % cls.name)
-            print(dedent(cls.__doc__))
-        elif what == 'filter':
-            cls = find_filter_class(name)
-            print("Help on the %s filter:" % name)
-            print(dedent(cls.__doc__))
-        return 0
-    except (AttributeError, ValueError):
-        print("%s not found!" % what, file=sys.stderr)
-        return 1
-
-
-def _print_list(what):
-    if what == 'lexer':
-        print()
-        print("Lexers:")
-        print("~~~~~~~")
-
-        info = []
-        for fullname, names, exts, _ in get_all_lexers():
-            tup = (', '.join(names)+':', fullname,
-                   exts and '(filenames ' + ', '.join(exts) + ')' or '')
-            info.append(tup)
-        info.sort()
-        for i in info:
-            print(('* %s\n    %s %s') % i)
-
-    elif what == 'formatter':
-        print()
-        print("Formatters:")
-        print("~~~~~~~~~~~")
-
-        info = []
-        for cls in get_all_formatters():
-            doc = docstring_headline(cls)
-            tup = (', '.join(cls.aliases) + ':', doc, cls.filenames and
-                   '(filenames ' + ', '.join(cls.filenames) + ')' or '')
-            info.append(tup)
-        info.sort()
-        for i in info:
-            print(('* %s\n    %s %s') % i)
-
-    elif what == 'filter':
-        print()
-        print("Filters:")
-        print("~~~~~~~~")
-
-        for name in get_all_filters():
-            cls = find_filter_class(name)
-            print("* " + name + ':')
-            print("    %s" % docstring_headline(cls))
-
-    elif what == 'style':
-        print()
-        print("Styles:")
-        print("~~~~~~~")
-
-        for name in get_all_styles():
-            cls = get_style_by_name(name)
-            print("* " + name + ':')
-            print("    %s" % docstring_headline(cls))
-
-
-def _print_list_as_json(requested_items):
-    import json
-    result = {}
-    if 'lexer' in requested_items:
-        info = {}
-        for fullname, names, filenames, mimetypes in get_all_lexers():
-            info[fullname] = {
-                'aliases': names,
-                'filenames': filenames,
-                'mimetypes': mimetypes
-            }
-        result['lexers'] = info
-
-    if 'formatter' in requested_items:
-        info = {}
-        for cls in get_all_formatters():
-            doc = docstring_headline(cls)
-            info[cls.name] = {
-                'aliases': cls.aliases,
-                'filenames': cls.filenames,
-                'doc': doc
-            }
-        result['formatters'] = info
-
-    if 'filter' in requested_items:
-        info = {}
-        for name in get_all_filters():
-            cls = find_filter_class(name)
-            info[name] = {
-                'doc': docstring_headline(cls)
-            }
-        result['filters'] = info
-
-    if 'style' in requested_items:
-        info = {}
-        for name in get_all_styles():
-            cls = get_style_by_name(name)
-            info[name] = {
-                'doc': docstring_headline(cls)
-            }
-        result['styles'] = info
-
-    json.dump(result, sys.stdout)
-
-def main_inner(parser, argns):
-    if argns.help:
-        parser.print_help()
-        return 0
-
-    if argns.V:
-        print('Pygments version %s, (c) 2006-2022 by Georg Brandl, Matthäus '
-              'Chajdas and contributors.' % __version__)
-        return 0
-
-    def is_only_option(opt):
-        return not any(v for (k, v) in vars(argns).items() if k != opt)
-
-    # handle ``pygmentize -L``
-    if argns.L is not None:
-        arg_set = set()
-        for k, v in vars(argns).items():
-            if v:
-                arg_set.add(k)
-
-        arg_set.discard('L')
-        arg_set.discard('json')
-
-        if arg_set:
-            parser.print_help(sys.stderr)
-            return 2
-
-        # print version
-        if not argns.json:
-            main(['', '-V'])
-        allowed_types = {'lexer', 'formatter', 'filter', 'style'}
-        largs = [arg.rstrip('s') for arg in argns.L]
-        if any(arg not in allowed_types for arg in largs):
-            parser.print_help(sys.stderr)
-            return 0
-        if not largs:
-            largs = allowed_types
-        if not argns.json:
-            for arg in largs:
-                _print_list(arg)
-        else:
-            _print_list_as_json(largs)
-        return 0
-
-    # handle ``pygmentize -H``
-    if argns.H:
-        if not is_only_option('H'):
-            parser.print_help(sys.stderr)
-            return 2
-        what, name = argns.H
-        if what not in ('lexer', 'formatter', 'filter'):
-            parser.print_help(sys.stderr)
-            return 2
-        return _print_help(what, name)
-
-    # parse -O options
-    parsed_opts = _parse_options(argns.O or [])
-
-    # parse -P options
-    for p_opt in argns.P or []:
-        try:
-            name, value = p_opt.split('=', 1)
-        except ValueError:
-            parsed_opts[p_opt] = True
-        else:
-            parsed_opts[name] = value
-
-    # encodings
-    inencoding = parsed_opts.get('inencoding', parsed_opts.get('encoding'))
-    outencoding = parsed_opts.get('outencoding', parsed_opts.get('encoding'))
-
-    # handle ``pygmentize -N``
-    if argns.N:
-        lexer = find_lexer_class_for_filename(argns.N)
-        if lexer is None:
-            lexer = TextLexer
-
-        print(lexer.aliases[0])
-        return 0
-
-    # handle ``pygmentize -C``
-    if argns.C:
-        inp = sys.stdin.buffer.read()
-        try:
-            lexer = guess_lexer(inp, inencoding=inencoding)
-        except ClassNotFound:
-            lexer = TextLexer
-
-        print(lexer.aliases[0])
-        return 0
-
-    # handle ``pygmentize -S``
-    S_opt = argns.S
-    a_opt = argns.a
-    if S_opt is not None:
-        f_opt = argns.f
-        if not f_opt:
-            parser.print_help(sys.stderr)
-            return 2
-        if argns.l or argns.INPUTFILE:
-            parser.print_help(sys.stderr)
-            return 2
-
-        try:
-            parsed_opts['style'] = S_opt
-            fmter = get_formatter_by_name(f_opt, **parsed_opts)
-        except ClassNotFound as err:
-            print(err, file=sys.stderr)
-            return 1
-
-        print(fmter.get_style_defs(a_opt or ''))
-        return 0
-
-    # if no -S is given, -a is not allowed
-    if argns.a is not None:
-        parser.print_help(sys.stderr)
-        return 2
-
-    # parse -F options
-    F_opts = _parse_filters(argns.F or [])
-
-    # -x: allow custom (eXternal) lexers and formatters
-    allow_custom_lexer_formatter = bool(argns.x)
-
-    # select lexer
-    lexer = None
-
-    # given by name?
-    lexername = argns.l
-    if lexername:
-        # custom lexer, located relative to user's cwd
-        if allow_custom_lexer_formatter and '.py' in lexername:
-            try:
-                filename = None
-                name = None
-                if ':' in lexername:
-                    filename, name = lexername.rsplit(':', 1)
-
-                    if '.py' in name:
-                        # This can happen on Windows: If the lexername is
-                        # C:\lexer.py -- return to normal load path in that case
-                        name = None
-
-                if filename and name:
-                    lexer = load_lexer_from_file(filename, name,
-                                                 **parsed_opts)
-                else:
-                    lexer = load_lexer_from_file(lexername, **parsed_opts)
-            except ClassNotFound as err:
-                print('Error:', err, file=sys.stderr)
-                return 1
-        else:
-            try:
-                lexer = get_lexer_by_name(lexername, **parsed_opts)
-            except (OptionError, ClassNotFound) as err:
-                print('Error:', err, file=sys.stderr)
-                return 1
-
-    # read input code
-    code = None
-
-    if argns.INPUTFILE:
-        if argns.s:
-            print('Error: -s option not usable when input file specified',
-                  file=sys.stderr)
-            return 2
-
-        infn = argns.INPUTFILE
-        try:
-            with open(infn, 'rb') as infp:
-                code = infp.read()
-        except Exception as err:
-            print('Error: cannot read infile:', err, file=sys.stderr)
-            return 1
-        if not inencoding:
-            code, inencoding = guess_decode(code)
-
-        # do we have to guess the lexer?
-        if not lexer:
-            try:
-                lexer = get_lexer_for_filename(infn, code, **parsed_opts)
-            except ClassNotFound as err:
-                if argns.g:
-                    try:
-                        lexer = guess_lexer(code, **parsed_opts)
-                    except ClassNotFound:
-                        lexer = TextLexer(**parsed_opts)
-                else:
-                    print('Error:', err, file=sys.stderr)
-                    return 1
-            except OptionError as err:
-                print('Error:', err, file=sys.stderr)
-                return 1
-
-    elif not argns.s:  # treat stdin as full file (-s support is later)
-        # read code from terminal, always in binary mode since we want to
-        # decode ourselves and be tolerant with it
-        code = sys.stdin.buffer.read()  # use .buffer to get a binary stream
-        if not inencoding:
-            code, inencoding = guess_decode_from_terminal(code, sys.stdin)
-            # else the lexer will do the decoding
-        if not lexer:
-            try:
-                lexer = guess_lexer(code, **parsed_opts)
-            except ClassNotFound:
-                lexer = TextLexer(**parsed_opts)
-
-    else:  # -s option needs a lexer with -l
-        if not lexer:
-            print('Error: when using -s a lexer has to be selected with -l',
-                  file=sys.stderr)
-            return 2
-
-    # process filters
-    for fname, fopts in F_opts:
-        try:
-            lexer.add_filter(fname, **fopts)
-        except ClassNotFound as err:
-            print('Error:', err, file=sys.stderr)
-            return 1
-
-    # select formatter
-    outfn = argns.o
-    fmter = argns.f
-    if fmter:
-        # custom formatter, located relative to user's cwd
-        if allow_custom_lexer_formatter and '.py' in fmter:
-            try:
-                filename = None
-                name = None
-                if ':' in fmter:
-                    # Same logic as above for custom lexer
-                    filename, name = fmter.rsplit(':', 1)
-
-                    if '.py' in name:
-                        name = None
-
-                if filename and name:
-                    fmter = load_formatter_from_file(filename, name,
-                                                     **parsed_opts)
-                else:
-                    fmter = load_formatter_from_file(fmter, **parsed_opts)
-            except ClassNotFound as err:
-                print('Error:', err, file=sys.stderr)
-                return 1
-        else:
-            try:
-                fmter = get_formatter_by_name(fmter, **parsed_opts)
-            except (OptionError, ClassNotFound) as err:
-                print('Error:', err, file=sys.stderr)
-                return 1
-
-    if outfn:
-        if not fmter:
-            try:
-                fmter = get_formatter_for_filename(outfn, **parsed_opts)
-            except (OptionError, ClassNotFound) as err:
-                print('Error:', err, file=sys.stderr)
-                return 1
-        try:
-            outfile = open(outfn, 'wb')
-        except Exception as err:
-            print('Error: cannot open outfile:', err, file=sys.stderr)
-            return 1
-    else:
-        if not fmter:
-            if os.environ.get('COLORTERM','') in ('truecolor', '24bit'):
-                fmter = TerminalTrueColorFormatter(**parsed_opts)
-            elif '256' in os.environ.get('TERM', ''):
-                fmter = Terminal256Formatter(**parsed_opts)
-            else:
-                fmter = TerminalFormatter(**parsed_opts)
-        outfile = sys.stdout.buffer
-
-    # determine output encoding if not explicitly selected
-    if not outencoding:
-        if outfn:
-            # output file? use lexer encoding for now (can still be None)
-            fmter.encoding = inencoding
-        else:
-            # else use terminal encoding
-            fmter.encoding = terminal_encoding(sys.stdout)
-
-    # provide coloring under Windows, if possible
-    if not outfn and sys.platform in ('win32', 'cygwin') and \
-       fmter.name in ('Terminal', 'Terminal256'):  # pragma: no cover
-        # unfortunately colorama doesn't support binary streams on Py3
-        outfile = UnclosingTextIOWrapper(outfile, encoding=fmter.encoding)
-        fmter.encoding = None
-        try:
-            import pip._vendor.colorama.initialise as colorama_initialise
-        except ImportError:
-            pass
-        else:
-            outfile = colorama_initialise.wrap_stream(
-                outfile, convert=None, strip=None, autoreset=False, wrap=True)
-
-    # When using the LaTeX formatter and the option `escapeinside` is
-    # specified, we need a special lexer which collects escaped text
-    # before running the chosen language lexer.
-    escapeinside = parsed_opts.get('escapeinside', '')
-    if len(escapeinside) == 2 and isinstance(fmter, LatexFormatter):
-        left = escapeinside[0]
-        right = escapeinside[1]
-        lexer = LatexEmbeddedLexer(left, right, lexer)
-
-    # ... and do it!
-    if not argns.s:
-        # process whole input as per normal...
-        try:
-            highlight(code, lexer, fmter, outfile)
-        finally:
-            if outfn:
-                outfile.close()
-        return 0
-    else:
-        # line by line processing of stdin (eg: for 'tail -f')...
-        try:
-            while 1:
-                line = sys.stdin.buffer.readline()
-                if not line:
-                    break
-                if not inencoding:
-                    line = guess_decode_from_terminal(line, sys.stdin)[0]
-                highlight(line, lexer, fmter, outfile)
-                if hasattr(outfile, 'flush'):
-                    outfile.flush()
-            return 0
-        except KeyboardInterrupt:  # pragma: no cover
-            return 0
-        finally:
-            if outfn:
-                outfile.close()
-
-
-class HelpFormatter(argparse.HelpFormatter):
-    def __init__(self, prog, indent_increment=2, max_help_position=16, width=None):
-        if width is None:
-            try:
-                width = shutil.get_terminal_size().columns - 2
-            except Exception:
-                pass
-        argparse.HelpFormatter.__init__(self, prog, indent_increment,
-                                        max_help_position, width)
-
-
-def main(args=sys.argv):
-    """
-    Main command line entry point.
-    """
-    desc = "Highlight an input file and write the result to an output file."
-    parser = argparse.ArgumentParser(description=desc, add_help=False,
-                                     formatter_class=HelpFormatter)
-
-    operation = parser.add_argument_group('Main operation')
-    lexersel = operation.add_mutually_exclusive_group()
-    lexersel.add_argument(
-        '-l', metavar='LEXER',
-        help='Specify the lexer to use.  (Query names with -L.)  If not '
-        'given and -g is not present, the lexer is guessed from the filename.')
-    lexersel.add_argument(
-        '-g', action='store_true',
-        help='Guess the lexer from the file contents, or pass through '
-        'as plain text if nothing can be guessed.')
-    operation.add_argument(
-        '-F', metavar='FILTER[:options]', action='append',
-        help='Add a filter to the token stream.  (Query names with -L.) '
-        'Filter options are given after a colon if necessary.')
-    operation.add_argument(
-        '-f', metavar='FORMATTER',
-        help='Specify the formatter to use.  (Query names with -L.) '
-        'If not given, the formatter is guessed from the output filename, '
-        'and defaults to the terminal formatter if the output is to the '
-        'terminal or an unknown file extension.')
-    operation.add_argument(
-        '-O', metavar='OPTION=value[,OPTION=value,...]', action='append',
-        help='Give options to the lexer and formatter as a comma-separated '
-        'list of key-value pairs. '
-        'Example: `-O bg=light,python=cool`.')
-    operation.add_argument(
-        '-P', metavar='OPTION=value', action='append',
-        help='Give a single option to the lexer and formatter - with this '
-        'you can pass options whose value contains commas and equal signs. '
-        'Example: `-P "heading=Pygments, the Python highlighter"`.')
-    operation.add_argument(
-        '-o', metavar='OUTPUTFILE',
-        help='Where to write the output.  Defaults to standard output.')
-
-    operation.add_argument(
-        'INPUTFILE', nargs='?',
-        help='Where to read the input.  Defaults to standard input.')
-
-    flags = parser.add_argument_group('Operation flags')
-    flags.add_argument(
-        '-v', action='store_true',
-        help='Print a detailed traceback on unhandled exceptions, which '
-        'is useful for debugging and bug reports.')
-    flags.add_argument(
-        '-s', action='store_true',
-        help='Process lines one at a time until EOF, rather than waiting to '
-        'process the entire file.  This only works for stdin, only for lexers '
-        'with no line-spanning constructs, and is intended for streaming '
-        'input such as you get from `tail -f`. '
-        'Example usage: `tail -f sql.log | pygmentize -s -l sql`.')
-    flags.add_argument(
-        '-x', action='store_true',
-        help='Allow custom lexers and formatters to be loaded from a .py file '
-        'relative to the current working directory. For example, '
-        '`-l ./customlexer.py -x`. By default, this option expects a file '
-        'with a class named CustomLexer or CustomFormatter; you can also '
-        'specify your own class name with a colon (`-l ./lexer.py:MyLexer`). '
-        'Users should be very careful not to use this option with untrusted '
-        'files, because it will import and run them.')
-    flags.add_argument('--json', help='Output as JSON. This can '
-        'be only used in conjunction with -L.',
-        default=False,
-        action='store_true')
-
-    special_modes_group = parser.add_argument_group(
-        'Special modes - do not do any highlighting')
-    special_modes = special_modes_group.add_mutually_exclusive_group()
-    special_modes.add_argument(
-        '-S', metavar='STYLE -f formatter',
-        help='Print style definitions for STYLE for a formatter '
-        'given with -f. The argument given by -a is formatter '
-        'dependent.')
-    special_modes.add_argument(
-        '-L', nargs='*', metavar='WHAT',
-        help='List lexers, formatters, styles or filters -- '
-        'give additional arguments for the thing(s) you want to list '
-        '(e.g. "styles"), or omit them to list everything.')
-    special_modes.add_argument(
-        '-N', metavar='FILENAME',
-        help='Guess and print out a lexer name based solely on the given '
-        'filename. Does not take input or highlight anything. If no specific '
-        'lexer can be determined, "text" is printed.')
-    special_modes.add_argument(
-        '-C', action='store_true',
-        help='Like -N, but print out a lexer name based solely on '
-        'a given content from standard input.')
-    special_modes.add_argument(
-        '-H', action='store', nargs=2, metavar=('NAME', 'TYPE'),
-        help='Print detailed help for the object <name> of type <type>, '
-        'where <type> is one of "lexer", "formatter" or "filter".')
-    special_modes.add_argument(
-        '-V', action='store_true',
-        help='Print the package version.')
-    special_modes.add_argument(
-        '-h', '--help', action='store_true',
-        help='Print this help.')
-    special_modes_group.add_argument(
-        '-a', metavar='ARG',
-        help='Formatter-specific additional argument for the -S (print '
-        'style sheet) mode.')
-
-    argns = parser.parse_args(args[1:])
-
-    try:
-        return main_inner(parser, argns)
-    except BrokenPipeError:
-        # someone closed our stdout, e.g. by quitting a pager.
-        return 0
-    except Exception:
-        if argns.v:
-            print(file=sys.stderr)
-            print('*' * 65, file=sys.stderr)
-            print('An unhandled exception occurred while highlighting.',
-                  file=sys.stderr)
-            print('Please report the whole traceback to the issue tracker at',
-                  file=sys.stderr)
-            print('<https://github.com/pygments/pygments/issues>.',
-                  file=sys.stderr)
-            print('*' * 65, file=sys.stderr)
-            print(file=sys.stderr)
-            raise
-        import traceback
-        info = traceback.format_exception(*sys.exc_info())
-        msg = info[-1].strip()
-        if len(info) >= 3:
-            # extract relevant file and position info
-            msg += '\n   (f%s)' % info[-2].split('\n')[0].strip()[1:]
-        print(file=sys.stderr)
-        print('*** Error while highlighting:', file=sys.stderr)
-        print(msg, file=sys.stderr)
-        print('*** If this is a bug you want to report, please rerun with -v.',
-              file=sys.stderr)
-        return 1
diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/pkg_resources/_vendor/importlib_resources/_legacy.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/pkg_resources/_vendor/importlib_resources/_legacy.py
deleted file mode 100644
index 1d5d3f1fbb1f6c69d0da2a50e1d4492ad3378f17..0000000000000000000000000000000000000000
--- a/spaces/Big-Web/MMSD/env/Lib/site-packages/pkg_resources/_vendor/importlib_resources/_legacy.py
+++ /dev/null
@@ -1,121 +0,0 @@
-import functools
-import os
-import pathlib
-import types
-import warnings
-
-from typing import Union, Iterable, ContextManager, BinaryIO, TextIO, Any
-
-from . import _common
-
-Package = Union[types.ModuleType, str]
-Resource = str
-
-
-def deprecated(func):
-    @functools.wraps(func)
-    def wrapper(*args, **kwargs):
-        warnings.warn(
-            f"{func.__name__} is deprecated. Use files() instead. "
-            "Refer to https://importlib-resources.readthedocs.io"
-            "/en/latest/using.html#migrating-from-legacy for migration advice.",
-            DeprecationWarning,
-            stacklevel=2,
-        )
-        return func(*args, **kwargs)
-
-    return wrapper
-
-
-def normalize_path(path):
-    # type: (Any) -> str
-    """Normalize a path by ensuring it is a string.
-
-    If the resulting string contains path separators, an exception is raised.
-    """
-    str_path = str(path)
-    parent, file_name = os.path.split(str_path)
-    if parent:
-        raise ValueError(f'{path!r} must be only a file name')
-    return file_name
-
-
-@deprecated
-def open_binary(package: Package, resource: Resource) -> BinaryIO:
-    """Return a file-like object opened for binary reading of the resource."""
-    return (_common.files(package) / normalize_path(resource)).open('rb')
-
-
-@deprecated
-def read_binary(package: Package, resource: Resource) -> bytes:
-    """Return the binary contents of the resource."""
-    return (_common.files(package) / normalize_path(resource)).read_bytes()
-
-
-@deprecated
-def open_text(
-    package: Package,
-    resource: Resource,
-    encoding: str = 'utf-8',
-    errors: str = 'strict',
-) -> TextIO:
-    """Return a file-like object opened for text reading of the resource."""
-    return (_common.files(package) / normalize_path(resource)).open(
-        'r', encoding=encoding, errors=errors
-    )
-
-
-@deprecated
-def read_text(
-    package: Package,
-    resource: Resource,
-    encoding: str = 'utf-8',
-    errors: str = 'strict',
-) -> str:
-    """Return the decoded string of the resource.
-
-    The decoding-related arguments have the same semantics as those of
-    bytes.decode().
-    """
-    with open_text(package, resource, encoding, errors) as fp:
-        return fp.read()
-
-
-@deprecated
-def contents(package: Package) -> Iterable[str]:
-    """Return an iterable of entries in `package`.
-
-    Note that not all entries are resources.  Specifically, directories are
-    not considered resources.  Use `is_resource()` on each entry returned here
-    to check if it is a resource or not.
-    """
-    return [path.name for path in _common.files(package).iterdir()]
-
-
-@deprecated
-def is_resource(package: Package, name: str) -> bool:
-    """True if `name` is a resource inside `package`.
-
-    Directories are *not* resources.
-    """
-    resource = normalize_path(name)
-    return any(
-        traversable.name == resource and traversable.is_file()
-        for traversable in _common.files(package).iterdir()
-    )
-
-
-@deprecated
-def path(
-    package: Package,
-    resource: Resource,
-) -> ContextManager[pathlib.Path]:
-    """A context manager providing a file path object to the resource.
-
-    If the resource does not already exist on its own on the file system,
-    a temporary file will be created. If the file was created, the file
-    will be deleted upon exiting the context manager (no exception is
-    raised if the file was deleted prior to the context manager
-    exiting).
-    """
-    return _common.as_file(_common.files(package) / normalize_path(resource))
diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/pkg_resources/_vendor/packaging/_manylinux.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/pkg_resources/_vendor/packaging/_manylinux.py
deleted file mode 100644
index 4c379aa6f69ff56c8f19612002c6e3e939ea6012..0000000000000000000000000000000000000000
--- a/spaces/Big-Web/MMSD/env/Lib/site-packages/pkg_resources/_vendor/packaging/_manylinux.py
+++ /dev/null
@@ -1,301 +0,0 @@
-import collections
-import functools
-import os
-import re
-import struct
-import sys
-import warnings
-from typing import IO, Dict, Iterator, NamedTuple, Optional, Tuple
-
-
-# Python does not provide platform information at sufficient granularity to
-# identify the architecture of the running executable in some cases, so we
-# determine it dynamically by reading the information from the running
-# process. This only applies on Linux, which uses the ELF format.
-class _ELFFileHeader:
-    # https://en.wikipedia.org/wiki/Executable_and_Linkable_Format#File_header
-    class _InvalidELFFileHeader(ValueError):
-        """
-        An invalid ELF file header was found.
-        """
-
-    ELF_MAGIC_NUMBER = 0x7F454C46
-    ELFCLASS32 = 1
-    ELFCLASS64 = 2
-    ELFDATA2LSB = 1
-    ELFDATA2MSB = 2
-    EM_386 = 3
-    EM_S390 = 22
-    EM_ARM = 40
-    EM_X86_64 = 62
-    EF_ARM_ABIMASK = 0xFF000000
-    EF_ARM_ABI_VER5 = 0x05000000
-    EF_ARM_ABI_FLOAT_HARD = 0x00000400
-
-    def __init__(self, file: IO[bytes]) -> None:
-        def unpack(fmt: str) -> int:
-            try:
-                data = file.read(struct.calcsize(fmt))
-                result: Tuple[int, ...] = struct.unpack(fmt, data)
-            except struct.error:
-                raise _ELFFileHeader._InvalidELFFileHeader()
-            return result[0]
-
-        self.e_ident_magic = unpack(">I")
-        if self.e_ident_magic != self.ELF_MAGIC_NUMBER:
-            raise _ELFFileHeader._InvalidELFFileHeader()
-        self.e_ident_class = unpack("B")
-        if self.e_ident_class not in {self.ELFCLASS32, self.ELFCLASS64}:
-            raise _ELFFileHeader._InvalidELFFileHeader()
-        self.e_ident_data = unpack("B")
-        if self.e_ident_data not in {self.ELFDATA2LSB, self.ELFDATA2MSB}:
-            raise _ELFFileHeader._InvalidELFFileHeader()
-        self.e_ident_version = unpack("B")
-        self.e_ident_osabi = unpack("B")
-        self.e_ident_abiversion = unpack("B")
-        self.e_ident_pad = file.read(7)
-        format_h = "<H" if self.e_ident_data == self.ELFDATA2LSB else ">H"
-        format_i = "<I" if self.e_ident_data == self.ELFDATA2LSB else ">I"
-        format_q = "<Q" if self.e_ident_data == self.ELFDATA2LSB else ">Q"
-        format_p = format_i if self.e_ident_class == self.ELFCLASS32 else format_q
-        self.e_type = unpack(format_h)
-        self.e_machine = unpack(format_h)
-        self.e_version = unpack(format_i)
-        self.e_entry = unpack(format_p)
-        self.e_phoff = unpack(format_p)
-        self.e_shoff = unpack(format_p)
-        self.e_flags = unpack(format_i)
-        self.e_ehsize = unpack(format_h)
-        self.e_phentsize = unpack(format_h)
-        self.e_phnum = unpack(format_h)
-        self.e_shentsize = unpack(format_h)
-        self.e_shnum = unpack(format_h)
-        self.e_shstrndx = unpack(format_h)
-
-
-def _get_elf_header() -> Optional[_ELFFileHeader]:
-    try:
-        with open(sys.executable, "rb") as f:
-            elf_header = _ELFFileHeader(f)
-    except (OSError, TypeError, _ELFFileHeader._InvalidELFFileHeader):
-        return None
-    return elf_header
-
-
-def _is_linux_armhf() -> bool:
-    # hard-float ABI can be detected from the ELF header of the running
-    # process
-    # https://static.docs.arm.com/ihi0044/g/aaelf32.pdf
-    elf_header = _get_elf_header()
-    if elf_header is None:
-        return False
-    result = elf_header.e_ident_class == elf_header.ELFCLASS32
-    result &= elf_header.e_ident_data == elf_header.ELFDATA2LSB
-    result &= elf_header.e_machine == elf_header.EM_ARM
-    result &= (
-        elf_header.e_flags & elf_header.EF_ARM_ABIMASK
-    ) == elf_header.EF_ARM_ABI_VER5
-    result &= (
-        elf_header.e_flags & elf_header.EF_ARM_ABI_FLOAT_HARD
-    ) == elf_header.EF_ARM_ABI_FLOAT_HARD
-    return result
-
-
-def _is_linux_i686() -> bool:
-    elf_header = _get_elf_header()
-    if elf_header is None:
-        return False
-    result = elf_header.e_ident_class == elf_header.ELFCLASS32
-    result &= elf_header.e_ident_data == elf_header.ELFDATA2LSB
-    result &= elf_header.e_machine == elf_header.EM_386
-    return result
-
-
-def _have_compatible_abi(arch: str) -> bool:
-    if arch == "armv7l":
-        return _is_linux_armhf()
-    if arch == "i686":
-        return _is_linux_i686()
-    return arch in {"x86_64", "aarch64", "ppc64", "ppc64le", "s390x"}
-
-
-# If glibc ever changes its major version, we need to know what the last
-# minor version was, so we can build the complete list of all versions.
-# For now, guess what the highest minor version might be, assume it will
-# be 50 for testing. Once this actually happens, update the dictionary
-# with the actual value.
-_LAST_GLIBC_MINOR: Dict[int, int] = collections.defaultdict(lambda: 50)
-
-
-class _GLibCVersion(NamedTuple):
-    major: int
-    minor: int
-
-
-def _glibc_version_string_confstr() -> Optional[str]:
-    """
-    Primary implementation of glibc_version_string using os.confstr.
-    """
-    # os.confstr is quite a bit faster than ctypes.DLL. It's also less likely
-    # to be broken or missing. This strategy is used in the standard library
-    # platform module.
-    # https://github.com/python/cpython/blob/fcf1d003bf4f0100c/Lib/platform.py#L175-L183
-    try:
-        # os.confstr("CS_GNU_LIBC_VERSION") returns a string like "glibc 2.17".
-        version_string = os.confstr("CS_GNU_LIBC_VERSION")
-        assert version_string is not None
-        _, version = version_string.split()
-    except (AssertionError, AttributeError, OSError, ValueError):
-        # os.confstr() or CS_GNU_LIBC_VERSION not available (or a bad value)...
-        return None
-    return version
-
-
-def _glibc_version_string_ctypes() -> Optional[str]:
-    """
-    Fallback implementation of glibc_version_string using ctypes.
-    """
-    try:
-        import ctypes
-    except ImportError:
-        return None
-
-    # ctypes.CDLL(None) internally calls dlopen(NULL), and as the dlopen
-    # manpage says, "If filename is NULL, then the returned handle is for the
-    # main program". This way we can let the linker do the work to figure out
-    # which libc our process is actually using.
-    #
-    # We must also handle the special case where the executable is not a
-    # dynamically linked executable. This can occur when using musl libc,
-    # for example. In this situation, dlopen() will error, leading to an
-    # OSError. Interestingly, at least in the case of musl, there is no
-    # errno set on the OSError. The single string argument used to construct
-    # OSError comes from libc itself and is therefore not portable to
-    # hard code here. In any case, failure to call dlopen() means we
-    # can proceed, so we bail on our attempt.
-    try:
-        process_namespace = ctypes.CDLL(None)
-    except OSError:
-        return None
-
-    try:
-        gnu_get_libc_version = process_namespace.gnu_get_libc_version
-    except AttributeError:
-        # Symbol doesn't exist -> therefore, we are not linked to
-        # glibc.
-        return None
-
-    # Call gnu_get_libc_version, which returns a string like "2.5"
-    gnu_get_libc_version.restype = ctypes.c_char_p
-    version_str: str = gnu_get_libc_version()
-    # py2 / py3 compatibility:
-    if not isinstance(version_str, str):
-        version_str = version_str.decode("ascii")
-
-    return version_str
-
-
-def _glibc_version_string() -> Optional[str]:
-    """Returns glibc version string, or None if not using glibc."""
-    return _glibc_version_string_confstr() or _glibc_version_string_ctypes()
-
-
-def _parse_glibc_version(version_str: str) -> Tuple[int, int]:
-    """Parse glibc version.
-
-    We use a regexp instead of str.split because we want to discard any
-    random junk that might come after the minor version -- this might happen
-    in patched/forked versions of glibc (e.g. Linaro's version of glibc
-    uses version strings like "2.20-2014.11"). See gh-3588.
-    """
-    m = re.match(r"(?P<major>[0-9]+)\.(?P<minor>[0-9]+)", version_str)
-    if not m:
-        warnings.warn(
-            "Expected glibc version with 2 components major.minor,"
-            " got: %s" % version_str,
-            RuntimeWarning,
-        )
-        return -1, -1
-    return int(m.group("major")), int(m.group("minor"))
-
-
-@functools.lru_cache()
-def _get_glibc_version() -> Tuple[int, int]:
-    version_str = _glibc_version_string()
-    if version_str is None:
-        return (-1, -1)
-    return _parse_glibc_version(version_str)
-
-
-# From PEP 513, PEP 600
-def _is_compatible(name: str, arch: str, version: _GLibCVersion) -> bool:
-    sys_glibc = _get_glibc_version()
-    if sys_glibc < version:
-        return False
-    # Check for presence of _manylinux module.
-    try:
-        import _manylinux  # noqa
-    except ImportError:
-        return True
-    if hasattr(_manylinux, "manylinux_compatible"):
-        result = _manylinux.manylinux_compatible(version[0], version[1], arch)
-        if result is not None:
-            return bool(result)
-        return True
-    if version == _GLibCVersion(2, 5):
-        if hasattr(_manylinux, "manylinux1_compatible"):
-            return bool(_manylinux.manylinux1_compatible)
-    if version == _GLibCVersion(2, 12):
-        if hasattr(_manylinux, "manylinux2010_compatible"):
-            return bool(_manylinux.manylinux2010_compatible)
-    if version == _GLibCVersion(2, 17):
-        if hasattr(_manylinux, "manylinux2014_compatible"):
-            return bool(_manylinux.manylinux2014_compatible)
-    return True
-
-
-_LEGACY_MANYLINUX_MAP = {
-    # CentOS 7 w/ glibc 2.17 (PEP 599)
-    (2, 17): "manylinux2014",
-    # CentOS 6 w/ glibc 2.12 (PEP 571)
-    (2, 12): "manylinux2010",
-    # CentOS 5 w/ glibc 2.5 (PEP 513)
-    (2, 5): "manylinux1",
-}
-
-
-def platform_tags(linux: str, arch: str) -> Iterator[str]:
-    if not _have_compatible_abi(arch):
-        return
-    # Oldest glibc to be supported regardless of architecture is (2, 17).
-    too_old_glibc2 = _GLibCVersion(2, 16)
-    if arch in {"x86_64", "i686"}:
-        # On x86/i686 also oldest glibc to be supported is (2, 5).
-        too_old_glibc2 = _GLibCVersion(2, 4)
-    current_glibc = _GLibCVersion(*_get_glibc_version())
-    glibc_max_list = [current_glibc]
-    # We can assume compatibility across glibc major versions.
-    # https://sourceware.org/bugzilla/show_bug.cgi?id=24636
-    #
-    # Build a list of maximum glibc versions so that we can
-    # output the canonical list of all glibc from current_glibc
-    # down to too_old_glibc2, including all intermediary versions.
-    for glibc_major in range(current_glibc.major - 1, 1, -1):
-        glibc_minor = _LAST_GLIBC_MINOR[glibc_major]
-        glibc_max_list.append(_GLibCVersion(glibc_major, glibc_minor))
-    for glibc_max in glibc_max_list:
-        if glibc_max.major == too_old_glibc2.major:
-            min_minor = too_old_glibc2.minor
-        else:
-            # For other glibc major versions oldest supported is (x, 0).
-            min_minor = -1
-        for glibc_minor in range(glibc_max.minor, min_minor, -1):
-            glibc_version = _GLibCVersion(glibc_max.major, glibc_minor)
-            tag = "manylinux_{}_{}".format(*glibc_version)
-            if _is_compatible(tag, arch, glibc_version):
-                yield linux.replace("linux", tag)
-            # Handle the legacy manylinux1, manylinux2010, manylinux2014 tags.
-            if glibc_version in _LEGACY_MANYLINUX_MAP:
-                legacy_tag = _LEGACY_MANYLINUX_MAP[glibc_version]
-                if _is_compatible(legacy_tag, arch, glibc_version):
-                    yield linux.replace("linux", legacy_tag)
diff --git a/spaces/CVPR/v-doc_abstractive_mac/descrip.md b/spaces/CVPR/v-doc_abstractive_mac/descrip.md
deleted file mode 100644
index d965b314baefdecfd61c003e97dcdc68f385782e..0000000000000000000000000000000000000000
--- a/spaces/CVPR/v-doc_abstractive_mac/descrip.md
+++ /dev/null
@@ -1,35 +0,0 @@
-# V-Doc : Visual questions answers with Documents
-This repository contains code for the paper [V-Doc : Visual questions answers with Documents](https://arxiv.org/pdf/2205.13724.pdf). The demo videos can be accessed by this [link](https://drive.google.com/file/d/1Ztp9LBcrEcJA3NlbFWn1RfNyfwt8Y6Qk/view).
-
-<h4 align="center">
-  <b>Ding, Y.*, Huang, Z.*, Wang, R., Zhang, Y., Chen, X., Ma, Y., Chung, H., & Han, C. (CVPR 2022) <br/><a href="https://arxiv.org/pdf/2205.13724.pdf">V-Doc : Visual questions answers with Documents</a><br/></b></span>
-</h4>
-
-<p align="center">
-  <img src="https://github.com/usydnlp/vdoc/blob/main/images/system_architecture.png">
-</p>
-
-### Dataset in Dataset Storage Module
-
-The dataset we used to trained the model is provided in following links:
-
-
- [PubVQA Dataset](https://drive.google.com/drive/folders/1YMuctGPJbsy45Iz23ygcN1VGHWQp3aaU?ths=true) for training Mac-Network.
-
-Dataset for training LayoutLMv2([FUNSD-QA](https://drive.google.com/file/d/1Ev_sLTx3U9nAr2TGgUT5BXB1rpfLMlcq/view?usp=sharing)). 
-
-### Dataset Generation
-To run the scene based question generation code, we need to fetch the JSON files from the source.  
-
-#### Extract OCR information
-```bash
-python3 ./document_collection.py
-```
-After the step above, a new folder called <code>./input_ocr</code> will be generated.
-#### Generate questions
-```bash
-python3 ./scene_based/pdf_generate_question.py
-```
-To limit the number of generated questions, you can change the code in <code>pdf_generate_question.py</code> line 575 and line 591-596
-
-After the steps above, you can see a json file under the <code>./output_qa_dataset</code>.
diff --git a/spaces/Catspindev/monadical-labs-minecraft-skin-generator/README.md b/spaces/Catspindev/monadical-labs-minecraft-skin-generator/README.md
deleted file mode 100644
index 55742e18742f322e1bb4673ee07f37168e44c580..0000000000000000000000000000000000000000
--- a/spaces/Catspindev/monadical-labs-minecraft-skin-generator/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Monadical Labs Minecraft Skin Generator
-emoji: 🚀
-colorFrom: gray
-colorTo: gray
-sdk: gradio
-sdk_version: 3.43.2
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
\ No newline at end of file
diff --git a/spaces/Chomkwoy/Nilkessye/__init__.py b/spaces/Chomkwoy/Nilkessye/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/Chomkwoy/Nilkessye/app.py b/spaces/Chomkwoy/Nilkessye/app.py
deleted file mode 100644
index 791a0bb3b2161b3d38527042a9828081a89df7bb..0000000000000000000000000000000000000000
--- a/spaces/Chomkwoy/Nilkessye/app.py
+++ /dev/null
@@ -1,253 +0,0 @@
-import gradio as gr
-import numpy as np
-import torch
-import io
-from PIL import Image
-from transformers import PreTrainedModel, VisionEncoderDecoderModel, VisionEncoderDecoderConfig
-import cv2
-from tqdm.auto import tqdm
-
-import load_book
-import utils.hangul
-from model import exkp
-import syllable_model
-import ocr_utils
-
-
-class OcrModel(PreTrainedModel):
-    config_class = VisionEncoderDecoderConfig
-
-    def __init__(self, config):
-        super().__init__(config)
-        self.centernet = exkp(
-            n=5,
-            nstack=4,
-            dims=[256, 256, 384, 384, 384, 512],
-            modules=[2, 2, 2, 2, 2, 4],
-            num_classes=4
-        )
-        self.recog = VisionEncoderDecoderModel(config)
-
-    def forward(self, pixel_values, **kwargs):
-        outputs = self.centernet(pixel_values, **kwargs)
-        return outputs
-
-
-def main():
-    model = OcrModel.from_pretrained('Chomkwoy/nilkessye')
-    if torch.cuda.is_available():
-        print("Enabling CUDA")
-        model = model.cuda()
-    recog = syllable_model.SyllableRecognizer(model.recog)
-
-    def upload_file(file):
-        yield (
-            [],  # gallery
-            "",  # output_textbox
-            gr.Textbox(show_label=False, visible=True),  # progress_indicator
-        )
-
-        image = Image.open(io.BytesIO(file))
-        yield (
-            [image],  # gallery
-            "",  # output_textbox
-            "처리중... 이미지 자르는 중",  # progress_indicator
-        )
-
-        image_np = np.array(image)[..., :3]
-        generator = recognize_page(
-            image_np,
-            model, recog,
-            return_line_infos=True,
-            batch_size=16
-        )
-
-        # Crop image
-        image = next(generator)
-        yield (
-            [Image.fromarray(image)],  # gallery
-            "",  # output_textbox
-            "처리중... 글자 위치 인식 중",  # progress_indicator
-        )
-
-        # Get lines
-        line_infos = next(generator)
-        image = draw_detections(image, line_infos)
-
-        # Read syllables
-        num_batches = next(generator)
-
-        yield (
-            [Image.fromarray(image)],  # gallery
-            "",  # output_textbox
-            f"처리중... 글자 읽는 중 (0/{num_batches})",  # progress_indicator
-        )
-
-        # Free memory
-        i = 0
-        while True:
-            try:
-                pred_syllables = next(generator)
-                i += 1
-                yield (
-                    [Image.fromarray(image)],  # gallery
-                    gen_html(pred_syllables, line_infos),  # output_textbox
-                    f"처리중... 글자 읽는 중 ({i}/{num_batches})",  # progress_indicator
-                )
-            except StopIteration:
-                break
-
-        yield (
-            [Image.fromarray(image)],  # gallery
-            gen_html(pred_syllables, line_infos),  # output_textbox
-            gr.Textbox(visible=False),  # progress_indicator
-        )
-
-    with gr.Blocks() as demo:
-        gr.Markdown("""
-        # 닐거쎠: 옛한글 글자 인식기
-
-        이미지 파일을 업로드해보세요. 한자는 인식되지 않습니다.
-        
-        만든사람: ᄎᆞᆷ괴
-        """)
-
-        progress_indicator = gr.Textbox(visible=False)
-
-        with gr.Row():
-            gallery = gr.Gallery(
-                columns=1,
-                allow_preview=False,
-                object_fit="contain",
-                label="보기"
-            )
-
-            with gr.Column():
-                upload_button = gr.UploadButton(
-                    '파일 올리기',
-                    type='binary'
-                )
-                output_textbox = gr.HTML(
-                    label="인식 결과",
-                    value="여기에 결과가 표시됩니다."
-                )
-
-        upload_button.upload(
-            fn=upload_file,
-            inputs=upload_button,
-            outputs=[gallery, output_textbox, progress_indicator]
-        )
-
-    demo.queue(max_size=20).launch(server_name='0.0.0.0')
-
-
-def gen_html(pred_syllables, line_infos):
-    output_lines = []
-    offset = 0
-    for line in line_infos:
-        if offset >= len(pred_syllables):
-            break
-        line_len = len(line['line'])
-        cur_line = '.'.join(pred_syllables[offset:offset + line_len])
-        cur_line_hangul = utils.hangul.convert_yale_to_hangul(cur_line)
-        output_lines.append({
-            'is_anno': line['is_anno'],
-            'text': cur_line_hangul
-        })
-        offset += line_len
-
-    output_html = ""
-    for line in output_lines:
-        if line['is_anno']:
-            output_html += f"<span style='letter-spacing: .1rem;'>{line['text']}</span>"
-        else:
-            output_html += f"<span style='font-size: 1.5em; letter-spacing: .1rem;'>{line['text']}</span>"
-
-    return output_html
-
-
-def draw_detections(image, line_infos):
-    image = image.copy()
-    for line_idx, line_info in enumerate(line_infos):
-        cv2.rectangle(image,
-                      (int(line_info['bbox'][0][0]), int(line_info['bbox'][0][1])),
-                      (int(line_info['bbox'][1][0]), int(line_info['bbox'][1][1])),
-                      [255, 255, 255], 6)
-
-    for line_idx, line_info in enumerate(line_infos):
-        for bbox, center, seq, cls in line_info['line']:
-            color = [[160, 158, 255], [212, 56, 13], [107, 255, 171], [255, 205, 66]][int(cls)]
-            shapes = image.copy()
-            cv2.rectangle(shapes, *bbox, color, cv2.FILLED)
-            alpha = 0.75
-            image = cv2.addWeighted(image, alpha, shapes, 1 - alpha, 0)
-            cv2.rectangle(image, *bbox, color, 2)
-
-    for line_idx, line_info in enumerate(line_infos):
-        cv2.putText(
-            image, f"{line_idx}",
-            (int(line_info['bbox'][0][0]), int(line_info['bbox'][0][1]) + 15),
-            cv2.FONT_HERSHEY_SIMPLEX, 0.7, [250, 225, 0], 2
-        )
-    return image
-
-
-def recognize_page(orig_image, centernet, syllable_recognizer, return_line_infos=False, batch_size=32):
-    orig_image, bbox, orig_size = load_book.process_page(orig_image)
-    yield orig_image
-
-    orig_size = (orig_image.shape[1], orig_image.shape[0])
-    image = cv2.resize(orig_image, dsize=(512, 512), interpolation=cv2.INTER_AREA)
-
-    image = image.astype(np.float32) / 255. - .5  # to [-.5, +.5] range
-    image = image.transpose((2, 0, 1))  # [H, W, C] to [C, H, W]
-    image = torch.as_tensor(image)
-
-    # Run object detection
-    centernet.eval()
-    with torch.no_grad():
-        output = centernet(torch.as_tensor(image)[None].to(centernet.device))
-
-    sw, sh = orig_size[0] * 4 / 512, orig_size[1] * 4 / 512
-
-    tiles = ocr_utils.get_pred_detections(
-        output, sw=sw, sh=sh,
-        threshold=0.3,
-        ae_threshold=20.0
-    )
-
-    line_infos = ocr_utils.detect_lines(tiles)
-    yield line_infos
-
-    yield from recognize_lines(line_infos, orig_image, syllable_recognizer, batch_size=batch_size)
-
-
-def recognize_lines(line_infos, orig_image, syllable_recognizer, batch_size=32):
-    tiles = []
-    for line_idx, line_info in enumerate(line_infos):
-        for bbox, center, seq, cls in line_info['line']:
-            (tlx, tly), (brx, bry) = bbox
-            w, h = brx - tlx, bry - tly
-            pw, ph = w / 5, h / 5
-            tile = orig_image[
-                   max(0, int(tly - ph)):min(orig_image.shape[0], int(bry + ph)),
-                   max(0, int(tlx - pw)):min(orig_image.shape[1], int(brx + pw)),
-                   ]
-            tiles.append((tile, bbox, center, seq, cls))
-
-    hangul_tiles = [(i, tile) for i, (tile, _, _, _, cls) in enumerate(tiles) if cls in [0, 2]]
-
-    pred_syllables = ["〓"] * len(tiles)
-    batches = list(ocr_utils.batched(hangul_tiles, batch_size))
-    yield len(batches)
-
-    for batch in tqdm(batches):
-        indices, images = zip(*batch)
-        batch_pred_syllables = syllable_recognizer.recognize(images)
-        for i, pred_syllable in zip(indices, batch_pred_syllables):
-            pred_syllables[i] = pred_syllable
-        yield pred_syllables[:i + 1]
-
-
-if __name__ == "__main__":
-    main()
diff --git a/spaces/CikeyQI/meme-api/meme_generator/memes/interview/__init__.py b/spaces/CikeyQI/meme-api/meme_generator/memes/interview/__init__.py
deleted file mode 100644
index a8d226fad8b20c09cbedc2e5dd0ecab0beb9b904..0000000000000000000000000000000000000000
--- a/spaces/CikeyQI/meme-api/meme_generator/memes/interview/__init__.py
+++ /dev/null
@@ -1,45 +0,0 @@
-from pathlib import Path
-from typing import List
-
-from pil_utils import BuildImage
-
-from meme_generator import add_meme
-from meme_generator.exception import TextOverLength
-
-img_dir = Path(__file__).parent / "images"
-
-
-def interview(images: List[BuildImage], texts: List[str], args):
-    if len(images) == 2:
-        self_img = images[0]
-        user_img = images[1]
-    else:
-        self_img = BuildImage.open(img_dir / "huaji.png")
-        user_img = images[0]
-    self_img = self_img.convert("RGBA").square().resize((124, 124))
-    user_img = user_img.convert("RGBA").square().resize((124, 124))
-
-    text = texts[0] if texts else "采访大佬经验"
-
-    frame = BuildImage.new("RGBA", (600, 310), "white")
-    microphone = BuildImage.open(img_dir / "microphone.png")
-    frame.paste(microphone, (330, 103), alpha=True)
-    frame.paste(self_img, (419, 40), alpha=True)
-    frame.paste(user_img, (57, 40), alpha=True)
-    try:
-        frame.draw_text((20, 200, 580, 310), text, max_fontsize=50, min_fontsize=20)
-    except ValueError:
-        raise TextOverLength(text)
-    return frame.save_jpg()
-
-
-add_meme(
-    "interview",
-    interview,
-    min_images=1,
-    max_images=2,
-    min_texts=0,
-    max_texts=1,
-    default_texts=["采访大佬经验"],
-    keywords=["采访"],
-)
diff --git a/spaces/CognitiveLabs/GPT-auto-webscraping/ExcecuteFunction.py b/spaces/CognitiveLabs/GPT-auto-webscraping/ExcecuteFunction.py
deleted file mode 100644
index edce83a720f72e05fdad7bb6b29464375e7be28c..0000000000000000000000000000000000000000
--- a/spaces/CognitiveLabs/GPT-auto-webscraping/ExcecuteFunction.py
+++ /dev/null
@@ -1,9 +0,0 @@
-import importlib
-
-def execute_function():
-    module = "output"
-    function = "extract_info"
-    module = importlib.import_module(module)
-    function = getattr(module, function)
-    print("returning function")
-    return function
diff --git a/spaces/CognitiveLabs/Research-Assistant/test/test3.py b/spaces/CognitiveLabs/Research-Assistant/test/test3.py
deleted file mode 100644
index b19587e7b045df07ae61eaec8949524850600394..0000000000000000000000000000000000000000
--- a/spaces/CognitiveLabs/Research-Assistant/test/test3.py
+++ /dev/null
@@ -1,21 +0,0 @@
-import openai
-
-openai.api_key = "sk-DQ1nFYzAVzGMznofdi0nig7MebfA9PWrTxCHlLIZIqc4X8xu"
-openai.api_base = "https://api.chatanywhere.cn/v1"
-
-def generator():
-    messages = [{
-            "role": "user",
-            "content": "What is the meaning of life?",
-    }]
-    response = ""
-    for chunk in openai.ChatCompletion.create(
-            model="gpt-3.5-turbo",
-            messages=messages,
-            temperature=0.9,
-            stream=True,
-    ):
-        content = chunk["choices"][0].get("delta", {}).get("content")
-        if content:
-            response += content
-        yield response
\ No newline at end of file
diff --git a/spaces/DEEMOSTECH/ChatAvatar/static/js/main.2966234b.js b/spaces/DEEMOSTECH/ChatAvatar/static/js/main.2966234b.js
deleted file mode 100644
index 6d2087431ea2c3a02a763556395cd41902e4488f..0000000000000000000000000000000000000000
--- a/spaces/DEEMOSTECH/ChatAvatar/static/js/main.2966234b.js
+++ /dev/null
@@ -1,3 +0,0 @@
-/*! For license information please see main.2966234b.js.LICENSE.txt */
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P P P          P P     P + +    P P P P P P P P P P P P P P P P P P P P P P     P      P    P      + +                + P P P P P P P P P P P P P P P P P P P P P P P P P    + +  + P P P P P P P P P P P + + + + + + + + + + + + + + + + + + + + + P P P P P P P P P P P P P P P P P P P P P + P P P P P P P P P P + + + + + + + + + + +                                              P P P P P P P P P P P P P P P P P P P P P P P P P P P P P P    P                   P        P P P P P P P P P P     K K K K K K K K K K  P P P P P P P P P P P P P P P P    + P P P P P P P P + + P P + + P P P P P P P P P P P P P P + P P P P P P P + P + + + P P P P + +  P        + +   + +    P + + + + + + + +  + + + + P P + P P P   + + K K K K K K K K K K P P   P P P P P L   P   + +    + P P P P P P + + + + P P + + P P P P P P P P P P P P P P + P P P P P P P + P P + P P + P P + +  +      + + + +   + +    + + +  + + + + + + + P P P P + P + + + + + + + K K K K K K K K K K   P P P   + + + + + + + + + +    + P P P P P P P P P + P P P + P P P P P P P P P P P P P P + P P P P P P P + P P + P P P P P + +  P         +    +    + + P + + + + + + + + + + + + + + + P P   + + K K K K K K K K K K   + + + + + + + P       +    + P P P P P P P P + + P P + + P P P P P P P P P P P P P      + +   + +    + + + + + + +    + + + + P P + P P P   + + K K K K K K K K K K  P P P P P P P + + + + + + + + + +  P + P P P P P P + + + P P P + P P P P + + + P P + P + P P + + + P P + + + P P P + + + P P P P P P P P P P P P + + + +      + + +    +     + + P + + + + + +  + + + + + + + + + + K K K K K K K K K K P P P         + + + + +      P P P P P P P P + P P P + P P P P P P P P P P P P P P P + P P P P P P P P P P P P P P P P + + + P        +    +     + + + + + + +   + P P P + + + + + P P   + + K K K K K K K K K K + + + + + + +  P P P P P P P  P     P P P P P P P P + P P P + P P P P P P P P P P P P P P P + P P P P P P P P P P + P P P P P + +  P        +    +     + + + + + + +   + + + + + + + P + P P   + + K K K K K K K K K K + P P + + + + + + + + + + + + +     P P P P P P P P P + P P P + P P P P P P P P P P P P P P P P P P P P P P P P P P P P P   P        +    +     P  + + + + P P P  P P P P P P P P P P   + + K K K K K K K K K K P P P P P P P P P  P P P P P P +    + P P P P P P P P P P P P P P P P P P + + + P P P P P P P P P P P P P P P P P P P P + P P P P P P P P P + P + + P P P P P P P + + +  + + + +       +  +         + + + + + + K K K K K K K K K K + +    + + + + + + + + + + + + \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ * \ \ * * * * * * * + + + +  \ \ \ \ \ \ \ * * * * * * * *  K K K K K K K K K K   + + + + + \ \ + \ + \ \ \ \ \ + \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ + \ + \ \ \ \ \ \ \ \ \ \ * \ \ * * * * * * * * * \ + + \ \ \ \ \ + \ + * * * * * * + + K K K K K K K K K K + + \ \ \ \ P        	    	      	              K K K K K K K K K K P P P P P P P P P P             P P P P P P P P + P P P P P P P P P P P P P P P P P P P P P P P P + + + +                        P P P P P            +                                 +                +            	 	 + + + + + \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ * * * * * * * * * * * * * * * * * * * * \ K K K K K K K K K K       \ \ \ \ \ \ * * * * \ \ \ \ * * * \ * * * \ \ * * * * * * * \ \ \ * * * * \ \ \ \ \ \ \ \ \ \ \ \ \ * * * * * * * * * * * * \ * K K K K K K K K K K * * * * * * P P P P P P + P + + + + + P + + P P P P P P P P P P P P P P P P P P P P P P P P P P P  P P P P X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z P P P P P P P P P + P P P P + + P P P P P P P + P + P P P P + + P P P P P P P P P + P P P P + + P P P P P P P P P P P P P P P P P + P P P P + + P P P P P P P + P + P P P P + + P P P P P P P P P P P P P P P + P P P P P P P P P P P P P P P P P + P P P P + + P P P P P P P P P P P P P P P P P P P P P P P P P P P + +             P P P P P P P P P P P P P P P P P P P P + + + P P P P P P P P P P P P P P P P           + + + + + + P P P P P P P P P P P P P P P P P P P P P P + + P P P P P P + +  P P P P P P P P P P P P P P P P P P P P P P P P P P P P P P P P   P P P P P P P P P P P P P P P P P  P P P P P P P P P P P P P P P P P P P P P P P P P P   + + + P P P P P P P P P P P    P P P P P P P P P P P + + + + + + + P P P P P P P P P P P P P + P P P P    + + + + + + + + + + + P P P P P P P P P P P P P P P P P P      + + + + + + + + + P P P P P P P P P P P P P P P P P P   + + + + + + + + + + + + P P P P P P P P P P P P P + P P P +   + + + + + + + + + + + + \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ * * * * * * * * * * * * * * * * * * * *    \     \ * + + K K K K K K K K K K + + + + + + P P P P P P P P P P + + + + + +               	 + K K K K K K K K K K + + + + + + P P P P P P P P P P P P P P P P P P P P P P P P P + + + + + + + P P P P P   P P P P P P P P P P P P P P P P P P P P P P P P P P  P + + + + + P P P P P P P P P P P P P P P P P P P P P P + + + + + + + + + + P P P P P P P P P P P P P P P P P P P P P P P P P P P P P P P +             + + + +             + + + +  + + +   K K K K K K K K K K \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ + + \ \ \ \ \ + + + + + + + + + + + \ \ \ \ \ \ \ \ \ \ \ \ + + + + \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ + + + + + + K K K K K K K K K K \ + + + * * P P P P P P P P P P P P P P P P P P P P P P P      + +   \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ * * * * * * * * * * + * * * * * * * * * * * * * * * * * * * * * * * * * * * * * + +  K K K K K K K K K K + + + + + + K K K K K K K K K K + + + + + + * * * * * * * \ * * * * * * + +                  + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +      P P P P P P P P P P P P P P P P P P P P P P P P P P P      P P P P P P P + + + + K K K K K K K K K K                                    + + +    P P P P P P P P P P P P P P P P P P P P P P P P P P P P P P              P P K K K K K K K K K K P P P P P P P P               + + + + + + + +     P P P P                     + + +      K K K K K K K K K K + + + P P P K K K K K K K K K K P P P P P P P P P P P P P P P P P P P P P P P P P P P P P P P P   P P P P P P P P P + + + + + + + P P P P P P P P P P P P P P P P P P P P P P P P P P P + + P P P         + + + + + + + +                  P P P P  P P P P P P  P P    P + + + + +                           +                            + + P P P P P P + + P P P P P P P P + P + P + P +                                + +                      +         P    P P P +           P P P P + +       +                    + + P P P +          +        	          	                              	                                                      +           P P + + O P P P P P      O P O O O O P P P P P      + P P P P P P P P P P P P P + + +                                                                   P     P            O  P    P P P P P       P  P  P  P O P P        P P P P P                 P  P P P P O O P P P P P O P P O P O O O O O O O O O O O O P P P P O O O O O O O O O O P P P P P P P P P   P P P P O   + + + +                                                                                                                                                                                                                                                                                           % %                                 % % % %                + + + + + + + + + + + + + + + + + + + + + + + + + O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O                                   O O O O O O O O O O O O O O O O O O O O O P                                                                                                                                                                             % % % %                 % %   %  % % %   % %                          % % %                                             %                       %                                                              % % % % % % % % % % % %     %  % % %  % %    % %   %   % % %         %       % % % % %  % %   %   % % % % % % % %    % %                                               %                  O O O O O O O O O O O O O O O O O O O O O O                                                                                                                                                                                                             + +                         +                              P P P P                     + + + + +     P   P P P P P P P P + + + + + + + P  + + + + + + + + + + + + + +  P P P P P P P P P P P P P P P P P P P P P P P + + + + + + + + + P P P P P P P + P P P P P P P + P P P P P P P + P P P P P P P +                                                P                                    + + + + + + + + + + + + + % % % % % % % % % % % % % % % % % % % % % % % % % % + % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % + + + + + + + + + + + + % % % % % % % % % % % % % % % % % % % % % % + + + + + + + + + + + + + + + + + + % % % % % % % % % % % % + + + +    % % G W W           % %             % W W W W W W W W W       % W W W W 6 % % W W W G G % % % + Q W Q W Q W Q W Q W W W W W W W W W W W W W W W W W W W W W W W W W Q W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W Q W Q W Q W W W W W W Q W W W W W W Q Q + +     G G W  Q W Q W Q W Q W Q W W W W W W W W W W W W W W W W W W W W W W W W W Q W Q W Q W W W W W W Q W W W W W W Q Q W W W W  Q G G W + + + + + W W W W W W W W W W W W W W W W W W W W W W W W W W W + W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W + % % W W W W % % % % % % % % % % + + + + + + + + + + + + Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % + W W W W W W W W W W % % % % % % % % % % % % % % % % % % % % % % O O O O O O O O % W W W W W W W W W W W W W W W % % % % % % % % % % % % % % % % % W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W % % % W W W W W W W W W W W W W W W W W W W W W G W W W W W W W W W W W W W W W + + + % % % % % % % % % % % % % % % % % % % + + + + + + + + + P P P P P P P P P P P P P P P P P    P P P P P P P P P P P P P P P P K K K K K K K K K K P P + + + + + + + + + + + + + + + + + + + +               P                 P                             P P   P P P P P P P P P P P P P P P P         + + + + + + + +                        P P P P P P P P P                 P                P       P                 + +          + + + + + + + + + + + + + + + + + + + + + +   P P P P P P P P P P P  P P P  P P P P  P P P P P P P P P P P P P P P P P P P P P P P           + + + P P P P P P     + + + + + + P P P P P P P P P P P P P P P P P P P P     + + + + + + + +   P P P P P P P P P P P P P P P P P P P P P P P P P P P P P P       + + + + + + + +   K K K K K K K K K K + + + + + +                   P P P P P P    P  P P  P P P P P P           P P P P P P P P P P P P P P P P P P P              + + + + + + + + + + +  X X X X X X X X X X X X X X X X X X X X X X X X X X X X X + + +               + P K K K K K K K K K K + + + +   \ \ \ \ \ * \ \ \ \ \ \ \ \ \ \ K K K K K K K K K K \ \ \ \ \ + P P P P P P P P P               + + + + + + + + + P P P  P P P P P P P P   + + K K K K K K K K K K + +     \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ * * * \ * * * \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ * \ * * * \ \ * * \ \ \ \ \ * * \ * \ + + + + + + + + + + + + + + + + + + + + + + + + \ \ \ * * P P P P P P P P P P P        P P P   + + + + + + + + + + P P P P P P + + P P P P P P + + P P P P P P + + + + + + + + + P P P P P P P + P P P P P P P + P P P P P P P P P P P P P P P P P P   + + + + P P P P P P P P P P P P P P P P P P P            + + K K K K K K K K K K + + + + + + T U U U U U U U U U U U U U U U U U U U U U U U U U U U T U U U U U U U U U U U U U U U U U U U U U U U U U U U + + + + + + + + + + + + Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y + + + + Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z + + + +                                 W W W W W W W W W W W W W W % % W W W W W W W W W W W W W W W W W W W W W W W W W W % % % % % % P P P P P P P + + + + + + + + + + + + P P P P P + + + + + V  V V V V V V V V V V  V V V V V V V V V V V V V + V V V V V + V + V V + V V + V V V V V V V V V V P P P P P P P P P P P P P P P P P P                 + + + + + + + + + + + + + + + + + P P P P P P P P P P P P P P P P P P P P P P P P P P P P P P P   P P P P P P P P P P P P P P P P + + P P P P P P P P P P P P P P + + + + + + + + + + + + + + + + + + + + + + + + P P P P P P P P P P P P   + +                           + + + + + +                 % % % % %             % %   % % % % % % %  %  +     %       % % % % % % % % + %   % + + + + P P P P P + P P P P P P P P P P P P P P P P P P P P P P P P P P P P P P P + +  +  % %   % %   % %  %  % W W W W W W W W W W   % % %  % W W W W W W W W W W W W W W W W W W W W W W W W W W  %  % % % W W W W W W W W W W W W W W W W W W W W W W W W W W  %  %        W Q Q Q Q Q Q Q Q Q Q W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W G G + + W W W W W W + + W W W W W W + + W W W W W W + + W W W + + +   % % %   +        + + + + + + + + + +      + + P P P P P P P P P P P P + P P P P P P P P P P P P P P P P P P P P P P + P P P P P P P P P P P P P P P P P P P + P P + P P P P P P P P P P P P P P P + + P P P P P P P P P P P P P P + + P P P P P P P P P P P P P P P P P P P P P P P P P P P + + + + +    + + + + P P P P P P P P P P P P P P P P P P P P P P P P P + + +          P P P P P P P P P P P P P P P P P P P P P P P P P              P P    +              + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +                               + + P P P P P P P P P P P P P P P P P P P P P P P P P P P P P + + + + + + + + + + + + + + +  P P P P P P P P P P P P P P P P P P P P P P P P P P P + + + + + + + + + P P P P P P P P P P P P P P P P P P P P P P + + + + + P P P P P P P P P P P P P P P P P P P P P P      + + + + + P P P P P P P P P P P P P P P P P P P P P P P P P P P P P P +  P P P P + + + + P P P P P P P P  P P P P P + + + + + + + + + + P P P P P P P P P P P P P P P P P P P P P P P P P P P P P P + + P P P P P P P P P P P P P P P P P P P P + + + + P P P P P P P P P P P P P P P P P P P P P P P P P P P P + + + + + + + + P P P P P P P P P P P P P P P P + + + + + + + + + + +  + + + + + + + + + + + + + + + + P P P P P P + + P + P P P P P P P P P P P P P P P P P P P P P P P P + P P + + + P + + P P P P P P P P P P P P P P P P P P P P P P P +  P P P P P P P P P P P P P P P P P P P P P P P   P P P P P P P + + + + + + + P P P P P P P P P + + + + + + + + + + + + + + + + P P P P P P P P P P P P P P P P P P P + P P + + + + + P P P P P P P P P P P P P P P P P P P P P P P P P P P P P + + +  P P P P P P P P P P P P P P P P P P P P P P P P P P + + + + +  P P P P P P P P P P P P P P P P P P P P P P P P + + + + P P P P P    +   + + + + +     P P P P + P P P + P P P P P P P P P P P P P P P P P P P P P P P P P + +    + + + +  P P P P P P P P P + + + + + + +          + + + + + + + P P P P P P P P P P P P P P P P P P P P P P P P P P P P P P P  P P P P P P P P  P P P P P P P P P P P P P P P P P P P P P P P P   + + + + P P P P P        + + + + + + + + + P P P P P P P P P P P P P P P P P P P P P P + + +        P P P P P P P P P P P P P P P P P P + + + + + + +     + + + + + + + + + + + + P P P P P P P + + + + + + + + + + + + + + + + P P P P P P P P P + + + + + + + + + + + + + + + + + + + + + + + P P P P P P P P P P P P P P P P P P P + + + + + + + + + + + + + P P P P P P P P P P P P P P P P P P P + + + + + + + P P P P P P     + + + + + + + + K K K K K K K K K K + + + + + + P P P P P P P P P P +    + + P P + + + + + + + + + + + + + + P P P P P P            P P P P      + + + + + +               + + + + P P P P P P P P P P P P P P P P K K K K K K K K K K + + + + + + + + + + + + + + +  P P P P P P P P P P P P P P P P                   + + + + + + + + + + +  + + P P P P P P P P P P P P P P P P P + + + + + + + K K K K K K K K K K + + + + + + P P P P P P P               + K K K K K K K K K K     P   P + + + + + + + + P P P P P P P P P P P P P P P P P P P    P + + + + + + + + +  P P P P            K K K K K K K K K K P  P    + P P P P P P P P P P P P P P P P P P P P + + + + + + + + + + + P P P P P P P P P P P P P P P P P P + P P P P P P P P P P P P P                    + P P P P P P P + P + P P P P + P P P P P P P P P P P P P P P + P P P P P P P P P P  + + + + + + P P P P P P P P P P P P P P P P            + + + + + K K K K K K K K K K + + + + + +     + P P P P P P P P + + P P + + P P P P P P P P P P P P P P + P P P P P P P + P P + P P P P P +   P        + +   + +    + + P + + + + + +  + + + + + P P P P P   + +        + + +      + + + + + + + + + + +        P P P P      K K K K K K K K K K   +   P P P + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +     P P  P + + + + + + + + K K K K K K K K K K + + + + + + P P P P P P P P P P P P P P P        + +                                 P P P P   + +     P + + + + + + + + + + + K K K K K K K K K K + + + + + +              + + + + + + + + + + + + + + + + + + + P P P P P P P P P P P              P + + + + + + + K K K K K K K K K K + + + + + + + + + + + + + + + + + + + + + + \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ + + * * * * * * * * * * * * * * * + + + + K K K K K K K K K K \ \    * P P P P P P P P P P P P                 + + + + K K K K K K K K K K P P P P P P P P P + + + + + + + + + + + + P P P P P P P P + + P + + P P P P P P P P + P P + P P P P P P P P P P P P P P P P       +   + +     P  P      + + + + + + + + + K K K K K K K K K K + + + + + + P P P P P P P P + + P P P P P P P P P P P P P P P P P P P P P P P        + +        P  P  + + + + + + + + + + + + + + + + + + + + + + + + + + + P           P P P P P P P P P P P P P P P P P P P P P P P P        P              + + + + + + + + P            P P P P P P P P P P                    P      + + + + + + + + + + + + + + + + + + + + + + + + + + + + + P P P P P P P P P + P P P P P P P P P P P P P P P P P P P P P P P P P         +         P      + + + + + + + + + + K K K K K K K K K K P P P P P P P P P P P P P P P + + +   P P P P P P P P P P P P P P P P P P + +               +               + + + + + + + + + P P P P P P P + P P + P P P P P P P P P P P P P P P P P P P P P P       + + +  +   +        P  + + + + + + + + K K K K K K K K K K + + + + + + P P P P P P + P P + P P P P P P P P P P P P P P P P P P P P P P P P      +   +      P + + + + + + + P P P P P P P P P P P P P P P P P P P       + + + + + + + + + + + + + + + + + + + P + + + + + + + + + + + + + + + P P P P P P P P P P P P P P P P P P P P P                              + + + + + + + + + + + + +  P P P P P P P P P P P P P P P P P P P P P P P P P P + + + + + + P P P P P P P P P P P P P P P +      + + + + + + + + + + + P P P P + + + + + + + + + + + + + + + + + + + + + + + + + + + + P P P P P P P P P P P P P P P P P P P P P P P P H H H C C C P P P P C P P P H C H C P P P P P P P P P P P P P P P P P P P P P P P P P P P H C C P P P P P P P P P P P P P P P + 	 	 	 	 	 	 	   + + + + + 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e===ot?en(t)||rn(t,n):!!en(e)||!(e!==qe||!rn(e,t))},an=function(e,t,n){return e===bt||e===ot?!!Wt(t)||t===Et&&Wt(n):Wt(e===Et?t:e)},on=function(e){var t=0,n=1;e[t]!==bt&&e[t]!==ot||(e[t]===ot&&(n=-1),t++);for(var r=[];Wt(e[t]);)r.push(e[t++]);var i=r.length?parseInt(u.apply(void 0,r),10):0;e[t]===Et&&t++;for(var A=[];Wt(e[t]);)A.push(e[t++]);var a=A.length,o=a?parseInt(u.apply(void 0,A),10):0;e[t]!==Vt&&e[t]!==Rt||t++;var s=1;e[t]!==bt&&e[t]!==ot||(e[t]===ot&&(s=-1),t++);for(var l=[];Wt(e[t]);)l.push(e[t++]);var c=l.length?parseInt(u.apply(void 0,l),10):0;return n*(i+o*Math.pow(10,-a))*Math.pow(10,s*c)},sn={type:2},ln={type:3},un={type:4},cn={type:13},dn={type:8},hn={type:21},fn={type:9},pn={type:10},gn={type:11},mn={type:12},vn={type:14},yn={type:23},wn={type:1},Bn={type:25},_n={type:24},bn={type:26},xn={type:27},Cn={type:28},Sn={type:29},En={type:31},Un={type:32},Mn=function(){function e(){this._value=[]}return e.prototype.write=function(e){this._value=this._value.concat(l(e))},e.prototype.read=function(){for(var e=[],t=this.consumeToken();t!==Un;)e.push(t),t=this.consumeToken();return e},e.prototype.consumeToken=function(){var e=this.consumeCodePoint();switch(e){case Ze:return this.consumeStringToken(Ze);case et:var t=this.peekCodePoint(0),n=this.peekCodePoint(1),r=this.peekCodePoint(2);if(tn(t)||rn(n,r)){var i=An(t,n,r)?Ge:ze;return{type:5,value:this.consumeName(),flags:i}}break;case tt:if(this.peekCodePoint(0)===$e)return this.consumeCodePoint(),cn;break;case rt:return this.consumeStringToken(rt);case it:return sn;case At:return ln;case _t:if(this.peekCodePoint(0)===$e)return this.consumeCodePoint(),vn;break;case bt:if(an(e,this.peekCodePoint(0),this.peekCodePoint(1)))return this.reconsumeCodePoint(e),this.consumeNumericToken();break;case xt:return un;case ot:var A=e,a=this.peekCodePoint(0),o=this.peekCodePoint(1);if(an(A,a,o))return this.reconsumeCodePoint(e),this.consumeNumericToken();if(An(A,a,o))return this.reconsumeCodePoint(e),this.consumeIdentLikeToken();if(a===ot&&o===ut)return this.consumeCodePoint(),this.consumeCodePoint(),_n;break;case Et:if(an(e,this.peekCodePoint(0),this.peekCodePoint(1)))return this.reconsumeCodePoint(e),this.consumeNumericToken();break;case Xe:if(this.peekCodePoint(0)===_t)for(this.consumeCodePoint();;){var s=this.consumeCodePoint();if(s===_t&&(s=this.consumeCodePoint())===Xe)return this.consumeToken();if(s===Lt)return this.consumeToken()}break;case Ct:return bn;case St:return xn;case lt:if(this.peekCodePoint(0)===st&&this.peekCodePoint(1)===ot&&this.peekCodePoint(2)===ot)return this.consumeCodePoint(),this.consumeCodePoint(),Bn;break;case ct:var l=this.peekCodePoint(0),c=this.peekCodePoint(1),d=this.peekCodePoint(2);if(An(l,c,d))return{type:7,value:this.consumeName()};break;case dt:return Cn;case qe:if(rn(e,this.peekCodePoint(0)))return this.reconsumeCodePoint(e),this.consumeIdentLikeToken();break;case ht:return Sn;case ft:if(this.peekCodePoint(0)===$e)return this.consumeCodePoint(),dn;break;case pt:return gn;case mt:return mn;case Pt:case Gt:var h=this.peekCodePoint(0),f=this.peekCodePoint(1);return h!==bt||!Xt(f)&&f!==gt||(this.consumeCodePoint(),this.consumeUnicodeRangeToken()),this.reconsumeCodePoint(e),this.consumeIdentLikeToken();case vt:if(this.peekCodePoint(0)===$e)return this.consumeCodePoint(),fn;if(this.peekCodePoint(0)===vt)return this.consumeCodePoint(),hn;break;case yt:if(this.peekCodePoint(0)===$e)return this.consumeCodePoint(),pn;break;case Lt:return Un}return $t(e)?(this.consumeWhiteSpace(),En):Wt(e)?(this.reconsumeCodePoint(e),this.consumeNumericToken()):en(e)?(this.reconsumeCodePoint(e),this.consumeIdentLikeToken()):{type:6,value:u(e)}},e.prototype.consumeCodePoint=function(){var e=this._value.shift();return"undefined"===typeof e?-1:e},e.prototype.reconsumeCodePoint=function(e){this._value.unshift(e)},e.prototype.peekCodePoint=function(e){return e>=this._value.length?-1:this._value[e]},e.prototype.consumeUnicodeRangeToken=function(){for(var e=[],t=this.consumeCodePoint();Xt(t)&&e.length<6;)e.push(t),t=this.consumeCodePoint();for(var n=!1;t===gt&&e.length<6;)e.push(t),t=this.consumeCodePoint(),n=!0;if(n)return{type:30,start:parseInt(u.apply(void 0,e.map((function(e){return e===gt?Dt:e}))),16),end:parseInt(u.apply(void 0,e.map((function(e){return e===gt?zt:e}))),16)};var r=parseInt(u.apply(void 0,e),16);if(this.peekCodePoint(0)===ot&&Xt(this.peekCodePoint(1))){this.consumeCodePoint(),t=this.consumeCodePoint();for(var i=[];Xt(t)&&i.length<6;)i.push(t),t=this.consumeCodePoint();return{type:30,start:r,end:parseInt(u.apply(void 0,i),16)}}return{type:30,start:r,end:r}},e.prototype.consumeIdentLikeToken=function(){var e=this.consumeName();return"url"===e.toLowerCase()&&this.peekCodePoint(0)===it?(this.consumeCodePoint(),this.consumeUrlToken()):this.peekCodePoint(0)===it?(this.consumeCodePoint(),{type:19,value:e}):{type:20,value:e}},e.prototype.consumeUrlToken=function(){var e=[];if(this.consumeWhiteSpace(),this.peekCodePoint(0)===Lt)return{type:22,value:""};var t=this.peekCodePoint(0);if(t===rt||t===Ze){var n=this.consumeStringToken(this.consumeCodePoint());return 0===n.type&&(this.consumeWhiteSpace(),this.peekCodePoint(0)===Lt||this.peekCodePoint(0)===At)?(this.consumeCodePoint(),{type:22,value:n.value}):(this.consumeBadUrlRemnants(),yn)}for(;;){var r=this.consumeCodePoint();if(r===Lt||r===At)return{type:22,value:u.apply(void 0,e)};if($t(r))return this.consumeWhiteSpace(),this.peekCodePoint(0)===Lt||this.peekCodePoint(0)===At?(this.consumeCodePoint(),{type:22,value:u.apply(void 0,e)}):(this.consumeBadUrlRemnants(),yn);if(r===Ze||r===rt||r===it||nn(r))return this.consumeBadUrlRemnants(),yn;if(r===qe){if(!rn(r,this.peekCodePoint(0)))return this.consumeBadUrlRemnants(),yn;e.push(this.consumeEscapedCodePoint())}else e.push(r)}},e.prototype.consumeWhiteSpace=function(){for(;$t(this.peekCodePoint(0));)this.consumeCodePoint()},e.prototype.consumeBadUrlRemnants=function(){for(;;){var e=this.consumeCodePoint();if(e===At||e===Lt)return;rn(e,this.peekCodePoint(0))&&this.consumeEscapedCodePoint()}},e.prototype.consumeStringSlice=function(e){for(var t=5e4,n="";e>0;){var r=Math.min(t,e);n+=u.apply(void 0,this._value.splice(0,r)),e-=r}return this._value.shift(),n},e.prototype.consumeStringToken=function(e){for(var t="",n=0;;){var r=this._value[n];if(r===Lt||void 0===r||r===e)return{type:0,value:t+=this.consumeStringSlice(n)};if(r===je)return this._value.splice(0,n),wn;if(r===qe){var i=this._value[n+1];i!==Lt&&void 0!==i&&(i===je?(t+=this.consumeStringSlice(n),n=-1,this._value.shift()):rn(r,i)&&(t+=this.consumeStringSlice(n),t+=u(this.consumeEscapedCodePoint()),n=-1))}n++}},e.prototype.consumeNumber=function(){var e=[],t=Ke,n=this.peekCodePoint(0);for(n!==bt&&n!==ot||e.push(this.consumeCodePoint());Wt(this.peekCodePoint(0));)e.push(this.consumeCodePoint());n=this.peekCodePoint(0);var r=this.peekCodePoint(1);if(n===Et&&Wt(r))for(e.push(this.consumeCodePoint(),this.consumeCodePoint()),t=We;Wt(this.peekCodePoint(0));)e.push(this.consumeCodePoint());n=this.peekCodePoint(0),r=this.peekCodePoint(1);var i=this.peekCodePoint(2);if((n===Vt||n===Rt)&&((r===bt||r===ot)&&Wt(i)||Wt(r)))for(e.push(this.consumeCodePoint(),this.consumeCodePoint()),t=We;Wt(this.peekCodePoint(0));)e.push(this.consumeCodePoint());return[on(e),t]},e.prototype.consumeNumericToken=function(){var e=this.consumeNumber(),t=e[0],n=e[1],r=this.peekCodePoint(0),i=this.peekCodePoint(1),A=this.peekCodePoint(2);return An(r,i,A)?{type:15,number:t,flags:n,unit:this.consumeName()}:r===nt?(this.consumeCodePoint(),{type:16,number:t,flags:n}):{type:17,number:t,flags:n}},e.prototype.consumeEscapedCodePoint=function(){var e=this.consumeCodePoint();if(Xt(e)){for(var t=u(e);Xt(this.peekCodePoint(0))&&t.length<6;)t+=u(this.consumeCodePoint());$t(this.peekCodePoint(0))&&this.consumeCodePoint();var n=parseInt(t,16);return 0===n||jt(n)||n>1114111?Bt:n}return e===Lt?Bt:e},e.prototype.consumeName=function(){for(var e="";;){var t=this.consumeCodePoint();if(tn(t))e+=u(t);else{if(!rn(t,this.peekCodePoint(0)))return this.reconsumeCodePoint(t),e;e+=u(this.consumeEscapedCodePoint())}}},e}(),Fn=function(){function e(e){this._tokens=e}return e.create=function(t){var n=new Mn;return n.write(t),new e(n.read())},e.parseValue=function(t){return e.create(t).parseComponentValue()},e.parseValues=function(t){return e.create(t).parseComponentValues()},e.prototype.parseComponentValue=function(){for(var e=this.consumeToken();31===e.type;)e=this.consumeToken();if(32===e.type)throw new SyntaxError("Error parsing CSS component value, unexpected EOF");this.reconsumeToken(e);var t=this.consumeComponentValue();do{e=this.consumeToken()}while(31===e.type);if(32===e.type)return t;throw new SyntaxError("Error parsing CSS component value, multiple values found when expecting only one")},e.prototype.parseComponentValues=function(){for(var e=[];;){var t=this.consumeComponentValue();if(32===t.type)return e;e.push(t),e.push()}},e.prototype.consumeComponentValue=function(){var e=this.consumeToken();switch(e.type){case 11:case 28:case 2:return this.consumeSimpleBlock(e.type);case 19:return this.consumeFunction(e)}return e},e.prototype.consumeSimpleBlock=function(e){for(var t={type:e,values:[]},n=this.consumeToken();;){if(32===n.type||Pn(n,e))return t;this.reconsumeToken(n),t.values.push(this.consumeComponentValue()),n=this.consumeToken()}},e.prototype.consumeFunction=function(e){for(var t={name:e.value,values:[],type:18};;){var n=this.consumeToken();if(32===n.type||3===n.type)return t;this.reconsumeToken(n),t.values.push(this.consumeComponentValue())}},e.prototype.consumeToken=function(){var e=this._tokens.shift();return"undefined"===typeof e?Un:e},e.prototype.reconsumeToken=function(e){this._tokens.unshift(e)},e}(),Tn=function(e){return 15===e.type},kn=function(e){return 17===e.type},Qn=function(e){return 20===e.type},Ln=function(e){return 0===e.type},Dn=function(e,t){return Qn(e)&&e.value===t},In=function(e){return 31!==e.type},Rn=function(e){return 31!==e.type&&4!==e.type},Hn=function(e){var t=[],n=[];return e.forEach((function(e){if(4===e.type){if(0===n.length)throw new Error("Error parsing function args, zero tokens for arg");return t.push(n),void(n=[])}31!==e.type&&n.push(e)})),n.length&&t.push(n),t},Pn=function(e,t){return 11===t&&12===e.type||28===t&&29===e.type||2===t&&3===e.type},Nn=function(e){return 17===e.type||15===e.type},On=function(e){return 16===e.type||Nn(e)},Vn=function(e){return e.length>1?[e[0],e[1]]:[e[0]]},zn={type:17,number:0,flags:Ke},Gn={type:16,number:50,flags:Ke},Kn={type:16,number:100,flags:Ke},Wn=function(e,t,n){var r=e[0],i=e[1];return[jn(r,t),jn("undefined"!==typeof i?i:r,n)]},jn=function(e,t){if(16===e.type)return e.number/100*t;if(Tn(e))switch(e.unit){case"rem":case"em":return 16*e.number;default:return e.number}return e.number},Xn="deg",qn="grad",Yn="rad",Jn="turn",Zn={name:"angle",parse:function(e,t){if(15===t.type)switch(t.unit){case Xn:return Math.PI*t.number/180;case qn:return Math.PI/200*t.number;case Yn:return t.number;case Jn:return 2*Math.PI*t.number}throw new Error("Unsupported angle type")}},$n=function(e){return 15===e.type&&(e.unit===Xn||e.unit===qn||e.unit===Yn||e.unit===Jn)},er=function(e){switch(e.filter(Qn).map((function(e){return e.value})).join(" ")){case"to bottom right":case"to right bottom":case"left top":case"top left":return[zn,zn];case"to top":case"bottom":return tr(0);case"to 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f=null,p=null;if(On(a)?f=jn(a,n.width):o&&On(o)&&(p=jn(o,n.height)),null===f||o&&!tl(o)||(p=l&&u?f/r*i:n.height),null!==p&&tl(a)&&(f=l&&u?p/i*r:n.width),null!==f&&null!==p)return[f,p];throw new Error("Unable to calculate background-size for element")},il=function(e,t){var n=e[t];return"undefined"===typeof n?e[0]:n},Al=function(e,t,n,r,i){var A=t[0],a=t[1],o=n[0],s=n[1];switch(e){case 2:return[new _s(Math.round(r.left),Math.round(r.top+a)),new _s(Math.round(r.left+r.width),Math.round(r.top+a)),new _s(Math.round(r.left+r.width),Math.round(s+r.top+a)),new _s(Math.round(r.left),Math.round(s+r.top+a))];case 3:return[new _s(Math.round(r.left+A),Math.round(r.top)),new _s(Math.round(r.left+A+o),Math.round(r.top)),new _s(Math.round(r.left+A+o),Math.round(r.height+r.top)),new _s(Math.round(r.left+A),Math.round(r.height+r.top))];case 1:return[new _s(Math.round(r.left+A),Math.round(r.top+a)),new _s(Math.round(r.left+A+o),Math.round(r.top+a)),new _s(Math.round(r.left+A+o),Math.round(r.top+a+s)),new _s(Math.round(r.left+A),Math.round(r.top+a+s))];default:return[new _s(Math.round(i.left),Math.round(i.top)),new _s(Math.round(i.left+i.width),Math.round(i.top)),new _s(Math.round(i.left+i.width),Math.round(i.height+i.top)),new _s(Math.round(i.left),Math.round(i.height+i.top))]}},al="data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7",ol="Hidden Text",sl=function(){function e(e){this._data={},this._document=e}return e.prototype.parseMetrics=function(e,t){var n=this._document.createElement("div"),r=this._document.createElement("img"),i=this._document.createElement("span"),A=this._document.body;n.style.visibility="hidden",n.style.fontFamily=e,n.style.fontSize=t,n.style.margin="0",n.style.padding="0",n.style.whiteSpace="nowrap",A.appendChild(n),r.src=al,r.width=1,r.height=1,r.style.margin="0",r.style.padding="0",r.style.verticalAlign="baseline",i.style.fontFamily=e,i.style.fontSize=t,i.style.margin="0",i.style.padding="0",i.appendChild(this._document.createTextNode(ol)),n.appendChild(i),n.appendChild(r);var a=r.offsetTop-i.offsetTop+2;n.removeChild(i),n.appendChild(this._document.createTextNode(ol)),n.style.lineHeight="normal",r.style.verticalAlign="super";var o=r.offsetTop-n.offsetTop+2;return A.removeChild(n),{baseline:a,middle:o}},e.prototype.getMetrics=function(e,t){var n=e+" "+t;return"undefined"===typeof this._data[n]&&(this._data[n]=this.parseMetrics(e,t)),this._data[n]},e}(),ll=function(){function e(e,t){this.context=e,this.options=t}return e}(),ul=1e4,cl=function(e){function n(t,n){var r=e.call(this,t,n)||this;return r._activeEffects=[],r.canvas=n.canvas?n.canvas:document.createElement("canvas"),r.ctx=r.canvas.getContext("2d"),n.canvas||(r.canvas.width=Math.floor(n.width*n.scale),r.canvas.height=Math.floor(n.height*n.scale),r.canvas.style.width=n.width+"px",r.canvas.style.height=n.height+"px"),r.fontMetrics=new sl(document),r.ctx.scale(r.options.scale,r.options.scale),r.ctx.translate(-n.x,-n.y),r.ctx.textBaseline="bottom",r._activeEffects=[],r.context.logger.debug("Canvas renderer initialized ("+n.width+"x"+n.height+") with scale "+n.scale),r}return t(n,e),n.prototype.applyEffects=function(e){for(var t=this;this._activeEffects.length;)this.popEffect();e.forEach((function(e){return t.applyEffect(e)}))},n.prototype.applyEffect=function(e){this.ctx.save(),Is(e)&&(this.ctx.globalAlpha=e.opacity),Ls(e)&&(this.ctx.translate(e.offsetX,e.offsetY),this.ctx.transform(e.matrix[0],e.matrix[1],e.matrix[2],e.matrix[3],e.matrix[4],e.matrix[5]),this.ctx.translate(-e.offsetX,-e.offsetY)),Ds(e)&&(this.path(e.path),this.ctx.clip()),this._activeEffects.push(e)},n.prototype.popEffect=function(){this._activeEffects.pop(),this.ctx.restore()},n.prototype.renderStack=function(e){return r(this,void 0,void 0,(function(){return i(this,(function(t){switch(t.label){case 0:return e.element.container.styles.isVisible()?[4,this.renderStackContent(e)]:[3,2];case 1:t.sent(),t.label=2;case 2:return[2]}}))}))},n.prototype.renderNode=function(e){return r(this,void 0,void 0,(function(){return i(this,(function(t){switch(t.label){case 0:return iA(e.container.flags,16),e.container.styles.isVisible()?[4,this.renderNodeBackgroundAndBorders(e)]:[3,3];case 1:return t.sent(),[4,this.renderNodeContent(e)];case 2:t.sent(),t.label=3;case 3:return[2]}}))}))},n.prototype.renderTextWithLetterSpacing=function(e,t,n){var r=this;0===t?this.ctx.fillText(e.text,e.bounds.left,e.bounds.top+n):Ma(e.text).reduce((function(t,i){return r.ctx.fillText(i,t,e.bounds.top+n),t+r.ctx.measureText(i).width}),e.bounds.left)},n.prototype.createFontStyle=function(e){var t=e.fontVariant.filter((function(e){return"normal"===e||"small-caps"===e})).join(""),n=gl(e.fontFamily).join(", "),r=Tn(e.fontSize)?""+e.fontSize.number+e.fontSize.unit:e.fontSize.number+"px";return[[e.fontStyle,t,e.fontWeight,r,n].join(" "),n,r]},n.prototype.renderTextNode=function(e,t){return r(this,void 0,void 0,(function(){var n,r,A,a,o,s,l,u,c=this;return i(this,(function(i){return n=this.createFontStyle(t),r=n[0],A=n[1],a=n[2],this.ctx.font=r,this.ctx.direction=1===t.direction?"rtl":"ltr",this.ctx.textAlign="left",this.ctx.textBaseline="alphabetic",o=this.fontMetrics.getMetrics(A,a),s=o.baseline,l=o.middle,u=t.paintOrder,e.textBounds.forEach((function(e){u.forEach((function(n){switch(n){case 0:c.ctx.fillStyle=ir(t.color),c.renderTextWithLetterSpacing(e,t.letterSpacing,s);var r=t.textShadow;r.length&&e.text.trim().length&&(r.slice(0).reverse().forEach((function(n){c.ctx.shadowColor=ir(n.color),c.ctx.shadowOffsetX=n.offsetX.number*c.options.scale,c.ctx.shadowOffsetY=n.offsetY.number*c.options.scale,c.ctx.shadowBlur=n.blur.number,c.renderTextWithLetterSpacing(e,t.letterSpacing,s)})),c.ctx.shadowColor="",c.ctx.shadowOffsetX=0,c.ctx.shadowOffsetY=0,c.ctx.shadowBlur=0),t.textDecorationLine.length&&(c.ctx.fillStyle=ir(t.textDecorationColor||t.color),t.textDecorationLine.forEach((function(t){switch(t){case 1:c.ctx.fillRect(e.bounds.left,Math.round(e.bounds.top+s),e.bounds.width,1);break;case 2:c.ctx.fillRect(e.bounds.left,Math.round(e.bounds.top),e.bounds.width,1);break;case 3:c.ctx.fillRect(e.bounds.left,Math.ceil(e.bounds.top+l),e.bounds.width,1)}})));break;case 1:t.webkitTextStrokeWidth&&e.text.trim().length&&(c.ctx.strokeStyle=ir(t.webkitTextStrokeColor),c.ctx.lineWidth=t.webkitTextStrokeWidth,c.ctx.lineJoin=window.chrome?"miter":"round",c.ctx.strokeText(e.text,e.bounds.left,e.bounds.top+s)),c.ctx.strokeStyle="",c.ctx.lineWidth=0,c.ctx.lineJoin="miter"}}))})),[2]}))}))},n.prototype.renderReplacedElement=function(e,t,n){if(n&&e.intrinsicWidth>0&&e.intrinsicHeight>0){var r=Js(e),i=Fs(t);this.path(i),this.ctx.save(),this.ctx.clip(),this.ctx.drawImage(n,0,0,e.intrinsicWidth,e.intrinsicHeight,r.left,r.top,r.width,r.height),this.ctx.restore()}},n.prototype.renderNodeContent=function(e){return r(this,void 0,void 0,(function(){var t,r,A,o,s,l,u,c,d,h,f,p,g,m,v,y,w,B;return i(this,(function(i){switch(i.label){case 0:this.applyEffects(e.getEffects(4)),t=e.container,r=e.curves,A=t.styles,o=0,s=t.textNodes,i.label=1;case 1:return o<s.length?(l=s[o],[4,this.renderTextNode(l,A)]):[3,4];case 2:i.sent(),i.label=3;case 3:return o++,[3,1];case 4:if(!(t instanceof Ha))return[3,8];i.label=5;case 5:return i.trys.push([5,7,,8]),[4,this.context.cache.match(t.src)];case 6:return v=i.sent(),this.renderReplacedElement(t,r,v),[3,8];case 7:return i.sent(),this.context.logger.error("Error loading image "+t.src),[3,8];case 8:if(t instanceof Pa&&this.renderReplacedElement(t,r,t.canvas),!(t instanceof Na))return[3,12];i.label=9;case 9:return i.trys.push([9,11,,12]),[4,this.context.cache.match(t.svg)];case 10:return v=i.sent(),this.renderReplacedElement(t,r,v),[3,12];case 11:return i.sent(),this.context.logger.error("Error loading svg "+t.svg.substring(0,255)),[3,12];case 12:return t instanceof eo&&t.tree?[4,new n(this.context,{scale:this.options.scale,backgroundColor:t.backgroundColor,x:0,y:0,width:t.width,height:t.height}).render(t.tree)]:[3,14];case 13:u=i.sent(),t.width&&t.height&&this.ctx.drawImage(u,0,0,t.width,t.height,t.bounds.left,t.bounds.top,t.bounds.width,t.bounds.height),i.label=14;case 14:if(t instanceof Ja&&(c=Math.min(t.bounds.width,t.bounds.height),t.type===ja?t.checked&&(this.ctx.save(),this.path([new _s(t.bounds.left+.39363*c,t.bounds.top+.79*c),new _s(t.bounds.left+.16*c,t.bounds.top+.5549*c),new _s(t.bounds.left+.27347*c,t.bounds.top+.44071*c),new _s(t.bounds.left+.39694*c,t.bounds.top+.5649*c),new _s(t.bounds.left+.72983*c,t.bounds.top+.23*c),new _s(t.bounds.left+.84*c,t.bounds.top+.34085*c),new _s(t.bounds.left+.39363*c,t.bounds.top+.79*c)]),this.ctx.fillStyle=ir(Ya),this.ctx.fill(),this.ctx.restore()):t.type===Xa&&t.checked&&(this.ctx.save(),this.ctx.beginPath(),this.ctx.arc(t.bounds.left+c/2,t.bounds.top+c/2,c/4,0,2*Math.PI,!0),this.ctx.fillStyle=ir(Ya),this.ctx.fill(),this.ctx.restore())),dl(t)&&t.value.length){switch(d=this.createFontStyle(A),w=d[0],h=d[1],f=this.fontMetrics.getMetrics(w,h).baseline,this.ctx.font=w,this.ctx.fillStyle=ir(A.color),this.ctx.textBaseline="alphabetic",this.ctx.textAlign=fl(t.styles.textAlign),B=Js(t),p=0,t.styles.textAlign){case 1:p+=B.width/2;break;case 2:p+=B.width}g=B.add(p,0,0,-B.height/2+1),this.ctx.save(),this.path([new _s(B.left,B.top),new _s(B.left+B.width,B.top),new _s(B.left+B.width,B.top+B.height),new _s(B.left,B.top+B.height)]),this.ctx.clip(),this.renderTextWithLetterSpacing(new Ca(t.value,g),A.letterSpacing,f),this.ctx.restore(),this.ctx.textBaseline="alphabetic",this.ctx.textAlign="left"}if(!iA(t.styles.display,2048))return[3,20];if(null===t.styles.listStyleImage)return[3,19];if(0!==(m=t.styles.listStyleImage).type)return[3,18];v=void 0,y=m.url,i.label=15;case 15:return i.trys.push([15,17,,18]),[4,this.context.cache.match(y)];case 16:return v=i.sent(),this.ctx.drawImage(v,t.bounds.left-(v.width+10),t.bounds.top),[3,18];case 17:return i.sent(),this.context.logger.error("Error loading list-style-image "+y),[3,18];case 18:return[3,20];case 19:e.listValue&&-1!==t.styles.listStyleType&&(w=this.createFontStyle(A)[0],this.ctx.font=w,this.ctx.fillStyle=ir(A.color),this.ctx.textBaseline="middle",this.ctx.textAlign="right",B=new a(t.bounds.left,t.bounds.top+jn(t.styles.paddingTop,t.bounds.width),t.bounds.width,Bi(A.lineHeight,A.fontSize.number)/2+1),this.renderTextWithLetterSpacing(new Ca(e.listValue,B),A.letterSpacing,Bi(A.lineHeight,A.fontSize.number)/2+2),this.ctx.textBaseline="bottom",this.ctx.textAlign="left"),i.label=20;case 20:return[2]}}))}))},n.prototype.renderStackContent=function(e){return r(this,void 0,void 0,(function(){var t,n,r,A,a,o,s,l,u,c,d,h,f,p,g;return i(this,(function(i){switch(i.label){case 0:return iA(e.element.container.flags,16),[4,this.renderNodeBackgroundAndBorders(e.element)];case 1:i.sent(),t=0,n=e.negativeZIndex,i.label=2;case 2:return t<n.length?(g=n[t],[4,this.renderStack(g)]):[3,5];case 3:i.sent(),i.label=4;case 4:return t++,[3,2];case 5:return[4,this.renderNodeContent(e.element)];case 6:i.sent(),r=0,A=e.nonInlineLevel,i.label=7;case 7:return r<A.length?(g=A[r],[4,this.renderNode(g)]):[3,10];case 8:i.sent(),i.label=9;case 9:return r++,[3,7];case 10:a=0,o=e.nonPositionedFloats,i.label=11;case 11:return a<o.length?(g=o[a],[4,this.renderStack(g)]):[3,14];case 12:i.sent(),i.label=13;case 13:return a++,[3,11];case 14:s=0,l=e.nonPositionedInlineLevel,i.label=15;case 15:return s<l.length?(g=l[s],[4,this.renderStack(g)]):[3,18];case 16:i.sent(),i.label=17;case 17:return s++,[3,15];case 18:u=0,c=e.inlineLevel,i.label=19;case 19:return u<c.length?(g=c[u],[4,this.renderNode(g)]):[3,22];case 20:i.sent(),i.label=21;case 21:return u++,[3,19];case 22:d=0,h=e.zeroOrAutoZIndexOrTransformedOrOpacity,i.label=23;case 23:return d<h.length?(g=h[d],[4,this.renderStack(g)]):[3,26];case 24:i.sent(),i.label=25;case 25:return d++,[3,23];case 26:f=0,p=e.positiveZIndex,i.label=27;case 27:return f<p.length?(g=p[f],[4,this.renderStack(g)]):[3,30];case 28:i.sent(),i.label=29;case 29:return f++,[3,27];case 30:return[2]}}))}))},n.prototype.mask=function(e){this.ctx.beginPath(),this.ctx.moveTo(0,0),this.ctx.lineTo(this.canvas.width,0),this.ctx.lineTo(this.canvas.width,this.canvas.height),this.ctx.lineTo(0,this.canvas.height),this.ctx.lineTo(0,0),this.formatPath(e.slice(0).reverse()),this.ctx.closePath()},n.prototype.path=function(e){this.ctx.beginPath(),this.formatPath(e),this.ctx.closePath()},n.prototype.formatPath=function(e){var t=this;e.forEach((function(e,n){var r=Cs(e)?e.start:e;0===n?t.ctx.moveTo(r.x,r.y):t.ctx.lineTo(r.x,r.y),Cs(e)&&t.ctx.bezierCurveTo(e.startControl.x,e.startControl.y,e.endControl.x,e.endControl.y,e.end.x,e.end.y)}))},n.prototype.renderRepeat=function(e,t,n,r){this.path(e),this.ctx.fillStyle=t,this.ctx.translate(n,r),this.ctx.fill(),this.ctx.translate(-n,-r)},n.prototype.resizeImage=function(e,t,n){var r;if(e.width===t&&e.height===n)return e;var i=(null!==(r=this.canvas.ownerDocument)&&void 0!==r?r:document).createElement("canvas");return i.width=Math.max(1,t),i.height=Math.max(1,n),i.getContext("2d").drawImage(e,0,0,e.width,e.height,0,0,t,n),i},n.prototype.renderBackgroundImage=function(e){return r(this,void 0,void 0,(function(){var t,n,r,A,a,o;return i(this,(function(s){switch(s.label){case 0:t=e.styles.backgroundImage.length-1,n=function(n){var A,a,o,s,l,u,c,d,h,f,p,g,m,v,y,w,B,_,b,x,C,S,E,U,M,F,T,k,Q,L,D;return i(this,(function(i){switch(i.label){case 0:if(0!==n.type)return[3,5];A=void 0,a=n.url,i.label=1;case 1:return i.trys.push([1,3,,4]),[4,r.context.cache.match(a)];case 2:return A=i.sent(),[3,4];case 3:return i.sent(),r.context.logger.error("Error loading background-image "+a),[3,4];case 4:return A&&(o=el(e,t,[A.width,A.height,A.width/A.height]),w=o[0],S=o[1],E=o[2],b=o[3],x=o[4],v=r.ctx.createPattern(r.resizeImage(A,b,x),"repeat"),r.renderRepeat(w,v,S,E)),[3,6];case 5:kr(n)?(s=el(e,t,[null,null,null]),w=s[0],S=s[1],E=s[2],b=s[3],x=s[4],l=vr(n.angle,b,x),u=l[0],c=l[1],d=l[2],h=l[3],f=l[4],(p=document.createElement("canvas")).width=b,p.height=x,g=p.getContext("2d"),m=g.createLinearGradient(c,h,d,f),gr(n.stops,u).forEach((function(e){return m.addColorStop(e.stop,ir(e.color))})),g.fillStyle=m,g.fillRect(0,0,b,x),b>0&&x>0&&(v=r.ctx.createPattern(p,"repeat"),r.renderRepeat(w,v,S,E))):Qr(n)&&(y=el(e,t,[null,null,null]),w=y[0],B=y[1],_=y[2],b=y[3],x=y[4],C=0===n.position.length?[Gn]:n.position,S=jn(C[0],b),E=jn(C[C.length-1],x),U=Br(n,S,E,b,x),M=U[0],F=U[1],M>0&&F>0&&(T=r.ctx.createRadialGradient(B+S,_+E,0,B+S,_+E,M),gr(n.stops,2*M).forEach((function(e){return T.addColorStop(e.stop,ir(e.color))})),r.path(w),r.ctx.fillStyle=T,M!==F?(k=e.bounds.left+.5*e.bounds.width,Q=e.bounds.top+.5*e.bounds.height,D=1/(L=F/M),r.ctx.save(),r.ctx.translate(k,Q),r.ctx.transform(1,0,0,L,0,0),r.ctx.translate(-k,-Q),r.ctx.fillRect(B,D*(_-Q)+Q,b,x*D),r.ctx.restore()):r.ctx.fill())),i.label=6;case 6:return t--,[2]}}))},r=this,A=0,a=e.styles.backgroundImage.slice(0).reverse(),s.label=1;case 1:return A<a.length?(o=a[A],[5,n(o)]):[3,4];case 2:s.sent(),s.label=3;case 3:return A++,[3,1];case 4:return[2]}}))}))},n.prototype.renderSolidBorder=function(e,t,n){return r(this,void 0,void 0,(function(){return i(this,(function(r){return this.path(Gs(n,t)),this.ctx.fillStyle=ir(e),this.ctx.fill(),[2]}))}))},n.prototype.renderDoubleBorder=function(e,t,n,A){return r(this,void 0,void 0,(function(){var r,a;return i(this,(function(i){switch(i.label){case 0:return t<3?[4,this.renderSolidBorder(e,n,A)]:[3,2];case 1:return i.sent(),[2];case 2:return r=Ks(A,n),this.path(r),this.ctx.fillStyle=ir(e),this.ctx.fill(),a=Ws(A,n),this.path(a),this.ctx.fill(),[2]}}))}))},n.prototype.renderNodeBackgroundAndBorders=function(e){return r(this,void 0,void 0,(function(){var t,n,r,A,a,o,s,l,u=this;return i(this,(function(i){switch(i.label){case 0:return this.applyEffects(e.getEffects(2)),t=e.container.styles,n=!rr(t.backgroundColor)||t.backgroundImage.length,r=[{style:t.borderTopStyle,color:t.borderTopColor,width:t.borderTopWidth},{style:t.borderRightStyle,color:t.borderRightColor,width:t.borderRightWidth},{style:t.borderBottomStyle,color:t.borderBottomColor,width:t.borderBottomWidth},{style:t.borderLeftStyle,color:t.borderLeftColor,width:t.borderLeftWidth}],A=hl(il(t.backgroundClip,0),e.curves),n||t.boxShadow.length?(this.ctx.save(),this.path(A),this.ctx.clip(),rr(t.backgroundColor)||(this.ctx.fillStyle=ir(t.backgroundColor),this.ctx.fill()),[4,this.renderBackgroundImage(e.container)]):[3,2];case 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function(){t||(t=!0,e._release())}}},{key:"autoRelease_INTERNAL",value:function(){var e=this;tA||window.setTimeout((function(){return e._release()}),10)}},{key:"_release",value:function(){if(this._refCount--,0===this._refCount){if(this._store.getState().nodeCleanupFunctions.forEach((function(e){return e()})),this._store.getState().nodeCleanupFunctions.clear(),!vn("recoil_memory_managament_2020"))return}else this._refCount}},{key:"isRetained",value:function(){return this._refCount>0}},{key:"checkRefCount_INTERNAL",value:function(){vn("recoil_memory_managament_2020")&&this._refCount}},{key:"getStore_INTERNAL",value:function(){return this.checkRefCount_INTERNAL(),this._store}},{key:"getID",value:function(){return this.checkRefCount_INTERNAL(),this._store.getState().currentTree.stateID}},{key:"getStoreID",value:function(){return this.checkRefCount_INTERNAL(),this._store.storeID}}]),e}();function iA(e,t){var n=arguments.length>2&&void 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new rA(iA(e,i),e.storeID)}),(function(e,t){var n,r;return String(t)+String(e.storeID)+String(null===(n=e.getState().nextTree)||void 0===n?void 0:n.version)+String(e.getState().currentTree.version)+String(null===(r=e.getState().previousTree)||void 0===r?void 0:r.version)})),2),aA=AA[0],oA=AA[1];Zi(oA);var sA=function(e){C(n,e);var t=M(n);function n(e,r){var i;return B(this,n),Xt(U(i=t.call(this,iA(e.getStore_INTERNAL(),e.getStore_INTERNAL().getState().currentTree,!0),e.getStoreID())),"_batch",void 0),Xt(U(i),"set",(function(e,t){i.checkRefCount_INTERNAL();var n=i.getStore_INTERNAL();i._batch((function(){Ji(n,e.key,1),qi(i.getStore_INTERNAL(),e,t)}))})),Xt(U(i),"reset",(function(e){i.checkRefCount_INTERNAL();var t=i.getStore_INTERNAL();i._batch((function(){Ji(t,e.key,1),qi(i.getStore_INTERNAL(),e,Gi)}))})),Xt(U(i),"setUnvalidatedAtomValues_DEPRECATED",(function(e){i.checkRefCount_INTERNAL();var t=i.getStore_INTERNAL();Pi((function(){var 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e&&(t.current=e()),t},vA=vr.getNextTreeStateVersion,yA=vr.makeEmptyStoreState,wA=Pr,BA=Vr,_A=Hr,bA=Rr,xA=Nr,CA=ar,SA=Ar,EA=dr,UA=Un.createMutableSource,MA=Un.reactMode,FA=hi.applyAtomValueWrites,TA=Ei,kA=fA.freshSnapshot,QA=r.useCallback,LA=r.useContext,DA=r.useEffect,IA=r.useMemo,RA=r.useRef,HA=r.useState;function PA(){throw Kt("This component must be used inside a <RecoilRoot> component.")}var NA=Object.freeze({storeID:EA(),getState:PA,replaceState:PA,getGraph:PA,subscribeToTransactions:PA,addTransactionMetadata:PA}),OA=!1;function VA(e){if(OA)throw Kt("An atom update was triggered within the execution of a state updater function. 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n()}},{key:"poll",value:function(){this.polling=!0,this.doPoll(),this.emitReserved("poll")}},{key:"onData",value:function(e){var t=this;(function(e,t){for(var n=e.split(sd),r=[],i=0;i<n.length;i++){var A=od(n[i],t);if(r.push(A),"error"===A.type)break}return r})(e,this.socket.binaryType).forEach((function(e){if("opening"===t.readyState&&"open"===e.type&&t.onOpen(),"close"===e.type)return t.onClose({description:"transport closed by the server"}),!1;t.onPacket(e)})),"closed"!==this.readyState&&(this.polling=!1,this.emitReserved("pollComplete"),"open"===this.readyState&&this.poll())}},{key:"doClose",value:function(){var e=this,t=function(){e.write([{type:"close"}])};"open"===this.readyState?t():this.once("open",t)}},{key:"write",value:function(e){var t=this;this.writable=!1,function(e,t){var n=e.length,r=new Array(n),i=0;e.forEach((function(e,A){ed(e,!1,(function(e){r[A]=e,++i===n&&t(r.join(sd))}))}))}(e,(function(e){t.doWrite(e,(function(){t.writable=!0,t.emitReserved("drain")}))}))}},{key:"uri",value:function(){var e=this.query||{},t=this.opts.secure?"https":"http",n="";!1!==this.opts.timestampRequests&&(e[this.opts.timestampParam]=Cd()),this.supportsBinary||e.sid||(e.b64=1),this.opts.port&&("https"===t&&443!==Number(this.opts.port)||"http"===t&&80!==Number(this.opts.port))&&(n=":"+this.opts.port);var r=Sd(e);return t+"://"+(-1!==this.opts.hostname.indexOf(":")?"["+this.opts.hostname+"]":this.opts.hostname)+n+this.opts.path+(r.length?"?"+r:"")}},{key:"request",value:function(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:{};return Object.assign(e,{xd:this.xd,xs:this.xs},this.opts),new Qd(this.uri(),e)}},{key:"doWrite",value:function(e,t){var n=this,r=this.request({method:"POST",data:e});r.on("success",t),r.on("error",(function(e,t){n.onError("xhr post error",e,t)}))}},{key:"doPoll",value:function(){var e=this,t=this.request();t.on("data",this.onData.bind(this)),t.on("error",(function(t,n){e.onError("xhr poll error",t,n)})),this.pollXhr=t}}]),n}(vd),Qd=function(e){C(n,e);var t=M(n);function n(e,r){var i;return B(this,n),fd(U(i=t.call(this)),r),i.opts=r,i.method=r.method||"GET",i.uri=e,i.async=!1!==r.async,i.data=void 0!==r.data?r.data:null,i.create(),i}return b(n,[{key:"create",value:function(){var e=this,t=cd(this.opts,"agent","pfx","key","passphrase","cert","ca","ciphers","rejectUnauthorized","autoUnref");t.xdomain=!!this.opts.xd,t.xscheme=!!this.opts.xs;var r=this.xhr=new Md(t);try{r.open(this.method,this.uri,this.async);try{if(this.opts.extraHeaders)for(var i in r.setDisableHeaderCheck&&r.setDisableHeaderCheck(!0),this.opts.extraHeaders)this.opts.extraHeaders.hasOwnProperty(i)&&r.setRequestHeader(i,this.opts.extraHeaders[i])}catch(A){}if("POST"===this.method)try{r.setRequestHeader("Content-type","text/plain;charset=UTF-8")}catch(A){}try{r.setRequestHeader("Accept","*/*")}catch(A){}"withCredentials"in r&&(r.withCredentials=this.opts.withCredentials),this.opts.requestTimeout&&(r.timeout=this.opts.requestTimeout),r.onreadystatechange=function(){4===r.readyState&&(200===r.status||1223===r.status?e.onLoad():e.setTimeoutFn((function(){e.onError("number"===typeof r.status?r.status:0)}),0))},r.send(this.data)}catch(A){return void this.setTimeoutFn((function(){e.onError(A)}),0)}"undefined"!==typeof document&&(this.index=n.requestsCount++,n.requests[this.index]=this)}},{key:"onError",value:function(e){this.emitReserved("error",e,this.xhr),this.cleanup(!0)}},{key:"cleanup",value:function(e){if("undefined"!==typeof this.xhr&&null!==this.xhr){if(this.xhr.onreadystatechange=Fd,e)try{this.xhr.abort()}catch(t){}"undefined"!==typeof document&&delete n.requests[this.index],this.xhr=null}}},{key:"onLoad",value:function(){var e=this.xhr.responseText;null!==e&&(this.emitReserved("data",e),this.emitReserved("success"),this.cleanup())}},{key:"abort",value:function(){this.cleanup()}}]),n}(ld);if(Qd.requestsCount=0,Qd.requests={},"undefined"!==typeof document)if("function"===typeof attachEvent)attachEvent("onunload",Ld);else if("function"===typeof addEventListener){addEventListener("onpagehide"in ud?"pagehide":"unload",Ld,!1)}function Ld(){for(var e in Qd.requests)Qd.requests.hasOwnProperty(e)&&Qd.requests[e].abort()}var Dd="function"===typeof Promise&&"function"===typeof Promise.resolve?function(e){return Promise.resolve().then(e)}:function(e,t){return t(e,0)},Id=ud.WebSocket||ud.MozWebSocket,Rd="undefined"!==typeof navigator&&"string"===typeof navigator.product&&"reactnative"===navigator.product.toLowerCase(),Hd=function(e){C(n,e);var t=M(n);function n(e){var r;return B(this,n),(r=t.call(this,e)).supportsBinary=!e.forceBase64,r}return b(n,[{key:"name",get:function(){return"websocket"}},{key:"doOpen",value:function(){if(this.check()){var e=this.uri(),t=this.opts.protocols,n=Rd?{}:cd(this.opts,"agent","perMessageDeflate","pfx","key","passphrase","cert","ca","ciphers","rejectUnauthorized","localAddress","protocolVersion","origin","maxPayload","family","checkServerIdentity");this.opts.extraHeaders&&(n.headers=this.opts.extraHeaders);try{this.ws=Rd?new Id(e,t,n):t?new Id(e,t):new Id(e)}catch(Xx){return this.emitReserved("error",Xx)}this.ws.binaryType=this.socket.binaryType||"arraybuffer",this.addEventListeners()}}},{key:"addEventListeners",value:function(){var e=this;this.ws.onopen=function(){e.opts.autoUnref&&e.ws._socket.unref(),e.onOpen()},this.ws.onclose=function(t){return e.onClose({description:"websocket connection closed",context:t})},this.ws.onmessage=function(t){return e.onData(t.data)},this.ws.onerror=function(t){return e.onError("websocket error",t)}}},{key:"write",value:function(e){var t=this;this.writable=!1;for(var n=function(){var n=e[r],i=r===e.length-1;ed(n,t.supportsBinary,(function(e){try{t.ws.send(e)}catch(n){}i&&Dd((function(){t.writable=!0,t.emitReserved("drain")}),t.setTimeoutFn)}))},r=0;r<e.length;r++)n()}},{key:"doClose",value:function(){"undefined"!==typeof this.ws&&(this.ws.close(),this.ws=null)}},{key:"uri",value:function(){var e=this.query||{},t=this.opts.secure?"wss":"ws",n="";this.opts.port&&("wss"===t&&443!==Number(this.opts.port)||"ws"===t&&80!==Number(this.opts.port))&&(n=":"+this.opts.port),this.opts.timestampRequests&&(e[this.opts.timestampParam]=Cd()),this.supportsBinary||(e.b64=1);var r=Sd(e);return t+"://"+(-1!==this.opts.hostname.indexOf(":")?"["+this.opts.hostname+"]":this.opts.hostname)+n+this.opts.path+(r.length?"?"+r:"")}},{key:"check",value:function(){return!!Id}}]),n}(vd),Pd={websocket:Hd,polling:kd},Nd=/^(?:(?![^:@\/?#]+:[^:@\/]*@)(http|https|ws|wss):\/\/)?((?:(([^:@\/?#]*)(?::([^:@\/?#]*))?)?@)?((?:[a-f0-9]{0,4}:){2,7}[a-f0-9]{0,4}|[^:\/?#]*)(?::(\d*))?)(((\/(?:[^?#](?![^?#\/]*\.[^?#\/.]+(?:[?#]|$)))*\/?)?([^?#\/]*))(?:\?([^#]*))?(?:#(.*))?)/,Od=["source","protocol","authority","userInfo","user","password","host","port","relative","path","directory","file","query","anchor"];function Vd(e){var t=e,n=e.indexOf("["),r=e.indexOf("]");-1!=n&&-1!=r&&(e=e.substring(0,n)+e.substring(n,r).replace(/:/g,";")+e.substring(r,e.length));for(var i=Nd.exec(e||""),A={},a=14;a--;)A[Od[a]]=i[a]||"";return-1!=n&&-1!=r&&(A.source=t,A.host=A.host.substring(1,A.host.length-1).replace(/;/g,":"),A.authority=A.authority.replace("[","").replace("]","").replace(/;/g,":"),A.ipv6uri=!0),A.pathNames=function(e,t){var n=/\/{2,9}/g,r=t.replace(n,"/").split("/");"/"!=t.slice(0,1)&&0!==t.length||r.splice(0,1);"/"==t.slice(-1)&&r.splice(r.length-1,1);return r}(0,A.path),A.queryKey=function(e,t){var n={};return t.replace(/(?:^|&)([^&=]*)=?([^&]*)/g,(function(e,t,r){t&&(n[t]=r)})),n}(0,A.query),A}var zd=function(e){C(n,e);var t=M(n);function n(e){var r,i=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};return B(this,n),(r=t.call(this)).writeBuffer=[],e&&"object"===typeof e&&(i=e,e=null),e?(e=Vd(e),i.hostname=e.host,i.secure="https"===e.protocol||"wss"===e.protocol,i.port=e.port,e.query&&(i.query=e.query)):i.host&&(i.hostname=Vd(i.host).host),fd(U(r),i),r.secure=null!=i.secure?i.secure:"undefined"!==typeof location&&"https:"===location.protocol,i.hostname&&!i.port&&(i.port=r.secure?"443":"80"),r.hostname=i.hostname||("undefined"!==typeof location?location.hostname:"localhost"),r.port=i.port||("undefined"!==typeof location&&location.port?location.port:r.secure?"443":"80"),r.transports=i.transports||["polling","websocket"],r.writeBuffer=[],r.prevBufferLen=0,r.opts=Object.assign({path:"/engine.io",agent:!1,withCredentials:!1,upgrade:!0,timestampParam:"t",rememberUpgrade:!1,addTrailingSlash:!0,rejectUnauthorized:!0,perMessageDeflate:{threshold:1024},transportOptions:{},closeOnBeforeunload:!0},i),r.opts.path=r.opts.path.replace(/\/$/,"")+(r.opts.addTrailingSlash?"/":""),"string"===typeof r.opts.query&&(r.opts.query=function(e){for(var t={},n=e.split("&"),r=0,i=n.length;r<i;r++){var A=n[r].split("=");t[decodeURIComponent(A[0])]=decodeURIComponent(A[1])}return t}(r.opts.query)),r.id=null,r.upgrades=null,r.pingInterval=null,r.pingTimeout=null,r.pingTimeoutTimer=null,"function"===typeof addEventListener&&(r.opts.closeOnBeforeunload&&(r.beforeunloadEventListener=function(){r.transport&&(r.transport.removeAllListeners(),r.transport.close())},addEventListener("beforeunload",r.beforeunloadEventListener,!1)),"localhost"!==r.hostname&&(r.offlineEventListener=function(){r.onClose("transport close",{description:"network connection lost"})},addEventListener("offline",r.offlineEventListener,!1))),r.open(),r}return b(n,[{key:"createTransport",value:function(e){var t=Object.assign({},this.opts.query);t.EIO=4,t.transport=e,this.id&&(t.sid=this.id);var n=Object.assign({},this.opts.transportOptions[e],this.opts,{query:t,socket:this,hostname:this.hostname,secure:this.secure,port:this.port});return new Pd[e](n)}},{key:"open",value:function(){var e,t=this;if(this.opts.rememberUpgrade&&n.priorWebsocketSuccess&&-1!==this.transports.indexOf("websocket"))e="websocket";else{if(0===this.transports.length)return void this.setTimeoutFn((function(){t.emitReserved("error","No transports available")}),0);e=this.transports[0]}this.readyState="opening";try{e=this.createTransport(e)}catch(r){return this.transports.shift(),void this.open()}e.open(),this.setTransport(e)}},{key:"setTransport",value:function(e){var t=this;this.transport&&this.transport.removeAllListeners(),this.transport=e,e.on("drain",this.onDrain.bind(this)).on("packet",this.onPacket.bind(this)).on("error",this.onError.bind(this)).on("close",(function(e){return t.onClose("transport close",e)}))}},{key:"probe",value:function(e){var t=this,r=this.createTransport(e),i=!1;n.priorWebsocketSuccess=!1;var A=function(){i||(r.send([{type:"ping",data:"probe"}]),r.once("packet",(function(e){if(!i)if("pong"===e.type&&"probe"===e.data){if(t.upgrading=!0,t.emitReserved("upgrading",r),!r)return;n.priorWebsocketSuccess="websocket"===r.name,t.transport.pause((function(){i||"closed"!==t.readyState&&(c(),t.setTransport(r),r.send([{type:"upgrade"}]),t.emitReserved("upgrade",r),r=null,t.upgrading=!1,t.flush())}))}else{var A=new Error("probe error");A.transport=r.name,t.emitReserved("upgradeError",A)}})))};function a(){i||(i=!0,c(),r.close(),r=null)}var o=function(e){var n=new Error("probe error: "+e);n.transport=r.name,a(),t.emitReserved("upgradeError",n)};function s(){o("transport closed")}function l(){o("socket closed")}function u(e){r&&e.name!==r.name&&a()}var c=function(){r.removeListener("open",A),r.removeListener("error",o),r.removeListener("close",s),t.off("close",l),t.off("upgrading",u)};r.once("open",A),r.once("error",o),r.once("close",s),this.once("close",l),this.once("upgrading",u),r.open()}},{key:"onOpen",value:function(){if(this.readyState="open",n.priorWebsocketSuccess="websocket"===this.transport.name,this.emitReserved("open"),this.flush(),"open"===this.readyState&&this.opts.upgrade)for(var e=0,t=this.upgrades.length;e<t;e++)this.probe(this.upgrades[e])}},{key:"onPacket",value:function(e){if("opening"===this.readyState||"open"===this.readyState||"closing"===this.readyState)switch(this.emitReserved("packet",e),this.emitReserved("heartbeat"),e.type){case"open":this.onHandshake(JSON.parse(e.data));break;case"ping":this.resetPingTimeout(),this.sendPacket("pong"),this.emitReserved("ping"),this.emitReserved("pong");break;case"error":var t=new Error("server error");t.code=e.data,this.onError(t);break;case"message":this.emitReserved("data",e.data),this.emitReserved("message",e.data)}}},{key:"onHandshake",value:function(e){this.emitReserved("handshake",e),this.id=e.sid,this.transport.query.sid=e.sid,this.upgrades=this.filterUpgrades(e.upgrades),this.pingInterval=e.pingInterval,this.pingTimeout=e.pingTimeout,this.maxPayload=e.maxPayload,this.onOpen(),"closed"!==this.readyState&&this.resetPingTimeout()}},{key:"resetPingTimeout",value:function(){var e=this;this.clearTimeoutFn(this.pingTimeoutTimer),this.pingTimeoutTimer=this.setTimeoutFn((function(){e.onClose("ping timeout")}),this.pingInterval+this.pingTimeout),this.opts.autoUnref&&this.pingTimeoutTimer.unref()}},{key:"onDrain",value:function(){this.writeBuffer.splice(0,this.prevBufferLen),this.prevBufferLen=0,0===this.writeBuffer.length?this.emitReserved("drain"):this.flush()}},{key:"flush",value:function(){if("closed"!==this.readyState&&this.transport.writable&&!this.upgrading&&this.writeBuffer.length){var e=this.getWritablePackets();this.transport.send(e),this.prevBufferLen=e.length,this.emitReserved("flush")}}},{key:"getWritablePackets",value:function(){if(!(this.maxPayload&&"polling"===this.transport.name&&this.writeBuffer.length>1))return this.writeBuffer;for(var e,t=1,n=0;n<this.writeBuffer.length;n++){var r=this.writeBuffer[n].data;if(r&&(t+="string"===typeof(e=r)?function(e){for(var t=0,n=0,r=0,i=e.length;r<i;r++)(t=e.charCodeAt(r))<128?n+=1:t<2048?n+=2:t<55296||t>=57344?n+=3:(r++,n+=4);return n}(e):Math.ceil((e.byteLength||e.size)*pd)),n>0&&t>this.maxPayload)return this.writeBuffer.slice(0,n);t+=2}return this.writeBuffer}},{key:"write",value:function(e,t,n){return this.sendPacket("message",e,t,n),this}},{key:"send",value:function(e,t,n){return this.sendPacket("message",e,t,n),this}},{key:"sendPacket",value:function(e,t,n,r){if("function"===typeof t&&(r=t,t=void 0),"function"===typeof n&&(r=n,n=null),"closing"!==this.readyState&&"closed"!==this.readyState){(n=n||{}).compress=!1!==n.compress;var i={type:e,data:t,options:n};this.emitReserved("packetCreate",i),this.writeBuffer.push(i),r&&this.once("flush",r),this.flush()}}},{key:"close",value:function(){var e=this,t=function(){e.onClose("forced close"),e.transport.close()},n=function n(){e.off("upgrade",n),e.off("upgradeError",n),t()},r=function(){e.once("upgrade",n),e.once("upgradeError",n)};return"opening"!==this.readyState&&"open"!==this.readyState||(this.readyState="closing",this.writeBuffer.length?this.once("drain",(function(){e.upgrading?r():t()})):this.upgrading?r():t()),this}},{key:"onError",value:function(e){n.priorWebsocketSuccess=!1,this.emitReserved("error",e),this.onClose("transport error",e)}},{key:"onClose",value:function(e,t){"opening"!==this.readyState&&"open"!==this.readyState&&"closing"!==this.readyState||(this.clearTimeoutFn(this.pingTimeoutTimer),this.transport.removeAllListeners("close"),this.transport.close(),this.transport.removeAllListeners(),"function"===typeof removeEventListener&&(removeEventListener("beforeunload",this.beforeunloadEventListener,!1),removeEventListener("offline",this.offlineEventListener,!1)),this.readyState="closed",this.id=null,this.emitReserved("close",e,t),this.writeBuffer=[],this.prevBufferLen=0)}},{key:"filterUpgrades",value:function(e){for(var t=[],n=0,r=e.length;n<r;n++)~this.transports.indexOf(e[n])&&t.push(e[n]);return t}}]),n}(ld);zd.protocol=4;zd.protocol;var Gd="function"===typeof ArrayBuffer,Kd=function(e){return"function"===typeof ArrayBuffer.isView?ArrayBuffer.isView(e):e.buffer instanceof ArrayBuffer},Wd=Object.prototype.toString,jd="function"===typeof Blob||"undefined"!==typeof Blob&&"[object BlobConstructor]"===Wd.call(Blob),Xd="function"===typeof File||"undefined"!==typeof File&&"[object FileConstructor]"===Wd.call(File);function qd(e){return Gd&&(e instanceof ArrayBuffer||Kd(e))||jd&&e instanceof Blob||Xd&&e instanceof File}function Yd(e,t){if(!e||"object"!==typeof e)return!1;if(Array.isArray(e)){for(var n=0,r=e.length;n<r;n++)if(Yd(e[n]))return!0;return!1}if(qd(e))return!0;if(e.toJSON&&"function"===typeof e.toJSON&&1===arguments.length)return Yd(e.toJSON(),!0);for(var i in e)if(Object.prototype.hasOwnProperty.call(e,i)&&Yd(e[i]))return!0;return!1}function Jd(e){var t=[],n=e.data,r=e;return r.data=Zd(n,t),r.attachments=t.length,{packet:r,buffers:t}}function Zd(e,t){if(!e)return e;if(qd(e)){var n={_placeholder:!0,num:t.length};return t.push(e),n}if(Array.isArray(e)){for(var r=new Array(e.length),i=0;i<e.length;i++)r[i]=Zd(e[i],t);return r}if("object"===typeof e&&!(e instanceof Date)){var A={};for(var a in e)Object.prototype.hasOwnProperty.call(e,a)&&(A[a]=Zd(e[a],t));return A}return e}function $d(e,t){return e.data=eh(e.data,t),delete e.attachments,e}function eh(e,t){if(!e)return e;if(e&&!0===e._placeholder){if("number"===typeof e.num&&e.num>=0&&e.num<t.length)return t[e.num];throw new Error("illegal attachments")}if(Array.isArray(e))for(var n=0;n<e.length;n++)e[n]=eh(e[n],t);else if("object"===typeof e)for(var r in e)Object.prototype.hasOwnProperty.call(e,r)&&(e[r]=eh(e[r],t));return e}var th,nh=5;!function(e){e[e.CONNECT=0]="CONNECT",e[e.DISCONNECT=1]="DISCONNECT",e[e.EVENT=2]="EVENT",e[e.ACK=3]="ACK",e[e.CONNECT_ERROR=4]="CONNECT_ERROR",e[e.BINARY_EVENT=5]="BINARY_EVENT",e[e.BINARY_ACK=6]="BINARY_ACK"}(th||(th={}));var rh=function(){function e(t){B(this,e),this.replacer=t}return b(e,[{key:"encode",value:function(e){return e.type!==th.EVENT&&e.type!==th.ACK||!Yd(e)?[this.encodeAsString(e)]:this.encodeAsBinary({type:e.type===th.EVENT?th.BINARY_EVENT:th.BINARY_ACK,nsp:e.nsp,data:e.data,id:e.id})}},{key:"encodeAsString",value:function(e){var t=""+e.type;return e.type!==th.BINARY_EVENT&&e.type!==th.BINARY_ACK||(t+=e.attachments+"-"),e.nsp&&"/"!==e.nsp&&(t+=e.nsp+","),null!=e.id&&(t+=e.id),null!=e.data&&(t+=JSON.stringify(e.data,this.replacer)),t}},{key:"encodeAsBinary",value:function(e){var t=Jd(e),n=this.encodeAsString(t.packet),r=t.buffers;return r.unshift(n),r}}]),e}(),ih=function(e){C(n,e);var t=M(n);function n(e){var r;return B(this,n),(r=t.call(this)).reviver=e,r}return b(n,[{key:"add",value:function(e){var t;if("string"===typeof e){if(this.reconstructor)throw new Error("got plaintext data when reconstructing a packet");var r=(t=this.decodeString(e)).type===th.BINARY_EVENT;r||t.type===th.BINARY_ACK?(t.type=r?th.EVENT:th.ACK,this.reconstructor=new Ah(t),0===t.attachments&&jc(S(n.prototype),"emitReserved",this).call(this,"decoded",t)):jc(S(n.prototype),"emitReserved",this).call(this,"decoded",t)}else{if(!qd(e)&&!e.base64)throw new Error("Unknown type: "+e);if(!this.reconstructor)throw new Error("got binary data when not reconstructing a packet");(t=this.reconstructor.takeBinaryData(e))&&(this.reconstructor=null,jc(S(n.prototype),"emitReserved",this).call(this,"decoded",t))}}},{key:"decodeString",value:function(e){var t=0,r={type:Number(e.charAt(0))};if(void 0===th[r.type])throw new Error("unknown packet type "+r.type);if(r.type===th.BINARY_EVENT||r.type===th.BINARY_ACK){for(var i=t+1;"-"!==e.charAt(++t)&&t!=e.length;);var A=e.substring(i,t);if(A!=Number(A)||"-"!==e.charAt(t))throw new Error("Illegal attachments");r.attachments=Number(A)}if("/"===e.charAt(t+1)){for(var a=t+1;++t;){if(","===e.charAt(t))break;if(t===e.length)break}r.nsp=e.substring(a,t)}else r.nsp="/";var o=e.charAt(t+1);if(""!==o&&Number(o)==o){for(var s=t+1;++t;){var l=e.charAt(t);if(null==l||Number(l)!=l){--t;break}if(t===e.length)break}r.id=Number(e.substring(s,t+1))}if(e.charAt(++t)){var u=this.tryParse(e.substr(t));if(!n.isPayloadValid(r.type,u))throw new Error("invalid payload");r.data=u}return r}},{key:"tryParse",value:function(e){try{return JSON.parse(e,this.reviver)}catch(t){return!1}}},{key:"destroy",value:function(){this.reconstructor&&(this.reconstructor.finishedReconstruction(),this.reconstructor=null)}}],[{key:"isPayloadValid",value:function(e,t){switch(e){case th.CONNECT:return"object"===typeof t;case th.DISCONNECT:return void 0===t;case th.CONNECT_ERROR:return"string"===typeof t||"object"===typeof t;case th.EVENT:case th.BINARY_EVENT:return Array.isArray(t)&&t.length>0;case th.ACK:case th.BINARY_ACK:return Array.isArray(t)}}}]),n}(ld),Ah=function(){function e(t){B(this,e),this.packet=t,this.buffers=[],this.reconPack=t}return b(e,[{key:"takeBinaryData",value:function(e){if(this.buffers.push(e),this.buffers.length===this.reconPack.attachments){var t=$d(this.reconPack,this.buffers);return this.finishedReconstruction(),t}return null}},{key:"finishedReconstruction",value:function(){this.reconPack=null,this.buffers=[]}}]),e}();function ah(e,t,n){return e.on(t,n),function(){e.off(t,n)}}var oh=Object.freeze({connect:1,connect_error:1,disconnect:1,disconnecting:1,newListener:1,removeListener:1}),sh=function(e){C(n,e);var t=M(n);function n(e,r,i){var A;return B(this,n),(A=t.call(this)).connected=!1,A.recovered=!1,A.receiveBuffer=[],A.sendBuffer=[],A._queue=[],A._queueSeq=0,A.ids=0,A.acks={},A.flags={},A.io=e,A.nsp=r,i&&i.auth&&(A.auth=i.auth),A._opts=Object.assign({},i),A.io._autoConnect&&A.open(),A}return b(n,[{key:"disconnected",get:function(){return!this.connected}},{key:"subEvents",value:function(){if(!this.subs){var e=this.io;this.subs=[ah(e,"open",this.onopen.bind(this)),ah(e,"packet",this.onpacket.bind(this)),ah(e,"error",this.onerror.bind(this)),ah(e,"close",this.onclose.bind(this))]}}},{key:"active",get:function(){return!!this.subs}},{key:"connect",value:function(){return this.connected||(this.subEvents(),this.io._reconnecting||this.io.open(),"open"===this.io._readyState&&this.onopen()),this}},{key:"open",value:function(){return this.connect()}},{key:"send",value:function(){for(var e=arguments.length,t=new Array(e),n=0;n<e;n++)t[n]=arguments[n];return t.unshift("message"),this.emit.apply(this,t),this}},{key:"emit",value:function(e){if(oh.hasOwnProperty(e))throw new Error('"'+e.toString()+'" is a reserved event name');for(var t=arguments.length,n=new Array(t>1?t-1:0),r=1;r<t;r++)n[r-1]=arguments[r];if(n.unshift(e),this._opts.retries&&!this.flags.fromQueue&&!this.flags.volatile)return this._addToQueue(n),this;var i={type:th.EVENT,data:n,options:{}};if(i.options.compress=!1!==this.flags.compress,"function"===typeof n[n.length-1]){var A=this.ids++,a=n.pop();this._registerAckCallback(A,a),i.id=A}var o=this.io.engine&&this.io.engine.transport&&this.io.engine.transport.writable;return this.flags.volatile&&(!o||!this.connected)||(this.connected?(this.notifyOutgoingListeners(i),this.packet(i)):this.sendBuffer.push(i)),this.flags={},this}},{key:"_registerAckCallback",value:function(e,t){var n,r=this,i=null!==(n=this.flags.timeout)&&void 0!==n?n:this._opts.ackTimeout;if(void 0!==i){var A=this.io.setTimeoutFn((function(){delete r.acks[e];for(var n=0;n<r.sendBuffer.length;n++)r.sendBuffer[n].id===e&&r.sendBuffer.splice(n,1);t.call(r,new Error("operation has timed out"))}),i);this.acks[e]=function(){r.io.clearTimeoutFn(A);for(var e=arguments.length,n=new Array(e),i=0;i<e;i++)n[i]=arguments[i];t.apply(r,[null].concat(n))}}else this.acks[e]=t}},{key:"emitWithAck",value:function(e){for(var t=this,n=arguments.length,r=new Array(n>1?n-1:0),i=1;i<n;i++)r[i-1]=arguments[i];var A=void 0!==this.flags.timeout||void 0!==this._opts.ackTimeout;return new Promise((function(n,i){r.push((function(e,t){return A?e?i(e):n(t):n(e)})),t.emit.apply(t,[e].concat(r))}))}},{key:"_addToQueue",value:function(e){var t,n=this;"function"===typeof e[e.length-1]&&(t=e.pop());var r={id:this._queueSeq++,tryCount:0,pending:!1,args:e,flags:Object.assign({fromQueue:!0},this.flags)};e.push((function(e){if(r===n._queue[0]){if(null!==e)r.tryCount>n._opts.retries&&(n._queue.shift(),t&&t(e));else if(n._queue.shift(),t){for(var i=arguments.length,A=new Array(i>1?i-1:0),a=1;a<i;a++)A[a-1]=arguments[a];t.apply(void 0,[null].concat(A))}return r.pending=!1,n._drainQueue()}})),this._queue.push(r),this._drainQueue()}},{key:"_drainQueue",value:function(){var e=arguments.length>0&&void 0!==arguments[0]&&arguments[0];if(this.connected&&0!==this._queue.length){var t=this._queue[0];t.pending&&!e||(t.pending=!0,t.tryCount++,this.flags=t.flags,this.emit.apply(this,t.args))}}},{key:"packet",value:function(e){e.nsp=this.nsp,this.io._packet(e)}},{key:"onopen",value:function(){var e=this;"function"==typeof this.auth?this.auth((function(t){e._sendConnectPacket(t)})):this._sendConnectPacket(this.auth)}},{key:"_sendConnectPacket",value:function(e){this.packet({type:th.CONNECT,data:this._pid?Object.assign({pid:this._pid,offset:this._lastOffset},e):e})}},{key:"onerror",value:function(e){this.connected||this.emitReserved("connect_error",e)}},{key:"onclose",value:function(e,t){this.connected=!1,delete this.id,this.emitReserved("disconnect",e,t)}},{key:"onpacket",value:function(e){if(e.nsp===this.nsp)switch(e.type){case th.CONNECT:e.data&&e.data.sid?this.onconnect(e.data.sid,e.data.pid):this.emitReserved("connect_error",new Error("It seems you are trying to reach a Socket.IO server in v2.x with a v3.x client, but they are not compatible (more information here: https://socket.io/docs/v3/migrating-from-2-x-to-3-0/)"));break;case th.EVENT:case th.BINARY_EVENT:this.onevent(e);break;case th.ACK:case th.BINARY_ACK:this.onack(e);break;case th.DISCONNECT:this.ondisconnect();break;case th.CONNECT_ERROR:this.destroy();var t=new Error(e.data.message);t.data=e.data.data,this.emitReserved("connect_error",t)}}},{key:"onevent",value:function(e){var t=e.data||[];null!=e.id&&t.push(this.ack(e.id)),this.connected?this.emitEvent(t):this.receiveBuffer.push(Object.freeze(t))}},{key:"emitEvent",value:function(e){if(this._anyListeners&&this._anyListeners.length){var t,r=k(this._anyListeners.slice());try{for(r.s();!(t=r.n()).done;){t.value.apply(this,e)}}catch(Xx){r.e(Xx)}finally{r.f()}}jc(S(n.prototype),"emit",this).apply(this,e),this._pid&&e.length&&"string"===typeof e[e.length-1]&&(this._lastOffset=e[e.length-1])}},{key:"ack",value:function(e){var t=this,n=!1;return function(){if(!n){n=!0;for(var r=arguments.length,i=new Array(r),A=0;A<r;A++)i[A]=arguments[A];t.packet({type:th.ACK,id:e,data:i})}}}},{key:"onack",value:function(e){var t=this.acks[e.id];"function"===typeof t&&(t.apply(this,e.data),delete this.acks[e.id])}},{key:"onconnect",value:function(e,t){this.id=e,this.recovered=t&&this._pid===t,this._pid=t,this.connected=!0,this.emitBuffered(),this.emitReserved("connect"),this._drainQueue(!0)}},{key:"emitBuffered",value:function(){var e=this;this.receiveBuffer.forEach((function(t){return e.emitEvent(t)})),this.receiveBuffer=[],this.sendBuffer.forEach((function(t){e.notifyOutgoingListeners(t),e.packet(t)})),this.sendBuffer=[]}},{key:"ondisconnect",value:function(){this.destroy(),this.onclose("io server disconnect")}},{key:"destroy",value:function(){this.subs&&(this.subs.forEach((function(e){return e()})),this.subs=void 0),this.io._destroy(this)}},{key:"disconnect",value:function(){return this.connected&&this.packet({type:th.DISCONNECT}),this.destroy(),this.connected&&this.onclose("io client disconnect"),this}},{key:"close",value:function(){return this.disconnect()}},{key:"compress",value:function(e){return this.flags.compress=e,this}},{key:"volatile",get:function(){return this.flags.volatile=!0,this}},{key:"timeout",value:function(e){return this.flags.timeout=e,this}},{key:"onAny",value:function(e){return this._anyListeners=this._anyListeners||[],this._anyListeners.push(e),this}},{key:"prependAny",value:function(e){return this._anyListeners=this._anyListeners||[],this._anyListeners.unshift(e),this}},{key:"offAny",value:function(e){if(!this._anyListeners)return this;if(e){for(var t=this._anyListeners,n=0;n<t.length;n++)if(e===t[n])return t.splice(n,1),this}else this._anyListeners=[];return this}},{key:"listenersAny",value:function(){return this._anyListeners||[]}},{key:"onAnyOutgoing",value:function(e){return this._anyOutgoingListeners=this._anyOutgoingListeners||[],this._anyOutgoingListeners.push(e),this}},{key:"prependAnyOutgoing",value:function(e){return this._anyOutgoingListeners=this._anyOutgoingListeners||[],this._anyOutgoingListeners.unshift(e),this}},{key:"offAnyOutgoing",value:function(e){if(!this._anyOutgoingListeners)return this;if(e){for(var t=this._anyOutgoingListeners,n=0;n<t.length;n++)if(e===t[n])return t.splice(n,1),this}else this._anyOutgoingListeners=[];return this}},{key:"listenersAnyOutgoing",value:function(){return this._anyOutgoingListeners||[]}},{key:"notifyOutgoingListeners",value:function(e){if(this._anyOutgoingListeners&&this._anyOutgoingListeners.length){var t,n=k(this._anyOutgoingListeners.slice());try{for(n.s();!(t=n.n()).done;){t.value.apply(this,e.data)}}catch(Xx){n.e(Xx)}finally{n.f()}}}}]),n}(ld);function lh(e){e=e||{},this.ms=e.min||100,this.max=e.max||1e4,this.factor=e.factor||2,this.jitter=e.jitter>0&&e.jitter<=1?e.jitter:0,this.attempts=0}lh.prototype.duration=function(){var e=this.ms*Math.pow(this.factor,this.attempts++);if(this.jitter){var t=Math.random(),n=Math.floor(t*this.jitter*e);e=0==(1&Math.floor(10*t))?e-n:e+n}return 0|Math.min(e,this.max)},lh.prototype.reset=function(){this.attempts=0},lh.prototype.setMin=function(e){this.ms=e},lh.prototype.setMax=function(e){this.max=e},lh.prototype.setJitter=function(e){this.jitter=e};var uh=function(t){C(r,t);var n=M(r);function r(t,i){var A,a;B(this,r),(A=n.call(this)).nsps={},A.subs=[],t&&"object"===typeof t&&(i=t,t=void 0),(i=i||{}).path=i.path||"/socket.io",A.opts=i,fd(U(A),i),A.reconnection(!1!==i.reconnection),A.reconnectionAttempts(i.reconnectionAttempts||1/0),A.reconnectionDelay(i.reconnectionDelay||1e3),A.reconnectionDelayMax(i.reconnectionDelayMax||5e3),A.randomizationFactor(null!==(a=i.randomizationFactor)&&void 0!==a?a:.5),A.backoff=new lh({min:A.reconnectionDelay(),max:A.reconnectionDelayMax(),jitter:A.randomizationFactor()}),A.timeout(null==i.timeout?2e4:i.timeout),A._readyState="closed",A.uri=t;var o=i.parser||e;return A.encoder=new o.Encoder,A.decoder=new o.Decoder,A._autoConnect=!1!==i.autoConnect,A._autoConnect&&A.open(),A}return b(r,[{key:"reconnection",value:function(e){return arguments.length?(this._reconnection=!!e,this):this._reconnection}},{key:"reconnectionAttempts",value:function(e){return void 0===e?this._reconnectionAttempts:(this._reconnectionAttempts=e,this)}},{key:"reconnectionDelay",value:function(e){var t;return void 0===e?this._reconnectionDelay:(this._reconnectionDelay=e,null===(t=this.backoff)||void 0===t||t.setMin(e),this)}},{key:"randomizationFactor",value:function(e){var t;return void 0===e?this._randomizationFactor:(this._randomizationFactor=e,null===(t=this.backoff)||void 0===t||t.setJitter(e),this)}},{key:"reconnectionDelayMax",value:function(e){var t;return void 0===e?this._reconnectionDelayMax:(this._reconnectionDelayMax=e,null===(t=this.backoff)||void 0===t||t.setMax(e),this)}},{key:"timeout",value:function(e){return arguments.length?(this._timeout=e,this):this._timeout}},{key:"maybeReconnectOnOpen",value:function(){!this._reconnecting&&this._reconnection&&0===this.backoff.attempts&&this.reconnect()}},{key:"open",value:function(e){var t=this;if(~this._readyState.indexOf("open"))return this;this.engine=new zd(this.uri,this.opts);var n=this.engine,r=this;this._readyState="opening",this.skipReconnect=!1;var i=ah(n,"open",(function(){r.onopen(),e&&e()})),A=ah(n,"error",(function(n){r.cleanup(),r._readyState="closed",t.emitReserved("error",n),e?e(n):r.maybeReconnectOnOpen()}));if(!1!==this._timeout){var a=this._timeout;0===a&&i();var o=this.setTimeoutFn((function(){i(),n.close(),n.emit("error",new Error("timeout"))}),a);this.opts.autoUnref&&o.unref(),this.subs.push((function(){clearTimeout(o)}))}return this.subs.push(i),this.subs.push(A),this}},{key:"connect",value:function(e){return this.open(e)}},{key:"onopen",value:function(){this.cleanup(),this._readyState="open",this.emitReserved("open");var e=this.engine;this.subs.push(ah(e,"ping",this.onping.bind(this)),ah(e,"data",this.ondata.bind(this)),ah(e,"error",this.onerror.bind(this)),ah(e,"close",this.onclose.bind(this)),ah(this.decoder,"decoded",this.ondecoded.bind(this)))}},{key:"onping",value:function(){this.emitReserved("ping")}},{key:"ondata",value:function(e){try{this.decoder.add(e)}catch(t){this.onclose("parse error",t)}}},{key:"ondecoded",value:function(e){var t=this;Dd((function(){t.emitReserved("packet",e)}),this.setTimeoutFn)}},{key:"onerror",value:function(e){this.emitReserved("error",e)}},{key:"socket",value:function(e,t){var n=this.nsps[e];return n?this._autoConnect&&!n.active&&n.connect():(n=new sh(this,e,t),this.nsps[e]=n),n}},{key:"_destroy",value:function(e){for(var t=0,n=Object.keys(this.nsps);t<n.length;t++){var r=n[t];if(this.nsps[r].active)return}this._close()}},{key:"_packet",value:function(e){for(var t=this.encoder.encode(e),n=0;n<t.length;n++)this.engine.write(t[n],e.options)}},{key:"cleanup",value:function(){this.subs.forEach((function(e){return e()})),this.subs.length=0,this.decoder.destroy()}},{key:"_close",value:function(){this.skipReconnect=!0,this._reconnecting=!1,this.onclose("forced close"),this.engine&&this.engine.close()}},{key:"disconnect",value:function(){return this._close()}},{key:"onclose",value:function(e,t){this.cleanup(),this.backoff.reset(),this._readyState="closed",this.emitReserved("close",e,t),this._reconnection&&!this.skipReconnect&&this.reconnect()}},{key:"reconnect",value:function(){var e=this;if(this._reconnecting||this.skipReconnect)return this;var t=this;if(this.backoff.attempts>=this._reconnectionAttempts)this.backoff.reset(),this.emitReserved("reconnect_failed"),this._reconnecting=!1;else{var n=this.backoff.duration();this._reconnecting=!0;var r=this.setTimeoutFn((function(){t.skipReconnect||(e.emitReserved("reconnect_attempt",t.backoff.attempts),t.skipReconnect||t.open((function(n){n?(t._reconnecting=!1,t.reconnect(),e.emitReserved("reconnect_error",n)):t.onreconnect()})))}),n);this.opts.autoUnref&&r.unref(),this.subs.push((function(){clearTimeout(r)}))}}},{key:"onreconnect",value:function(){var e=this.backoff.attempts;this._reconnecting=!1,this.backoff.reset(),this.emitReserved("reconnect",e)}}]),r}(ld),ch={};function dh(e,t){"object"===typeof e&&(t=e,e=void 0);var n,r=function(e){var t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:"",n=arguments.length>2?arguments[2]:void 0,r=e;n=n||"undefined"!==typeof location&&location,null==e&&(e=n.protocol+"//"+n.host),"string"===typeof e&&("/"===e.charAt(0)&&(e="/"===e.charAt(1)?n.protocol+e:n.host+e),/^(https?|wss?):\/\//.test(e)||(e="undefined"!==typeof n?n.protocol+"//"+e:"https://"+e),r=Vd(e)),r.port||(/^(http|ws)$/.test(r.protocol)?r.port="80":/^(http|ws)s$/.test(r.protocol)&&(r.port="443")),r.path=r.path||"/";var i=-1!==r.host.indexOf(":")?"["+r.host+"]":r.host;return r.id=r.protocol+"://"+i+":"+r.port+t,r.href=r.protocol+"://"+i+(n&&n.port===r.port?"":":"+r.port),r}(e,(t=t||{}).path||"/socket.io"),i=r.source,A=r.id,a=r.path,o=ch[A]&&a in ch[A].nsps;return t.forceNew||t["force new connection"]||!1===t.multiplex||o?n=new uh(i,t):(ch[A]||(ch[A]=new uh(i,t)),n=ch[A]),r.query&&!t.query&&(t.query=r.queryKey),n.socket(r.path,t)}Object.assign(dh,{Manager:uh,Socket:sh,io:dh,connect:dh});var hh,fh="https://hyperhuman.deemos.com/api",ph=Wc,gh=function(e){e&&(ph=Wc.create({headers:{Authorization:"Bearer ".concat(e)}}))},mh={username:"huggingface",email:"merife6322@duiter.com",password:"123456"},vh=function(e){var t=e.email,n=e.password;return ph.post("".concat(fh,"/user/login"),{email:t,password:n})},yh=function(e){var t=e.user_uuid,n=e.username;return ph.post("".concat(fh,"/user/get_info"),{user_uuid:t,username:n})},wh=function(){var e=l(o().mark((function e(){return o().wrap((function(e){for(;;)switch(e.prev=e.next){case 0:if(!hh){e.next=2;break}return e.abrupt("return",!1);case 2:return console.log("start chat"),e.abrupt("return",ph.get("".concat(fh,"/chat").concat("")));case 4:case"end":return e.stop()}}),e)})));return function(){return e.apply(this,arguments)}}(),Bh=function(){hh&&hh.connected&&(console.log("close ws"),hh.close(),console.log(hh))},_h=function(e){var t=e.task_uuid,n=e.prompt;return ph.post("".concat(fh,"/task/generate"),{task_uuid:t,prompt:n})},bh=function(e){return ph.post("".concat(fh,"/task/check_progress/").concat(e))},xh=function(e){var t=e.type,n=e.page_num;return Wc.post("".concat(fh,"/task/cards"),{type:t,page_num:n})},Ch=function(e){return Wc.post("".concat(fh,"/task/card/").concat(e))},Sh=function(e){var t=e.task_uuid,n=e.type,r=e.name,i=e.token;return 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this.x=Math.random(),this.y=Math.random(),this}},{key:Symbol.iterator,value:o().mark((function e(){return o().wrap((function(e){for(;;)switch(e.prev=e.next){case 0:return e.next=2,this.x;case 2:return e.next=4,this.y;case 4:case"end":return e.stop()}}),e,this)}))}]),t}(),Gg=function(){function e(){B(this,e),e.prototype.isMatrix3=!0,this.elements=[1,0,0,0,1,0,0,0,1]}return b(e,[{key:"set",value:function(e,t,n,r,i,A,a,o,s){var l=this.elements;return l[0]=e,l[1]=r,l[2]=a,l[3]=t,l[4]=i,l[5]=o,l[6]=n,l[7]=A,l[8]=s,this}},{key:"identity",value:function(){return this.set(1,0,0,0,1,0,0,0,1),this}},{key:"copy",value:function(e){var t=this.elements,n=e.elements;return t[0]=n[0],t[1]=n[1],t[2]=n[2],t[3]=n[3],t[4]=n[4],t[5]=n[5],t[6]=n[6],t[7]=n[7],t[8]=n[8],this}},{key:"extractBasis",value:function(e,t,n){return e.setFromMatrix3Column(this,0),t.setFromMatrix3Column(this,1),n.setFromMatrix3Column(this,2),this}},{key:"setFromMatrix4",value:function(e){var t=e.elements;return this.set(t[0],t[4],t[8],t[1],t[5],t[9],t[2],t[6],t[10]),this}},{key:"multiply",value:function(e){return this.multiplyMatrices(this,e)}},{key:"premultiply",value:function(e){return this.multiplyMatrices(e,this)}},{key:"multiplyMatrices",value:function(e,t){var n=e.elements,r=t.elements,i=this.elements,A=n[0],a=n[3],o=n[6],s=n[1],l=n[4],u=n[7],c=n[2],d=n[5],h=n[8],f=r[0],p=r[3],g=r[6],m=r[1],v=r[4],y=r[7],w=r[2],B=r[5],_=r[8];return i[0]=A*f+a*m+o*w,i[3]=A*p+a*v+o*B,i[6]=A*g+a*y+o*_,i[1]=s*f+l*m+u*w,i[4]=s*p+l*v+u*B,i[7]=s*g+l*y+u*_,i[2]=c*f+d*m+h*w,i[5]=c*p+d*v+h*B,i[8]=c*g+d*y+h*_,this}},{key:"multiplyScalar",value:function(e){var t=this.elements;return t[0]*=e,t[3]*=e,t[6]*=e,t[1]*=e,t[4]*=e,t[7]*=e,t[2]*=e,t[5]*=e,t[8]*=e,this}},{key:"determinant",value:function(){var e=this.elements,t=e[0],n=e[1],r=e[2],i=e[3],A=e[4],a=e[5],o=e[6],s=e[7],l=e[8];return t*A*l-t*a*s-n*i*l+n*a*o+r*i*s-r*A*o}},{key:"invert",value:function(){var e=this.elements,t=e[0],n=e[1],r=e[2],i=e[3],A=e[4],a=e[5],o=e[6],s=e[7],l=e[8],u=l*A-a*s,c=a*o-l*i,d=s*i-A*o,h=t*u+n*c+r*d;if(0===h)return this.set(0,0,0,0,0,0,0,0,0);var f=1/h;return e[0]=u*f,e[1]=(r*s-l*n)*f,e[2]=(a*n-r*A)*f,e[3]=c*f,e[4]=(l*t-r*o)*f,e[5]=(r*i-a*t)*f,e[6]=d*f,e[7]=(n*o-s*t)*f,e[8]=(A*t-n*i)*f,this}},{key:"transpose",value:function(){var e,t=this.elements;return e=t[1],t[1]=t[3],t[3]=e,e=t[2],t[2]=t[6],t[6]=e,e=t[5],t[5]=t[7],t[7]=e,this}},{key:"getNormalMatrix",value:function(e){return this.setFromMatrix4(e).invert().transpose()}},{key:"transposeIntoArray",value:function(e){var t=this.elements;return e[0]=t[0],e[1]=t[3],e[2]=t[6],e[3]=t[1],e[4]=t[4],e[5]=t[7],e[6]=t[2],e[7]=t[5],e[8]=t[8],this}},{key:"setUvTransform",value:function(e,t,n,r,i,A,a){var o=Math.cos(i),s=Math.sin(i);return this.set(n*o,n*s,-n*(o*A+s*a)+A+e,-r*s,r*o,-r*(-s*A+o*a)+a+t,0,0,1),this}},{key:"scale",value:function(e,t){return this.premultiply(Kg.makeScale(e,t)),this}},{key:"rotate",value:function(e){return this.premultiply(Kg.makeRotation(-e)),this}},{key:"translate",value:function(e,t){return this.premultiply(Kg.makeTranslation(e,t)),this}},{key:"makeTranslation",value:function(e,t){return this.set(1,0,e,0,1,t,0,0,1),this}},{key:"makeRotation",value:function(e){var t=Math.cos(e),n=Math.sin(e);return this.set(t,-n,0,n,t,0,0,0,1),this}},{key:"makeScale",value:function(e,t){return this.set(e,0,0,0,t,0,0,0,1),this}},{key:"equals",value:function(e){for(var t=this.elements,n=e.elements,r=0;r<9;r++)if(t[r]!==n[r])return!1;return!0}},{key:"fromArray",value:function(e){for(var t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0,n=0;n<9;n++)this.elements[n]=e[n+t];return this}},{key:"toArray",value:function(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:[],t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0,n=this.elements;return e[t]=n[0],e[t+1]=n[1],e[t+2]=n[2],e[t+3]=n[3],e[t+4]=n[4],e[t+5]=n[5],e[t+6]=n[6],e[t+7]=n[7],e[t+8]=n[8],e}},{key:"clone",value:function(){return(new this.constructor).fromArray(this.elements)}}]),e}(),Kg=new Gg;function Wg(e){for(var t=e.length-1;t>=0;--t)if(e[t]>=65535)return!0;return!1}Int8Array,Uint8Array,Uint8ClampedArray,Int16Array,Uint16Array,Int32Array,Uint32Array,Float32Array,Float64Array;function jg(e){return document.createElementNS("http://www.w3.org/1999/xhtml",e)}function Xg(e){return e<.04045?.0773993808*e:Math.pow(.9478672986*e+.0521327014,2.4)}function qg(e){return e<.0031308?12.92*e:1.055*Math.pow(e,.41666)-.055}var Yg=(c(qh={},Bg,c({},_g,Xg)),c(qh,_g,c({},Bg,qg)),qh),Jg={legacyMode:!0,get workingColorSpace(){return _g},set workingColorSpace(e){console.warn("THREE.ColorManagement: .workingColorSpace is readonly.")},convert:function(e,t,n){if(this.legacyMode||t===n||!t||!n)return e;if(Yg[t]&&void 0!==Yg[t][n]){var r=Yg[t][n];return e.r=r(e.r),e.g=r(e.g),e.b=r(e.b),e}throw new Error("Unsupported color space conversion.")},fromWorkingColorSpace:function(e,t){return this.convert(e,this.workingColorSpace,t)},toWorkingColorSpace:function(e,t){return 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nm(e,t,n){return n<0&&(n+=1),n>1&&(n-=1),n<1/6?e+6*(t-e)*n:n<.5?t:n<2/3?e+6*(t-e)*(2/3-n):e}function rm(e,t){return t.r=e.r,t.g=e.g,t.b=e.b,t}var im,Am=function(e){function t(e,n,r){return B(this,t),this.isColor=!0,this.r=1,this.g=1,this.b=1,void 0===n&&void 0===r?this.set(e):this.setRGB(e,n,r)}return b(t,[{key:"set",value:function(e){return e&&e.isColor?this.copy(e):"number"===typeof e?this.setHex(e):"string"===typeof e&&this.setStyle(e),this}},{key:"setScalar",value:function(e){return this.r=e,this.g=e,this.b=e,this}},{key:"setHex",value:function(e){var t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:Bg;return e=Math.floor(e),this.r=(e>>16&255)/255,this.g=(e>>8&255)/255,this.b=(255&e)/255,Jg.toWorkingColorSpace(this,t),this}},{key:"setRGB",value:function(e,t,n){var r=arguments.length>3&&void 0!==arguments[3]?arguments[3]:Jg.workingColorSpace;return this.r=e,this.g=t,this.b=n,Jg.toWorkingColorSpace(this,r),this}},{key:"setHSL",value:function(e,t,n){var r=arguments.length>3&&void 0!==arguments[3]?arguments[3]:Jg.workingColorSpace;if(e=Dg(e,1),t=Lg(t,0,1),n=Lg(n,0,1),0===t)this.r=this.g=this.b=n;else{var i=n<=.5?n*(1+t):n+t-n*t,A=2*n-i;this.r=nm(A,i,e+1/3),this.g=nm(A,i,e),this.b=nm(A,i,e-1/3)}return Jg.toWorkingColorSpace(this,r),this}},{key:"setStyle",value:function(e){var t,n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:Bg;function r(t){void 0!==t&&parseFloat(t)<1&&console.warn("THREE.Color: Alpha component of "+e+" will be ignored.")}if(t=/^((?:rgb|hsl)a?)\(([^\)]*)\)/.exec(e)){var i,A=t[1],a=t[2];switch(A){case"rgb":case"rgba":if(i=/^\s*(\d+)\s*,\s*(\d+)\s*,\s*(\d+)\s*(?:,\s*(\d*\.?\d+)\s*)?$/.exec(a))return this.r=Math.min(255,parseInt(i[1],10))/255,this.g=Math.min(255,parseInt(i[2],10))/255,this.b=Math.min(255,parseInt(i[3],10))/255,Jg.toWorkingColorSpace(this,n),r(i[4]),this;if(i=/^\s*(\d+)\%\s*,\s*(\d+)\%\s*,\s*(\d+)\%\s*(?:,\s*(\d*\.?\d+)\s*)?$/.exec(a))return this.r=Math.min(100,parseInt(i[1],10))/100,this.g=Math.min(100,parseInt(i[2],10))/100,this.b=Math.min(100,parseInt(i[3],10))/100,Jg.toWorkingColorSpace(this,n),r(i[4]),this;break;case"hsl":case"hsla":if(i=/^\s*(\d*\.?\d+)\s*,\s*(\d*\.?\d+)\%\s*,\s*(\d*\.?\d+)\%\s*(?:,\s*(\d*\.?\d+)\s*)?$/.exec(a)){var o=parseFloat(i[1])/360,s=parseFloat(i[2])/100,l=parseFloat(i[3])/100;return r(i[4]),this.setHSL(o,s,l,n)}}}else if(t=/^\#([A-Fa-f\d]+)$/.exec(e)){var u=t[1],c=u.length;if(3===c)return this.r=parseInt(u.charAt(0)+u.charAt(0),16)/255,this.g=parseInt(u.charAt(1)+u.charAt(1),16)/255,this.b=parseInt(u.charAt(2)+u.charAt(2),16)/255,Jg.toWorkingColorSpace(this,n),this;if(6===c)return this.r=parseInt(u.charAt(0)+u.charAt(1),16)/255,this.g=parseInt(u.charAt(2)+u.charAt(3),16)/255,this.b=parseInt(u.charAt(4)+u.charAt(5),16)/255,Jg.toWorkingColorSpace(this,n),this}return e&&e.length>0?this.setColorName(e,n):this}},{key:"setColorName",value:function(e){var t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:Bg,n=Zg[e.toLowerCase()];return void 0!==n?this.setHex(n,t):console.warn("THREE.Color: Unknown color "+e),this}},{key:"clone",value:function(){return new this.constructor(this.r,this.g,this.b)}},{key:"copy",value:function(e){return this.r=e.r,this.g=e.g,this.b=e.b,this}},{key:"copySRGBToLinear",value:function(e){return this.r=Xg(e.r),this.g=Xg(e.g),this.b=Xg(e.b),this}},{key:"copyLinearToSRGB",value:function(e){return this.r=qg(e.r),this.g=qg(e.g),this.b=qg(e.b),this}},{key:"convertSRGBToLinear",value:function(){return this.copySRGBToLinear(this),this}},{key:"convertLinearToSRGB",value:function(){return this.copyLinearToSRGB(this),this}},{key:"getHex",value:function(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:Bg;return Jg.fromWorkingColorSpace(rm(this,$g),e),Lg(255*$g.r,0,255)<<16^Lg(255*$g.g,0,255)<<8^Lg(255*$g.b,0,255)<<0}},{key:"getHexString",value:function(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:Bg;return("000000"+this.getHex(e).toString(16)).slice(-6)}},{key:"getHSL",value:function(e){var t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:Jg.workingColorSpace;Jg.fromWorkingColorSpace(rm(this,$g),t);var n,r,i=$g.r,A=$g.g,a=$g.b,o=Math.max(i,A,a),s=Math.min(i,A,a),l=(s+o)/2;if(s===o)n=0,r=0;else{var u=o-s;switch(r=l<=.5?u/(o+s):u/(2-o-s),o){case i:n=(A-a)/u+(A<a?6:0);break;case A:n=(a-i)/u+2;break;case a:n=(i-A)/u+4}n/=6}return e.h=n,e.s=r,e.l=l,e}},{key:"getRGB",value:function(e){var t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:Jg.workingColorSpace;return Jg.fromWorkingColorSpace(rm(this,$g),t),e.r=$g.r,e.g=$g.g,e.b=$g.b,e}},{key:"getStyle",value:function(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:Bg;return Jg.fromWorkingColorSpace(rm(this,$g),e),e!==Bg?"color(".concat(e," ").concat($g.r," ").concat($g.g," ").concat($g.b,")"):"rgb(".concat(255*$g.r|0,",").concat(255*$g.g|0,",").concat(255*$g.b|0,")")}},{key:"offsetHSL",value:function(e,t,n){return this.getHSL(em),em.h+=e,em.s+=t,em.l+=n,this.setHSL(em.h,em.s,em.l),this}},{key:"add",value:function(e){return this.r+=e.r,this.g+=e.g,this.b+=e.b,this}},{key:"addColors",value:function(e,t){return this.r=e.r+t.r,this.g=e.g+t.g,this.b=e.b+t.b,this}},{key:"addScalar",value:function(e){return this.r+=e,this.g+=e,this.b+=e,this}},{key:"sub",value:function(e){return this.r=Math.max(0,this.r-e.r),this.g=Math.max(0,this.g-e.g),this.b=Math.max(0,this.b-e.b),this}},{key:"multiply",value:function(e){return this.r*=e.r,this.g*=e.g,this.b*=e.b,this}},{key:"multiplyScalar",value:function(e){return this.r*=e,this.g*=e,this.b*=e,this}},{key:"lerp",value:function(e,t){return this.r+=(e.r-this.r)*t,this.g+=(e.g-this.g)*t,this.b+=(e.b-this.b)*t,this}},{key:"lerpColors",value:function(e,t,n){return this.r=e.r+(t.r-e.r)*n,this.g=e.g+(t.g-e.g)*n,this.b=e.b+(t.b-e.b)*n,this}},{key:"lerpHSL",value:function(e,t){this.getHSL(em),e.getHSL(tm);var n=Ig(em.h,tm.h,t),r=Ig(em.s,tm.s,t),i=Ig(em.l,tm.l,t);return this.setHSL(n,r,i),this}},{key:"equals",value:function(e){return e.r===this.r&&e.g===this.g&&e.b===this.b}},{key:"fromArray",value:function(e){var t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0;return this.r=e[t],this.g=e[t+1],this.b=e[t+2],this}},{key:"toArray",value:function(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:[],t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0;return e[t]=this.r,e[t+1]=this.g,e[t+2]=this.b,e}},{key:"fromBufferAttribute",value:function(e,t){return this.r=e.getX(t),this.g=e.getY(t),this.b=e.getZ(t),this}},{key:"toJSON",value:function(){return this.getHex()}},{key:Symbol.iterator,value:o().mark((function e(){return o().wrap((function(e){for(;;)switch(e.prev=e.next){case 0:return e.next=2,this.r;case 2:return e.next=4,this.g;case 4:return e.next=6,this.b;case 6:case"end":return e.stop()}}),e,this)}))}]),t}();Am.NAMES=Zg;var am=function(){function e(){B(this,e)}return b(e,null,[{key:"getDataURL",value:function(e){if(/^data:/i.test(e.src))return e.src;if("undefined"==typeof HTMLCanvasElement)return e.src;var t;if(e instanceof HTMLCanvasElement)t=e;else{void 0===im&&(im=jg("canvas")),im.width=e.width,im.height=e.height;var n=im.getContext("2d");e instanceof ImageData?n.putImageData(e,0,0):n.drawImage(e,0,0,e.width,e.height),t=im}return t.width>2048||t.height>2048?(console.warn("THREE.ImageUtils.getDataURL: Image converted to jpg for performance reasons",e),t.toDataURL("image/jpeg",.6)):t.toDataURL("image/png")}},{key:"sRGBToLinear",value:function(e){if("undefined"!==typeof HTMLImageElement&&e instanceof HTMLImageElement||"undefined"!==typeof HTMLCanvasElement&&e instanceof HTMLCanvasElement||"undefined"!==typeof ImageBitmap&&e instanceof ImageBitmap){var t=jg("canvas");t.width=e.width,t.height=e.height;var n=t.getContext("2d");n.drawImage(e,0,0,e.width,e.height);for(var r=n.getImageData(0,0,e.width,e.height),i=r.data,A=0;A<i.length;A++)i[A]=255*Xg(i[A]/255);return n.putImageData(r,0,0),t}if(e.data){for(var a=e.data.slice(0),o=0;o<a.length;o++)a instanceof Uint8Array||a instanceof Uint8ClampedArray?a[o]=Math.floor(255*Xg(a[o]/255)):a[o]=Xg(a[o]);return{data:a,width:e.width,height:e.height}}return console.warn("THREE.ImageUtils.sRGBToLinear(): Unsupported image type. No color space conversion applied."),e}}]),e}(),om=function(){function e(){var t=arguments.length>0&&void 0!==arguments[0]?arguments[0]:null;B(this,e),this.isSource=!0,this.uuid=Qg(),this.data=t,this.version=0}return b(e,[{key:"needsUpdate",set:function(e){!0===e&&this.version++}},{key:"toJSON",value:function(e){var t=void 0===e||"string"===typeof e;if(!t&&void 0!==e.images[this.uuid])return e.images[this.uuid];var n={uuid:this.uuid,url:""},r=this.data;if(null!==r){var i;if(Array.isArray(r)){i=[];for(var A=0,a=r.length;A<a;A++)r[A].isDataTexture?i.push(sm(r[A].image)):i.push(sm(r[A]))}else i=sm(r);n.url=i}return t||(e.images[this.uuid]=n),n}}]),e}();function sm(e){return"undefined"!==typeof HTMLImageElement&&e instanceof HTMLImageElement||"undefined"!==typeof HTMLCanvasElement&&e instanceof HTMLCanvasElement||"undefined"!==typeof ImageBitmap&&e instanceof ImageBitmap?am.getDataURL(e):e.data?{data:Array.from(e.data),width:e.width,height:e.height,type:e.data.constructor.name}:(console.warn("THREE.Texture: Unable to serialize Texture."),{})}var lm=0,um=function(e){C(n,e);var t=M(n);function n(){var e,r=arguments.length>0&&void 0!==arguments[0]?arguments[0]:n.DEFAULT_IMAGE,i=arguments.length>1&&void 0!==arguments[1]?arguments[1]:n.DEFAULT_MAPPING,A=arguments.length>2&&void 0!==arguments[2]?arguments[2]:np,a=arguments.length>3&&void 0!==arguments[3]?arguments[3]:np,o=arguments.length>4&&void 0!==arguments[4]?arguments[4]:op,s=arguments.length>5&&void 0!==arguments[5]?arguments[5]:lp,l=arguments.length>6&&void 0!==arguments[6]?arguments[6]:bp,u=arguments.length>7&&void 0!==arguments[7]?arguments[7]:cp,c=arguments.length>8&&void 0!==arguments[8]?arguments[8]:n.DEFAULT_ANISOTROPY,d=arguments.length>9&&void 0!==arguments[9]?arguments[9]:pg;return B(this,n),(e=t.call(this)).isTexture=!0,Object.defineProperty(U(e),"id",{value:lm++}),e.uuid=Qg(),e.name="",e.source=new om(r),e.mipmaps=[],e.mapping=i,e.wrapS=A,e.wrapT=a,e.magFilter=o,e.minFilter=s,e.anisotropy=c,e.format=l,e.internalFormat=null,e.type=u,e.offset=new zg(0,0),e.repeat=new zg(1,1),e.center=new zg(0,0),e.rotation=0,e.matrixAutoUpdate=!0,e.matrix=new Gg,e.generateMipmaps=!0,e.premultiplyAlpha=!1,e.flipY=!0,e.unpackAlignment=4,e.encoding=d,e.userData={},e.version=0,e.onUpdate=null,e.isRenderTargetTexture=!1,e.needsPMREMUpdate=!1,e}return b(n,[{key:"image",get:function(){return this.source.data},set:function(e){this.source.data=e}},{key:"updateMatrix",value:function(){this.matrix.setUvTransform(this.offset.x,this.offset.y,this.repeat.x,this.repeat.y,this.rotation,this.center.x,this.center.y)}},{key:"clone",value:function(){return(new this.constructor).copy(this)}},{key:"copy",value:function(e){return this.name=e.name,this.source=e.source,this.mipmaps=e.mipmaps.slice(0),this.mapping=e.mapping,this.wrapS=e.wrapS,this.wrapT=e.wrapT,this.magFilter=e.magFilter,this.minFilter=e.minFilter,this.anisotropy=e.anisotropy,this.format=e.format,this.internalFormat=e.internalFormat,this.type=e.type,this.offset.copy(e.offset),this.repeat.copy(e.repeat),this.center.copy(e.center),this.rotation=e.rotation,this.matrixAutoUpdate=e.matrixAutoUpdate,this.matrix.copy(e.matrix),this.generateMipmaps=e.generateMipmaps,this.premultiplyAlpha=e.premultiplyAlpha,this.flipY=e.flipY,this.unpackAlignment=e.unpackAlignment,this.encoding=e.encoding,this.userData=JSON.parse(JSON.stringify(e.userData)),this.needsUpdate=!0,this}},{key:"toJSON",value:function(e){var t=void 0===e||"string"===typeof e;if(!t&&void 0!==e.textures[this.uuid])return e.textures[this.uuid];var n={metadata:{version:4.5,type:"Texture",generator:"Texture.toJSON"},uuid:this.uuid,name:this.name,image:this.source.toJSON(e).uuid,mapping:this.mapping,repeat:[this.repeat.x,this.repeat.y],offset:[this.offset.x,this.offset.y],center:[this.center.x,this.center.y],rotation:this.rotation,wrap:[this.wrapS,this.wrapT],format:this.format,type:this.type,encoding:this.encoding,minFilter:this.minFilter,magFilter:this.magFilter,anisotropy:this.anisotropy,flipY:this.flipY,generateMipmaps:this.generateMipmaps,premultiplyAlpha:this.premultiplyAlpha,unpackAlignment:this.unpackAlignment};return Object.keys(this.userData).length>0&&(n.userData=this.userData),t||(e.textures[this.uuid]=n),n}},{key:"dispose",value:function(){this.dispatchEvent({type:"dispose"})}},{key:"transformUv",value:function(e){if(300!==this.mapping)return e;if(e.applyMatrix3(this.matrix),e.x<0||e.x>1)switch(this.wrapS){case tp:e.x=e.x-Math.floor(e.x);break;case np:e.x=e.x<0?0:1;break;case rp:1===Math.abs(Math.floor(e.x)%2)?e.x=Math.ceil(e.x)-e.x:e.x=e.x-Math.floor(e.x)}if(e.y<0||e.y>1)switch(this.wrapT){case tp:e.y=e.y-Math.floor(e.y);break;case np:e.y=e.y<0?0:1;break;case rp:1===Math.abs(Math.floor(e.y)%2)?e.y=Math.ceil(e.y)-e.y:e.y=e.y-Math.floor(e.y)}return this.flipY&&(e.y=1-e.y),e}},{key:"needsUpdate",set:function(e){!0===e&&(this.version++,this.source.needsUpdate=!0)}}]),n}(Ug);um.DEFAULT_IMAGE=null,um.DEFAULT_MAPPING=300,um.DEFAULT_ANISOTROPY=1;var cm=function(e){function t(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0,r=arguments.length>2&&void 0!==arguments[2]?arguments[2]:0,i=arguments.length>3&&void 0!==arguments[3]?arguments[3]:1;B(this,t),t.prototype.isVector4=!0,this.x=e,this.y=n,this.z=r,this.w=i}return b(t,[{key:"width",get:function(){return this.z},set:function(e){this.z=e}},{key:"height",get:function(){return this.w},set:function(e){this.w=e}},{key:"set",value:function(e,t,n,r){return this.x=e,this.y=t,this.z=n,this.w=r,this}},{key:"setScalar",value:function(e){return this.x=e,this.y=e,this.z=e,this.w=e,this}},{key:"setX",value:function(e){return this.x=e,this}},{key:"setY",value:function(e){return this.y=e,this}},{key:"setZ",value:function(e){return this.z=e,this}},{key:"setW",value:function(e){return this.w=e,this}},{key:"setComponent",value:function(e,t){switch(e){case 0:this.x=t;break;case 1:this.y=t;break;case 2:this.z=t;break;case 3:this.w=t;break;default:throw new Error("index is out of range: "+e)}return this}},{key:"getComponent",value:function(e){switch(e){case 0:return this.x;case 1:return this.y;case 2:return this.z;case 3:return this.w;default:throw new Error("index is out of range: "+e)}}},{key:"clone",value:function(){return new this.constructor(this.x,this.y,this.z,this.w)}},{key:"copy",value:function(e){return this.x=e.x,this.y=e.y,this.z=e.z,this.w=void 0!==e.w?e.w:1,this}},{key:"add",value:function(e){return this.x+=e.x,this.y+=e.y,this.z+=e.z,this.w+=e.w,this}},{key:"addScalar",value:function(e){return this.x+=e,this.y+=e,this.z+=e,this.w+=e,this}},{key:"addVectors",value:function(e,t){return this.x=e.x+t.x,this.y=e.y+t.y,this.z=e.z+t.z,this.w=e.w+t.w,this}},{key:"addScaledVector",value:function(e,t){return this.x+=e.x*t,this.y+=e.y*t,this.z+=e.z*t,this.w+=e.w*t,this}},{key:"sub",value:function(e){return this.x-=e.x,this.y-=e.y,this.z-=e.z,this.w-=e.w,this}},{key:"subScalar",value:function(e){return this.x-=e,this.y-=e,this.z-=e,this.w-=e,this}},{key:"subVectors",value:function(e,t){return this.x=e.x-t.x,this.y=e.y-t.y,this.z=e.z-t.z,this.w=e.w-t.w,this}},{key:"multiply",value:function(e){return this.x*=e.x,this.y*=e.y,this.z*=e.z,this.w*=e.w,this}},{key:"multiplyScalar",value:function(e){return this.x*=e,this.y*=e,this.z*=e,this.w*=e,this}},{key:"applyMatrix4",value:function(e){var t=this.x,n=this.y,r=this.z,i=this.w,A=e.elements;return this.x=A[0]*t+A[4]*n+A[8]*r+A[12]*i,this.y=A[1]*t+A[5]*n+A[9]*r+A[13]*i,this.z=A[2]*t+A[6]*n+A[10]*r+A[14]*i,this.w=A[3]*t+A[7]*n+A[11]*r+A[15]*i,this}},{key:"divideScalar",value:function(e){return this.multiplyScalar(1/e)}},{key:"setAxisAngleFromQuaternion",value:function(e){this.w=2*Math.acos(e.w);var t=Math.sqrt(1-e.w*e.w);return t<1e-4?(this.x=1,this.y=0,this.z=0):(this.x=e.x/t,this.y=e.y/t,this.z=e.z/t),this}},{key:"setAxisAngleFromRotationMatrix",value:function(e){var t,n,r,i,A=.01,a=.1,o=e.elements,s=o[0],l=o[4],u=o[8],c=o[1],d=o[5],h=o[9],f=o[2],p=o[6],g=o[10];if(Math.abs(l-c)<A&&Math.abs(u-f)<A&&Math.abs(h-p)<A){if(Math.abs(l+c)<a&&Math.abs(u+f)<a&&Math.abs(h+p)<a&&Math.abs(s+d+g-3)<a)return this.set(1,0,0,0),this;t=Math.PI;var m=(s+1)/2,v=(d+1)/2,y=(g+1)/2,w=(l+c)/4,B=(u+f)/4,_=(h+p)/4;return m>v&&m>y?m<A?(n=0,r=.707106781,i=.707106781):(r=w/(n=Math.sqrt(m)),i=B/n):v>y?v<A?(n=.707106781,r=0,i=.707106781):(n=w/(r=Math.sqrt(v)),i=_/r):y<A?(n=.707106781,r=.707106781,i=0):(n=B/(i=Math.sqrt(y)),r=_/i),this.set(n,r,i,t),this}var b=Math.sqrt((p-h)*(p-h)+(u-f)*(u-f)+(c-l)*(c-l));return Math.abs(b)<.001&&(b=1),this.x=(p-h)/b,this.y=(u-f)/b,this.z=(c-l)/b,this.w=Math.acos((s+d+g-1)/2),this}},{key:"min",value:function(e){return this.x=Math.min(this.x,e.x),this.y=Math.min(this.y,e.y),this.z=Math.min(this.z,e.z),this.w=Math.min(this.w,e.w),this}},{key:"max",value:function(e){return this.x=Math.max(this.x,e.x),this.y=Math.max(this.y,e.y),this.z=Math.max(this.z,e.z),this.w=Math.max(this.w,e.w),this}},{key:"clamp",value:function(e,t){return this.x=Math.max(e.x,Math.min(t.x,this.x)),this.y=Math.max(e.y,Math.min(t.y,this.y)),this.z=Math.max(e.z,Math.min(t.z,this.z)),this.w=Math.max(e.w,Math.min(t.w,this.w)),this}},{key:"clampScalar",value:function(e,t){return this.x=Math.max(e,Math.min(t,this.x)),this.y=Math.max(e,Math.min(t,this.y)),this.z=Math.max(e,Math.min(t,this.z)),this.w=Math.max(e,Math.min(t,this.w)),this}},{key:"clampLength",value:function(e,t){var n=this.length();return this.divideScalar(n||1).multiplyScalar(Math.max(e,Math.min(t,n)))}},{key:"floor",value:function(){return this.x=Math.floor(this.x),this.y=Math.floor(this.y),this.z=Math.floor(this.z),this.w=Math.floor(this.w),this}},{key:"ceil",value:function(){return this.x=Math.ceil(this.x),this.y=Math.ceil(this.y),this.z=Math.ceil(this.z),this.w=Math.ceil(this.w),this}},{key:"round",value:function(){return this.x=Math.round(this.x),this.y=Math.round(this.y),this.z=Math.round(this.z),this.w=Math.round(this.w),this}},{key:"roundToZero",value:function(){return this.x=this.x<0?Math.ceil(this.x):Math.floor(this.x),this.y=this.y<0?Math.ceil(this.y):Math.floor(this.y),this.z=this.z<0?Math.ceil(this.z):Math.floor(this.z),this.w=this.w<0?Math.ceil(this.w):Math.floor(this.w),this}},{key:"negate",value:function(){return this.x=-this.x,this.y=-this.y,this.z=-this.z,this.w=-this.w,this}},{key:"dot",value:function(e){return this.x*e.x+this.y*e.y+this.z*e.z+this.w*e.w}},{key:"lengthSq",value:function(){return this.x*this.x+this.y*this.y+this.z*this.z+this.w*this.w}},{key:"length",value:function(){return Math.sqrt(this.x*this.x+this.y*this.y+this.z*this.z+this.w*this.w)}},{key:"manhattanLength",value:function(){return Math.abs(this.x)+Math.abs(this.y)+Math.abs(this.z)+Math.abs(this.w)}},{key:"normalize",value:function(){return this.divideScalar(this.length()||1)}},{key:"setLength",value:function(e){return this.normalize().multiplyScalar(e)}},{key:"lerp",value:function(e,t){return this.x+=(e.x-this.x)*t,this.y+=(e.y-this.y)*t,this.z+=(e.z-this.z)*t,this.w+=(e.w-this.w)*t,this}},{key:"lerpVectors",value:function(e,t,n){return this.x=e.x+(t.x-e.x)*n,this.y=e.y+(t.y-e.y)*n,this.z=e.z+(t.z-e.z)*n,this.w=e.w+(t.w-e.w)*n,this}},{key:"equals",value:function(e){return e.x===this.x&&e.y===this.y&&e.z===this.z&&e.w===this.w}},{key:"fromArray",value:function(e){var t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0;return this.x=e[t],this.y=e[t+1],this.z=e[t+2],this.w=e[t+3],this}},{key:"toArray",value:function(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:[],t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0;return e[t]=this.x,e[t+1]=this.y,e[t+2]=this.z,e[t+3]=this.w,e}},{key:"fromBufferAttribute",value:function(e,t){return this.x=e.getX(t),this.y=e.getY(t),this.z=e.getZ(t),this.w=e.getW(t),this}},{key:"random",value:function(){return this.x=Math.random(),this.y=Math.random(),this.z=Math.random(),this.w=Math.random(),this}},{key:Symbol.iterator,value:o().mark((function e(){return o().wrap((function(e){for(;;)switch(e.prev=e.next){case 0:return e.next=2,this.x;case 2:return e.next=4,this.y;case 4:return e.next=6,this.z;case 6:return e.next=8,this.w;case 8:case"end":return e.stop()}}),e,this)}))}]),t}(),dm=function(e){C(n,e);var t=M(n);function n(){var e,r=arguments.length>0&&void 0!==arguments[0]?arguments[0]:1,i=arguments.length>1&&void 0!==arguments[1]?arguments[1]:1,A=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};B(this,n),(e=t.call(this)).isWebGLRenderTarget=!0,e.width=r,e.height=i,e.depth=1,e.scissor=new cm(0,0,r,i),e.scissorTest=!1,e.viewport=new cm(0,0,r,i);var a={width:r,height:i,depth:1};return e.texture=new um(a,A.mapping,A.wrapS,A.wrapT,A.magFilter,A.minFilter,A.format,A.type,A.anisotropy,A.encoding),e.texture.isRenderTargetTexture=!0,e.texture.flipY=!1,e.texture.generateMipmaps=void 0!==A.generateMipmaps&&A.generateMipmaps,e.texture.internalFormat=void 0!==A.internalFormat?A.internalFormat:null,e.texture.minFilter=void 0!==A.minFilter?A.minFilter:op,e.depthBuffer=void 0===A.depthBuffer||A.depthBuffer,e.stencilBuffer=void 0!==A.stencilBuffer&&A.stencilBuffer,e.depthTexture=void 0!==A.depthTexture?A.depthTexture:null,e.samples=void 0!==A.samples?A.samples:0,e}return b(n,[{key:"setSize",value:function(e,t){var n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:1;this.width===e&&this.height===t&&this.depth===n||(this.width=e,this.height=t,this.depth=n,this.texture.image.width=e,this.texture.image.height=t,this.texture.image.depth=n,this.dispose()),this.viewport.set(0,0,e,t),this.scissor.set(0,0,e,t)}},{key:"clone",value:function(){return(new this.constructor).copy(this)}},{key:"copy",value:function(e){this.width=e.width,this.height=e.height,this.depth=e.depth,this.viewport.copy(e.viewport),this.texture=e.texture.clone(),this.texture.isRenderTargetTexture=!0;var t=Object.assign({},e.texture.image);return this.texture.source=new om(t),this.depthBuffer=e.depthBuffer,this.stencilBuffer=e.stencilBuffer,null!==e.depthTexture&&(this.depthTexture=e.depthTexture.clone()),this.samples=e.samples,this}},{key:"dispose",value:function(){this.dispatchEvent({type:"dispose"})}}]),n}(Ug),hm=function(e){C(n,e);var t=M(n);function n(){var e,r=arguments.length>0&&void 0!==arguments[0]?arguments[0]:null,i=arguments.length>1&&void 0!==arguments[1]?arguments[1]:1,A=arguments.length>2&&void 0!==arguments[2]?arguments[2]:1,a=arguments.length>3&&void 0!==arguments[3]?arguments[3]:1;return B(this,n),(e=t.call(this,null)).isDataArrayTexture=!0,e.image={data:r,width:i,height:A,depth:a},e.magFilter=ip,e.minFilter=ip,e.wrapR=np,e.generateMipmaps=!1,e.flipY=!1,e.unpackAlignment=1,e}return b(n)}(um),fm=function(e){C(n,e);var t=M(n);function n(){var e,r=arguments.length>0&&void 0!==arguments[0]?arguments[0]:null,i=arguments.length>1&&void 0!==arguments[1]?arguments[1]:1,A=arguments.length>2&&void 0!==arguments[2]?arguments[2]:1,a=arguments.length>3&&void 0!==arguments[3]?arguments[3]:1;return B(this,n),(e=t.call(this,null)).isData3DTexture=!0,e.image={data:r,width:i,height:A,depth:a},e.magFilter=ip,e.minFilter=ip,e.wrapR=np,e.generateMipmaps=!1,e.flipY=!1,e.unpackAlignment=1,e}return b(n)}(um),pm=function(e){function t(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0,r=arguments.length>2&&void 0!==arguments[2]?arguments[2]:0,i=arguments.length>3&&void 0!==arguments[3]?arguments[3]:1;B(this,t),this.isQuaternion=!0,this._x=e,this._y=n,this._z=r,this._w=i}return b(t,[{key:"x",get:function(){return this._x},set:function(e){this._x=e,this._onChangeCallback()}},{key:"y",get:function(){return this._y},set:function(e){this._y=e,this._onChangeCallback()}},{key:"z",get:function(){return this._z},set:function(e){this._z=e,this._onChangeCallback()}},{key:"w",get:function(){return this._w},set:function(e){this._w=e,this._onChangeCallback()}},{key:"set",value:function(e,t,n,r){return this._x=e,this._y=t,this._z=n,this._w=r,this._onChangeCallback(),this}},{key:"clone",value:function(){return new this.constructor(this._x,this._y,this._z,this._w)}},{key:"copy",value:function(e){return this._x=e.x,this._y=e.y,this._z=e.z,this._w=e.w,this._onChangeCallback(),this}},{key:"setFromEuler",value:function(e,t){var n=e._x,r=e._y,i=e._z,A=e._order,a=Math.cos,o=Math.sin,s=a(n/2),l=a(r/2),u=a(i/2),c=o(n/2),d=o(r/2),h=o(i/2);switch(A){case"XYZ":this._x=c*l*u+s*d*h,this._y=s*d*u-c*l*h,this._z=s*l*h+c*d*u,this._w=s*l*u-c*d*h;break;case"YXZ":this._x=c*l*u+s*d*h,this._y=s*d*u-c*l*h,this._z=s*l*h-c*d*u,this._w=s*l*u+c*d*h;break;case"ZXY":this._x=c*l*u-s*d*h,this._y=s*d*u+c*l*h,this._z=s*l*h+c*d*u,this._w=s*l*u-c*d*h;break;case"ZYX":this._x=c*l*u-s*d*h,this._y=s*d*u+c*l*h,this._z=s*l*h-c*d*u,this._w=s*l*u+c*d*h;break;case"YZX":this._x=c*l*u+s*d*h,this._y=s*d*u+c*l*h,this._z=s*l*h-c*d*u,this._w=s*l*u-c*d*h;break;case"XZY":this._x=c*l*u-s*d*h,this._y=s*d*u-c*l*h,this._z=s*l*h+c*d*u,this._w=s*l*u+c*d*h;break;default:console.warn("THREE.Quaternion: .setFromEuler() encountered an unknown order: "+A)}return!1!==t&&this._onChangeCallback(),this}},{key:"setFromAxisAngle",value:function(e,t){var n=t/2,r=Math.sin(n);return this._x=e.x*r,this._y=e.y*r,this._z=e.z*r,this._w=Math.cos(n),this._onChangeCallback(),this}},{key:"setFromRotationMatrix",value:function(e){var t=e.elements,n=t[0],r=t[4],i=t[8],A=t[1],a=t[5],o=t[9],s=t[2],l=t[6],u=t[10],c=n+a+u;if(c>0){var d=.5/Math.sqrt(c+1);this._w=.25/d,this._x=(l-o)*d,this._y=(i-s)*d,this._z=(A-r)*d}else if(n>a&&n>u){var h=2*Math.sqrt(1+n-a-u);this._w=(l-o)/h,this._x=.25*h,this._y=(r+A)/h,this._z=(i+s)/h}else if(a>u){var f=2*Math.sqrt(1+a-n-u);this._w=(i-s)/f,this._x=(r+A)/f,this._y=.25*f,this._z=(o+l)/f}else{var p=2*Math.sqrt(1+u-n-a);this._w=(A-r)/p,this._x=(i+s)/p,this._y=(o+l)/p,this._z=.25*p}return this._onChangeCallback(),this}},{key:"setFromUnitVectors",value:function(e,t){var n=e.dot(t)+1;return n<Number.EPSILON?(n=0,Math.abs(e.x)>Math.abs(e.z)?(this._x=-e.y,this._y=e.x,this._z=0,this._w=n):(this._x=0,this._y=-e.z,this._z=e.y,this._w=n)):(this._x=e.y*t.z-e.z*t.y,this._y=e.z*t.x-e.x*t.z,this._z=e.x*t.y-e.y*t.x,this._w=n),this.normalize()}},{key:"angleTo",value:function(e){return 2*Math.acos(Math.abs(Lg(this.dot(e),-1,1)))}},{key:"rotateTowards",value:function(e,t){var n=this.angleTo(e);if(0===n)return this;var r=Math.min(1,t/n);return this.slerp(e,r),this}},{key:"identity",value:function(){return this.set(0,0,0,1)}},{key:"invert",value:function(){return this.conjugate()}},{key:"conjugate",value:function(){return this._x*=-1,this._y*=-1,this._z*=-1,this._onChangeCallback(),this}},{key:"dot",value:function(e){return this._x*e._x+this._y*e._y+this._z*e._z+this._w*e._w}},{key:"lengthSq",value:function(){return this._x*this._x+this._y*this._y+this._z*this._z+this._w*this._w}},{key:"length",value:function(){return Math.sqrt(this._x*this._x+this._y*this._y+this._z*this._z+this._w*this._w)}},{key:"normalize",value:function(){var e=this.length();return 0===e?(this._x=0,this._y=0,this._z=0,this._w=1):(e=1/e,this._x=this._x*e,this._y=this._y*e,this._z=this._z*e,this._w=this._w*e),this._onChangeCallback(),this}},{key:"multiply",value:function(e){return this.multiplyQuaternions(this,e)}},{key:"premultiply",value:function(e){return this.multiplyQuaternions(e,this)}},{key:"multiplyQuaternions",value:function(e,t){var n=e._x,r=e._y,i=e._z,A=e._w,a=t._x,o=t._y,s=t._z,l=t._w;return this._x=n*l+A*a+r*s-i*o,this._y=r*l+A*o+i*a-n*s,this._z=i*l+A*s+n*o-r*a,this._w=A*l-n*a-r*o-i*s,this._onChangeCallback(),this}},{key:"slerp",value:function(e,t){if(0===t)return this;if(1===t)return this.copy(e);var n=this._x,r=this._y,i=this._z,A=this._w,a=A*e._w+n*e._x+r*e._y+i*e._z;if(a<0?(this._w=-e._w,this._x=-e._x,this._y=-e._y,this._z=-e._z,a=-a):this.copy(e),a>=1)return this._w=A,this._x=n,this._y=r,this._z=i,this;var o=1-a*a;if(o<=Number.EPSILON){var s=1-t;return this._w=s*A+t*this._w,this._x=s*n+t*this._x,this._y=s*r+t*this._y,this._z=s*i+t*this._z,this.normalize(),this._onChangeCallback(),this}var l=Math.sqrt(o),u=Math.atan2(l,a),c=Math.sin((1-t)*u)/l,d=Math.sin(t*u)/l;return this._w=A*c+this._w*d,this._x=n*c+this._x*d,this._y=r*c+this._y*d,this._z=i*c+this._z*d,this._onChangeCallback(),this}},{key:"slerpQuaternions",value:function(e,t,n){return this.copy(e).slerp(t,n)}},{key:"random",value:function(){var e=Math.random(),t=Math.sqrt(1-e),n=Math.sqrt(e),r=2*Math.PI*Math.random(),i=2*Math.PI*Math.random();return this.set(t*Math.cos(r),n*Math.sin(i),n*Math.cos(i),t*Math.sin(r))}},{key:"equals",value:function(e){return e._x===this._x&&e._y===this._y&&e._z===this._z&&e._w===this._w}},{key:"fromArray",value:function(e){var t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0;return this._x=e[t],this._y=e[t+1],this._z=e[t+2],this._w=e[t+3],this._onChangeCallback(),this}},{key:"toArray",value:function(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:[],t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0;return e[t]=this._x,e[t+1]=this._y,e[t+2]=this._z,e[t+3]=this._w,e}},{key:"fromBufferAttribute",value:function(e,t){return this._x=e.getX(t),this._y=e.getY(t),this._z=e.getZ(t),this._w=e.getW(t),this}},{key:"_onChange",value:function(e){return this._onChangeCallback=e,this}},{key:"_onChangeCallback",value:function(){}},{key:Symbol.iterator,value:o().mark((function e(){return o().wrap((function(e){for(;;)switch(e.prev=e.next){case 0:return e.next=2,this._x;case 2:return e.next=4,this._y;case 4:return e.next=6,this._z;case 6:return e.next=8,this._w;case 8:case"end":return e.stop()}}),e,this)}))}],[{key:"slerpFlat",value:function(e,t,n,r,i,A,a){var o=n[r+0],s=n[r+1],l=n[r+2],u=n[r+3],c=i[A+0],d=i[A+1],h=i[A+2],f=i[A+3];if(0===a)return e[t+0]=o,e[t+1]=s,e[t+2]=l,void(e[t+3]=u);if(1===a)return e[t+0]=c,e[t+1]=d,e[t+2]=h,void(e[t+3]=f);if(u!==f||o!==c||s!==d||l!==h){var p=1-a,g=o*c+s*d+l*h+u*f,m=g>=0?1:-1,v=1-g*g;if(v>Number.EPSILON){var y=Math.sqrt(v),w=Math.atan2(y,g*m);p=Math.sin(p*w)/y,a=Math.sin(a*w)/y}var B=a*m;if(o=o*p+c*B,s=s*p+d*B,l=l*p+h*B,u=u*p+f*B,p===1-a){var _=1/Math.sqrt(o*o+s*s+l*l+u*u);o*=_,s*=_,l*=_,u*=_}}e[t]=o,e[t+1]=s,e[t+2]=l,e[t+3]=u}},{key:"multiplyQuaternionsFlat",value:function(e,t,n,r,i,A){var a=n[r],o=n[r+1],s=n[r+2],l=n[r+3],u=i[A],c=i[A+1],d=i[A+2],h=i[A+3];return e[t]=a*h+l*u+o*d-s*c,e[t+1]=o*h+l*c+s*u-a*d,e[t+2]=s*h+l*d+a*c-o*u,e[t+3]=l*h-a*u-o*c-s*d,e}}]),t}(),gm=function(e){function t(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0,r=arguments.length>2&&void 0!==arguments[2]?arguments[2]:0;B(this,t),t.prototype.isVector3=!0,this.x=e,this.y=n,this.z=r}return b(t,[{key:"set",value:function(e,t,n){return void 0===n&&(n=this.z),this.x=e,this.y=t,this.z=n,this}},{key:"setScalar",value:function(e){return this.x=e,this.y=e,this.z=e,this}},{key:"setX",value:function(e){return this.x=e,this}},{key:"setY",value:function(e){return this.y=e,this}},{key:"setZ",value:function(e){return this.z=e,this}},{key:"setComponent",value:function(e,t){switch(e){case 0:this.x=t;break;case 1:this.y=t;break;case 2:this.z=t;break;default:throw new Error("index is out of range: "+e)}return this}},{key:"getComponent",value:function(e){switch(e){case 0:return this.x;case 1:return this.y;case 2:return this.z;default:throw new Error("index is out of range: "+e)}}},{key:"clone",value:function(){return new this.constructor(this.x,this.y,this.z)}},{key:"copy",value:function(e){return this.x=e.x,this.y=e.y,this.z=e.z,this}},{key:"add",value:function(e){return this.x+=e.x,this.y+=e.y,this.z+=e.z,this}},{key:"addScalar",value:function(e){return this.x+=e,this.y+=e,this.z+=e,this}},{key:"addVectors",value:function(e,t){return this.x=e.x+t.x,this.y=e.y+t.y,this.z=e.z+t.z,this}},{key:"addScaledVector",value:function(e,t){return this.x+=e.x*t,this.y+=e.y*t,this.z+=e.z*t,this}},{key:"sub",value:function(e){return this.x-=e.x,this.y-=e.y,this.z-=e.z,this}},{key:"subScalar",value:function(e){return this.x-=e,this.y-=e,this.z-=e,this}},{key:"subVectors",value:function(e,t){return this.x=e.x-t.x,this.y=e.y-t.y,this.z=e.z-t.z,this}},{key:"multiply",value:function(e){return this.x*=e.x,this.y*=e.y,this.z*=e.z,this}},{key:"multiplyScalar",value:function(e){return this.x*=e,this.y*=e,this.z*=e,this}},{key:"multiplyVectors",value:function(e,t){return this.x=e.x*t.x,this.y=e.y*t.y,this.z=e.z*t.z,this}},{key:"applyEuler",value:function(e){return this.applyQuaternion(vm.setFromEuler(e))}},{key:"applyAxisAngle",value:function(e,t){return this.applyQuaternion(vm.setFromAxisAngle(e,t))}},{key:"applyMatrix3",value:function(e){var t=this.x,n=this.y,r=this.z,i=e.elements;return this.x=i[0]*t+i[3]*n+i[6]*r,this.y=i[1]*t+i[4]*n+i[7]*r,this.z=i[2]*t+i[5]*n+i[8]*r,this}},{key:"applyNormalMatrix",value:function(e){return this.applyMatrix3(e).normalize()}},{key:"applyMatrix4",value:function(e){var t=this.x,n=this.y,r=this.z,i=e.elements,A=1/(i[3]*t+i[7]*n+i[11]*r+i[15]);return this.x=(i[0]*t+i[4]*n+i[8]*r+i[12])*A,this.y=(i[1]*t+i[5]*n+i[9]*r+i[13])*A,this.z=(i[2]*t+i[6]*n+i[10]*r+i[14])*A,this}},{key:"applyQuaternion",value:function(e){var t=this.x,n=this.y,r=this.z,i=e.x,A=e.y,a=e.z,o=e.w,s=o*t+A*r-a*n,l=o*n+a*t-i*r,u=o*r+i*n-A*t,c=-i*t-A*n-a*r;return this.x=s*o+c*-i+l*-a-u*-A,this.y=l*o+c*-A+u*-i-s*-a,this.z=u*o+c*-a+s*-A-l*-i,this}},{key:"project",value:function(e){return this.applyMatrix4(e.matrixWorldInverse).applyMatrix4(e.projectionMatrix)}},{key:"unproject",value:function(e){return this.applyMatrix4(e.projectionMatrixInverse).applyMatrix4(e.matrixWorld)}},{key:"transformDirection",value:function(e){var t=this.x,n=this.y,r=this.z,i=e.elements;return this.x=i[0]*t+i[4]*n+i[8]*r,this.y=i[1]*t+i[5]*n+i[9]*r,this.z=i[2]*t+i[6]*n+i[10]*r,this.normalize()}},{key:"divide",value:function(e){return this.x/=e.x,this.y/=e.y,this.z/=e.z,this}},{key:"divideScalar",value:function(e){return this.multiplyScalar(1/e)}},{key:"min",value:function(e){return this.x=Math.min(this.x,e.x),this.y=Math.min(this.y,e.y),this.z=Math.min(this.z,e.z),this}},{key:"max",value:function(e){return this.x=Math.max(this.x,e.x),this.y=Math.max(this.y,e.y),this.z=Math.max(this.z,e.z),this}},{key:"clamp",value:function(e,t){return this.x=Math.max(e.x,Math.min(t.x,this.x)),this.y=Math.max(e.y,Math.min(t.y,this.y)),this.z=Math.max(e.z,Math.min(t.z,this.z)),this}},{key:"clampScalar",value:function(e,t){return this.x=Math.max(e,Math.min(t,this.x)),this.y=Math.max(e,Math.min(t,this.y)),this.z=Math.max(e,Math.min(t,this.z)),this}},{key:"clampLength",value:function(e,t){var n=this.length();return this.divideScalar(n||1).multiplyScalar(Math.max(e,Math.min(t,n)))}},{key:"floor",value:function(){return this.x=Math.floor(this.x),this.y=Math.floor(this.y),this.z=Math.floor(this.z),this}},{key:"ceil",value:function(){return this.x=Math.ceil(this.x),this.y=Math.ceil(this.y),this.z=Math.ceil(this.z),this}},{key:"round",value:function(){return this.x=Math.round(this.x),this.y=Math.round(this.y),this.z=Math.round(this.z),this}},{key:"roundToZero",value:function(){return this.x=this.x<0?Math.ceil(this.x):Math.floor(this.x),this.y=this.y<0?Math.ceil(this.y):Math.floor(this.y),this.z=this.z<0?Math.ceil(this.z):Math.floor(this.z),this}},{key:"negate",value:function(){return this.x=-this.x,this.y=-this.y,this.z=-this.z,this}},{key:"dot",value:function(e){return this.x*e.x+this.y*e.y+this.z*e.z}},{key:"lengthSq",value:function(){return this.x*this.x+this.y*this.y+this.z*this.z}},{key:"length",value:function(){return Math.sqrt(this.x*this.x+this.y*this.y+this.z*this.z)}},{key:"manhattanLength",value:function(){return Math.abs(this.x)+Math.abs(this.y)+Math.abs(this.z)}},{key:"normalize",value:function(){return this.divideScalar(this.length()||1)}},{key:"setLength",value:function(e){return this.normalize().multiplyScalar(e)}},{key:"lerp",value:function(e,t){return this.x+=(e.x-this.x)*t,this.y+=(e.y-this.y)*t,this.z+=(e.z-this.z)*t,this}},{key:"lerpVectors",value:function(e,t,n){return this.x=e.x+(t.x-e.x)*n,this.y=e.y+(t.y-e.y)*n,this.z=e.z+(t.z-e.z)*n,this}},{key:"cross",value:function(e){return this.crossVectors(this,e)}},{key:"crossVectors",value:function(e,t){var n=e.x,r=e.y,i=e.z,A=t.x,a=t.y,o=t.z;return this.x=r*o-i*a,this.y=i*A-n*o,this.z=n*a-r*A,this}},{key:"projectOnVector",value:function(e){var t=e.lengthSq();if(0===t)return this.set(0,0,0);var n=e.dot(this)/t;return this.copy(e).multiplyScalar(n)}},{key:"projectOnPlane",value:function(e){return mm.copy(this).projectOnVector(e),this.sub(mm)}},{key:"reflect",value:function(e){return this.sub(mm.copy(e).multiplyScalar(2*this.dot(e)))}},{key:"angleTo",value:function(e){var t=Math.sqrt(this.lengthSq()*e.lengthSq());if(0===t)return Math.PI/2;var n=this.dot(e)/t;return Math.acos(Lg(n,-1,1))}},{key:"distanceTo",value:function(e){return Math.sqrt(this.distanceToSquared(e))}},{key:"distanceToSquared",value:function(e){var t=this.x-e.x,n=this.y-e.y,r=this.z-e.z;return t*t+n*n+r*r}},{key:"manhattanDistanceTo",value:function(e){return Math.abs(this.x-e.x)+Math.abs(this.y-e.y)+Math.abs(this.z-e.z)}},{key:"setFromSpherical",value:function(e){return this.setFromSphericalCoords(e.radius,e.phi,e.theta)}},{key:"setFromSphericalCoords",value:function(e,t,n){var r=Math.sin(t)*e;return this.x=r*Math.sin(n),this.y=Math.cos(t)*e,this.z=r*Math.cos(n),this}},{key:"setFromCylindrical",value:function(e){return this.setFromCylindricalCoords(e.radius,e.theta,e.y)}},{key:"setFromCylindricalCoords",value:function(e,t,n){return this.x=e*Math.sin(t),this.y=n,this.z=e*Math.cos(t),this}},{key:"setFromMatrixPosition",value:function(e){var t=e.elements;return this.x=t[12],this.y=t[13],this.z=t[14],this}},{key:"setFromMatrixScale",value:function(e){var t=this.setFromMatrixColumn(e,0).length(),n=this.setFromMatrixColumn(e,1).length(),r=this.setFromMatrixColumn(e,2).length();return this.x=t,this.y=n,this.z=r,this}},{key:"setFromMatrixColumn",value:function(e,t){return this.fromArray(e.elements,4*t)}},{key:"setFromMatrix3Column",value:function(e,t){return this.fromArray(e.elements,3*t)}},{key:"setFromEuler",value:function(e){return this.x=e._x,this.y=e._y,this.z=e._z,this}},{key:"equals",value:function(e){return e.x===this.x&&e.y===this.y&&e.z===this.z}},{key:"fromArray",value:function(e){var t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0;return this.x=e[t],this.y=e[t+1],this.z=e[t+2],this}},{key:"toArray",value:function(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:[],t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0;return e[t]=this.x,e[t+1]=this.y,e[t+2]=this.z,e}},{key:"fromBufferAttribute",value:function(e,t){return this.x=e.getX(t),this.y=e.getY(t),this.z=e.getZ(t),this}},{key:"random",value:function(){return this.x=Math.random(),this.y=Math.random(),this.z=Math.random(),this}},{key:"randomDirection",value:function(){var e=2*(Math.random()-.5),t=Math.random()*Math.PI*2,n=Math.sqrt(1-Math.pow(e,2));return this.x=n*Math.cos(t),this.y=n*Math.sin(t),this.z=e,this}},{key:Symbol.iterator,value:o().mark((function e(){return o().wrap((function(e){for(;;)switch(e.prev=e.next){case 0:return e.next=2,this.x;case 2:return e.next=4,this.y;case 4:return e.next=6,this.z;case 6:case"end":return e.stop()}}),e,this)}))}]),t}(),mm=new gm,vm=new pm,ym=function(){function e(){var t=arguments.length>0&&void 0!==arguments[0]?arguments[0]:new gm(1/0,1/0,1/0),n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:new gm(-1/0,-1/0,-1/0);B(this,e),this.isBox3=!0,this.min=t,this.max=n}return b(e,[{key:"set",value:function(e,t){return this.min.copy(e),this.max.copy(t),this}},{key:"setFromArray",value:function(e){for(var t=1/0,n=1/0,r=1/0,i=-1/0,A=-1/0,a=-1/0,o=0,s=e.length;o<s;o+=3){var l=e[o],u=e[o+1],c=e[o+2];l<t&&(t=l),u<n&&(n=u),c<r&&(r=c),l>i&&(i=l),u>A&&(A=u),c>a&&(a=c)}return this.min.set(t,n,r),this.max.set(i,A,a),this}},{key:"setFromBufferAttribute",value:function(e){for(var t=1/0,n=1/0,r=1/0,i=-1/0,A=-1/0,a=-1/0,o=0,s=e.count;o<s;o++){var l=e.getX(o),u=e.getY(o),c=e.getZ(o);l<t&&(t=l),u<n&&(n=u),c<r&&(r=c),l>i&&(i=l),u>A&&(A=u),c>a&&(a=c)}return this.min.set(t,n,r),this.max.set(i,A,a),this}},{key:"setFromPoints",value:function(e){this.makeEmpty();for(var t=0,n=e.length;t<n;t++)this.expandByPoint(e[t]);return this}},{key:"setFromCenterAndSize",value:function(e,t){var n=Bm.copy(t).multiplyScalar(.5);return this.min.copy(e).sub(n),this.max.copy(e).add(n),this}},{key:"setFromObject",value:function(e){var t=arguments.length>1&&void 0!==arguments[1]&&arguments[1];return this.makeEmpty(),this.expandByObject(e,t)}},{key:"clone",value:function(){return(new this.constructor).copy(this)}},{key:"copy",value:function(e){return this.min.copy(e.min),this.max.copy(e.max),this}},{key:"makeEmpty",value:function(){return this.min.x=this.min.y=this.min.z=1/0,this.max.x=this.max.y=this.max.z=-1/0,this}},{key:"isEmpty",value:function(){return this.max.x<this.min.x||this.max.y<this.min.y||this.max.z<this.min.z}},{key:"getCenter",value:function(e){return this.isEmpty()?e.set(0,0,0):e.addVectors(this.min,this.max).multiplyScalar(.5)}},{key:"getSize",value:function(e){return this.isEmpty()?e.set(0,0,0):e.subVectors(this.max,this.min)}},{key:"expandByPoint",value:function(e){return this.min.min(e),this.max.max(e),this}},{key:"expandByVector",value:function(e){return this.min.sub(e),this.max.add(e),this}},{key:"expandByScalar",value:function(e){return this.min.addScalar(-e),this.max.addScalar(e),this}},{key:"expandByObject",value:function(e){var t=arguments.length>1&&void 0!==arguments[1]&&arguments[1];e.updateWorldMatrix(!1,!1);var n=e.geometry;if(void 0!==n)if(t&&void 0!=n.attributes&&void 0!==n.attributes.position)for(var r=n.attributes.position,i=0,A=r.count;i<A;i++)Bm.fromBufferAttribute(r,i).applyMatrix4(e.matrixWorld),this.expandByPoint(Bm);else null===n.boundingBox&&n.computeBoundingBox(),_m.copy(n.boundingBox),_m.applyMatrix4(e.matrixWorld),this.union(_m);for(var a=e.children,o=0,s=a.length;o<s;o++)this.expandByObject(a[o],t);return this}},{key:"containsPoint",value:function(e){return!(e.x<this.min.x||e.x>this.max.x||e.y<this.min.y||e.y>this.max.y||e.z<this.min.z||e.z>this.max.z)}},{key:"containsBox",value:function(e){return this.min.x<=e.min.x&&e.max.x<=this.max.x&&this.min.y<=e.min.y&&e.max.y<=this.max.y&&this.min.z<=e.min.z&&e.max.z<=this.max.z}},{key:"getParameter",value:function(e,t){return t.set((e.x-this.min.x)/(this.max.x-this.min.x),(e.y-this.min.y)/(this.max.y-this.min.y),(e.z-this.min.z)/(this.max.z-this.min.z))}},{key:"intersectsBox",value:function(e){return!(e.max.x<this.min.x||e.min.x>this.max.x||e.max.y<this.min.y||e.min.y>this.max.y||e.max.z<this.min.z||e.min.z>this.max.z)}},{key:"intersectsSphere",value:function(e){return this.clampPoint(e.center,Bm),Bm.distanceToSquared(e.center)<=e.radius*e.radius}},{key:"intersectsPlane",value:function(e){var t,n;return e.normal.x>0?(t=e.normal.x*this.min.x,n=e.normal.x*this.max.x):(t=e.normal.x*this.max.x,n=e.normal.x*this.min.x),e.normal.y>0?(t+=e.normal.y*this.min.y,n+=e.normal.y*this.max.y):(t+=e.normal.y*this.max.y,n+=e.normal.y*this.min.y),e.normal.z>0?(t+=e.normal.z*this.min.z,n+=e.normal.z*this.max.z):(t+=e.normal.z*this.max.z,n+=e.normal.z*this.min.z),t<=-e.constant&&n>=-e.constant}},{key:"intersectsTriangle",value:function(e){if(this.isEmpty())return!1;this.getCenter(Mm),Fm.subVectors(this.max,Mm),bm.subVectors(e.a,Mm),xm.subVectors(e.b,Mm),Cm.subVectors(e.c,Mm),Sm.subVectors(xm,bm),Em.subVectors(Cm,xm),Um.subVectors(bm,Cm);var t=[0,-Sm.z,Sm.y,0,-Em.z,Em.y,0,-Um.z,Um.y,Sm.z,0,-Sm.x,Em.z,0,-Em.x,Um.z,0,-Um.x,-Sm.y,Sm.x,0,-Em.y,Em.x,0,-Um.y,Um.x,0];return!!Qm(t,bm,xm,Cm,Fm)&&(!!Qm(t=[1,0,0,0,1,0,0,0,1],bm,xm,Cm,Fm)&&(Tm.crossVectors(Sm,Em),Qm(t=[Tm.x,Tm.y,Tm.z],bm,xm,Cm,Fm)))}},{key:"clampPoint",value:function(e,t){return t.copy(e).clamp(this.min,this.max)}},{key:"distanceToPoint",value:function(e){return Bm.copy(e).clamp(this.min,this.max).sub(e).length()}},{key:"getBoundingSphere",value:function(e){return this.getCenter(e.center),e.radius=.5*this.getSize(Bm).length(),e}},{key:"intersect",value:function(e){return this.min.max(e.min),this.max.min(e.max),this.isEmpty()&&this.makeEmpty(),this}},{key:"union",value:function(e){return this.min.min(e.min),this.max.max(e.max),this}},{key:"applyMatrix4",value:function(e){return this.isEmpty()||(wm[0].set(this.min.x,this.min.y,this.min.z).applyMatrix4(e),wm[1].set(this.min.x,this.min.y,this.max.z).applyMatrix4(e),wm[2].set(this.min.x,this.max.y,this.min.z).applyMatrix4(e),wm[3].set(this.min.x,this.max.y,this.max.z).applyMatrix4(e),wm[4].set(this.max.x,this.min.y,this.min.z).applyMatrix4(e),wm[5].set(this.max.x,this.min.y,this.max.z).applyMatrix4(e),wm[6].set(this.max.x,this.max.y,this.min.z).applyMatrix4(e),wm[7].set(this.max.x,this.max.y,this.max.z).applyMatrix4(e),this.setFromPoints(wm)),this}},{key:"translate",value:function(e){return this.min.add(e),this.max.add(e),this}},{key:"equals",value:function(e){return e.min.equals(this.min)&&e.max.equals(this.max)}}]),e}(),wm=[new gm,new gm,new gm,new gm,new gm,new gm,new gm,new gm],Bm=new gm,_m=new ym,bm=new gm,xm=new gm,Cm=new gm,Sm=new gm,Em=new gm,Um=new gm,Mm=new gm,Fm=new gm,Tm=new gm,km=new gm;function Qm(e,t,n,r,i){for(var A=0,a=e.length-3;A<=a;A+=3){km.fromArray(e,A);var o=i.x*Math.abs(km.x)+i.y*Math.abs(km.y)+i.z*Math.abs(km.z),s=t.dot(km),l=n.dot(km),u=r.dot(km);if(Math.max(-Math.max(s,l,u),Math.min(s,l,u))>o)return!1}return!0}var Lm=new ym,Dm=new gm,Im=new gm,Rm=function(){function e(){var t=arguments.length>0&&void 0!==arguments[0]?arguments[0]:new gm,n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:-1;B(this,e),this.center=t,this.radius=n}return b(e,[{key:"set",value:function(e,t){return this.center.copy(e),this.radius=t,this}},{key:"setFromPoints",value:function(e,t){var n=this.center;void 0!==t?n.copy(t):Lm.setFromPoints(e).getCenter(n);for(var r=0,i=0,A=e.length;i<A;i++)r=Math.max(r,n.distanceToSquared(e[i]));return this.radius=Math.sqrt(r),this}},{key:"copy",value:function(e){return this.center.copy(e.center),this.radius=e.radius,this}},{key:"isEmpty",value:function(){return this.radius<0}},{key:"makeEmpty",value:function(){return this.center.set(0,0,0),this.radius=-1,this}},{key:"containsPoint",value:function(e){return e.distanceToSquared(this.center)<=this.radius*this.radius}},{key:"distanceToPoint",value:function(e){return e.distanceTo(this.center)-this.radius}},{key:"intersectsSphere",value:function(e){var t=this.radius+e.radius;return e.center.distanceToSquared(this.center)<=t*t}},{key:"intersectsBox",value:function(e){return e.intersectsSphere(this)}},{key:"intersectsPlane",value:function(e){return Math.abs(e.distanceToPoint(this.center))<=this.radius}},{key:"clampPoint",value:function(e,t){var n=this.center.distanceToSquared(e);return t.copy(e),n>this.radius*this.radius&&(t.sub(this.center).normalize(),t.multiplyScalar(this.radius).add(this.center)),t}},{key:"getBoundingBox",value:function(e){return this.isEmpty()?(e.makeEmpty(),e):(e.set(this.center,this.center),e.expandByScalar(this.radius),e)}},{key:"applyMatrix4",value:function(e){return this.center.applyMatrix4(e),this.radius=this.radius*e.getMaxScaleOnAxis(),this}},{key:"translate",value:function(e){return this.center.add(e),this}},{key:"expandByPoint",value:function(e){if(this.isEmpty())return this.center.copy(e),this.radius=0,this;Dm.subVectors(e,this.center);var t=Dm.lengthSq();if(t>this.radius*this.radius){var n=Math.sqrt(t),r=.5*(n-this.radius);this.center.addScaledVector(Dm,r/n),this.radius+=r}return this}},{key:"union",value:function(e){return e.isEmpty()?this:this.isEmpty()?(this.copy(e),this):(!0===this.center.equals(e.center)?this.radius=Math.max(this.radius,e.radius):(Im.subVectors(e.center,this.center).setLength(e.radius),this.expandByPoint(Dm.copy(e.center).add(Im)),this.expandByPoint(Dm.copy(e.center).sub(Im))),this)}},{key:"equals",value:function(e){return e.center.equals(this.center)&&e.radius===this.radius}},{key:"clone",value:function(){return(new this.constructor).copy(this)}}]),e}(),Hm=new gm,Pm=new gm,Nm=new gm,Om=new gm,Vm=new gm,zm=new gm,Gm=new gm,Km=function(){function e(){var t=arguments.length>0&&void 0!==arguments[0]?arguments[0]:new gm,n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:new gm(0,0,-1);B(this,e),this.origin=t,this.direction=n}return b(e,[{key:"set",value:function(e,t){return this.origin.copy(e),this.direction.copy(t),this}},{key:"copy",value:function(e){return this.origin.copy(e.origin),this.direction.copy(e.direction),this}},{key:"at",value:function(e,t){return t.copy(this.direction).multiplyScalar(e).add(this.origin)}},{key:"lookAt",value:function(e){return this.direction.copy(e).sub(this.origin).normalize(),this}},{key:"recast",value:function(e){return this.origin.copy(this.at(e,Hm)),this}},{key:"closestPointToPoint",value:function(e,t){t.subVectors(e,this.origin);var n=t.dot(this.direction);return n<0?t.copy(this.origin):t.copy(this.direction).multiplyScalar(n).add(this.origin)}},{key:"distanceToPoint",value:function(e){return Math.sqrt(this.distanceSqToPoint(e))}},{key:"distanceSqToPoint",value:function(e){var t=Hm.subVectors(e,this.origin).dot(this.direction);return t<0?this.origin.distanceToSquared(e):(Hm.copy(this.direction).multiplyScalar(t).add(this.origin),Hm.distanceToSquared(e))}},{key:"distanceSqToSegment",value:function(e,t,n,r){Pm.copy(e).add(t).multiplyScalar(.5),Nm.copy(t).sub(e).normalize(),Om.copy(this.origin).sub(Pm);var i,A,a,o,s=.5*e.distanceTo(t),l=-this.direction.dot(Nm),u=Om.dot(this.direction),c=-Om.dot(Nm),d=Om.lengthSq(),h=Math.abs(1-l*l);if(h>0)if(A=l*u-c,o=s*h,(i=l*c-u)>=0)if(A>=-o)if(A<=o){var f=1/h;a=(i*=f)*(i+l*(A*=f)+2*u)+A*(l*i+A+2*c)+d}else A=s,a=-(i=Math.max(0,-(l*A+u)))*i+A*(A+2*c)+d;else A=-s,a=-(i=Math.max(0,-(l*A+u)))*i+A*(A+2*c)+d;else A<=-o?a=-(i=Math.max(0,-(-l*s+u)))*i+(A=i>0?-s:Math.min(Math.max(-s,-c),s))*(A+2*c)+d:A<=o?(i=0,a=(A=Math.min(Math.max(-s,-c),s))*(A+2*c)+d):a=-(i=Math.max(0,-(l*s+u)))*i+(A=i>0?s:Math.min(Math.max(-s,-c),s))*(A+2*c)+d;else A=l>0?-s:s,a=-(i=Math.max(0,-(l*A+u)))*i+A*(A+2*c)+d;return n&&n.copy(this.direction).multiplyScalar(i).add(this.origin),r&&r.copy(Nm).multiplyScalar(A).add(Pm),a}},{key:"intersectSphere",value:function(e,t){Hm.subVectors(e.center,this.origin);var n=Hm.dot(this.direction),r=Hm.dot(Hm)-n*n,i=e.radius*e.radius;if(r>i)return null;var A=Math.sqrt(i-r),a=n-A,o=n+A;return a<0&&o<0?null:a<0?this.at(o,t):this.at(a,t)}},{key:"intersectsSphere",value:function(e){return this.distanceSqToPoint(e.center)<=e.radius*e.radius}},{key:"distanceToPlane",value:function(e){var t=e.normal.dot(this.direction);if(0===t)return 0===e.distanceToPoint(this.origin)?0:null;var n=-(this.origin.dot(e.normal)+e.constant)/t;return n>=0?n:null}},{key:"intersectPlane",value:function(e,t){var n=this.distanceToPlane(e);return null===n?null:this.at(n,t)}},{key:"intersectsPlane",value:function(e){var t=e.distanceToPoint(this.origin);return 0===t||e.normal.dot(this.direction)*t<0}},{key:"intersectBox",value:function(e,t){var n,r,i,A,a,o,s=1/this.direction.x,l=1/this.direction.y,u=1/this.direction.z,c=this.origin;return s>=0?(n=(e.min.x-c.x)*s,r=(e.max.x-c.x)*s):(n=(e.max.x-c.x)*s,r=(e.min.x-c.x)*s),l>=0?(i=(e.min.y-c.y)*l,A=(e.max.y-c.y)*l):(i=(e.max.y-c.y)*l,A=(e.min.y-c.y)*l),n>A||i>r?null:((i>n||isNaN(n))&&(n=i),(A<r||isNaN(r))&&(r=A),u>=0?(a=(e.min.z-c.z)*u,o=(e.max.z-c.z)*u):(a=(e.max.z-c.z)*u,o=(e.min.z-c.z)*u),n>o||a>r?null:((a>n||n!==n)&&(n=a),(o<r||r!==r)&&(r=o),r<0?null:this.at(n>=0?n:r,t)))}},{key:"intersectsBox",value:function(e){return null!==this.intersectBox(e,Hm)}},{key:"intersectTriangle",value:function(e,t,n,r,i){Vm.subVectors(t,e),zm.subVectors(n,e),Gm.crossVectors(Vm,zm);var A,a=this.direction.dot(Gm);if(a>0){if(r)return null;A=1}else{if(!(a<0))return null;A=-1,a=-a}Om.subVectors(this.origin,e);var o=A*this.direction.dot(zm.crossVectors(Om,zm));if(o<0)return null;var s=A*this.direction.dot(Vm.cross(Om));if(s<0)return null;if(o+s>a)return null;var l=-A*Om.dot(Gm);return l<0?null:this.at(l/a,i)}},{key:"applyMatrix4",value:function(e){return this.origin.applyMatrix4(e),this.direction.transformDirection(e),this}},{key:"equals",value:function(e){return e.origin.equals(this.origin)&&e.direction.equals(this.direction)}},{key:"clone",value:function(){return(new this.constructor).copy(this)}}]),e}(),Wm=function(){function e(){B(this,e),e.prototype.isMatrix4=!0,this.elements=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1]}return b(e,[{key:"set",value:function(e,t,n,r,i,A,a,o,s,l,u,c,d,h,f,p){var g=this.elements;return g[0]=e,g[4]=t,g[8]=n,g[12]=r,g[1]=i,g[5]=A,g[9]=a,g[13]=o,g[2]=s,g[6]=l,g[10]=u,g[14]=c,g[3]=d,g[7]=h,g[11]=f,g[15]=p,this}},{key:"identity",value:function(){return this.set(1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1),this}},{key:"clone",value:function(){return(new e).fromArray(this.elements)}},{key:"copy",value:function(e){var t=this.elements,n=e.elements;return t[0]=n[0],t[1]=n[1],t[2]=n[2],t[3]=n[3],t[4]=n[4],t[5]=n[5],t[6]=n[6],t[7]=n[7],t[8]=n[8],t[9]=n[9],t[10]=n[10],t[11]=n[11],t[12]=n[12],t[13]=n[13],t[14]=n[14],t[15]=n[15],this}},{key:"copyPosition",value:function(e){var t=this.elements,n=e.elements;return t[12]=n[12],t[13]=n[13],t[14]=n[14],this}},{key:"setFromMatrix3",value:function(e){var t=e.elements;return this.set(t[0],t[3],t[6],0,t[1],t[4],t[7],0,t[2],t[5],t[8],0,0,0,0,1),this}},{key:"extractBasis",value:function(e,t,n){return e.setFromMatrixColumn(this,0),t.setFromMatrixColumn(this,1),n.setFromMatrixColumn(this,2),this}},{key:"makeBasis",value:function(e,t,n){return this.set(e.x,t.x,n.x,0,e.y,t.y,n.y,0,e.z,t.z,n.z,0,0,0,0,1),this}},{key:"extractRotation",value:function(e){var t=this.elements,n=e.elements,r=1/jm.setFromMatrixColumn(e,0).length(),i=1/jm.setFromMatrixColumn(e,1).length(),A=1/jm.setFromMatrixColumn(e,2).length();return t[0]=n[0]*r,t[1]=n[1]*r,t[2]=n[2]*r,t[3]=0,t[4]=n[4]*i,t[5]=n[5]*i,t[6]=n[6]*i,t[7]=0,t[8]=n[8]*A,t[9]=n[9]*A,t[10]=n[10]*A,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,this}},{key:"makeRotationFromEuler",value:function(e){var t=this.elements,n=e.x,r=e.y,i=e.z,A=Math.cos(n),a=Math.sin(n),o=Math.cos(r),s=Math.sin(r),l=Math.cos(i),u=Math.sin(i);if("XYZ"===e.order){var c=A*l,d=A*u,h=a*l,f=a*u;t[0]=o*l,t[4]=-o*u,t[8]=s,t[1]=d+h*s,t[5]=c-f*s,t[9]=-a*o,t[2]=f-c*s,t[6]=h+d*s,t[10]=A*o}else if("YXZ"===e.order){var p=o*l,g=o*u,m=s*l,v=s*u;t[0]=p+v*a,t[4]=m*a-g,t[8]=A*s,t[1]=A*u,t[5]=A*l,t[9]=-a,t[2]=g*a-m,t[6]=v+p*a,t[10]=A*o}else if("ZXY"===e.order){var y=o*l,w=o*u,B=s*l,_=s*u;t[0]=y-_*a,t[4]=-A*u,t[8]=B+w*a,t[1]=w+B*a,t[5]=A*l,t[9]=_-y*a,t[2]=-A*s,t[6]=a,t[10]=A*o}else if("ZYX"===e.order){var b=A*l,x=A*u,C=a*l,S=a*u;t[0]=o*l,t[4]=C*s-x,t[8]=b*s+S,t[1]=o*u,t[5]=S*s+b,t[9]=x*s-C,t[2]=-s,t[6]=a*o,t[10]=A*o}else if("YZX"===e.order){var E=A*o,U=A*s,M=a*o,F=a*s;t[0]=o*l,t[4]=F-E*u,t[8]=M*u+U,t[1]=u,t[5]=A*l,t[9]=-a*l,t[2]=-s*l,t[6]=U*u+M,t[10]=E-F*u}else if("XZY"===e.order){var T=A*o,k=A*s,Q=a*o,L=a*s;t[0]=o*l,t[4]=-u,t[8]=s*l,t[1]=T*u+L,t[5]=A*l,t[9]=k*u-Q,t[2]=Q*u-k,t[6]=a*l,t[10]=L*u+T}return t[3]=0,t[7]=0,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,this}},{key:"makeRotationFromQuaternion",value:function(e){return this.compose(qm,e,Ym)}},{key:"lookAt",value:function(e,t,n){var r=this.elements;return $m.subVectors(e,t),0===$m.lengthSq()&&($m.z=1),$m.normalize(),Jm.crossVectors(n,$m),0===Jm.lengthSq()&&(1===Math.abs(n.z)?$m.x+=1e-4:$m.z+=1e-4,$m.normalize(),Jm.crossVectors(n,$m)),Jm.normalize(),Zm.crossVectors($m,Jm),r[0]=Jm.x,r[4]=Zm.x,r[8]=$m.x,r[1]=Jm.y,r[5]=Zm.y,r[9]=$m.y,r[2]=Jm.z,r[6]=Zm.z,r[10]=$m.z,this}},{key:"multiply",value:function(e){return this.multiplyMatrices(this,e)}},{key:"premultiply",value:function(e){return this.multiplyMatrices(e,this)}},{key:"multiplyMatrices",value:function(e,t){var n=e.elements,r=t.elements,i=this.elements,A=n[0],a=n[4],o=n[8],s=n[12],l=n[1],u=n[5],c=n[9],d=n[13],h=n[2],f=n[6],p=n[10],g=n[14],m=n[3],v=n[7],y=n[11],w=n[15],B=r[0],_=r[4],b=r[8],x=r[12],C=r[1],S=r[5],E=r[9],U=r[13],M=r[2],F=r[6],T=r[10],k=r[14],Q=r[3],L=r[7],D=r[11],I=r[15];return i[0]=A*B+a*C+o*M+s*Q,i[4]=A*_+a*S+o*F+s*L,i[8]=A*b+a*E+o*T+s*D,i[12]=A*x+a*U+o*k+s*I,i[1]=l*B+u*C+c*M+d*Q,i[5]=l*_+u*S+c*F+d*L,i[9]=l*b+u*E+c*T+d*D,i[13]=l*x+u*U+c*k+d*I,i[2]=h*B+f*C+p*M+g*Q,i[6]=h*_+f*S+p*F+g*L,i[10]=h*b+f*E+p*T+g*D,i[14]=h*x+f*U+p*k+g*I,i[3]=m*B+v*C+y*M+w*Q,i[7]=m*_+v*S+y*F+w*L,i[11]=m*b+v*E+y*T+w*D,i[15]=m*x+v*U+y*k+w*I,this}},{key:"multiplyScalar",value:function(e){var t=this.elements;return t[0]*=e,t[4]*=e,t[8]*=e,t[12]*=e,t[1]*=e,t[5]*=e,t[9]*=e,t[13]*=e,t[2]*=e,t[6]*=e,t[10]*=e,t[14]*=e,t[3]*=e,t[7]*=e,t[11]*=e,t[15]*=e,this}},{key:"determinant",value:function(){var e=this.elements,t=e[0],n=e[4],r=e[8],i=e[12],A=e[1],a=e[5],o=e[9],s=e[13],l=e[2],u=e[6],c=e[10],d=e[14];return e[3]*(+i*o*u-r*s*u-i*a*c+n*s*c+r*a*d-n*o*d)+e[7]*(+t*o*d-t*s*c+i*A*c-r*A*d+r*s*l-i*o*l)+e[11]*(+t*s*u-t*a*d-i*A*u+n*A*d+i*a*l-n*s*l)+e[15]*(-r*a*l-t*o*u+t*a*c+r*A*u-n*A*c+n*o*l)}},{key:"transpose",value:function(){var e,t=this.elements;return e=t[1],t[1]=t[4],t[4]=e,e=t[2],t[2]=t[8],t[8]=e,e=t[6],t[6]=t[9],t[9]=e,e=t[3],t[3]=t[12],t[12]=e,e=t[7],t[7]=t[13],t[13]=e,e=t[11],t[11]=t[14],t[14]=e,this}},{key:"setPosition",value:function(e,t,n){var r=this.elements;return e.isVector3?(r[12]=e.x,r[13]=e.y,r[14]=e.z):(r[12]=e,r[13]=t,r[14]=n),this}},{key:"invert",value:function(){var e=this.elements,t=e[0],n=e[1],r=e[2],i=e[3],A=e[4],a=e[5],o=e[6],s=e[7],l=e[8],u=e[9],c=e[10],d=e[11],h=e[12],f=e[13],p=e[14],g=e[15],m=u*p*s-f*c*s+f*o*d-a*p*d-u*o*g+a*c*g,v=h*c*s-l*p*s-h*o*d+A*p*d+l*o*g-A*c*g,y=l*f*s-h*u*s+h*a*d-A*f*d-l*a*g+A*u*g,w=h*u*o-l*f*o-h*a*c+A*f*c+l*a*p-A*u*p,B=t*m+n*v+r*y+i*w;if(0===B)return this.set(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0);var _=1/B;return e[0]=m*_,e[1]=(f*c*i-u*p*i-f*r*d+n*p*d+u*r*g-n*c*g)*_,e[2]=(a*p*i-f*o*i+f*r*s-n*p*s-a*r*g+n*o*g)*_,e[3]=(u*o*i-a*c*i-u*r*s+n*c*s+a*r*d-n*o*d)*_,e[4]=v*_,e[5]=(l*p*i-h*c*i+h*r*d-t*p*d-l*r*g+t*c*g)*_,e[6]=(h*o*i-A*p*i-h*r*s+t*p*s+A*r*g-t*o*g)*_,e[7]=(A*c*i-l*o*i+l*r*s-t*c*s-A*r*d+t*o*d)*_,e[8]=y*_,e[9]=(h*u*i-l*f*i-h*n*d+t*f*d+l*n*g-t*u*g)*_,e[10]=(A*f*i-h*a*i+h*n*s-t*f*s-A*n*g+t*a*g)*_,e[11]=(l*a*i-A*u*i-l*n*s+t*u*s+A*n*d-t*a*d)*_,e[12]=w*_,e[13]=(l*f*r-h*u*r+h*n*c-t*f*c-l*n*p+t*u*p)*_,e[14]=(h*a*r-A*f*r-h*n*o+t*f*o+A*n*p-t*a*p)*_,e[15]=(A*u*r-l*a*r+l*n*o-t*u*o-A*n*c+t*a*c)*_,this}},{key:"scale",value:function(e){var t=this.elements,n=e.x,r=e.y,i=e.z;return t[0]*=n,t[4]*=r,t[8]*=i,t[1]*=n,t[5]*=r,t[9]*=i,t[2]*=n,t[6]*=r,t[10]*=i,t[3]*=n,t[7]*=r,t[11]*=i,this}},{key:"getMaxScaleOnAxis",value:function(){var e=this.elements,t=e[0]*e[0]+e[1]*e[1]+e[2]*e[2],n=e[4]*e[4]+e[5]*e[5]+e[6]*e[6],r=e[8]*e[8]+e[9]*e[9]+e[10]*e[10];return Math.sqrt(Math.max(t,n,r))}},{key:"makeTranslation",value:function(e,t,n){return this.set(1,0,0,e,0,1,0,t,0,0,1,n,0,0,0,1),this}},{key:"makeRotationX",value:function(e){var t=Math.cos(e),n=Math.sin(e);return this.set(1,0,0,0,0,t,-n,0,0,n,t,0,0,0,0,1),this}},{key:"makeRotationY",value:function(e){var t=Math.cos(e),n=Math.sin(e);return this.set(t,0,n,0,0,1,0,0,-n,0,t,0,0,0,0,1),this}},{key:"makeRotationZ",value:function(e){var t=Math.cos(e),n=Math.sin(e);return this.set(t,-n,0,0,n,t,0,0,0,0,1,0,0,0,0,1),this}},{key:"makeRotationAxis",value:function(e,t){var n=Math.cos(t),r=Math.sin(t),i=1-n,A=e.x,a=e.y,o=e.z,s=i*A,l=i*a;return this.set(s*A+n,s*a-r*o,s*o+r*a,0,s*a+r*o,l*a+n,l*o-r*A,0,s*o-r*a,l*o+r*A,i*o*o+n,0,0,0,0,1),this}},{key:"makeScale",value:function(e,t,n){return this.set(e,0,0,0,0,t,0,0,0,0,n,0,0,0,0,1),this}},{key:"makeShear",value:function(e,t,n,r,i,A){return this.set(1,n,i,0,e,1,A,0,t,r,1,0,0,0,0,1),this}},{key:"compose",value:function(e,t,n){var r=this.elements,i=t._x,A=t._y,a=t._z,o=t._w,s=i+i,l=A+A,u=a+a,c=i*s,d=i*l,h=i*u,f=A*l,p=A*u,g=a*u,m=o*s,v=o*l,y=o*u,w=n.x,B=n.y,_=n.z;return r[0]=(1-(f+g))*w,r[1]=(d+y)*w,r[2]=(h-v)*w,r[3]=0,r[4]=(d-y)*B,r[5]=(1-(c+g))*B,r[6]=(p+m)*B,r[7]=0,r[8]=(h+v)*_,r[9]=(p-m)*_,r[10]=(1-(c+f))*_,r[11]=0,r[12]=e.x,r[13]=e.y,r[14]=e.z,r[15]=1,this}},{key:"decompose",value:function(e,t,n){var r=this.elements,i=jm.set(r[0],r[1],r[2]).length(),A=jm.set(r[4],r[5],r[6]).length(),a=jm.set(r[8],r[9],r[10]).length();this.determinant()<0&&(i=-i),e.x=r[12],e.y=r[13],e.z=r[14],Xm.copy(this);var o=1/i,s=1/A,l=1/a;return Xm.elements[0]*=o,Xm.elements[1]*=o,Xm.elements[2]*=o,Xm.elements[4]*=s,Xm.elements[5]*=s,Xm.elements[6]*=s,Xm.elements[8]*=l,Xm.elements[9]*=l,Xm.elements[10]*=l,t.setFromRotationMatrix(Xm),n.x=i,n.y=A,n.z=a,this}},{key:"makePerspective",value:function(e,t,n,r,i,A){var a=this.elements,o=2*i/(t-e),s=2*i/(n-r),l=(t+e)/(t-e),u=(n+r)/(n-r),c=-(A+i)/(A-i),d=-2*A*i/(A-i);return a[0]=o,a[4]=0,a[8]=l,a[12]=0,a[1]=0,a[5]=s,a[9]=u,a[13]=0,a[2]=0,a[6]=0,a[10]=c,a[14]=d,a[3]=0,a[7]=0,a[11]=-1,a[15]=0,this}},{key:"makeOrthographic",value:function(e,t,n,r,i,A){var a=this.elements,o=1/(t-e),s=1/(n-r),l=1/(A-i),u=(t+e)*o,c=(n+r)*s,d=(A+i)*l;return a[0]=2*o,a[4]=0,a[8]=0,a[12]=-u,a[1]=0,a[5]=2*s,a[9]=0,a[13]=-c,a[2]=0,a[6]=0,a[10]=-2*l,a[14]=-d,a[3]=0,a[7]=0,a[11]=0,a[15]=1,this}},{key:"equals",value:function(e){for(var t=this.elements,n=e.elements,r=0;r<16;r++)if(t[r]!==n[r])return!1;return!0}},{key:"fromArray",value:function(e){for(var t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0,n=0;n<16;n++)this.elements[n]=e[n+t];return this}},{key:"toArray",value:function(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:[],t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0,n=this.elements;return e[t]=n[0],e[t+1]=n[1],e[t+2]=n[2],e[t+3]=n[3],e[t+4]=n[4],e[t+5]=n[5],e[t+6]=n[6],e[t+7]=n[7],e[t+8]=n[8],e[t+9]=n[9],e[t+10]=n[10],e[t+11]=n[11],e[t+12]=n[12],e[t+13]=n[13],e[t+14]=n[14],e[t+15]=n[15],e}}]),e}(),jm=new gm,Xm=new Wm,qm=new gm(0,0,0),Ym=new gm(1,1,1),Jm=new gm,Zm=new gm,$m=new gm,ev=new Wm,tv=new pm,nv=function(e){function t(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0,r=arguments.length>2&&void 0!==arguments[2]?arguments[2]:0,i=arguments.length>3&&void 0!==arguments[3]?arguments[3]:t.DEFAULT_ORDER;B(this,t),this.isEuler=!0,this._x=e,this._y=n,this._z=r,this._order=i}return b(t,[{key:"x",get:function(){return this._x},set:function(e){this._x=e,this._onChangeCallback()}},{key:"y",get:function(){return this._y},set:function(e){this._y=e,this._onChangeCallback()}},{key:"z",get:function(){return this._z},set:function(e){this._z=e,this._onChangeCallback()}},{key:"order",get:function(){return this._order},set:function(e){this._order=e,this._onChangeCallback()}},{key:"set",value:function(e,t,n){var r=arguments.length>3&&void 0!==arguments[3]?arguments[3]:this._order;return this._x=e,this._y=t,this._z=n,this._order=r,this._onChangeCallback(),this}},{key:"clone",value:function(){return new this.constructor(this._x,this._y,this._z,this._order)}},{key:"copy",value:function(e){return this._x=e._x,this._y=e._y,this._z=e._z,this._order=e._order,this._onChangeCallback(),this}},{key:"setFromRotationMatrix",value:function(e){var t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:this._order,n=!(arguments.length>2&&void 0!==arguments[2])||arguments[2],r=e.elements,i=r[0],A=r[4],a=r[8],o=r[1],s=r[5],l=r[9],u=r[2],c=r[6],d=r[10];switch(t){case"XYZ":this._y=Math.asin(Lg(a,-1,1)),Math.abs(a)<.9999999?(this._x=Math.atan2(-l,d),this._z=Math.atan2(-A,i)):(this._x=Math.atan2(c,s),this._z=0);break;case"YXZ":this._x=Math.asin(-Lg(l,-1,1)),Math.abs(l)<.9999999?(this._y=Math.atan2(a,d),this._z=Math.atan2(o,s)):(this._y=Math.atan2(-u,i),this._z=0);break;case"ZXY":this._x=Math.asin(Lg(c,-1,1)),Math.abs(c)<.9999999?(this._y=Math.atan2(-u,d),this._z=Math.atan2(-A,s)):(this._y=0,this._z=Math.atan2(o,i));break;case"ZYX":this._y=Math.asin(-Lg(u,-1,1)),Math.abs(u)<.9999999?(this._x=Math.atan2(c,d),this._z=Math.atan2(o,i)):(this._x=0,this._z=Math.atan2(-A,s));break;case"YZX":this._z=Math.asin(Lg(o,-1,1)),Math.abs(o)<.9999999?(this._x=Math.atan2(-l,s),this._y=Math.atan2(-u,i)):(this._x=0,this._y=Math.atan2(a,d));break;case"XZY":this._z=Math.asin(-Lg(A,-1,1)),Math.abs(A)<.9999999?(this._x=Math.atan2(c,s),this._y=Math.atan2(a,i)):(this._x=Math.atan2(-l,d),this._y=0);break;default:console.warn("THREE.Euler: .setFromRotationMatrix() encountered an unknown order: "+t)}return this._order=t,!0===n&&this._onChangeCallback(),this}},{key:"setFromQuaternion",value:function(e,t,n){return ev.makeRotationFromQuaternion(e),this.setFromRotationMatrix(ev,t,n)}},{key:"setFromVector3",value:function(e){var t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:this._order;return this.set(e.x,e.y,e.z,t)}},{key:"reorder",value:function(e){return tv.setFromEuler(this),this.setFromQuaternion(tv,e)}},{key:"equals",value:function(e){return e._x===this._x&&e._y===this._y&&e._z===this._z&&e._order===this._order}},{key:"fromArray",value:function(e){return this._x=e[0],this._y=e[1],this._z=e[2],void 0!==e[3]&&(this._order=e[3]),this._onChangeCallback(),this}},{key:"toArray",value:function(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:[],t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0;return e[t]=this._x,e[t+1]=this._y,e[t+2]=this._z,e[t+3]=this._order,e}},{key:"_onChange",value:function(e){return this._onChangeCallback=e,this}},{key:"_onChangeCallback",value:function(){}},{key:Symbol.iterator,value:o().mark((function e(){return o().wrap((function(e){for(;;)switch(e.prev=e.next){case 0:return e.next=2,this._x;case 2:return e.next=4,this._y;case 4:return e.next=6,this._z;case 6:return e.next=8,this._order;case 8:case"end":return e.stop()}}),e,this)}))}]),t}();nv.DEFAULT_ORDER="XYZ";var rv=function(){function e(){B(this,e),this.mask=1}return b(e,[{key:"set",value:function(e){this.mask=(1<<e|0)>>>0}},{key:"enable",value:function(e){this.mask|=1<<e|0}},{key:"enableAll",value:function(){this.mask=-1}},{key:"toggle",value:function(e){this.mask^=1<<e|0}},{key:"disable",value:function(e){this.mask&=~(1<<e|0)}},{key:"disableAll",value:function(){this.mask=0}},{key:"test",value:function(e){return 0!==(this.mask&e.mask)}},{key:"isEnabled",value:function(e){return 0!==(this.mask&(1<<e|0))}}]),e}(),iv=0,Av=new gm,av=new pm,ov=new Wm,sv=new gm,lv=new gm,uv=new gm,cv=new pm,dv=new gm(1,0,0),hv=new gm(0,1,0),fv=new gm(0,0,1),pv={type:"added"},gv={type:"removed"},mv=function(e){C(n,e);var t=M(n);function n(){var e;B(this,n),(e=t.call(this)).isObject3D=!0,Object.defineProperty(U(e),"id",{value:iv++}),e.uuid=Qg(),e.name="",e.type="Object3D",e.parent=null,e.children=[],e.up=n.DEFAULT_UP.clone();var r=new gm,i=new nv,A=new pm,a=new gm(1,1,1);return i._onChange((function(){A.setFromEuler(i,!1)})),A._onChange((function(){i.setFromQuaternion(A,void 0,!1)})),Object.defineProperties(U(e),{position:{configurable:!0,enumerable:!0,value:r},rotation:{configurable:!0,enumerable:!0,value:i},quaternion:{configurable:!0,enumerable:!0,value:A},scale:{configurable:!0,enumerable:!0,value:a},modelViewMatrix:{value:new Wm},normalMatrix:{value:new Gg}}),e.matrix=new Wm,e.matrixWorld=new Wm,e.matrixAutoUpdate=n.DEFAULT_MATRIX_AUTO_UPDATE,e.matrixWorldNeedsUpdate=!1,e.matrixWorldAutoUpdate=n.DEFAULT_MATRIX_WORLD_AUTO_UPDATE,e.layers=new rv,e.visible=!0,e.castShadow=!1,e.receiveShadow=!1,e.frustumCulled=!0,e.renderOrder=0,e.animations=[],e.userData={},e}return b(n,[{key:"onBeforeRender",value:function(){}},{key:"onAfterRender",value:function(){}},{key:"applyMatrix4",value:function(e){this.matrixAutoUpdate&&this.updateMatrix(),this.matrix.premultiply(e),this.matrix.decompose(this.position,this.quaternion,this.scale)}},{key:"applyQuaternion",value:function(e){return this.quaternion.premultiply(e),this}},{key:"setRotationFromAxisAngle",value:function(e,t){this.quaternion.setFromAxisAngle(e,t)}},{key:"setRotationFromEuler",value:function(e){this.quaternion.setFromEuler(e,!0)}},{key:"setRotationFromMatrix",value:function(e){this.quaternion.setFromRotationMatrix(e)}},{key:"setRotationFromQuaternion",value:function(e){this.quaternion.copy(e)}},{key:"rotateOnAxis",value:function(e,t){return av.setFromAxisAngle(e,t),this.quaternion.multiply(av),this}},{key:"rotateOnWorldAxis",value:function(e,t){return av.setFromAxisAngle(e,t),this.quaternion.premultiply(av),this}},{key:"rotateX",value:function(e){return this.rotateOnAxis(dv,e)}},{key:"rotateY",value:function(e){return this.rotateOnAxis(hv,e)}},{key:"rotateZ",value:function(e){return this.rotateOnAxis(fv,e)}},{key:"translateOnAxis",value:function(e,t){return Av.copy(e).applyQuaternion(this.quaternion),this.position.add(Av.multiplyScalar(t)),this}},{key:"translateX",value:function(e){return this.translateOnAxis(dv,e)}},{key:"translateY",value:function(e){return this.translateOnAxis(hv,e)}},{key:"translateZ",value:function(e){return this.translateOnAxis(fv,e)}},{key:"localToWorld",value:function(e){return this.updateWorldMatrix(!0,!1),e.applyMatrix4(this.matrixWorld)}},{key:"worldToLocal",value:function(e){return this.updateWorldMatrix(!0,!1),e.applyMatrix4(ov.copy(this.matrixWorld).invert())}},{key:"lookAt",value:function(e,t,n){e.isVector3?sv.copy(e):sv.set(e,t,n);var r=this.parent;this.updateWorldMatrix(!0,!1),lv.setFromMatrixPosition(this.matrixWorld),this.isCamera||this.isLight?ov.lookAt(lv,sv,this.up):ov.lookAt(sv,lv,this.up),this.quaternion.setFromRotationMatrix(ov),r&&(ov.extractRotation(r.matrixWorld),av.setFromRotationMatrix(ov),this.quaternion.premultiply(av.invert()))}},{key:"add",value:function(e){if(arguments.length>1){for(var t=0;t<arguments.length;t++)this.add(arguments[t]);return this}return e===this?(console.error("THREE.Object3D.add: object can't be added as a child of itself.",e),this):(e&&e.isObject3D?(null!==e.parent&&e.parent.remove(e),e.parent=this,this.children.push(e),e.dispatchEvent(pv)):console.error("THREE.Object3D.add: object not an instance of THREE.Object3D.",e),this)}},{key:"remove",value:function(e){if(arguments.length>1){for(var t=0;t<arguments.length;t++)this.remove(arguments[t]);return this}var n=this.children.indexOf(e);return-1!==n&&(e.parent=null,this.children.splice(n,1),e.dispatchEvent(gv)),this}},{key:"removeFromParent",value:function(){var e=this.parent;return null!==e&&e.remove(this),this}},{key:"clear",value:function(){for(var e=0;e<this.children.length;e++){var t=this.children[e];t.parent=null,t.dispatchEvent(gv)}return this.children.length=0,this}},{key:"attach",value:function(e){return this.updateWorldMatrix(!0,!1),ov.copy(this.matrixWorld).invert(),null!==e.parent&&(e.parent.updateWorldMatrix(!0,!1),ov.multiply(e.parent.matrixWorld)),e.applyMatrix4(ov),this.add(e),e.updateWorldMatrix(!1,!0),this}},{key:"getObjectById",value:function(e){return this.getObjectByProperty("id",e)}},{key:"getObjectByName",value:function(e){return this.getObjectByProperty("name",e)}},{key:"getObjectByProperty",value:function(e,t){if(this[e]===t)return this;for(var n=0,r=this.children.length;n<r;n++){var i=this.children[n].getObjectByProperty(e,t);if(void 0!==i)return i}}},{key:"getObjectsByProperty",value:function(e,t){var n=[];this[e]===t&&n.push(this);for(var r=0,i=this.children.length;r<i;r++){var A=this.children[r].getObjectsByProperty(e,t);A.length>0&&(n=n.concat(A))}return n}},{key:"getWorldPosition",value:function(e){return this.updateWorldMatrix(!0,!1),e.setFromMatrixPosition(this.matrixWorld)}},{key:"getWorldQuaternion",value:function(e){return this.updateWorldMatrix(!0,!1),this.matrixWorld.decompose(lv,e,uv),e}},{key:"getWorldScale",value:function(e){return this.updateWorldMatrix(!0,!1),this.matrixWorld.decompose(lv,cv,e),e}},{key:"getWorldDirection",value:function(e){this.updateWorldMatrix(!0,!1);var t=this.matrixWorld.elements;return e.set(t[8],t[9],t[10]).normalize()}},{key:"raycast",value:function(){}},{key:"traverse",value:function(e){e(this);for(var t=this.children,n=0,r=t.length;n<r;n++)t[n].traverse(e)}},{key:"traverseVisible",value:function(e){if(!1!==this.visible){e(this);for(var t=this.children,n=0,r=t.length;n<r;n++)t[n].traverseVisible(e)}}},{key:"traverseAncestors",value:function(e){var t=this.parent;null!==t&&(e(t),t.traverseAncestors(e))}},{key:"updateMatrix",value:function(){this.matrix.compose(this.position,this.quaternion,this.scale),this.matrixWorldNeedsUpdate=!0}},{key:"updateMatrixWorld",value:function(e){this.matrixAutoUpdate&&this.updateMatrix(),(this.matrixWorldNeedsUpdate||e)&&(null===this.parent?this.matrixWorld.copy(this.matrix):this.matrixWorld.multiplyMatrices(this.parent.matrixWorld,this.matrix),this.matrixWorldNeedsUpdate=!1,e=!0);for(var t=this.children,n=0,r=t.length;n<r;n++){var i=t[n];!0!==i.matrixWorldAutoUpdate&&!0!==e||i.updateMatrixWorld(e)}}},{key:"updateWorldMatrix",value:function(e,t){var n=this.parent;if(!0===e&&null!==n&&!0===n.matrixWorldAutoUpdate&&n.updateWorldMatrix(!0,!1),this.matrixAutoUpdate&&this.updateMatrix(),null===this.parent?this.matrixWorld.copy(this.matrix):this.matrixWorld.multiplyMatrices(this.parent.matrixWorld,this.matrix),!0===t)for(var r=this.children,i=0,A=r.length;i<A;i++){var a=r[i];!0===a.matrixWorldAutoUpdate&&a.updateWorldMatrix(!1,!0)}}},{key:"toJSON",value:function(e){var t=void 0===e||"string"===typeof e,n={};t&&(e={geometries:{},materials:{},textures:{},images:{},shapes:{},skeletons:{},animations:{},nodes:{}},n.metadata={version:4.5,type:"Object",generator:"Object3D.toJSON"});var r={};function i(t,n){return void 0===t[n.uuid]&&(t[n.uuid]=n.toJSON(e)),n.uuid}if(r.uuid=this.uuid,r.type=this.type,""!==this.name&&(r.name=this.name),!0===this.castShadow&&(r.castShadow=!0),!0===this.receiveShadow&&(r.receiveShadow=!0),!1===this.visible&&(r.visible=!1),!1===this.frustumCulled&&(r.frustumCulled=!1),0!==this.renderOrder&&(r.renderOrder=this.renderOrder),Object.keys(this.userData).length>0&&(r.userData=this.userData),r.layers=this.layers.mask,r.matrix=this.matrix.toArray(),!1===this.matrixAutoUpdate&&(r.matrixAutoUpdate=!1),this.isInstancedMesh&&(r.type="InstancedMesh",r.count=this.count,r.instanceMatrix=this.instanceMatrix.toJSON(),null!==this.instanceColor&&(r.instanceColor=this.instanceColor.toJSON())),this.isScene)this.background&&(this.background.isColor?r.background=this.background.toJSON():this.background.isTexture&&(r.background=this.background.toJSON(e).uuid)),this.environment&&this.environment.isTexture&&!0!==this.environment.isRenderTargetTexture&&(r.environment=this.environment.toJSON(e).uuid);else if(this.isMesh||this.isLine||this.isPoints){r.geometry=i(e.geometries,this.geometry);var A=this.geometry.parameters;if(void 0!==A&&void 0!==A.shapes){var a=A.shapes;if(Array.isArray(a))for(var o=0,s=a.length;o<s;o++){var l=a[o];i(e.shapes,l)}else i(e.shapes,a)}}if(this.isSkinnedMesh&&(r.bindMode=this.bindMode,r.bindMatrix=this.bindMatrix.toArray(),void 0!==this.skeleton&&(i(e.skeletons,this.skeleton),r.skeleton=this.skeleton.uuid)),void 0!==this.material)if(Array.isArray(this.material)){for(var u=[],c=0,d=this.material.length;c<d;c++)u.push(i(e.materials,this.material[c]));r.material=u}else r.material=i(e.materials,this.material);if(this.children.length>0){r.children=[];for(var h=0;h<this.children.length;h++)r.children.push(this.children[h].toJSON(e).object)}if(this.animations.length>0){r.animations=[];for(var f=0;f<this.animations.length;f++){var p=this.animations[f];r.animations.push(i(e.animations,p))}}if(t){var g=x(e.geometries),m=x(e.materials),v=x(e.textures),y=x(e.images),w=x(e.shapes),B=x(e.skeletons),_=x(e.animations),b=x(e.nodes);g.length>0&&(n.geometries=g),m.length>0&&(n.materials=m),v.length>0&&(n.textures=v),y.length>0&&(n.images=y),w.length>0&&(n.shapes=w),B.length>0&&(n.skeletons=B),_.length>0&&(n.animations=_),b.length>0&&(n.nodes=b)}return n.object=r,n;function x(e){var t=[];for(var n in e){var r=e[n];delete r.metadata,t.push(r)}return t}}},{key:"clone",value:function(e){return(new this.constructor).copy(this,e)}},{key:"copy",value:function(e){var t=!(arguments.length>1&&void 0!==arguments[1])||arguments[1];if(this.name=e.name,this.up.copy(e.up),this.position.copy(e.position),this.rotation.order=e.rotation.order,this.quaternion.copy(e.quaternion),this.scale.copy(e.scale),this.matrix.copy(e.matrix),this.matrixWorld.copy(e.matrixWorld),this.matrixAutoUpdate=e.matrixAutoUpdate,this.matrixWorldNeedsUpdate=e.matrixWorldNeedsUpdate,this.matrixWorldAutoUpdate=e.matrixWorldAutoUpdate,this.layers.mask=e.layers.mask,this.visible=e.visible,this.castShadow=e.castShadow,this.receiveShadow=e.receiveShadow,this.frustumCulled=e.frustumCulled,this.renderOrder=e.renderOrder,this.userData=JSON.parse(JSON.stringify(e.userData)),!0===t)for(var n=0;n<e.children.length;n++){var r=e.children[n];this.add(r.clone())}return this}}]),n}(Ug);mv.DEFAULT_UP=new gm(0,1,0),mv.DEFAULT_MATRIX_AUTO_UPDATE=!0,mv.DEFAULT_MATRIX_WORLD_AUTO_UPDATE=!0;var vv=new gm,yv=new gm,wv=new gm,Bv=new gm,_v=new gm,bv=new gm,xv=new gm,Cv=new gm,Sv=new gm,Ev=new gm,Uv=function(){function e(){var t=arguments.length>0&&void 0!==arguments[0]?arguments[0]:new gm,n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:new gm,r=arguments.length>2&&void 0!==arguments[2]?arguments[2]:new gm;B(this,e),this.a=t,this.b=n,this.c=r}return b(e,[{key:"set",value:function(e,t,n){return this.a.copy(e),this.b.copy(t),this.c.copy(n),this}},{key:"setFromPointsAndIndices",value:function(e,t,n,r){return this.a.copy(e[t]),this.b.copy(e[n]),this.c.copy(e[r]),this}},{key:"setFromAttributeAndIndices",value:function(e,t,n,r){return this.a.fromBufferAttribute(e,t),this.b.fromBufferAttribute(e,n),this.c.fromBufferAttribute(e,r),this}},{key:"clone",value:function(){return(new this.constructor).copy(this)}},{key:"copy",value:function(e){return this.a.copy(e.a),this.b.copy(e.b),this.c.copy(e.c),this}},{key:"getArea",value:function(){return vv.subVectors(this.c,this.b),yv.subVectors(this.a,this.b),.5*vv.cross(yv).length()}},{key:"getMidpoint",value:function(e){return e.addVectors(this.a,this.b).add(this.c).multiplyScalar(1/3)}},{key:"getNormal",value:function(t){return e.getNormal(this.a,this.b,this.c,t)}},{key:"getPlane",value:function(e){return e.setFromCoplanarPoints(this.a,this.b,this.c)}},{key:"getBarycoord",value:function(t,n){return e.getBarycoord(t,this.a,this.b,this.c,n)}},{key:"getUV",value:function(t,n,r,i,A){return e.getUV(t,this.a,this.b,this.c,n,r,i,A)}},{key:"containsPoint",value:function(t){return e.containsPoint(t,this.a,this.b,this.c)}},{key:"isFrontFacing",value:function(t){return e.isFrontFacing(this.a,this.b,this.c,t)}},{key:"intersectsBox",value:function(e){return e.intersectsTriangle(this)}},{key:"closestPointToPoint",value:function(e,t){var n,r,i=this.a,A=this.b,a=this.c;_v.subVectors(A,i),bv.subVectors(a,i),Cv.subVectors(e,i);var o=_v.dot(Cv),s=bv.dot(Cv);if(o<=0&&s<=0)return t.copy(i);Sv.subVectors(e,A);var l=_v.dot(Sv),u=bv.dot(Sv);if(l>=0&&u<=l)return t.copy(A);var c=o*u-l*s;if(c<=0&&o>=0&&l<=0)return n=o/(o-l),t.copy(i).addScaledVector(_v,n);Ev.subVectors(e,a);var d=_v.dot(Ev),h=bv.dot(Ev);if(h>=0&&d<=h)return t.copy(a);var f=d*s-o*h;if(f<=0&&s>=0&&h<=0)return r=s/(s-h),t.copy(i).addScaledVector(bv,r);var p=l*h-d*u;if(p<=0&&u-l>=0&&d-h>=0)return xv.subVectors(a,A),r=(u-l)/(u-l+(d-h)),t.copy(A).addScaledVector(xv,r);var g=1/(p+f+c);return n=f*g,r=c*g,t.copy(i).addScaledVector(_v,n).addScaledVector(bv,r)}},{key:"equals",value:function(e){return e.a.equals(this.a)&&e.b.equals(this.b)&&e.c.equals(this.c)}}],[{key:"getNormal",value:function(e,t,n,r){r.subVectors(n,t),vv.subVectors(e,t),r.cross(vv);var i=r.lengthSq();return i>0?r.multiplyScalar(1/Math.sqrt(i)):r.set(0,0,0)}},{key:"getBarycoord",value:function(e,t,n,r,i){vv.subVectors(r,t),yv.subVectors(n,t),wv.subVectors(e,t);var A=vv.dot(vv),a=vv.dot(yv),o=vv.dot(wv),s=yv.dot(yv),l=yv.dot(wv),u=A*s-a*a;if(0===u)return i.set(-2,-1,-1);var c=1/u,d=(s*o-a*l)*c,h=(A*l-a*o)*c;return i.set(1-d-h,h,d)}},{key:"containsPoint",value:function(e,t,n,r){return this.getBarycoord(e,t,n,r,Bv),Bv.x>=0&&Bv.y>=0&&Bv.x+Bv.y<=1}},{key:"getUV",value:function(e,t,n,r,i,A,a,o){return this.getBarycoord(e,t,n,r,Bv),o.set(0,0),o.addScaledVector(i,Bv.x),o.addScaledVector(A,Bv.y),o.addScaledVector(a,Bv.z),o}},{key:"isFrontFacing",value:function(e,t,n,r){return vv.subVectors(n,t),yv.subVectors(e,t),vv.cross(yv).dot(r)<0}}]),e}(),Mv=0,Fv=function(e){C(n,e);var t=M(n);function n(){var e;return B(this,n),(e=t.call(this)).isMaterial=!0,Object.defineProperty(U(e),"id",{value:Mv++}),e.uuid=Qg(),e.name="",e.type="Material",e.blending=df,e.side=sf,e.vertexColors=!1,e.opacity=1,e.transparent=!1,e.blendSrc=Sf,e.blendDst=Ef,e.blendEquation=mf,e.blendSrcAlpha=null,e.blendDstAlpha=null,e.blendEquationAlpha=null,e.depthFunc=If,e.depthTest=!0,e.depthWrite=!0,e.stencilWriteMask=255,e.stencilFunc=xg,e.stencilRef=0,e.stencilFuncMask=255,e.stencilFail=bg,e.stencilZFail=bg,e.stencilZPass=bg,e.stencilWrite=!1,e.clippingPlanes=null,e.clipIntersection=!1,e.clipShadows=!1,e.shadowSide=null,e.colorWrite=!0,e.precision=null,e.polygonOffset=!1,e.polygonOffsetFactor=0,e.polygonOffsetUnits=0,e.dithering=!1,e.alphaToCoverage=!1,e.premultipliedAlpha=!1,e.forceSinglePass=!1,e.visible=!0,e.toneMapped=!0,e.userData={},e.version=0,e._alphaTest=0,e}return b(n,[{key:"alphaTest",get:function(){return this._alphaTest},set:function(e){this._alphaTest>0!==e>0&&this.version++,this._alphaTest=e}},{key:"onBuild",value:function(){}},{key:"onBeforeRender",value:function(){}},{key:"onBeforeCompile",value:function(){}},{key:"customProgramCacheKey",value:function(){return this.onBeforeCompile.toString()}},{key:"setValues",value:function(e){if(void 0!==e)for(var t in e){var n=e[t];if(void 0!==n){var r=this[t];void 0!==r?r&&r.isColor?r.set(n):r&&r.isVector3&&n&&n.isVector3?r.copy(n):this[t]=n:console.warn("THREE."+this.type+": '"+t+"' is not a property of this material.")}else console.warn("THREE.Material: '"+t+"' parameter is undefined.")}}},{key:"toJSON",value:function(e){var t=void 0===e||"string"===typeof e;t&&(e={textures:{},images:{}});var n={metadata:{version:4.5,type:"Material",generator:"Material.toJSON"}};function r(e){var t=[];for(var n in e){var r=e[n];delete r.metadata,t.push(r)}return t}if(n.uuid=this.uuid,n.type=this.type,""!==this.name&&(n.name=this.name),this.color&&this.color.isColor&&(n.color=this.color.getHex()),void 0!==this.roughness&&(n.roughness=this.roughness),void 0!==this.metalness&&(n.metalness=this.metalness),void 0!==this.sheen&&(n.sheen=this.sheen),this.sheenColor&&this.sheenColor.isColor&&(n.sheenColor=this.sheenColor.getHex()),void 0!==this.sheenRoughness&&(n.sheenRoughness=this.sheenRoughness),this.emissive&&this.emissive.isColor&&(n.emissive=this.emissive.getHex()),this.emissiveIntensity&&1!==this.emissiveIntensity&&(n.emissiveIntensity=this.emissiveIntensity),this.specular&&this.specular.isColor&&(n.specular=this.specular.getHex()),void 0!==this.specularIntensity&&(n.specularIntensity=this.specularIntensity),this.specularColor&&this.specularColor.isColor&&(n.specularColor=this.specularColor.getHex()),void 0!==this.shininess&&(n.shininess=this.shininess),void 0!==this.clearcoat&&(n.clearcoat=this.clearcoat),void 0!==this.clearcoatRoughness&&(n.clearcoatRoughness=this.clearcoatRoughness),this.clearcoatMap&&this.clearcoatMap.isTexture&&(n.clearcoatMap=this.clearcoatMap.toJSON(e).uuid),this.clearcoatRoughnessMap&&this.clearcoatRoughnessMap.isTexture&&(n.clearcoatRoughnessMap=this.clearcoatRoughnessMap.toJSON(e).uuid),this.clearcoatNormalMap&&this.clearcoatNormalMap.isTexture&&(n.clearcoatNormalMap=this.clearcoatNormalMap.toJSON(e).uuid,n.clearcoatNormalScale=this.clearcoatNormalScale.toArray()),void 0!==this.iridescence&&(n.iridescence=this.iridescence),void 0!==this.iridescenceIOR&&(n.iridescenceIOR=this.iridescenceIOR),void 0!==this.iridescenceThicknessRange&&(n.iridescenceThicknessRange=this.iridescenceThicknessRange),this.iridescenceMap&&this.iridescenceMap.isTexture&&(n.iridescenceMap=this.iridescenceMap.toJSON(e).uuid),this.iridescenceThicknessMap&&this.iridescenceThicknessMap.isTexture&&(n.iridescenceThicknessMap=this.iridescenceThicknessMap.toJSON(e).uuid),this.map&&this.map.isTexture&&(n.map=this.map.toJSON(e).uuid),this.matcap&&this.matcap.isTexture&&(n.matcap=this.matcap.toJSON(e).uuid),this.alphaMap&&this.alphaMap.isTexture&&(n.alphaMap=this.alphaMap.toJSON(e).uuid),this.lightMap&&this.lightMap.isTexture&&(n.lightMap=this.lightMap.toJSON(e).uuid,n.lightMapIntensity=this.lightMapIntensity),this.aoMap&&this.aoMap.isTexture&&(n.aoMap=this.aoMap.toJSON(e).uuid,n.aoMapIntensity=this.aoMapIntensity),this.bumpMap&&this.bumpMap.isTexture&&(n.bumpMap=this.bumpMap.toJSON(e).uuid,n.bumpScale=this.bumpScale),this.normalMap&&this.normalMap.isTexture&&(n.normalMap=this.normalMap.toJSON(e).uuid,n.normalMapType=this.normalMapType,n.normalScale=this.normalScale.toArray()),this.displacementMap&&this.displacementMap.isTexture&&(n.displacementMap=this.displacementMap.toJSON(e).uuid,n.displacementScale=this.displacementScale,n.displacementBias=this.displacementBias),this.roughnessMap&&this.roughnessMap.isTexture&&(n.roughnessMap=this.roughnessMap.toJSON(e).uuid),this.metalnessMap&&this.metalnessMap.isTexture&&(n.metalnessMap=this.metalnessMap.toJSON(e).uuid),this.emissiveMap&&this.emissiveMap.isTexture&&(n.emissiveMap=this.emissiveMap.toJSON(e).uuid),this.specularMap&&this.specularMap.isTexture&&(n.specularMap=this.specularMap.toJSON(e).uuid),this.specularIntensityMap&&this.specularIntensityMap.isTexture&&(n.specularIntensityMap=this.specularIntensityMap.toJSON(e).uuid),this.specularColorMap&&this.specularColorMap.isTexture&&(n.specularColorMap=this.specularColorMap.toJSON(e).uuid),this.envMap&&this.envMap.isTexture&&(n.envMap=this.envMap.toJSON(e).uuid,void 0!==this.combine&&(n.combine=this.combine)),void 0!==this.envMapIntensity&&(n.envMapIntensity=this.envMapIntensity),void 0!==this.reflectivity&&(n.reflectivity=this.reflectivity),void 0!==this.refractionRatio&&(n.refractionRatio=this.refractionRatio),this.gradientMap&&this.gradientMap.isTexture&&(n.gradientMap=this.gradientMap.toJSON(e).uuid),void 0!==this.transmission&&(n.transmission=this.transmission),this.transmissionMap&&this.transmissionMap.isTexture&&(n.transmissionMap=this.transmissionMap.toJSON(e).uuid),void 0!==this.thickness&&(n.thickness=this.thickness),this.thicknessMap&&this.thicknessMap.isTexture&&(n.thicknessMap=this.thicknessMap.toJSON(e).uuid),void 0!==this.attenuationDistance&&this.attenuationDistance!==1/0&&(n.attenuationDistance=this.attenuationDistance),void 0!==this.attenuationColor&&(n.attenuationColor=this.attenuationColor.getHex()),void 0!==this.size&&(n.size=this.size),null!==this.shadowSide&&(n.shadowSide=this.shadowSide),void 0!==this.sizeAttenuation&&(n.sizeAttenuation=this.sizeAttenuation),this.blending!==df&&(n.blending=this.blending),this.side!==sf&&(n.side=this.side),this.vertexColors&&(n.vertexColors=!0),this.opacity<1&&(n.opacity=this.opacity),!0===this.transparent&&(n.transparent=this.transparent),n.depthFunc=this.depthFunc,n.depthTest=this.depthTest,n.depthWrite=this.depthWrite,n.colorWrite=this.colorWrite,n.stencilWrite=this.stencilWrite,n.stencilWriteMask=this.stencilWriteMask,n.stencilFunc=this.stencilFunc,n.stencilRef=this.stencilRef,n.stencilFuncMask=this.stencilFuncMask,n.stencilFail=this.stencilFail,n.stencilZFail=this.stencilZFail,n.stencilZPass=this.stencilZPass,void 0!==this.rotation&&0!==this.rotation&&(n.rotation=this.rotation),!0===this.polygonOffset&&(n.polygonOffset=!0),0!==this.polygonOffsetFactor&&(n.polygonOffsetFactor=this.polygonOffsetFactor),0!==this.polygonOffsetUnits&&(n.polygonOffsetUnits=this.polygonOffsetUnits),void 0!==this.linewidth&&1!==this.linewidth&&(n.linewidth=this.linewidth),void 0!==this.dashSize&&(n.dashSize=this.dashSize),void 0!==this.gapSize&&(n.gapSize=this.gapSize),void 0!==this.scale&&(n.scale=this.scale),!0===this.dithering&&(n.dithering=!0),this.alphaTest>0&&(n.alphaTest=this.alphaTest),!0===this.alphaToCoverage&&(n.alphaToCoverage=this.alphaToCoverage),!0===this.premultipliedAlpha&&(n.premultipliedAlpha=this.premultipliedAlpha),!0===this.forceSinglePass&&(n.forceSinglePass=this.forceSinglePass),!0===this.wireframe&&(n.wireframe=this.wireframe),this.wireframeLinewidth>1&&(n.wireframeLinewidth=this.wireframeLinewidth),"round"!==this.wireframeLinecap&&(n.wireframeLinecap=this.wireframeLinecap),"round"!==this.wireframeLinejoin&&(n.wireframeLinejoin=this.wireframeLinejoin),!0===this.flatShading&&(n.flatShading=this.flatShading),!1===this.visible&&(n.visible=!1),!1===this.toneMapped&&(n.toneMapped=!1),!1===this.fog&&(n.fog=!1),Object.keys(this.userData).length>0&&(n.userData=this.userData),t){var i=r(e.textures),A=r(e.images);i.length>0&&(n.textures=i),A.length>0&&(n.images=A)}return n}},{key:"clone",value:function(){return(new this.constructor).copy(this)}},{key:"copy",value:function(e){this.name=e.name,this.blending=e.blending,this.side=e.side,this.vertexColors=e.vertexColors,this.opacity=e.opacity,this.transparent=e.transparent,this.blendSrc=e.blendSrc,this.blendDst=e.blendDst,this.blendEquation=e.blendEquation,this.blendSrcAlpha=e.blendSrcAlpha,this.blendDstAlpha=e.blendDstAlpha,this.blendEquationAlpha=e.blendEquationAlpha,this.depthFunc=e.depthFunc,this.depthTest=e.depthTest,this.depthWrite=e.depthWrite,this.stencilWriteMask=e.stencilWriteMask,this.stencilFunc=e.stencilFunc,this.stencilRef=e.stencilRef,this.stencilFuncMask=e.stencilFuncMask,this.stencilFail=e.stencilFail,this.stencilZFail=e.stencilZFail,this.stencilZPass=e.stencilZPass,this.stencilWrite=e.stencilWrite;var t=e.clippingPlanes,n=null;if(null!==t){var r=t.length;n=new Array(r);for(var i=0;i!==r;++i)n[i]=t[i].clone()}return this.clippingPlanes=n,this.clipIntersection=e.clipIntersection,this.clipShadows=e.clipShadows,this.shadowSide=e.shadowSide,this.colorWrite=e.colorWrite,this.precision=e.precision,this.polygonOffset=e.polygonOffset,this.polygonOffsetFactor=e.polygonOffsetFactor,this.polygonOffsetUnits=e.polygonOffsetUnits,this.dithering=e.dithering,this.alphaTest=e.alphaTest,this.alphaToCoverage=e.alphaToCoverage,this.premultipliedAlpha=e.premultipliedAlpha,this.forceSinglePass=e.forceSinglePass,this.visible=e.visible,this.toneMapped=e.toneMapped,this.userData=JSON.parse(JSON.stringify(e.userData)),this}},{key:"dispose",value:function(){this.dispatchEvent({type:"dispose"})}},{key:"needsUpdate",set:function(e){!0===e&&this.version++}}]),n}(Ug),Tv=function(e){C(n,e);var t=M(n);function n(e){var r;return B(this,n),(r=t.call(this)).isMeshBasicMaterial=!0,r.type="MeshBasicMaterial",r.color=new Am(16777215),r.map=null,r.lightMap=null,r.lightMapIntensity=1,r.aoMap=null,r.aoMapIntensity=1,r.specularMap=null,r.alphaMap=null,r.envMap=null,r.combine=Of,r.reflectivity=1,r.refractionRatio=.98,r.wireframe=!1,r.wireframeLinewidth=1,r.wireframeLinecap="round",r.wireframeLinejoin="round",r.fog=!0,r.setValues(e),r}return b(n,[{key:"copy",value:function(e){return jc(S(n.prototype),"copy",this).call(this,e),this.color.copy(e.color),this.map=e.map,this.lightMap=e.lightMap,this.lightMapIntensity=e.lightMapIntensity,this.aoMap=e.aoMap,this.aoMapIntensity=e.aoMapIntensity,this.specularMap=e.specularMap,this.alphaMap=e.alphaMap,this.envMap=e.envMap,this.combine=e.combine,this.reflectivity=e.reflectivity,this.refractionRatio=e.refractionRatio,this.wireframe=e.wireframe,this.wireframeLinewidth=e.wireframeLinewidth,this.wireframeLinecap=e.wireframeLinecap,this.wireframeLinejoin=e.wireframeLinejoin,this.fog=e.fog,this}}]),n}(Fv),kv=new gm,Qv=new zg,Lv=function(){function e(t,n){var r=arguments.length>2&&void 0!==arguments[2]&&arguments[2];if(B(this,e),Array.isArray(t))throw new TypeError("THREE.BufferAttribute: array should be a Typed Array.");this.isBufferAttribute=!0,this.name="",this.array=t,this.itemSize=n,this.count=void 0!==t?t.length/n:0,this.normalized=r,this.usage=Cg,this.updateRange={offset:0,count:-1},this.version=0}return b(e,[{key:"onUploadCallback",value:function(){}},{key:"needsUpdate",set:function(e){!0===e&&this.version++}},{key:"setUsage",value:function(e){return this.usage=e,this}},{key:"copy",value:function(e){return this.name=e.name,this.array=new e.array.constructor(e.array),this.itemSize=e.itemSize,this.count=e.count,this.normalized=e.normalized,this.usage=e.usage,this}},{key:"copyAt",value:function(e,t,n){e*=this.itemSize,n*=t.itemSize;for(var r=0,i=this.itemSize;r<i;r++)this.array[e+r]=t.array[n+r];return this}},{key:"copyArray",value:function(e){return this.array.set(e),this}},{key:"applyMatrix3",value:function(e){if(2===this.itemSize)for(var t=0,n=this.count;t<n;t++)Qv.fromBufferAttribute(this,t),Qv.applyMatrix3(e),this.setXY(t,Qv.x,Qv.y);else if(3===this.itemSize)for(var r=0,i=this.count;r<i;r++)kv.fromBufferAttribute(this,r),kv.applyMatrix3(e),this.setXYZ(r,kv.x,kv.y,kv.z);return this}},{key:"applyMatrix4",value:function(e){for(var t=0,n=this.count;t<n;t++)kv.fromBufferAttribute(this,t),kv.applyMatrix4(e),this.setXYZ(t,kv.x,kv.y,kv.z);return this}},{key:"applyNormalMatrix",value:function(e){for(var t=0,n=this.count;t<n;t++)kv.fromBufferAttribute(this,t),kv.applyNormalMatrix(e),this.setXYZ(t,kv.x,kv.y,kv.z);return this}},{key:"transformDirection",value:function(e){for(var t=0,n=this.count;t<n;t++)kv.fromBufferAttribute(this,t),kv.transformDirection(e),this.setXYZ(t,kv.x,kv.y,kv.z);return this}},{key:"set",value:function(e){var t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0;return this.array.set(e,t),this}},{key:"getX",value:function(e){var t=this.array[e*this.itemSize];return this.normalized&&(t=Ng(t,this.array)),t}},{key:"setX",value:function(e,t){return this.normalized&&(t=Og(t,this.array)),this.array[e*this.itemSize]=t,this}},{key:"getY",value:function(e){var t=this.array[e*this.itemSize+1];return this.normalized&&(t=Ng(t,this.array)),t}},{key:"setY",value:function(e,t){return this.normalized&&(t=Og(t,this.array)),this.array[e*this.itemSize+1]=t,this}},{key:"getZ",value:function(e){var t=this.array[e*this.itemSize+2];return this.normalized&&(t=Ng(t,this.array)),t}},{key:"setZ",value:function(e,t){return this.normalized&&(t=Og(t,this.array)),this.array[e*this.itemSize+2]=t,this}},{key:"getW",value:function(e){var t=this.array[e*this.itemSize+3];return this.normalized&&(t=Ng(t,this.array)),t}},{key:"setW",value:function(e,t){return this.normalized&&(t=Og(t,this.array)),this.array[e*this.itemSize+3]=t,this}},{key:"setXY",value:function(e,t,n){return e*=this.itemSize,this.normalized&&(t=Og(t,this.array),n=Og(n,this.array)),this.array[e+0]=t,this.array[e+1]=n,this}},{key:"setXYZ",value:function(e,t,n,r){return e*=this.itemSize,this.normalized&&(t=Og(t,this.array),n=Og(n,this.array),r=Og(r,this.array)),this.array[e+0]=t,this.array[e+1]=n,this.array[e+2]=r,this}},{key:"setXYZW",value:function(e,t,n,r,i){return e*=this.itemSize,this.normalized&&(t=Og(t,this.array),n=Og(n,this.array),r=Og(r,this.array),i=Og(i,this.array)),this.array[e+0]=t,this.array[e+1]=n,this.array[e+2]=r,this.array[e+3]=i,this}},{key:"onUpload",value:function(e){return this.onUploadCallback=e,this}},{key:"clone",value:function(){return new this.constructor(this.array,this.itemSize).copy(this)}},{key:"toJSON",value:function(){var e={itemSize:this.itemSize,type:this.array.constructor.name,array:Array.from(this.array),normalized:this.normalized};return""!==this.name&&(e.name=this.name),this.usage!==Cg&&(e.usage=this.usage),0===this.updateRange.offset&&-1===this.updateRange.count||(e.updateRange=this.updateRange),e}},{key:"copyColorsArray",value:function(){console.error("THREE.BufferAttribute: copyColorsArray() was removed in r144.")}},{key:"copyVector2sArray",value:function(){console.error("THREE.BufferAttribute: copyVector2sArray() was removed in r144.")}},{key:"copyVector3sArray",value:function(){console.error("THREE.BufferAttribute: copyVector3sArray() was removed in r144.")}},{key:"copyVector4sArray",value:function(){console.error("THREE.BufferAttribute: copyVector4sArray() was removed in r144.")}}]),e}(),Dv=function(e){C(n,e);var t=M(n);function n(e,r,i){return B(this,n),t.call(this,new Uint16Array(e),r,i)}return b(n)}(Lv),Iv=function(e){C(n,e);var t=M(n);function n(e,r,i){return B(this,n),t.call(this,new Uint32Array(e),r,i)}return b(n)}(Lv),Rv=function(e){C(n,e);var t=M(n);function n(e,r,i){return B(this,n),t.call(this,new Float32Array(e),r,i)}return b(n)}(Lv),Hv=0,Pv=new Wm,Nv=new mv,Ov=new gm,Vv=new ym,zv=new ym,Gv=new gm,Kv=function(e){C(n,e);var t=M(n);function n(){var e;return B(this,n),(e=t.call(this)).isBufferGeometry=!0,Object.defineProperty(U(e),"id",{value:Hv++}),e.uuid=Qg(),e.name="",e.type="BufferGeometry",e.index=null,e.attributes={},e.morphAttributes={},e.morphTargetsRelative=!1,e.groups=[],e.boundingBox=null,e.boundingSphere=null,e.drawRange={start:0,count:1/0},e.userData={},e}return b(n,[{key:"getIndex",value:function(){return this.index}},{key:"setIndex",value:function(e){return Array.isArray(e)?this.index=new(Wg(e)?Iv:Dv)(e,1):this.index=e,this}},{key:"getAttribute",value:function(e){return this.attributes[e]}},{key:"setAttribute",value:function(e,t){return this.attributes[e]=t,this}},{key:"deleteAttribute",value:function(e){return delete this.attributes[e],this}},{key:"hasAttribute",value:function(e){return void 0!==this.attributes[e]}},{key:"addGroup",value:function(e,t){var n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:0;this.groups.push({start:e,count:t,materialIndex:n})}},{key:"clearGroups",value:function(){this.groups=[]}},{key:"setDrawRange",value:function(e,t){this.drawRange.start=e,this.drawRange.count=t}},{key:"applyMatrix4",value:function(e){var t=this.attributes.position;void 0!==t&&(t.applyMatrix4(e),t.needsUpdate=!0);var n=this.attributes.normal;if(void 0!==n){var r=(new Gg).getNormalMatrix(e);n.applyNormalMatrix(r),n.needsUpdate=!0}var i=this.attributes.tangent;return void 0!==i&&(i.transformDirection(e),i.needsUpdate=!0),null!==this.boundingBox&&this.computeBoundingBox(),null!==this.boundingSphere&&this.computeBoundingSphere(),this}},{key:"applyQuaternion",value:function(e){return Pv.makeRotationFromQuaternion(e),this.applyMatrix4(Pv),this}},{key:"rotateX",value:function(e){return Pv.makeRotationX(e),this.applyMatrix4(Pv),this}},{key:"rotateY",value:function(e){return Pv.makeRotationY(e),this.applyMatrix4(Pv),this}},{key:"rotateZ",value:function(e){return Pv.makeRotationZ(e),this.applyMatrix4(Pv),this}},{key:"translate",value:function(e,t,n){return Pv.makeTranslation(e,t,n),this.applyMatrix4(Pv),this}},{key:"scale",value:function(e,t,n){return Pv.makeScale(e,t,n),this.applyMatrix4(Pv),this}},{key:"lookAt",value:function(e){return Nv.lookAt(e),Nv.updateMatrix(),this.applyMatrix4(Nv.matrix),this}},{key:"center",value:function(){return this.computeBoundingBox(),this.boundingBox.getCenter(Ov).negate(),this.translate(Ov.x,Ov.y,Ov.z),this}},{key:"setFromPoints",value:function(e){for(var t=[],n=0,r=e.length;n<r;n++){var i=e[n];t.push(i.x,i.y,i.z||0)}return this.setAttribute("position",new Rv(t,3)),this}},{key:"computeBoundingBox",value:function(){null===this.boundingBox&&(this.boundingBox=new ym);var e=this.attributes.position,t=this.morphAttributes.position;if(e&&e.isGLBufferAttribute)return console.error('THREE.BufferGeometry.computeBoundingBox(): GLBufferAttribute requires a manual bounding box. Alternatively set "mesh.frustumCulled" to "false".',this),void this.boundingBox.set(new gm(-1/0,-1/0,-1/0),new gm(1/0,1/0,1/0));if(void 0!==e){if(this.boundingBox.setFromBufferAttribute(e),t)for(var n=0,r=t.length;n<r;n++){var i=t[n];Vv.setFromBufferAttribute(i),this.morphTargetsRelative?(Gv.addVectors(this.boundingBox.min,Vv.min),this.boundingBox.expandByPoint(Gv),Gv.addVectors(this.boundingBox.max,Vv.max),this.boundingBox.expandByPoint(Gv)):(this.boundingBox.expandByPoint(Vv.min),this.boundingBox.expandByPoint(Vv.max))}}else this.boundingBox.makeEmpty();(isNaN(this.boundingBox.min.x)||isNaN(this.boundingBox.min.y)||isNaN(this.boundingBox.min.z))&&console.error('THREE.BufferGeometry.computeBoundingBox(): Computed min/max have NaN values. The "position" attribute is likely to have NaN values.',this)}},{key:"computeBoundingSphere",value:function(){null===this.boundingSphere&&(this.boundingSphere=new Rm);var e=this.attributes.position,t=this.morphAttributes.position;if(e&&e.isGLBufferAttribute)return console.error('THREE.BufferGeometry.computeBoundingSphere(): GLBufferAttribute requires a manual bounding sphere. Alternatively set "mesh.frustumCulled" to "false".',this),void this.boundingSphere.set(new gm,1/0);if(e){var n=this.boundingSphere.center;if(Vv.setFromBufferAttribute(e),t)for(var r=0,i=t.length;r<i;r++){var A=t[r];zv.setFromBufferAttribute(A),this.morphTargetsRelative?(Gv.addVectors(Vv.min,zv.min),Vv.expandByPoint(Gv),Gv.addVectors(Vv.max,zv.max),Vv.expandByPoint(Gv)):(Vv.expandByPoint(zv.min),Vv.expandByPoint(zv.max))}Vv.getCenter(n);for(var a=0,o=0,s=e.count;o<s;o++)Gv.fromBufferAttribute(e,o),a=Math.max(a,n.distanceToSquared(Gv));if(t)for(var l=0,u=t.length;l<u;l++)for(var c=t[l],d=this.morphTargetsRelative,h=0,f=c.count;h<f;h++)Gv.fromBufferAttribute(c,h),d&&(Ov.fromBufferAttribute(e,h),Gv.add(Ov)),a=Math.max(a,n.distanceToSquared(Gv));this.boundingSphere.radius=Math.sqrt(a),isNaN(this.boundingSphere.radius)&&console.error('THREE.BufferGeometry.computeBoundingSphere(): Computed radius is NaN. The "position" attribute is likely to have NaN values.',this)}}},{key:"computeTangents",value:function(){var e=this.index,t=this.attributes;if(null!==e&&void 0!==t.position&&void 0!==t.normal&&void 0!==t.uv){var n=e.array,r=t.position.array,i=t.normal.array,A=t.uv.array,a=r.length/3;!1===this.hasAttribute("tangent")&&this.setAttribute("tangent",new Lv(new Float32Array(4*a),4));for(var o=this.getAttribute("tangent").array,s=[],l=[],u=0;u<a;u++)s[u]=new gm,l[u]=new gm;var c=new gm,d=new gm,h=new gm,f=new zg,p=new zg,g=new zg,m=new gm,v=new gm,y=this.groups;0===y.length&&(y=[{start:0,count:n.length}]);for(var w=0,B=y.length;w<B;++w)for(var _=y[w],b=_.start,x=b,C=b+_.count;x<C;x+=3)I(n[x+0],n[x+1],n[x+2]);for(var S=new gm,E=new gm,U=new gm,M=new gm,F=0,T=y.length;F<T;++F)for(var k=y[F],Q=k.start,L=Q,D=Q+k.count;L<D;L+=3)R(n[L+0]),R(n[L+1]),R(n[L+2])}else console.error("THREE.BufferGeometry: .computeTangents() failed. Missing required attributes (index, position, normal or uv)");function I(e,t,n){c.fromArray(r,3*e),d.fromArray(r,3*t),h.fromArray(r,3*n),f.fromArray(A,2*e),p.fromArray(A,2*t),g.fromArray(A,2*n),d.sub(c),h.sub(c),p.sub(f),g.sub(f);var i=1/(p.x*g.y-g.x*p.y);isFinite(i)&&(m.copy(d).multiplyScalar(g.y).addScaledVector(h,-p.y).multiplyScalar(i),v.copy(h).multiplyScalar(p.x).addScaledVector(d,-g.x).multiplyScalar(i),s[e].add(m),s[t].add(m),s[n].add(m),l[e].add(v),l[t].add(v),l[n].add(v))}function R(e){U.fromArray(i,3*e),M.copy(U);var t=s[e];S.copy(t),S.sub(U.multiplyScalar(U.dot(t))).normalize(),E.crossVectors(M,t);var n=E.dot(l[e])<0?-1:1;o[4*e]=S.x,o[4*e+1]=S.y,o[4*e+2]=S.z,o[4*e+3]=n}}},{key:"computeVertexNormals",value:function(){var e=this.index,t=this.getAttribute("position");if(void 0!==t){var n=this.getAttribute("normal");if(void 0===n)n=new Lv(new Float32Array(3*t.count),3),this.setAttribute("normal",n);else for(var r=0,i=n.count;r<i;r++)n.setXYZ(r,0,0,0);var A=new gm,a=new gm,o=new gm,s=new gm,l=new gm,u=new gm,c=new gm,d=new gm;if(e)for(var h=0,f=e.count;h<f;h+=3){var p=e.getX(h+0),g=e.getX(h+1),m=e.getX(h+2);A.fromBufferAttribute(t,p),a.fromBufferAttribute(t,g),o.fromBufferAttribute(t,m),c.subVectors(o,a),d.subVectors(A,a),c.cross(d),s.fromBufferAttribute(n,p),l.fromBufferAttribute(n,g),u.fromBufferAttribute(n,m),s.add(c),l.add(c),u.add(c),n.setXYZ(p,s.x,s.y,s.z),n.setXYZ(g,l.x,l.y,l.z),n.setXYZ(m,u.x,u.y,u.z)}else for(var v=0,y=t.count;v<y;v+=3)A.fromBufferAttribute(t,v+0),a.fromBufferAttribute(t,v+1),o.fromBufferAttribute(t,v+2),c.subVectors(o,a),d.subVectors(A,a),c.cross(d),n.setXYZ(v+0,c.x,c.y,c.z),n.setXYZ(v+1,c.x,c.y,c.z),n.setXYZ(v+2,c.x,c.y,c.z);this.normalizeNormals(),n.needsUpdate=!0}}},{key:"merge",value:function(){return console.error("THREE.BufferGeometry.merge() has been removed. Use THREE.BufferGeometryUtils.mergeBufferGeometries() instead."),this}},{key:"normalizeNormals",value:function(){for(var e=this.attributes.normal,t=0,n=e.count;t<n;t++)Gv.fromBufferAttribute(e,t),Gv.normalize(),e.setXYZ(t,Gv.x,Gv.y,Gv.z)}},{key:"toNonIndexed",value:function(){function e(e,t){for(var n=e.array,r=e.itemSize,i=e.normalized,A=new n.constructor(t.length*r),a=0,o=0,s=0,l=t.length;s<l;s++){a=e.isInterleavedBufferAttribute?t[s]*e.data.stride+e.offset:t[s]*r;for(var u=0;u<r;u++)A[o++]=n[a++]}return new Lv(A,r,i)}if(null===this.index)return console.warn("THREE.BufferGeometry.toNonIndexed(): BufferGeometry is already non-indexed."),this;var t=new n,r=this.index.array,i=this.attributes;for(var A in i){var a=e(i[A],r);t.setAttribute(A,a)}var o=this.morphAttributes;for(var s in o){for(var l=[],u=o[s],c=0,d=u.length;c<d;c++){var h=e(u[c],r);l.push(h)}t.morphAttributes[s]=l}t.morphTargetsRelative=this.morphTargetsRelative;for(var f=this.groups,p=0,g=f.length;p<g;p++){var m=f[p];t.addGroup(m.start,m.count,m.materialIndex)}return t}},{key:"toJSON",value:function(){var e={metadata:{version:4.5,type:"BufferGeometry",generator:"BufferGeometry.toJSON"}};if(e.uuid=this.uuid,e.type=this.type,""!==this.name&&(e.name=this.name),Object.keys(this.userData).length>0&&(e.userData=this.userData),void 0!==this.parameters){var t=this.parameters;for(var n in t)void 0!==t[n]&&(e[n]=t[n]);return e}e.data={attributes:{}};var r=this.index;null!==r&&(e.data.index={type:r.array.constructor.name,array:Array.prototype.slice.call(r.array)});var i=this.attributes;for(var A in i){var a=i[A];e.data.attributes[A]=a.toJSON(e.data)}var o={},s=!1;for(var l in this.morphAttributes){for(var u=this.morphAttributes[l],c=[],d=0,h=u.length;d<h;d++){var f=u[d];c.push(f.toJSON(e.data))}c.length>0&&(o[l]=c,s=!0)}s&&(e.data.morphAttributes=o,e.data.morphTargetsRelative=this.morphTargetsRelative);var p=this.groups;p.length>0&&(e.data.groups=JSON.parse(JSON.stringify(p)));var g=this.boundingSphere;return null!==g&&(e.data.boundingSphere={center:g.center.toArray(),radius:g.radius}),e}},{key:"clone",value:function(){return(new this.constructor).copy(this)}},{key:"copy",value:function(e){this.index=null,this.attributes={},this.morphAttributes={},this.groups=[],this.boundingBox=null,this.boundingSphere=null;var t={};this.name=e.name;var n=e.index;null!==n&&this.setIndex(n.clone(t));var r=e.attributes;for(var i in r){var A=r[i];this.setAttribute(i,A.clone(t))}var a=e.morphAttributes;for(var o in a){for(var s=[],l=a[o],u=0,c=l.length;u<c;u++)s.push(l[u].clone(t));this.morphAttributes[o]=s}this.morphTargetsRelative=e.morphTargetsRelative;for(var d=e.groups,h=0,f=d.length;h<f;h++){var p=d[h];this.addGroup(p.start,p.count,p.materialIndex)}var g=e.boundingBox;null!==g&&(this.boundingBox=g.clone());var m=e.boundingSphere;return null!==m&&(this.boundingSphere=m.clone()),this.drawRange.start=e.drawRange.start,this.drawRange.count=e.drawRange.count,this.userData=e.userData,void 0!==e.parameters&&(this.parameters=Object.assign({},e.parameters)),this}},{key:"dispose",value:function(){this.dispatchEvent({type:"dispose"})}}]),n}(Ug),Wv=new Wm,jv=new Km,Xv=new Rm,qv=new gm,Yv=new gm,Jv=new gm,Zv=new gm,$v=new gm,ey=new zg,ty=new zg,ny=new zg,ry=new gm,iy=new gm,Ay=function(e){C(n,e);var t=M(n);function n(){var e,r=arguments.length>0&&void 0!==arguments[0]?arguments[0]:new Kv,i=arguments.length>1&&void 0!==arguments[1]?arguments[1]:new Tv;return B(this,n),(e=t.call(this)).isMesh=!0,e.type="Mesh",e.geometry=r,e.material=i,e.updateMorphTargets(),e}return b(n,[{key:"copy",value:function(e,t){return jc(S(n.prototype),"copy",this).call(this,e,t),void 0!==e.morphTargetInfluences&&(this.morphTargetInfluences=e.morphTargetInfluences.slice()),void 0!==e.morphTargetDictionary&&(this.morphTargetDictionary=Object.assign({},e.morphTargetDictionary)),this.material=e.material,this.geometry=e.geometry,this}},{key:"updateMorphTargets",value:function(){var e=this.geometry.morphAttributes,t=Object.keys(e);if(t.length>0){var n=e[t[0]];if(void 0!==n){this.morphTargetInfluences=[],this.morphTargetDictionary={};for(var r=0,i=n.length;r<i;r++){var A=n[r].name||String(r);this.morphTargetInfluences.push(0),this.morphTargetDictionary[A]=r}}}}},{key:"getVertexPosition",value:function(e,t){var n=this.geometry,r=n.attributes.position,i=n.morphAttributes.position,A=n.morphTargetsRelative;t.fromBufferAttribute(r,e);var a=this.morphTargetInfluences;if(i&&a){$v.set(0,0,0);for(var o=0,s=i.length;o<s;o++){var l=a[o],u=i[o];0!==l&&(Zv.fromBufferAttribute(u,e),A?$v.addScaledVector(Zv,l):$v.addScaledVector(Zv.sub(t),l))}t.add($v)}return this.isSkinnedMesh&&this.boneTransform(e,t),t}},{key:"raycast",value:function(e,t){var n=this.geometry,r=this.material,i=this.matrixWorld;if(void 0!==r&&(null===n.boundingSphere&&n.computeBoundingSphere(),Xv.copy(n.boundingSphere),Xv.applyMatrix4(i),!1!==e.ray.intersectsSphere(Xv)&&(Wv.copy(i).invert(),jv.copy(e.ray).applyMatrix4(Wv),null===n.boundingBox||!1!==jv.intersectsBox(n.boundingBox)))){var A,a=n.index,o=n.attributes.position,s=n.attributes.uv,l=n.attributes.uv2,u=n.groups,c=n.drawRange;if(null!==a)if(Array.isArray(r))for(var d=0,h=u.length;d<h;d++)for(var f=u[d],p=r[f.materialIndex],g=Math.max(f.start,c.start),m=Math.min(a.count,Math.min(f.start+f.count,c.start+c.count));g<m;g+=3){var v=a.getX(g),y=a.getX(g+1),w=a.getX(g+2);(A=ay(this,p,e,jv,s,l,v,y,w))&&(A.faceIndex=Math.floor(g/3),A.face.materialIndex=f.materialIndex,t.push(A))}else for(var B=Math.max(0,c.start),_=Math.min(a.count,c.start+c.count);B<_;B+=3){var b=a.getX(B),x=a.getX(B+1),C=a.getX(B+2);(A=ay(this,r,e,jv,s,l,b,x,C))&&(A.faceIndex=Math.floor(B/3),t.push(A))}else if(void 0!==o)if(Array.isArray(r))for(var S=0,E=u.length;S<E;S++)for(var U=u[S],M=r[U.materialIndex],F=Math.max(U.start,c.start),T=Math.min(o.count,Math.min(U.start+U.count,c.start+c.count));F<T;F+=3){(A=ay(this,M,e,jv,s,l,F,F+1,F+2))&&(A.faceIndex=Math.floor(F/3),A.face.materialIndex=U.materialIndex,t.push(A))}else for(var k=Math.max(0,c.start),Q=Math.min(o.count,c.start+c.count);k<Q;k+=3){(A=ay(this,r,e,jv,s,l,k,k+1,k+2))&&(A.faceIndex=Math.floor(k/3),t.push(A))}}}}]),n}(mv);function ay(e,t,n,r,i,A,a,o,s){e.getVertexPosition(a,qv),e.getVertexPosition(o,Yv),e.getVertexPosition(s,Jv);var l=function(e,t,n,r,i,A,a,o){if(null===(t.side===lf?r.intersectTriangle(a,A,i,!0,o):r.intersectTriangle(i,A,a,t.side===sf,o)))return null;iy.copy(o),iy.applyMatrix4(e.matrixWorld);var s=n.ray.origin.distanceTo(iy);return s<n.near||s>n.far?null:{distance:s,point:iy.clone(),object:e}}(e,t,n,r,qv,Yv,Jv,ry);if(l){i&&(ey.fromBufferAttribute(i,a),ty.fromBufferAttribute(i,o),ny.fromBufferAttribute(i,s),l.uv=Uv.getUV(ry,qv,Yv,Jv,ey,ty,ny,new zg)),A&&(ey.fromBufferAttribute(A,a),ty.fromBufferAttribute(A,o),ny.fromBufferAttribute(A,s),l.uv2=Uv.getUV(ry,qv,Yv,Jv,ey,ty,ny,new zg));var u={a:a,b:o,c:s,normal:new gm,materialIndex:0};Uv.getNormal(qv,Yv,Jv,u.normal),l.face=u}return l}var oy=function(e){C(n,e);var t=M(n);function n(){var e,r=arguments.length>0&&void 0!==arguments[0]?arguments[0]:1,i=arguments.length>1&&void 0!==arguments[1]?arguments[1]:1,A=arguments.length>2&&void 0!==arguments[2]?arguments[2]:1,a=arguments.length>3&&void 0!==arguments[3]?arguments[3]:1,o=arguments.length>4&&void 0!==arguments[4]?arguments[4]:1,s=arguments.length>5&&void 0!==arguments[5]?arguments[5]:1;B(this,n),(e=t.call(this)).type="BoxGeometry",e.parameters={width:r,height:i,depth:A,widthSegments:a,heightSegments:o,depthSegments:s};var l=U(e);a=Math.floor(a),o=Math.floor(o),s=Math.floor(s);var u=[],c=[],d=[],h=[],f=0,p=0;function g(e,t,n,r,i,A,a,o,s,g,m){for(var v=A/s,y=a/g,w=A/2,B=a/2,_=o/2,b=s+1,x=g+1,C=0,S=0,E=new gm,U=0;U<x;U++)for(var M=U*y-B,F=0;F<b;F++){var T=F*v-w;E[e]=T*r,E[t]=M*i,E[n]=_,c.push(E.x,E.y,E.z),E[e]=0,E[t]=0,E[n]=o>0?1:-1,d.push(E.x,E.y,E.z),h.push(F/s),h.push(1-U/g),C+=1}for(var k=0;k<g;k++)for(var Q=0;Q<s;Q++){var L=f+Q+b*k,D=f+Q+b*(k+1),I=f+(Q+1)+b*(k+1),R=f+(Q+1)+b*k;u.push(L,D,R),u.push(D,I,R),S+=6}l.addGroup(p,S,m),p+=S,f+=C}return g("z","y","x",-1,-1,A,i,r,s,o,0),g("z","y","x",1,-1,A,i,-r,s,o,1),g("x","z","y",1,1,r,A,i,a,s,2),g("x","z","y",1,-1,r,A,-i,a,s,3),g("x","y","z",1,-1,r,i,A,a,o,4),g("x","y","z",-1,-1,r,i,-A,a,o,5),e.setIndex(u),e.setAttribute("position",new Rv(c,3)),e.setAttribute("normal",new Rv(d,3)),e.setAttribute("uv",new Rv(h,2)),e}return b(n,null,[{key:"fromJSON",value:function(e){return new n(e.width,e.height,e.depth,e.widthSegments,e.heightSegments,e.depthSegments)}}]),n}(Kv);function sy(e){var t={};for(var n in e)for(var r in t[n]={},e[n]){var i=e[n][r];i&&(i.isColor||i.isMatrix3||i.isMatrix4||i.isVector2||i.isVector3||i.isVector4||i.isTexture||i.isQuaternion)?t[n][r]=i.clone():Array.isArray(i)?t[n][r]=i.slice():t[n][r]=i}return t}function ly(e){for(var t={},n=0;n<e.length;n++){var r=sy(e[n]);for(var i in r)t[i]=r[i]}return t}function uy(e){return null===e.getRenderTarget()&&e.outputEncoding===gg?Bg:_g}var cy={clone:sy,merge:ly},dy="void main() {\n\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\n}",hy="void main() {\n\tgl_FragColor = vec4( 1.0, 0.0, 0.0, 1.0 );\n}",fy=function(e){C(n,e);var t=M(n);function n(e){var r;return B(this,n),(r=t.call(this)).isShaderMaterial=!0,r.type="ShaderMaterial",r.defines={},r.uniforms={},r.uniformsGroups=[],r.vertexShader=dy,r.fragmentShader=hy,r.linewidth=1,r.wireframe=!1,r.wireframeLinewidth=1,r.fog=!1,r.lights=!1,r.clipping=!1,r.extensions={derivatives:!1,fragDepth:!1,drawBuffers:!1,shaderTextureLOD:!1},r.defaultAttributeValues={color:[1,1,1],uv:[0,0],uv2:[0,0]},r.index0AttributeName=void 0,r.uniformsNeedUpdate=!1,r.glslVersion=null,void 0!==e&&r.setValues(e),r}return b(n,[{key:"copy",value:function(e){return jc(S(n.prototype),"copy",this).call(this,e),this.fragmentShader=e.fragmentShader,this.vertexShader=e.vertexShader,this.uniforms=sy(e.uniforms),this.uniformsGroups=function(e){for(var t=[],n=0;n<e.length;n++)t.push(e[n].clone());return t}(e.uniformsGroups),this.defines=Object.assign({},e.defines),this.wireframe=e.wireframe,this.wireframeLinewidth=e.wireframeLinewidth,this.fog=e.fog,this.lights=e.lights,this.clipping=e.clipping,this.extensions=Object.assign({},e.extensions),this.glslVersion=e.glslVersion,this}},{key:"toJSON",value:function(e){var t=jc(S(n.prototype),"toJSON",this).call(this,e);for(var r in t.glslVersion=this.glslVersion,t.uniforms={},this.uniforms){var i=this.uniforms[r].value;i&&i.isTexture?t.uniforms[r]={type:"t",value:i.toJSON(e).uuid}:i&&i.isColor?t.uniforms[r]={type:"c",value:i.getHex()}:i&&i.isVector2?t.uniforms[r]={type:"v2",value:i.toArray()}:i&&i.isVector3?t.uniforms[r]={type:"v3",value:i.toArray()}:i&&i.isVector4?t.uniforms[r]={type:"v4",value:i.toArray()}:i&&i.isMatrix3?t.uniforms[r]={type:"m3",value:i.toArray()}:i&&i.isMatrix4?t.uniforms[r]={type:"m4",value:i.toArray()}:t.uniforms[r]={value:i}}Object.keys(this.defines).length>0&&(t.defines=this.defines),t.vertexShader=this.vertexShader,t.fragmentShader=this.fragmentShader;var A={};for(var a in this.extensions)!0===this.extensions[a]&&(A[a]=!0);return Object.keys(A).length>0&&(t.extensions=A),t}}]),n}(Fv),py=function(e){C(n,e);var t=M(n);function n(){var e;return B(this,n),(e=t.call(this)).isCamera=!0,e.type="Camera",e.matrixWorldInverse=new Wm,e.projectionMatrix=new Wm,e.projectionMatrixInverse=new Wm,e}return b(n,[{key:"copy",value:function(e,t){return jc(S(n.prototype),"copy",this).call(this,e,t),this.matrixWorldInverse.copy(e.matrixWorldInverse),this.projectionMatrix.copy(e.projectionMatrix),this.projectionMatrixInverse.copy(e.projectionMatrixInverse),this}},{key:"getWorldDirection",value:function(e){this.updateWorldMatrix(!0,!1);var t=this.matrixWorld.elements;return e.set(-t[8],-t[9],-t[10]).normalize()}},{key:"updateMatrixWorld",value:function(e){jc(S(n.prototype),"updateMatrixWorld",this).call(this,e),this.matrixWorldInverse.copy(this.matrixWorld).invert()}},{key:"updateWorldMatrix",value:function(e,t){jc(S(n.prototype),"updateWorldMatrix",this).call(this,e,t),this.matrixWorldInverse.copy(this.matrixWorld).invert()}},{key:"clone",value:function(){return(new this.constructor).copy(this)}}]),n}(mv),gy=function(e){C(n,e);var t=M(n);function n(){var e,r=arguments.length>0&&void 0!==arguments[0]?arguments[0]:50,i=arguments.length>1&&void 0!==arguments[1]?arguments[1]:1,A=arguments.length>2&&void 0!==arguments[2]?arguments[2]:.1,a=arguments.length>3&&void 0!==arguments[3]?arguments[3]:2e3;return B(this,n),(e=t.call(this)).isPerspectiveCamera=!0,e.type="PerspectiveCamera",e.fov=r,e.zoom=1,e.near=A,e.far=a,e.focus=10,e.aspect=i,e.view=null,e.filmGauge=35,e.filmOffset=0,e.updateProjectionMatrix(),e}return b(n,[{key:"copy",value:function(e,t){return jc(S(n.prototype),"copy",this).call(this,e,t),this.fov=e.fov,this.zoom=e.zoom,this.near=e.near,this.far=e.far,this.focus=e.focus,this.aspect=e.aspect,this.view=null===e.view?null:Object.assign({},e.view),this.filmGauge=e.filmGauge,this.filmOffset=e.filmOffset,this}},{key:"setFocalLength",value:function(e){var t=.5*this.getFilmHeight()/e;this.fov=2*kg*Math.atan(t),this.updateProjectionMatrix()}},{key:"getFocalLength",value:function(){var e=Math.tan(.5*Tg*this.fov);return.5*this.getFilmHeight()/e}},{key:"getEffectiveFOV",value:function(){return 2*kg*Math.atan(Math.tan(.5*Tg*this.fov)/this.zoom)}},{key:"getFilmWidth",value:function(){return this.filmGauge*Math.min(this.aspect,1)}},{key:"getFilmHeight",value:function(){return this.filmGauge/Math.max(this.aspect,1)}},{key:"setViewOffset",value:function(e,t,n,r,i,A){this.aspect=e/t,null===this.view&&(this.view={enabled:!0,fullWidth:1,fullHeight:1,offsetX:0,offsetY:0,width:1,height:1}),this.view.enabled=!0,this.view.fullWidth=e,this.view.fullHeight=t,this.view.offsetX=n,this.view.offsetY=r,this.view.width=i,this.view.height=A,this.updateProjectionMatrix()}},{key:"clearViewOffset",value:function(){null!==this.view&&(this.view.enabled=!1),this.updateProjectionMatrix()}},{key:"updateProjectionMatrix",value:function(){var e=this.near,t=e*Math.tan(.5*Tg*this.fov)/this.zoom,n=2*t,r=this.aspect*n,i=-.5*r,A=this.view;if(null!==this.view&&this.view.enabled){var a=A.fullWidth,o=A.fullHeight;i+=A.offsetX*r/a,t-=A.offsetY*n/o,r*=A.width/a,n*=A.height/o}var s=this.filmOffset;0!==s&&(i+=e*s/this.getFilmWidth()),this.projectionMatrix.makePerspective(i,i+r,t,t-n,e,this.far),this.projectionMatrixInverse.copy(this.projectionMatrix).invert()}},{key:"toJSON",value:function(e){var t=jc(S(n.prototype),"toJSON",this).call(this,e);return t.object.fov=this.fov,t.object.zoom=this.zoom,t.object.near=this.near,t.object.far=this.far,t.object.focus=this.focus,t.object.aspect=this.aspect,null!==this.view&&(t.object.view=Object.assign({},this.view)),t.object.filmGauge=this.filmGauge,t.object.filmOffset=this.filmOffset,t}}]),n}(py),my=-90,vy=1,yy=function(e){C(n,e);var t=M(n);function n(e,r,i){var A;B(this,n),(A=t.call(this)).type="CubeCamera",A.renderTarget=i;var a=new gy(my,vy,e,r);a.layers=A.layers,a.up.set(0,1,0),a.lookAt(1,0,0),A.add(a);var o=new gy(my,vy,e,r);o.layers=A.layers,o.up.set(0,1,0),o.lookAt(-1,0,0),A.add(o);var s=new gy(my,vy,e,r);s.layers=A.layers,s.up.set(0,0,-1),s.lookAt(0,1,0),A.add(s);var l=new gy(my,vy,e,r);l.layers=A.layers,l.up.set(0,0,1),l.lookAt(0,-1,0),A.add(l);var u=new gy(my,vy,e,r);u.layers=A.layers,u.up.set(0,1,0),u.lookAt(0,0,1),A.add(u);var c=new gy(my,vy,e,r);return c.layers=A.layers,c.up.set(0,1,0),c.lookAt(0,0,-1),A.add(c),A}return b(n,[{key:"update",value:function(e,t){null===this.parent&&this.updateMatrixWorld();var n=this.renderTarget,r=v(this.children,6),i=r[0],A=r[1],a=r[2],o=r[3],s=r[4],l=r[5],u=e.getRenderTarget(),c=e.toneMapping,d=e.xr.enabled;e.toneMapping=Gf,e.xr.enabled=!1;var h=n.texture.generateMipmaps;n.texture.generateMipmaps=!1,e.setRenderTarget(n,0),e.render(t,i),e.setRenderTarget(n,1),e.render(t,A),e.setRenderTarget(n,2),e.render(t,a),e.setRenderTarget(n,3),e.render(t,o),e.setRenderTarget(n,4),e.render(t,s),n.texture.generateMipmaps=h,e.setRenderTarget(n,5),e.render(t,l),e.setRenderTarget(u),e.toneMapping=c,e.xr.enabled=d,n.texture.needsPMREMUpdate=!0}}]),n}(mv),wy=function(e){C(n,e);var t=M(n);function n(e,r,i,A,a,o,s,l,u,c){var d;return B(this,n),e=void 0!==e?e:[],r=void 0!==r?r:Yf,(d=t.call(this,e,r,i,A,a,o,s,l,u,c)).isCubeTexture=!0,d.flipY=!1,d}return b(n,[{key:"images",get:function(){return this.image},set:function(e){this.image=e}}]),n}(um),By=function(e){C(n,e);var t=M(n);function n(){var e,r=arguments.length>0&&void 0!==arguments[0]?arguments[0]:1,i=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};B(this,n),(e=t.call(this,r,r,i)).isWebGLCubeRenderTarget=!0;var A={width:r,height:r,depth:1},a=[A,A,A,A,A,A];return e.texture=new wy(a,i.mapping,i.wrapS,i.wrapT,i.magFilter,i.minFilter,i.format,i.type,i.anisotropy,i.encoding),e.texture.isRenderTargetTexture=!0,e.texture.generateMipmaps=void 0!==i.generateMipmaps&&i.generateMipmaps,e.texture.minFilter=void 0!==i.minFilter?i.minFilter:op,e}return b(n,[{key:"fromEquirectangularTexture",value:function(e,t){this.texture.type=t.type,this.texture.encoding=t.encoding,this.texture.generateMipmaps=t.generateMipmaps,this.texture.minFilter=t.minFilter,this.texture.magFilter=t.magFilter;var n={tEquirect:{value:null}},r="\n\n\t\t\t\tvarying vec3 vWorldDirection;\n\n\t\t\t\tvec3 transformDirection( in vec3 dir, in mat4 matrix ) {\n\n\t\t\t\t\treturn normalize( ( matrix * vec4( dir, 0.0 ) ).xyz );\n\n\t\t\t\t}\n\n\t\t\t\tvoid main() {\n\n\t\t\t\t\tvWorldDirection = transformDirection( position, modelMatrix );\n\n\t\t\t\t\t#include <begin_vertex>\n\t\t\t\t\t#include <project_vertex>\n\n\t\t\t\t}\n\t\t\t",i="\n\n\t\t\t\tuniform sampler2D tEquirect;\n\n\t\t\t\tvarying vec3 vWorldDirection;\n\n\t\t\t\t#include <common>\n\n\t\t\t\tvoid main() {\n\n\t\t\t\t\tvec3 direction = normalize( vWorldDirection );\n\n\t\t\t\t\tvec2 sampleUV = equirectUv( direction );\n\n\t\t\t\t\tgl_FragColor = texture2D( tEquirect, sampleUV );\n\n\t\t\t\t}\n\t\t\t",A=new oy(5,5,5),a=new fy({name:"CubemapFromEquirect",uniforms:sy(n),vertexShader:r,fragmentShader:i,side:lf,blending:cf});a.uniforms.tEquirect.value=t;var o=new Ay(A,a),s=t.minFilter;return t.minFilter===lp&&(t.minFilter=op),new yy(1,10,this).update(e,o),t.minFilter=s,o.geometry.dispose(),o.material.dispose(),this}},{key:"clear",value:function(e,t,n,r){for(var i=e.getRenderTarget(),A=0;A<6;A++)e.setRenderTarget(this,A),e.clear(t,n,r);e.setRenderTarget(i)}}]),n}(dm),_y=new gm,by=new gm,xy=new Gg,Cy=function(){function e(){var t=arguments.length>0&&void 0!==arguments[0]?arguments[0]:new gm(1,0,0),n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0;B(this,e),this.isPlane=!0,this.normal=t,this.constant=n}return b(e,[{key:"set",value:function(e,t){return this.normal.copy(e),this.constant=t,this}},{key:"setComponents",value:function(e,t,n,r){return this.normal.set(e,t,n),this.constant=r,this}},{key:"setFromNormalAndCoplanarPoint",value:function(e,t){return this.normal.copy(e),this.constant=-t.dot(this.normal),this}},{key:"setFromCoplanarPoints",value:function(e,t,n){var r=_y.subVectors(n,t).cross(by.subVectors(e,t)).normalize();return this.setFromNormalAndCoplanarPoint(r,e),this}},{key:"copy",value:function(e){return this.normal.copy(e.normal),this.constant=e.constant,this}},{key:"normalize",value:function(){var e=1/this.normal.length();return this.normal.multiplyScalar(e),this.constant*=e,this}},{key:"negate",value:function(){return this.constant*=-1,this.normal.negate(),this}},{key:"distanceToPoint",value:function(e){return this.normal.dot(e)+this.constant}},{key:"distanceToSphere",value:function(e){return this.distanceToPoint(e.center)-e.radius}},{key:"projectPoint",value:function(e,t){return t.copy(this.normal).multiplyScalar(-this.distanceToPoint(e)).add(e)}},{key:"intersectLine",value:function(e,t){var n=e.delta(_y),r=this.normal.dot(n);if(0===r)return 0===this.distanceToPoint(e.start)?t.copy(e.start):null;var i=-(e.start.dot(this.normal)+this.constant)/r;return i<0||i>1?null:t.copy(n).multiplyScalar(i).add(e.start)}},{key:"intersectsLine",value:function(e){var t=this.distanceToPoint(e.start),n=this.distanceToPoint(e.end);return t<0&&n>0||n<0&&t>0}},{key:"intersectsBox",value:function(e){return e.intersectsPlane(this)}},{key:"intersectsSphere",value:function(e){return e.intersectsPlane(this)}},{key:"coplanarPoint",value:function(e){return e.copy(this.normal).multiplyScalar(-this.constant)}},{key:"applyMatrix4",value:function(e,t){var n=t||xy.getNormalMatrix(e),r=this.coplanarPoint(_y).applyMatrix4(e),i=this.normal.applyMatrix3(n).normalize();return this.constant=-r.dot(i),this}},{key:"translate",value:function(e){return this.constant-=e.dot(this.normal),this}},{key:"equals",value:function(e){return e.normal.equals(this.normal)&&e.constant===this.constant}},{key:"clone",value:function(){return(new this.constructor).copy(this)}}]),e}(),Sy=new Rm,Ey=new gm,Uy=function(){function e(){var t=arguments.length>0&&void 0!==arguments[0]?arguments[0]:new Cy,n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:new Cy,r=arguments.length>2&&void 0!==arguments[2]?arguments[2]:new Cy,i=arguments.length>3&&void 0!==arguments[3]?arguments[3]:new Cy,A=arguments.length>4&&void 0!==arguments[4]?arguments[4]:new Cy,a=arguments.length>5&&void 0!==arguments[5]?arguments[5]:new Cy;B(this,e),this.planes=[t,n,r,i,A,a]}return b(e,[{key:"set",value:function(e,t,n,r,i,A){var a=this.planes;return a[0].copy(e),a[1].copy(t),a[2].copy(n),a[3].copy(r),a[4].copy(i),a[5].copy(A),this}},{key:"copy",value:function(e){for(var t=this.planes,n=0;n<6;n++)t[n].copy(e.planes[n]);return this}},{key:"setFromProjectionMatrix",value:function(e){var t=this.planes,n=e.elements,r=n[0],i=n[1],A=n[2],a=n[3],o=n[4],s=n[5],l=n[6],u=n[7],c=n[8],d=n[9],h=n[10],f=n[11],p=n[12],g=n[13],m=n[14],v=n[15];return t[0].setComponents(a-r,u-o,f-c,v-p).normalize(),t[1].setComponents(a+r,u+o,f+c,v+p).normalize(),t[2].setComponents(a+i,u+s,f+d,v+g).normalize(),t[3].setComponents(a-i,u-s,f-d,v-g).normalize(),t[4].setComponents(a-A,u-l,f-h,v-m).normalize(),t[5].setComponents(a+A,u+l,f+h,v+m).normalize(),this}},{key:"intersectsObject",value:function(e){var t=e.geometry;return null===t.boundingSphere&&t.computeBoundingSphere(),Sy.copy(t.boundingSphere).applyMatrix4(e.matrixWorld),this.intersectsSphere(Sy)}},{key:"intersectsSprite",value:function(e){return Sy.center.set(0,0,0),Sy.radius=.7071067811865476,Sy.applyMatrix4(e.matrixWorld),this.intersectsSphere(Sy)}},{key:"intersectsSphere",value:function(e){for(var t=this.planes,n=e.center,r=-e.radius,i=0;i<6;i++){if(t[i].distanceToPoint(n)<r)return!1}return!0}},{key:"intersectsBox",value:function(e){for(var t=this.planes,n=0;n<6;n++){var r=t[n];if(Ey.x=r.normal.x>0?e.max.x:e.min.x,Ey.y=r.normal.y>0?e.max.y:e.min.y,Ey.z=r.normal.z>0?e.max.z:e.min.z,r.distanceToPoint(Ey)<0)return!1}return!0}},{key:"containsPoint",value:function(e){for(var t=this.planes,n=0;n<6;n++)if(t[n].distanceToPoint(e)<0)return!1;return!0}},{key:"clone",value:function(){return(new this.constructor).copy(this)}}]),e}();function My(){var e=null,t=!1,n=null,r=null;function i(t,A){n(t,A),r=e.requestAnimationFrame(i)}return{start:function(){!0!==t&&null!==n&&(r=e.requestAnimationFrame(i),t=!0)},stop:function(){e.cancelAnimationFrame(r),t=!1},setAnimationLoop:function(e){n=e},setContext:function(t){e=t}}}function Fy(e,t){var n=t.isWebGL2,r=new WeakMap;return{get:function(e){return e.isInterleavedBufferAttribute&&(e=e.data),r.get(e)},remove:function(t){t.isInterleavedBufferAttribute&&(t=t.data);var n=r.get(t);n&&(e.deleteBuffer(n.buffer),r.delete(t))},update:function(t,i){if(t.isGLBufferAttribute){var A=r.get(t);(!A||A.version<t.version)&&r.set(t,{buffer:t.buffer,type:t.type,bytesPerElement:t.elementSize,version:t.version})}else{t.isInterleavedBufferAttribute&&(t=t.data);var a=r.get(t);void 0===a?r.set(t,function(t,r){var i,A=t.array,a=t.usage,o=e.createBuffer();if(e.bindBuffer(r,o),e.bufferData(r,A,a),t.onUploadCallback(),A instanceof Float32Array)i=5126;else if(A instanceof Uint16Array)if(t.isFloat16BufferAttribute){if(!n)throw new Error("THREE.WebGLAttributes: Usage of Float16BufferAttribute requires WebGL2.");i=5131}else i=5123;else if(A instanceof Int16Array)i=5122;else if(A instanceof Uint32Array)i=5125;else if(A instanceof Int32Array)i=5124;else if(A instanceof Int8Array)i=5120;else if(A instanceof Uint8Array)i=5121;else{if(!(A instanceof Uint8ClampedArray))throw new Error("THREE.WebGLAttributes: Unsupported buffer data format: "+A);i=5121}return{buffer:o,type:i,bytesPerElement:A.BYTES_PER_ELEMENT,version:t.version}}(t,i)):a.version<t.version&&(!function(t,r,i){var A=r.array,a=r.updateRange;e.bindBuffer(i,t),-1===a.count?e.bufferSubData(i,0,A):(n?e.bufferSubData(i,a.offset*A.BYTES_PER_ELEMENT,A,a.offset,a.count):e.bufferSubData(i,a.offset*A.BYTES_PER_ELEMENT,A.subarray(a.offset,a.offset+a.count)),a.count=-1),r.onUploadCallback()}(a.buffer,t,i),a.version=t.version)}}}}var Ty=function(e){C(n,e);var t=M(n);function n(){var e,r=arguments.length>0&&void 0!==arguments[0]?arguments[0]:1,i=arguments.length>1&&void 0!==arguments[1]?arguments[1]:1,A=arguments.length>2&&void 0!==arguments[2]?arguments[2]:1,a=arguments.length>3&&void 0!==arguments[3]?arguments[3]:1;B(this,n),(e=t.call(this)).type="PlaneGeometry",e.parameters={width:r,height:i,widthSegments:A,heightSegments:a};for(var o=r/2,s=i/2,l=Math.floor(A),u=Math.floor(a),c=l+1,d=u+1,h=r/l,f=i/u,p=[],g=[],m=[],v=[],y=0;y<d;y++)for(var w=y*f-s,_=0;_<c;_++){var b=_*h-o;g.push(b,-w,0),m.push(0,0,1),v.push(_/l),v.push(1-y/u)}for(var x=0;x<u;x++)for(var C=0;C<l;C++){var S=C+c*x,E=C+c*(x+1),U=C+1+c*(x+1),M=C+1+c*x;p.push(S,E,M),p.push(E,U,M)}return e.setIndex(p),e.setAttribute("position",new Rv(g,3)),e.setAttribute("normal",new Rv(m,3)),e.setAttribute("uv",new Rv(v,2)),e}return b(n,null,[{key:"fromJSON",value:function(e){return new n(e.width,e.height,e.widthSegments,e.heightSegments)}}]),n}(Kv),ky={alphamap_fragment:"#ifdef USE_ALPHAMAP\n\tdiffuseColor.a *= texture2D( alphaMap, vUv ).g;\n#endif",alphamap_pars_fragment:"#ifdef USE_ALPHAMAP\n\tuniform sampler2D alphaMap;\n#endif",alphatest_fragment:"#ifdef USE_ALPHATEST\n\tif ( diffuseColor.a < alphaTest ) discard;\n#endif",alphatest_pars_fragment:"#ifdef USE_ALPHATEST\n\tuniform float alphaTest;\n#endif",aomap_fragment:"#ifdef USE_AOMAP\n\tfloat ambientOcclusion = ( texture2D( aoMap, vUv2 ).r - 1.0 ) * aoMapIntensity + 1.0;\n\treflectedLight.indirectDiffuse *= ambientOcclusion;\n\t#if defined( USE_ENVMAP ) && defined( STANDARD )\n\t\tfloat dotNV = saturate( dot( geometry.normal, geometry.viewDir ) );\n\t\treflectedLight.indirectSpecular *= computeSpecularOcclusion( dotNV, ambientOcclusion, material.roughness );\n\t#endif\n#endif",aomap_pars_fragment:"#ifdef USE_AOMAP\n\tuniform sampler2D aoMap;\n\tuniform float aoMapIntensity;\n#endif",begin_vertex:"vec3 transformed = vec3( position );",beginnormal_vertex:"vec3 objectNormal = vec3( normal );\n#ifdef USE_TANGENT\n\tvec3 objectTangent = vec3( tangent.xyz );\n#endif",bsdfs:"vec3 BRDF_Lambert( const in vec3 diffuseColor ) {\n\treturn RECIPROCAL_PI * diffuseColor;\n}\nvec3 F_Schlick( const in vec3 f0, const in float f90, const in float dotVH ) {\n\tfloat fresnel = exp2( ( - 5.55473 * dotVH - 6.98316 ) * dotVH );\n\treturn f0 * ( 1.0 - fresnel ) + ( f90 * fresnel );\n}\nfloat F_Schlick( const in float f0, const in float f90, const in float dotVH ) {\n\tfloat fresnel = exp2( ( - 5.55473 * dotVH - 6.98316 ) * dotVH );\n\treturn f0 * ( 1.0 - fresnel ) + ( f90 * fresnel );\n}\nvec3 Schlick_to_F0( const in vec3 f, const in float f90, const in float dotVH ) {\n    float x = clamp( 1.0 - dotVH, 0.0, 1.0 );\n    float x2 = x * x;\n    float x5 = clamp( x * x2 * x2, 0.0, 0.9999 );\n    return ( f - vec3( f90 ) * x5 ) / ( 1.0 - x5 );\n}\nfloat V_GGX_SmithCorrelated( const in float alpha, const in float dotNL, const in float dotNV ) {\n\tfloat a2 = pow2( alpha );\n\tfloat gv = dotNL * sqrt( a2 + ( 1.0 - a2 ) * pow2( dotNV ) );\n\tfloat gl = dotNV * sqrt( a2 + ( 1.0 - a2 ) * pow2( dotNL ) );\n\treturn 0.5 / max( gv + gl, EPSILON );\n}\nfloat D_GGX( const in float alpha, const in float dotNH ) {\n\tfloat a2 = pow2( alpha );\n\tfloat denom = pow2( dotNH ) * ( a2 - 1.0 ) + 1.0;\n\treturn RECIPROCAL_PI * a2 / pow2( denom );\n}\nvec3 BRDF_GGX( const in vec3 lightDir, const in vec3 viewDir, const in vec3 normal, const in vec3 f0, const in float f90, const in float roughness ) {\n\tfloat alpha = pow2( roughness );\n\tvec3 halfDir = normalize( lightDir + viewDir );\n\tfloat dotNL = saturate( dot( normal, lightDir ) );\n\tfloat dotNV = saturate( dot( normal, viewDir ) );\n\tfloat dotNH = saturate( dot( normal, halfDir ) );\n\tfloat dotVH = saturate( dot( viewDir, halfDir ) );\n\tvec3 F = F_Schlick( f0, f90, dotVH );\n\tfloat V = V_GGX_SmithCorrelated( alpha, dotNL, dotNV );\n\tfloat D = D_GGX( alpha, dotNH );\n\treturn F * ( V * D );\n}\n#ifdef USE_IRIDESCENCE\n\tvec3 BRDF_GGX_Iridescence( const in vec3 lightDir, const in vec3 viewDir, const in vec3 normal, const in vec3 f0, const in float f90, const in float iridescence, const in vec3 iridescenceFresnel, const in float roughness ) {\n\t\tfloat alpha = pow2( roughness );\n\t\tvec3 halfDir = normalize( lightDir + viewDir );\n\t\tfloat dotNL = saturate( dot( normal, lightDir ) );\n\t\tfloat dotNV = saturate( dot( normal, viewDir ) );\n\t\tfloat dotNH = saturate( dot( normal, halfDir ) );\n\t\tfloat dotVH = saturate( dot( viewDir, halfDir ) );\n\t\tvec3 F = mix( F_Schlick( f0, f90, dotVH ), iridescenceFresnel, iridescence );\n\t\tfloat V = V_GGX_SmithCorrelated( alpha, dotNL, dotNV );\n\t\tfloat D = D_GGX( alpha, dotNH );\n\t\treturn F * ( V * D );\n\t}\n#endif\nvec2 LTC_Uv( const in vec3 N, const in vec3 V, const in float roughness ) {\n\tconst float LUT_SIZE = 64.0;\n\tconst float LUT_SCALE = ( LUT_SIZE - 1.0 ) / LUT_SIZE;\n\tconst float LUT_BIAS = 0.5 / LUT_SIZE;\n\tfloat dotNV = saturate( dot( N, V ) );\n\tvec2 uv = vec2( roughness, sqrt( 1.0 - dotNV ) );\n\tuv = uv * LUT_SCALE + LUT_BIAS;\n\treturn uv;\n}\nfloat LTC_ClippedSphereFormFactor( const in vec3 f ) {\n\tfloat l = length( f );\n\treturn max( ( l * l + f.z ) / ( l + 1.0 ), 0.0 );\n}\nvec3 LTC_EdgeVectorFormFactor( const in vec3 v1, const in vec3 v2 ) {\n\tfloat x = dot( v1, v2 );\n\tfloat y = abs( x );\n\tfloat a = 0.8543985 + ( 0.4965155 + 0.0145206 * y ) * y;\n\tfloat b = 3.4175940 + ( 4.1616724 + y ) * y;\n\tfloat v = a / b;\n\tfloat theta_sintheta = ( x > 0.0 ) ? v : 0.5 * inversesqrt( max( 1.0 - x * x, 1e-7 ) ) - v;\n\treturn cross( v1, v2 ) * theta_sintheta;\n}\nvec3 LTC_Evaluate( const in vec3 N, const in vec3 V, const in vec3 P, const in mat3 mInv, const in vec3 rectCoords[ 4 ] ) {\n\tvec3 v1 = rectCoords[ 1 ] - rectCoords[ 0 ];\n\tvec3 v2 = rectCoords[ 3 ] - rectCoords[ 0 ];\n\tvec3 lightNormal = cross( v1, v2 );\n\tif( dot( lightNormal, P - rectCoords[ 0 ] ) < 0.0 ) return vec3( 0.0 );\n\tvec3 T1, T2;\n\tT1 = normalize( V - N * dot( V, N ) );\n\tT2 = - cross( N, T1 );\n\tmat3 mat = mInv * transposeMat3( mat3( T1, T2, N ) );\n\tvec3 coords[ 4 ];\n\tcoords[ 0 ] = mat * ( rectCoords[ 0 ] - P );\n\tcoords[ 1 ] = mat * ( rectCoords[ 1 ] - P );\n\tcoords[ 2 ] = mat * ( rectCoords[ 2 ] - P );\n\tcoords[ 3 ] = mat * ( rectCoords[ 3 ] - P );\n\tcoords[ 0 ] = normalize( coords[ 0 ] );\n\tcoords[ 1 ] = normalize( coords[ 1 ] );\n\tcoords[ 2 ] = normalize( coords[ 2 ] );\n\tcoords[ 3 ] = normalize( coords[ 3 ] );\n\tvec3 vectorFormFactor = vec3( 0.0 );\n\tvectorFormFactor += LTC_EdgeVectorFormFactor( coords[ 0 ], coords[ 1 ] );\n\tvectorFormFactor += LTC_EdgeVectorFormFactor( coords[ 1 ], coords[ 2 ] );\n\tvectorFormFactor += LTC_EdgeVectorFormFactor( coords[ 2 ], coords[ 3 ] );\n\tvectorFormFactor += LTC_EdgeVectorFormFactor( coords[ 3 ], coords[ 0 ] );\n\tfloat result = LTC_ClippedSphereFormFactor( vectorFormFactor );\n\treturn vec3( result );\n}\nfloat G_BlinnPhong_Implicit( ) {\n\treturn 0.25;\n}\nfloat D_BlinnPhong( const in float shininess, const in float dotNH ) {\n\treturn RECIPROCAL_PI * ( shininess * 0.5 + 1.0 ) * pow( dotNH, shininess );\n}\nvec3 BRDF_BlinnPhong( const in vec3 lightDir, const in vec3 viewDir, const in vec3 normal, const in vec3 specularColor, const in float shininess ) {\n\tvec3 halfDir = normalize( lightDir + viewDir );\n\tfloat dotNH = saturate( dot( normal, halfDir ) );\n\tfloat dotVH = saturate( dot( viewDir, halfDir ) );\n\tvec3 F = F_Schlick( specularColor, 1.0, dotVH );\n\tfloat G = G_BlinnPhong_Implicit( );\n\tfloat D = D_BlinnPhong( shininess, dotNH );\n\treturn F * ( G * D );\n}\n#if defined( USE_SHEEN )\nfloat D_Charlie( float roughness, float dotNH ) {\n\tfloat alpha = pow2( roughness );\n\tfloat invAlpha = 1.0 / alpha;\n\tfloat cos2h = dotNH * dotNH;\n\tfloat sin2h = max( 1.0 - cos2h, 0.0078125 );\n\treturn ( 2.0 + invAlpha ) * pow( sin2h, invAlpha * 0.5 ) / ( 2.0 * PI );\n}\nfloat V_Neubelt( float dotNV, float dotNL ) {\n\treturn saturate( 1.0 / ( 4.0 * ( dotNL + dotNV - dotNL * dotNV ) ) );\n}\nvec3 BRDF_Sheen( const in vec3 lightDir, const in vec3 viewDir, const in vec3 normal, vec3 sheenColor, const in float sheenRoughness ) {\n\tvec3 halfDir = normalize( lightDir + viewDir );\n\tfloat dotNL = saturate( dot( normal, lightDir ) );\n\tfloat dotNV = saturate( dot( normal, viewDir ) );\n\tfloat dotNH = saturate( dot( normal, halfDir ) );\n\tfloat D = D_Charlie( sheenRoughness, dotNH );\n\tfloat V = V_Neubelt( dotNV, dotNL );\n\treturn sheenColor * ( D * V );\n}\n#endif",iridescence_fragment:"#ifdef USE_IRIDESCENCE\n\tconst mat3 XYZ_TO_REC709 = mat3(\n\t\t 3.2404542, -0.9692660,  0.0556434,\n\t\t-1.5371385,  1.8760108, -0.2040259,\n\t\t-0.4985314,  0.0415560,  1.0572252\n\t);\n\tvec3 Fresnel0ToIor( vec3 fresnel0 ) {\n\t\tvec3 sqrtF0 = sqrt( fresnel0 );\n\t\treturn ( vec3( 1.0 ) + sqrtF0 ) / ( vec3( 1.0 ) - sqrtF0 );\n\t}\n\tvec3 IorToFresnel0( vec3 transmittedIor, float incidentIor ) {\n\t\treturn pow2( ( transmittedIor - vec3( incidentIor ) ) / ( transmittedIor + vec3( incidentIor ) ) );\n\t}\n\tfloat IorToFresnel0( float transmittedIor, float incidentIor ) {\n\t\treturn pow2( ( transmittedIor - incidentIor ) / ( transmittedIor + incidentIor ));\n\t}\n\tvec3 evalSensitivity( float OPD, vec3 shift ) {\n\t\tfloat phase = 2.0 * PI * OPD * 1.0e-9;\n\t\tvec3 val = vec3( 5.4856e-13, 4.4201e-13, 5.2481e-13 );\n\t\tvec3 pos = vec3( 1.6810e+06, 1.7953e+06, 2.2084e+06 );\n\t\tvec3 var = vec3( 4.3278e+09, 9.3046e+09, 6.6121e+09 );\n\t\tvec3 xyz = val * sqrt( 2.0 * PI * var ) * cos( pos * phase + shift ) * exp( - pow2( phase ) * var );\n\t\txyz.x += 9.7470e-14 * sqrt( 2.0 * PI * 4.5282e+09 ) * cos( 2.2399e+06 * phase + shift[ 0 ] ) * exp( - 4.5282e+09 * pow2( phase ) );\n\t\txyz /= 1.0685e-7;\n\t\tvec3 rgb = XYZ_TO_REC709 * xyz;\n\t\treturn rgb;\n\t}\n\tvec3 evalIridescence( float outsideIOR, float eta2, float cosTheta1, float thinFilmThickness, vec3 baseF0 ) {\n\t\tvec3 I;\n\t\tfloat iridescenceIOR = mix( outsideIOR, eta2, smoothstep( 0.0, 0.03, thinFilmThickness ) );\n\t\tfloat sinTheta2Sq = pow2( outsideIOR / iridescenceIOR ) * ( 1.0 - pow2( cosTheta1 ) );\n\t\tfloat cosTheta2Sq = 1.0 - sinTheta2Sq;\n\t\tif ( cosTheta2Sq < 0.0 ) {\n\t\t\t return vec3( 1.0 );\n\t\t}\n\t\tfloat cosTheta2 = sqrt( cosTheta2Sq );\n\t\tfloat R0 = IorToFresnel0( iridescenceIOR, outsideIOR );\n\t\tfloat R12 = F_Schlick( R0, 1.0, cosTheta1 );\n\t\tfloat R21 = R12;\n\t\tfloat T121 = 1.0 - R12;\n\t\tfloat phi12 = 0.0;\n\t\tif ( iridescenceIOR < outsideIOR ) phi12 = PI;\n\t\tfloat phi21 = PI - phi12;\n\t\tvec3 baseIOR = Fresnel0ToIor( clamp( baseF0, 0.0, 0.9999 ) );\t\tvec3 R1 = IorToFresnel0( baseIOR, iridescenceIOR );\n\t\tvec3 R23 = F_Schlick( R1, 1.0, cosTheta2 );\n\t\tvec3 phi23 = vec3( 0.0 );\n\t\tif ( baseIOR[ 0 ] < iridescenceIOR ) phi23[ 0 ] = PI;\n\t\tif ( baseIOR[ 1 ] < iridescenceIOR ) phi23[ 1 ] = PI;\n\t\tif ( baseIOR[ 2 ] < iridescenceIOR ) phi23[ 2 ] = PI;\n\t\tfloat OPD = 2.0 * iridescenceIOR * thinFilmThickness * cosTheta2;\n\t\tvec3 phi = vec3( phi21 ) + phi23;\n\t\tvec3 R123 = clamp( R12 * R23, 1e-5, 0.9999 );\n\t\tvec3 r123 = sqrt( R123 );\n\t\tvec3 Rs = pow2( T121 ) * R23 / ( vec3( 1.0 ) - R123 );\n\t\tvec3 C0 = R12 + Rs;\n\t\tI = C0;\n\t\tvec3 Cm = Rs - T121;\n\t\tfor ( int m = 1; m <= 2; ++ m ) {\n\t\t\tCm *= r123;\n\t\t\tvec3 Sm = 2.0 * evalSensitivity( float( m ) * OPD, float( m ) * phi );\n\t\t\tI += Cm * Sm;\n\t\t}\n\t\treturn max( I, vec3( 0.0 ) );\n\t}\n#endif",bumpmap_pars_fragment:"#ifdef USE_BUMPMAP\n\tuniform sampler2D bumpMap;\n\tuniform float bumpScale;\n\tvec2 dHdxy_fwd() {\n\t\tvec2 dSTdx = dFdx( vUv );\n\t\tvec2 dSTdy = dFdy( vUv );\n\t\tfloat Hll = bumpScale * texture2D( bumpMap, vUv ).x;\n\t\tfloat dBx = bumpScale * texture2D( bumpMap, vUv + dSTdx ).x - Hll;\n\t\tfloat dBy = bumpScale * texture2D( bumpMap, vUv + dSTdy ).x - Hll;\n\t\treturn vec2( dBx, dBy );\n\t}\n\tvec3 perturbNormalArb( vec3 surf_pos, vec3 surf_norm, vec2 dHdxy, float faceDirection ) {\n\t\tvec3 vSigmaX = dFdx( surf_pos.xyz );\n\t\tvec3 vSigmaY = dFdy( surf_pos.xyz );\n\t\tvec3 vN = surf_norm;\n\t\tvec3 R1 = cross( vSigmaY, vN );\n\t\tvec3 R2 = cross( vN, vSigmaX );\n\t\tfloat fDet = dot( vSigmaX, R1 ) * faceDirection;\n\t\tvec3 vGrad = sign( fDet ) * ( dHdxy.x * R1 + dHdxy.y * R2 );\n\t\treturn normalize( abs( fDet ) * surf_norm - vGrad );\n\t}\n#endif",clipping_planes_fragment:"#if NUM_CLIPPING_PLANES > 0\n\tvec4 plane;\n\t#pragma unroll_loop_start\n\tfor ( int i = 0; i < UNION_CLIPPING_PLANES; i ++ ) {\n\t\tplane = clippingPlanes[ i ];\n\t\tif ( dot( vClipPosition, plane.xyz ) > plane.w ) discard;\n\t}\n\t#pragma unroll_loop_end\n\t#if UNION_CLIPPING_PLANES < NUM_CLIPPING_PLANES\n\t\tbool clipped = true;\n\t\t#pragma unroll_loop_start\n\t\tfor ( int i = UNION_CLIPPING_PLANES; i < NUM_CLIPPING_PLANES; i ++ ) {\n\t\t\tplane = clippingPlanes[ i ];\n\t\t\tclipped = ( dot( vClipPosition, plane.xyz ) > plane.w ) && clipped;\n\t\t}\n\t\t#pragma unroll_loop_end\n\t\tif ( clipped ) discard;\n\t#endif\n#endif",clipping_planes_pars_fragment:"#if NUM_CLIPPING_PLANES > 0\n\tvarying vec3 vClipPosition;\n\tuniform vec4 clippingPlanes[ NUM_CLIPPING_PLANES ];\n#endif",clipping_planes_pars_vertex:"#if NUM_CLIPPING_PLANES > 0\n\tvarying vec3 vClipPosition;\n#endif",clipping_planes_vertex:"#if NUM_CLIPPING_PLANES > 0\n\tvClipPosition = - mvPosition.xyz;\n#endif",color_fragment:"#if defined( USE_COLOR_ALPHA )\n\tdiffuseColor *= vColor;\n#elif defined( USE_COLOR )\n\tdiffuseColor.rgb *= vColor;\n#endif",color_pars_fragment:"#if defined( USE_COLOR_ALPHA )\n\tvarying vec4 vColor;\n#elif defined( USE_COLOR )\n\tvarying vec3 vColor;\n#endif",color_pars_vertex:"#if defined( USE_COLOR_ALPHA )\n\tvarying vec4 vColor;\n#elif defined( USE_COLOR ) || defined( USE_INSTANCING_COLOR )\n\tvarying vec3 vColor;\n#endif",color_vertex:"#if defined( USE_COLOR_ALPHA )\n\tvColor = vec4( 1.0 );\n#elif defined( USE_COLOR ) || defined( USE_INSTANCING_COLOR )\n\tvColor = vec3( 1.0 );\n#endif\n#ifdef USE_COLOR\n\tvColor *= color;\n#endif\n#ifdef USE_INSTANCING_COLOR\n\tvColor.xyz *= instanceColor.xyz;\n#endif",common:"#define PI 3.141592653589793\n#define PI2 6.283185307179586\n#define PI_HALF 1.5707963267948966\n#define RECIPROCAL_PI 0.3183098861837907\n#define RECIPROCAL_PI2 0.15915494309189535\n#define EPSILON 1e-6\n#ifndef saturate\n#define saturate( a ) clamp( a, 0.0, 1.0 )\n#endif\n#define whiteComplement( a ) ( 1.0 - saturate( a ) )\nfloat pow2( const in float x ) { return x*x; }\nvec3 pow2( const in vec3 x ) { return x*x; }\nfloat pow3( const in float x ) { return x*x*x; }\nfloat pow4( const in float x ) { float x2 = x*x; return x2*x2; }\nfloat max3( const in vec3 v ) { return max( max( v.x, v.y ), v.z ); }\nfloat average( const in vec3 v ) { return dot( v, vec3( 0.3333333 ) ); }\nhighp float rand( const in vec2 uv ) {\n\tconst highp float a = 12.9898, b = 78.233, c = 43758.5453;\n\thighp float dt = dot( uv.xy, vec2( a,b ) ), sn = mod( dt, PI );\n\treturn fract( sin( sn ) * c );\n}\n#ifdef HIGH_PRECISION\n\tfloat precisionSafeLength( vec3 v ) { return length( v ); }\n#else\n\tfloat precisionSafeLength( vec3 v ) {\n\t\tfloat maxComponent = max3( abs( v ) );\n\t\treturn length( v / maxComponent ) * maxComponent;\n\t}\n#endif\nstruct IncidentLight {\n\tvec3 color;\n\tvec3 direction;\n\tbool visible;\n};\nstruct ReflectedLight {\n\tvec3 directDiffuse;\n\tvec3 directSpecular;\n\tvec3 indirectDiffuse;\n\tvec3 indirectSpecular;\n};\nstruct GeometricContext {\n\tvec3 position;\n\tvec3 normal;\n\tvec3 viewDir;\n#ifdef USE_CLEARCOAT\n\tvec3 clearcoatNormal;\n#endif\n};\nvec3 transformDirection( in vec3 dir, in mat4 matrix ) {\n\treturn normalize( ( matrix * vec4( dir, 0.0 ) ).xyz );\n}\nvec3 inverseTransformDirection( in vec3 dir, in mat4 matrix ) {\n\treturn normalize( ( vec4( dir, 0.0 ) * matrix ).xyz );\n}\nmat3 transposeMat3( const in mat3 m ) {\n\tmat3 tmp;\n\ttmp[ 0 ] = vec3( m[ 0 ].x, m[ 1 ].x, m[ 2 ].x );\n\ttmp[ 1 ] = vec3( m[ 0 ].y, m[ 1 ].y, m[ 2 ].y );\n\ttmp[ 2 ] = vec3( m[ 0 ].z, m[ 1 ].z, m[ 2 ].z );\n\treturn tmp;\n}\nfloat luminance( const in vec3 rgb ) {\n\tconst vec3 weights = vec3( 0.2126729, 0.7151522, 0.0721750 );\n\treturn dot( weights, rgb );\n}\nbool isPerspectiveMatrix( mat4 m ) {\n\treturn m[ 2 ][ 3 ] == - 1.0;\n}\nvec2 equirectUv( in vec3 dir ) {\n\tfloat u = atan( dir.z, dir.x ) * RECIPROCAL_PI2 + 0.5;\n\tfloat v = asin( clamp( dir.y, - 1.0, 1.0 ) ) * RECIPROCAL_PI + 0.5;\n\treturn vec2( u, v );\n}",cube_uv_reflection_fragment:"#ifdef ENVMAP_TYPE_CUBE_UV\n\t#define cubeUV_minMipLevel 4.0\n\t#define cubeUV_minTileSize 16.0\n\tfloat getFace( vec3 direction ) {\n\t\tvec3 absDirection = abs( direction );\n\t\tfloat face = - 1.0;\n\t\tif ( absDirection.x > absDirection.z ) {\n\t\t\tif ( absDirection.x > absDirection.y )\n\t\t\t\tface = direction.x > 0.0 ? 0.0 : 3.0;\n\t\t\telse\n\t\t\t\tface = direction.y > 0.0 ? 1.0 : 4.0;\n\t\t} else {\n\t\t\tif ( absDirection.z > absDirection.y )\n\t\t\t\tface = direction.z > 0.0 ? 2.0 : 5.0;\n\t\t\telse\n\t\t\t\tface = direction.y > 0.0 ? 1.0 : 4.0;\n\t\t}\n\t\treturn face;\n\t}\n\tvec2 getUV( vec3 direction, float face ) {\n\t\tvec2 uv;\n\t\tif ( face == 0.0 ) {\n\t\t\tuv = vec2( direction.z, direction.y ) / abs( direction.x );\n\t\t} else if ( face == 1.0 ) {\n\t\t\tuv = vec2( - direction.x, - direction.z ) / abs( direction.y );\n\t\t} else if ( face == 2.0 ) {\n\t\t\tuv = vec2( - direction.x, direction.y ) / abs( direction.z );\n\t\t} else if ( face == 3.0 ) {\n\t\t\tuv = vec2( - direction.z, direction.y ) / abs( direction.x );\n\t\t} else if ( face == 4.0 ) {\n\t\t\tuv = vec2( - direction.x, direction.z ) / abs( direction.y );\n\t\t} else {\n\t\t\tuv = vec2( direction.x, direction.y ) / abs( direction.z );\n\t\t}\n\t\treturn 0.5 * ( uv + 1.0 );\n\t}\n\tvec3 bilinearCubeUV( sampler2D envMap, vec3 direction, float mipInt ) {\n\t\tfloat face = getFace( direction );\n\t\tfloat filterInt = max( cubeUV_minMipLevel - mipInt, 0.0 );\n\t\tmipInt = max( mipInt, cubeUV_minMipLevel );\n\t\tfloat faceSize = exp2( mipInt );\n\t\thighp vec2 uv = getUV( direction, face ) * ( faceSize - 2.0 ) + 1.0;\n\t\tif ( face > 2.0 ) {\n\t\t\tuv.y += faceSize;\n\t\t\tface -= 3.0;\n\t\t}\n\t\tuv.x += face * faceSize;\n\t\tuv.x += filterInt * 3.0 * cubeUV_minTileSize;\n\t\tuv.y += 4.0 * ( exp2( CUBEUV_MAX_MIP ) - faceSize );\n\t\tuv.x *= CUBEUV_TEXEL_WIDTH;\n\t\tuv.y *= CUBEUV_TEXEL_HEIGHT;\n\t\t#ifdef texture2DGradEXT\n\t\t\treturn texture2DGradEXT( envMap, uv, vec2( 0.0 ), vec2( 0.0 ) ).rgb;\n\t\t#else\n\t\t\treturn texture2D( envMap, uv ).rgb;\n\t\t#endif\n\t}\n\t#define cubeUV_r0 1.0\n\t#define cubeUV_v0 0.339\n\t#define cubeUV_m0 - 2.0\n\t#define cubeUV_r1 0.8\n\t#define cubeUV_v1 0.276\n\t#define cubeUV_m1 - 1.0\n\t#define cubeUV_r4 0.4\n\t#define cubeUV_v4 0.046\n\t#define cubeUV_m4 2.0\n\t#define cubeUV_r5 0.305\n\t#define cubeUV_v5 0.016\n\t#define cubeUV_m5 3.0\n\t#define cubeUV_r6 0.21\n\t#define cubeUV_v6 0.0038\n\t#define cubeUV_m6 4.0\n\tfloat roughnessToMip( float roughness ) {\n\t\tfloat mip = 0.0;\n\t\tif ( roughness >= cubeUV_r1 ) {\n\t\t\tmip = ( cubeUV_r0 - roughness ) * ( cubeUV_m1 - cubeUV_m0 ) / ( cubeUV_r0 - cubeUV_r1 ) + cubeUV_m0;\n\t\t} else if ( roughness >= cubeUV_r4 ) {\n\t\t\tmip = ( cubeUV_r1 - roughness ) * ( cubeUV_m4 - cubeUV_m1 ) / ( cubeUV_r1 - cubeUV_r4 ) + cubeUV_m1;\n\t\t} else if ( roughness >= cubeUV_r5 ) {\n\t\t\tmip = ( cubeUV_r4 - roughness ) * ( cubeUV_m5 - cubeUV_m4 ) / ( cubeUV_r4 - cubeUV_r5 ) + cubeUV_m4;\n\t\t} else if ( roughness >= cubeUV_r6 ) {\n\t\t\tmip = ( cubeUV_r5 - roughness ) * ( cubeUV_m6 - cubeUV_m5 ) / ( cubeUV_r5 - cubeUV_r6 ) + cubeUV_m5;\n\t\t} else {\n\t\t\tmip = - 2.0 * log2( 1.16 * roughness );\t\t}\n\t\treturn mip;\n\t}\n\tvec4 textureCubeUV( sampler2D envMap, vec3 sampleDir, float roughness ) {\n\t\tfloat mip = clamp( roughnessToMip( roughness ), cubeUV_m0, CUBEUV_MAX_MIP );\n\t\tfloat mipF = fract( mip );\n\t\tfloat mipInt = floor( mip );\n\t\tvec3 color0 = bilinearCubeUV( envMap, sampleDir, mipInt );\n\t\tif ( mipF == 0.0 ) {\n\t\t\treturn vec4( color0, 1.0 );\n\t\t} else {\n\t\t\tvec3 color1 = bilinearCubeUV( envMap, sampleDir, mipInt + 1.0 );\n\t\t\treturn vec4( mix( color0, color1, mipF ), 1.0 );\n\t\t}\n\t}\n#endif",defaultnormal_vertex:"vec3 transformedNormal = objectNormal;\n#ifdef USE_INSTANCING\n\tmat3 m = mat3( instanceMatrix );\n\ttransformedNormal /= vec3( dot( m[ 0 ], m[ 0 ] ), dot( m[ 1 ], m[ 1 ] ), dot( m[ 2 ], m[ 2 ] ) );\n\ttransformedNormal = m * transformedNormal;\n#endif\ntransformedNormal = normalMatrix * transformedNormal;\n#ifdef FLIP_SIDED\n\ttransformedNormal = - transformedNormal;\n#endif\n#ifdef USE_TANGENT\n\tvec3 transformedTangent = ( modelViewMatrix * vec4( objectTangent, 0.0 ) ).xyz;\n\t#ifdef FLIP_SIDED\n\t\ttransformedTangent = - transformedTangent;\n\t#endif\n#endif",displacementmap_pars_vertex:"#ifdef USE_DISPLACEMENTMAP\n\tuniform sampler2D displacementMap;\n\tuniform float displacementScale;\n\tuniform float displacementBias;\n#endif",displacementmap_vertex:"#ifdef USE_DISPLACEMENTMAP\n\ttransformed += normalize( objectNormal ) * ( texture2D( displacementMap, vUv ).x * displacementScale + displacementBias );\n#endif",emissivemap_fragment:"#ifdef USE_EMISSIVEMAP\n\tvec4 emissiveColor = texture2D( emissiveMap, vUv );\n\ttotalEmissiveRadiance *= emissiveColor.rgb;\n#endif",emissivemap_pars_fragment:"#ifdef USE_EMISSIVEMAP\n\tuniform sampler2D emissiveMap;\n#endif",encodings_fragment:"gl_FragColor = linearToOutputTexel( gl_FragColor );",encodings_pars_fragment:"vec4 LinearToLinear( in vec4 value ) {\n\treturn value;\n}\nvec4 LinearTosRGB( in vec4 value ) {\n\treturn vec4( mix( pow( value.rgb, vec3( 0.41666 ) ) * 1.055 - vec3( 0.055 ), value.rgb * 12.92, vec3( lessThanEqual( value.rgb, vec3( 0.0031308 ) ) ) ), value.a );\n}",envmap_fragment:"#ifdef USE_ENVMAP\n\t#ifdef ENV_WORLDPOS\n\t\tvec3 cameraToFrag;\n\t\tif ( isOrthographic ) {\n\t\t\tcameraToFrag = normalize( vec3( - viewMatrix[ 0 ][ 2 ], - viewMatrix[ 1 ][ 2 ], - viewMatrix[ 2 ][ 2 ] ) );\n\t\t} else {\n\t\t\tcameraToFrag = normalize( vWorldPosition - cameraPosition );\n\t\t}\n\t\tvec3 worldNormal = inverseTransformDirection( normal, viewMatrix );\n\t\t#ifdef ENVMAP_MODE_REFLECTION\n\t\t\tvec3 reflectVec = reflect( cameraToFrag, worldNormal );\n\t\t#else\n\t\t\tvec3 reflectVec = refract( cameraToFrag, worldNormal, refractionRatio );\n\t\t#endif\n\t#else\n\t\tvec3 reflectVec = vReflect;\n\t#endif\n\t#ifdef ENVMAP_TYPE_CUBE\n\t\tvec4 envColor = textureCube( envMap, vec3( flipEnvMap * reflectVec.x, reflectVec.yz ) );\n\t#else\n\t\tvec4 envColor = vec4( 0.0 );\n\t#endif\n\t#ifdef ENVMAP_BLENDING_MULTIPLY\n\t\toutgoingLight = mix( outgoingLight, outgoingLight * envColor.xyz, specularStrength * reflectivity );\n\t#elif defined( ENVMAP_BLENDING_MIX )\n\t\toutgoingLight = mix( outgoingLight, envColor.xyz, specularStrength * reflectivity );\n\t#elif defined( ENVMAP_BLENDING_ADD )\n\t\toutgoingLight += envColor.xyz * specularStrength * reflectivity;\n\t#endif\n#endif",envmap_common_pars_fragment:"#ifdef USE_ENVMAP\n\tuniform float envMapIntensity;\n\tuniform float flipEnvMap;\n\t#ifdef ENVMAP_TYPE_CUBE\n\t\tuniform samplerCube envMap;\n\t#else\n\t\tuniform sampler2D envMap;\n\t#endif\n\t\n#endif",envmap_pars_fragment:"#ifdef USE_ENVMAP\n\tuniform float reflectivity;\n\t#if defined( USE_BUMPMAP ) || defined( USE_NORMALMAP ) || defined( PHONG ) || defined( LAMBERT )\n\t\t#define ENV_WORLDPOS\n\t#endif\n\t#ifdef ENV_WORLDPOS\n\t\tvarying vec3 vWorldPosition;\n\t\tuniform float refractionRatio;\n\t#else\n\t\tvarying vec3 vReflect;\n\t#endif\n#endif",envmap_pars_vertex:"#ifdef USE_ENVMAP\n\t#if defined( USE_BUMPMAP ) || defined( USE_NORMALMAP ) || defined( PHONG ) || defined( LAMBERT )\n\t\t#define ENV_WORLDPOS\n\t#endif\n\t#ifdef ENV_WORLDPOS\n\t\t\n\t\tvarying vec3 vWorldPosition;\n\t#else\n\t\tvarying vec3 vReflect;\n\t\tuniform float refractionRatio;\n\t#endif\n#endif",envmap_physical_pars_fragment:"#if defined( USE_ENVMAP )\n\tvec3 getIBLIrradiance( const in vec3 normal ) {\n\t\t#if defined( ENVMAP_TYPE_CUBE_UV )\n\t\t\tvec3 worldNormal = inverseTransformDirection( normal, viewMatrix );\n\t\t\tvec4 envMapColor = textureCubeUV( envMap, worldNormal, 1.0 );\n\t\t\treturn PI * envMapColor.rgb * envMapIntensity;\n\t\t#else\n\t\t\treturn vec3( 0.0 );\n\t\t#endif\n\t}\n\tvec3 getIBLRadiance( const in vec3 viewDir, const in vec3 normal, const in float roughness ) {\n\t\t#if defined( ENVMAP_TYPE_CUBE_UV )\n\t\t\tvec3 reflectVec = reflect( - viewDir, normal );\n\t\t\treflectVec = normalize( mix( reflectVec, normal, roughness * roughness) );\n\t\t\treflectVec = inverseTransformDirection( reflectVec, viewMatrix );\n\t\t\tvec4 envMapColor = textureCubeUV( envMap, reflectVec, roughness );\n\t\t\treturn envMapColor.rgb * envMapIntensity;\n\t\t#else\n\t\t\treturn vec3( 0.0 );\n\t\t#endif\n\t}\n#endif",envmap_vertex:"#ifdef USE_ENVMAP\n\t#ifdef ENV_WORLDPOS\n\t\tvWorldPosition = worldPosition.xyz;\n\t#else\n\t\tvec3 cameraToVertex;\n\t\tif ( isOrthographic ) {\n\t\t\tcameraToVertex = normalize( vec3( - viewMatrix[ 0 ][ 2 ], - viewMatrix[ 1 ][ 2 ], - viewMatrix[ 2 ][ 2 ] ) );\n\t\t} else {\n\t\t\tcameraToVertex = normalize( worldPosition.xyz - cameraPosition );\n\t\t}\n\t\tvec3 worldNormal = inverseTransformDirection( transformedNormal, viewMatrix );\n\t\t#ifdef ENVMAP_MODE_REFLECTION\n\t\t\tvReflect = reflect( cameraToVertex, worldNormal );\n\t\t#else\n\t\t\tvReflect = refract( cameraToVertex, worldNormal, refractionRatio );\n\t\t#endif\n\t#endif\n#endif",fog_vertex:"#ifdef USE_FOG\n\tvFogDepth = - mvPosition.z;\n#endif",fog_pars_vertex:"#ifdef USE_FOG\n\tvarying float vFogDepth;\n#endif",fog_fragment:"#ifdef USE_FOG\n\t#ifdef FOG_EXP2\n\t\tfloat fogFactor = 1.0 - exp( - fogDensity * fogDensity * vFogDepth * vFogDepth );\n\t#else\n\t\tfloat fogFactor = smoothstep( fogNear, fogFar, vFogDepth );\n\t#endif\n\tgl_FragColor.rgb = mix( gl_FragColor.rgb, fogColor, fogFactor );\n#endif",fog_pars_fragment:"#ifdef USE_FOG\n\tuniform vec3 fogColor;\n\tvarying float vFogDepth;\n\t#ifdef FOG_EXP2\n\t\tuniform float fogDensity;\n\t#else\n\t\tuniform float fogNear;\n\t\tuniform float fogFar;\n\t#endif\n#endif",gradientmap_pars_fragment:"#ifdef USE_GRADIENTMAP\n\tuniform sampler2D gradientMap;\n#endif\nvec3 getGradientIrradiance( vec3 normal, vec3 lightDirection ) {\n\tfloat dotNL = dot( normal, lightDirection );\n\tvec2 coord = vec2( dotNL * 0.5 + 0.5, 0.0 );\n\t#ifdef USE_GRADIENTMAP\n\t\treturn vec3( texture2D( gradientMap, coord ).r );\n\t#else\n\t\tvec2 fw = fwidth( coord ) * 0.5;\n\t\treturn mix( vec3( 0.7 ), vec3( 1.0 ), smoothstep( 0.7 - fw.x, 0.7 + fw.x, coord.x ) );\n\t#endif\n}",lightmap_fragment:"#ifdef USE_LIGHTMAP\n\tvec4 lightMapTexel = texture2D( lightMap, vUv2 );\n\tvec3 lightMapIrradiance = lightMapTexel.rgb * lightMapIntensity;\n\treflectedLight.indirectDiffuse += lightMapIrradiance;\n#endif",lightmap_pars_fragment:"#ifdef USE_LIGHTMAP\n\tuniform sampler2D lightMap;\n\tuniform float lightMapIntensity;\n#endif",lights_lambert_fragment:"LambertMaterial material;\nmaterial.diffuseColor = diffuseColor.rgb;\nmaterial.specularStrength = specularStrength;",lights_lambert_pars_fragment:"varying vec3 vViewPosition;\nstruct LambertMaterial {\n\tvec3 diffuseColor;\n\tfloat specularStrength;\n};\nvoid RE_Direct_Lambert( const in IncidentLight directLight, const in GeometricContext geometry, const in LambertMaterial material, inout ReflectedLight reflectedLight ) {\n\tfloat dotNL = saturate( dot( geometry.normal, directLight.direction ) );\n\tvec3 irradiance = dotNL * directLight.color;\n\treflectedLight.directDiffuse += irradiance * BRDF_Lambert( material.diffuseColor );\n}\nvoid RE_IndirectDiffuse_Lambert( const in vec3 irradiance, const in GeometricContext geometry, const in LambertMaterial material, inout ReflectedLight reflectedLight ) {\n\treflectedLight.indirectDiffuse += irradiance * BRDF_Lambert( material.diffuseColor );\n}\n#define RE_Direct\t\t\t\tRE_Direct_Lambert\n#define RE_IndirectDiffuse\t\tRE_IndirectDiffuse_Lambert",lights_pars_begin:"uniform bool receiveShadow;\nuniform vec3 ambientLightColor;\nuniform vec3 lightProbe[ 9 ];\nvec3 shGetIrradianceAt( in vec3 normal, in vec3 shCoefficients[ 9 ] ) {\n\tfloat x = normal.x, y = normal.y, z = normal.z;\n\tvec3 result = shCoefficients[ 0 ] * 0.886227;\n\tresult += shCoefficients[ 1 ] * 2.0 * 0.511664 * y;\n\tresult += shCoefficients[ 2 ] * 2.0 * 0.511664 * z;\n\tresult += shCoefficients[ 3 ] * 2.0 * 0.511664 * x;\n\tresult += shCoefficients[ 4 ] * 2.0 * 0.429043 * x * y;\n\tresult += shCoefficients[ 5 ] * 2.0 * 0.429043 * y * z;\n\tresult += shCoefficients[ 6 ] * ( 0.743125 * z * z - 0.247708 );\n\tresult += shCoefficients[ 7 ] * 2.0 * 0.429043 * x * z;\n\tresult += shCoefficients[ 8 ] * 0.429043 * ( x * x - y * y );\n\treturn result;\n}\nvec3 getLightProbeIrradiance( const in vec3 lightProbe[ 9 ], const in vec3 normal ) {\n\tvec3 worldNormal = inverseTransformDirection( normal, viewMatrix );\n\tvec3 irradiance = shGetIrradianceAt( worldNormal, lightProbe );\n\treturn irradiance;\n}\nvec3 getAmbientLightIrradiance( const in vec3 ambientLightColor ) {\n\tvec3 irradiance = ambientLightColor;\n\treturn irradiance;\n}\nfloat getDistanceAttenuation( const in float lightDistance, const in float cutoffDistance, const in float decayExponent ) {\n\t#if defined ( PHYSICALLY_CORRECT_LIGHTS )\n\t\tfloat distanceFalloff = 1.0 / max( pow( lightDistance, decayExponent ), 0.01 );\n\t\tif ( cutoffDistance > 0.0 ) {\n\t\t\tdistanceFalloff *= pow2( saturate( 1.0 - pow4( lightDistance / cutoffDistance ) ) );\n\t\t}\n\t\treturn distanceFalloff;\n\t#else\n\t\tif ( cutoffDistance > 0.0 && decayExponent > 0.0 ) {\n\t\t\treturn pow( saturate( - lightDistance / cutoffDistance + 1.0 ), decayExponent );\n\t\t}\n\t\treturn 1.0;\n\t#endif\n}\nfloat getSpotAttenuation( const in float coneCosine, const in float penumbraCosine, const in float angleCosine ) {\n\treturn smoothstep( coneCosine, penumbraCosine, angleCosine );\n}\n#if NUM_DIR_LIGHTS > 0\n\tstruct DirectionalLight {\n\t\tvec3 direction;\n\t\tvec3 color;\n\t};\n\tuniform DirectionalLight directionalLights[ NUM_DIR_LIGHTS ];\n\tvoid getDirectionalLightInfo( const in DirectionalLight directionalLight, const in GeometricContext geometry, out IncidentLight light ) {\n\t\tlight.color = directionalLight.color;\n\t\tlight.direction = directionalLight.direction;\n\t\tlight.visible = true;\n\t}\n#endif\n#if NUM_POINT_LIGHTS > 0\n\tstruct PointLight {\n\t\tvec3 position;\n\t\tvec3 color;\n\t\tfloat distance;\n\t\tfloat decay;\n\t};\n\tuniform PointLight pointLights[ NUM_POINT_LIGHTS ];\n\tvoid getPointLightInfo( const in PointLight pointLight, const in GeometricContext geometry, out IncidentLight light ) {\n\t\tvec3 lVector = pointLight.position - geometry.position;\n\t\tlight.direction = normalize( lVector );\n\t\tfloat lightDistance = length( lVector );\n\t\tlight.color = pointLight.color;\n\t\tlight.color *= getDistanceAttenuation( lightDistance, pointLight.distance, pointLight.decay );\n\t\tlight.visible = ( light.color != vec3( 0.0 ) );\n\t}\n#endif\n#if NUM_SPOT_LIGHTS > 0\n\tstruct SpotLight {\n\t\tvec3 position;\n\t\tvec3 direction;\n\t\tvec3 color;\n\t\tfloat distance;\n\t\tfloat decay;\n\t\tfloat coneCos;\n\t\tfloat penumbraCos;\n\t};\n\tuniform SpotLight spotLights[ NUM_SPOT_LIGHTS ];\n\tvoid getSpotLightInfo( const in SpotLight spotLight, const in GeometricContext geometry, out IncidentLight light ) {\n\t\tvec3 lVector = spotLight.position - geometry.position;\n\t\tlight.direction = normalize( lVector );\n\t\tfloat angleCos = dot( light.direction, spotLight.direction );\n\t\tfloat spotAttenuation = getSpotAttenuation( spotLight.coneCos, spotLight.penumbraCos, angleCos );\n\t\tif ( spotAttenuation > 0.0 ) {\n\t\t\tfloat lightDistance = length( lVector );\n\t\t\tlight.color = spotLight.color * spotAttenuation;\n\t\t\tlight.color *= getDistanceAttenuation( lightDistance, spotLight.distance, spotLight.decay );\n\t\t\tlight.visible = ( light.color != vec3( 0.0 ) );\n\t\t} else {\n\t\t\tlight.color = vec3( 0.0 );\n\t\t\tlight.visible = false;\n\t\t}\n\t}\n#endif\n#if NUM_RECT_AREA_LIGHTS > 0\n\tstruct RectAreaLight {\n\t\tvec3 color;\n\t\tvec3 position;\n\t\tvec3 halfWidth;\n\t\tvec3 halfHeight;\n\t};\n\tuniform sampler2D ltc_1;\tuniform sampler2D ltc_2;\n\tuniform RectAreaLight rectAreaLights[ NUM_RECT_AREA_LIGHTS ];\n#endif\n#if NUM_HEMI_LIGHTS > 0\n\tstruct HemisphereLight {\n\t\tvec3 direction;\n\t\tvec3 skyColor;\n\t\tvec3 groundColor;\n\t};\n\tuniform HemisphereLight hemisphereLights[ NUM_HEMI_LIGHTS ];\n\tvec3 getHemisphereLightIrradiance( const in HemisphereLight hemiLight, const in vec3 normal ) {\n\t\tfloat dotNL = dot( normal, hemiLight.direction );\n\t\tfloat hemiDiffuseWeight = 0.5 * dotNL + 0.5;\n\t\tvec3 irradiance = mix( hemiLight.groundColor, hemiLight.skyColor, hemiDiffuseWeight );\n\t\treturn irradiance;\n\t}\n#endif",lights_toon_fragment:"ToonMaterial material;\nmaterial.diffuseColor = diffuseColor.rgb;",lights_toon_pars_fragment:"varying vec3 vViewPosition;\nstruct ToonMaterial {\n\tvec3 diffuseColor;\n};\nvoid RE_Direct_Toon( const in IncidentLight directLight, const in GeometricContext geometry, const in ToonMaterial material, inout ReflectedLight reflectedLight ) {\n\tvec3 irradiance = getGradientIrradiance( geometry.normal, directLight.direction ) * directLight.color;\n\treflectedLight.directDiffuse += irradiance * BRDF_Lambert( material.diffuseColor );\n}\nvoid RE_IndirectDiffuse_Toon( const in vec3 irradiance, const in GeometricContext geometry, const in ToonMaterial material, inout ReflectedLight reflectedLight ) {\n\treflectedLight.indirectDiffuse += irradiance * BRDF_Lambert( material.diffuseColor );\n}\n#define RE_Direct\t\t\t\tRE_Direct_Toon\n#define RE_IndirectDiffuse\t\tRE_IndirectDiffuse_Toon",lights_phong_fragment:"BlinnPhongMaterial material;\nmaterial.diffuseColor = diffuseColor.rgb;\nmaterial.specularColor = specular;\nmaterial.specularShininess = shininess;\nmaterial.specularStrength = specularStrength;",lights_phong_pars_fragment:"varying vec3 vViewPosition;\nstruct BlinnPhongMaterial {\n\tvec3 diffuseColor;\n\tvec3 specularColor;\n\tfloat specularShininess;\n\tfloat specularStrength;\n};\nvoid RE_Direct_BlinnPhong( const in IncidentLight directLight, const in GeometricContext geometry, const in BlinnPhongMaterial material, inout ReflectedLight reflectedLight ) {\n\tfloat dotNL = saturate( dot( geometry.normal, directLight.direction ) );\n\tvec3 irradiance = dotNL * directLight.color;\n\treflectedLight.directDiffuse += irradiance * BRDF_Lambert( material.diffuseColor );\n\treflectedLight.directSpecular += irradiance * BRDF_BlinnPhong( directLight.direction, geometry.viewDir, geometry.normal, material.specularColor, material.specularShininess ) * material.specularStrength;\n}\nvoid RE_IndirectDiffuse_BlinnPhong( const in vec3 irradiance, const in GeometricContext geometry, const in BlinnPhongMaterial material, inout ReflectedLight reflectedLight ) {\n\treflectedLight.indirectDiffuse += irradiance * BRDF_Lambert( material.diffuseColor );\n}\n#define RE_Direct\t\t\t\tRE_Direct_BlinnPhong\n#define RE_IndirectDiffuse\t\tRE_IndirectDiffuse_BlinnPhong",lights_physical_fragment:"PhysicalMaterial material;\nmaterial.diffuseColor = diffuseColor.rgb * ( 1.0 - metalnessFactor );\nvec3 dxy = max( abs( dFdx( geometryNormal ) ), abs( dFdy( geometryNormal ) ) );\nfloat geometryRoughness = max( max( dxy.x, dxy.y ), dxy.z );\nmaterial.roughness = max( roughnessFactor, 0.0525 );material.roughness += geometryRoughness;\nmaterial.roughness = min( material.roughness, 1.0 );\n#ifdef IOR\n\tmaterial.ior = ior;\n\t#ifdef SPECULAR\n\t\tfloat specularIntensityFactor = specularIntensity;\n\t\tvec3 specularColorFactor = specularColor;\n\t\t#ifdef USE_SPECULARINTENSITYMAP\n\t\t\tspecularIntensityFactor *= texture2D( specularIntensityMap, vUv ).a;\n\t\t#endif\n\t\t#ifdef USE_SPECULARCOLORMAP\n\t\t\tspecularColorFactor *= texture2D( specularColorMap, vUv ).rgb;\n\t\t#endif\n\t\tmaterial.specularF90 = mix( specularIntensityFactor, 1.0, metalnessFactor );\n\t#else\n\t\tfloat specularIntensityFactor = 1.0;\n\t\tvec3 specularColorFactor = vec3( 1.0 );\n\t\tmaterial.specularF90 = 1.0;\n\t#endif\n\tmaterial.specularColor = mix( min( pow2( ( material.ior - 1.0 ) / ( material.ior + 1.0 ) ) * specularColorFactor, vec3( 1.0 ) ) * specularIntensityFactor, diffuseColor.rgb, metalnessFactor );\n#else\n\tmaterial.specularColor = mix( vec3( 0.04 ), diffuseColor.rgb, metalnessFactor );\n\tmaterial.specularF90 = 1.0;\n#endif\n#ifdef USE_CLEARCOAT\n\tmaterial.clearcoat = clearcoat;\n\tmaterial.clearcoatRoughness = clearcoatRoughness;\n\tmaterial.clearcoatF0 = vec3( 0.04 );\n\tmaterial.clearcoatF90 = 1.0;\n\t#ifdef USE_CLEARCOATMAP\n\t\tmaterial.clearcoat *= texture2D( clearcoatMap, vUv ).x;\n\t#endif\n\t#ifdef USE_CLEARCOAT_ROUGHNESSMAP\n\t\tmaterial.clearcoatRoughness *= texture2D( clearcoatRoughnessMap, vUv ).y;\n\t#endif\n\tmaterial.clearcoat = saturate( material.clearcoat );\tmaterial.clearcoatRoughness = max( material.clearcoatRoughness, 0.0525 );\n\tmaterial.clearcoatRoughness += geometryRoughness;\n\tmaterial.clearcoatRoughness = min( material.clearcoatRoughness, 1.0 );\n#endif\n#ifdef USE_IRIDESCENCE\n\tmaterial.iridescence = iridescence;\n\tmaterial.iridescenceIOR = iridescenceIOR;\n\t#ifdef USE_IRIDESCENCEMAP\n\t\tmaterial.iridescence *= texture2D( iridescenceMap, vUv ).r;\n\t#endif\n\t#ifdef USE_IRIDESCENCE_THICKNESSMAP\n\t\tmaterial.iridescenceThickness = (iridescenceThicknessMaximum - iridescenceThicknessMinimum) * texture2D( iridescenceThicknessMap, vUv ).g + iridescenceThicknessMinimum;\n\t#else\n\t\tmaterial.iridescenceThickness = iridescenceThicknessMaximum;\n\t#endif\n#endif\n#ifdef USE_SHEEN\n\tmaterial.sheenColor = sheenColor;\n\t#ifdef USE_SHEENCOLORMAP\n\t\tmaterial.sheenColor *= texture2D( sheenColorMap, vUv ).rgb;\n\t#endif\n\tmaterial.sheenRoughness = clamp( sheenRoughness, 0.07, 1.0 );\n\t#ifdef USE_SHEENROUGHNESSMAP\n\t\tmaterial.sheenRoughness *= texture2D( sheenRoughnessMap, vUv ).a;\n\t#endif\n#endif",lights_physical_pars_fragment:"struct PhysicalMaterial {\n\tvec3 diffuseColor;\n\tfloat roughness;\n\tvec3 specularColor;\n\tfloat specularF90;\n\t#ifdef USE_CLEARCOAT\n\t\tfloat clearcoat;\n\t\tfloat clearcoatRoughness;\n\t\tvec3 clearcoatF0;\n\t\tfloat clearcoatF90;\n\t#endif\n\t#ifdef USE_IRIDESCENCE\n\t\tfloat iridescence;\n\t\tfloat iridescenceIOR;\n\t\tfloat iridescenceThickness;\n\t\tvec3 iridescenceFresnel;\n\t\tvec3 iridescenceF0;\n\t#endif\n\t#ifdef USE_SHEEN\n\t\tvec3 sheenColor;\n\t\tfloat sheenRoughness;\n\t#endif\n\t#ifdef IOR\n\t\tfloat ior;\n\t#endif\n\t#ifdef USE_TRANSMISSION\n\t\tfloat transmission;\n\t\tfloat transmissionAlpha;\n\t\tfloat thickness;\n\t\tfloat attenuationDistance;\n\t\tvec3 attenuationColor;\n\t#endif\n};\nvec3 clearcoatSpecular = vec3( 0.0 );\nvec3 sheenSpecular = vec3( 0.0 );\nfloat IBLSheenBRDF( const in vec3 normal, const in vec3 viewDir, const in float roughness ) {\n\tfloat dotNV = saturate( dot( normal, viewDir ) );\n\tfloat r2 = roughness * roughness;\n\tfloat a = roughness < 0.25 ? -339.2 * r2 + 161.4 * roughness - 25.9 : -8.48 * r2 + 14.3 * roughness - 9.95;\n\tfloat b = roughness < 0.25 ? 44.0 * r2 - 23.7 * roughness + 3.26 : 1.97 * r2 - 3.27 * roughness + 0.72;\n\tfloat DG = exp( a * dotNV + b ) + ( roughness < 0.25 ? 0.0 : 0.1 * ( roughness - 0.25 ) );\n\treturn saturate( DG * RECIPROCAL_PI );\n}\nvec2 DFGApprox( const in vec3 normal, const in vec3 viewDir, const in float roughness ) {\n\tfloat dotNV = saturate( dot( normal, viewDir ) );\n\tconst vec4 c0 = vec4( - 1, - 0.0275, - 0.572, 0.022 );\n\tconst vec4 c1 = vec4( 1, 0.0425, 1.04, - 0.04 );\n\tvec4 r = roughness * c0 + c1;\n\tfloat a004 = min( r.x * r.x, exp2( - 9.28 * dotNV ) ) * r.x + r.y;\n\tvec2 fab = vec2( - 1.04, 1.04 ) * a004 + r.zw;\n\treturn fab;\n}\nvec3 EnvironmentBRDF( const in vec3 normal, const in vec3 viewDir, const in vec3 specularColor, const in float specularF90, const in float roughness ) {\n\tvec2 fab = DFGApprox( normal, viewDir, roughness );\n\treturn specularColor * fab.x + specularF90 * fab.y;\n}\n#ifdef USE_IRIDESCENCE\nvoid computeMultiscatteringIridescence( const in vec3 normal, const in vec3 viewDir, const in vec3 specularColor, const in float specularF90, const in float iridescence, const in vec3 iridescenceF0, const in float roughness, inout vec3 singleScatter, inout vec3 multiScatter ) {\n#else\nvoid computeMultiscattering( const in vec3 normal, const in vec3 viewDir, const in vec3 specularColor, const in float specularF90, const in float roughness, inout vec3 singleScatter, inout vec3 multiScatter ) {\n#endif\n\tvec2 fab = DFGApprox( normal, viewDir, roughness );\n\t#ifdef USE_IRIDESCENCE\n\t\tvec3 Fr = mix( specularColor, iridescenceF0, iridescence );\n\t#else\n\t\tvec3 Fr = specularColor;\n\t#endif\n\tvec3 FssEss = Fr * fab.x + specularF90 * fab.y;\n\tfloat Ess = fab.x + fab.y;\n\tfloat Ems = 1.0 - Ess;\n\tvec3 Favg = Fr + ( 1.0 - Fr ) * 0.047619;\tvec3 Fms = FssEss * Favg / ( 1.0 - Ems * Favg );\n\tsingleScatter += FssEss;\n\tmultiScatter += Fms * Ems;\n}\n#if NUM_RECT_AREA_LIGHTS > 0\n\tvoid RE_Direct_RectArea_Physical( const in RectAreaLight rectAreaLight, const in GeometricContext geometry, const in PhysicalMaterial material, inout ReflectedLight reflectedLight ) {\n\t\tvec3 normal = geometry.normal;\n\t\tvec3 viewDir = geometry.viewDir;\n\t\tvec3 position = geometry.position;\n\t\tvec3 lightPos = rectAreaLight.position;\n\t\tvec3 halfWidth = rectAreaLight.halfWidth;\n\t\tvec3 halfHeight = rectAreaLight.halfHeight;\n\t\tvec3 lightColor = rectAreaLight.color;\n\t\tfloat roughness = material.roughness;\n\t\tvec3 rectCoords[ 4 ];\n\t\trectCoords[ 0 ] = lightPos + halfWidth - halfHeight;\t\trectCoords[ 1 ] = lightPos - halfWidth - halfHeight;\n\t\trectCoords[ 2 ] = lightPos - halfWidth + halfHeight;\n\t\trectCoords[ 3 ] = lightPos + halfWidth + halfHeight;\n\t\tvec2 uv = LTC_Uv( normal, viewDir, roughness );\n\t\tvec4 t1 = texture2D( ltc_1, uv );\n\t\tvec4 t2 = texture2D( ltc_2, uv );\n\t\tmat3 mInv = mat3(\n\t\t\tvec3( t1.x, 0, t1.y ),\n\t\t\tvec3(    0, 1,    0 ),\n\t\t\tvec3( t1.z, 0, t1.w )\n\t\t);\n\t\tvec3 fresnel = ( material.specularColor * t2.x + ( vec3( 1.0 ) - material.specularColor ) * t2.y );\n\t\treflectedLight.directSpecular += lightColor * fresnel * LTC_Evaluate( normal, viewDir, position, mInv, rectCoords );\n\t\treflectedLight.directDiffuse += lightColor * material.diffuseColor * LTC_Evaluate( normal, viewDir, position, mat3( 1.0 ), rectCoords );\n\t}\n#endif\nvoid RE_Direct_Physical( const in IncidentLight directLight, const in GeometricContext geometry, const in PhysicalMaterial material, inout ReflectedLight reflectedLight ) {\n\tfloat dotNL = saturate( dot( geometry.normal, directLight.direction ) );\n\tvec3 irradiance = dotNL * directLight.color;\n\t#ifdef USE_CLEARCOAT\n\t\tfloat dotNLcc = saturate( dot( geometry.clearcoatNormal, directLight.direction ) );\n\t\tvec3 ccIrradiance = dotNLcc * directLight.color;\n\t\tclearcoatSpecular += ccIrradiance * BRDF_GGX( directLight.direction, geometry.viewDir, geometry.clearcoatNormal, material.clearcoatF0, material.clearcoatF90, material.clearcoatRoughness );\n\t#endif\n\t#ifdef USE_SHEEN\n\t\tsheenSpecular += irradiance * BRDF_Sheen( directLight.direction, geometry.viewDir, geometry.normal, material.sheenColor, material.sheenRoughness );\n\t#endif\n\t#ifdef USE_IRIDESCENCE\n\t\treflectedLight.directSpecular += irradiance * BRDF_GGX_Iridescence( directLight.direction, geometry.viewDir, geometry.normal, material.specularColor, material.specularF90, material.iridescence, material.iridescenceFresnel, material.roughness );\n\t#else\n\t\treflectedLight.directSpecular += irradiance * BRDF_GGX( directLight.direction, geometry.viewDir, geometry.normal, material.specularColor, material.specularF90, material.roughness );\n\t#endif\n\treflectedLight.directDiffuse += irradiance * BRDF_Lambert( material.diffuseColor );\n}\nvoid RE_IndirectDiffuse_Physical( const in vec3 irradiance, const in GeometricContext geometry, const in PhysicalMaterial material, inout ReflectedLight reflectedLight ) {\n\treflectedLight.indirectDiffuse += irradiance * BRDF_Lambert( material.diffuseColor );\n}\nvoid RE_IndirectSpecular_Physical( const in vec3 radiance, const in vec3 irradiance, const in vec3 clearcoatRadiance, const in GeometricContext geometry, const in PhysicalMaterial material, inout ReflectedLight reflectedLight) {\n\t#ifdef USE_CLEARCOAT\n\t\tclearcoatSpecular += clearcoatRadiance * EnvironmentBRDF( geometry.clearcoatNormal, geometry.viewDir, material.clearcoatF0, material.clearcoatF90, material.clearcoatRoughness );\n\t#endif\n\t#ifdef USE_SHEEN\n\t\tsheenSpecular += irradiance * material.sheenColor * IBLSheenBRDF( geometry.normal, geometry.viewDir, material.sheenRoughness );\n\t#endif\n\tvec3 singleScattering = vec3( 0.0 );\n\tvec3 multiScattering = vec3( 0.0 );\n\tvec3 cosineWeightedIrradiance = irradiance * RECIPROCAL_PI;\n\t#ifdef USE_IRIDESCENCE\n\t\tcomputeMultiscatteringIridescence( geometry.normal, geometry.viewDir, material.specularColor, material.specularF90, material.iridescence, material.iridescenceFresnel, material.roughness, singleScattering, multiScattering );\n\t#else\n\t\tcomputeMultiscattering( geometry.normal, geometry.viewDir, material.specularColor, material.specularF90, material.roughness, singleScattering, multiScattering );\n\t#endif\n\tvec3 totalScattering = singleScattering + multiScattering;\n\tvec3 diffuse = material.diffuseColor * ( 1.0 - max( max( totalScattering.r, totalScattering.g ), totalScattering.b ) );\n\treflectedLight.indirectSpecular += radiance * singleScattering;\n\treflectedLight.indirectSpecular += multiScattering * cosineWeightedIrradiance;\n\treflectedLight.indirectDiffuse += diffuse * cosineWeightedIrradiance;\n}\n#define RE_Direct\t\t\t\tRE_Direct_Physical\n#define RE_Direct_RectArea\t\tRE_Direct_RectArea_Physical\n#define RE_IndirectDiffuse\t\tRE_IndirectDiffuse_Physical\n#define RE_IndirectSpecular\t\tRE_IndirectSpecular_Physical\nfloat computeSpecularOcclusion( const in float dotNV, const in float ambientOcclusion, const in float roughness ) {\n\treturn saturate( pow( dotNV + ambientOcclusion, exp2( - 16.0 * roughness - 1.0 ) ) - 1.0 + ambientOcclusion );\n}",lights_fragment_begin:"\nGeometricContext geometry;\ngeometry.position = - vViewPosition;\ngeometry.normal = normal;\ngeometry.viewDir = ( isOrthographic ) ? vec3( 0, 0, 1 ) : normalize( vViewPosition );\n#ifdef USE_CLEARCOAT\n\tgeometry.clearcoatNormal = clearcoatNormal;\n#endif\n#ifdef USE_IRIDESCENCE\n\tfloat dotNVi = saturate( dot( normal, geometry.viewDir ) );\n\tif ( material.iridescenceThickness == 0.0 ) {\n\t\tmaterial.iridescence = 0.0;\n\t} else {\n\t\tmaterial.iridescence = saturate( material.iridescence );\n\t}\n\tif ( material.iridescence > 0.0 ) {\n\t\tmaterial.iridescenceFresnel = evalIridescence( 1.0, material.iridescenceIOR, dotNVi, material.iridescenceThickness, material.specularColor );\n\t\tmaterial.iridescenceF0 = Schlick_to_F0( material.iridescenceFresnel, 1.0, dotNVi );\n\t}\n#endif\nIncidentLight directLight;\n#if ( NUM_POINT_LIGHTS > 0 ) && defined( RE_Direct )\n\tPointLight pointLight;\n\t#if defined( USE_SHADOWMAP ) && NUM_POINT_LIGHT_SHADOWS > 0\n\tPointLightShadow pointLightShadow;\n\t#endif\n\t#pragma unroll_loop_start\n\tfor ( int i = 0; i < NUM_POINT_LIGHTS; i ++ ) {\n\t\tpointLight = pointLights[ i ];\n\t\tgetPointLightInfo( pointLight, geometry, directLight );\n\t\t#if defined( USE_SHADOWMAP ) && ( UNROLLED_LOOP_INDEX < NUM_POINT_LIGHT_SHADOWS )\n\t\tpointLightShadow = pointLightShadows[ i ];\n\t\tdirectLight.color *= ( directLight.visible && receiveShadow ) ? getPointShadow( pointShadowMap[ i ], pointLightShadow.shadowMapSize, pointLightShadow.shadowBias, pointLightShadow.shadowRadius, vPointShadowCoord[ i ], pointLightShadow.shadowCameraNear, pointLightShadow.shadowCameraFar ) : 1.0;\n\t\t#endif\n\t\tRE_Direct( directLight, geometry, material, reflectedLight );\n\t}\n\t#pragma unroll_loop_end\n#endif\n#if ( NUM_SPOT_LIGHTS > 0 ) && defined( RE_Direct )\n\tSpotLight spotLight;\n\tvec4 spotColor;\n\tvec3 spotLightCoord;\n\tbool inSpotLightMap;\n\t#if defined( USE_SHADOWMAP ) && NUM_SPOT_LIGHT_SHADOWS > 0\n\tSpotLightShadow spotLightShadow;\n\t#endif\n\t#pragma unroll_loop_start\n\tfor ( int i = 0; i < NUM_SPOT_LIGHTS; i ++ ) {\n\t\tspotLight = spotLights[ i ];\n\t\tgetSpotLightInfo( spotLight, geometry, directLight );\n\t\t#if ( UNROLLED_LOOP_INDEX < NUM_SPOT_LIGHT_SHADOWS_WITH_MAPS )\n\t\t#define SPOT_LIGHT_MAP_INDEX UNROLLED_LOOP_INDEX\n\t\t#elif ( UNROLLED_LOOP_INDEX < NUM_SPOT_LIGHT_SHADOWS )\n\t\t#define SPOT_LIGHT_MAP_INDEX NUM_SPOT_LIGHT_MAPS\n\t\t#else\n\t\t#define SPOT_LIGHT_MAP_INDEX ( UNROLLED_LOOP_INDEX - NUM_SPOT_LIGHT_SHADOWS + NUM_SPOT_LIGHT_SHADOWS_WITH_MAPS )\n\t\t#endif\n\t\t#if ( SPOT_LIGHT_MAP_INDEX < NUM_SPOT_LIGHT_MAPS )\n\t\t\tspotLightCoord = vSpotLightCoord[ i ].xyz / vSpotLightCoord[ i ].w;\n\t\t\tinSpotLightMap = all( lessThan( abs( spotLightCoord * 2. - 1. ), vec3( 1.0 ) ) );\n\t\t\tspotColor = texture2D( spotLightMap[ SPOT_LIGHT_MAP_INDEX ], spotLightCoord.xy );\n\t\t\tdirectLight.color = inSpotLightMap ? directLight.color * spotColor.rgb : directLight.color;\n\t\t#endif\n\t\t#undef SPOT_LIGHT_MAP_INDEX\n\t\t#if defined( USE_SHADOWMAP ) && ( UNROLLED_LOOP_INDEX < NUM_SPOT_LIGHT_SHADOWS )\n\t\tspotLightShadow = spotLightShadows[ i ];\n\t\tdirectLight.color *= ( directLight.visible && receiveShadow ) ? getShadow( spotShadowMap[ i ], spotLightShadow.shadowMapSize, spotLightShadow.shadowBias, spotLightShadow.shadowRadius, vSpotLightCoord[ i ] ) : 1.0;\n\t\t#endif\n\t\tRE_Direct( directLight, geometry, material, reflectedLight );\n\t}\n\t#pragma unroll_loop_end\n#endif\n#if ( NUM_DIR_LIGHTS > 0 ) && defined( RE_Direct )\n\tDirectionalLight directionalLight;\n\t#if defined( USE_SHADOWMAP ) && NUM_DIR_LIGHT_SHADOWS > 0\n\tDirectionalLightShadow directionalLightShadow;\n\t#endif\n\t#pragma unroll_loop_start\n\tfor ( int i = 0; i < NUM_DIR_LIGHTS; i ++ ) {\n\t\tdirectionalLight = directionalLights[ i ];\n\t\tgetDirectionalLightInfo( directionalLight, geometry, directLight );\n\t\t#if defined( USE_SHADOWMAP ) && ( UNROLLED_LOOP_INDEX < NUM_DIR_LIGHT_SHADOWS )\n\t\tdirectionalLightShadow = directionalLightShadows[ i ];\n\t\tdirectLight.color *= ( directLight.visible && receiveShadow ) ? getShadow( directionalShadowMap[ i ], directionalLightShadow.shadowMapSize, directionalLightShadow.shadowBias, directionalLightShadow.shadowRadius, vDirectionalShadowCoord[ i ] ) : 1.0;\n\t\t#endif\n\t\tRE_Direct( directLight, geometry, material, reflectedLight );\n\t}\n\t#pragma unroll_loop_end\n#endif\n#if ( NUM_RECT_AREA_LIGHTS > 0 ) && defined( RE_Direct_RectArea )\n\tRectAreaLight rectAreaLight;\n\t#pragma unroll_loop_start\n\tfor ( int i = 0; i < NUM_RECT_AREA_LIGHTS; i ++ ) {\n\t\trectAreaLight = rectAreaLights[ i ];\n\t\tRE_Direct_RectArea( rectAreaLight, geometry, material, reflectedLight );\n\t}\n\t#pragma unroll_loop_end\n#endif\n#if defined( RE_IndirectDiffuse )\n\tvec3 iblIrradiance = vec3( 0.0 );\n\tvec3 irradiance = getAmbientLightIrradiance( ambientLightColor );\n\tirradiance += getLightProbeIrradiance( lightProbe, geometry.normal );\n\t#if ( NUM_HEMI_LIGHTS > 0 )\n\t\t#pragma unroll_loop_start\n\t\tfor ( int i = 0; i < NUM_HEMI_LIGHTS; i ++ ) {\n\t\t\tirradiance += getHemisphereLightIrradiance( hemisphereLights[ i ], geometry.normal );\n\t\t}\n\t\t#pragma unroll_loop_end\n\t#endif\n#endif\n#if defined( RE_IndirectSpecular )\n\tvec3 radiance = vec3( 0.0 );\n\tvec3 clearcoatRadiance = vec3( 0.0 );\n#endif",lights_fragment_maps:"#if defined( RE_IndirectDiffuse )\n\t#ifdef USE_LIGHTMAP\n\t\tvec4 lightMapTexel = texture2D( lightMap, vUv2 );\n\t\tvec3 lightMapIrradiance = lightMapTexel.rgb * lightMapIntensity;\n\t\tirradiance += lightMapIrradiance;\n\t#endif\n\t#if defined( USE_ENVMAP ) && defined( STANDARD ) && defined( ENVMAP_TYPE_CUBE_UV )\n\t\tiblIrradiance += getIBLIrradiance( geometry.normal );\n\t#endif\n#endif\n#if defined( USE_ENVMAP ) && defined( RE_IndirectSpecular )\n\tradiance += getIBLRadiance( geometry.viewDir, geometry.normal, material.roughness );\n\t#ifdef USE_CLEARCOAT\n\t\tclearcoatRadiance += getIBLRadiance( geometry.viewDir, geometry.clearcoatNormal, material.clearcoatRoughness );\n\t#endif\n#endif",lights_fragment_end:"#if defined( RE_IndirectDiffuse )\n\tRE_IndirectDiffuse( irradiance, geometry, material, reflectedLight );\n#endif\n#if defined( RE_IndirectSpecular )\n\tRE_IndirectSpecular( radiance, iblIrradiance, clearcoatRadiance, geometry, material, reflectedLight );\n#endif",logdepthbuf_fragment:"#if defined( USE_LOGDEPTHBUF ) && defined( USE_LOGDEPTHBUF_EXT )\n\tgl_FragDepthEXT = vIsPerspective == 0.0 ? gl_FragCoord.z : log2( vFragDepth ) * logDepthBufFC * 0.5;\n#endif",logdepthbuf_pars_fragment:"#if defined( USE_LOGDEPTHBUF ) && defined( USE_LOGDEPTHBUF_EXT )\n\tuniform float logDepthBufFC;\n\tvarying float vFragDepth;\n\tvarying float vIsPerspective;\n#endif",logdepthbuf_pars_vertex:"#ifdef USE_LOGDEPTHBUF\n\t#ifdef USE_LOGDEPTHBUF_EXT\n\t\tvarying float vFragDepth;\n\t\tvarying float vIsPerspective;\n\t#else\n\t\tuniform float logDepthBufFC;\n\t#endif\n#endif",logdepthbuf_vertex:"#ifdef USE_LOGDEPTHBUF\n\t#ifdef USE_LOGDEPTHBUF_EXT\n\t\tvFragDepth = 1.0 + gl_Position.w;\n\t\tvIsPerspective = float( isPerspectiveMatrix( projectionMatrix ) );\n\t#else\n\t\tif ( isPerspectiveMatrix( projectionMatrix ) ) {\n\t\t\tgl_Position.z = log2( max( EPSILON, gl_Position.w + 1.0 ) ) * logDepthBufFC - 1.0;\n\t\t\tgl_Position.z *= gl_Position.w;\n\t\t}\n\t#endif\n#endif",map_fragment:"#ifdef USE_MAP\n\tvec4 sampledDiffuseColor = texture2D( map, vUv );\n\t#ifdef DECODE_VIDEO_TEXTURE\n\t\tsampledDiffuseColor = vec4( mix( pow( sampledDiffuseColor.rgb * 0.9478672986 + vec3( 0.0521327014 ), vec3( 2.4 ) ), sampledDiffuseColor.rgb * 0.0773993808, vec3( lessThanEqual( sampledDiffuseColor.rgb, vec3( 0.04045 ) ) ) ), sampledDiffuseColor.w );\n\t#endif\n\tdiffuseColor *= sampledDiffuseColor;\n#endif",map_pars_fragment:"#ifdef USE_MAP\n\tuniform sampler2D map;\n#endif",map_particle_fragment:"#if defined( USE_MAP ) || defined( USE_ALPHAMAP )\n\tvec2 uv = ( uvTransform * vec3( gl_PointCoord.x, 1.0 - gl_PointCoord.y, 1 ) ).xy;\n#endif\n#ifdef USE_MAP\n\tdiffuseColor *= texture2D( map, uv );\n#endif\n#ifdef USE_ALPHAMAP\n\tdiffuseColor.a *= texture2D( alphaMap, uv ).g;\n#endif",map_particle_pars_fragment:"#if defined( USE_MAP ) || defined( USE_ALPHAMAP )\n\tuniform mat3 uvTransform;\n#endif\n#ifdef USE_MAP\n\tuniform sampler2D map;\n#endif\n#ifdef USE_ALPHAMAP\n\tuniform sampler2D alphaMap;\n#endif",metalnessmap_fragment:"float metalnessFactor = metalness;\n#ifdef USE_METALNESSMAP\n\tvec4 texelMetalness = texture2D( metalnessMap, vUv );\n\tmetalnessFactor *= texelMetalness.b;\n#endif",metalnessmap_pars_fragment:"#ifdef USE_METALNESSMAP\n\tuniform sampler2D metalnessMap;\n#endif",morphcolor_vertex:"#if defined( USE_MORPHCOLORS ) && defined( MORPHTARGETS_TEXTURE )\n\tvColor *= morphTargetBaseInfluence;\n\tfor ( int i = 0; i < MORPHTARGETS_COUNT; i ++ ) {\n\t\t#if defined( USE_COLOR_ALPHA )\n\t\t\tif ( morphTargetInfluences[ i ] != 0.0 ) vColor += getMorph( gl_VertexID, i, 2 ) * morphTargetInfluences[ i ];\n\t\t#elif defined( USE_COLOR )\n\t\t\tif ( morphTargetInfluences[ i ] != 0.0 ) vColor += getMorph( gl_VertexID, i, 2 ).rgb * morphTargetInfluences[ i ];\n\t\t#endif\n\t}\n#endif",morphnormal_vertex:"#ifdef USE_MORPHNORMALS\n\tobjectNormal *= morphTargetBaseInfluence;\n\t#ifdef MORPHTARGETS_TEXTURE\n\t\tfor ( int i = 0; i < MORPHTARGETS_COUNT; i ++ ) {\n\t\t\tif ( morphTargetInfluences[ i ] != 0.0 ) objectNormal += getMorph( gl_VertexID, i, 1 ).xyz * morphTargetInfluences[ i ];\n\t\t}\n\t#else\n\t\tobjectNormal += morphNormal0 * morphTargetInfluences[ 0 ];\n\t\tobjectNormal += morphNormal1 * morphTargetInfluences[ 1 ];\n\t\tobjectNormal += morphNormal2 * morphTargetInfluences[ 2 ];\n\t\tobjectNormal += morphNormal3 * morphTargetInfluences[ 3 ];\n\t#endif\n#endif",morphtarget_pars_vertex:"#ifdef USE_MORPHTARGETS\n\tuniform float morphTargetBaseInfluence;\n\t#ifdef MORPHTARGETS_TEXTURE\n\t\tuniform float morphTargetInfluences[ MORPHTARGETS_COUNT ];\n\t\tuniform sampler2DArray morphTargetsTexture;\n\t\tuniform ivec2 morphTargetsTextureSize;\n\t\tvec4 getMorph( const in int vertexIndex, const in int morphTargetIndex, const in int offset ) {\n\t\t\tint texelIndex = vertexIndex * MORPHTARGETS_TEXTURE_STRIDE + offset;\n\t\t\tint y = texelIndex / morphTargetsTextureSize.x;\n\t\t\tint x = texelIndex - y * morphTargetsTextureSize.x;\n\t\t\tivec3 morphUV = ivec3( x, y, morphTargetIndex );\n\t\t\treturn texelFetch( morphTargetsTexture, morphUV, 0 );\n\t\t}\n\t#else\n\t\t#ifndef USE_MORPHNORMALS\n\t\t\tuniform float morphTargetInfluences[ 8 ];\n\t\t#else\n\t\t\tuniform float morphTargetInfluences[ 4 ];\n\t\t#endif\n\t#endif\n#endif",morphtarget_vertex:"#ifdef USE_MORPHTARGETS\n\ttransformed *= morphTargetBaseInfluence;\n\t#ifdef MORPHTARGETS_TEXTURE\n\t\tfor ( int i = 0; i < MORPHTARGETS_COUNT; i ++ ) {\n\t\t\tif ( morphTargetInfluences[ i ] != 0.0 ) transformed += getMorph( gl_VertexID, i, 0 ).xyz * morphTargetInfluences[ i ];\n\t\t}\n\t#else\n\t\ttransformed += morphTarget0 * morphTargetInfluences[ 0 ];\n\t\ttransformed += morphTarget1 * morphTargetInfluences[ 1 ];\n\t\ttransformed += morphTarget2 * morphTargetInfluences[ 2 ];\n\t\ttransformed += morphTarget3 * morphTargetInfluences[ 3 ];\n\t\t#ifndef USE_MORPHNORMALS\n\t\t\ttransformed += morphTarget4 * morphTargetInfluences[ 4 ];\n\t\t\ttransformed += morphTarget5 * morphTargetInfluences[ 5 ];\n\t\t\ttransformed += morphTarget6 * morphTargetInfluences[ 6 ];\n\t\t\ttransformed += morphTarget7 * morphTargetInfluences[ 7 ];\n\t\t#endif\n\t#endif\n#endif",normal_fragment_begin:"float faceDirection = gl_FrontFacing ? 1.0 : - 1.0;\n#ifdef FLAT_SHADED\n\tvec3 fdx = dFdx( vViewPosition );\n\tvec3 fdy = dFdy( vViewPosition );\n\tvec3 normal = normalize( cross( fdx, fdy ) );\n#else\n\tvec3 normal = normalize( vNormal );\n\t#ifdef DOUBLE_SIDED\n\t\tnormal = normal * faceDirection;\n\t#endif\n\t#ifdef USE_TANGENT\n\t\tvec3 tangent = normalize( vTangent );\n\t\tvec3 bitangent = normalize( vBitangent );\n\t\t#ifdef DOUBLE_SIDED\n\t\t\ttangent = tangent * faceDirection;\n\t\t\tbitangent = bitangent * faceDirection;\n\t\t#endif\n\t\t#if defined( TANGENTSPACE_NORMALMAP ) || defined( USE_CLEARCOAT_NORMALMAP )\n\t\t\tmat3 vTBN = mat3( tangent, bitangent, normal );\n\t\t#endif\n\t#endif\n#endif\nvec3 geometryNormal = normal;",normal_fragment_maps:"#ifdef OBJECTSPACE_NORMALMAP\n\tnormal = texture2D( normalMap, vUv ).xyz * 2.0 - 1.0;\n\t#ifdef FLIP_SIDED\n\t\tnormal = - normal;\n\t#endif\n\t#ifdef DOUBLE_SIDED\n\t\tnormal = normal * faceDirection;\n\t#endif\n\tnormal = normalize( normalMatrix * normal );\n#elif defined( TANGENTSPACE_NORMALMAP )\n\tvec3 mapN = texture2D( normalMap, vUv ).xyz * 2.0 - 1.0;\n\tmapN.xy *= normalScale;\n\t#ifdef USE_TANGENT\n\t\tnormal = normalize( vTBN * mapN );\n\t#else\n\t\tnormal = perturbNormal2Arb( - vViewPosition, normal, mapN, faceDirection );\n\t#endif\n#elif defined( USE_BUMPMAP )\n\tnormal = perturbNormalArb( - vViewPosition, normal, dHdxy_fwd(), faceDirection );\n#endif",normal_pars_fragment:"#ifndef FLAT_SHADED\n\tvarying vec3 vNormal;\n\t#ifdef USE_TANGENT\n\t\tvarying vec3 vTangent;\n\t\tvarying vec3 vBitangent;\n\t#endif\n#endif",normal_pars_vertex:"#ifndef FLAT_SHADED\n\tvarying vec3 vNormal;\n\t#ifdef USE_TANGENT\n\t\tvarying vec3 vTangent;\n\t\tvarying vec3 vBitangent;\n\t#endif\n#endif",normal_vertex:"#ifndef FLAT_SHADED\n\tvNormal = normalize( transformedNormal );\n\t#ifdef USE_TANGENT\n\t\tvTangent = normalize( transformedTangent );\n\t\tvBitangent = normalize( cross( vNormal, vTangent ) * tangent.w );\n\t#endif\n#endif",normalmap_pars_fragment:"#ifdef USE_NORMALMAP\n\tuniform sampler2D normalMap;\n\tuniform vec2 normalScale;\n#endif\n#ifdef OBJECTSPACE_NORMALMAP\n\tuniform mat3 normalMatrix;\n#endif\n#if ! defined ( USE_TANGENT ) && ( defined ( TANGENTSPACE_NORMALMAP ) || defined ( USE_CLEARCOAT_NORMALMAP ) )\n\tvec3 perturbNormal2Arb( vec3 eye_pos, vec3 surf_norm, vec3 mapN, float faceDirection ) {\n\t\tvec3 q0 = dFdx( eye_pos.xyz );\n\t\tvec3 q1 = dFdy( eye_pos.xyz );\n\t\tvec2 st0 = dFdx( vUv.st );\n\t\tvec2 st1 = dFdy( vUv.st );\n\t\tvec3 N = surf_norm;\n\t\tvec3 q1perp = cross( q1, N );\n\t\tvec3 q0perp = cross( N, q0 );\n\t\tvec3 T = q1perp * st0.x + q0perp * st1.x;\n\t\tvec3 B = q1perp * st0.y + q0perp * st1.y;\n\t\tfloat det = max( dot( T, T ), dot( B, B ) );\n\t\tfloat scale = ( det == 0.0 ) ? 0.0 : faceDirection * inversesqrt( det );\n\t\treturn normalize( T * ( mapN.x * scale ) + B * ( mapN.y * scale ) + N * mapN.z );\n\t}\n#endif",clearcoat_normal_fragment_begin:"#ifdef USE_CLEARCOAT\n\tvec3 clearcoatNormal = geometryNormal;\n#endif",clearcoat_normal_fragment_maps:"#ifdef USE_CLEARCOAT_NORMALMAP\n\tvec3 clearcoatMapN = texture2D( clearcoatNormalMap, vUv ).xyz * 2.0 - 1.0;\n\tclearcoatMapN.xy *= clearcoatNormalScale;\n\t#ifdef USE_TANGENT\n\t\tclearcoatNormal = normalize( vTBN * clearcoatMapN );\n\t#else\n\t\tclearcoatNormal = perturbNormal2Arb( - vViewPosition, clearcoatNormal, clearcoatMapN, faceDirection );\n\t#endif\n#endif",clearcoat_pars_fragment:"#ifdef USE_CLEARCOATMAP\n\tuniform sampler2D clearcoatMap;\n#endif\n#ifdef USE_CLEARCOAT_ROUGHNESSMAP\n\tuniform sampler2D clearcoatRoughnessMap;\n#endif\n#ifdef USE_CLEARCOAT_NORMALMAP\n\tuniform sampler2D clearcoatNormalMap;\n\tuniform vec2 clearcoatNormalScale;\n#endif",iridescence_pars_fragment:"#ifdef USE_IRIDESCENCEMAP\n\tuniform sampler2D iridescenceMap;\n#endif\n#ifdef USE_IRIDESCENCE_THICKNESSMAP\n\tuniform sampler2D iridescenceThicknessMap;\n#endif",output_fragment:"#ifdef OPAQUE\ndiffuseColor.a = 1.0;\n#endif\n#ifdef USE_TRANSMISSION\ndiffuseColor.a *= material.transmissionAlpha + 0.1;\n#endif\ngl_FragColor = vec4( outgoingLight, diffuseColor.a );",packing:"vec3 packNormalToRGB( const in vec3 normal ) {\n\treturn normalize( normal ) * 0.5 + 0.5;\n}\nvec3 unpackRGBToNormal( const in vec3 rgb ) {\n\treturn 2.0 * rgb.xyz - 1.0;\n}\nconst float PackUpscale = 256. / 255.;const float UnpackDownscale = 255. / 256.;\nconst vec3 PackFactors = vec3( 256. * 256. * 256., 256. * 256., 256. );\nconst vec4 UnpackFactors = UnpackDownscale / vec4( PackFactors, 1. );\nconst float ShiftRight8 = 1. / 256.;\nvec4 packDepthToRGBA( const in float v ) {\n\tvec4 r = vec4( fract( v * PackFactors ), v );\n\tr.yzw -= r.xyz * ShiftRight8;\treturn r * PackUpscale;\n}\nfloat unpackRGBAToDepth( const in vec4 v ) {\n\treturn dot( v, UnpackFactors );\n}\nvec2 packDepthToRG( in highp float v ) {\n\treturn packDepthToRGBA( v ).yx;\n}\nfloat unpackRGToDepth( const in highp vec2 v ) {\n\treturn unpackRGBAToDepth( vec4( v.xy, 0.0, 0.0 ) );\n}\nvec4 pack2HalfToRGBA( vec2 v ) {\n\tvec4 r = vec4( v.x, fract( v.x * 255.0 ), v.y, fract( v.y * 255.0 ) );\n\treturn vec4( r.x - r.y / 255.0, r.y, r.z - r.w / 255.0, r.w );\n}\nvec2 unpackRGBATo2Half( vec4 v ) {\n\treturn vec2( v.x + ( v.y / 255.0 ), v.z + ( v.w / 255.0 ) );\n}\nfloat viewZToOrthographicDepth( const in float viewZ, const in float near, const in float far ) {\n\treturn ( viewZ + near ) / ( near - far );\n}\nfloat orthographicDepthToViewZ( const in float linearClipZ, const in float near, const in float far ) {\n\treturn linearClipZ * ( near - far ) - near;\n}\nfloat viewZToPerspectiveDepth( const in float viewZ, const in float near, const in float far ) {\n\treturn ( ( near + viewZ ) * far ) / ( ( far - near ) * viewZ );\n}\nfloat perspectiveDepthToViewZ( const in float invClipZ, const in float near, const in float far ) {\n\treturn ( near * far ) / ( ( far - near ) * invClipZ - far );\n}",premultiplied_alpha_fragment:"#ifdef PREMULTIPLIED_ALPHA\n\tgl_FragColor.rgb *= gl_FragColor.a;\n#endif",project_vertex:"vec4 mvPosition = vec4( transformed, 1.0 );\n#ifdef USE_INSTANCING\n\tmvPosition = instanceMatrix * mvPosition;\n#endif\nmvPosition = modelViewMatrix * mvPosition;\ngl_Position = projectionMatrix * mvPosition;",dithering_fragment:"#ifdef DITHERING\n\tgl_FragColor.rgb = dithering( gl_FragColor.rgb );\n#endif",dithering_pars_fragment:"#ifdef DITHERING\n\tvec3 dithering( vec3 color ) {\n\t\tfloat grid_position = rand( gl_FragCoord.xy );\n\t\tvec3 dither_shift_RGB = vec3( 0.25 / 255.0, -0.25 / 255.0, 0.25 / 255.0 );\n\t\tdither_shift_RGB = mix( 2.0 * dither_shift_RGB, -2.0 * dither_shift_RGB, grid_position );\n\t\treturn color + dither_shift_RGB;\n\t}\n#endif",roughnessmap_fragment:"float roughnessFactor = roughness;\n#ifdef USE_ROUGHNESSMAP\n\tvec4 texelRoughness = texture2D( roughnessMap, vUv );\n\troughnessFactor *= texelRoughness.g;\n#endif",roughnessmap_pars_fragment:"#ifdef USE_ROUGHNESSMAP\n\tuniform sampler2D roughnessMap;\n#endif",shadowmap_pars_fragment:"#if NUM_SPOT_LIGHT_COORDS > 0\n  varying vec4 vSpotLightCoord[ NUM_SPOT_LIGHT_COORDS ];\n#endif\n#if NUM_SPOT_LIGHT_MAPS > 0\n  uniform sampler2D spotLightMap[ NUM_SPOT_LIGHT_MAPS ];\n#endif\n#ifdef USE_SHADOWMAP\n\t#if NUM_DIR_LIGHT_SHADOWS > 0\n\t\tuniform sampler2D directionalShadowMap[ NUM_DIR_LIGHT_SHADOWS ];\n\t\tvarying vec4 vDirectionalShadowCoord[ NUM_DIR_LIGHT_SHADOWS ];\n\t\tstruct DirectionalLightShadow {\n\t\t\tfloat shadowBias;\n\t\t\tfloat shadowNormalBias;\n\t\t\tfloat shadowRadius;\n\t\t\tvec2 shadowMapSize;\n\t\t};\n\t\tuniform DirectionalLightShadow directionalLightShadows[ NUM_DIR_LIGHT_SHADOWS ];\n\t#endif\n\t#if NUM_SPOT_LIGHT_SHADOWS > 0\n\t\tuniform sampler2D spotShadowMap[ NUM_SPOT_LIGHT_SHADOWS ];\n\t\tstruct SpotLightShadow {\n\t\t\tfloat shadowBias;\n\t\t\tfloat shadowNormalBias;\n\t\t\tfloat shadowRadius;\n\t\t\tvec2 shadowMapSize;\n\t\t};\n\t\tuniform SpotLightShadow spotLightShadows[ NUM_SPOT_LIGHT_SHADOWS ];\n\t#endif\n\t#if NUM_POINT_LIGHT_SHADOWS > 0\n\t\tuniform sampler2D pointShadowMap[ NUM_POINT_LIGHT_SHADOWS ];\n\t\tvarying vec4 vPointShadowCoord[ NUM_POINT_LIGHT_SHADOWS ];\n\t\tstruct PointLightShadow {\n\t\t\tfloat shadowBias;\n\t\t\tfloat shadowNormalBias;\n\t\t\tfloat shadowRadius;\n\t\t\tvec2 shadowMapSize;\n\t\t\tfloat shadowCameraNear;\n\t\t\tfloat shadowCameraFar;\n\t\t};\n\t\tuniform PointLightShadow pointLightShadows[ NUM_POINT_LIGHT_SHADOWS ];\n\t#endif\n\tfloat texture2DCompare( sampler2D depths, vec2 uv, float compare ) {\n\t\treturn step( compare, unpackRGBAToDepth( texture2D( depths, uv ) ) );\n\t}\n\tvec2 texture2DDistribution( sampler2D shadow, vec2 uv ) {\n\t\treturn unpackRGBATo2Half( texture2D( shadow, uv ) );\n\t}\n\tfloat VSMShadow (sampler2D shadow, vec2 uv, float compare ){\n\t\tfloat occlusion = 1.0;\n\t\tvec2 distribution = texture2DDistribution( shadow, uv );\n\t\tfloat hard_shadow = step( compare , distribution.x );\n\t\tif (hard_shadow != 1.0 ) {\n\t\t\tfloat distance = compare - distribution.x ;\n\t\t\tfloat variance = max( 0.00000, distribution.y * distribution.y );\n\t\t\tfloat softness_probability = variance / (variance + distance * distance );\t\t\tsoftness_probability = clamp( ( softness_probability - 0.3 ) / ( 0.95 - 0.3 ), 0.0, 1.0 );\t\t\tocclusion = clamp( max( hard_shadow, softness_probability ), 0.0, 1.0 );\n\t\t}\n\t\treturn occlusion;\n\t}\n\tfloat getShadow( sampler2D shadowMap, vec2 shadowMapSize, float shadowBias, float shadowRadius, vec4 shadowCoord ) {\n\t\tfloat shadow = 1.0;\n\t\tshadowCoord.xyz /= shadowCoord.w;\n\t\tshadowCoord.z += shadowBias;\n\t\tbool inFrustum = shadowCoord.x >= 0.0 && shadowCoord.x <= 1.0 && shadowCoord.y >= 0.0 && shadowCoord.y <= 1.0;\n\t\tbool frustumTest = inFrustum && shadowCoord.z <= 1.0;\n\t\tif ( frustumTest ) {\n\t\t#if defined( SHADOWMAP_TYPE_PCF )\n\t\t\tvec2 texelSize = vec2( 1.0 ) / shadowMapSize;\n\t\t\tfloat dx0 = - texelSize.x * shadowRadius;\n\t\t\tfloat dy0 = - texelSize.y * shadowRadius;\n\t\t\tfloat dx1 = + texelSize.x * shadowRadius;\n\t\t\tfloat dy1 = + texelSize.y * shadowRadius;\n\t\t\tfloat dx2 = dx0 / 2.0;\n\t\t\tfloat dy2 = dy0 / 2.0;\n\t\t\tfloat dx3 = dx1 / 2.0;\n\t\t\tfloat dy3 = dy1 / 2.0;\n\t\t\tshadow = (\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx0, dy0 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( 0.0, dy0 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx1, dy0 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx2, dy2 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( 0.0, dy2 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx3, dy2 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx0, 0.0 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx2, 0.0 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy, shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx3, 0.0 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx1, 0.0 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx2, dy3 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( 0.0, dy3 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx3, dy3 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx0, dy1 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( 0.0, dy1 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx1, dy1 ), shadowCoord.z )\n\t\t\t) * ( 1.0 / 17.0 );\n\t\t#elif defined( SHADOWMAP_TYPE_PCF_SOFT )\n\t\t\tvec2 texelSize = vec2( 1.0 ) / shadowMapSize;\n\t\t\tfloat dx = texelSize.x;\n\t\t\tfloat dy = texelSize.y;\n\t\t\tvec2 uv = shadowCoord.xy;\n\t\t\tvec2 f = fract( uv * shadowMapSize + 0.5 );\n\t\t\tuv -= f * texelSize;\n\t\t\tshadow = (\n\t\t\t\ttexture2DCompare( shadowMap, uv, shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, uv + vec2( dx, 0.0 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, uv + vec2( 0.0, dy ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, uv + texelSize, shadowCoord.z ) +\n\t\t\t\tmix( texture2DCompare( shadowMap, uv + vec2( -dx, 0.0 ), shadowCoord.z ),\n\t\t\t\t\t texture2DCompare( shadowMap, uv + vec2( 2.0 * dx, 0.0 ), shadowCoord.z ),\n\t\t\t\t\t f.x ) +\n\t\t\t\tmix( texture2DCompare( shadowMap, uv + vec2( -dx, dy ), shadowCoord.z ),\n\t\t\t\t\t texture2DCompare( shadowMap, uv + vec2( 2.0 * dx, dy ), shadowCoord.z ),\n\t\t\t\t\t f.x ) +\n\t\t\t\tmix( texture2DCompare( shadowMap, uv + vec2( 0.0, -dy ), shadowCoord.z ),\n\t\t\t\t\t texture2DCompare( shadowMap, uv + vec2( 0.0, 2.0 * dy ), shadowCoord.z ),\n\t\t\t\t\t f.y ) +\n\t\t\t\tmix( texture2DCompare( shadowMap, uv + vec2( dx, -dy ), shadowCoord.z ),\n\t\t\t\t\t texture2DCompare( shadowMap, uv + vec2( dx, 2.0 * dy ), shadowCoord.z ),\n\t\t\t\t\t f.y ) +\n\t\t\t\tmix( mix( texture2DCompare( shadowMap, uv + vec2( -dx, -dy ), shadowCoord.z ),\n\t\t\t\t\t\t  texture2DCompare( shadowMap, uv + vec2( 2.0 * dx, -dy ), shadowCoord.z ),\n\t\t\t\t\t\t  f.x ),\n\t\t\t\t\t mix( texture2DCompare( shadowMap, uv + vec2( -dx, 2.0 * dy ), shadowCoord.z ),\n\t\t\t\t\t\t  texture2DCompare( shadowMap, uv + vec2( 2.0 * dx, 2.0 * dy ), shadowCoord.z ),\n\t\t\t\t\t\t  f.x ),\n\t\t\t\t\t f.y )\n\t\t\t) * ( 1.0 / 9.0 );\n\t\t#elif defined( SHADOWMAP_TYPE_VSM )\n\t\t\tshadow = VSMShadow( shadowMap, shadowCoord.xy, shadowCoord.z );\n\t\t#else\n\t\t\tshadow = texture2DCompare( shadowMap, shadowCoord.xy, shadowCoord.z );\n\t\t#endif\n\t\t}\n\t\treturn shadow;\n\t}\n\tvec2 cubeToUV( vec3 v, float texelSizeY ) {\n\t\tvec3 absV = abs( v );\n\t\tfloat scaleToCube = 1.0 / max( absV.x, max( absV.y, absV.z ) );\n\t\tabsV *= scaleToCube;\n\t\tv *= scaleToCube * ( 1.0 - 2.0 * texelSizeY );\n\t\tvec2 planar = v.xy;\n\t\tfloat almostATexel = 1.5 * texelSizeY;\n\t\tfloat almostOne = 1.0 - almostATexel;\n\t\tif ( absV.z >= almostOne ) {\n\t\t\tif ( v.z > 0.0 )\n\t\t\t\tplanar.x = 4.0 - v.x;\n\t\t} else if ( absV.x >= almostOne ) {\n\t\t\tfloat signX = sign( v.x );\n\t\t\tplanar.x = v.z * signX + 2.0 * signX;\n\t\t} else if ( absV.y >= almostOne ) {\n\t\t\tfloat signY = sign( v.y );\n\t\t\tplanar.x = v.x + 2.0 * signY + 2.0;\n\t\t\tplanar.y = v.z * signY - 2.0;\n\t\t}\n\t\treturn vec2( 0.125, 0.25 ) * planar + vec2( 0.375, 0.75 );\n\t}\n\tfloat getPointShadow( sampler2D shadowMap, vec2 shadowMapSize, float shadowBias, float shadowRadius, vec4 shadowCoord, float shadowCameraNear, float shadowCameraFar ) {\n\t\tvec2 texelSize = vec2( 1.0 ) / ( shadowMapSize * vec2( 4.0, 2.0 ) );\n\t\tvec3 lightToPosition = shadowCoord.xyz;\n\t\tfloat dp = ( length( lightToPosition ) - shadowCameraNear ) / ( shadowCameraFar - shadowCameraNear );\t\tdp += shadowBias;\n\t\tvec3 bd3D = normalize( lightToPosition );\n\t\t#if defined( SHADOWMAP_TYPE_PCF ) || defined( SHADOWMAP_TYPE_PCF_SOFT ) || defined( SHADOWMAP_TYPE_VSM )\n\t\t\tvec2 offset = vec2( - 1, 1 ) * shadowRadius * texelSize.y;\n\t\t\treturn (\n\t\t\t\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.xyy, texelSize.y ), dp ) +\n\t\t\t\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.yyy, texelSize.y ), dp ) +\n\t\t\t\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.xyx, texelSize.y ), dp ) +\n\t\t\t\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.yyx, texelSize.y ), dp ) +\n\t\t\t\ttexture2DCompare( shadowMap, cubeToUV( bd3D, texelSize.y ), dp ) +\n\t\t\t\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.xxy, texelSize.y ), dp ) +\n\t\t\t\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.yxy, texelSize.y ), dp ) +\n\t\t\t\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.xxx, texelSize.y ), dp ) +\n\t\t\t\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.yxx, texelSize.y ), dp )\n\t\t\t) * ( 1.0 / 9.0 );\n\t\t#else\n\t\t\treturn texture2DCompare( shadowMap, cubeToUV( bd3D, texelSize.y ), dp );\n\t\t#endif\n\t}\n#endif",shadowmap_pars_vertex:"#if NUM_SPOT_LIGHT_COORDS > 0\n  uniform mat4 spotLightMatrix[ NUM_SPOT_LIGHT_COORDS ];\n  varying vec4 vSpotLightCoord[ NUM_SPOT_LIGHT_COORDS ];\n#endif\n#ifdef USE_SHADOWMAP\n\t#if NUM_DIR_LIGHT_SHADOWS > 0\n\t\tuniform mat4 directionalShadowMatrix[ NUM_DIR_LIGHT_SHADOWS ];\n\t\tvarying vec4 vDirectionalShadowCoord[ NUM_DIR_LIGHT_SHADOWS ];\n\t\tstruct DirectionalLightShadow {\n\t\t\tfloat shadowBias;\n\t\t\tfloat shadowNormalBias;\n\t\t\tfloat shadowRadius;\n\t\t\tvec2 shadowMapSize;\n\t\t};\n\t\tuniform DirectionalLightShadow directionalLightShadows[ NUM_DIR_LIGHT_SHADOWS ];\n\t#endif\n\t#if NUM_SPOT_LIGHT_SHADOWS > 0\n\t\tstruct SpotLightShadow {\n\t\t\tfloat shadowBias;\n\t\t\tfloat shadowNormalBias;\n\t\t\tfloat shadowRadius;\n\t\t\tvec2 shadowMapSize;\n\t\t};\n\t\tuniform SpotLightShadow spotLightShadows[ NUM_SPOT_LIGHT_SHADOWS ];\n\t#endif\n\t#if NUM_POINT_LIGHT_SHADOWS > 0\n\t\tuniform mat4 pointShadowMatrix[ NUM_POINT_LIGHT_SHADOWS ];\n\t\tvarying vec4 vPointShadowCoord[ NUM_POINT_LIGHT_SHADOWS ];\n\t\tstruct PointLightShadow {\n\t\t\tfloat shadowBias;\n\t\t\tfloat shadowNormalBias;\n\t\t\tfloat shadowRadius;\n\t\t\tvec2 shadowMapSize;\n\t\t\tfloat shadowCameraNear;\n\t\t\tfloat shadowCameraFar;\n\t\t};\n\t\tuniform PointLightShadow pointLightShadows[ NUM_POINT_LIGHT_SHADOWS ];\n\t#endif\n#endif",shadowmap_vertex:"#if ( defined( USE_SHADOWMAP ) && ( NUM_DIR_LIGHT_SHADOWS > 0 || NUM_POINT_LIGHT_SHADOWS > 0 ) ) || ( NUM_SPOT_LIGHT_COORDS > 0 )\n\tvec3 shadowWorldNormal = inverseTransformDirection( transformedNormal, viewMatrix );\n\tvec4 shadowWorldPosition;\n#endif\n#if defined( USE_SHADOWMAP )\n\t#if NUM_DIR_LIGHT_SHADOWS > 0\n\t\t#pragma unroll_loop_start\n\t\tfor ( int i = 0; i < NUM_DIR_LIGHT_SHADOWS; i ++ ) {\n\t\t\tshadowWorldPosition = worldPosition + vec4( shadowWorldNormal * directionalLightShadows[ i ].shadowNormalBias, 0 );\n\t\t\tvDirectionalShadowCoord[ i ] = directionalShadowMatrix[ i ] * shadowWorldPosition;\n\t\t}\n\t\t#pragma unroll_loop_end\n\t#endif\n\t#if NUM_POINT_LIGHT_SHADOWS > 0\n\t\t#pragma unroll_loop_start\n\t\tfor ( int i = 0; i < NUM_POINT_LIGHT_SHADOWS; i ++ ) {\n\t\t\tshadowWorldPosition = worldPosition + vec4( shadowWorldNormal * pointLightShadows[ i ].shadowNormalBias, 0 );\n\t\t\tvPointShadowCoord[ i ] = pointShadowMatrix[ i ] * shadowWorldPosition;\n\t\t}\n\t\t#pragma unroll_loop_end\n\t#endif\n#endif\n#if NUM_SPOT_LIGHT_COORDS > 0\n\t#pragma unroll_loop_start\n\tfor ( int i = 0; i < NUM_SPOT_LIGHT_COORDS; i ++ ) {\n\t\tshadowWorldPosition = worldPosition;\n\t\t#if ( defined( USE_SHADOWMAP ) && UNROLLED_LOOP_INDEX < NUM_SPOT_LIGHT_SHADOWS )\n\t\t\tshadowWorldPosition.xyz += shadowWorldNormal * spotLightShadows[ i ].shadowNormalBias;\n\t\t#endif\n\t\tvSpotLightCoord[ i ] = spotLightMatrix[ i ] * shadowWorldPosition;\n\t}\n\t#pragma unroll_loop_end\n#endif",shadowmask_pars_fragment:"float getShadowMask() {\n\tfloat shadow = 1.0;\n\t#ifdef USE_SHADOWMAP\n\t#if NUM_DIR_LIGHT_SHADOWS > 0\n\tDirectionalLightShadow directionalLight;\n\t#pragma unroll_loop_start\n\tfor ( int i = 0; i < NUM_DIR_LIGHT_SHADOWS; i ++ ) {\n\t\tdirectionalLight = directionalLightShadows[ i ];\n\t\tshadow *= receiveShadow ? getShadow( directionalShadowMap[ i ], directionalLight.shadowMapSize, directionalLight.shadowBias, directionalLight.shadowRadius, vDirectionalShadowCoord[ i ] ) : 1.0;\n\t}\n\t#pragma unroll_loop_end\n\t#endif\n\t#if NUM_SPOT_LIGHT_SHADOWS > 0\n\tSpotLightShadow spotLight;\n\t#pragma unroll_loop_start\n\tfor ( int i = 0; i < NUM_SPOT_LIGHT_SHADOWS; i ++ ) {\n\t\tspotLight = spotLightShadows[ i ];\n\t\tshadow *= receiveShadow ? getShadow( spotShadowMap[ i ], spotLight.shadowMapSize, spotLight.shadowBias, spotLight.shadowRadius, vSpotLightCoord[ i ] ) : 1.0;\n\t}\n\t#pragma unroll_loop_end\n\t#endif\n\t#if NUM_POINT_LIGHT_SHADOWS > 0\n\tPointLightShadow pointLight;\n\t#pragma unroll_loop_start\n\tfor ( int i = 0; i < NUM_POINT_LIGHT_SHADOWS; i ++ ) {\n\t\tpointLight = pointLightShadows[ i ];\n\t\tshadow *= receiveShadow ? getPointShadow( pointShadowMap[ i ], pointLight.shadowMapSize, pointLight.shadowBias, pointLight.shadowRadius, vPointShadowCoord[ i ], pointLight.shadowCameraNear, pointLight.shadowCameraFar ) : 1.0;\n\t}\n\t#pragma unroll_loop_end\n\t#endif\n\t#endif\n\treturn shadow;\n}",skinbase_vertex:"#ifdef USE_SKINNING\n\tmat4 boneMatX = getBoneMatrix( skinIndex.x );\n\tmat4 boneMatY = getBoneMatrix( skinIndex.y );\n\tmat4 boneMatZ = getBoneMatrix( skinIndex.z );\n\tmat4 boneMatW = getBoneMatrix( skinIndex.w );\n#endif",skinning_pars_vertex:"#ifdef USE_SKINNING\n\tuniform mat4 bindMatrix;\n\tuniform mat4 bindMatrixInverse;\n\tuniform highp sampler2D boneTexture;\n\tuniform int boneTextureSize;\n\tmat4 getBoneMatrix( const in float i ) {\n\t\tfloat j = i * 4.0;\n\t\tfloat x = mod( j, float( boneTextureSize ) );\n\t\tfloat y = floor( j / float( boneTextureSize ) );\n\t\tfloat dx = 1.0 / float( boneTextureSize );\n\t\tfloat dy = 1.0 / float( boneTextureSize );\n\t\ty = dy * ( y + 0.5 );\n\t\tvec4 v1 = texture2D( boneTexture, vec2( dx * ( x + 0.5 ), y ) );\n\t\tvec4 v2 = texture2D( boneTexture, vec2( dx * ( x + 1.5 ), y ) );\n\t\tvec4 v3 = texture2D( boneTexture, vec2( dx * ( x + 2.5 ), y ) );\n\t\tvec4 v4 = texture2D( boneTexture, vec2( dx * ( x + 3.5 ), y ) );\n\t\tmat4 bone = mat4( v1, v2, v3, v4 );\n\t\treturn bone;\n\t}\n#endif",skinning_vertex:"#ifdef USE_SKINNING\n\tvec4 skinVertex = bindMatrix * vec4( transformed, 1.0 );\n\tvec4 skinned = vec4( 0.0 );\n\tskinned += boneMatX * skinVertex * skinWeight.x;\n\tskinned += boneMatY * skinVertex * skinWeight.y;\n\tskinned += boneMatZ * skinVertex * skinWeight.z;\n\tskinned += boneMatW * skinVertex * skinWeight.w;\n\ttransformed = ( bindMatrixInverse * skinned ).xyz;\n#endif",skinnormal_vertex:"#ifdef USE_SKINNING\n\tmat4 skinMatrix = mat4( 0.0 );\n\tskinMatrix += skinWeight.x * boneMatX;\n\tskinMatrix += skinWeight.y * boneMatY;\n\tskinMatrix += skinWeight.z * boneMatZ;\n\tskinMatrix += skinWeight.w * boneMatW;\n\tskinMatrix = bindMatrixInverse * skinMatrix * bindMatrix;\n\tobjectNormal = vec4( skinMatrix * vec4( objectNormal, 0.0 ) ).xyz;\n\t#ifdef USE_TANGENT\n\t\tobjectTangent = vec4( skinMatrix * vec4( objectTangent, 0.0 ) ).xyz;\n\t#endif\n#endif",specularmap_fragment:"float specularStrength;\n#ifdef USE_SPECULARMAP\n\tvec4 texelSpecular = texture2D( specularMap, vUv );\n\tspecularStrength = texelSpecular.r;\n#else\n\tspecularStrength = 1.0;\n#endif",specularmap_pars_fragment:"#ifdef USE_SPECULARMAP\n\tuniform sampler2D specularMap;\n#endif",tonemapping_fragment:"#if defined( TONE_MAPPING )\n\tgl_FragColor.rgb = toneMapping( gl_FragColor.rgb );\n#endif",tonemapping_pars_fragment:"#ifndef saturate\n#define saturate( a ) clamp( a, 0.0, 1.0 )\n#endif\nuniform float toneMappingExposure;\nvec3 LinearToneMapping( vec3 color ) {\n\treturn toneMappingExposure * color;\n}\nvec3 ReinhardToneMapping( vec3 color ) {\n\tcolor *= toneMappingExposure;\n\treturn saturate( color / ( vec3( 1.0 ) + color ) );\n}\nvec3 OptimizedCineonToneMapping( vec3 color ) {\n\tcolor *= toneMappingExposure;\n\tcolor = max( vec3( 0.0 ), color - 0.004 );\n\treturn pow( ( color * ( 6.2 * color + 0.5 ) ) / ( color * ( 6.2 * color + 1.7 ) + 0.06 ), vec3( 2.2 ) );\n}\nvec3 RRTAndODTFit( vec3 v ) {\n\tvec3 a = v * ( v + 0.0245786 ) - 0.000090537;\n\tvec3 b = v * ( 0.983729 * v + 0.4329510 ) + 0.238081;\n\treturn a / b;\n}\nvec3 ACESFilmicToneMapping( vec3 color ) {\n\tconst mat3 ACESInputMat = mat3(\n\t\tvec3( 0.59719, 0.07600, 0.02840 ),\t\tvec3( 0.35458, 0.90834, 0.13383 ),\n\t\tvec3( 0.04823, 0.01566, 0.83777 )\n\t);\n\tconst mat3 ACESOutputMat = mat3(\n\t\tvec3(  1.60475, -0.10208, -0.00327 ),\t\tvec3( -0.53108,  1.10813, -0.07276 ),\n\t\tvec3( -0.07367, -0.00605,  1.07602 )\n\t);\n\tcolor *= toneMappingExposure / 0.6;\n\tcolor = ACESInputMat * color;\n\tcolor = RRTAndODTFit( color );\n\tcolor = ACESOutputMat * color;\n\treturn saturate( color );\n}\nvec3 CustomToneMapping( vec3 color ) { return color; }",transmission_fragment:"#ifdef USE_TRANSMISSION\n\tmaterial.transmission = transmission;\n\tmaterial.transmissionAlpha = 1.0;\n\tmaterial.thickness = thickness;\n\tmaterial.attenuationDistance = attenuationDistance;\n\tmaterial.attenuationColor = attenuationColor;\n\t#ifdef USE_TRANSMISSIONMAP\n\t\tmaterial.transmission *= texture2D( transmissionMap, vUv ).r;\n\t#endif\n\t#ifdef USE_THICKNESSMAP\n\t\tmaterial.thickness *= texture2D( thicknessMap, vUv ).g;\n\t#endif\n\tvec3 pos = vWorldPosition;\n\tvec3 v = normalize( cameraPosition - pos );\n\tvec3 n = inverseTransformDirection( normal, viewMatrix );\n\tvec4 transmission = getIBLVolumeRefraction(\n\t\tn, v, material.roughness, material.diffuseColor, material.specularColor, material.specularF90,\n\t\tpos, modelMatrix, viewMatrix, projectionMatrix, material.ior, material.thickness,\n\t\tmaterial.attenuationColor, material.attenuationDistance );\n\tmaterial.transmissionAlpha = mix( material.transmissionAlpha, transmission.a, material.transmission );\n\ttotalDiffuse = mix( totalDiffuse, transmission.rgb, material.transmission );\n#endif",transmission_pars_fragment:"#ifdef USE_TRANSMISSION\n\tuniform float transmission;\n\tuniform float thickness;\n\tuniform float attenuationDistance;\n\tuniform vec3 attenuationColor;\n\t#ifdef USE_TRANSMISSIONMAP\n\t\tuniform sampler2D transmissionMap;\n\t#endif\n\t#ifdef USE_THICKNESSMAP\n\t\tuniform sampler2D thicknessMap;\n\t#endif\n\tuniform vec2 transmissionSamplerSize;\n\tuniform sampler2D transmissionSamplerMap;\n\tuniform mat4 modelMatrix;\n\tuniform mat4 projectionMatrix;\n\tvarying vec3 vWorldPosition;\n\tvec3 getVolumeTransmissionRay( const in vec3 n, const in vec3 v, const in float thickness, const in float ior, const in mat4 modelMatrix ) {\n\t\tvec3 refractionVector = refract( - v, normalize( n ), 1.0 / ior );\n\t\tvec3 modelScale;\n\t\tmodelScale.x = length( vec3( modelMatrix[ 0 ].xyz ) );\n\t\tmodelScale.y = length( vec3( modelMatrix[ 1 ].xyz ) );\n\t\tmodelScale.z = length( vec3( modelMatrix[ 2 ].xyz ) );\n\t\treturn normalize( refractionVector ) * thickness * modelScale;\n\t}\n\tfloat applyIorToRoughness( const in float roughness, const in float ior ) {\n\t\treturn roughness * clamp( ior * 2.0 - 2.0, 0.0, 1.0 );\n\t}\n\tvec4 getTransmissionSample( const in vec2 fragCoord, const in float roughness, const in float ior ) {\n\t\tfloat framebufferLod = log2( transmissionSamplerSize.x ) * applyIorToRoughness( roughness, ior );\n\t\t#ifdef texture2DLodEXT\n\t\t\treturn texture2DLodEXT( transmissionSamplerMap, fragCoord.xy, framebufferLod );\n\t\t#else\n\t\t\treturn texture2D( transmissionSamplerMap, fragCoord.xy, framebufferLod );\n\t\t#endif\n\t}\n\tvec3 applyVolumeAttenuation( const in vec3 radiance, const in float transmissionDistance, const in vec3 attenuationColor, const in float attenuationDistance ) {\n\t\tif ( isinf( attenuationDistance ) ) {\n\t\t\treturn radiance;\n\t\t} else {\n\t\t\tvec3 attenuationCoefficient = -log( attenuationColor ) / attenuationDistance;\n\t\t\tvec3 transmittance = exp( - attenuationCoefficient * transmissionDistance );\t\t\treturn transmittance * radiance;\n\t\t}\n\t}\n\tvec4 getIBLVolumeRefraction( const in vec3 n, const in vec3 v, const in float roughness, const in vec3 diffuseColor,\n\t\tconst in vec3 specularColor, const in float specularF90, const in vec3 position, const in mat4 modelMatrix,\n\t\tconst in mat4 viewMatrix, const in mat4 projMatrix, const in float ior, const in float thickness,\n\t\tconst in vec3 attenuationColor, const in float attenuationDistance ) {\n\t\tvec3 transmissionRay = getVolumeTransmissionRay( n, v, thickness, ior, modelMatrix );\n\t\tvec3 refractedRayExit = position + transmissionRay;\n\t\tvec4 ndcPos = projMatrix * viewMatrix * vec4( refractedRayExit, 1.0 );\n\t\tvec2 refractionCoords = ndcPos.xy / ndcPos.w;\n\t\trefractionCoords += 1.0;\n\t\trefractionCoords /= 2.0;\n\t\tvec4 transmittedLight = getTransmissionSample( refractionCoords, roughness, ior );\n\t\tvec3 attenuatedColor = applyVolumeAttenuation( transmittedLight.rgb, length( transmissionRay ), attenuationColor, attenuationDistance );\n\t\tvec3 F = EnvironmentBRDF( n, v, specularColor, specularF90, roughness );\n\t\treturn vec4( ( 1.0 - F ) * attenuatedColor * diffuseColor, transmittedLight.a );\n\t}\n#endif",uv_pars_fragment:"#if ( defined( USE_UV ) && ! defined( UVS_VERTEX_ONLY ) )\n\tvarying vec2 vUv;\n#endif",uv_pars_vertex:"#ifdef USE_UV\n\t#ifdef UVS_VERTEX_ONLY\n\t\tvec2 vUv;\n\t#else\n\t\tvarying vec2 vUv;\n\t#endif\n\tuniform mat3 uvTransform;\n#endif",uv_vertex:"#ifdef USE_UV\n\tvUv = ( uvTransform * vec3( uv, 1 ) ).xy;\n#endif",uv2_pars_fragment:"#if defined( USE_LIGHTMAP ) || defined( USE_AOMAP )\n\tvarying vec2 vUv2;\n#endif",uv2_pars_vertex:"#if defined( USE_LIGHTMAP ) || defined( USE_AOMAP )\n\tattribute vec2 uv2;\n\tvarying vec2 vUv2;\n\tuniform mat3 uv2Transform;\n#endif",uv2_vertex:"#if defined( USE_LIGHTMAP ) || defined( USE_AOMAP )\n\tvUv2 = ( uv2Transform * vec3( uv2, 1 ) ).xy;\n#endif",worldpos_vertex:"#if defined( USE_ENVMAP ) || defined( DISTANCE ) || defined ( USE_SHADOWMAP ) || defined ( USE_TRANSMISSION ) || NUM_SPOT_LIGHT_COORDS > 0\n\tvec4 worldPosition = vec4( transformed, 1.0 );\n\t#ifdef USE_INSTANCING\n\t\tworldPosition = instanceMatrix * worldPosition;\n\t#endif\n\tworldPosition = modelMatrix * worldPosition;\n#endif",background_vert:"varying vec2 vUv;\nuniform mat3 uvTransform;\nvoid main() {\n\tvUv = ( uvTransform * vec3( uv, 1 ) ).xy;\n\tgl_Position = vec4( position.xy, 1.0, 1.0 );\n}",background_frag:"uniform sampler2D t2D;\nuniform float backgroundIntensity;\nvarying vec2 vUv;\nvoid main() {\n\tvec4 texColor = texture2D( t2D, vUv );\n\t#ifdef DECODE_VIDEO_TEXTURE\n\t\ttexColor = vec4( mix( pow( texColor.rgb * 0.9478672986 + vec3( 0.0521327014 ), vec3( 2.4 ) ), texColor.rgb * 0.0773993808, vec3( lessThanEqual( texColor.rgb, vec3( 0.04045 ) ) ) ), texColor.w );\n\t#endif\n\ttexColor.rgb *= backgroundIntensity;\n\tgl_FragColor = texColor;\n\t#include <tonemapping_fragment>\n\t#include <encodings_fragment>\n}",backgroundCube_vert:"varying vec3 vWorldDirection;\n#include <common>\nvoid main() {\n\tvWorldDirection = transformDirection( position, modelMatrix );\n\t#include <begin_vertex>\n\t#include <project_vertex>\n\tgl_Position.z = gl_Position.w;\n}",backgroundCube_frag:"#ifdef ENVMAP_TYPE_CUBE\n\tuniform samplerCube envMap;\n#elif defined( ENVMAP_TYPE_CUBE_UV )\n\tuniform sampler2D envMap;\n#endif\nuniform float flipEnvMap;\nuniform float backgroundBlurriness;\nuniform float backgroundIntensity;\nvarying vec3 vWorldDirection;\n#include <cube_uv_reflection_fragment>\nvoid main() {\n\t#ifdef ENVMAP_TYPE_CUBE\n\t\tvec4 texColor = textureCube( envMap, vec3( flipEnvMap * vWorldDirection.x, vWorldDirection.yz ) );\n\t#elif defined( ENVMAP_TYPE_CUBE_UV )\n\t\tvec4 texColor = textureCubeUV( envMap, vWorldDirection, backgroundBlurriness );\n\t#else\n\t\tvec4 texColor = vec4( 0.0, 0.0, 0.0, 1.0 );\n\t#endif\n\ttexColor.rgb *= backgroundIntensity;\n\tgl_FragColor = texColor;\n\t#include <tonemapping_fragment>\n\t#include <encodings_fragment>\n}",cube_vert:"varying vec3 vWorldDirection;\n#include <common>\nvoid main() {\n\tvWorldDirection = transformDirection( position, modelMatrix );\n\t#include <begin_vertex>\n\t#include <project_vertex>\n\tgl_Position.z = gl_Position.w;\n}",cube_frag:"uniform samplerCube tCube;\nuniform float tFlip;\nuniform float opacity;\nvarying vec3 vWorldDirection;\nvoid main() {\n\tvec4 texColor = textureCube( tCube, vec3( tFlip * vWorldDirection.x, vWorldDirection.yz ) );\n\tgl_FragColor = texColor;\n\tgl_FragColor.a *= opacity;\n\t#include <tonemapping_fragment>\n\t#include <encodings_fragment>\n}",depth_vert:"#include <common>\n#include <uv_pars_vertex>\n#include <displacementmap_pars_vertex>\n#include <morphtarget_pars_vertex>\n#include <skinning_pars_vertex>\n#include <logdepthbuf_pars_vertex>\n#include <clipping_planes_pars_vertex>\nvarying vec2 vHighPrecisionZW;\nvoid main() {\n\t#include <uv_vertex>\n\t#include <skinbase_vertex>\n\t#ifdef USE_DISPLACEMENTMAP\n\t\t#include <beginnormal_vertex>\n\t\t#include <morphnormal_vertex>\n\t\t#include <skinnormal_vertex>\n\t#endif\n\t#include <begin_vertex>\n\t#include <morphtarget_vertex>\n\t#include <skinning_vertex>\n\t#include <displacementmap_vertex>\n\t#include <project_vertex>\n\t#include <logdepthbuf_vertex>\n\t#include <clipping_planes_vertex>\n\tvHighPrecisionZW = gl_Position.zw;\n}",depth_frag:"#if DEPTH_PACKING == 3200\n\tuniform float opacity;\n#endif\n#include <common>\n#include <packing>\n#include <uv_pars_fragment>\n#include <map_pars_fragment>\n#include <alphamap_pars_fragment>\n#include <alphatest_pars_fragment>\n#include <logdepthbuf_pars_fragment>\n#include <clipping_planes_pars_fragment>\nvarying vec2 vHighPrecisionZW;\nvoid main() {\n\t#include <clipping_planes_fragment>\n\tvec4 diffuseColor = vec4( 1.0 );\n\t#if DEPTH_PACKING == 3200\n\t\tdiffuseColor.a = opacity;\n\t#endif\n\t#include <map_fragment>\n\t#include <alphamap_fragment>\n\t#include <alphatest_fragment>\n\t#include <logdepthbuf_fragment>\n\tfloat fragCoordZ = 0.5 * vHighPrecisionZW[0] / vHighPrecisionZW[1] + 0.5;\n\t#if DEPTH_PACKING == 3200\n\t\tgl_FragColor = vec4( vec3( 1.0 - fragCoordZ ), opacity );\n\t#elif DEPTH_PACKING == 3201\n\t\tgl_FragColor = packDepthToRGBA( fragCoordZ );\n\t#endif\n}",distanceRGBA_vert:"#define DISTANCE\nvarying vec3 vWorldPosition;\n#include <common>\n#include <uv_pars_vertex>\n#include <displacementmap_pars_vertex>\n#include <morphtarget_pars_vertex>\n#include <skinning_pars_vertex>\n#include <clipping_planes_pars_vertex>\nvoid main() {\n\t#include <uv_vertex>\n\t#include <skinbase_vertex>\n\t#ifdef USE_DISPLACEMENTMAP\n\t\t#include <beginnormal_vertex>\n\t\t#include <morphnormal_vertex>\n\t\t#include <skinnormal_vertex>\n\t#endif\n\t#include <begin_vertex>\n\t#include <morphtarget_vertex>\n\t#include <skinning_vertex>\n\t#include <displacementmap_vertex>\n\t#include <project_vertex>\n\t#include <worldpos_vertex>\n\t#include <clipping_planes_vertex>\n\tvWorldPosition = worldPosition.xyz;\n}",distanceRGBA_frag:"#define DISTANCE\nuniform vec3 referencePosition;\nuniform float nearDistance;\nuniform float farDistance;\nvarying vec3 vWorldPosition;\n#include <common>\n#include <packing>\n#include <uv_pars_fragment>\n#include <map_pars_fragment>\n#include <alphamap_pars_fragment>\n#include <alphatest_pars_fragment>\n#include <clipping_planes_pars_fragment>\nvoid main () {\n\t#include <clipping_planes_fragment>\n\tvec4 diffuseColor = vec4( 1.0 );\n\t#include <map_fragment>\n\t#include <alphamap_fragment>\n\t#include <alphatest_fragment>\n\tfloat dist = length( vWorldPosition - referencePosition );\n\tdist = ( dist - nearDistance ) / ( farDistance - nearDistance );\n\tdist = saturate( dist );\n\tgl_FragColor = packDepthToRGBA( dist );\n}",equirect_vert:"varying vec3 vWorldDirection;\n#include <common>\nvoid main() {\n\tvWorldDirection = transformDirection( position, modelMatrix );\n\t#include <begin_vertex>\n\t#include <project_vertex>\n}",equirect_frag:"uniform sampler2D tEquirect;\nvarying vec3 vWorldDirection;\n#include <common>\nvoid main() {\n\tvec3 direction = normalize( vWorldDirection );\n\tvec2 sampleUV = equirectUv( direction );\n\tgl_FragColor = texture2D( tEquirect, sampleUV );\n\t#include <tonemapping_fragment>\n\t#include <encodings_fragment>\n}",linedashed_vert:"uniform float scale;\nattribute float lineDistance;\nvarying float vLineDistance;\n#include <common>\n#include <color_pars_vertex>\n#include <fog_pars_vertex>\n#include <morphtarget_pars_vertex>\n#include <logdepthbuf_pars_vertex>\n#include <clipping_planes_pars_vertex>\nvoid main() {\n\tvLineDistance = scale * lineDistance;\n\t#include <color_vertex>\n\t#include <morphcolor_vertex>\n\t#include <begin_vertex>\n\t#include <morphtarget_vertex>\n\t#include <project_vertex>\n\t#include <logdepthbuf_vertex>\n\t#include <clipping_planes_vertex>\n\t#include <fog_vertex>\n}",linedashed_frag:"uniform vec3 diffuse;\nuniform float opacity;\nuniform float dashSize;\nuniform float totalSize;\nvarying float vLineDistance;\n#include <common>\n#include <color_pars_fragment>\n#include <fog_pars_fragment>\n#include <logdepthbuf_pars_fragment>\n#include <clipping_planes_pars_fragment>\nvoid main() {\n\t#include <clipping_planes_fragment>\n\tif ( mod( vLineDistance, totalSize ) > dashSize ) {\n\t\tdiscard;\n\t}\n\tvec3 outgoingLight = vec3( 0.0 );\n\tvec4 diffuseColor = vec4( diffuse, opacity );\n\t#include <logdepthbuf_fragment>\n\t#include <color_fragment>\n\toutgoingLight = diffuseColor.rgb;\n\t#include <output_fragment>\n\t#include <tonemapping_fragment>\n\t#include <encodings_fragment>\n\t#include <fog_fragment>\n\t#include <premultiplied_alpha_fragment>\n}",meshbasic_vert:"#include <common>\n#include <uv_pars_vertex>\n#include <uv2_pars_vertex>\n#include <envmap_pars_vertex>\n#include <color_pars_vertex>\n#include <fog_pars_vertex>\n#include <morphtarget_pars_vertex>\n#include <skinning_pars_vertex>\n#include <logdepthbuf_pars_vertex>\n#include <clipping_planes_pars_vertex>\nvoid main() {\n\t#include <uv_vertex>\n\t#include <uv2_vertex>\n\t#include <color_vertex>\n\t#include <morphcolor_vertex>\n\t#if defined ( USE_ENVMAP ) || defined ( USE_SKINNING )\n\t\t#include <beginnormal_vertex>\n\t\t#include <morphnormal_vertex>\n\t\t#include <skinbase_vertex>\n\t\t#include <skinnormal_vertex>\n\t\t#include <defaultnormal_vertex>\n\t#endif\n\t#include <begin_vertex>\n\t#include <morphtarget_vertex>\n\t#include <skinning_vertex>\n\t#include <project_vertex>\n\t#include <logdepthbuf_vertex>\n\t#include <clipping_planes_vertex>\n\t#include <worldpos_vertex>\n\t#include <envmap_vertex>\n\t#include <fog_vertex>\n}",meshbasic_frag:"uniform vec3 diffuse;\nuniform float opacity;\n#ifndef FLAT_SHADED\n\tvarying vec3 vNormal;\n#endif\n#include <common>\n#include <dithering_pars_fragment>\n#include <color_pars_fragment>\n#include <uv_pars_fragment>\n#include <uv2_pars_fragment>\n#include <map_pars_fragment>\n#include <alphamap_pars_fragment>\n#include <alphatest_pars_fragment>\n#include <aomap_pars_fragment>\n#include <lightmap_pars_fragment>\n#include <envmap_common_pars_fragment>\n#include <envmap_pars_fragment>\n#include <fog_pars_fragment>\n#include <specularmap_pars_fragment>\n#include <logdepthbuf_pars_fragment>\n#include <clipping_planes_pars_fragment>\nvoid main() {\n\t#include <clipping_planes_fragment>\n\tvec4 diffuseColor = vec4( diffuse, opacity );\n\t#include <logdepthbuf_fragment>\n\t#include <map_fragment>\n\t#include <color_fragment>\n\t#include <alphamap_fragment>\n\t#include <alphatest_fragment>\n\t#include <specularmap_fragment>\n\tReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\n\t#ifdef USE_LIGHTMAP\n\t\tvec4 lightMapTexel = texture2D( lightMap, vUv2 );\n\t\treflectedLight.indirectDiffuse += lightMapTexel.rgb * lightMapIntensity * RECIPROCAL_PI;\n\t#else\n\t\treflectedLight.indirectDiffuse += vec3( 1.0 );\n\t#endif\n\t#include <aomap_fragment>\n\treflectedLight.indirectDiffuse *= diffuseColor.rgb;\n\tvec3 outgoingLight = reflectedLight.indirectDiffuse;\n\t#include <envmap_fragment>\n\t#include <output_fragment>\n\t#include <tonemapping_fragment>\n\t#include <encodings_fragment>\n\t#include <fog_fragment>\n\t#include <premultiplied_alpha_fragment>\n\t#include <dithering_fragment>\n}",meshlambert_vert:"#define LAMBERT\nvarying vec3 vViewPosition;\n#include <common>\n#include <uv_pars_vertex>\n#include <uv2_pars_vertex>\n#include <displacementmap_pars_vertex>\n#include <envmap_pars_vertex>\n#include <color_pars_vertex>\n#include <fog_pars_vertex>\n#include <normal_pars_vertex>\n#include <morphtarget_pars_vertex>\n#include <skinning_pars_vertex>\n#include <shadowmap_pars_vertex>\n#include <logdepthbuf_pars_vertex>\n#include <clipping_planes_pars_vertex>\nvoid main() {\n\t#include <uv_vertex>\n\t#include <uv2_vertex>\n\t#include <color_vertex>\n\t#include <morphcolor_vertex>\n\t#include <beginnormal_vertex>\n\t#include <morphnormal_vertex>\n\t#include <skinbase_vertex>\n\t#include <skinnormal_vertex>\n\t#include <defaultnormal_vertex>\n\t#include <normal_vertex>\n\t#include <begin_vertex>\n\t#include <morphtarget_vertex>\n\t#include <skinning_vertex>\n\t#include <displacementmap_vertex>\n\t#include <project_vertex>\n\t#include <logdepthbuf_vertex>\n\t#include <clipping_planes_vertex>\n\tvViewPosition = - mvPosition.xyz;\n\t#include <worldpos_vertex>\n\t#include <envmap_vertex>\n\t#include <shadowmap_vertex>\n\t#include <fog_vertex>\n}",meshlambert_frag:"#define LAMBERT\nuniform vec3 diffuse;\nuniform vec3 emissive;\nuniform float opacity;\n#include <common>\n#include <packing>\n#include <dithering_pars_fragment>\n#include <color_pars_fragment>\n#include <uv_pars_fragment>\n#include <uv2_pars_fragment>\n#include <map_pars_fragment>\n#include <alphamap_pars_fragment>\n#include <alphatest_pars_fragment>\n#include <aomap_pars_fragment>\n#include <lightmap_pars_fragment>\n#include <emissivemap_pars_fragment>\n#include <envmap_common_pars_fragment>\n#include <envmap_pars_fragment>\n#include <fog_pars_fragment>\n#include <bsdfs>\n#include <lights_pars_begin>\n#include <normal_pars_fragment>\n#include <lights_lambert_pars_fragment>\n#include <shadowmap_pars_fragment>\n#include <bumpmap_pars_fragment>\n#include <normalmap_pars_fragment>\n#include <specularmap_pars_fragment>\n#include <logdepthbuf_pars_fragment>\n#include <clipping_planes_pars_fragment>\nvoid main() {\n\t#include <clipping_planes_fragment>\n\tvec4 diffuseColor = vec4( diffuse, opacity );\n\tReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\n\tvec3 totalEmissiveRadiance = emissive;\n\t#include <logdepthbuf_fragment>\n\t#include <map_fragment>\n\t#include <color_fragment>\n\t#include <alphamap_fragment>\n\t#include <alphatest_fragment>\n\t#include <specularmap_fragment>\n\t#include <normal_fragment_begin>\n\t#include <normal_fragment_maps>\n\t#include <emissivemap_fragment>\n\t#include <lights_lambert_fragment>\n\t#include <lights_fragment_begin>\n\t#include <lights_fragment_maps>\n\t#include <lights_fragment_end>\n\t#include <aomap_fragment>\n\tvec3 outgoingLight = reflectedLight.directDiffuse + reflectedLight.indirectDiffuse + totalEmissiveRadiance;\n\t#include <envmap_fragment>\n\t#include <output_fragment>\n\t#include <tonemapping_fragment>\n\t#include <encodings_fragment>\n\t#include <fog_fragment>\n\t#include <premultiplied_alpha_fragment>\n\t#include <dithering_fragment>\n}",meshmatcap_vert:"#define MATCAP\nvarying vec3 vViewPosition;\n#include <common>\n#include <uv_pars_vertex>\n#include <color_pars_vertex>\n#include <displacementmap_pars_vertex>\n#include <fog_pars_vertex>\n#include <normal_pars_vertex>\n#include <morphtarget_pars_vertex>\n#include <skinning_pars_vertex>\n#include <logdepthbuf_pars_vertex>\n#include <clipping_planes_pars_vertex>\nvoid main() {\n\t#include <uv_vertex>\n\t#include <color_vertex>\n\t#include <morphcolor_vertex>\n\t#include <beginnormal_vertex>\n\t#include <morphnormal_vertex>\n\t#include <skinbase_vertex>\n\t#include <skinnormal_vertex>\n\t#include <defaultnormal_vertex>\n\t#include <normal_vertex>\n\t#include <begin_vertex>\n\t#include <morphtarget_vertex>\n\t#include <skinning_vertex>\n\t#include <displacementmap_vertex>\n\t#include <project_vertex>\n\t#include <logdepthbuf_vertex>\n\t#include <clipping_planes_vertex>\n\t#include <fog_vertex>\n\tvViewPosition = - mvPosition.xyz;\n}",meshmatcap_frag:"#define MATCAP\nuniform vec3 diffuse;\nuniform float opacity;\nuniform sampler2D matcap;\nvarying vec3 vViewPosition;\n#include <common>\n#include <dithering_pars_fragment>\n#include <color_pars_fragment>\n#include <uv_pars_fragment>\n#include <map_pars_fragment>\n#include <alphamap_pars_fragment>\n#include <alphatest_pars_fragment>\n#include <fog_pars_fragment>\n#include <normal_pars_fragment>\n#include <bumpmap_pars_fragment>\n#include <normalmap_pars_fragment>\n#include <logdepthbuf_pars_fragment>\n#include <clipping_planes_pars_fragment>\nvoid main() {\n\t#include <clipping_planes_fragment>\n\tvec4 diffuseColor = vec4( diffuse, opacity );\n\t#include <logdepthbuf_fragment>\n\t#include <map_fragment>\n\t#include <color_fragment>\n\t#include <alphamap_fragment>\n\t#include <alphatest_fragment>\n\t#include <normal_fragment_begin>\n\t#include <normal_fragment_maps>\n\tvec3 viewDir = normalize( vViewPosition );\n\tvec3 x = normalize( vec3( viewDir.z, 0.0, - viewDir.x ) );\n\tvec3 y = cross( viewDir, x );\n\tvec2 uv = vec2( dot( x, normal ), dot( y, normal ) ) * 0.495 + 0.5;\n\t#ifdef USE_MATCAP\n\t\tvec4 matcapColor = texture2D( matcap, uv );\n\t#else\n\t\tvec4 matcapColor = vec4( vec3( mix( 0.2, 0.8, uv.y ) ), 1.0 );\n\t#endif\n\tvec3 outgoingLight = diffuseColor.rgb * matcapColor.rgb;\n\t#include <output_fragment>\n\t#include <tonemapping_fragment>\n\t#include <encodings_fragment>\n\t#include <fog_fragment>\n\t#include <premultiplied_alpha_fragment>\n\t#include <dithering_fragment>\n}",meshnormal_vert:"#define NORMAL\n#if defined( FLAT_SHADED ) || defined( USE_BUMPMAP ) || defined( TANGENTSPACE_NORMALMAP )\n\tvarying vec3 vViewPosition;\n#endif\n#include <common>\n#include <uv_pars_vertex>\n#include <displacementmap_pars_vertex>\n#include <normal_pars_vertex>\n#include <morphtarget_pars_vertex>\n#include <skinning_pars_vertex>\n#include <logdepthbuf_pars_vertex>\n#include <clipping_planes_pars_vertex>\nvoid main() {\n\t#include <uv_vertex>\n\t#include <beginnormal_vertex>\n\t#include <morphnormal_vertex>\n\t#include <skinbase_vertex>\n\t#include <skinnormal_vertex>\n\t#include <defaultnormal_vertex>\n\t#include <normal_vertex>\n\t#include <begin_vertex>\n\t#include <morphtarget_vertex>\n\t#include <skinning_vertex>\n\t#include <displacementmap_vertex>\n\t#include <project_vertex>\n\t#include <logdepthbuf_vertex>\n\t#include <clipping_planes_vertex>\n#if defined( FLAT_SHADED ) || defined( USE_BUMPMAP ) || defined( TANGENTSPACE_NORMALMAP )\n\tvViewPosition = - mvPosition.xyz;\n#endif\n}",meshnormal_frag:"#define NORMAL\nuniform float opacity;\n#if defined( FLAT_SHADED ) || defined( USE_BUMPMAP ) || defined( TANGENTSPACE_NORMALMAP )\n\tvarying vec3 vViewPosition;\n#endif\n#include <packing>\n#include <uv_pars_fragment>\n#include <normal_pars_fragment>\n#include <bumpmap_pars_fragment>\n#include <normalmap_pars_fragment>\n#include <logdepthbuf_pars_fragment>\n#include <clipping_planes_pars_fragment>\nvoid main() {\n\t#include <clipping_planes_fragment>\n\t#include <logdepthbuf_fragment>\n\t#include <normal_fragment_begin>\n\t#include <normal_fragment_maps>\n\tgl_FragColor = vec4( packNormalToRGB( normal ), opacity );\n\t#ifdef OPAQUE\n\t\tgl_FragColor.a = 1.0;\n\t#endif\n}",meshphong_vert:"#define PHONG\nvarying vec3 vViewPosition;\n#include <common>\n#include <uv_pars_vertex>\n#include <uv2_pars_vertex>\n#include <displacementmap_pars_vertex>\n#include <envmap_pars_vertex>\n#include <color_pars_vertex>\n#include <fog_pars_vertex>\n#include <normal_pars_vertex>\n#include <morphtarget_pars_vertex>\n#include <skinning_pars_vertex>\n#include <shadowmap_pars_vertex>\n#include <logdepthbuf_pars_vertex>\n#include <clipping_planes_pars_vertex>\nvoid main() {\n\t#include <uv_vertex>\n\t#include <uv2_vertex>\n\t#include <color_vertex>\n\t#include <morphcolor_vertex>\n\t#include <beginnormal_vertex>\n\t#include <morphnormal_vertex>\n\t#include <skinbase_vertex>\n\t#include <skinnormal_vertex>\n\t#include <defaultnormal_vertex>\n\t#include <normal_vertex>\n\t#include <begin_vertex>\n\t#include <morphtarget_vertex>\n\t#include <skinning_vertex>\n\t#include <displacementmap_vertex>\n\t#include <project_vertex>\n\t#include <logdepthbuf_vertex>\n\t#include <clipping_planes_vertex>\n\tvViewPosition = - mvPosition.xyz;\n\t#include <worldpos_vertex>\n\t#include <envmap_vertex>\n\t#include <shadowmap_vertex>\n\t#include <fog_vertex>\n}",meshphong_frag:"#define PHONG\nuniform vec3 diffuse;\nuniform vec3 emissive;\nuniform vec3 specular;\nuniform float shininess;\nuniform float opacity;\n#include <common>\n#include <packing>\n#include <dithering_pars_fragment>\n#include <color_pars_fragment>\n#include <uv_pars_fragment>\n#include <uv2_pars_fragment>\n#include <map_pars_fragment>\n#include <alphamap_pars_fragment>\n#include <alphatest_pars_fragment>\n#include <aomap_pars_fragment>\n#include <lightmap_pars_fragment>\n#include <emissivemap_pars_fragment>\n#include <envmap_common_pars_fragment>\n#include <envmap_pars_fragment>\n#include <fog_pars_fragment>\n#include <bsdfs>\n#include <lights_pars_begin>\n#include <normal_pars_fragment>\n#include <lights_phong_pars_fragment>\n#include <shadowmap_pars_fragment>\n#include <bumpmap_pars_fragment>\n#include <normalmap_pars_fragment>\n#include <specularmap_pars_fragment>\n#include <logdepthbuf_pars_fragment>\n#include <clipping_planes_pars_fragment>\nvoid main() {\n\t#include <clipping_planes_fragment>\n\tvec4 diffuseColor = vec4( diffuse, opacity );\n\tReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\n\tvec3 totalEmissiveRadiance = emissive;\n\t#include <logdepthbuf_fragment>\n\t#include <map_fragment>\n\t#include <color_fragment>\n\t#include <alphamap_fragment>\n\t#include <alphatest_fragment>\n\t#include <specularmap_fragment>\n\t#include <normal_fragment_begin>\n\t#include <normal_fragment_maps>\n\t#include <emissivemap_fragment>\n\t#include <lights_phong_fragment>\n\t#include <lights_fragment_begin>\n\t#include <lights_fragment_maps>\n\t#include <lights_fragment_end>\n\t#include <aomap_fragment>\n\tvec3 outgoingLight = reflectedLight.directDiffuse + reflectedLight.indirectDiffuse + reflectedLight.directSpecular + reflectedLight.indirectSpecular + totalEmissiveRadiance;\n\t#include <envmap_fragment>\n\t#include <output_fragment>\n\t#include <tonemapping_fragment>\n\t#include <encodings_fragment>\n\t#include <fog_fragment>\n\t#include <premultiplied_alpha_fragment>\n\t#include <dithering_fragment>\n}",meshphysical_vert:"#define STANDARD\nvarying vec3 vViewPosition;\n#ifdef USE_TRANSMISSION\n\tvarying vec3 vWorldPosition;\n#endif\n#include <common>\n#include <uv_pars_vertex>\n#include <uv2_pars_vertex>\n#include <displacementmap_pars_vertex>\n#include <color_pars_vertex>\n#include <fog_pars_vertex>\n#include <normal_pars_vertex>\n#include <morphtarget_pars_vertex>\n#include <skinning_pars_vertex>\n#include <shadowmap_pars_vertex>\n#include <logdepthbuf_pars_vertex>\n#include <clipping_planes_pars_vertex>\nvoid main() {\n\t#include <uv_vertex>\n\t#include <uv2_vertex>\n\t#include <color_vertex>\n\t#include <morphcolor_vertex>\n\t#include <beginnormal_vertex>\n\t#include <morphnormal_vertex>\n\t#include <skinbase_vertex>\n\t#include <skinnormal_vertex>\n\t#include <defaultnormal_vertex>\n\t#include <normal_vertex>\n\t#include <begin_vertex>\n\t#include <morphtarget_vertex>\n\t#include <skinning_vertex>\n\t#include <displacementmap_vertex>\n\t#include <project_vertex>\n\t#include <logdepthbuf_vertex>\n\t#include <clipping_planes_vertex>\n\tvViewPosition = - mvPosition.xyz;\n\t#include <worldpos_vertex>\n\t#include <shadowmap_vertex>\n\t#include <fog_vertex>\n#ifdef USE_TRANSMISSION\n\tvWorldPosition = worldPosition.xyz;\n#endif\n}",meshphysical_frag:"#define STANDARD\n#ifdef PHYSICAL\n\t#define IOR\n\t#define SPECULAR\n#endif\nuniform vec3 diffuse;\nuniform vec3 emissive;\nuniform float roughness;\nuniform float metalness;\nuniform float opacity;\n#ifdef IOR\n\tuniform float ior;\n#endif\n#ifdef SPECULAR\n\tuniform float specularIntensity;\n\tuniform vec3 specularColor;\n\t#ifdef USE_SPECULARINTENSITYMAP\n\t\tuniform sampler2D specularIntensityMap;\n\t#endif\n\t#ifdef USE_SPECULARCOLORMAP\n\t\tuniform sampler2D specularColorMap;\n\t#endif\n#endif\n#ifdef USE_CLEARCOAT\n\tuniform float clearcoat;\n\tuniform float clearcoatRoughness;\n#endif\n#ifdef USE_IRIDESCENCE\n\tuniform float iridescence;\n\tuniform float iridescenceIOR;\n\tuniform float iridescenceThicknessMinimum;\n\tuniform float iridescenceThicknessMaximum;\n#endif\n#ifdef USE_SHEEN\n\tuniform vec3 sheenColor;\n\tuniform float sheenRoughness;\n\t#ifdef USE_SHEENCOLORMAP\n\t\tuniform sampler2D sheenColorMap;\n\t#endif\n\t#ifdef USE_SHEENROUGHNESSMAP\n\t\tuniform sampler2D sheenRoughnessMap;\n\t#endif\n#endif\nvarying vec3 vViewPosition;\n#include <common>\n#include <packing>\n#include <dithering_pars_fragment>\n#include <color_pars_fragment>\n#include <uv_pars_fragment>\n#include <uv2_pars_fragment>\n#include <map_pars_fragment>\n#include <alphamap_pars_fragment>\n#include <alphatest_pars_fragment>\n#include <aomap_pars_fragment>\n#include <lightmap_pars_fragment>\n#include <emissivemap_pars_fragment>\n#include <bsdfs>\n#include <iridescence_fragment>\n#include <cube_uv_reflection_fragment>\n#include <envmap_common_pars_fragment>\n#include <envmap_physical_pars_fragment>\n#include <fog_pars_fragment>\n#include <lights_pars_begin>\n#include <normal_pars_fragment>\n#include <lights_physical_pars_fragment>\n#include <transmission_pars_fragment>\n#include <shadowmap_pars_fragment>\n#include <bumpmap_pars_fragment>\n#include <normalmap_pars_fragment>\n#include <clearcoat_pars_fragment>\n#include <iridescence_pars_fragment>\n#include <roughnessmap_pars_fragment>\n#include <metalnessmap_pars_fragment>\n#include <logdepthbuf_pars_fragment>\n#include <clipping_planes_pars_fragment>\nvoid main() {\n\t#include <clipping_planes_fragment>\n\tvec4 diffuseColor = vec4( diffuse, opacity );\n\tReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\n\tvec3 totalEmissiveRadiance = emissive;\n\t#include <logdepthbuf_fragment>\n\t#include <map_fragment>\n\t#include <color_fragment>\n\t#include <alphamap_fragment>\n\t#include <alphatest_fragment>\n\t#include <roughnessmap_fragment>\n\t#include <metalnessmap_fragment>\n\t#include <normal_fragment_begin>\n\t#include <normal_fragment_maps>\n\t#include <clearcoat_normal_fragment_begin>\n\t#include <clearcoat_normal_fragment_maps>\n\t#include <emissivemap_fragment>\n\t#include <lights_physical_fragment>\n\t#include <lights_fragment_begin>\n\t#include <lights_fragment_maps>\n\t#include <lights_fragment_end>\n\t#include <aomap_fragment>\n\tvec3 totalDiffuse = reflectedLight.directDiffuse + reflectedLight.indirectDiffuse;\n\tvec3 totalSpecular = reflectedLight.directSpecular + reflectedLight.indirectSpecular;\n\t#include <transmission_fragment>\n\tvec3 outgoingLight = totalDiffuse + totalSpecular + totalEmissiveRadiance;\n\t#ifdef USE_SHEEN\n\t\tfloat sheenEnergyComp = 1.0 - 0.157 * max3( material.sheenColor );\n\t\toutgoingLight = outgoingLight * sheenEnergyComp + sheenSpecular;\n\t#endif\n\t#ifdef USE_CLEARCOAT\n\t\tfloat dotNVcc = saturate( dot( geometry.clearcoatNormal, geometry.viewDir ) );\n\t\tvec3 Fcc = F_Schlick( material.clearcoatF0, material.clearcoatF90, dotNVcc );\n\t\toutgoingLight = outgoingLight * ( 1.0 - material.clearcoat * Fcc ) + clearcoatSpecular * material.clearcoat;\n\t#endif\n\t#include <output_fragment>\n\t#include <tonemapping_fragment>\n\t#include <encodings_fragment>\n\t#include <fog_fragment>\n\t#include <premultiplied_alpha_fragment>\n\t#include <dithering_fragment>\n}",meshtoon_vert:"#define TOON\nvarying vec3 vViewPosition;\n#include <common>\n#include <uv_pars_vertex>\n#include <uv2_pars_vertex>\n#include <displacementmap_pars_vertex>\n#include <color_pars_vertex>\n#include <fog_pars_vertex>\n#include <normal_pars_vertex>\n#include <morphtarget_pars_vertex>\n#include <skinning_pars_vertex>\n#include <shadowmap_pars_vertex>\n#include <logdepthbuf_pars_vertex>\n#include <clipping_planes_pars_vertex>\nvoid main() {\n\t#include <uv_vertex>\n\t#include <uv2_vertex>\n\t#include <color_vertex>\n\t#include <morphcolor_vertex>\n\t#include <beginnormal_vertex>\n\t#include <morphnormal_vertex>\n\t#include <skinbase_vertex>\n\t#include <skinnormal_vertex>\n\t#include <defaultnormal_vertex>\n\t#include <normal_vertex>\n\t#include <begin_vertex>\n\t#include <morphtarget_vertex>\n\t#include <skinning_vertex>\n\t#include <displacementmap_vertex>\n\t#include <project_vertex>\n\t#include <logdepthbuf_vertex>\n\t#include <clipping_planes_vertex>\n\tvViewPosition = - mvPosition.xyz;\n\t#include <worldpos_vertex>\n\t#include <shadowmap_vertex>\n\t#include <fog_vertex>\n}",meshtoon_frag:"#define TOON\nuniform vec3 diffuse;\nuniform vec3 emissive;\nuniform float opacity;\n#include <common>\n#include <packing>\n#include <dithering_pars_fragment>\n#include <color_pars_fragment>\n#include <uv_pars_fragment>\n#include <uv2_pars_fragment>\n#include <map_pars_fragment>\n#include <alphamap_pars_fragment>\n#include <alphatest_pars_fragment>\n#include <aomap_pars_fragment>\n#include <lightmap_pars_fragment>\n#include <emissivemap_pars_fragment>\n#include <gradientmap_pars_fragment>\n#include <fog_pars_fragment>\n#include <bsdfs>\n#include <lights_pars_begin>\n#include <normal_pars_fragment>\n#include <lights_toon_pars_fragment>\n#include <shadowmap_pars_fragment>\n#include <bumpmap_pars_fragment>\n#include <normalmap_pars_fragment>\n#include <logdepthbuf_pars_fragment>\n#include <clipping_planes_pars_fragment>\nvoid main() {\n\t#include <clipping_planes_fragment>\n\tvec4 diffuseColor = vec4( diffuse, opacity );\n\tReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\n\tvec3 totalEmissiveRadiance = emissive;\n\t#include <logdepthbuf_fragment>\n\t#include <map_fragment>\n\t#include <color_fragment>\n\t#include <alphamap_fragment>\n\t#include <alphatest_fragment>\n\t#include <normal_fragment_begin>\n\t#include <normal_fragment_maps>\n\t#include <emissivemap_fragment>\n\t#include <lights_toon_fragment>\n\t#include <lights_fragment_begin>\n\t#include <lights_fragment_maps>\n\t#include <lights_fragment_end>\n\t#include <aomap_fragment>\n\tvec3 outgoingLight = reflectedLight.directDiffuse + reflectedLight.indirectDiffuse + totalEmissiveRadiance;\n\t#include <output_fragment>\n\t#include <tonemapping_fragment>\n\t#include <encodings_fragment>\n\t#include <fog_fragment>\n\t#include <premultiplied_alpha_fragment>\n\t#include <dithering_fragment>\n}",points_vert:"uniform float size;\nuniform float scale;\n#include <common>\n#include <color_pars_vertex>\n#include <fog_pars_vertex>\n#include <morphtarget_pars_vertex>\n#include <logdepthbuf_pars_vertex>\n#include <clipping_planes_pars_vertex>\nvoid main() {\n\t#include <color_vertex>\n\t#include <morphcolor_vertex>\n\t#include <begin_vertex>\n\t#include <morphtarget_vertex>\n\t#include <project_vertex>\n\tgl_PointSize = size;\n\t#ifdef USE_SIZEATTENUATION\n\t\tbool isPerspective = isPerspectiveMatrix( projectionMatrix );\n\t\tif ( isPerspective ) gl_PointSize *= ( scale / - mvPosition.z );\n\t#endif\n\t#include <logdepthbuf_vertex>\n\t#include <clipping_planes_vertex>\n\t#include <worldpos_vertex>\n\t#include <fog_vertex>\n}",points_frag:"uniform vec3 diffuse;\nuniform float opacity;\n#include <common>\n#include <color_pars_fragment>\n#include <map_particle_pars_fragment>\n#include <alphatest_pars_fragment>\n#include <fog_pars_fragment>\n#include <logdepthbuf_pars_fragment>\n#include <clipping_planes_pars_fragment>\nvoid main() {\n\t#include <clipping_planes_fragment>\n\tvec3 outgoingLight = vec3( 0.0 );\n\tvec4 diffuseColor = vec4( diffuse, opacity );\n\t#include <logdepthbuf_fragment>\n\t#include <map_particle_fragment>\n\t#include <color_fragment>\n\t#include <alphatest_fragment>\n\toutgoingLight = diffuseColor.rgb;\n\t#include <output_fragment>\n\t#include <tonemapping_fragment>\n\t#include <encodings_fragment>\n\t#include <fog_fragment>\n\t#include <premultiplied_alpha_fragment>\n}",shadow_vert:"#include <common>\n#include <fog_pars_vertex>\n#include <morphtarget_pars_vertex>\n#include <skinning_pars_vertex>\n#include <shadowmap_pars_vertex>\nvoid main() {\n\t#include <beginnormal_vertex>\n\t#include <morphnormal_vertex>\n\t#include <skinbase_vertex>\n\t#include <skinnormal_vertex>\n\t#include <defaultnormal_vertex>\n\t#include <begin_vertex>\n\t#include <morphtarget_vertex>\n\t#include <skinning_vertex>\n\t#include <project_vertex>\n\t#include <worldpos_vertex>\n\t#include <shadowmap_vertex>\n\t#include <fog_vertex>\n}",shadow_frag:"uniform vec3 color;\nuniform float opacity;\n#include <common>\n#include <packing>\n#include <fog_pars_fragment>\n#include <bsdfs>\n#include <lights_pars_begin>\n#include <shadowmap_pars_fragment>\n#include <shadowmask_pars_fragment>\nvoid main() {\n\tgl_FragColor = vec4( color, opacity * ( 1.0 - getShadowMask() ) );\n\t#include <tonemapping_fragment>\n\t#include <encodings_fragment>\n\t#include <fog_fragment>\n}",sprite_vert:"uniform float rotation;\nuniform vec2 center;\n#include <common>\n#include <uv_pars_vertex>\n#include <fog_pars_vertex>\n#include <logdepthbuf_pars_vertex>\n#include <clipping_planes_pars_vertex>\nvoid main() {\n\t#include <uv_vertex>\n\tvec4 mvPosition = modelViewMatrix * vec4( 0.0, 0.0, 0.0, 1.0 );\n\tvec2 scale;\n\tscale.x = length( vec3( modelMatrix[ 0 ].x, modelMatrix[ 0 ].y, modelMatrix[ 0 ].z ) );\n\tscale.y = length( vec3( modelMatrix[ 1 ].x, modelMatrix[ 1 ].y, modelMatrix[ 1 ].z ) );\n\t#ifndef USE_SIZEATTENUATION\n\t\tbool 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if(l="drawArraysInstancedANGLE",null===(s=t.get("ANGLE_instanced_arrays")))return void console.error("THREE.WebGLBufferRenderer: using THREE.InstancedBufferGeometry but hardware does not support extension ANGLE_instanced_arrays.");s[l](i,r,a,o),n.update(a,i,o)}}}function Py(e,t,n){var r;function i(t){if("highp"===t){if(e.getShaderPrecisionFormat(35633,36338).precision>0&&e.getShaderPrecisionFormat(35632,36338).precision>0)return"highp";t="mediump"}return"mediump"===t&&e.getShaderPrecisionFormat(35633,36337).precision>0&&e.getShaderPrecisionFormat(35632,36337).precision>0?"mediump":"lowp"}var A="undefined"!==typeof WebGL2RenderingContext&&e instanceof WebGL2RenderingContext,a=void 0!==n.precision?n.precision:"highp",o=i(a);o!==a&&(console.warn("THREE.WebGLRenderer:",a,"not supported, using",o,"instead."),a=o);var s=A||t.has("WEBGL_draw_buffers"),l=!0===n.logarithmicDepthBuffer,u=e.getParameter(34930),c=e.getParameter(35660),d=e.getParameter(3379),h=e.getParameter(34076),f=e.getParameter(34921),p=e.getParameter(36347),g=e.getParameter(36348),m=e.getParameter(36349),v=c>0,y=A||t.has("OES_texture_float");return{isWebGL2:A,drawBuffers:s,getMaxAnisotropy:function(){if(void 0!==r)return r;if(!0===t.has("EXT_texture_filter_anisotropic")){var n=t.get("EXT_texture_filter_anisotropic");r=e.getParameter(n.MAX_TEXTURE_MAX_ANISOTROPY_EXT)}else r=0;return r},getMaxPrecision:i,precision:a,logarithmicDepthBuffer:l,maxTextures:u,maxVertexTextures:c,maxTextureSize:d,maxCubemapSize:h,maxAttributes:f,maxVertexUniforms:p,maxVaryings:g,maxFragmentUniforms:m,vertexTextures:v,floatFragmentTextures:y,floatVertexTextures:v&&y,maxSamples:A?e.getParameter(36183):0}}function Ny(e){var t=this,n=null,r=0,i=!1,A=!1,a=new Cy,o=new Gg,s={value:null,needsUpdate:!1};function l(e,n,r,i){var A=null!==e?e.length:0,l=null;if(0!==A){if(l=s.value,!0!==i||null===l){var u=r+4*A,c=n.matrixWorldInverse;o.getNormalMatrix(c),(null===l||l.length<u)&&(l=new Float32Array(u));for(var d=0,h=r;d!==A;++d,h+=4)a.copy(e[d]).applyMatrix4(c,o),a.normal.toArray(l,h),l[h+3]=a.constant}s.value=l,s.needsUpdate=!0}return t.numPlanes=A,t.numIntersection=0,l}this.uniform=s,this.numPlanes=0,this.numIntersection=0,this.init=function(e,t){var n=0!==e.length||t||0!==r||i;return i=t,r=e.length,n},this.beginShadows=function(){A=!0,l(null)},this.endShadows=function(){A=!1},this.setGlobalState=function(e,t){n=l(e,t,0)},this.setState=function(a,o,u){var c=a.clippingPlanes,d=a.clipIntersection,h=a.clipShadows,f=e.get(a);if(!i||null===c||0===c.length||A&&!h)A?l(null):function(){s.value!==n&&(s.value=n,s.needsUpdate=r>0);t.numPlanes=r,t.numIntersection=0}();else{var p=A?0:r,g=4*p,m=f.clippingState||null;s.value=m,m=l(c,o,g,u);for(var v=0;v!==g;++v)m[v]=n[v];f.clippingState=m,this.numIntersection=d?this.numPlanes:0,this.numPlanes+=p}}}function Oy(e){var t=new WeakMap;function n(e,t){return t===Zf?e.mapping=Yf:t===$f&&(e.mapping=Jf),e}function r(e){var n=e.target;n.removeEventListener("dispose",r);var i=t.get(n);void 0!==i&&(t.delete(n),i.dispose())}return{get:function(i){if(i&&i.isTexture&&!1===i.isRenderTargetTexture){var A=i.mapping;if(A===Zf||A===$f){if(t.has(i))return n(t.get(i).texture,i.mapping);var a=i.image;if(a&&a.height>0){var o=new By(a.height/2);return o.fromEquirectangularTexture(e,i),t.set(i,o),i.addEventListener("dispose",r),n(o.texture,i.mapping)}return null}}return i},dispose:function(){t=new WeakMap}}}var Vy=function(e){C(n,e);var t=M(n);function n(){var e,r=arguments.length>0&&void 0!==arguments[0]?arguments[0]:-1,i=arguments.length>1&&void 0!==arguments[1]?arguments[1]:1,A=arguments.length>2&&void 0!==arguments[2]?arguments[2]:1,a=arguments.length>3&&void 0!==arguments[3]?arguments[3]:-1,o=arguments.length>4&&void 0!==arguments[4]?arguments[4]:.1,s=arguments.length>5&&void 0!==arguments[5]?arguments[5]:2e3;return B(this,n),(e=t.call(this)).isOrthographicCamera=!0,e.type="OrthographicCamera",e.zoom=1,e.view=null,e.left=r,e.right=i,e.top=A,e.bottom=a,e.near=o,e.far=s,e.updateProjectionMatrix(),e}return b(n,[{key:"copy",value:function(e,t){return jc(S(n.prototype),"copy",this).call(this,e,t),this.left=e.left,this.right=e.right,this.top=e.top,this.bottom=e.bottom,this.near=e.near,this.far=e.far,this.zoom=e.zoom,this.view=null===e.view?null:Object.assign({},e.view),this}},{key:"setViewOffset",value:function(e,t,n,r,i,A){null===this.view&&(this.view={enabled:!0,fullWidth:1,fullHeight:1,offsetX:0,offsetY:0,width:1,height:1}),this.view.enabled=!0,this.view.fullWidth=e,this.view.fullHeight=t,this.view.offsetX=n,this.view.offsetY=r,this.view.width=i,this.view.height=A,this.updateProjectionMatrix()}},{key:"clearViewOffset",value:function(){null!==this.view&&(this.view.enabled=!1),this.updateProjectionMatrix()}},{key:"updateProjectionMatrix",value:function(){var e=(this.right-this.left)/(2*this.zoom),t=(this.top-this.bottom)/(2*this.zoom),n=(this.right+this.left)/2,r=(this.top+this.bottom)/2,i=n-e,A=n+e,a=r+t,o=r-t;if(null!==this.view&&this.view.enabled){var s=(this.right-this.left)/this.view.fullWidth/this.zoom,l=(this.top-this.bottom)/this.view.fullHeight/this.zoom;A=(i+=s*this.view.offsetX)+s*this.view.width,o=(a-=l*this.view.offsetY)-l*this.view.height}this.projectionMatrix.makeOrthographic(i,A,a,o,this.near,this.far),this.projectionMatrixInverse.copy(this.projectionMatrix).invert()}},{key:"toJSON",value:function(e){var t=jc(S(n.prototype),"toJSON",this).call(this,e);return t.object.zoom=this.zoom,t.object.left=this.left,t.object.right=this.right,t.object.top=this.top,t.object.bottom=this.bottom,t.object.near=this.near,t.object.far=this.far,null!==this.view&&(t.object.view=Object.assign({},this.view)),t}}]),n}(py),zy=4,Gy=[.125,.215,.35,.446,.526,.582],Ky=20,Wy=new Vy,jy=new Am,Xy=null,qy=(1+Math.sqrt(5))/2,Yy=1/qy,Jy=[new gm(1,1,1),new gm(-1,1,1),new gm(1,1,-1),new gm(-1,1,-1),new gm(0,qy,Yy),new gm(0,qy,-Yy),new gm(Yy,0,qy),new gm(-Yy,0,qy),new gm(qy,Yy,0),new gm(-qy,Yy,0)],Zy=function(){function e(t){B(this,e),this._renderer=t,this._pingPongRenderTarget=null,this._lodMax=0,this._cubeSize=0,this._lodPlanes=[],this._sizeLods=[],this._sigmas=[],this._blurMaterial=null,this._cubemapMaterial=null,this._equirectMaterial=null,this._compileMaterial(this._blurMaterial)}return b(e,[{key:"fromScene",value:function(e){var t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0,n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:.1,r=arguments.length>3&&void 0!==arguments[3]?arguments[3]:100;Xy=this._renderer.getRenderTarget(),this._setSize(256);var i=this._allocateTargets();return i.depthBuffer=!0,this._sceneToCubeUV(e,n,r,i),t>0&&this._blur(i,0,0,t),this._applyPMREM(i),this._cleanup(i),i}},{key:"fromEquirectangular",value:function(e){var t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null;return this._fromTexture(e,t)}},{key:"fromCubemap",value:function(e){var t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null;return this._fromTexture(e,t)}},{key:"compileCubemapShader",value:function(){null===this._cubemapMaterial&&(this._cubemapMaterial=nw(),this._compileMaterial(this._cubemapMaterial))}},{key:"compileEquirectangularShader",value:function(){null===this._equirectMaterial&&(this._equirectMaterial=tw(),this._compileMaterial(this._equirectMaterial))}},{key:"dispose",value:function(){this._dispose(),null!==this._cubemapMaterial&&this._cubemapMaterial.dispose(),null!==this._equirectMaterial&&this._equirectMaterial.dispose()}},{key:"_setSize",value:function(e){this._lodMax=Math.floor(Math.log2(e)),this._cubeSize=Math.pow(2,this._lodMax)}},{key:"_dispose",value:function(){null!==this._blurMaterial&&this._blurMaterial.dispose(),null!==this._pingPongRenderTarget&&this._pingPongRenderTarget.dispose();for(var e=0;e<this._lodPlanes.length;e++)this._lodPlanes[e].dispose()}},{key:"_cleanup",value:function(e){this._renderer.setRenderTarget(Xy),e.scissorTest=!1,ew(e,0,0,e.width,e.height)}},{key:"_fromTexture",value:function(e,t){e.mapping===Yf||e.mapping===Jf?this._setSize(0===e.image.length?16:e.image[0].width||e.image[0].image.width):this._setSize(e.image.width/4),Xy=this._renderer.getRenderTarget();var n=t||this._allocateTargets();return this._textureToCubeUV(e,n),this._applyPMREM(n),this._cleanup(n),n}},{key:"_allocateTargets",value:function(){var e=3*Math.max(this._cubeSize,112),t=4*this._cubeSize,n={magFilter:op,minFilter:op,generateMipmaps:!1,type:vp,format:bp,encoding:pg,depthBuffer:!1},r=$y(e,t,n);if(null===this._pingPongRenderTarget||this._pingPongRenderTarget.width!==e||this._pingPongRenderTarget.height!==t){null!==this._pingPongRenderTarget&&this._dispose(),this._pingPongRenderTarget=$y(e,t,n);var i=this._lodMax,A=function(e){for(var t=[],n=[],r=[],i=e,A=e-zy+1+Gy.length,a=0;a<A;a++){var o=Math.pow(2,i);n.push(o);var s=1/o;a>e-zy?s=Gy[a-e+zy-1]:0===a&&(s=0),r.push(s);for(var l=1/(o-2),u=-l,c=1+l,d=[u,u,c,u,c,c,u,u,c,c,u,c],h=6,f=6,p=3,g=2,m=1,v=new Float32Array(p*f*h),y=new Float32Array(g*f*h),w=new Float32Array(m*f*h),B=0;B<h;B++){var _=B%3*2/3-1,b=B>2?0:-1,x=[_,b,0,_+2/3,b,0,_+2/3,b+1,0,_,b,0,_+2/3,b+1,0,_,b+1,0];v.set(x,p*f*B),y.set(d,g*f*B);var C=[B,B,B,B,B,B];w.set(C,m*f*B)}var S=new Kv;S.setAttribute("position",new Lv(v,p)),S.setAttribute("uv",new Lv(y,g)),S.setAttribute("faceIndex",new Lv(w,m)),t.push(S),i>zy&&i--}return{lodPlanes:t,sizeLods:n,sigmas:r}}(i);this._sizeLods=A.sizeLods,this._lodPlanes=A.lodPlanes,this._sigmas=A.sigmas,this._blurMaterial=function(e,t,n){var r=new Float32Array(Ky),i=new gm(0,1,0),A=new fy({name:"SphericalGaussianBlur",defines:{n:Ky,CUBEUV_TEXEL_WIDTH:1/t,CUBEUV_TEXEL_HEIGHT:1/n,CUBEUV_MAX_MIP:"".concat(e,".0")},uniforms:{envMap:{value:null},samples:{value:1},weights:{value:r},latitudinal:{value:!1},dTheta:{value:0},mipInt:{value:0},poleAxis:{value:i}},vertexShader:"\n\n\t\tprecision mediump float;\n\t\tprecision mediump int;\n\n\t\tattribute float faceIndex;\n\n\t\tvarying vec3 vOutputDirection;\n\n\t\t// RH coordinate system; PMREM face-indexing convention\n\t\tvec3 getDirection( vec2 uv, float face ) {\n\n\t\t\tuv = 2.0 * uv - 1.0;\n\n\t\t\tvec3 direction = vec3( uv, 1.0 );\n\n\t\t\tif ( face == 0.0 ) {\n\n\t\t\t\tdirection = direction.zyx; // ( 1, v, u ) pos x\n\n\t\t\t} else if ( face == 1.0 ) {\n\n\t\t\t\tdirection = direction.xzy;\n\t\t\t\tdirection.xz *= -1.0; // ( -u, 1, -v ) pos y\n\n\t\t\t} else if ( face == 2.0 ) {\n\n\t\t\t\tdirection.x *= -1.0; // ( -u, v, 1 ) pos z\n\n\t\t\t} else if ( face == 3.0 ) {\n\n\t\t\t\tdirection = direction.zyx;\n\t\t\t\tdirection.xz *= -1.0; // ( -1, v, -u ) neg x\n\n\t\t\t} else if ( face == 4.0 ) {\n\n\t\t\t\tdirection = direction.xzy;\n\t\t\t\tdirection.xy *= -1.0; // ( -u, -1, v ) neg y\n\n\t\t\t} else if ( face == 5.0 ) {\n\n\t\t\t\tdirection.z *= -1.0; // ( u, v, -1 ) neg z\n\n\t\t\t}\n\n\t\t\treturn direction;\n\n\t\t}\n\n\t\tvoid main() {\n\n\t\t\tvOutputDirection = getDirection( uv, faceIndex );\n\t\t\tgl_Position = vec4( position, 1.0 );\n\n\t\t}\n\t",fragmentShader:"\n\n\t\t\tprecision mediump float;\n\t\t\tprecision mediump int;\n\n\t\t\tvarying vec3 vOutputDirection;\n\n\t\t\tuniform sampler2D envMap;\n\t\t\tuniform int samples;\n\t\t\tuniform float weights[ n ];\n\t\t\tuniform bool latitudinal;\n\t\t\tuniform float dTheta;\n\t\t\tuniform float mipInt;\n\t\t\tuniform vec3 poleAxis;\n\n\t\t\t#define ENVMAP_TYPE_CUBE_UV\n\t\t\t#include <cube_uv_reflection_fragment>\n\n\t\t\tvec3 getSample( float theta, vec3 axis ) {\n\n\t\t\t\tfloat cosTheta = cos( theta );\n\t\t\t\t// Rodrigues' axis-angle rotation\n\t\t\t\tvec3 sampleDirection = vOutputDirection * cosTheta\n\t\t\t\t\t+ cross( axis, vOutputDirection ) * sin( theta )\n\t\t\t\t\t+ axis * dot( axis, vOutputDirection ) * ( 1.0 - cosTheta );\n\n\t\t\t\treturn bilinearCubeUV( envMap, sampleDirection, mipInt );\n\n\t\t\t}\n\n\t\t\tvoid main() {\n\n\t\t\t\tvec3 axis = latitudinal ? poleAxis : cross( poleAxis, vOutputDirection );\n\n\t\t\t\tif ( all( equal( axis, vec3( 0.0 ) ) ) ) {\n\n\t\t\t\t\taxis = vec3( vOutputDirection.z, 0.0, - vOutputDirection.x );\n\n\t\t\t\t}\n\n\t\t\t\taxis = normalize( axis );\n\n\t\t\t\tgl_FragColor = vec4( 0.0, 0.0, 0.0, 1.0 );\n\t\t\t\tgl_FragColor.rgb += weights[ 0 ] * getSample( 0.0, axis );\n\n\t\t\t\tfor ( int i = 1; i < n; i++ ) {\n\n\t\t\t\t\tif ( i >= samples ) {\n\n\t\t\t\t\t\tbreak;\n\n\t\t\t\t\t}\n\n\t\t\t\t\tfloat theta = dTheta * float( i );\n\t\t\t\t\tgl_FragColor.rgb += weights[ i ] * getSample( -1.0 * theta, axis );\n\t\t\t\t\tgl_FragColor.rgb += weights[ i ] * getSample( theta, axis );\n\n\t\t\t\t}\n\n\t\t\t}\n\t\t",blending:cf,depthTest:!1,depthWrite:!1});return A}(i,e,t)}return r}},{key:"_compileMaterial",value:function(e){var t=new Ay(this._lodPlanes[0],e);this._renderer.compile(t,Wy)}},{key:"_sceneToCubeUV",value:function(e,t,n,r){var i=new gy(90,1,t,n),A=[1,-1,1,1,1,1],a=[1,1,1,-1,-1,-1],o=this._renderer,s=o.autoClear,l=o.toneMapping;o.getClearColor(jy),o.toneMapping=Gf,o.autoClear=!1;var u=new Tv({name:"PMREM.Background",side:lf,depthWrite:!1,depthTest:!1}),c=new Ay(new oy,u),d=!1,h=e.background;h?h.isColor&&(u.color.copy(h),e.background=null,d=!0):(u.color.copy(jy),d=!0);for(var f=0;f<6;f++){var p=f%3;0===p?(i.up.set(0,A[f],0),i.lookAt(a[f],0,0)):1===p?(i.up.set(0,0,A[f]),i.lookAt(0,a[f],0)):(i.up.set(0,A[f],0),i.lookAt(0,0,a[f]));var g=this._cubeSize;ew(r,p*g,f>2?g:0,g,g),o.setRenderTarget(r),d&&o.render(c,i),o.render(e,i)}c.geometry.dispose(),c.material.dispose(),o.toneMapping=l,o.autoClear=s,e.background=h}},{key:"_textureToCubeUV",value:function(e,t){var n=this._renderer,r=e.mapping===Yf||e.mapping===Jf;r?(null===this._cubemapMaterial&&(this._cubemapMaterial=nw()),this._cubemapMaterial.uniforms.flipEnvMap.value=!1===e.isRenderTargetTexture?-1:1):null===this._equirectMaterial&&(this._equirectMaterial=tw());var i=r?this._cubemapMaterial:this._equirectMaterial,A=new Ay(this._lodPlanes[0],i);i.uniforms.envMap.value=e;var a=this._cubeSize;ew(t,0,0,3*a,2*a),n.setRenderTarget(t),n.render(A,Wy)}},{key:"_applyPMREM",value:function(e){var t=this._renderer,n=t.autoClear;t.autoClear=!1;for(var r=1;r<this._lodPlanes.length;r++){var i=Math.sqrt(this._sigmas[r]*this._sigmas[r]-this._sigmas[r-1]*this._sigmas[r-1]),A=Jy[(r-1)%Jy.length];this._blur(e,r-1,r,i,A)}t.autoClear=n}},{key:"_blur",value:function(e,t,n,r,i){var A=this._pingPongRenderTarget;this._halfBlur(e,A,t,n,r,"latitudinal",i),this._halfBlur(A,e,n,n,r,"longitudinal",i)}},{key:"_halfBlur",value:function(e,t,n,r,i,A,a){var o=this._renderer,s=this._blurMaterial;"latitudinal"!==A&&"longitudinal"!==A&&console.error("blur direction must be either latitudinal or longitudinal!");var l=new Ay(this._lodPlanes[r],s),u=s.uniforms,c=this._sizeLods[n]-1,d=isFinite(i)?Math.PI/(2*c):2*Math.PI/(2*Ky-1),h=i/d,f=isFinite(i)?1+Math.floor(3*h):Ky;f>Ky&&console.warn("sigmaRadians, ".concat(i,", is too large and will clip, as it requested ").concat(f," samples when the maximum is set to ").concat(Ky));for(var p=[],g=0,m=0;m<Ky;++m){var v=m/h,y=Math.exp(-v*v/2);p.push(y),0===m?g+=y:m<f&&(g+=2*y)}for(var w=0;w<p.length;w++)p[w]=p[w]/g;u.envMap.value=e.texture,u.samples.value=f,u.weights.value=p,u.latitudinal.value="latitudinal"===A,a&&(u.poleAxis.value=a);var B=this._lodMax;u.dTheta.value=d,u.mipInt.value=B-n;var _=this._sizeLods[r];ew(t,3*_*(r>B-zy?r-B+zy:0),4*(this._cubeSize-_),3*_,2*_),o.setRenderTarget(t),o.render(l,Wy)}}]),e}();function $y(e,t,n){var r=new dm(e,t,n);return r.texture.mapping=ep,r.texture.name="PMREM.cubeUv",r.scissorTest=!0,r}function ew(e,t,n,r,i){e.viewport.set(t,n,r,i),e.scissor.set(t,n,r,i)}function tw(){return new fy({name:"EquirectangularToCubeUV",uniforms:{envMap:{value:null}},vertexShader:"\n\n\t\tprecision mediump float;\n\t\tprecision mediump int;\n\n\t\tattribute float faceIndex;\n\n\t\tvarying vec3 vOutputDirection;\n\n\t\t// RH coordinate system; PMREM face-indexing convention\n\t\tvec3 getDirection( vec2 uv, float face ) {\n\n\t\t\tuv = 2.0 * uv - 1.0;\n\n\t\t\tvec3 direction = vec3( uv, 1.0 );\n\n\t\t\tif ( face == 0.0 ) {\n\n\t\t\t\tdirection = direction.zyx; // ( 1, v, u ) pos x\n\n\t\t\t} else if ( face == 1.0 ) {\n\n\t\t\t\tdirection = direction.xzy;\n\t\t\t\tdirection.xz *= -1.0; // ( -u, 1, -v ) pos y\n\n\t\t\t} else if ( face == 2.0 ) {\n\n\t\t\t\tdirection.x *= -1.0; // ( -u, v, 1 ) pos z\n\n\t\t\t} else if ( face == 3.0 ) {\n\n\t\t\t\tdirection = direction.zyx;\n\t\t\t\tdirection.xz *= -1.0; // ( -1, v, -u ) neg x\n\n\t\t\t} else if ( face == 4.0 ) {\n\n\t\t\t\tdirection = direction.xzy;\n\t\t\t\tdirection.xy *= -1.0; // ( -u, -1, v ) neg y\n\n\t\t\t} else if ( face == 5.0 ) {\n\n\t\t\t\tdirection.z *= -1.0; // ( u, v, -1 ) neg z\n\n\t\t\t}\n\n\t\t\treturn direction;\n\n\t\t}\n\n\t\tvoid main() {\n\n\t\t\tvOutputDirection = getDirection( uv, faceIndex );\n\t\t\tgl_Position = vec4( position, 1.0 );\n\n\t\t}\n\t",fragmentShader:"\n\n\t\t\tprecision mediump float;\n\t\t\tprecision mediump int;\n\n\t\t\tvarying vec3 vOutputDirection;\n\n\t\t\tuniform sampler2D envMap;\n\n\t\t\t#include <common>\n\n\t\t\tvoid main() {\n\n\t\t\t\tvec3 outputDirection = normalize( vOutputDirection );\n\t\t\t\tvec2 uv = equirectUv( outputDirection );\n\n\t\t\t\tgl_FragColor = vec4( texture2D ( envMap, uv ).rgb, 1.0 );\n\n\t\t\t}\n\t\t",blending:cf,depthTest:!1,depthWrite:!1})}function nw(){return new fy({name:"CubemapToCubeUV",uniforms:{envMap:{value:null},flipEnvMap:{value:-1}},vertexShader:"\n\n\t\tprecision mediump float;\n\t\tprecision mediump int;\n\n\t\tattribute float faceIndex;\n\n\t\tvarying vec3 vOutputDirection;\n\n\t\t// RH coordinate system; PMREM face-indexing convention\n\t\tvec3 getDirection( vec2 uv, float face ) {\n\n\t\t\tuv = 2.0 * uv - 1.0;\n\n\t\t\tvec3 direction = vec3( uv, 1.0 );\n\n\t\t\tif ( face == 0.0 ) {\n\n\t\t\t\tdirection = direction.zyx; // ( 1, v, u ) pos x\n\n\t\t\t} else if ( face == 1.0 ) {\n\n\t\t\t\tdirection = direction.xzy;\n\t\t\t\tdirection.xz *= -1.0; // ( -u, 1, -v ) pos y\n\n\t\t\t} else if ( face == 2.0 ) {\n\n\t\t\t\tdirection.x *= -1.0; // ( -u, v, 1 ) pos z\n\n\t\t\t} else if ( face == 3.0 ) {\n\n\t\t\t\tdirection = direction.zyx;\n\t\t\t\tdirection.xz *= -1.0; // ( -1, v, -u ) neg x\n\n\t\t\t} else if ( face == 4.0 ) {\n\n\t\t\t\tdirection = direction.xzy;\n\t\t\t\tdirection.xy *= -1.0; // ( -u, -1, v ) neg y\n\n\t\t\t} else if ( face == 5.0 ) {\n\n\t\t\t\tdirection.z *= -1.0; // ( u, v, -1 ) neg z\n\n\t\t\t}\n\n\t\t\treturn direction;\n\n\t\t}\n\n\t\tvoid main() {\n\n\t\t\tvOutputDirection = getDirection( uv, faceIndex );\n\t\t\tgl_Position = vec4( position, 1.0 );\n\n\t\t}\n\t",fragmentShader:"\n\n\t\t\tprecision mediump float;\n\t\t\tprecision mediump int;\n\n\t\t\tuniform float flipEnvMap;\n\n\t\t\tvarying vec3 vOutputDirection;\n\n\t\t\tuniform samplerCube envMap;\n\n\t\t\tvoid main() {\n\n\t\t\t\tgl_FragColor = textureCube( envMap, vec3( flipEnvMap * vOutputDirection.x, vOutputDirection.yz ) );\n\n\t\t\t}\n\t\t",blending:cf,depthTest:!1,depthWrite:!1})}function rw(e){var t=new WeakMap,n=null;function r(e){var n=e.target;n.removeEventListener("dispose",r);var i=t.get(n);void 0!==i&&(t.delete(n),i.dispose())}return{get:function(i){if(i&&i.isTexture){var A=i.mapping,a=A===Zf||A===$f,o=A===Yf||A===Jf;if(a||o){if(i.isRenderTargetTexture&&!0===i.needsPMREMUpdate){i.needsPMREMUpdate=!1;var s=t.get(i);return null===n&&(n=new Zy(e)),s=a?n.fromEquirectangular(i,s):n.fromCubemap(i,s),t.set(i,s),s.texture}if(t.has(i))return t.get(i).texture;var l=i.image;if(a&&l&&l.height>0||o&&l&&function(e){for(var t=0,n=6,r=0;r<n;r++)void 0!==e[r]&&t++;return t===n}(l)){null===n&&(n=new Zy(e));var u=a?n.fromEquirectangular(i):n.fromCubemap(i);return t.set(i,u),i.addEventListener("dispose",r),u.texture}return null}}return i},dispose:function(){t=new WeakMap,null!==n&&(n.dispose(),n=null)}}}function iw(e){var t={};function n(n){if(void 0!==t[n])return t[n];var r;switch(n){case"WEBGL_depth_texture":r=e.getExtension("WEBGL_depth_texture")||e.getExtension("MOZ_WEBGL_depth_texture")||e.getExtension("WEBKIT_WEBGL_depth_texture");break;case"EXT_texture_filter_anisotropic":r=e.getExtension("EXT_texture_filter_anisotropic")||e.getExtension("MOZ_EXT_texture_filter_anisotropic")||e.getExtension("WEBKIT_EXT_texture_filter_anisotropic");break;case"WEBGL_compressed_texture_s3tc":r=e.getExtension("WEBGL_compressed_texture_s3tc")||e.getExtension("MOZ_WEBGL_compressed_texture_s3tc")||e.getExtension("WEBKIT_WEBGL_compressed_texture_s3tc");break;case"WEBGL_compressed_texture_pvrtc":r=e.getExtension("WEBGL_compressed_texture_pvrtc")||e.getExtension("WEBKIT_WEBGL_compressed_texture_pvrtc");break;default:r=e.getExtension(n)}return t[n]=r,r}return{has:function(e){return null!==n(e)},init:function(e){e.isWebGL2?n("EXT_color_buffer_float"):(n("WEBGL_depth_texture"),n("OES_texture_float"),n("OES_texture_half_float"),n("OES_texture_half_float_linear"),n("OES_standard_derivatives"),n("OES_element_index_uint"),n("OES_vertex_array_object"),n("ANGLE_instanced_arrays")),n("OES_texture_float_linear"),n("EXT_color_buffer_half_float"),n("WEBGL_multisampled_render_to_texture")},get:function(e){var t=n(e);return null===t&&console.warn("THREE.WebGLRenderer: "+e+" extension not supported."),t}}}function Aw(e,t,n,r){var i={},A=new WeakMap;function a(e){var o=e.target;for(var s in null!==o.index&&t.remove(o.index),o.attributes)t.remove(o.attributes[s]);o.removeEventListener("dispose",a),delete i[o.id];var l=A.get(o);l&&(t.remove(l),A.delete(o)),r.releaseStatesOfGeometry(o),!0===o.isInstancedBufferGeometry&&delete o._maxInstanceCount,n.memory.geometries--}function o(e){var n=[],r=e.index,i=e.attributes.position,a=0;if(null!==r){var o=r.array;a=r.version;for(var s=0,l=o.length;s<l;s+=3){var u=o[s+0],c=o[s+1],d=o[s+2];n.push(u,c,c,d,d,u)}}else{var h=i.array;a=i.version;for(var f=0,p=h.length/3-1;f<p;f+=3){var g=f+0,m=f+1,v=f+2;n.push(g,m,m,v,v,g)}}var y=new(Wg(n)?Iv:Dv)(n,1);y.version=a;var w=A.get(e);w&&t.remove(w),A.set(e,y)}return{get:function(e,t){return!0===i[t.id]||(t.addEventListener("dispose",a),i[t.id]=!0,n.memory.geometries++),t},update:function(e){var n=e.attributes;for(var r in n)t.update(n[r],34962);var i=e.morphAttributes;for(var A in i)for(var a=i[A],o=0,s=a.length;o<s;o++)t.update(a[o],34962)},getWireframeAttribute:function(e){var t=A.get(e);if(t){var n=e.index;null!==n&&t.version<n.version&&o(e)}else o(e);return A.get(e)}}}function aw(e,t,n,r){var i,A,a,o=r.isWebGL2;this.setMode=function(e){i=e},this.setIndex=function(e){A=e.type,a=e.bytesPerElement},this.render=function(t,r){e.drawElements(i,r,A,t*a),n.update(r,i,1)},this.renderInstances=function(r,s,l){if(0!==l){var u,c;if(o)u=e,c="drawElementsInstanced";else if(c="drawElementsInstancedANGLE",null===(u=t.get("ANGLE_instanced_arrays")))return void console.error("THREE.WebGLIndexedBufferRenderer: using THREE.InstancedBufferGeometry but hardware does not support extension ANGLE_instanced_arrays.");u[c](i,s,A,r*a,l),n.update(s,i,l)}}}function ow(e){var t={frame:0,calls:0,triangles:0,points:0,lines:0};return{memory:{geometries:0,textures:0},render:t,programs:null,autoReset:!0,reset:function(){t.frame++,t.calls=0,t.triangles=0,t.points=0,t.lines=0},update:function(e,n,r){switch(t.calls++,n){case 4:t.triangles+=r*(e/3);break;case 1:t.lines+=r*(e/2);break;case 3:t.lines+=r*(e-1);break;case 2:t.lines+=r*e;break;case 0:t.points+=r*e;break;default:console.error("THREE.WebGLInfo: Unknown draw mode:",n)}}}}function sw(e,t){return e[0]-t[0]}function lw(e,t){return Math.abs(t[1])-Math.abs(e[1])}function uw(e,t,n){for(var r={},i=new Float32Array(8),A=new WeakMap,a=new cm,o=[],s=0;s<8;s++)o[s]=[s,0];return{update:function(s,l,u,c){var 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n=[];if(t.shaderID?n.push(t.shaderID):(n.push(t.customVertexShaderID),n.push(t.customFragmentShaderID)),void 0!==t.defines)for(var r in t.defines)n.push(r),n.push(t.defines[r]);return!1===t.isRawShaderMaterial&&(!function(e,t){e.push(t.precision),e.push(t.outputEncoding),e.push(t.envMapMode),e.push(t.envMapCubeUVHeight),e.push(t.combine),e.push(t.vertexUvs),e.push(t.fogExp2),e.push(t.sizeAttenuation),e.push(t.morphTargetsCount),e.push(t.morphAttributeCount),e.push(t.numDirLights),e.push(t.numPointLights),e.push(t.numSpotLights),e.push(t.numSpotLightMaps),e.push(t.numHemiLights),e.push(t.numRectAreaLights),e.push(t.numDirLightShadows),e.push(t.numPointLightShadows),e.push(t.numSpotLightShadows),e.push(t.numSpotLightShadowsWithMaps),e.push(t.shadowMapType),e.push(t.toneMapping),e.push(t.numClippingPlanes),e.push(t.numClipIntersection),e.push(t.depthPacking)}(n,t),function(e,t){o.disableAll(),t.isWebGL2&&o.enable(0);t.supportsVertexTextures&&o.enable(1);t.instancing&&o.enable(2);t.instancingColor&&o.enable(3);t.map&&o.enable(4);t.matcap&&o.enable(5);t.envMap&&o.enable(6);t.lightMap&&o.enable(7);t.aoMap&&o.enable(8);t.emissiveMap&&o.enable(9);t.bumpMap&&o.enable(10);t.normalMap&&o.enable(11);t.objectSpaceNormalMap&&o.enable(12);t.tangentSpaceNormalMap&&o.enable(13);t.clearcoat&&o.enable(14);t.clearcoatMap&&o.enable(15);t.clearcoatRoughnessMap&&o.enable(16);t.clearcoatNormalMap&&o.enable(17);t.iridescence&&o.enable(18);t.iridescenceMap&&o.enable(19);t.iridescenceThicknessMap&&o.enable(20);t.displacementMap&&o.enable(21);t.specularMap&&o.enable(22);t.roughnessMap&&o.enable(23);t.metalnessMap&&o.enable(24);t.gradientMap&&o.enable(25);t.alphaMap&&o.enable(26);t.alphaTest&&o.enable(27);t.vertexColors&&o.enable(28);t.vertexAlphas&&o.enable(29);t.vertexUvs&&o.enable(30);t.vertexTangents&&o.enable(31);t.uvsVertexOnly&&o.enable(32);e.push(o.mask),o.disableAll(),t.fog&&o.enable(0);t.useFog&&o.enable(1);t.flatShading&&o.enable(2);t.logarithmicDepthBuffer&&o.enable(3);t.skinning&&o.enable(4);t.morphTargets&&o.enable(5);t.morphNormals&&o.enable(6);t.morphColors&&o.enable(7);t.premultipliedAlpha&&o.enable(8);t.shadowMapEnabled&&o.enable(9);t.physicallyCorrectLights&&o.enable(10);t.doubleSided&&o.enable(11);t.flipSided&&o.enable(12);t.useDepthPacking&&o.enable(13);t.dithering&&o.enable(14);t.specularIntensityMap&&o.enable(15);t.specularColorMap&&o.enable(16);t.transmission&&o.enable(17);t.transmissionMap&&o.enable(18);t.thicknessMap&&o.enable(19);t.sheen&&o.enable(20);t.sheenColorMap&&o.enable(21);t.sheenRoughnessMap&&o.enable(22);t.decodeVideoTexture&&o.enable(23);t.opaque&&o.enable(24);e.push(o.mask)}(n,t),n.push(e.outputEncoding)),n.push(t.customProgramCacheKey),n.join()},getUniforms:function(e){var t,n=f[e.type];if(n){var r=Ly[n];t=cy.clone(r.uniforms)}else t=e.uniforms;return t},acquireProgram:function(t,n){for(var r,i=0,a=l.length;i<a;i++){var o=l[i];if(o.cacheKey===n){++(r=o).usedTimes;break}}return void 0===r&&(r=new TB(e,n,t,A),l.push(r)),r},releaseProgram:function(e){if(0===--e.usedTimes){var t=l.indexOf(e);l[t]=l[l.length-1],l.pop(),e.destroy()}},releaseShaderCache:function(e){s.remove(e)},programs:l,dispose:function(){s.dispose()}}}function IB(){var e=new WeakMap;return{get:function(t){var n=e.get(t);return void 0===n&&(n={},e.set(t,n)),n},remove:function(t){e.delete(t)},update:function(t,n,r){e.get(t)[n]=r},dispose:function(){e=new WeakMap}}}function RB(e,t){return e.groupOrder!==t.groupOrder?e.groupOrder-t.groupOrder:e.renderOrder!==t.renderOrder?e.renderOrder-t.renderOrder:e.material.id!==t.material.id?e.material.id-t.material.id:e.z!==t.z?e.z-t.z:e.id-t.id}function HB(e,t){return e.groupOrder!==t.groupOrder?e.groupOrder-t.groupOrder:e.renderOrder!==t.renderOrder?e.renderOrder-t.renderOrder:e.z!==t.z?t.z-e.z:e.id-t.id}function PB(){var e=[],t=0,n=[],r=[],i=[];function A(n,r,i,A,a,o){var s=e[t];return void 0===s?(s={id:n.id,object:n,geometry:r,material:i,groupOrder:A,renderOrder:n.renderOrder,z:a,group:o},e[t]=s):(s.id=n.id,s.object=n,s.geometry=r,s.material=i,s.groupOrder=A,s.renderOrder=n.renderOrder,s.z=a,s.group=o),t++,s}return{opaque:n,transmissive:r,transparent:i,init:function(){t=0,n.length=0,r.length=0,i.length=0},push:function(e,t,a,o,s,l){var u=A(e,t,a,o,s,l);a.transmission>0?r.push(u):!0===a.transparent?i.push(u):n.push(u)},unshift:function(e,t,a,o,s,l){var u=A(e,t,a,o,s,l);a.transmission>0?r.unshift(u):!0===a.transparent?i.unshift(u):n.unshift(u)},finish:function(){for(var n=t,r=e.length;n<r;n++){var i=e[n];if(null===i.id)break;i.id=null,i.object=null,i.geometry=null,i.material=null,i.group=null}},sort:function(e,t){n.length>1&&n.sort(e||RB),r.length>1&&r.sort(t||HB),i.length>1&&i.sort(t||HB)}}}function NB(){var e=new WeakMap;return{get:function(t,n){var r,i=e.get(t);return void 0===i?(r=new PB,e.set(t,[r])):n>=i.length?(r=new PB,i.push(r)):r=i[n],r},dispose:function(){e=new WeakMap}}}function OB(){var e={};return{get:function(t){if(void 0!==e[t.id])return e[t.id];var n;switch(t.type){case"DirectionalLight":n={direction:new gm,color:new Am};break;case"SpotLight":n={position:new gm,direction:new gm,color:new Am,distance:0,coneCos:0,penumbraCos:0,decay:0};break;case"PointLight":n={position:new gm,color:new Am,distance:0,decay:0};break;case"HemisphereLight":n={direction:new gm,skyColor:new Am,groundColor:new Am};break;case"RectAreaLight":n={color:new Am,position:new gm,halfWidth:new gm,halfHeight:new gm}}return e[t.id]=n,n}}}var VB=0;function zB(e,t){return(t.castShadow?2:0)-(e.castShadow?2:0)+(t.map?1:0)-(e.map?1:0)}function GB(e,t){for(var n=new OB,r=function(){var e={};return{get:function(t){if(void 0!==e[t.id])return e[t.id];var n;switch(t.type){case"DirectionalLight":case"SpotLight":n={shadowBias:0,shadowNormalBias:0,shadowRadius:1,shadowMapSize:new zg};break;case"PointLight":n={shadowBias:0,shadowNormalBias:0,shadowRadius:1,shadowMapSize:new zg,shadowCameraNear:1,shadowCameraFar:1e3}}return e[t.id]=n,n}}}(),i={version:0,hash:{directionalLength:-1,pointLength:-1,spotLength:-1,rectAreaLength:-1,hemiLength:-1,numDirectionalShadows:-1,numPointShadows:-1,numSpotShadows:-1,numSpotMaps:-1},ambient:[0,0,0],probe:[],directional:[],directionalShadow:[],directionalShadowMap:[],directionalShadowMatrix:[],spot:[],spotLightMap:[],spotShadow:[],spotShadowMap:[],spotLightMatrix:[],rectArea:[],rectAreaLTC1:null,rectAreaLTC2:null,point:[],pointShadow:[],pointShadowMap:[],pointShadowMatrix:[],hemi:[],numSpotLightShadowsWithMaps:0},A=0;A<9;A++)i.probe.push(new gm);var a=new gm,o=new Wm,s=new Wm;return{setup:function(A,a){for(var o=0,s=0,l=0,u=0;u<9;u++)i.probe[u].set(0,0,0);var c=0,d=0,h=0,f=0,p=0,g=0,m=0,v=0,y=0,w=0;A.sort(zB);for(var B=!0!==a?Math.PI:1,_=0,b=A.length;_<b;_++){var x=A[_],C=x.color,S=x.intensity,E=x.distance,U=x.shadow&&x.shadow.map?x.shadow.map.texture:null;if(x.isAmbientLight)o+=C.r*S*B,s+=C.g*S*B,l+=C.b*S*B;else if(x.isLightProbe)for(var M=0;M<9;M++)i.probe[M].addScaledVector(x.sh.coefficients[M],S);else if(x.isDirectionalLight){var F=n.get(x);if(F.color.copy(x.color).multiplyScalar(x.intensity*B),x.castShadow){var T=x.shadow,k=r.get(x);k.shadowBias=T.bias,k.shadowNormalBias=T.normalBias,k.shadowRadius=T.radius,k.shadowMapSize=T.mapSize,i.directionalShadow[c]=k,i.directionalShadowMap[c]=U,i.directionalShadowMatrix[c]=x.shadow.matrix,g++}i.directional[c]=F,c++}else if(x.isSpotLight){var Q=n.get(x);Q.position.setFromMatrixPosition(x.matrixWorld),Q.color.copy(C).multiplyScalar(S*B),Q.distance=E,Q.coneCos=Math.cos(x.angle),Q.penumbraCos=Math.cos(x.angle*(1-x.penumbra)),Q.decay=x.decay,i.spot[h]=Q;var L=x.shadow;if(x.map&&(i.spotLightMap[y]=x.map,y++,L.updateMatrices(x),x.castShadow&&w++),i.spotLightMatrix[h]=L.matrix,x.castShadow){var D=r.get(x);D.shadowBias=L.bias,D.shadowNormalBias=L.normalBias,D.shadowRadius=L.radius,D.shadowMapSize=L.mapSize,i.spotShadow[h]=D,i.spotShadowMap[h]=U,v++}h++}else if(x.isRectAreaLight){var I=n.get(x);I.color.copy(C).multiplyScalar(S),I.halfWidth.set(.5*x.width,0,0),I.halfHeight.set(0,.5*x.height,0),i.rectArea[f]=I,f++}else if(x.isPointLight){var R=n.get(x);if(R.color.copy(x.color).multiplyScalar(x.intensity*B),R.distance=x.distance,R.decay=x.decay,x.castShadow){var H=x.shadow,P=r.get(x);P.shadowBias=H.bias,P.shadowNormalBias=H.normalBias,P.shadowRadius=H.radius,P.shadowMapSize=H.mapSize,P.shadowCameraNear=H.camera.near,P.shadowCameraFar=H.camera.far,i.pointShadow[d]=P,i.pointShadowMap[d]=U,i.pointShadowMatrix[d]=x.shadow.matrix,m++}i.point[d]=R,d++}else if(x.isHemisphereLight){var N=n.get(x);N.skyColor.copy(x.color).multiplyScalar(S*B),N.groundColor.copy(x.groundColor).multiplyScalar(S*B),i.hemi[p]=N,p++}}f>0&&(t.isWebGL2||!0===e.has("OES_texture_float_linear")?(i.rectAreaLTC1=Qy.LTC_FLOAT_1,i.rectAreaLTC2=Qy.LTC_FLOAT_2):!0===e.has("OES_texture_half_float_linear")?(i.rectAreaLTC1=Qy.LTC_HALF_1,i.rectAreaLTC2=Qy.LTC_HALF_2):console.error("THREE.WebGLRenderer: Unable to use RectAreaLight. Missing WebGL extensions.")),i.ambient[0]=o,i.ambient[1]=s,i.ambient[2]=l;var O=i.hash;O.directionalLength===c&&O.pointLength===d&&O.spotLength===h&&O.rectAreaLength===f&&O.hemiLength===p&&O.numDirectionalShadows===g&&O.numPointShadows===m&&O.numSpotShadows===v&&O.numSpotMaps===y||(i.directional.length=c,i.spot.length=h,i.rectArea.length=f,i.point.length=d,i.hemi.length=p,i.directionalShadow.length=g,i.directionalShadowMap.length=g,i.pointShadow.length=m,i.pointShadowMap.length=m,i.spotShadow.length=v,i.spotShadowMap.length=v,i.directionalShadowMatrix.length=g,i.pointShadowMatrix.length=m,i.spotLightMatrix.length=v+y-w,i.spotLightMap.length=y,i.numSpotLightShadowsWithMaps=w,O.directionalLength=c,O.pointLength=d,O.spotLength=h,O.rectAreaLength=f,O.hemiLength=p,O.numDirectionalShadows=g,O.numPointShadows=m,O.numSpotShadows=v,O.numSpotMaps=y,i.version=VB++)},setupView:function(e,t){for(var n=0,r=0,A=0,l=0,u=0,c=t.matrixWorldInverse,d=0,h=e.length;d<h;d++){var f=e[d];if(f.isDirectionalLight){var p=i.directional[n];p.direction.setFromMatrixPosition(f.matrixWorld),a.setFromMatrixPosition(f.target.matrixWorld),p.direction.sub(a),p.direction.transformDirection(c),n++}else if(f.isSpotLight){var g=i.spot[A];g.position.setFromMatrixPosition(f.matrixWorld),g.position.applyMatrix4(c),g.direction.setFromMatrixPosition(f.matrixWorld),a.setFromMatrixPosition(f.target.matrixWorld),g.direction.sub(a),g.direction.transformDirection(c),A++}else if(f.isRectAreaLight){var m=i.rectArea[l];m.position.setFromMatrixPosition(f.matrixWorld),m.position.applyMatrix4(c),s.identity(),o.copy(f.matrixWorld),o.premultiply(c),s.extractRotation(o),m.halfWidth.set(.5*f.width,0,0),m.halfHeight.set(0,.5*f.height,0),m.halfWidth.applyMatrix4(s),m.halfHeight.applyMatrix4(s),l++}else if(f.isPointLight){var v=i.point[r];v.position.setFromMatrixPosition(f.matrixWorld),v.position.applyMatrix4(c),r++}else if(f.isHemisphereLight){var y=i.hemi[u];y.direction.setFromMatrixPosition(f.matrixWorld),y.direction.transformDirection(c),u++}}},state:i}}function KB(e,t){var n=new GB(e,t),r=[],i=[];return{init:function(){r.length=0,i.length=0},state:{lightsArray:r,shadowsArray:i,lights:n},setupLights:function(e){n.setup(r,e)},setupLightsView:function(e){n.setupView(r,e)},pushLight:function(e){r.push(e)},pushShadow:function(e){i.push(e)}}}function WB(e,t){var n=new WeakMap;return{get:function(r){var i,A=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0,a=n.get(r);return void 0===a?(i=new KB(e,t),n.set(r,[i])):A>=a.length?(i=new KB(e,t),a.push(i)):i=a[A],i},dispose:function(){n=new WeakMap}}}var jB=function(e){C(n,e);var t=M(n);function n(e){var r;return B(this,n),(r=t.call(this)).isMeshDepthMaterial=!0,r.type="MeshDepthMaterial",r.depthPacking=mg,r.map=null,r.alphaMap=null,r.displacementMap=null,r.displacementScale=1,r.displacementBias=0,r.wireframe=!1,r.wireframeLinewidth=1,r.setValues(e),r}return b(n,[{key:"copy",value:function(e){return jc(S(n.prototype),"copy",this).call(this,e),this.depthPacking=e.depthPacking,this.map=e.map,this.alphaMap=e.alphaMap,this.displacementMap=e.displacementMap,this.displacementScale=e.displacementScale,this.displacementBias=e.displacementBias,this.wireframe=e.wireframe,this.wireframeLinewidth=e.wireframeLinewidth,this}}]),n}(Fv),XB=function(e){C(n,e);var t=M(n);function n(e){var r;return B(this,n),(r=t.call(this)).isMeshDistanceMaterial=!0,r.type="MeshDistanceMaterial",r.referencePosition=new gm,r.nearDistance=1,r.farDistance=1e3,r.map=null,r.alphaMap=null,r.displacementMap=null,r.displacementScale=1,r.displacementBias=0,r.setValues(e),r}return b(n,[{key:"copy",value:function(e){return jc(S(n.prototype),"copy",this).call(this,e),this.referencePosition.copy(e.referencePosition),this.nearDistance=e.nearDistance,this.farDistance=e.farDistance,this.map=e.map,this.alphaMap=e.alphaMap,this.displacementMap=e.displacementMap,this.displacementScale=e.displacementScale,this.displacementBias=e.displacementBias,this}}]),n}(Fv),qB="void main() {\n\tgl_Position = vec4( position, 1.0 );\n}",YB="uniform sampler2D shadow_pass;\nuniform vec2 resolution;\nuniform float radius;\n#include <packing>\nvoid main() {\n\tconst float samples = float( VSM_SAMPLES );\n\tfloat mean = 0.0;\n\tfloat squared_mean = 0.0;\n\tfloat uvStride = samples <= 1.0 ? 0.0 : 2.0 / ( samples - 1.0 );\n\tfloat uvStart = samples <= 1.0 ? 0.0 : - 1.0;\n\tfor ( float i = 0.0; i < samples; i ++ ) {\n\t\tfloat uvOffset = uvStart + i * uvStride;\n\t\t#ifdef HORIZONTAL_PASS\n\t\t\tvec2 distribution = unpackRGBATo2Half( texture2D( shadow_pass, ( gl_FragCoord.xy + vec2( uvOffset, 0.0 ) * radius ) / resolution ) );\n\t\t\tmean += distribution.x;\n\t\t\tsquared_mean += distribution.y * distribution.y + distribution.x * distribution.x;\n\t\t#else\n\t\t\tfloat depth = unpackRGBAToDepth( texture2D( shadow_pass, ( gl_FragCoord.xy + vec2( 0.0, uvOffset ) * radius ) / resolution ) );\n\t\t\tmean += depth;\n\t\t\tsquared_mean += depth * depth;\n\t\t#endif\n\t}\n\tmean = mean / samples;\n\tsquared_mean = squared_mean / samples;\n\tfloat std_dev = sqrt( squared_mean - mean * mean );\n\tgl_FragColor = pack2HalfToRGBA( vec2( mean, std_dev ) );\n}";function JB(e,t,n){var r,i=new Uy,A=new zg,a=new zg,o=new cm,s=new jB({depthPacking:vg}),l=new XB,u={},d=n.maxTextureSize,h=(c(r={},sf,lf),c(r,lf,sf),c(r,uf,uf),r),f=new fy({defines:{VSM_SAMPLES:8},uniforms:{shadow_pass:{value:null},resolution:{value:new zg},radius:{value:4}},vertexShader:qB,fragmentShader:YB}),p=f.clone();p.defines.HORIZONTAL_PASS=1;var g=new Kv;g.setAttribute("position",new Lv(new Float32Array([-1,-1,.5,3,-1,.5,-1,3,.5]),3));var m=new Ay(g,f),v=this;function y(n,r){var i=t.update(m);f.defines.VSM_SAMPLES!==n.blurSamples&&(f.defines.VSM_SAMPLES=n.blurSamples,p.defines.VSM_SAMPLES=n.blurSamples,f.needsUpdate=!0,p.needsUpdate=!0),null===n.mapPass&&(n.mapPass=new dm(A.x,A.y)),f.uniforms.shadow_pass.value=n.map.texture,f.uniforms.resolution.value=n.mapSize,f.uniforms.radius.value=n.radius,e.setRenderTarget(n.mapPass),e.clear(),e.renderBufferDirect(r,null,i,f,m,null),p.uniforms.shadow_pass.value=n.mapPass.texture,p.uniforms.resolution.value=n.mapSize,p.uniforms.radius.value=n.radius,e.setRenderTarget(n.map),e.clear(),e.renderBufferDirect(r,null,i,p,m,null)}function w(t,n,r,i,A,a){var o=null,c=!0===r.isPointLight?t.customDistanceMaterial:t.customDepthMaterial;if(void 0!==c)o=c;else if(o=!0===r.isPointLight?l:s,e.localClippingEnabled&&!0===n.clipShadows&&Array.isArray(n.clippingPlanes)&&0!==n.clippingPlanes.length||n.displacementMap&&0!==n.displacementScale||n.alphaMap&&n.alphaTest>0||n.map&&n.alphaTest>0){var d=o.uuid,f=n.uuid,p=u[d];void 0===p&&(p={},u[d]=p);var g=p[f];void 0===g&&(g=o.clone(),p[f]=g),o=g}return o.visible=n.visible,o.wireframe=n.wireframe,o.side=a===of?null!==n.shadowSide?n.shadowSide:n.side:null!==n.shadowSide?n.shadowSide:h[n.side],o.alphaMap=n.alphaMap,o.alphaTest=n.alphaTest,o.map=n.map,o.clipShadows=n.clipShadows,o.clippingPlanes=n.clippingPlanes,o.clipIntersection=n.clipIntersection,o.displacementMap=n.displacementMap,o.displacementScale=n.displacementScale,o.displacementBias=n.displacementBias,o.wireframeLinewidth=n.wireframeLinewidth,o.linewidth=n.linewidth,!0===r.isPointLight&&!0===o.isMeshDistanceMaterial&&(o.referencePosition.setFromMatrixPosition(r.matrixWorld),o.nearDistance=i,o.farDistance=A),o}function B(n,r,A,a,o){if(!1!==n.visible){if(n.layers.test(r.layers)&&(n.isMesh||n.isLine||n.isPoints)&&(n.castShadow||n.receiveShadow&&o===of)&&(!n.frustumCulled||i.intersectsObject(n))){n.modelViewMatrix.multiplyMatrices(A.matrixWorldInverse,n.matrixWorld);var s=t.update(n),l=n.material;if(Array.isArray(l))for(var u=s.groups,c=0,d=u.length;c<d;c++){var h=u[c],f=l[h.materialIndex];if(f&&f.visible){var p=w(n,f,a,A.near,A.far,o);e.renderBufferDirect(A,null,s,p,n,h)}}else if(l.visible){var g=w(n,l,a,A.near,A.far,o);e.renderBufferDirect(A,null,s,g,n,null)}}for(var m=n.children,v=0,y=m.length;v<y;v++)B(m[v],r,A,a,o)}}this.enabled=!1,this.autoUpdate=!0,this.needsUpdate=!1,this.type=Af,this.render=function(t,n,r){if(!1!==v.enabled&&(!1!==v.autoUpdate||!1!==v.needsUpdate)&&0!==t.length){var s=e.getRenderTarget(),l=e.getActiveCubeFace(),u=e.getActiveMipmapLevel(),c=e.state;c.setBlending(cf),c.buffers.color.setClear(1,1,1,1),c.buffers.depth.setTest(!0),c.setScissorTest(!1);for(var h=0,f=t.length;h<f;h++){var p=t[h],g=p.shadow;if(void 0!==g){if(!1!==g.autoUpdate||!1!==g.needsUpdate){A.copy(g.mapSize);var m=g.getFrameExtents();if(A.multiply(m),a.copy(g.mapSize),(A.x>d||A.y>d)&&(A.x>d&&(a.x=Math.floor(d/m.x),A.x=a.x*m.x,g.mapSize.x=a.x),A.y>d&&(a.y=Math.floor(d/m.y),A.y=a.y*m.y,g.mapSize.y=a.y)),null===g.map){var w=this.type!==of?{minFilter:ip,magFilter:ip}:{};g.map=new dm(A.x,A.y,w),g.map.texture.name=p.name+".shadowMap",g.camera.updateProjectionMatrix()}e.setRenderTarget(g.map),e.clear();for(var _=g.getViewportCount(),b=0;b<_;b++){var x=g.getViewport(b);o.set(a.x*x.x,a.y*x.y,a.x*x.z,a.y*x.w),c.viewport(o),g.updateMatrices(p,b),i=g.getFrustum(),B(n,r,g.camera,p,this.type)}!0!==g.isPointLightShadow&&this.type===of&&y(g,r),g.needsUpdate=!1}}else console.warn("THREE.WebGLShadowMap:",p,"has no shadow.")}v.needsUpdate=!1,e.setRenderTarget(s,l,u)}}}function ZB(e,t,n){var r,i,A=n.isWebGL2;var a=new function(){var t=!1,n=new cm,r=null,i=new cm(0,0,0,0);return{setMask:function(n){r===n||t||(e.colorMask(n,n,n,n),r=n)},setLocked:function(e){t=e},setClear:function(t,r,A,a,o){!0===o&&(t*=a,r*=a,A*=a),n.set(t,r,A,a),!1===i.equals(n)&&(e.clearColor(t,r,A,a),i.copy(n))},reset:function(){t=!1,r=null,i.set(-1,0,0,0)}}},o=new function(){var t=!1,n=null,r=null,i=null;return{setTest:function(e){e?z(2929):G(2929)},setMask:function(r){n===r||t||(e.depthMask(r),n=r)},setFunc:function(t){if(r!==t){switch(t){case Qf:e.depthFunc(512);break;case Lf:e.depthFunc(519);break;case Df:e.depthFunc(513);break;case If:e.depthFunc(515);break;case Rf:e.depthFunc(514);break;case Hf:e.depthFunc(518);break;case Pf:e.depthFunc(516);break;case Nf:e.depthFunc(517);break;default:e.depthFunc(515)}r=t}},setLocked:function(e){t=e},setClear:function(t){i!==t&&(e.clearDepth(t),i=t)},reset:function(){t=!1,n=null,r=null,i=null}}},s=new function(){var t=!1,n=null,r=null,i=null,A=null,a=null,o=null,s=null,l=null;return{setTest:function(e){t||(e?z(2960):G(2960))},setMask:function(r){n===r||t||(e.stencilMask(r),n=r)},setFunc:function(t,n,a){r===t&&i===n&&A===a||(e.stencilFunc(t,n,a),r=t,i=n,A=a)},setOp:function(t,n,r){a===t&&o===n&&s===r||(e.stencilOp(t,n,r),a=t,o=n,s=r)},setLocked:function(e){t=e},setClear:function(t){l!==t&&(e.clearStencil(t),l=t)},reset:function(){t=!1,n=null,r=null,i=null,A=null,a=null,o=null,s=null,l=null}}},l=new WeakMap,u=new WeakMap,d={},h={},f=new WeakMap,p=[],g=null,m=!1,v=null,y=null,w=null,B=null,_=null,b=null,x=null,C=!1,S=null,E=null,U=null,M=null,F=null,T=e.getParameter(35661),k=!1,Q=0,L=e.getParameter(7938);-1!==L.indexOf("WebGL")?(Q=parseFloat(/^WebGL (\d)/.exec(L)[1]),k=Q>=1):-1!==L.indexOf("OpenGL ES")&&(Q=parseFloat(/^OpenGL ES (\d)/.exec(L)[1]),k=Q>=2);var D=null,I={},R=e.getParameter(3088),H=e.getParameter(2978),P=(new cm).fromArray(R),N=(new cm).fromArray(H);function O(t,n,r){var i=new Uint8Array(4),A=e.createTexture();e.bindTexture(t,A),e.texParameteri(t,10241,9728),e.texParameteri(t,10240,9728);for(var a=0;a<r;a++)e.texImage2D(n+a,0,6408,1,1,0,6408,5121,i);return A}var V={};function z(t){!0!==d[t]&&(e.enable(t),d[t]=!0)}function G(t){!1!==d[t]&&(e.disable(t),d[t]=!1)}V[3553]=O(3553,3553,1),V[34067]=O(34067,34069,6),a.setClear(0,0,0,1),o.setClear(1),s.setClear(0),z(2929),o.setFunc(If),q(!1),Y(nf),z(2884),X(cf);var K=(c(r={},mf,32774),c(r,vf,32778),c(r,yf,32779),r);if(A)K[wf]=32775,K[Bf]=32776;else{var W=t.get("EXT_blend_minmax");null!==W&&(K[wf]=W.MIN_EXT,K[Bf]=W.MAX_EXT)}var j=(c(i={},_f,0),c(i,bf,1),c(i,xf,768),c(i,Sf,770),c(i,kf,776),c(i,Ff,774),c(i,Uf,772),c(i,Cf,769),c(i,Ef,771),c(i,Tf,775),c(i,Mf,773),i);function X(t,n,r,i,A,a,o,s){if(t!==cf){if(!1===m&&(z(3042),m=!0),t===gf)A=A||n,a=a||r,o=o||i,n===y&&A===_||(e.blendEquationSeparate(K[n],K[A]),y=n,_=A),r===w&&i===B&&a===b&&o===x||(e.blendFuncSeparate(j[r],j[i],j[a],j[o]),w=r,B=i,b=a,x=o),v=t,C=!1;else if(t!==v||s!==C){if(y===mf&&_===mf||(e.blendEquation(32774),y=mf,_=mf),s)switch(t){case df:e.blendFuncSeparate(1,771,1,771);break;case hf:e.blendFunc(1,1);break;case ff:e.blendFuncSeparate(0,769,0,1);break;case pf:e.blendFuncSeparate(0,768,0,770);break;default:console.error("THREE.WebGLState: Invalid blending: ",t)}else switch(t){case df:e.blendFuncSeparate(770,771,1,771);break;case hf:e.blendFunc(770,1);break;case ff:e.blendFuncSeparate(0,769,0,1);break;case pf:e.blendFunc(0,768);break;default:console.error("THREE.WebGLState: Invalid blending: ",t)}w=null,B=null,b=null,x=null,v=t,C=s}}else!0===m&&(G(3042),m=!1)}function q(t){S!==t&&(t?e.frontFace(2304):e.frontFace(2305),S=t)}function Y(t){t!==tf?(z(2884),t!==E&&(t===nf?e.cullFace(1029):t===rf?e.cullFace(1028):e.cullFace(1032))):G(2884),E=t}function J(t,n,r){t?(z(32823),M===n&&F===r||(e.polygonOffset(n,r),M=n,F=r)):G(32823)}return{buffers:{color:a,depth:o,stencil:s},enable:z,disable:G,bindFramebuffer:function(t,n){return h[t]!==n&&(e.bindFramebuffer(t,n),h[t]=n,A&&(36009===t&&(h[36160]=n),36160===t&&(h[36009]=n)),!0)},drawBuffers:function(r,i){var A=p,a=!1;if(r)if(void 0===(A=f.get(i))&&(A=[],f.set(i,A)),r.isWebGLMultipleRenderTargets){var o=r.texture;if(A.length!==o.length||36064!==A[0]){for(var s=0,l=o.length;s<l;s++)A[s]=36064+s;A.length=o.length,a=!0}}else 36064!==A[0]&&(A[0]=36064,a=!0);else 1029!==A[0]&&(A[0]=1029,a=!0);a&&(n.isWebGL2?e.drawBuffers(A):t.get("WEBGL_draw_buffers").drawBuffersWEBGL(A))},useProgram:function(t){return g!==t&&(e.useProgram(t),g=t,!0)},setBlending:X,setMaterial:function(e,t){e.side===uf?G(2884):z(2884);var n=e.side===lf;t&&(n=!n),q(n),e.blending===df&&!1===e.transparent?X(cf):X(e.blending,e.blendEquation,e.blendSrc,e.blendDst,e.blendEquationAlpha,e.blendSrcAlpha,e.blendDstAlpha,e.premultipliedAlpha),o.setFunc(e.depthFunc),o.setTest(e.depthTest),o.setMask(e.depthWrite),a.setMask(e.colorWrite);var 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c=i.isCompressedTexture||i.image[0].isCompressedTexture,d=i.image[0]&&i.image[0].isDataTexture,f=[],p=0;p<6;p++)f[p]=c||d?d?i.image[p].image:i.image[p]:_(i.image[p],!1,!0,h),f[p]=G(i,f[p]);var g,m=f[0],v=b(m)||u,y=A.convert(i.format,i.encoding),w=A.convert(i.type),B=S(i.internalFormat,y,w,i.encoding),U=u&&!0!==i.isVideoTexture,M=void 0===l.__version||!0===o,F=E(i,m,v);if(I(34067,i,v),c){U&&M&&n.texStorage2D(34067,F,B,m.width,m.height);for(var T=0;T<6;T++){g=f[T].mipmaps;for(var k=0;k<g.length;k++){var Q=g[k];i.format!==bp?null!==y?U?n.compressedTexSubImage2D(34069+T,k,0,0,Q.width,Q.height,y,Q.data):n.compressedTexImage2D(34069+T,k,B,Q.width,Q.height,0,Q.data):console.warn("THREE.WebGLRenderer: Attempt to load unsupported compressed texture format in .setTextureCube()"):U?n.texSubImage2D(34069+T,k,0,0,Q.width,Q.height,y,w,Q.data):n.texImage2D(34069+T,k,B,Q.width,Q.height,0,y,w,Q.data)}}}else{g=i.mipmaps,U&&M&&(g.length>0&&F++,n.texStorage2D(34067,F,B,f[0].width,f[0].height));for(var L=0;L<6;L++)if(d){U?n.texSubImage2D(34069+L,0,0,0,f[L].width,f[L].height,y,w,f[L].data):n.texImage2D(34069+L,0,B,f[L].width,f[L].height,0,y,w,f[L].data);for(var D=0;D<g.length;D++){var H=g[D].image[L].image;U?n.texSubImage2D(34069+L,D+1,0,0,H.width,H.height,y,w,H.data):n.texImage2D(34069+L,D+1,B,H.width,H.height,0,y,w,H.data)}}else{U?n.texSubImage2D(34069+L,0,0,0,y,w,f[L]):n.texImage2D(34069+L,0,B,y,w,f[L]);for(var P=0;P<g.length;P++){var N=g[P];U?n.texSubImage2D(34069+L,P+1,0,0,y,w,N.image[L]):n.texImage2D(34069+L,P+1,B,y,w,N.image[L])}}}x(i,v)&&C(34067),l.__version=s.version,i.onUpdate&&i.onUpdate(i)}t.__version=i.version}(a,t,i):n.bindTexture(34067,a.__webglTexture,33984+i)},this.rebindTextures=function(e,t,n){var i=r.get(e);void 0!==t&&P(i.__webglFramebuffer,e,e.texture,36064,3553),void 0!==n&&O(e)},this.setupRenderTarget=function(t){var o=t.texture,s=r.get(t),l=r.get(o);t.addEventListener("dispose",F),!0!==t.isWebGLMultipleRenderTargets&&(void 0===l.__webglTexture&&(l.__webglTexture=e.createTexture()),l.__version=o.version,a.memory.textures++);var c=!0===t.isWebGLCubeRenderTarget,d=!0===t.isWebGLMultipleRenderTargets,h=b(t)||u;if(c){s.__webglFramebuffer=[];for(var f=0;f<6;f++)s.__webglFramebuffer[f]=e.createFramebuffer()}else{if(s.__webglFramebuffer=e.createFramebuffer(),d)if(i.drawBuffers)for(var p=t.texture,g=0,m=p.length;g<m;g++){var v=r.get(p[g]);void 0===v.__webglTexture&&(v.__webglTexture=e.createTexture(),a.memory.textures++)}else console.warn("THREE.WebGLRenderer: WebGLMultipleRenderTargets can only be used with WebGL2 or WEBGL_draw_buffers extension.");if(u&&t.samples>0&&!1===z(t)){var y=d?o:[o];s.__webglMultisampledFramebuffer=e.createFramebuffer(),s.__webglColorRenderbuffer=[],n.bindFramebuffer(36160,s.__webglMultisampledFramebuffer);for(var w=0;w<y.length;w++){var B=y[w];s.__webglColorRenderbuffer[w]=e.createRenderbuffer(),e.bindRenderbuffer(36161,s.__webglColorRenderbuffer[w]);var _=A.convert(B.format,B.encoding),E=A.convert(B.type),U=S(B.internalFormat,_,E,B.encoding,!0===t.isXRRenderTarget),M=V(t);e.renderbufferStorageMultisample(36161,M,U,t.width,t.height),e.framebufferRenderbuffer(36160,36064+w,36161,s.__webglColorRenderbuffer[w])}e.bindRenderbuffer(36161,null),t.depthBuffer&&(s.__webglDepthRenderbuffer=e.createRenderbuffer(),N(s.__webglDepthRenderbuffer,t,!0)),n.bindFramebuffer(36160,null)}}if(c){n.bindTexture(34067,l.__webglTexture),I(34067,o,h);for(var T=0;T<6;T++)P(s.__webglFramebuffer[T],t,o,36064,34069+T);x(o,h)&&C(34067),n.unbindTexture()}else if(d){for(var k=t.texture,Q=0,L=k.length;Q<L;Q++){var D=k[Q],R=r.get(D);n.bindTexture(3553,R.__webglTexture),I(3553,D,h),P(s.__webglFramebuffer,t,D,36064+Q,3553),x(D,h)&&C(3553)}n.unbindTexture()}else{var H=3553;(t.isWebGL3DRenderTarget||t.isWebGLArrayRenderTarget)&&(u?H=t.isWebGL3DRenderTarget?32879:35866:console.error("THREE.WebGLTextures: THREE.Data3DTexture and THREE.DataArrayTexture only supported with WebGL2.")),n.bindTexture(H,l.__webglTexture),I(H,o,h),P(s.__webglFramebuffer,t,o,36064,H),x(o,h)&&C(H),n.unbindTexture()}t.depthBuffer&&O(t)},this.updateRenderTargetMipmap=function(e){for(var t=b(e)||u,i=!0===e.isWebGLMultipleRenderTargets?e.texture:[e.texture],A=0,a=i.length;A<a;A++){var o=i[A];if(x(o,t)){var s=e.isWebGLCubeRenderTarget?34067:3553,l=r.get(o).__webglTexture;n.bindTexture(s,l),C(s),n.unbindTexture()}}},this.updateMultisampleRenderTarget=function(t){if(u&&t.samples>0&&!1===z(t)){var i=t.isWebGLMultipleRenderTargets?t.texture:[t.texture],A=t.width,a=t.height,o=16384,s=[],l=t.stencilBuffer?33306:36096,c=r.get(t),d=!0===t.isWebGLMultipleRenderTargets;if(d)for(var h=0;h<i.length;h++)n.bindFramebuffer(36160,c.__webglMultisampledFramebuffer),e.framebufferRenderbuffer(36160,36064+h,36161,null),n.bindFramebuffer(36160,c.__webglFramebuffer),e.framebufferTexture2D(36009,36064+h,3553,null,0);n.bindFramebuffer(36008,c.__webglMultisampledFramebuffer),n.bindFramebuffer(36009,c.__webglFramebuffer);for(var f=0;f<i.length;f++){s.push(36064+f),t.depthBuffer&&s.push(l);var p=void 0!==c.__ignoreDepthValues&&c.__ignoreDepthValues;if(!1===p&&(t.depthBuffer&&(o|=256),t.stencilBuffer&&(o|=1024)),d&&e.framebufferRenderbuffer(36008,36064,36161,c.__webglColorRenderbuffer[f]),!0===p&&(e.invalidateFramebuffer(36008,[l]),e.invalidateFramebuffer(36009,[l])),d){var g=r.get(i[f]).__webglTexture;e.framebufferTexture2D(36009,36064,3553,g,0)}e.blitFramebuffer(0,0,A,a,0,0,A,a,o,9728),m&&e.invalidateFramebuffer(36008,s)}if(n.bindFramebuffer(36008,null),n.bindFramebuffer(36009,null),d)for(var v=0;v<i.length;v++){n.bindFramebuffer(36160,c.__webglMultisampledFramebuffer),e.framebufferRenderbuffer(36160,36064+v,36161,c.__webglColorRenderbuffer[v]);var y=r.get(i[v]).__webglTexture;n.bindFramebuffer(36160,c.__webglFramebuffer),e.framebufferTexture2D(36009,36064+v,3553,y,0)}n.bindFramebuffer(36009,c.__webglMultisampledFramebuffer)}},this.setupDepthRenderbuffer=O,this.setupFrameBufferTexture=P,this.useMultisampledRTT=z}function e_(e,t,n){var r=n.isWebGL2;return{convert:function(n){var i,A=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null;if(n===cp)return 5121;if(n===yp)return 32819;if(n===wp)return 32820;if(n===dp)return 5120;if(n===hp)return 5122;if(n===fp)return 5123;if(n===pp)return 5124;if(n===gp)return 5125;if(n===mp)return 5126;if(n===vp)return r?5131:null!==(i=t.get("OES_texture_half_float"))?i.HALF_FLOAT_OES:null;if(n===_p)return 6406;if(n===bp)return 6408;if(n===xp)return 6409;if(n===Cp)return 6410;if(n===Sp)return 6402;if(n===Ep)return 34041;if(n===Eg)return null!==(i=t.get("EXT_sRGB"))?i.SRGB_ALPHA_EXT:null;if(n===Up)return 6403;if(n===Mp)return 36244;if(n===Fp)return 33319;if(n===Tp)return 33320;if(n===kp)return 36249;if(n===Qp||n===Lp||n===Dp||n===Ip)if(A===gg){if(null===(i=t.get("WEBGL_compressed_texture_s3tc_srgb")))return null;if(n===Qp)return i.COMPRESSED_SRGB_S3TC_DXT1_EXT;if(n===Lp)return i.COMPRESSED_SRGB_ALPHA_S3TC_DXT1_EXT;if(n===Dp)return i.COMPRESSED_SRGB_ALPHA_S3TC_DXT3_EXT;if(n===Ip)return i.COMPRESSED_SRGB_ALPHA_S3TC_DXT5_EXT}else{if(null===(i=t.get("WEBGL_compressed_texture_s3tc")))return null;if(n===Qp)return i.COMPRESSED_RGB_S3TC_DXT1_EXT;if(n===Lp)return i.COMPRESSED_RGBA_S3TC_DXT1_EXT;if(n===Dp)return i.COMPRESSED_RGBA_S3TC_DXT3_EXT;if(n===Ip)return i.COMPRESSED_RGBA_S3TC_DXT5_EXT}if(n===Rp||n===Hp||n===Pp||n===Np){if(null===(i=t.get("WEBGL_compressed_texture_pvrtc")))return null;if(n===Rp)return i.COMPRESSED_RGB_PVRTC_4BPPV1_IMG;if(n===Hp)return i.COMPRESSED_RGB_PVRTC_2BPPV1_IMG;if(n===Pp)return i.COMPRESSED_RGBA_PVRTC_4BPPV1_IMG;if(n===Np)return i.COMPRESSED_RGBA_PVRTC_2BPPV1_IMG}if(n===Op)return null!==(i=t.get("WEBGL_compressed_texture_etc1"))?i.COMPRESSED_RGB_ETC1_WEBGL:null;if(n===Vp||n===zp){if(null===(i=t.get("WEBGL_compressed_texture_etc")))return null;if(n===Vp)return A===gg?i.COMPRESSED_SRGB8_ETC2:i.COMPRESSED_RGB8_ETC2;if(n===zp)return A===gg?i.COMPRESSED_SRGB8_ALPHA8_ETC2_EAC:i.COMPRESSED_RGBA8_ETC2_EAC}if(n===Gp||n===Kp||n===Wp||n===jp||n===Xp||n===qp||n===Yp||n===Jp||n===Zp||n===$p||n===eg||n===tg||n===ng||n===rg){if(null===(i=t.get("WEBGL_compressed_texture_astc")))return null;if(n===Gp)return A===gg?i.COMPRESSED_SRGB8_ALPHA8_ASTC_4x4_KHR:i.COMPRESSED_RGBA_ASTC_4x4_KHR;if(n===Kp)return A===gg?i.COMPRESSED_SRGB8_ALPHA8_ASTC_5x4_KHR:i.COMPRESSED_RGBA_ASTC_5x4_KHR;if(n===Wp)return A===gg?i.COMPRESSED_SRGB8_ALPHA8_ASTC_5x5_KHR:i.COMPRESSED_RGBA_ASTC_5x5_KHR;if(n===jp)return A===gg?i.COMPRESSED_SRGB8_ALPHA8_ASTC_6x5_KHR:i.COMPRESSED_RGBA_ASTC_6x5_KHR;if(n===Xp)return A===gg?i.COMPRESSED_SRGB8_ALPHA8_ASTC_6x6_KHR:i.COMPRESSED_RGBA_ASTC_6x6_KHR;if(n===qp)return A===gg?i.COMPRESSED_SRGB8_ALPHA8_ASTC_8x5_KHR:i.COMPRESSED_RGBA_ASTC_8x5_KHR;if(n===Yp)return A===gg?i.COMPRESSED_SRGB8_ALPHA8_ASTC_8x6_KHR:i.COMPRESSED_RGBA_ASTC_8x6_KHR;if(n===Jp)return A===gg?i.COMPRESSED_SRGB8_ALPHA8_ASTC_8x8_KHR:i.COMPRESSED_RGBA_ASTC_8x8_KHR;if(n===Zp)return A===gg?i.COMPRESSED_SRGB8_ALPHA8_ASTC_10x5_KHR:i.COMPRESSED_RGBA_ASTC_10x5_KHR;if(n===$p)return A===gg?i.COMPRESSED_SRGB8_ALPHA8_ASTC_10x6_KHR:i.COMPRESSED_RGBA_ASTC_10x6_KHR;if(n===eg)return A===gg?i.COMPRESSED_SRGB8_ALPHA8_ASTC_10x8_KHR:i.COMPRESSED_RGBA_ASTC_10x8_KHR;if(n===tg)return A===gg?i.COMPRESSED_SRGB8_ALPHA8_ASTC_10x10_KHR:i.COMPRESSED_RGBA_ASTC_10x10_KHR;if(n===ng)return A===gg?i.COMPRESSED_SRGB8_ALPHA8_ASTC_12x10_KHR:i.COMPRESSED_RGBA_ASTC_12x10_KHR;if(n===rg)return A===gg?i.COMPRESSED_SRGB8_ALPHA8_ASTC_12x12_KHR:i.COMPRESSED_RGBA_ASTC_12x12_KHR}if(n===ig){if(null===(i=t.get("EXT_texture_compression_bptc")))return null;if(n===ig)return A===gg?i.COMPRESSED_SRGB_ALPHA_BPTC_UNORM_EXT:i.COMPRESSED_RGBA_BPTC_UNORM_EXT}if(n===Ag||n===ag||n===og||n===sg){if(null===(i=t.get("EXT_texture_compression_rgtc")))return null;if(n===ig)return i.COMPRESSED_RED_RGTC1_EXT;if(n===ag)return i.COMPRESSED_SIGNED_RED_RGTC1_EXT;if(n===og)return i.COMPRESSED_RED_GREEN_RGTC2_EXT;if(n===sg)return i.COMPRESSED_SIGNED_RED_GREEN_RGTC2_EXT}return n===Bp?r?34042:null!==(i=t.get("WEBGL_depth_texture"))?i.UNSIGNED_INT_24_8_WEBGL:null:void 0!==e[n]?e[n]:null}}}var t_=function(e){C(n,e);var t=M(n);function n(){var e,r=arguments.length>0&&void 0!==arguments[0]?arguments[0]:[];return B(this,n),(e=t.call(this)).isArrayCamera=!0,e.cameras=r,e}return b(n)}(gy),n_=function(e){C(n,e);var t=M(n);function n(){var e;return B(this,n),(e=t.call(this)).isGroup=!0,e.type="Group",e}return 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null!==this._targetRay&&this._targetRay.dispatchEvent(e),null!==this._grip&&this._grip.dispatchEvent(e),null!==this._hand&&this._hand.dispatchEvent(e),this}},{key:"connect",value:function(e){if(e&&e.hand){var t=this._hand;if(t){var n,r=k(e.hand.values());try{for(r.s();!(n=r.n()).done;){var i=n.value;this._getHandJoint(t,i)}}catch(Xx){r.e(Xx)}finally{r.f()}}}return this.dispatchEvent({type:"connected",data:e}),this}},{key:"disconnect",value:function(e){return this.dispatchEvent({type:"disconnected",data:e}),null!==this._targetRay&&(this._targetRay.visible=!1),null!==this._grip&&(this._grip.visible=!1),null!==this._hand&&(this._hand.visible=!1),this}},{key:"update",value:function(e,t,n){var r=null,i=null,A=null,a=this._targetRay,o=this._grip,s=this._hand;if(e&&"visible-blurred"!==t.session.visibilityState){if(s&&e.hand){A=!0;var l,u=k(e.hand.values());try{for(u.s();!(l=u.n()).done;){var c=l.value,d=t.getJointPose(c,n),h=this._getHandJoint(s,c);null!==d&&(h.matrix.fromArray(d.transform.matrix),h.matrix.decompose(h.position,h.rotation,h.scale),h.jointRadius=d.radius),h.visible=null!==d}}catch(Xx){u.e(Xx)}finally{u.f()}var f=s.joints["index-finger-tip"],p=s.joints["thumb-tip"],g=f.position.distanceTo(p.position);s.inputState.pinching&&g>.025?(s.inputState.pinching=!1,this.dispatchEvent({type:"pinchend",handedness:e.handedness,target:this})):!s.inputState.pinching&&g<=.015&&(s.inputState.pinching=!0,this.dispatchEvent({type:"pinchstart",handedness:e.handedness,target:this}))}else null!==o&&e.gripSpace&&null!==(i=t.getPose(e.gripSpace,n))&&(o.matrix.fromArray(i.transform.matrix),o.matrix.decompose(o.position,o.rotation,o.scale),i.linearVelocity?(o.hasLinearVelocity=!0,o.linearVelocity.copy(i.linearVelocity)):o.hasLinearVelocity=!1,i.angularVelocity?(o.hasAngularVelocity=!0,o.angularVelocity.copy(i.angularVelocity)):o.hasAngularVelocity=!1);null!==a&&(null===(r=t.getPose(e.targetRaySpace,n))&&null!==i&&(r=i),null!==r&&(a.matrix.fromArray(r.transform.matrix),a.matrix.decompose(a.position,a.rotation,a.scale),r.linearVelocity?(a.hasLinearVelocity=!0,a.linearVelocity.copy(r.linearVelocity)):a.hasLinearVelocity=!1,r.angularVelocity?(a.hasAngularVelocity=!0,a.angularVelocity.copy(r.angularVelocity)):a.hasAngularVelocity=!1,this.dispatchEvent(r_)))}return null!==a&&(a.visible=null!==r),null!==o&&(o.visible=null!==i),null!==s&&(s.visible=null!==A),this}},{key:"_getHandJoint",value:function(e,t){if(void 0===e.joints[t.jointName]){var n=new n_;n.matrixAutoUpdate=!1,n.visible=!1,e.joints[t.jointName]=n,e.add(n)}return e.joints[t.jointName]}}]),e}(),A_=function(e){C(n,e);var t=M(n);function n(e,r,i,A,a,o,s,l,u,c){var d;if(B(this,n),(c=void 0!==c?c:Sp)!==Sp&&c!==Ep)throw new Error("DepthTexture format must be either THREE.DepthFormat or THREE.DepthStencilFormat");return void 0===i&&c===Sp&&(i=gp),void 0===i&&c===Ep&&(i=Bp),(d=t.call(this,null,A,a,o,s,l,c,i,u)).isDepthTexture=!0,d.image={width:e,height:r},d.magFilter=void 0!==s?s:ip,d.minFilter=void 0!==l?l:ip,d.flipY=!1,d.generateMipmaps=!1,d}return b(n)}(um),a_=function(e){C(n,e);var t=M(n);function n(e,r){var i;B(this,n);var A=U(i=t.call(this)),a=null,s=1,u=null,c="local-floor",d=1,h=null,f=null,p=null,g=null,m=null,v=null,y=r.getContextAttributes(),w=null,_=null,b=[],x=[],C=new Set,S=new Map,E=new gy;E.layers.enable(1),E.viewport=new cm;var M=new gy;M.layers.enable(2),M.viewport=new cm;var F=[E,M],T=new t_;T.layers.enable(1),T.layers.enable(2);var Q=null,L=null;function D(e){var t=x.indexOf(e.inputSource);if(-1!==t){var n=b[t];void 0!==n&&n.dispatchEvent({type:e.type,data:e.inputSource})}}function I(){a.removeEventListener("select",D),a.removeEventListener("selectstart",D),a.removeEventListener("selectend",D),a.removeEventListener("squeeze",D),a.removeEventListener("squeezestart",D),a.removeEventListener("squeezeend",D),a.removeEventListener("end",I),a.removeEventListener("inputsourceschange",R);for(var t=0;t<b.length;t++){var n=x[t];null!==n&&(x[t]=null,b[t].disconnect(n))}Q=null,L=null,e.setRenderTarget(w),m=null,g=null,p=null,a=null,_=null,V.stop(),A.isPresenting=!1,A.dispatchEvent({type:"sessionend"})}function R(e){for(var t=0;t<e.removed.length;t++){var n=e.removed[t],r=x.indexOf(n);r>=0&&(x[r]=null,b[r].disconnect(n))}for(var i=0;i<e.added.length;i++){var A=e.added[i],a=x.indexOf(A);if(-1===a){for(var o=0;o<b.length;o++){if(o>=x.length){x.push(A),a=o;break}if(null===x[o]){x[o]=A,a=o;break}}if(-1===a)break}var s=b[a];s&&s.connect(A)}}i.cameraAutoUpdate=!0,i.enabled=!1,i.isPresenting=!1,i.getController=function(e){var t=b[e];return void 0===t&&(t=new i_,b[e]=t),t.getTargetRaySpace()},i.getControllerGrip=function(e){var t=b[e];return void 0===t&&(t=new i_,b[e]=t),t.getGripSpace()},i.getHand=function(e){var t=b[e];return void 0===t&&(t=new i_,b[e]=t),t.getHandSpace()},i.setFramebufferScaleFactor=function(e){s=e,!0===A.isPresenting&&console.warn("THREE.WebXRManager: Cannot change framebuffer scale while presenting.")},i.setReferenceSpaceType=function(e){c=e,!0===A.isPresenting&&console.warn("THREE.WebXRManager: Cannot change reference space type while presenting.")},i.getReferenceSpace=function(){return h||u},i.setReferenceSpace=function(e){h=e},i.getBaseLayer=function(){return null!==g?g:m},i.getBinding=function(){return p},i.getFrame=function(){return v},i.getSession=function(){return a},i.setSession=function(){var t=l(o().mark((function t(n){var i,l,f,v,B;return o().wrap((function(t){for(;;)switch(t.prev=t.next){case 0:if(null===(a=n)){t.next=25;break}if(w=e.getRenderTarget(),a.addEventListener("select",D),a.addEventListener("selectstart",D),a.addEventListener("selectend",D),a.addEventListener("squeeze",D),a.addEventListener("squeezestart",D),a.addEventListener("squeezeend",D),a.addEventListener("end",I),a.addEventListener("inputsourceschange",R),!0===y.xrCompatible){t.next=14;break}return t.next=14,r.makeXRCompatible();case 14:return void 0===a.renderState.layers||!1===e.capabilities.isWebGL2?(i={antialias:void 0!==a.renderState.layers||y.antialias,alpha:y.alpha,depth:y.depth,stencil:y.stencil,framebufferScaleFactor:s},m=new XRWebGLLayer(a,r,i),a.updateRenderState({baseLayer:m}),_=new dm(m.framebufferWidth,m.framebufferHeight,{format:bp,type:cp,encoding:e.outputEncoding,stencilBuffer:y.stencil})):(l=null,f=null,v=null,y.depth&&(v=y.stencil?35056:33190,l=y.stencil?Ep:Sp,f=y.stencil?Bp:gp),B={colorFormat:32856,depthFormat:v,scaleFactor:s},p=new 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N(e,t){null===t?e.matrixWorld.copy(e.matrix):e.matrixWorld.multiplyMatrices(t.matrixWorld,e.matrix),e.matrixWorldInverse.copy(e.matrixWorld).invert()}i.updateCamera=function(e){if(null!==a){T.near=M.near=E.near=e.near,T.far=M.far=E.far=e.far,Q===T.near&&L===T.far||(a.updateRenderState({depthNear:T.near,depthFar:T.far}),Q=T.near,L=T.far);var t=e.parent,n=T.cameras;N(T,t);for(var r=0;r<n.length;r++)N(n[r],t);T.matrixWorld.decompose(T.position,T.quaternion,T.scale),e.matrix.copy(T.matrix),e.matrix.decompose(e.position,e.quaternion,e.scale);for(var i=e.children,A=0,o=i.length;A<o;A++)i[A].updateMatrixWorld(!0);2===n.length?function(e,t,n){H.setFromMatrixPosition(t.matrixWorld),P.setFromMatrixPosition(n.matrixWorld);var r=H.distanceTo(P),i=t.projectionMatrix.elements,A=n.projectionMatrix.elements,a=i[14]/(i[10]-1),o=i[14]/(i[10]+1),s=(i[9]+1)/i[5],l=(i[9]-1)/i[5],u=(i[8]-1)/i[0],c=(A[8]+1)/A[0],d=a*u,h=a*c,f=r/(-u+c),p=f*-u;t.matrixWorld.decompose(e.position,e.quaternion,e.scale),e.translateX(p),e.translateZ(f),e.matrixWorld.compose(e.position,e.quaternion,e.scale),e.matrixWorldInverse.copy(e.matrixWorld).invert();var g=a+f,m=o+f,v=d-p,y=h+(r-p),w=s*o/m*g,B=l*o/m*g;e.projectionMatrix.makePerspective(v,y,w,B,g,m)}(T,E,M):T.projectionMatrix.copy(E.projectionMatrix)}},i.getCamera=function(){return T},i.getFoveation=function(){if(null!==g||null!==m)return d},i.setFoveation=function(e){d=e,null!==g&&(g.fixedFoveation=e),null!==m&&void 0!==m.fixedFoveation&&(m.fixedFoveation=e)},i.getPlanes=function(){return C};var O=null;var V=new My;return V.setAnimationLoop((function(t,n){if(f=n.getViewerPose(h||u),v=n,null!==f){var r=f.views;null!==m&&(e.setRenderTargetFramebuffer(_,m.framebuffer),e.setRenderTarget(_));var i=!1;r.length!==T.cameras.length&&(T.cameras.length=0,i=!0);for(var a=0;a<r.length;a++){var o=r[a],s=null;if(null!==m)s=m.getViewport(o);else{var l=p.getViewSubImage(g,o);s=l.viewport,0===a&&(e.setRenderTargetTextures(_,l.colorTexture,g.ignoreDepthValues?void 0:l.depthStencilTexture),e.setRenderTarget(_))}var c=F[a];void 0===c&&((c=new gy).layers.enable(a),c.viewport=new cm,F[a]=c),c.matrix.fromArray(o.transform.matrix),c.projectionMatrix.fromArray(o.projectionMatrix),c.viewport.set(s.x,s.y,s.width,s.height),0===a&&T.matrix.copy(c.matrix),!0===i&&T.cameras.push(c)}}for(var d=0;d<b.length;d++){var y=x[d],w=b[d];null!==y&&void 0!==w&&w.update(y,n,h||u)}if(O&&O(t,n),n.detectedPlanes){A.dispatchEvent({type:"planesdetected",data:n.detectedPlanes});var B,E=null,U=k(C);try{for(U.s();!(B=U.n()).done;){var M=B.value;n.detectedPlanes.has(M)||(null===E&&(E=[]),E.push(M))}}catch(Xx){U.e(Xx)}finally{U.f()}if(null!==E){var Q,L=k(E);try{for(L.s();!(Q=L.n()).done;){var D=Q.value;C.delete(D),S.delete(D),A.dispatchEvent({type:"planeremoved",data:D})}}catch(Xx){L.e(Xx)}finally{L.f()}}var I,R=k(n.detectedPlanes);try{for(R.s();!(I=R.n()).done;){var H=I.value;if(C.has(H)){var P=S.get(H);H.lastChangedTime>P&&(S.set(H,H.lastChangedTime),A.dispatchEvent({type:"planechanged",data:H}))}else C.add(H),S.set(H,n.lastChangedTime),A.dispatchEvent({type:"planeadded",data:H})}}catch(Xx){R.e(Xx)}finally{R.f()}}v=null})),i.setAnimationLoop=function(e){O=e},i.dispose=function(){},i}return b(n)}(Ug);function o_(e,t){function n(n,r){n.opacity.value=r.opacity,r.color&&n.diffuse.value.copy(r.color),r.emissive&&n.emissive.value.copy(r.emissive).multiplyScalar(r.emissiveIntensity),r.map&&(n.map.value=r.map),r.alphaMap&&(n.alphaMap.value=r.alphaMap),r.bumpMap&&(n.bumpMap.value=r.bumpMap,n.bumpScale.value=r.bumpScale,r.side===lf&&(n.bumpScale.value*=-1)),r.displacementMap&&(n.displacementMap.value=r.displacementMap,n.displacementScale.value=r.displacementScale,n.displacementBias.value=r.displacementBias),r.emissiveMap&&(n.emissiveMap.value=r.emissiveMap),r.normalMap&&(n.normalMap.value=r.normalMap,n.normalScale.value.copy(r.normalScale),r.side===lf&&n.normalScale.value.negate()),r.specularMap&&(n.specularMap.value=r.specularMap),r.alphaTest>0&&(n.alphaTest.value=r.alphaTest);var i,A,a=t.get(r).envMap;if(a&&(n.envMap.value=a,n.flipEnvMap.value=a.isCubeTexture&&!1===a.isRenderTargetTexture?-1:1,n.reflectivity.value=r.reflectivity,n.ior.value=r.ior,n.refractionRatio.value=r.refractionRatio),r.lightMap){n.lightMap.value=r.lightMap;var o=!0!==e.physicallyCorrectLights?Math.PI:1;n.lightMapIntensity.value=r.lightMapIntensity*o}r.aoMap&&(n.aoMap.value=r.aoMap,n.aoMapIntensity.value=r.aoMapIntensity),r.map?i=r.map:r.specularMap?i=r.specularMap:r.displacementMap?i=r.displacementMap:r.normalMap?i=r.normalMap:r.bumpMap?i=r.bumpMap:r.roughnessMap?i=r.roughnessMap:r.metalnessMap?i=r.metalnessMap:r.alphaMap?i=r.alphaMap:r.emissiveMap?i=r.emissiveMap:r.clearcoatMap?i=r.clearcoatMap:r.clearcoatNormalMap?i=r.clearcoatNormalMap:r.clearcoatRoughnessMap?i=r.clearcoatRoughnessMap:r.iridescenceMap?i=r.iridescenceMap:r.iridescenceThicknessMap?i=r.iridescenceThicknessMap:r.specularIntensityMap?i=r.specularIntensityMap:r.specularColorMap?i=r.specularColorMap:r.transmissionMap?i=r.transmissionMap:r.thicknessMap?i=r.thicknessMap:r.sheenColorMap?i=r.sheenColorMap:r.sheenRoughnessMap&&(i=r.sheenRoughnessMap),void 0!==i&&(i.isWebGLRenderTarget&&(i=i.texture),!0===i.matrixAutoUpdate&&i.updateMatrix(),n.uvTransform.value.copy(i.matrix)),r.aoMap?A=r.aoMap:r.lightMap&&(A=r.lightMap),void 0!==A&&(A.isWebGLRenderTarget&&(A=A.texture),!0===A.matrixAutoUpdate&&A.updateMatrix(),n.uv2Transform.value.copy(A.matrix))}return{refreshFogUniforms:function(t,n){n.color.getRGB(t.fogColor.value,uy(e)),n.isFog?(t.fogNear.value=n.near,t.fogFar.value=n.far):n.isFogExp2&&(t.fogDensity.value=n.density)},refreshMaterialUniforms:function(e,r,i,A,a){r.isMeshBasicMaterial||r.isMeshLambertMaterial?n(e,r):r.isMeshToonMaterial?(n(e,r),function(e,t){t.gradientMap&&(e.gradientMap.value=t.gradientMap)}(e,r)):r.isMeshPhongMaterial?(n(e,r),function(e,t){e.specular.value.copy(t.specular),e.shininess.value=Math.max(t.shininess,1e-4)}(e,r)):r.isMeshStandardMaterial?(n(e,r),function(e,n){e.roughness.value=n.roughness,e.metalness.value=n.metalness,n.roughnessMap&&(e.roughnessMap.value=n.roughnessMap);n.metalnessMap&&(e.metalnessMap.value=n.metalnessMap);var r=t.get(n).envMap;r&&(e.envMapIntensity.value=n.envMapIntensity)}(e,r),r.isMeshPhysicalMaterial&&function(e,t,n){e.ior.value=t.ior,t.sheen>0&&(e.sheenColor.value.copy(t.sheenColor).multiplyScalar(t.sheen),e.sheenRoughness.value=t.sheenRoughness,t.sheenColorMap&&(e.sheenColorMap.value=t.sheenColorMap),t.sheenRoughnessMap&&(e.sheenRoughnessMap.value=t.sheenRoughnessMap));t.clearcoat>0&&(e.clearcoat.value=t.clearcoat,e.clearcoatRoughness.value=t.clearcoatRoughness,t.clearcoatMap&&(e.clearcoatMap.value=t.clearcoatMap),t.clearcoatRoughnessMap&&(e.clearcoatRoughnessMap.value=t.clearcoatRoughnessMap),t.clearcoatNormalMap&&(e.clearcoatNormalScale.value.copy(t.clearcoatNormalScale),e.clearcoatNormalMap.value=t.clearcoatNormalMap,t.side===lf&&e.clearcoatNormalScale.value.negate()));t.iridescence>0&&(e.iridescence.value=t.iridescence,e.iridescenceIOR.value=t.iridescenceIOR,e.iridescenceThicknessMinimum.value=t.iridescenceThicknessRange[0],e.iridescenceThicknessMaximum.value=t.iridescenceThicknessRange[1],t.iridescenceMap&&(e.iridescenceMap.value=t.iridescenceMap),t.iridescenceThicknessMap&&(e.iridescenceThicknessMap.value=t.iridescenceThicknessMap));t.transmission>0&&(e.transmission.value=t.transmission,e.transmissionSamplerMap.value=n.texture,e.transmissionSamplerSize.value.set(n.width,n.height),t.transmissionMap&&(e.transmissionMap.value=t.transmissionMap),e.thickness.value=t.thickness,t.thicknessMap&&(e.thicknessMap.value=t.thicknessMap),e.attenuationDistance.value=t.attenuationDistance,e.attenuationColor.value.copy(t.attenuationColor));e.specularIntensity.value=t.specularIntensity,e.specularColor.value.copy(t.specularColor),t.specularIntensityMap&&(e.specularIntensityMap.value=t.specularIntensityMap);t.specularColorMap&&(e.specularColorMap.value=t.specularColorMap)}(e,r,a)):r.isMeshMatcapMaterial?(n(e,r),function(e,t){t.matcap&&(e.matcap.value=t.matcap)}(e,r)):r.isMeshDepthMaterial?n(e,r):r.isMeshDistanceMaterial?(n(e,r),function(e,t){e.referencePosition.value.copy(t.referencePosition),e.nearDistance.value=t.nearDistance,e.farDistance.value=t.farDistance}(e,r)):r.isMeshNormalMaterial?n(e,r):r.isLineBasicMaterial?(function(e,t){e.diffuse.value.copy(t.color),e.opacity.value=t.opacity}(e,r),r.isLineDashedMaterial&&function(e,t){e.dashSize.value=t.dashSize,e.totalSize.value=t.dashSize+t.gapSize,e.scale.value=t.scale}(e,r)):r.isPointsMaterial?function(e,t,n,r){e.diffuse.value.copy(t.color),e.opacity.value=t.opacity,e.size.value=t.size*n,e.scale.value=.5*r,t.map&&(e.map.value=t.map);t.alphaMap&&(e.alphaMap.value=t.alphaMap);t.alphaTest>0&&(e.alphaTest.value=t.alphaTest);var i;t.map?i=t.map:t.alphaMap&&(i=t.alphaMap);void 0!==i&&(!0===i.matrixAutoUpdate&&i.updateMatrix(),e.uvTransform.value.copy(i.matrix))}(e,r,i,A):r.isSpriteMaterial?function(e,t){e.diffuse.value.copy(t.color),e.opacity.value=t.opacity,e.rotation.value=t.rotation,t.map&&(e.map.value=t.map);t.alphaMap&&(e.alphaMap.value=t.alphaMap);t.alphaTest>0&&(e.alphaTest.value=t.alphaTest);var n;t.map?n=t.map:t.alphaMap&&(n=t.alphaMap);void 0!==n&&(!0===n.matrixAutoUpdate&&n.updateMatrix(),e.uvTransform.value.copy(n.matrix))}(e,r):r.isShadowMaterial?(e.color.value.copy(r.color),e.opacity.value=r.opacity):r.isShaderMaterial&&(r.uniformsNeedUpdate=!1)}}}function s_(e,t,n,r){var i={},A={},a=[],o=n.isWebGL2?e.getParameter(35375):0;function s(e,t,n){var r=e.value;if(void 0===n[t]){if("number"===typeof r)n[t]=r;else{for(var i=Array.isArray(r)?r:[r],A=[],a=0;a<i.length;a++)A.push(i[a].clone());n[t]=A}return!0}if("number"===typeof r){if(n[t]!==r)return n[t]=r,!0}else for(var o=Array.isArray(n[t])?n[t]:[n[t]],s=Array.isArray(r)?r:[r],l=0;l<o.length;l++){var u=o[l];if(!1===u.equals(s[l]))return u.copy(s[l]),!0}return!1}function l(e){var t={boundary:0,storage:0};return"number"===typeof e?(t.boundary=4,t.storage=4):e.isVector2?(t.boundary=8,t.storage=8):e.isVector3||e.isColor?(t.boundary=16,t.storage=12):e.isVector4?(t.boundary=16,t.storage=16):e.isMatrix3?(t.boundary=48,t.storage=48):e.isMatrix4?(t.boundary=64,t.storage=64):e.isTexture?console.warn("THREE.WebGLRenderer: Texture samplers can not be part of an uniforms group."):console.warn("THREE.WebGLRenderer: Unsupported uniform value type.",e),t}function u(t){var n=t.target;n.removeEventListener("dispose",u);var r=a.indexOf(n.__bindingPointIndex);a.splice(r,1),e.deleteBuffer(i[n.id]),delete i[n.id],delete A[n.id]}return{bind:function(e,t){var n=t.program;r.uniformBlockBinding(e,n)},update:function(n,c){var d=i[n.id];void 0===d&&(!function(e){for(var t=e.uniforms,n=0,r=16,i=0,A=0,a=t.length;A<a;A++){for(var o=t[A],s={boundary:0,storage:0},u=Array.isArray(o.value)?o.value:[o.value],c=0,d=u.length;c<d;c++){var h=l(u[c]);s.boundary+=h.boundary,s.storage+=h.storage}if(o.__data=new Float32Array(s.storage/Float32Array.BYTES_PER_ELEMENT),o.__offset=n,A>0)0!==(i=n%r)&&r-i-s.boundary<0&&(n+=r-i,o.__offset=n);n+=s.storage}(i=n%r)>0&&(n+=r-i);e.__size=n,e.__cache={}}(n),d=function(t){var n=function(){for(var e=0;e<o;e++)if(-1===a.indexOf(e))return a.push(e),e;return console.error("THREE.WebGLRenderer: Maximum number of simultaneously usable uniforms groups reached."),0}();t.__bindingPointIndex=n;var r=e.createBuffer(),i=t.__size,A=t.usage;return e.bindBuffer(35345,r),e.bufferData(35345,i,A),e.bindBuffer(35345,null),e.bindBufferBase(35345,n,r),r}(n),i[n.id]=d,n.addEventListener("dispose",u));var h=c.program;r.updateUBOMapping(n,h);var f=t.render.frame;A[n.id]!==f&&(!function(t){var n=i[t.id],r=t.uniforms,A=t.__cache;e.bindBuffer(35345,n);for(var a=0,o=r.length;a<o;a++){var 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A=t.fog,a=r.isMeshStandardMaterial?t.environment:null,o=null===y?p.outputEncoding:!0===y.isXRRenderTarget?y.texture.encoding:pg,s=(r.isMeshStandardMaterial?q:X).get(r.envMap||a),l=!0===r.vertexColors&&!!n.attributes.color&&4===n.attributes.color.itemSize,u=!!r.normalMap&&!!n.attributes.tangent,c=!!n.morphAttributes.position,h=!!n.morphAttributes.normal,f=!!n.morphAttributes.color,g=r.toneMapped?p.toneMapping:Gf,m=n.morphAttributes.position||n.morphAttributes.normal||n.morphAttributes.color,v=void 0!==m?m.length:0,_=W.get(r),b=d.state.lights;if(!0===L&&(!0===D||e!==B)){var x=e===B&&r.id===w;re.setState(r,e,x)}var C=!1;r.version===_.__version?_.needsLights&&_.lightsStateVersion!==b.state.version||_.outputEncoding!==o||i.isInstancedMesh&&!1===_.instancing?C=!0:i.isInstancedMesh||!0!==_.instancing?i.isSkinnedMesh&&!1===_.skinning?C=!0:i.isSkinnedMesh||!0!==_.skinning?_.envMap!==s||!0===r.fog&&_.fog!==A?C=!0:void 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r=z.isWebGL2;null===I&&(I=new dm(1,1,{generateMipmaps:!0,type:V.has("EXT_color_buffer_half_float")?vp:cp,minFilter:lp,samples:r&&!0===a?4:0}));p.getDrawingBufferSize(H),r?I.setSize(H.x,H.y):I.setSize(Pg(H.x),Pg(H.y));var i=p.getRenderTarget();p.setRenderTarget(I),p.clear();var A=p.toneMapping;p.toneMapping=Gf,Ue(e,t,n),p.toneMapping=A,j.updateMultisampleRenderTarget(I),j.updateRenderTargetMipmap(I),p.setRenderTarget(i)}(i,t,n),r&&G.viewport(_.copy(r)),i.length>0&&Ue(i,t,n),A.length>0&&Ue(A,t,n),o.length>0&&Ue(o,t,n),G.buffers.depth.setTest(!0),G.buffers.depth.setMask(!0),G.buffers.color.setMask(!0),G.setPolygonOffset(!1)}function Ue(e,t,n){for(var r=!0===t.isScene?t.overrideMaterial:null,i=0,A=e.length;i<A;i++){var a=e[i],o=a.object,s=a.geometry,l=null===r?a.material:r,u=a.group;o.layers.test(n.layers)&&Me(o,t,n,s,l,u)}}function Me(e,t,n,r,i,A){e.onBeforeRender(p,t,n,r,i,A),e.modelViewMatrix.multiplyMatrices(n.matrixWorldInverse,e.matrixWorld),e.normalMatrix.getNormalMatrix(e.modelViewMatrix),i.onBeforeRender(p,t,n,r,e,A),!0===i.transparent&&i.side===uf&&!1===i.forceSinglePass?(i.side=lf,i.needsUpdate=!0,p.renderBufferDirect(n,t,r,i,e,A),i.side=sf,i.needsUpdate=!0,p.renderBufferDirect(n,t,r,i,e,A),i.side=uf):p.renderBufferDirect(n,t,r,i,e,A),e.onAfterRender(p,t,n,r,i,A)}function Fe(e,t,n){!0!==t.isScene&&(t=N);var r=W.get(e),i=d.state.lights,A=d.state.shadowsArray,a=i.state.version,o=$.getParameters(e,i.state,A,t,n),s=$.getProgramCacheKey(o),l=r.programs;r.environment=e.isMeshStandardMaterial?t.environment:null,r.fog=t.fog,r.envMap=(e.isMeshStandardMaterial?q:X).get(e.envMap||r.environment),void 0===l&&(e.addEventListener("dispose",Be),l=new Map,r.programs=l);var u=l.get(s);if(void 0!==u){if(r.currentProgram===u&&r.lightsStateVersion===a)return Te(e,o),u}else o.uniforms=$.getUniforms(e),e.onBuild(n,o,p),e.onBeforeCompile(o,p),u=$.acquireProgram(o,s),l.set(s,u),r.uniforms=o.uniforms;var c=r.uniforms;(e.isShaderMaterial||e.isRawShaderMaterial)&&!0!==e.clipping||(c.clippingPlanes=re.uniform),Te(e,o),r.needsLights=function(e){return e.isMeshLambertMaterial||e.isMeshToonMaterial||e.isMeshPhongMaterial||e.isMeshStandardMaterial||e.isShadowMaterial||e.isShaderMaterial&&!0===e.lights}(e),r.lightsStateVersion=a,r.needsLights&&(c.ambientLightColor.value=i.state.ambient,c.lightProbe.value=i.state.probe,c.directionalLights.value=i.state.directional,c.directionalLightShadows.value=i.state.directionalShadow,c.spotLights.value=i.state.spot,c.spotLightShadows.value=i.state.spotShadow,c.rectAreaLights.value=i.state.rectArea,c.ltc_1.value=i.state.rectAreaLTC1,c.ltc_2.value=i.state.rectAreaLTC2,c.pointLights.value=i.state.point,c.pointLightShadows.value=i.state.pointShadow,c.hemisphereLights.value=i.state.hemi,c.directionalShadowMap.value=i.state.directionalShadowMap,c.directionalShadowMatrix.value=i.state.directionalShadowMatrix,c.spotShadowMap.value=i.state.spotShadowMap,c.spotLightMatrix.value=i.state.spotLightMatrix,c.spotLightMap.value=i.state.spotLightMap,c.pointShadowMap.value=i.state.pointShadowMap,c.pointShadowMatrix.value=i.state.pointShadowMatrix);var h=u.getUniforms(),f=pB.seqWithValue(h.seq,c);return r.currentProgram=u,r.uniformsList=f,u}function Te(e,t){var n=W.get(e);n.outputEncoding=t.outputEncoding,n.instancing=t.instancing,n.skinning=t.skinning,n.morphTargets=t.morphTargets,n.morphNormals=t.morphNormals,n.morphColors=t.morphColors,n.morphTargetsCount=t.morphTargetsCount,n.numClippingPlanes=t.numClippingPlanes,n.numIntersection=t.numClipIntersection,n.vertexAlphas=t.vertexAlphas,n.vertexTangents=t.vertexTangents,n.toneMapping=t.toneMapping}Ce.setAnimationLoop((function(e){_e&&_e(e)})),"undefined"!==typeof self&&Ce.setContext(self),this.setAnimationLoop=function(e){_e=e,me.setAnimationLoop(e),null===e?Ce.stop():Ce.start()},me.addEventListener("sessionstart",be),me.addEventListener("sessionend",xe),this.render=function(e,t){if(void 0===t||!0===t.isCamera){if(!0!==g){!0===e.matrixWorldAutoUpdate&&e.updateMatrixWorld(),null===t.parent&&!0===t.matrixWorldAutoUpdate&&t.updateMatrixWorld(),!0===me.enabled&&!0===me.isPresenting&&(!0===me.cameraAutoUpdate&&me.updateCamera(t),t=me.getCamera()),!0===e.isScene&&e.onBeforeRender(p,e,t,y),(d=ne.get(e,f.length)).init(),f.push(d),R.multiplyMatrices(t.projectionMatrix,t.matrixWorldInverse),Q.setFromProjectionMatrix(R),D=this.localClippingEnabled,L=re.init(this.clippingPlanes,D),(c=te.get(e,h.length)).init(),h.push(c),Se(e,t,0,p.sortObjects),c.finish(),!0===p.sortObjects&&c.sort(U,M),!0===L&&re.beginShadows();var n=d.state.shadowsArray;if(ie.render(n,e,t),!0===L&&re.endShadows(),!0===this.info.autoReset&&this.info.reset(),Ae.render(c,e),d.setupLights(p.physicallyCorrectLights),t.isArrayCamera)for(var r=t.cameras,i=0,A=r.length;i<A;i++){var a=r[i];Ee(c,e,a,a.viewport)}else Ee(c,e,t);null!==y&&(j.updateMultisampleRenderTarget(y),j.updateRenderTargetMipmap(y)),!0===e.isScene&&e.onAfterRender(p,e,t),ue.resetDefaultState(),w=-1,B=null,f.pop(),d=f.length>0?f[f.length-1]:null,h.pop(),c=h.length>0?h[h.length-1]:null}}else console.error("THREE.WebGLRenderer.render: camera is not an instance of THREE.Camera.")},this.getActiveCubeFace=function(){return m},this.getActiveMipmapLevel=function(){return v},this.getRenderTarget=function(){return y},this.setRenderTargetTextures=function(e,t,n){W.get(e.texture).__webglTexture=t,W.get(e.depthTexture).__webglTexture=n;var r=W.get(e);r.__hasExternalTextures=!0,r.__hasExternalTextures&&(r.__autoAllocateDepthBuffer=void 0===n,r.__autoAllocateDepthBuffer||!0===V.has("WEBGL_multisampled_render_to_texture")&&(console.warn("THREE.WebGLRenderer: Render-to-texture extension was disabled because an external texture was provided"),r.__useRenderToTexture=!1))},this.setRenderTargetFramebuffer=function(e,t){var n=W.get(e);n.__webglFramebuffer=t,n.__useDefaultFramebuffer=void 0===t},this.setRenderTarget=function(e){var t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0,n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:0;y=e,m=t,v=n;var r=!0,i=null,A=!1,a=!1;if(e){var o=W.get(e);void 0!==o.__useDefaultFramebuffer?(G.bindFramebuffer(36160,null),r=!1):void 0===o.__webglFramebuffer?j.setupRenderTarget(e):o.__hasExternalTextures&&j.rebindTextures(e,W.get(e.texture).__webglTexture,W.get(e.depthTexture).__webglTexture);var s=e.texture;(s.isData3DTexture||s.isDataArrayTexture||s.isCompressedArrayTexture)&&(a=!0);var l=W.get(e).__webglFramebuffer;e.isWebGLCubeRenderTarget?(i=l[t],A=!0):i=z.isWebGL2&&e.samples>0&&!1===j.useMultisampledRTT(e)?W.get(e).__webglMultisampledFramebuffer:l,_.copy(e.viewport),b.copy(e.scissor),x=e.scissorTest}else _.copy(F).multiplyScalar(E).floor(),b.copy(T).multiplyScalar(E).floor(),x=k;if(G.bindFramebuffer(36160,i)&&z.drawBuffers&&r&&G.drawBuffers(e,i),G.viewport(_),G.scissor(b),G.setScissorTest(x),A){var u=W.get(e.texture);de.framebufferTexture2D(36160,36064,34069+t,u.__webglTexture,n)}else if(a){var c=W.get(e.texture),d=t||0;de.framebufferTextureLayer(36160,36064,c.__webglTexture,n||0,d)}w=-1},this.readRenderTargetPixels=function(e,t,n,r,i,A,a){if(e&&e.isWebGLRenderTarget){var o=W.get(e).__webglFramebuffer;if(e.isWebGLCubeRenderTarget&&void 0!==a&&(o=o[a]),o){G.bindFramebuffer(36160,o);try{var s=e.texture,l=s.format,u=s.type;if(l!==bp&&le.convert(l)!==de.getParameter(35739))return void console.error("THREE.WebGLRenderer.readRenderTargetPixels: renderTarget is not in RGBA or implementation defined format.");var c=u===vp&&(V.has("EXT_color_buffer_half_float")||z.isWebGL2&&V.has("EXT_color_buffer_float"));if(u!==cp&&le.convert(u)!==de.getParameter(35738)&&(u!==mp||!(z.isWebGL2||V.has("OES_texture_float")||V.has("WEBGL_color_buffer_float")))&&!c)return void console.error("THREE.WebGLRenderer.readRenderTargetPixels: renderTarget is not in UnsignedByteType or implementation defined type.");t>=0&&t<=e.width-r&&n>=0&&n<=e.height-i&&de.readPixels(t,n,r,i,le.convert(l),le.convert(u),A)}finally{var d=null!==y?W.get(y).__webglFramebuffer:null;G.bindFramebuffer(36160,d)}}}else console.error("THREE.WebGLRenderer.readRenderTargetPixels: renderTarget is not THREE.WebGLRenderTarget.")},this.copyFramebufferToTexture=function(e,t){var n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:0,r=Math.pow(2,-n),i=Math.floor(t.image.width*r),A=Math.floor(t.image.height*r);j.setTexture2D(t,0),de.copyTexSubImage2D(3553,n,0,0,e.x,e.y,i,A),G.unbindTexture()},this.copyTextureToTexture=function(e,t,n){var r=arguments.length>3&&void 0!==arguments[3]?arguments[3]:0,i=t.image.width,A=t.image.height,a=le.convert(n.format),o=le.convert(n.type);j.setTexture2D(n,0),de.pixelStorei(37440,n.flipY),de.pixelStorei(37441,n.premultiplyAlpha),de.pixelStorei(3317,n.unpackAlignment),t.isDataTexture?de.texSubImage2D(3553,r,e.x,e.y,i,A,a,o,t.image.data):t.isCompressedTexture?de.compressedTexSubImage2D(3553,r,e.x,e.y,t.mipmaps[0].width,t.mipmaps[0].height,a,t.mipmaps[0].data):de.texSubImage2D(3553,r,e.x,e.y,a,o,t.image),0===r&&n.generateMipmaps&&de.generateMipmap(3553),G.unbindTexture()},this.copyTextureToTexture3D=function(e,t,n,r){var i=arguments.length>4&&void 0!==arguments[4]?arguments[4]:0;if(p.isWebGL1Renderer)console.warn("THREE.WebGLRenderer.copyTextureToTexture3D: can only be used with WebGL2.");else{var A,a=e.max.x-e.min.x+1,o=e.max.y-e.min.y+1,s=e.max.z-e.min.z+1,l=le.convert(r.format),u=le.convert(r.type);if(r.isData3DTexture)j.setTexture3D(r,0),A=32879;else{if(!r.isDataArrayTexture)return void console.warn("THREE.WebGLRenderer.copyTextureToTexture3D: only supports THREE.DataTexture3D and THREE.DataTexture2DArray.");j.setTexture2DArray(r,0),A=35866}de.pixelStorei(37440,r.flipY),de.pixelStorei(37441,r.premultiplyAlpha),de.pixelStorei(3317,r.unpackAlignment);var c=de.getParameter(3314),d=de.getParameter(32878),h=de.getParameter(3316),f=de.getParameter(3315),g=de.getParameter(32877),m=n.isCompressedTexture?n.mipmaps[0]:n.image;de.pixelStorei(3314,m.width),de.pixelStorei(32878,m.height),de.pixelStorei(3316,e.min.x),de.pixelStorei(3315,e.min.y),de.pixelStorei(32877,e.min.z),n.isDataTexture||n.isData3DTexture?de.texSubImage3D(A,i,t.x,t.y,t.z,a,o,s,l,u,m.data):n.isCompressedArrayTexture?(console.warn("THREE.WebGLRenderer.copyTextureToTexture3D: untested support for compressed srcTexture."),de.compressedTexSubImage3D(A,i,t.x,t.y,t.z,a,o,s,l,m.data)):de.texSubImage3D(A,i,t.x,t.y,t.z,a,o,s,l,u,m),de.pixelStorei(3314,c),de.pixelStorei(32878,d),de.pixelStorei(3316,h),de.pixelStorei(3315,f),de.pixelStorei(32877,g),0===i&&r.generateMipmaps&&de.generateMipmap(A),G.unbindTexture()}},this.initTexture=function(e){e.isCubeTexture?j.setTextureCube(e,0):e.isData3DTexture?j.setTexture3D(e,0):e.isDataArrayTexture||e.isCompressedArrayTexture?j.setTexture2DArray(e,0):j.setTexture2D(e,0),G.unbindTexture()},this.resetState=function(){m=0,v=0,y=null,G.reset(),ue.reset()},"undefined"!==typeof __THREE_DEVTOOLS__&&__THREE_DEVTOOLS__.dispatchEvent(new CustomEvent("observe",{detail:this}))}var u_=function(e){C(n,e);var t=M(n);function n(){return B(this,n),t.apply(this,arguments)}return b(n)}(l_);u_.prototype.isWebGL1Renderer=!0;var c_=function(e){C(n,e);var t=M(n);function n(){var e;return B(this,n),(e=t.call(this)).isScene=!0,e.type="Scene",e.background=null,e.environment=null,e.fog=null,e.backgroundBlurriness=0,e.backgroundIntensity=1,e.overrideMaterial=null,"undefined"!==typeof __THREE_DEVTOOLS__&&__THREE_DEVTOOLS__.dispatchEvent(new CustomEvent("observe",{detail:U(e)})),e}return b(n,[{key:"copy",value:function(e,t){return jc(S(n.prototype),"copy",this).call(this,e,t),null!==e.background&&(this.background=e.background.clone()),null!==e.environment&&(this.environment=e.environment.clone()),null!==e.fog&&(this.fog=e.fog.clone()),this.backgroundBlurriness=e.backgroundBlurriness,this.backgroundIntensity=e.backgroundIntensity,null!==e.overrideMaterial&&(this.overrideMaterial=e.overrideMaterial.clone()),this.matrixAutoUpdate=e.matrixAutoUpdate,this}},{key:"toJSON",value:function(e){var t=jc(S(n.prototype),"toJSON",this).call(this,e);return null!==this.fog&&(t.object.fog=this.fog.toJSON()),this.backgroundBlurriness>0&&(t.object.backgroundBlurriness=this.backgroundBlurriness),1!==this.backgroundIntensity&&(t.object.backgroundIntensity=this.backgroundIntensity),t}},{key:"autoUpdate",get:function(){return console.warn("THREE.Scene: autoUpdate was renamed to matrixWorldAutoUpdate in r144."),this.matrixWorldAutoUpdate},set:function(e){console.warn("THREE.Scene: autoUpdate was renamed to matrixWorldAutoUpdate in r144."),this.matrixWorldAutoUpdate=e}}]),n}(mv);var d_=function(e){C(n,e);var t=M(n);function n(){var e,r=arguments.length>0&&void 0!==arguments[0]?arguments[0]:null,i=arguments.length>1&&void 0!==arguments[1]?arguments[1]:1,A=arguments.length>2&&void 0!==arguments[2]?arguments[2]:1,a=arguments.length>3?arguments[3]:void 0,o=arguments.length>4?arguments[4]:void 0,s=arguments.length>5?arguments[5]:void 0,l=arguments.length>6?arguments[6]:void 0,u=arguments.length>7?arguments[7]:void 0,c=arguments.length>8&&void 0!==arguments[8]?arguments[8]:ip,d=arguments.length>9&&void 0!==arguments[9]?arguments[9]:ip,h=arguments.length>10?arguments[10]:void 0,f=arguments.length>11?arguments[11]:void 0;return B(this,n),(e=t.call(this,null,s,l,u,c,d,a,o,h,f)).isDataTexture=!0,e.image={data:r,width:i,height:A},e.generateMipmaps=!1,e.flipY=!1,e.unpackAlignment=1,e}return b(n)}(um),h_=function(e){C(n,e);var t=M(n);function n(e){var r;return B(this,n),(r=t.call(this)).isLineBasicMaterial=!0,r.type="LineBasicMaterial",r.color=new Am(16777215),r.linewidth=1,r.linecap="round",r.linejoin="round",r.fog=!0,r.setValues(e),r}return b(n,[{key:"copy",value:function(e){return jc(S(n.prototype),"copy",this).call(this,e),this.color.copy(e.color),this.linewidth=e.linewidth,this.linecap=e.linecap,this.linejoin=e.linejoin,this.fog=e.fog,this}}]),n}(Fv),f_=new gm,p_=new gm,g_=new Wm,m_=new Km,v_=new Rm,y_=function(e){C(n,e);var t=M(n);function n(){var e,r=arguments.length>0&&void 0!==arguments[0]?arguments[0]:new Kv,i=arguments.length>1&&void 0!==arguments[1]?arguments[1]:new h_;return B(this,n),(e=t.call(this)).isLine=!0,e.type="Line",e.geometry=r,e.material=i,e.updateMorphTargets(),e}return b(n,[{key:"copy",value:function(e,t){return jc(S(n.prototype),"copy",this).call(this,e,t),this.material=e.material,this.geometry=e.geometry,this}},{key:"computeLineDistances",value:function(){var e=this.geometry;if(null===e.index){for(var t=e.attributes.position,n=[0],r=1,i=t.count;r<i;r++)f_.fromBufferAttribute(t,r-1),p_.fromBufferAttribute(t,r),n[r]=n[r-1],n[r]+=f_.distanceTo(p_);e.setAttribute("lineDistance",new Rv(n,1))}else console.warn("THREE.Line.computeLineDistances(): Computation only possible with non-indexed BufferGeometry.");return this}},{key:"raycast",value:function(e,t){var n=this.geometry,r=this.matrixWorld,i=e.params.Line.threshold,A=n.drawRange;if(null===n.boundingSphere&&n.computeBoundingSphere(),v_.copy(n.boundingSphere),v_.applyMatrix4(r),v_.radius+=i,!1!==e.ray.intersectsSphere(v_)){g_.copy(r).invert(),m_.copy(e.ray).applyMatrix4(g_);var a=i/((this.scale.x+this.scale.y+this.scale.z)/3),o=a*a,s=new gm,l=new gm,u=new gm,c=new gm,d=this.isLineSegments?2:1,h=n.index,f=n.attributes.position;if(null!==h)for(var p=Math.max(0,A.start),g=Math.min(h.count,A.start+A.count)-1;p<g;p+=d){var m=h.getX(p),v=h.getX(p+1);if(s.fromBufferAttribute(f,m),l.fromBufferAttribute(f,v),!(m_.distanceSqToSegment(s,l,c,u)>o)){c.applyMatrix4(this.matrixWorld);var y=e.ray.origin.distanceTo(c);y<e.near||y>e.far||t.push({distance:y,point:u.clone().applyMatrix4(this.matrixWorld),index:p,face:null,faceIndex:null,object:this})}}else for(var w=Math.max(0,A.start),B=Math.min(f.count,A.start+A.count)-1;w<B;w+=d){if(s.fromBufferAttribute(f,w),l.fromBufferAttribute(f,w+1),!(m_.distanceSqToSegment(s,l,c,u)>o)){c.applyMatrix4(this.matrixWorld);var _=e.ray.origin.distanceTo(c);_<e.near||_>e.far||t.push({distance:_,point:u.clone().applyMatrix4(this.matrixWorld),index:w,face:null,faceIndex:null,object:this})}}}}},{key:"updateMorphTargets",value:function(){var e=this.geometry.morphAttributes,t=Object.keys(e);if(t.length>0){var n=e[t[0]];if(void 0!==n){this.morphTargetInfluences=[],this.morphTargetDictionary={};for(var r=0,i=n.length;r<i;r++){var A=n[r].name||String(r);this.morphTargetInfluences.push(0),this.morphTargetDictionary[A]=r}}}}}]),n}(mv),w_=new gm,B_=new gm,__=function(e){C(n,e);var t=M(n);function n(e,r){var i;return B(this,n),(i=t.call(this,e,r)).isLineSegments=!0,i.type="LineSegments",i}return b(n,[{key:"computeLineDistances",value:function(){var e=this.geometry;if(null===e.index){for(var t=e.attributes.position,n=[],r=0,i=t.count;r<i;r+=2)w_.fromBufferAttribute(t,r),B_.fromBufferAttribute(t,r+1),n[r]=0===r?0:n[r-1],n[r+1]=n[r]+w_.distanceTo(B_);e.setAttribute("lineDistance",new Rv(n,1))}else console.warn("THREE.LineSegments.computeLineDistances(): Computation only possible with non-indexed BufferGeometry.");return this}}]),n}(y_),b_=function(e){C(n,e);var t=M(n);function n(e){var r;return B(this,n),(r=t.call(this)).isPointsMaterial=!0,r.type="PointsMaterial",r.color=new Am(16777215),r.map=null,r.alphaMap=null,r.size=1,r.sizeAttenuation=!0,r.fog=!0,r.setValues(e),r}return b(n,[{key:"copy",value:function(e){return jc(S(n.prototype),"copy",this).call(this,e),this.color.copy(e.color),this.map=e.map,this.alphaMap=e.alphaMap,this.size=e.size,this.sizeAttenuation=e.sizeAttenuation,this.fog=e.fog,this}}]),n}(Fv),x_=new Wm,C_=new Km,S_=new Rm,E_=new gm,U_=function(e){C(n,e);var t=M(n);function n(){var e,r=arguments.length>0&&void 0!==arguments[0]?arguments[0]:new Kv,i=arguments.length>1&&void 0!==arguments[1]?arguments[1]:new b_;return B(this,n),(e=t.call(this)).isPoints=!0,e.type="Points",e.geometry=r,e.material=i,e.updateMorphTargets(),e}return b(n,[{key:"copy",value:function(e,t){return jc(S(n.prototype),"copy",this).call(this,e,t),this.material=e.material,this.geometry=e.geometry,this}},{key:"raycast",value:function(e,t){var n=this.geometry,r=this.matrixWorld,i=e.params.Points.threshold,A=n.drawRange;if(null===n.boundingSphere&&n.computeBoundingSphere(),S_.copy(n.boundingSphere),S_.applyMatrix4(r),S_.radius+=i,!1!==e.ray.intersectsSphere(S_)){x_.copy(r).invert(),C_.copy(e.ray).applyMatrix4(x_);var a=i/((this.scale.x+this.scale.y+this.scale.z)/3),o=a*a,s=n.index,l=n.attributes.position;if(null!==s)for(var u=Math.max(0,A.start),c=Math.min(s.count,A.start+A.count);u<c;u++){var d=s.getX(u);E_.fromBufferAttribute(l,d),M_(E_,d,o,r,e,t,this)}else for(var h=Math.max(0,A.start),f=Math.min(l.count,A.start+A.count);h<f;h++)E_.fromBufferAttribute(l,h),M_(E_,h,o,r,e,t,this)}}},{key:"updateMorphTargets",value:function(){var e=this.geometry.morphAttributes,t=Object.keys(e);if(t.length>0){var n=e[t[0]];if(void 0!==n){this.morphTargetInfluences=[],this.morphTargetDictionary={};for(var r=0,i=n.length;r<i;r++){var A=n[r].name||String(r);this.morphTargetInfluences.push(0),this.morphTargetDictionary[A]=r}}}}}]),n}(mv);function M_(e,t,n,r,i,A,a){var o=C_.distanceSqToPoint(e);if(o<n){var s=new gm;C_.closestPointToPoint(e,s),s.applyMatrix4(r);var l=i.ray.origin.distanceTo(s);if(l<i.near||l>i.far)return;A.push({distance:l,distanceToRay:Math.sqrt(o),point:s,index:t,face:null,object:a})}}var F_=function(e){C(n,e);var t=M(n);function n(e){var r;return B(this,n),(r=t.call(this)).isMeshPhongMaterial=!0,r.type="MeshPhongMaterial",r.color=new Am(16777215),r.specular=new Am(1118481),r.shininess=30,r.map=null,r.lightMap=null,r.lightMapIntensity=1,r.aoMap=null,r.aoMapIntensity=1,r.emissive=new Am(0),r.emissiveIntensity=1,r.emissiveMap=null,r.bumpMap=null,r.bumpScale=1,r.normalMap=null,r.normalMapType=yg,r.normalScale=new zg(1,1),r.displacementMap=null,r.displacementScale=1,r.displacementBias=0,r.specularMap=null,r.alphaMap=null,r.envMap=null,r.combine=Of,r.reflectivity=1,r.refractionRatio=.98,r.wireframe=!1,r.wireframeLinewidth=1,r.wireframeLinecap="round",r.wireframeLinejoin="round",r.flatShading=!1,r.fog=!0,r.setValues(e),r}return b(n,[{key:"copy",value:function(e){return jc(S(n.prototype),"copy",this).call(this,e),this.color.copy(e.color),this.specular.copy(e.specular),this.shininess=e.shininess,this.map=e.map,this.lightMap=e.lightMap,this.lightMapIntensity=e.lightMapIntensity,this.aoMap=e.aoMap,this.aoMapIntensity=e.aoMapIntensity,this.emissive.copy(e.emissive),this.emissiveMap=e.emissiveMap,this.emissiveIntensity=e.emissiveIntensity,this.bumpMap=e.bumpMap,this.bumpScale=e.bumpScale,this.normalMap=e.normalMap,this.normalMapType=e.normalMapType,this.normalScale.copy(e.normalScale),this.displacementMap=e.displacementMap,this.displacementScale=e.displacementScale,this.displacementBias=e.displacementBias,this.specularMap=e.specularMap,this.alphaMap=e.alphaMap,this.envMap=e.envMap,this.combine=e.combine,this.reflectivity=e.reflectivity,this.refractionRatio=e.refractionRatio,this.wireframe=e.wireframe,this.wireframeLinewidth=e.wireframeLinewidth,this.wireframeLinecap=e.wireframeLinecap,this.wireframeLinejoin=e.wireframeLinejoin,this.flatShading=e.flatShading,this.fog=e.fog,this}}]),n}(Fv);function T_(e,t,n){return Q_(e)?new e.constructor(e.subarray(t,void 0!==n?n:e.length)):e.slice(t,n)}function k_(e,t,n){return!e||!n&&e.constructor===t?e:"number"===typeof t.BYTES_PER_ELEMENT?new t(e):Array.prototype.slice.call(e)}function Q_(e){return ArrayBuffer.isView(e)&&!(e instanceof DataView)}var L_=function(){function e(t,n,r,i){B(this,e),this.parameterPositions=t,this._cachedIndex=0,this.resultBuffer=void 0!==i?i:new n.constructor(r),this.sampleValues=n,this.valueSize=r,this.settings=null,this.DefaultSettings_={}}return b(e,[{key:"evaluate",value:function(e){var t=this.parameterPositions,n=this._cachedIndex,r=t[n],i=t[n-1];e:{t:{var A;n:{r:if(!(e<r)){for(var a=n+2;;){if(void 0===r){if(e<i)break r;return n=t.length,this._cachedIndex=n,this.copySampleValue_(n-1)}if(n===a)break;if(i=r,e<(r=t[++n]))break t}A=t.length;break n}if(e>=i)break e;var o=t[1];e<o&&(n=2,i=o);for(var s=n-2;;){if(void 0===i)return this._cachedIndex=0,this.copySampleValue_(0);if(n===s)break;if(r=i,e>=(i=t[--n-1]))break 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this.values.length/this.times.length}},{key:"shift",value:function(e){if(0!==e)for(var t=this.times,n=0,r=t.length;n!==r;++n)t[n]+=e;return this}},{key:"scale",value:function(e){if(1!==e)for(var t=this.times,n=0,r=t.length;n!==r;++n)t[n]*=e;return this}},{key:"trim",value:function(e,t){for(var n=this.times,r=n.length,i=0,A=r-1;i!==r&&n[i]<e;)++i;for(;-1!==A&&n[A]>t;)--A;if(++A,0!==i||A!==r){i>=A&&(i=(A=Math.max(A,1))-1);var a=this.getValueSize();this.times=T_(n,i,A),this.values=T_(this.values,i*a,A*a)}return this}},{key:"validate",value:function(){var e=!0,t=this.getValueSize();t-Math.floor(t)!==0&&(console.error("THREE.KeyframeTrack: Invalid value size in track.",this),e=!1);var n=this.times,r=this.values,i=n.length;0===i&&(console.error("THREE.KeyframeTrack: Track is empty.",this),e=!1);for(var A=null,a=0;a!==i;a++){var o=n[a];if("number"===typeof o&&isNaN(o)){console.error("THREE.KeyframeTrack: Time is not a valid 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n.createInterpolant=this.createInterpolant,n}}],[{key:"toJSON",value:function(e){var t,n=e.constructor;if(n.toJSON!==this.toJSON)t=n.toJSON(e);else{t={name:e.name,times:k_(e.times,Array),values:k_(e.values,Array)};var r=e.getInterpolation();r!==e.DefaultInterpolation&&(t.interpolation=r)}return t.type=e.ValueTypeName,t}}]),e}();H_.prototype.TimeBufferType=Float32Array,H_.prototype.ValueBufferType=Float32Array,H_.prototype.DefaultInterpolation=ug;var P_=function(e){C(n,e);var t=M(n);function n(){return B(this,n),t.apply(this,arguments)}return b(n)}(H_);P_.prototype.ValueTypeName="bool",P_.prototype.ValueBufferType=Array,P_.prototype.DefaultInterpolation=lg,P_.prototype.InterpolantFactoryMethodLinear=void 0,P_.prototype.InterpolantFactoryMethodSmooth=void 0;var N_=function(e){C(n,e);var t=M(n);function n(){return B(this,n),t.apply(this,arguments)}return b(n)}(H_);N_.prototype.ValueTypeName="color";var O_=function(e){C(n,e);var t=M(n);function n(){return 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zg(512,512),this.map=null,this.mapPass=null,this.matrix=new Wm,this.autoUpdate=!0,this.needsUpdate=!1,this._frustum=new Uy,this._frameExtents=new zg(1,1),this._viewportCount=1,this._viewports=[new cm(0,0,1,1)]}return b(e,[{key:"getViewportCount",value:function(){return this._viewportCount}},{key:"getFrustum",value:function(){return this._frustum}},{key:"updateMatrices",value:function(e){var t=this.camera,n=this.matrix;ib.setFromMatrixPosition(e.matrixWorld),t.position.copy(ib),Ab.setFromMatrixPosition(e.target.matrixWorld),t.lookAt(Ab),t.updateMatrixWorld(),rb.multiplyMatrices(t.projectionMatrix,t.matrixWorldInverse),this._frustum.setFromProjectionMatrix(rb),n.set(.5,0,0,.5,0,.5,0,.5,0,0,.5,.5,0,0,0,1),n.multiply(rb)}},{key:"getViewport",value:function(e){return this._viewports[e]}},{key:"getFrameExtents",value:function(){return this._frameExtents}},{key:"dispose",value:function(){this.map&&this.map.dispose(),this.mapPass&&this.mapPass.dispose()}},{key:"copy",value:function(e){return this.camera=e.camera.clone(),this.bias=e.bias,this.radius=e.radius,this.mapSize.copy(e.mapSize),this}},{key:"clone",value:function(){return(new this.constructor).copy(this)}},{key:"toJSON",value:function(){var e={};return 0!==this.bias&&(e.bias=this.bias),0!==this.normalBias&&(e.normalBias=this.normalBias),1!==this.radius&&(e.radius=this.radius),512===this.mapSize.x&&512===this.mapSize.y||(e.mapSize=this.mapSize.toArray()),e.camera=this.camera.toJSON(!1).object,delete e.camera.matrix,e}}]),e}(),ob=function(e){C(n,e);var t=M(n);function n(){var e;return B(this,n),(e=t.call(this,new Vy(-5,5,5,-5,.5,500))).isDirectionalLightShadow=!0,e}return b(n)}(ab),sb=function(e){C(n,e);var t=M(n);function n(e,r){var i;return B(this,n),(i=t.call(this,e,r)).isDirectionalLight=!0,i.type="DirectionalLight",i.position.copy(mv.DEFAULT_UP),i.updateMatrix(),i.target=new mv,i.shadow=new ob,i}return b(n,[{key:"dispose",value:function(){this.shadow.dispose()}},{key:"copy",value:function(e){return jc(S(n.prototype),"copy",this).call(this,e),this.target=e.target.clone(),this.shadow=e.shadow.clone(),this}}]),n}(nb),lb=function(){function e(){var t=!(arguments.length>0&&void 0!==arguments[0])||arguments[0];B(this,e),this.autoStart=t,this.startTime=0,this.oldTime=0,this.elapsedTime=0,this.running=!1}return b(e,[{key:"start",value:function(){this.startTime=ub(),this.oldTime=this.startTime,this.elapsedTime=0,this.running=!0}},{key:"stop",value:function(){this.getElapsedTime(),this.running=!1,this.autoStart=!1}},{key:"getElapsedTime",value:function(){return this.getDelta(),this.elapsedTime}},{key:"getDelta",value:function(){var e=0;if(this.autoStart&&!this.running)return this.start(),0;if(this.running){var t=ub();e=(t-this.oldTime)/1e3,this.oldTime=t,this.elapsedTime+=e}return e}}]),e}();function ub(){return("undefined"===typeof performance?Date:performance).now()}var cb="\\[\\]\\.:\\/",db=new RegExp("["+cb+"]","g"),hb="[^"+cb+"]",fb="[^"+cb.replace("\\.","")+"]",pb=new RegExp("^"+/((?:WC+[\/:])*)/.source.replace("WC",hb)+/(WCOD+)?/.source.replace("WCOD",fb)+/(?:\.(WC+)(?:\[(.+)\])?)?/.source.replace("WC",hb)+/\.(WC+)(?:\[(.+)\])?/.source.replace("WC",hb)+"$"),gb=["material","materials","bones","map"],mb=function(){function e(t,n,r){B(this,e);var i=r||vb.parseTrackName(n);this._targetGroup=t,this._bindings=t.subscribe_(n,i)}return b(e,[{key:"getValue",value:function(e,t){this.bind();var n=this._targetGroup.nCachedObjects_,r=this._bindings[n];void 0!==r&&r.getValue(e,t)}},{key:"setValue",value:function(e,t){for(var n=this._bindings,r=this._targetGroup.nCachedObjects_,i=n.length;r!==i;++r)n[r].setValue(e,t)}},{key:"bind",value:function(){for(var e=this._bindings,t=this._targetGroup.nCachedObjects_,n=e.length;t!==n;++t)e[t].bind()}},{key:"unbind",value:function(){for(var e=this._bindings,t=this._targetGroup.nCachedObjects_,n=e.length;t!==n;++t)e[t].unbind()}}]),e}(),vb=function(){function e(t,n,r){B(this,e),this.path=n,this.parsedPath=r||e.parseTrackName(n),this.node=e.findNode(t,this.parsedPath.nodeName)||t,this.rootNode=t,this.getValue=this._getValue_unbound,this.setValue=this._setValue_unbound}return b(e,[{key:"_getValue_unavailable",value:function(){}},{key:"_setValue_unavailable",value:function(){}},{key:"_getValue_direct",value:function(e,t){e[t]=this.targetObject[this.propertyName]}},{key:"_getValue_array",value:function(e,t){for(var n=this.resolvedProperty,r=0,i=n.length;r!==i;++r)e[t++]=n[r]}},{key:"_getValue_arrayElement",value:function(e,t){e[t]=this.resolvedProperty[this.propertyIndex]}},{key:"_getValue_toArray",value:function(e,t){this.resolvedProperty.toArray(e,t)}},{key:"_setValue_direct",value:function(e,t){this.targetObject[this.propertyName]=e[t]}},{key:"_setValue_direct_setNeedsUpdate",value:function(e,t){this.targetObject[this.propertyName]=e[t],this.targetObject.needsUpdate=!0}},{key:"_setValue_direct_setMatrixWorldNeedsUpdate",value:function(e,t){this.targetObject[this.propertyName]=e[t],this.targetObject.matrixWorldNeedsUpdate=!0}},{key:"_setValue_array",value:function(e,t){for(var n=this.resolvedProperty,r=0,i=n.length;r!==i;++r)n[r]=e[t++]}},{key:"_setValue_array_setNeedsUpdate",value:function(e,t){for(var n=this.resolvedProperty,r=0,i=n.length;r!==i;++r)n[r]=e[t++];this.targetObject.needsUpdate=!0}},{key:"_setValue_array_setMatrixWorldNeedsUpdate",value:function(e,t){for(var n=this.resolvedProperty,r=0,i=n.length;r!==i;++r)n[r]=e[t++];this.targetObject.matrixWorldNeedsUpdate=!0}},{key:"_setValue_arrayElement",value:function(e,t){this.resolvedProperty[this.propertyIndex]=e[t]}},{key:"_setValue_arrayElement_setNeedsUpdate",value:function(e,t){this.resolvedProperty[this.propertyIndex]=e[t],this.targetObject.needsUpdate=!0}},{key:"_setValue_arrayElement_setMatrixWorldNeedsUpdate",value:function(e,t){this.resolvedProperty[this.propertyIndex]=e[t],this.targetObject.matrixWorldNeedsUpdate=!0}},{key:"_setValue_fromArray",value:function(e,t){this.resolvedProperty.fromArray(e,t)}},{key:"_setValue_fromArray_setNeedsUpdate",value:function(e,t){this.resolvedProperty.fromArray(e,t),this.targetObject.needsUpdate=!0}},{key:"_setValue_fromArray_setMatrixWorldNeedsUpdate",value:function(e,t){this.resolvedProperty.fromArray(e,t),this.targetObject.matrixWorldNeedsUpdate=!0}},{key:"_getValue_unbound",value:function(e,t){this.bind(),this.getValue(e,t)}},{key:"_setValue_unbound",value:function(e,t){this.bind(),this.setValue(e,t)}},{key:"bind",value:function(){var t=this.node,n=this.parsedPath,r=n.objectName,i=n.propertyName,A=n.propertyIndex;if(t||(t=e.findNode(this.rootNode,n.nodeName)||this.rootNode,this.node=t),this.getValue=this._getValue_unavailable,this.setValue=this._setValue_unavailable,t){if(r){var a=n.objectIndex;switch(r){case"materials":if(!t.material)return void console.error("THREE.PropertyBinding: Can not bind to material as node does not have a material.",this);if(!t.material.materials)return void console.error("THREE.PropertyBinding: Can not bind to material.materials as node.material does not have a materials array.",this);t=t.material.materials;break;case"bones":if(!t.skeleton)return void console.error("THREE.PropertyBinding: Can not bind to bones as node does not have a skeleton.",this);t=t.skeleton.bones;for(var o=0;o<t.length;o++)if(t[o].name===a){a=o;break}break;case"map":if("map"in t){t=t.map;break}if(!t.material)return void console.error("THREE.PropertyBinding: Can not bind to material as node does not have a material.",this);if(!t.material.map)return void console.error("THREE.PropertyBinding: Can not bind to material.map as node.material does not have a map.",this);t=t.material.map;break;default:if(void 0===t[r])return void console.error("THREE.PropertyBinding: Can not bind to objectName of node undefined.",this);t=t[r]}if(void 0!==a){if(void 0===t[a])return void console.error("THREE.PropertyBinding: Trying to bind to objectIndex of objectName, but is undefined.",this,t);t=t[a]}}var s=t[i];if(void 0!==s){var l=this.Versioning.None;this.targetObject=t,void 0!==t.needsUpdate?l=this.Versioning.NeedsUpdate:void 0!==t.matrixWorldNeedsUpdate&&(l=this.Versioning.MatrixWorldNeedsUpdate);var u=this.BindingType.Direct;if(void 0!==A){if("morphTargetInfluences"===i){if(!t.geometry)return void console.error("THREE.PropertyBinding: Can not bind to morphTargetInfluences because node does not have a geometry.",this);if(!t.geometry.morphAttributes)return void console.error("THREE.PropertyBinding: Can not bind to morphTargetInfluences because node does not have a geometry.morphAttributes.",this);void 0!==t.morphTargetDictionary[A]&&(A=t.morphTargetDictionary[A])}u=this.BindingType.ArrayElement,this.resolvedProperty=s,this.propertyIndex=A}else void 0!==s.fromArray&&void 0!==s.toArray?(u=this.BindingType.HasFromToArray,this.resolvedProperty=s):Array.isArray(s)?(u=this.BindingType.EntireArray,this.resolvedProperty=s):this.propertyName=i;this.getValue=this.GetterByBindingType[u],this.setValue=this.SetterByBindingTypeAndVersioning[u][l]}else{var c=n.nodeName;console.error("THREE.PropertyBinding: Trying to update property for track: "+c+"."+i+" but it wasn't found.",t)}}else console.error("THREE.PropertyBinding: Trying to update node for track: "+this.path+" but it wasn't found.")}},{key:"unbind",value:function(){this.node=null,this.getValue=this._getValue_unbound,this.setValue=this._setValue_unbound}}],[{key:"create",value:function(t,n,r){return t&&t.isAnimationObjectGroup?new e.Composite(t,n,r):new e(t,n,r)}},{key:"sanitizeNodeName",value:function(e){return e.replace(/\s/g,"_").replace(db,"")}},{key:"parseTrackName",value:function(e){var t=pb.exec(e);if(null===t)throw new Error("PropertyBinding: Cannot parse trackName: "+e);var n={nodeName:t[2],objectName:t[3],objectIndex:t[4],propertyName:t[5],propertyIndex:t[6]},r=n.nodeName&&n.nodeName.lastIndexOf(".");if(void 0!==r&&-1!==r){var i=n.nodeName.substring(r+1);-1!==gb.indexOf(i)&&(n.nodeName=n.nodeName.substring(0,r),n.objectName=i)}if(null===n.propertyName||0===n.propertyName.length)throw new Error("PropertyBinding: can not parse propertyName from trackName: "+e);return n}},{key:"findNode",value:function(e,t){if(void 0===t||""===t||"."===t||-1===t||t===e.name||t===e.uuid)return e;if(e.skeleton){var n=e.skeleton.getBoneByName(t);if(void 0!==n)return n}if(e.children){var r=function e(n){for(var r=0;r<n.length;r++){var i=n[r];if(i.name===t||i.uuid===t)return i;var A=e(i.children);if(A)return A}return null}(e.children);if(r)return r}return null}}]),e}();vb.Composite=mb,vb.prototype.BindingType={Direct:0,EntireArray:1,ArrayElement:2,HasFromToArray:3},vb.prototype.Versioning={None:0,NeedsUpdate:1,MatrixWorldNeedsUpdate:2},vb.prototype.GetterByBindingType=[vb.prototype._getValue_direct,vb.prototype._getValue_array,vb.prototype._getValue_arrayElement,vb.prototype._getValue_toArray],vb.prototype.SetterByBindingTypeAndVersioning=[[vb.prototype._setValue_direct,vb.prototype._setValue_direct_setNeedsUpdate,vb.prototype._setValue_direct_setMatrixWorldNeedsUpdate],[vb.prototype._setValue_array,vb.prototype._setValue_array_setNeedsUpdate,vb.prototype._setValue_array_setMatrixWorldNeedsUpdate],[vb.prototype._setValue_arrayElement,vb.prototype._setValue_arrayElement_setNeedsUpdate,vb.prototype._setValue_arrayElement_setMatrixWorldNeedsUpdate],[vb.prototype._setValue_fromArray,vb.prototype._setValue_fromArray_setNeedsUpdate,vb.prototype._setValue_fromArray_setMatrixWorldNeedsUpdate]];new Float32Array(1);var yb=wb();function wb(){for(var e=new ArrayBuffer(4),t=new Float32Array(e),n=new Uint32Array(e),r=new Uint32Array(512),i=new Uint32Array(512),A=0;A<256;++A){var a=A-127;a<-27?(r[A]=0,r[256|A]=32768,i[A]=24,i[256|A]=24):a<-14?(r[A]=1024>>-a-14,r[256|A]=1024>>-a-14|32768,i[A]=-a-1,i[256|A]=-a-1):a<=15?(r[A]=a+15<<10,r[256|A]=a+15<<10|32768,i[A]=13,i[256|A]=13):a<128?(r[A]=31744,r[256|A]=64512,i[A]=24,i[256|A]=24):(r[A]=31744,r[256|A]=64512,i[A]=13,i[256|A]=13)}for(var o=new Uint32Array(2048),s=new Uint32Array(64),l=new Uint32Array(64),u=1;u<1024;++u){for(var c=u<<13,d=0;0===(8388608&c);)c<<=1,d-=8388608;c&=-8388609,d+=947912704,o[u]=c|d}for(var h=1024;h<2048;++h)o[h]=939524096+(h-1024<<13);for(var f=1;f<31;++f)s[f]=f<<23;s[31]=1199570944,s[32]=2147483648;for(var p=33;p<63;++p)s[p]=2147483648+(p-32<<23);s[63]=3347054592;for(var g=1;g<64;++g)32!==g&&(l[g]=1024);return{floatView:t,uint32View:n,baseTable:r,shiftTable:i,mantissaTable:o,exponentTable:s,offsetTable:l}}var Bb=Object.freeze({__proto__:null,fromHalfFloat:function(e){var t=e>>10;return yb.uint32View[0]=yb.mantissaTable[yb.offsetTable[t]+(1023&e)]+yb.exponentTable[t],yb.floatView[0]},toHalfFloat:function(e){Math.abs(e)>65504&&console.warn("THREE.DataUtils.toHalfFloat(): Value out of range."),e=Lg(e,-65504,65504),yb.floatView[0]=e;var t=yb.uint32View[0],n=t>>23&511;return yb.baseTable[n]+((8388607&t)>>yb.shiftTable[n])}});"undefined"!==typeof __THREE_DEVTOOLS__&&__THREE_DEVTOOLS__.dispatchEvent(new CustomEvent("register",{detail:{revision:ef}})),"undefined"!==typeof window&&(window.__THREE__?console.warn("WARNING: Multiple instances of Three.js being imported."):window.__THREE__=ef);var _b=/^[og]\s*(.+)?/,bb=/^mtllib /,xb=/^usemtl /,Cb=/^usemap /,Sb=/\s+/,Eb=new gm,Ub=new gm,Mb=new gm,Fb=new gm,Tb=new gm,kb=new Am;function Qb(){var e={objects:[],object:{},vertices:[],normals:[],colors:[],uvs:[],materials:{},materialLibraries:[],startObject:function(e,t){if(this.object&&!1===this.object.fromDeclaration)return this.object.name=e,void(this.object.fromDeclaration=!1!==t);var n=this.object&&"function"===typeof this.object.currentMaterial?this.object.currentMaterial():void 0;if(this.object&&"function"===typeof this.object._finalize&&this.object._finalize(!0),this.object={name:e||"",fromDeclaration:!1!==t,geometry:{vertices:[],normals:[],colors:[],uvs:[],hasUVIndices:!1},materials:[],smooth:!0,startMaterial:function(e,t){var n=this._finalize(!1);n&&(n.inherited||n.groupCount<=0)&&this.materials.splice(n.index,1);var r={index:this.materials.length,name:e||"",mtllib:Array.isArray(t)&&t.length>0?t[t.length-1]:"",smooth:void 0!==n?n.smooth:this.smooth,groupStart:void 0!==n?n.groupEnd:0,groupEnd:-1,groupCount:-1,inherited:!1,clone:function(e){var t={index:"number"===typeof e?e:this.index,name:this.name,mtllib:this.mtllib,smooth:this.smooth,groupStart:0,groupEnd:-1,groupCount:-1,inherited:!1};return t.clone=this.clone.bind(t),t}};return this.materials.push(r),r},currentMaterial:function(){if(this.materials.length>0)return this.materials[this.materials.length-1]},_finalize:function(e){var t=this.currentMaterial();if(t&&-1===t.groupEnd&&(t.groupEnd=this.geometry.vertices.length/3,t.groupCount=t.groupEnd-t.groupStart,t.inherited=!1),e&&this.materials.length>1)for(var n=this.materials.length-1;n>=0;n--)this.materials[n].groupCount<=0&&this.materials.splice(n,1);return e&&0===this.materials.length&&this.materials.push({name:"",smooth:this.smooth}),t}},n&&n.name&&"function"===typeof n.clone){var r=n.clone(0);r.inherited=!0,this.object.materials.push(r)}this.objects.push(this.object)},finalize:function(){this.object&&"function"===typeof this.object._finalize&&this.object._finalize(!0)},parseVertexIndex:function(e,t){var n=parseInt(e,10);return 3*(n>=0?n-1:n+t/3)},parseNormalIndex:function(e,t){var n=parseInt(e,10);return 3*(n>=0?n-1:n+t/3)},parseUVIndex:function(e,t){var n=parseInt(e,10);return 2*(n>=0?n-1:n+t/2)},addVertex:function(e,t,n){var r=this.vertices,i=this.object.geometry.vertices;i.push(r[e+0],r[e+1],r[e+2]),i.push(r[t+0],r[t+1],r[t+2]),i.push(r[n+0],r[n+1],r[n+2])},addVertexPoint:function(e){var t=this.vertices;this.object.geometry.vertices.push(t[e+0],t[e+1],t[e+2])},addVertexLine:function(e){var t=this.vertices;this.object.geometry.vertices.push(t[e+0],t[e+1],t[e+2])},addNormal:function(e,t,n){var r=this.normals,i=this.object.geometry.normals;i.push(r[e+0],r[e+1],r[e+2]),i.push(r[t+0],r[t+1],r[t+2]),i.push(r[n+0],r[n+1],r[n+2])},addFaceNormal:function(e,t,n){var r=this.vertices,i=this.object.geometry.normals;Eb.fromArray(r,e),Ub.fromArray(r,t),Mb.fromArray(r,n),Tb.subVectors(Mb,Ub),Fb.subVectors(Eb,Ub),Tb.cross(Fb),Tb.normalize(),i.push(Tb.x,Tb.y,Tb.z),i.push(Tb.x,Tb.y,Tb.z),i.push(Tb.x,Tb.y,Tb.z)},addColor:function(e,t,n){var r=this.colors,i=this.object.geometry.colors;void 0!==r[e]&&i.push(r[e+0],r[e+1],r[e+2]),void 0!==r[t]&&i.push(r[t+0],r[t+1],r[t+2]),void 0!==r[n]&&i.push(r[n+0],r[n+1],r[n+2])},addUV:function(e,t,n){var r=this.uvs,i=this.object.geometry.uvs;i.push(r[e+0],r[e+1]),i.push(r[t+0],r[t+1]),i.push(r[n+0],r[n+1])},addDefaultUV:function(){var e=this.object.geometry.uvs;e.push(0,0),e.push(0,0),e.push(0,0)},addUVLine:function(e){var t=this.uvs;this.object.geometry.uvs.push(t[e+0],t[e+1])},addFace:function(e,t,n,r,i,A,a,o,s){var l=this.vertices.length,u=this.parseVertexIndex(e,l),c=this.parseVertexIndex(t,l),d=this.parseVertexIndex(n,l);if(this.addVertex(u,c,d),this.addColor(u,c,d),void 0!==a&&""!==a){var h=this.normals.length;u=this.parseNormalIndex(a,h),c=this.parseNormalIndex(o,h),d=this.parseNormalIndex(s,h),this.addNormal(u,c,d)}else this.addFaceNormal(u,c,d);if(void 0!==r&&""!==r){var f=this.uvs.length;u=this.parseUVIndex(r,f),c=this.parseUVIndex(i,f),d=this.parseUVIndex(A,f),this.addUV(u,c,d),this.object.geometry.hasUVIndices=!0}else this.addDefaultUV()},addPointGeometry:function(e){this.object.geometry.type="Points";for(var t=this.vertices.length,n=0,r=e.length;n<r;n++){var i=this.parseVertexIndex(e[n],t);this.addVertexPoint(i),this.addColor(i)}},addLineGeometry:function(e,t){this.object.geometry.type="Line";for(var n=this.vertices.length,r=this.uvs.length,i=0,A=e.length;i<A;i++)this.addVertexLine(this.parseVertexIndex(e[i],n));for(var a=0,o=t.length;a<o;a++)this.addUVLine(this.parseUVIndex(t[a],r))}};return e.startObject("",!1),e}var Lb=function(e){C(n,e);var t=M(n);function n(e){var r;return B(this,n),(r=t.call(this,e)).materials=null,r}return b(n,[{key:"load",value:function(e,t,n,r){var i=this,A=new Z_(this.manager);A.setPath(this.path),A.setRequestHeader(this.requestHeader),A.setWithCredentials(this.withCredentials),A.load(e,(function(n){try{t(i.parse(n))}catch(A){r?r(A):console.error(A),i.manager.itemError(e)}}),n,r)}},{key:"setMaterials",value:function(e){return this.materials=e,this}},{key:"parse",value:function(e){var t=new Qb;-1!==e.indexOf("\r\n")&&(e=e.replace(/\r\n/g,"\n")),-1!==e.indexOf("\\\n")&&(e=e.replace(/\\\n/g,""));for(var n=e.split("\n"),r=[],i=0,A=n.length;i<A;i++){var a=n[i].trimStart();if(0!==a.length){var o=a.charAt(0);if("#"!==o)if("v"===o){var s=a.split(Sb);switch(s[0]){case"v":t.vertices.push(parseFloat(s[1]),parseFloat(s[2]),parseFloat(s[3])),s.length>=7?(kb.setRGB(parseFloat(s[4]),parseFloat(s[5]),parseFloat(s[6])).convertSRGBToLinear(),t.colors.push(kb.r,kb.g,kb.b)):t.colors.push(void 0,void 0,void 0);break;case"vn":t.normals.push(parseFloat(s[1]),parseFloat(s[2]),parseFloat(s[3]));break;case"vt":t.uvs.push(parseFloat(s[1]),parseFloat(s[2]))}}else if("f"===o){for(var l=a.slice(1).trim().split(Sb),u=[],c=0,d=l.length;c<d;c++){var h=l[c];if(h.length>0){var f=h.split("/");u.push(f)}}for(var p=u[0],g=1,m=u.length-1;g<m;g++){var v=u[g],y=u[g+1];t.addFace(p[0],v[0],y[0],p[1],v[1],y[1],p[2],v[2],y[2])}}else if("l"===o){var w=a.substring(1).trim().split(" "),B=[],_=[];if(-1===a.indexOf("/"))B=w;else for(var b=0,x=w.length;b<x;b++){var C=w[b].split("/");""!==C[0]&&B.push(C[0]),""!==C[1]&&_.push(C[1])}t.addLineGeometry(B,_)}else if("p"===o){var S=a.slice(1).trim().split(" ");t.addPointGeometry(S)}else if(null!==(r=_b.exec(a))){var E=(" "+r[0].slice(1).trim()).slice(1);t.startObject(E)}else if(xb.test(a))t.object.startMaterial(a.substring(7).trim(),t.materialLibraries);else if(bb.test(a))t.materialLibraries.push(a.substring(7).trim());else if(Cb.test(a))console.warn('THREE.OBJLoader: Rendering identifier "usemap" not supported. Textures must be defined in MTL files.');else if("s"===o){if((r=a.split(" ")).length>1){var U=r[1].trim().toLowerCase();t.object.smooth="0"!==U&&"off"!==U}else t.object.smooth=!0;var M=t.object.currentMaterial();M&&(M.smooth=t.object.smooth)}else{if("\0"===a)continue;console.warn('THREE.OBJLoader: Unexpected line: "'+a+'"')}}}t.finalize();var F=new n_;if(F.materialLibraries=[].concat(t.materialLibraries),!0===!(1===t.objects.length&&0===t.objects[0].geometry.vertices.length))for(var T=0,k=t.objects.length;T<k;T++){var Q=t.objects[T],L=Q.geometry,D=Q.materials,I="Line"===L.type,R="Points"===L.type,H=!1;if(0!==L.vertices.length){var P=new Kv;P.setAttribute("position",new Rv(L.vertices,3)),L.normals.length>0&&P.setAttribute("normal",new Rv(L.normals,3)),L.colors.length>0&&(H=!0,P.setAttribute("color",new Rv(L.colors,3))),!0===L.hasUVIndices&&P.setAttribute("uv",new Rv(L.uvs,2));for(var N=[],O=0,V=D.length;O<V;O++){var 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A}(A.subarray(A.pos),o,s);if(-1!==l){var u,c,d;switch(this.type){case mp:d=l.length/4;for(var h=new Float32Array(4*d),f=0;f<d;f++)r(l,4*f,h,4*f);u=h,c=mp;break;case vp:d=l.length/4;for(var p=new Uint16Array(4*d),g=0;g<d;g++)i(l,4*g,p,4*g);u=p,c=vp;break;default:console.error("THREE.RGBELoader: unsupported type: ",this.type)}return{width:o,height:s,data:u,header:a.string,gamma:a.gamma,exposure:a.exposure,type:c}}}return null}},{key:"setDataType",value:function(e){return this.type=e,this}},{key:"load",value:function(e,t,r,i){return jc(S(n.prototype),"load",this).call(this,e,(function(e,n){switch(e.type){case mp:case vp:e.encoding=pg,e.minFilter=op,e.magFilter=op,e.generateMipmaps=!1,e.flipY=!0}t&&t(e,n)}),r,i)}}]),n}(eb),Ib={get:function(e){return Ib.getInclude("include_common")+Ib[e+".glsl"]},getInclude:function(e){return Ib[e+".glsl"]+"\n"},"include_common.glsl":"\nvec4 encodeHDRE(vec3 color)\n{\n#ifdef HDRE\n    float maxValue = max(max(color.r, color.g), color.b) + .01;\n    float e = floor(max(log(maxValue), 0.0));\n    color /= exp(e);\n    return vec4(color, e / 5.0);\n#else\n    return vec4(color, 1.0);\n#endif\n}\n\nvec3 decodeHDRE(vec4 hdre)\n{\n#ifdef HDRE\n    return hdre.rgb * exp(hdre.a * 255.0 - 128.0);\n#else\n    return hdre.xyz;\n#endif\n}\n\nfloat luminance(vec3 color)\n{\n    return dot(color, vec3(.30, 0.59, .11));\n}\n\nfloat luminance(vec4 color)\n{\n    return luminance(color.xyz);\n}\n\nfloat linearStep(float lower, float upper, float x)\n{\n    return clamp((x - lower) / (upper - lower), 0.0, 1.0);\n}\n\n// Only for 0 - 1\nvec4 floatToRGBA8(float value)\n{\n    vec4 enc = value * vec4(1.0, 255.0, 65025.0, 16581375.0);\n    // cannot fract first value or 1 would not be encodable\n    enc.yzw = fract(enc.yzw);\n    return enc - enc.yzww * vec4(1.0/255.0, 1.0/255.0, 1.0/255.0, 0.0);\n}\n\nfloat RGBA8ToFloat(vec4 rgba)\n{\n    return dot(rgba, vec4(1.0, 1.0/255.0, 1.0/65025.0, 1.0/16581375.0));\n}\n\nvec2 floatToRG8(float value)\n{\n    vec2 enc = vec2(1.0, 255.0) * value;\n    enc.y = fract(enc.y);\n    enc.x -= enc.y / 255.0;\n    return enc;\n}\n\nfloat RG8ToFloat(vec2 rg)\n{\n    return dot(rg, vec2(1.0, 1.0/255.0));\n}\n\nvec3 intersectCubeMap(vec3 rayOrigin, vec3 rayDir, float cubeSize)\n{\n    vec3 t = (cubeSize * sign(rayDir) - rayOrigin) / rayDir;\n    float minT = min(min(t.x, t.y), t.z);\n    return rayOrigin + minT * rayDir;\n}\n\nvec4 sampleLatLong(sampler2D tex, vec3 uvw)\n{\n    vec2 uv;\n    uv.x = atan(uvw.z, uvw.x) / 6.283184 + 0.5;\n    uv.y = acos(-uvw.y) / 3.141592;\n    return texture2D(tex, uv);\n}\n","include_beckmann.glsl":"\nstruct PointLight {\n    vec3 position;\n    vec3 color;\n    float distance;\n    float decay;\n};\n\nstruct DirectionalLight {\n    vec3 direction;\n    vec3 color;\n};\n\n#if NUM_POINT_LIGHTS > 0\nuniform PointLight pointLights[ NUM_POINT_LIGHTS ];\n#endif\n\n#if NUM_DIR_LIGHTS > 0\nuniform DirectionalLight directionalLights[ NUM_DIR_LIGHTS ];\n#endif\n\n\nfloat beckmann_geometryTerm(vec3 normal, vec3 dir, float k)\n{\n    float d = max(dot(normal, dir), 0.0);\n    return d / (d * (1.0 - k) + k);\n}\n\n// schlick-beckman\nfloat beckmann_lightVisibility(vec3 normal, vec3 viewDir, vec3 lightDir, float roughness)\n{\n    float k = roughness + 1.0;\n    k = k * k * .125;\n    return beckmann_geometryTerm(normal, viewDir, k) * beckmann_geometryTerm(normal, lightDir, k);\n}\n\nfloat beckmann_distribution(float roughness, vec3 normal, vec3 halfVector)\n{\n    float roughSqr = roughness*roughness;\n    float halfDotNormal = max(dot(halfVector, normal), 0.00001);\n    float a = roughSqr * pow(halfDotNormal, 4.0);\n    float b = (halfDotNormal * halfDotNormal - 1.0) / (roughSqr * halfDotNormal * halfDotNormal);\n    return exp(b) / a;\n}\n\n// light dir is towards the light\n// view dir is toward the camera\nvoid beckmann_brdf(in vec3 normal, in float roughness, float normalSpecularReflectance, in vec3 lightDir, in vec3 viewDir, in vec3 viewPos, in vec3 lightColor, out vec3 diffuseLight, out vec3 specularLight)\n{\n    float nDotL = max(dot(lightDir, normal), 0.0);\n    vec3 irradiance = nDotL * lightColor;    // in fact irradiance / PI\n\n    vec3 halfVector = normalize(lightDir + viewDir);\n\n    float mappedRoughness =  roughness * roughness;\n\n    // adapted for 2-lobed skin\n    float distribution =\n        (beckmann_distribution(mappedRoughness *.8, normal, halfVector) * .05 +\n        beckmann_distribution(mappedRoughness, normal, halfVector)) * .95;\n\n    float halfDotLight = max(dot(halfVector, lightDir), 0.0);\n    float cosAngle = 1.0 - halfDotLight;\n    float fresnel = normalSpecularReflectance + (1.0 - normalSpecularReflectance) * pow(cosAngle, 5.0);\n\n    diffuseLight = irradiance;\n\n    specularLight = irradiance * fresnel * distribution;\n\n    specularLight *= beckmann_lightVisibility(normal, viewDir, lightDir, roughness);\n}\n\n// diffuse light only\nvec3 beckmann_getDiffuseLight(in vec3 normal, in vec3 viewPos, in float shadow)\n{\n    vec3 diffuseLight = vec3(0.0);\n\n#if NUM_DIR_LIGHTS > 0\n    for (int i = 0; i < NUM_DIR_LIGHTS; ++i) {\n        float nDotL = max(dot(directionalLights[i].direction, normal), 0.0);\n        diffuseLight += nDotL * directionalLights[i].color * shadow;\n    }\n#endif\n\n    return diffuseLight;\n}\n\nvoid beckmann_getLight(in vec3 normal, in float roughness, in float normalSpecularReflectance, in vec3 viewPos, in float shadow, out vec3 diffuseLight, out vec3 specularLight)\n{\n    vec3 viewDir = -normalize(viewPos);\n    diffuseLight = vec3(0.0);\n    specularLight = vec3(0.0);\n\n#if NUM_DIR_LIGHTS > 0\n    for (int i = 0; i < NUM_DIR_LIGHTS; ++i) {\n        vec3 diff;\n        vec3 spec;\n        beckmann_brdf(normal, roughness, normalSpecularReflectance, directionalLights[i].direction, viewDir, viewPos, directionalLights[i].color, diff, spec);\n        diffuseLight += diff * shadow;\n        specularLight += spec * shadow;\n    }\n#endif\n}\n","include_ggx.glsl":"\nstruct PointLight {\n    vec3 position;\n    vec3 color;\n    float distance;\n    float decay;\n};\n\nstruct DirectionalLight {\n    vec3 direction;\n    vec3 color;\n};\n\n#if NUM_POINT_LIGHTS > 0\nuniform PointLight pointLights[ NUM_POINT_LIGHTS ];\n#endif\n\n#if NUM_DIR_LIGHTS > 0\nuniform DirectionalLight directionalLights[ NUM_DIR_LIGHTS ];\n#endif\n\n\nfloat ggx_geometryTerm(vec3 normal, vec3 dir, float k)\n{\n    float d = max(dot(normal, dir), 0.0);\n    return d / (d * (1.0 - k) + k);\n}\n\n// schlick-beckman\nfloat ggx_lightVisibility(vec3 normal, vec3 viewDir, vec3 lightDir, float roughness)\n{\n    float k = roughness + 1.0;\n    k = k * k * .125;\n    return ggx_geometryTerm(normal, viewDir, k) * ggx_geometryTerm(normal, lightDir, k);\n}\n\nfloat ggx_distribution(float roughness, vec3 normal, vec3 halfVector)\n{\n    float roughSqr = roughness*roughness;\n    float halfDotNormal = max(dot(halfVector, normal), 0.0);\n    float denom = (halfDotNormal * halfDotNormal) * (roughSqr - 1.0) + 1.0;\n    return roughSqr / (denom * denom);\n}\n\n// light dir is towards the light\n// view dir is toward the camera\nvoid ggx_brdf(in vec3 normal, in float roughness, float normalSpecularReflectance, in vec3 lightDir, in vec3 viewDir, in vec3 viewPos, in vec3 lightColor, out vec3 diffuseLight, out vec3 specularLight)\n{\n    float nDotL = max(dot(lightDir, normal), 0.0);\n    vec3 irradiance = nDotL * lightColor;    // in fact irradiance / PI\n\n    vec3 halfVector = normalize(lightDir + viewDir);\n\n    float mappedRoughness =  roughness * roughness;\n\n    float distribution = ggx_distribution(mappedRoughness, normal, halfVector);\n\n    float halfDotLight = max(dot(halfVector, lightDir), 0.0);\n    float cosAngle = 1.0 - halfDotLight;\n    float fresnel = normalSpecularReflectance + (1.0 - normalSpecularReflectance) * pow(cosAngle, 5.0);\n\n    diffuseLight = irradiance;\n\n    specularLight = irradiance * fresnel * distribution;\n\n    specularLight *= ggx_lightVisibility(normal, viewDir, lightDir, roughness);\n}\n\n// diffuse light only\nvec3 ggx_getDiffuseLight(in vec3 normal, in vec3 viewPos, in float shadow)\n{\n    vec3 diffuseLight = vec3(0.0);\n\n#if NUM_DIR_LIGHTS > 0\n    for (int i = 0; i < NUM_DIR_LIGHTS; ++i) {\n        float nDotL = max(dot(directionalLights[i].direction, normal), 0.0);\n        diffuseLight += nDotL * directionalLights[i].color * shadow;\n    }\n#endif\n\n    return diffuseLight;\n}\n\nvoid ggx_getLight(in vec3 normal, in float roughness, in float normalSpecularReflectance, in vec3 viewPos, in float shadow, out vec3 diffuseLight, out vec3 specularLight)\n{\n    vec3 viewDir = -normalize(viewPos);\n    diffuseLight = vec3(0.0);\n    specularLight = vec3(0.0);\n\n#if NUM_DIR_LIGHTS > 0\n    for (int i = 0; i < NUM_DIR_LIGHTS; ++i) {\n        vec3 diff;\n        vec3 spec;\n        ggx_brdf(normal, roughness, normalSpecularReflectance, directionalLights[i].direction, viewDir, viewPos, directionalLights[i].color, diff, spec);\n        diffuseLight += diff * shadow;\n        specularLight += spec * shadow;\n    }\n#endif\n}\n","linear_depth_vertex.glsl":"\nvarying vec2 vUV;\nvarying float linearDepth;\n\nuniform float cameraNear;\nuniform float rcpCameraRange;\n\nvoid main()\n{\n    vec4 viewPosition = modelViewMatrix * vec4(position, 1.0);\n    linearDepth = (-viewPosition.z - cameraNear) * rcpCameraRange;\n    gl_Position = projectionMatrix * viewPosition;\n    vUV = uv;\n}\n","linear_depth_fragment.glsl":"\nvarying float linearDepth;\n\nvoid main()\n{\n    gl_FragColor = floatToRGBA8(linearDepth);\n}\n","linear_hair_depth_fragment.glsl":"\nvarying vec2 vUV;\nvarying float linearDepth;\nuniform sampler2D alphaMap;\nuniform float alpha_test;\n\n// uniform sampler2D depthMap_prev;\n// uniform vec2 screen_size;\nuniform int layer;\n\nvoid main()\n{\n    float alpha = texture2D(alphaMap, vUV).g;\n    if (alpha < alpha_test) discard;\n\n\n    if(layer==0){\n        gl_FragColor = floatToRGBA8(linearDepth);\n        // gl_FragColor = vec4(1.,0.,0.,1.);\n    } else {\n        // // gl_FragColor = texture2D(depthMap_prev,uv_screen)*2.0;\n        // gl_FragColor = floatToRGBA8(linearDepth);\n        // // gl_FragColor = vec4(1.0,1.0,1.0,1.0);\n        // return;\n\n        // vec2 uv_screen = gl_FragCoord.xy/screen_size;\n        // float depth_prev = RGBA8ToFloat(texture2D(depthMap_prev,uv_screen));\n\n        // float uDepthOffset=0.0;\n        // // if(depth_prev + uDepthOffset - linearDepth >= 0. ) discard;\n\n\n        // if (uv_screen.x<0.5){\n        //     gl_FragColor = floatToRGBA8(depth_prev);\n        //     gl_FragColor = vec4(uv_screen,0.0,1.0);\n        //     gl_FragColor = texture2D(depthMap_prev,uv_screen);\n        // }\n        // else\n        //     gl_FragColor = floatToRGBA8(linearDepth);\n    \n    }\n\n    // [0, 1]\n}\n","skin_vertex.glsl":"\nvarying vec2 vUV;\nvarying vec3 vViewNormal;\nvarying vec3 vViewPosition;\nvarying vec4 vShadowCoord;\nvarying vec4 vProjection;\n\nuniform mat4 shadowMapMatrix;\n\nvoid main() {\n    vec3 localPos = position;\n    vec4 viewPos = modelViewMatrix * vec4(localPos, 1.0);\n    vec4 worldPos = modelMatrix * vec4(localPos, 1.0);\n\n    gl_Position = vProjection = projectionMatrix * viewPos;\n    vUV = uv;\n\n    vViewNormal = normalMatrix * normal;\n    vViewPosition = viewPos.xyz;\n    vShadowCoord = shadowMapMatrix * worldPos * .5 + .5;\n}\n","skin_fragment.glsl":"\nvarying vec2 vUV;\nvarying vec3 vViewNormal;\nvarying vec3 vViewPosition;\nvarying vec4 vShadowCoord;\nvarying vec4 vProjection;\n\nuniform sampler2D diffuseMap;\nuniform sampler2D normalMap;\nuniform sampler2D roughnessAOThicknessMap;\nuniform sampler2D roughnessMap;\nuniform sampler2D roughnessDetailMap;\nuniform sampler2D irradianceMap;\nuniform sampler2D specularMap;\nuniform sampler2D shadowMap;\nuniform sampler2D sssMap;\n\nuniform float normalSpecularReflectance;\nuniform float roughnessMedian;\nuniform float roughnessMapRange;\nuniform float roughnessDetailRange;\nuniform float probeExposure;\nuniform float sssProfileScale;\nuniform float thicknessRange;\nuniform float specular_intensity;\nuniform vec3 sssTopLayerColor;\nuniform vec3 transmittanceColor;\nuniform mat4 shadowMapMatrix;\n\nuniform int return_stage;\n\nvec3 perturbNormal2Arb(vec3 position, vec3 normal, vec3 normalSample)\n{\n    vec3 q0 = dFdx( position.xyz );\n    vec3 q1 = dFdy( position.xyz );\n    vec2 st0 = dFdx( vUV.st );\n    vec2 st1 = dFdy( vUV.st );\n    vec3 S = normalize( q0 * st1.t - q1 * st0.t );\n    vec3 T = normalize( -q0 * st1.s + q1 * st0.s );\n    vec3 N = normalize( normal );\n    mat3 tsn = mat3( S, T, N );\n    return normalize( tsn * normalSample );\n}\n\nvec3 getNormal()\n{\n    vec4 normalSample = texture2D(normalMap, vUV);\n    vec3 normal = normalSample.xyz * 2.0 - 1.0;\n    return perturbNormal2Arb(vViewPosition, vViewNormal, normal);\n}\n\nvec2 getVSMMoments(vec2 uv)\n{\n    vec4 s = texture2D(shadowMap, uv);\n#ifdef VSM_FLOAT\n    return s.xy;\n#else\n    return vec2(RG8ToFloat(s.xy), RG8ToFloat(s.zw));\n#endif\n}\n\nfloat getShadow(float offset)\n{\n    vec4 coord = vShadowCoord;\n    coord.z -= offset;\n    vec2 moments = getVSMMoments(coord.xy);\n    float p = linearStep(coord.z - 0.002, coord.z, moments.x);\n    float variance = moments.y - moments.x * moments.x;\n    variance = max(variance, MIN_VARIANCE);\n\n    float diff = coord.z - moments.x;\n    float upperBound = variance / (variance + diff*diff);\n    float shadow = linearStep(LIGHT_BLEED_REDUCTION, 1.0, upperBound);\n    return clamp(max(shadow, p), 0.0, 1.0);\n}\n\nvoid main() {\n    vec3 viewNormal = getNormal();\n    vec3 worldNormal = viewNormal * mat3(viewMatrix);\n    vec4 diffuse = texture2D(diffuseMap, vUV);\n\n    vec3 diffuseLight = vec3(0.0);\n    vec3 specularLight = vec3(0.0);\n\n    vec4 roughnessAOThickness = texture2D(roughnessAOThicknessMap, vUV);\n    float roughness_sample = texture2D(roughnessMap, vUV).g;\n\n    // TODO: scale roughness UP by distance\n    float roughness = roughnessMedian + (roughness_sample - 0.5) * roughnessMapRange;\n    roughness = clamp(roughness, 0.001, 1.0);\n\n    float roughnessDetail = texture2D(roughnessDetailMap, vUV * 20.0).x;\n    roughness += -(roughnessDetail - 0.5) * roughnessDetailRange;\n\n    float shadow = getShadow(0.0);\n    beckmann_getLight(viewNormal, roughness, normalSpecularReflectance, vViewPosition, shadow, diffuseLight, specularLight);\n\n    vec4 irradianceSample = sampleLatLong(irradianceMap, worldNormal);\n    vec3 viewRefl = reflect(normalize(vViewPosition), viewNormal);\n    vec3 worldReflection = viewRefl * mat3(viewMatrix);\n    vec4 specularSample = sampleLatLong(specularMap, worldReflection);\n\n    // TODO: Do this more correctly (remember Seb Lagarde (?)'s post?)\n    float geomOcclusion = 1.0 - roughness * roughness;\n    float NdotL = max(dot(worldNormal, worldReflection), 0.0);\n    float cosAngle = 1.0 - NdotL;\n    float fresnel = normalSpecularReflectance + (1.0 - normalSpecularReflectance) * pow(cosAngle, 5.0);\n    // should be augmented with shadow test\n\n    float probeStrength = probeExposure * roughnessAOThickness.y;\n    diffuseLight += irradianceSample.xyz /** irradianceSample.xyz */* probeStrength;\n    specularLight += specularSample.xyz /** specularSample.xyz */* fresnel * geomOcclusion * probeStrength;\n\n    vec2 screenUV = vProjection.xy / vProjection.w * .5 + .5;\n    vec3 sss = texture2D(sssMap, screenUV).xyz;\n    \n    vec3 diffuseLight_sss = diffuseLight * sssTopLayerColor + sss;\n\n\n    float thickness = (1.0 - roughnessAOThickness.z) * thicknessRange + 0.01;\n    float offset = roughnessAOThickness.z * thicknessRange + 0.01;\n    float shadowTransmission = getShadow(2.0 * offset);\n    vec3 transmission_color = exp(-transmittanceColor * thickness);\n    vec3 shadow_diffuse_light = beckmann_getDiffuseLight(-viewNormal, vViewPosition, shadowTransmission);\n    vec3 transmission_diffuse_light = transmission_color*shadow_diffuse_light;\n    vec3 diffuseLight_sss_tm = diffuseLight_sss + transmission_diffuse_light;\n    vec3 col = diffuse.xyz * diffuseLight_sss_tm + specularLight * specular_intensity;\n\n\n    if (return_stage == 1) {\n        gl_FragColor = vec4(diffuseLight_sss, 1.0);\n    } else if (return_stage == 2) {\n        gl_FragColor = vec4(shadowTransmission);\n    } else if (return_stage == 3) {\n        gl_FragColor = vec4(shadow_diffuse_light, 1.0);\n    } else {\n        gl_FragColor = vec4(col, 1.0);\n    }\n}\n","hair_vertex.glsl":"\nvarying vec2 vUV;\nvarying vec3 vViewNormal;\nvarying vec3 vViewPosition;\nvarying vec4 vShadowCoord;\nvarying vec4 vProjection;\nvarying vec3 vTangent;\nvarying vec3 vNormal;\n\nuniform mat4 shadowMapMatrix;\n\nvoid main() {\n    vec3 localPos = position;\n    vec4 viewPos = modelViewMatrix * vec4(localPos, 1.0);\n    vec4 worldPos = modelMatrix * vec4(localPos, 1.0);\n\n    vTangent=(modelMatrix*tangent).xyz;\n    vNormal=(modelMatrix*vec4(normal,1.0)).xyz;\n\n    gl_Position = vProjection = projectionMatrix * viewPos;\n    vUV = uv;\n\n    vViewNormal = normalMatrix * normal;\n    vViewPosition = viewPos.xyz;\n    vShadowCoord = shadowMapMatrix * worldPos * .5 + .5;\n}\n","hair_fragment.glsl":'\nvarying vec2 vUV;\nvarying vec3 vViewNormal;\nvarying vec3 vViewPosition;\nvarying vec4 vShadowCoord;\nvarying vec4 vProjection;\nvarying vec3 vTangent;\nvarying vec3 vNormal;\n\nuniform float normalSpecularReflectance;\nuniform sampler2D diffuseMap;\nuniform sampler2D normalMap;\nuniform sampler2D irradianceMap;\nuniform sampler2D specularMap;\nuniform sampler2D shadowMap;\nuniform sampler2D alphaMap;\nuniform sampler2D roughnessMap;\n\nuniform sampler2D specular_map;\nuniform sampler2D scatter_map;\n\nuniform mat4 shadowMapMatrix;\nuniform float alpha_test;\n\nuniform float tangent_shift_0;\nuniform float specular_exp_0;\nuniform float tangent_shift_1;\nuniform float specular_exp_1;\n\nuniform int return_stage;\n\nvec2 getVSMMoments(vec2 uv)\n{\n    vec4 s = texture2D(shadowMap, uv);\n#ifdef VSM_FLOAT\n    return s.xy;\n#else\n    return vec2(RG8ToFloat(s.xy), RG8ToFloat(s.zw));\n#endif\n}\n\nfloat getShadow(float offset)\n{\n    vec4 coord = vShadowCoord;\n    coord.z -= offset;\n    vec2 moments = getVSMMoments(coord.xy);\n    float p = linearStep(coord.z - 0.002, coord.z, moments.x);\n    float variance = moments.y - moments.x * moments.x;\n    variance = max(variance, MIN_VARIANCE);\n\n    float diff = coord.z - moments.x;\n    float upperBound = variance / (variance + diff*diff);\n    float shadow = linearStep(LIGHT_BLEED_REDUCTION, 1.0, upperBound);\n    return clamp(max(shadow, p), 0.0, 1.0);\n}\n\nfloat getShadow2(float offset)\n{\n    vec4 coord = vShadowCoord;\n    coord.z -= offset;\n    vec2 moments = getVSMMoments(coord.xy);\n    float p = linearStep(coord.z - 0.2, coord.z, moments.x);\n    return p;\n    float variance = moments.y - moments.x * moments.x;\n    variance = max(variance, MIN_VARIANCE);\n\n    float diff = coord.z - moments.x;\n    float upperBound = variance / (variance + diff*diff);\n    float shadow = linearStep(LIGHT_BLEED_REDUCTION, 1.0, upperBound);\n    return clamp(max(shadow, p), 0.0, 1.0);\n}\n\nfloat StrandSpecular (vec3 strand_dir, vec3 normal_dir, vec3 view_dir, vec3 light_dir, float exponent){\n    vec3 half_dir = normalize(light_dir + view_dir);\n    float dotTH = dot(strand_dir, half_dir);\n    float sinTH = sqrt(1.0 - dotTH*dotTH);\n    float dirAtten = smoothstep(-1.0, 0.0, dotTH);\n\n    // float dotNH = dot(normal_dir, half_dir);\n    // dotNH=smoothstep(-1.0,1.0,dotNH);\n    // dotNH=pow(dotNH,20.0);\n\n\n    // return dotNH;\n    // return dirAtten * pow(sinTH*dotNH, exponent);\n    // return dirAtten * pow(dotNH, exponent);\n    return dirAtten * pow(sinTH, exponent);\n}\n\nvec3 ShiftTangent (vec3 strand_dir, vec3 normal_dir, float shift) {\n    vec3 strand_dir_shifted = strand_dir + shift * normal_dir;\n    return normalize (strand_dir_shifted);\n}\n\nfloat fresnel_dielectric_cos(float cosi, float eta)\n{\n  /* compute fresnel reflectance without explicitly computing\n   * the refracted direction */\n  float c = abs(cosi);\n  float g = eta * eta - 1.0 + c * c;\n  float result;\n\n  if (g > 0.0) {\n    g = sqrt(g);\n    float A = (g - c) / (g + c);\n    float B = (c * (g + c) - 1.0) / (c * (g - c) + 1.0);\n    result = 0.5 * A * A * (1.0 + B * B);\n  }\n  else\n    result = 1.0; /* TIR (no refracted component) */\n\n  return result;\n}\n\nfloat fresnel_approx(float cosi, float F0){\n    float NdotL = max(cosi, 0.0);\n    float cosAngle = 1.0 - NdotL;\n    float fresnel = normalSpecularReflectance + (1.0 - normalSpecularReflectance) * pow(cosAngle, 5.0);\n    return fresnel;\n}\n\n\n\n\n\n\n\n\n\n\n\n\n// #define _USE_LIGHT_FACING_NORMAL\n// #define _USE_ADVANCED_MULTIPLE_SCATTERING\n#define DEFAULT_HAIR_SPECULAR_VALUE 0.0465 // Hair is IOR 1.55\n\nstruct BSDFData {\n    vec3 fresnel0;\n    float cuticleAngleR;\n    float cuticleAngleTT;\n    float cuticleAngleTRT;\n    float roughnessR;\n    float roughnessTT;\n    float roughnessTRT;\n    vec3 absorption;\n    float perceptualRoughnessRadial;\n};\n\nstruct HairAngle\n{\n    float sinThetaI;\n    float sinThetaO;\n    float cosThetaI;\n    float cosThetaO;\n    float cosThetaD;\n    float thetaH;\n    float phiI;\n    float phiO;\n    float phi;\n    float cosPhi;\n    float sinThetaT;\n    float cosThetaT;\n};\n\nstruct CBSDF {\n    vec3 diffR;\n    vec3 specR;\n};\n\nfloat saturate(float value){\n    return clamp(value,0.0,1.0);\n}\n\nfloat rsqrt(float value)\n{\n  return pow(value, -0.5);\n}\n\nfloat SafeSqrt(float x)\n{\n    return sqrt(max(0.0, x));\n}\nfloat Sq(float x){\n    return x*x;\n}\nvec3 Sq(vec3 x){\n    return x*x;\n}\nfloat rcp(float x){\n    return 1.0/x;\n}\nvec3 rcp(vec3 x){\n    return 1.0/x;\n}\n\nfloat F_Schlick(float F0, float cosi){\n    cosi = max(cosi, 0.0);\n    float fresnel = F0 + (1.0 - F0) * pow(1.0 - cosi, 5.0);\n    return fresnel;\n}\nvec3 F_Schlick(vec3 F0, float cosi){\n    cosi = max(cosi, 0.0);\n    vec3 fresnel = F0 + (1.0 - F0) * pow(1.0 - cosi, 5.0);\n    return fresnel;\n}\n\nfloat ModifiedRefractionIndex(float cosThetaD)\n{\n    // Original derivation of modified refraction index for arbitrary IOR.\n    // float sinThetaD = sqrt(1 - Sq(cosThetaD));\n    // return sqrt(Sq(eta) - Sq(sinThetaD)) / cosThetaD;\n\n    // Karis approximation for the modified refraction index for human hair (1.55)\n    return 1.19 / cosThetaD + (0.36 * cosThetaD);\n}\n\nvoid GetHairAngleWorld(vec3 V, vec3 L, vec3 T, inout HairAngle angles)\n{\n    angles.sinThetaO = dot(T, V);\n    angles.sinThetaI = dot(T, L);\n\n    float thetaO = asin(angles.sinThetaO);\n    float thetaI = asin(angles.sinThetaI);\n    angles.thetaH = (thetaI + thetaO) * 0.5;\n\n    angles.cosThetaD = cos((thetaO - thetaI) * 0.5);\n    angles.cosThetaO = cos(thetaO);\n    angles.cosThetaI = cos(thetaI);\n\n    // Projection onto the normal plane, and since phi is the relative angle, we take the cosine in this projection.\n    vec3 VProj = V - angles.sinThetaO * T;\n    vec3 LProj = L - angles.sinThetaI * T;\n    angles.cosPhi = dot(LProj, VProj) * rsqrt(dot(LProj, LProj) * dot(VProj, VProj) + 1e-5); // zero-div guard\n    angles.phi = acos(angles.cosPhi);\n\n    // Fixed for approximate human hair IOR\n    angles.sinThetaT = angles.sinThetaO / 1.55;\n    angles.cosThetaT = SafeSqrt(1.0 - Sq(angles.sinThetaT));\n}\n\nvec3 D_LongitudinalScatteringGaussian(vec3 thetaH, vec3 beta)\n{\n    float sqrtTwoPi = 2.50662827463100050241;\n    return rcp(beta * sqrtTwoPi) * exp(-Sq(thetaH) / (2.0 * Sq(beta)));\n}\n\n#define PI 3.14159265359\n#define TWO_PI 6.28318530717958647693\n#define FOUR_PI ((2.0*TWO_PI))\n#define INV_PI 0.31830988618379067153776752674502872406891929148091\n\nvec3 GetRoughenedAzimuthalScatteringDistribution(float phi, float cosThetaD, float beta)\n{\n    float X = (phi + TWO_PI) / FOUR_PI;\n    float Y = cosThetaD;\n    float Z = beta;\n\n    // TODO: It should be possible to reduce the domain of the integration to 0 -> HALF/PI as it repeats. This will save memory.\n    // return SAMPLE_TEXTURE3D_LOD(_PreIntegratedAverageHairFiberScattering, s_linear_clamp_sampler, vec3(X, Y, Z), 0).xyz;\n    return texture(scatter_map, vec2(X,Y)).xyz*3.23;\n    return vec3(0.0);\n}\n\n\n#define HAIR_H_TT  0.0\n#define HAIR_H_TRT 0.86602540378\n\nvec3 AbsorptionFromReflectance(vec3 diffuseColor, float azimuthalRoughness)\n{\n    float beta  = azimuthalRoughness;\n    float beta2 = beta  * beta;\n    float beta3 = beta2 * beta;\n    float beta4 = beta3 * beta;\n    float beta5 = beta4 * beta;\n\n    // Least squares fit of an inverse mapping between scattering parameters and scattering albedo.\n    float denom = 5.969 - (0.215 * beta) + (2.532 * beta2) - (10.73 * beta3) + (5.574 * beta4) + (0.245 * beta5);\n\n    vec3 t = log(diffuseColor) / denom;\n    return t * t;\n}\n\nBSDFData init_bsdf(){\n    BSDFData bsdfData;\n\n    bsdfData.fresnel0=vec3(DEFAULT_HAIR_SPECULAR_VALUE);\n\n    float cuticleAngle=0.1;\n    // bsdfData.cuticleAngle    = -cuticleAngle;\n    bsdfData.cuticleAngleR   = -cuticleAngle;\n    bsdfData.cuticleAngleTT  =  cuticleAngle * 0.5;\n    bsdfData.cuticleAngleTRT =  cuticleAngle * 1.5;\n\n    float roughnessL=0.1;\n    bsdfData.roughnessR   = roughnessL;\n    bsdfData.roughnessTT  = roughnessL * 0.5;\n    bsdfData.roughnessTRT = roughnessL * 2.0;\n\n    bsdfData.perceptualRoughnessRadial=0.3;\n    bsdfData.absorption=AbsorptionFromReflectance(vec3(0.1),bsdfData.perceptualRoughnessRadial);\n\n    return bsdfData;\n}\n\nCBSDF EvaluateBSDF(vec3 V, vec3 L, vec3 T, vec3 N, BSDFData bsdfData)\n{\n\n    CBSDF cbsdf;\n\n\n    // Approximation of the three primary paths in a hair fiber (R, TT, TRT), with concepts from:\n    // "Strand-Based Hair Rendering in Frostbite" (Tafuri 2019)\n    // "A Practical and Controllable Hair and Fur Model for Production Path Tracing" (Chiang 2016)\n    // "Physically Based Hair Shading in Unreal" (Karis 2016)\n    // "An Energy-Conserving Hair Reflectance Model" (d\'Eon 2011)\n    // "Light Scattering from Human Hair Fibers" (Marschner 2003)\n\n    // Reminder: All of these flags are known at compile time and the compiler will strip away the unused paths.\n\n    // Retrieve angles via spherical coordinates in the hair shading space.\n    HairAngle angles;\n    GetHairAngleWorld(V, L, T, angles);\n\n    vec3 alpha = vec3(\n        bsdfData.cuticleAngleR,\n        bsdfData.cuticleAngleTT,\n        bsdfData.cuticleAngleTRT\n    );\n\n    vec3 beta = vec3(\n        bsdfData.roughnessR,\n        bsdfData.roughnessTT,\n        bsdfData.roughnessTRT\n    );\n\n    // The index of refraction that can be used to analyze scattering in the normal plane (Bravais\' Law).\n    float etaPrime = ModifiedRefractionIndex(angles.cosThetaD);\n\n    // Reduced absorption coefficient.\n    vec3 mu = bsdfData.absorption;\n\n    // Various misc. terms reused between lobe evaluation.\n    vec3 F=vec3(0.0);\n    vec3 Tr=vec3(0.0);\n    vec3 S=vec3(0.0);\n\n    // Evaluate the longitudinal scattering for all three paths.\n    vec3 M = D_LongitudinalScatteringGaussian(angles.thetaH - alpha, beta);\n    // greater thetaH => smaller M\n\n    // Save the attenuations in case of multiple scattering.\n    vec3 A[3];\n\n    // Fetch the preintegrated azimuthal distributions for each path\n    vec3 D = GetRoughenedAzimuthalScatteringDistribution(angles.phi, angles.cosThetaD, bsdfData.perceptualRoughnessRadial);\n    // phi=0 => max\n    // phi=+-pi => 0\n    // cosThetaD is cos(IO/2); greater IO angle => smaller cosThetaD; \n    // 0 angle => cosThetaD=1; \n    // full angle => cosThetaD=0;\n    // cosThetaD no affect on 0\n\n    // Solve the first three lobes (R, TT, TRT).\n\n    // R\n    {\n        // Attenuation for this path as proposed by d\'Eon et al, replaced with a trig identity for cos half phi.\n        A[0] = F_Schlick(bsdfData.fresnel0, sqrt(0.5 + 0.5 * dot(L, V)));\n        S += M[0] * A[0] * D[0];\n    }\n\n    // TT\n    if (true)\n    {\n        // Attenutation (Simplified for H = 0)\n        float cosGammaO = SafeSqrt(1.0 - Sq(HAIR_H_TT));\n        float cosTheta  = angles.cosThetaO * cosGammaO;\n        F = F_Schlick(bsdfData.fresnel0, cosTheta);\n\n        float sinGammaT = HAIR_H_TT / etaPrime;\n        float cosGammaT = SafeSqrt(1.0 - Sq(sinGammaT));\n        Tr = exp(-mu * (2.0 * cosGammaT / angles.cosThetaT));\n\n        A[1] = Sq(1.0 - F) * Tr;\n\n        S += M[1] * A[1] * D[1];\n    }\n\n    // TRT\n    {\n        // Attenutation (Simplified for H = \u221a3/2)\n        float cosGammaO = SafeSqrt(1.0 - Sq(HAIR_H_TRT));\n        float cosTheta  = angles.cosThetaO * cosGammaO;\n        F = F_Schlick(bsdfData.fresnel0, cosTheta);\n\n        float sinGammaT = HAIR_H_TRT / etaPrime;\n        float cosGammaT = SafeSqrt(1.0 - Sq(sinGammaT));\n        Tr = exp(-mu * (2.0 * cosGammaT / angles.cosThetaT));\n\n        A[2] = Sq(1.0 - F) * F * Sq(Tr);\n\n        S += M[2] * A[2] * D[2];\n    }\n\n    // TODO: Residual TRRT+ Lobe. (accounts for ~15% energy otherwise lost by the first three lobes).\n\n    // This seems necesarry to match the reference.\n    S *= INV_PI;\n\n    // Transmission event is built into the model.\n    // Some stubborn NaNs have cropped up due to the angle optimization, we suppress them here with a max for now.\n    cbsdf.specR = max(S, 0.0);\n\n    // gl_FragColor=vec4(cbsdf.specR,1.0);\n\n\n    // Multiple Scattering\n#ifdef _USE_ADVANCED_MULTIPLE_SCATTERING\n    if (true)\n    {\n        cbsdf.specR = EvaluateMultipleScattering(L, cbsdf.specR, bsdfData, alpha, beta, angles.thetaH, angles.sinThetaI, D, A);\n    }\n    else\n#endif\n    {\n\n        #ifdef _USE_LIGHT_FACING_NORMAL\n            // The Kajiya-Kay model has a "built-in" transmission, and the \'NdotL\' is always positive.\n            float cosTL = dot(T, L);\n            float sinTL = sqrt(saturate(1.0 - cosTL * cosTL));\n            float NdotL = sinTL; // Corresponds to the cosine w.r.t. the light-facing normal\n        #else\n            // Double-sided Lambert.\n            float NdotL = dot(N, L);\n        #endif\n        \n            float clampedNdotL = saturate(NdotL);\n\n\n        #ifdef _USE_LIGHT_FACING_NORMAL\n            // See "Analytic Tangent Irradiance Environment Maps for Anisotropic Surfaces".\n            cbsdf.diffR = vec3(rcp(PI * PI) * clampedNdotL);\n            // // Transmission is built into the model, and it\'s not exactly clear how to split it.\n            // cbsdf.diffT = 0;\n        #else\n            // Double-sided Lambert.\n            // cbsdf.diffR = vec3(Lambert() * clampedNdotL);\n            cbsdf.diffR = vec3(clampedNdotL);\n        #endif // _USE_LIGHT_FACING_NORMAL\n    }\n\n    return cbsdf;\n}\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nvoid main() {\n\n    float alpha = texture2D(alphaMap, vUV).g;\n    if (alpha < alpha_test) discard;\n    \n    float roughness = texture2D(roughnessMap, vUV).g;\n\n    // ======                                      \n    // normal                                      \n    // ======\n\n    vec3 tangent=normalize(vTangent);\n    vec3 normal=normalize(vNormal);\n    vec3 bitangent=normalize(cross(normal,tangent));\n\n    vec3 tangent_normal_sample = texture2D(normalMap, vUV).xyz * 2.0 - 1.0;\n    tangent_normal_sample.x*=-1.0;\n\n    mat3 BTN=mat3( bitangent, tangent, normal );\n    vec3 normal_perturb=normalize(BTN*tangent_normal_sample);\n    vec3 viewNormal = mat3(viewMatrix)* normal_perturb;\n\n    vec3 strand_dir=-bitangent;\n\n    vec3 view_dir=normalize(-vViewPosition) * mat3(viewMatrix);\n    vec3 light_dir=normalize(directionalLights[0].direction * mat3(viewMatrix));\n\n\n    // vec3 specular_color_0=vec3(0.2);\n    // vec3 specular_color_1=vec3(0.18,0.15,0.15);\n\n    // vec3 specular_hair_0 = specular_color_0* StrandSpecular(ShiftTangent(strand_dir,normal_perturb,tangent_shift_0),normal_perturb, view_dir, light_dir, specular_exp_0);\n    // vec3 specular_hair_1 = specular_color_1* StrandSpecular(ShiftTangent(strand_dir,normal_perturb,tangent_shift_1),normal_perturb, view_dir, light_dir, specular_exp_1);\n\n    // vec3 specular_color=(specular_hair_0+specular_hair_1)*(0.1+specularLight);\n\n    // vec3 color=diffuse * diffuseLight*0.2 + specular_color;\n\n\n    // ======\n    // anisotropic\n    // ======\n\n    float anisotropic_roughness=0.038;\n    float anisotropic_strength=1.0;\n    float specular_sample=texture(specular_map,vUV).g;\n    vec3 base_sample=texture(diffuseMap,vUV).rgb;\n    vec3 anisotropic_color=vec3(1.0,0.799,0.69);\n\n\n    float anisotropic_IOR=anisotropic_roughness*4.0+1.0;\n\n    float fresnel=fresnel_dielectric_cos(dot(view_dir,-strand_dir),anisotropic_IOR);\n    float specular_rate=fresnel*specular_sample;\n\n    vec3 final_anisotropic_color=mix(base_sample*0.2,anisotropic_color,specular_rate);\n\n\n\n\n    vec3 diffuseLight = vec3(0.0);\n    vec3 specularLight = vec3(0.0);\n\n    // float shadow = getShadow(0.05);\n    float shadow = getShadow2(0.0);\n    // gl_FragColor=vec4(shadow);\n    // return;\n\n    float normalSpecularReflectance=0.027;\n    beckmann_getLight(viewNormal, roughness, normalSpecularReflectance, vViewPosition, shadow, diffuseLight, specularLight);\n\n\n    float geomOcclusion=1.0-roughness*roughness;\n    \n    vec4 irradianceSample = sampleLatLong(irradianceMap, normal_perturb);\n    diffuseLight += irradianceSample.xyz;\n\n    vec3 env_specular_sample = sampleLatLong(specularMap, reflect(-view_dir,normal_perturb)).xyz;\n\n\n    vec3 env_specular_light=env_specular_sample * fresnel * geomOcclusion;\n    specularLight += env_specular_light;\n\n\n    // vec3 col = diffuse * diffuseLight + specularLight;\n    vec3 color = final_anisotropic_color * diffuseLight;\n\n\n    // gl_FragColor=vec4(color,1.0);\n    gl_FragColor=vec4(env_specular_light,1.0);\n\n\n\n    BSDFData b=init_bsdf();\n    CBSDF c=EvaluateBSDF(view_dir, light_dir, bitangent, normal_perturb, b);\n\n    // gl_FragColor=vec4(c.specR,1.0);\n    // gl_FragColor=vec4(c.specR*env_specular_sample*shadow+env_specular_light*0.2+base_sample* diffuseLight*0.5,1.0);\n\n    vec3 specular_light_color=c.specR*specular_sample*shadow*5.0*anisotropic_color;\n\n    gl_FragColor=vec4(specular_light_color+env_specular_light*0.2+base_sample* diffuseLight*0.5,1.0);\n\n}\n',"sky_vertex.glsl":"\nvarying vec3 worldViewDir;\n\nvoid main() {\n    vec3 worldPosition = (modelMatrix * vec4(position, 1.0)).xyz;\n    worldViewDir = worldPosition - cameraPosition;\n    gl_Position = projectionMatrix * modelViewMatrix * vec4(position, 1.0);\n}\n","sky_fragment.glsl":"\nvarying vec3 worldViewDir;\n\n#ifdef LAT_LONG\nuniform sampler2D envMap;\n#else\nuniform samplerCube envMap;\n#endif\n\nuniform float exposure;\n\nvoid main()\n{\n    vec3 elementDir = normalize(worldViewDir);\n#ifdef LAT_LONG\n    gl_FragColor = sampleLatLong(envMap, elementDir);\n#else\n    gl_FragColor = textureCube(envMap, elementDir);\n#endif\n\n#ifdef HDRE\n    gl_FragColor.xyz = decodeHDRE(gl_FragColor);\n    gl_FragColor.w = 1.0;\n#endif\n    gl_FragColor.xyz *= exposure;\n}\n","dof_fragment.glsl":"\nuniform sampler2D source;\nuniform sampler2D blurred1;\nuniform sampler2D blurred2;\nuniform sampler2D depth;\n\nuniform float strength;\nuniform float focusDepth;\nuniform float focusRange;\nuniform float focusFalloff;\n\nvarying vec2 vUV;\n\nvoid main()\n{\n    const float blendCutoff = .5;\n    float depth = RGBA8ToFloat(texture2D(depth, vUV));\n    float distance = abs(depth - focusDepth);\n\n    float blurAmount = clamp((distance - focusRange) / focusFalloff, 0.0, 1.0);\n\n    vec3 mainCol = decodeHDRE(texture2D(source, vUV));\n    vec3 blurredCol1 = decodeHDRE(texture2D(blurred1, vUV));\n    vec3 blurredCol2 = decodeHDRE(texture2D(blurred2, vUV));\n\n    // for little blurs (0.0 - 0.25), use smaller amount, for (.5, 1.0), use larger blur\n    float smallBlur = linearStep(0.0, blendCutoff, blurAmount);\n    float largeBlur = linearStep(blendCutoff, 1.0, blurAmount);\n    vec3 color = mix(blurredCol1, blurredCol2, largeBlur);\n    color = mix(mainCol, color, smallBlur * strength);\n\n    gl_FragColor = encodeHDRE(color);\n}\n","fxaa_tonemap_fragment.glsl":"\nuniform sampler2D tDiffuse;\nuniform vec2 rcpRenderTargetResolution;\nuniform float whitePoint;\n\n#define FXAA_REDUCE_MIN   (1.0/128.0)\n#define FXAA_REDUCE_MUL   (1.0/8.0)\n#define FXAA_SPAN_MAX     (8.0)\n\n// Jim Hejl and Richard Burgess-Dawson\n#define TM_A 6.2\n#define TM_B .5\n#define TM_C 6.2\n#define TM_D 1.7\n#define TM_E 0.06\n\nvec3 toneMap(vec3 color)\n{\n    return (color*(TM_A*color+TM_B))/(color*(TM_C*color+TM_D)+TM_E);\n}\n\nfloat toneMap(float val)\n{\n    return (val*(TM_A*val+TM_B))/(val*(TM_C*val+TM_D)+TM_E);\n}\n\n// refer to: https://github.com/mattdesl/glsl-fxaa/blob/master/fxaa.glsl\nvoid main() {\n    \n    vec3 rgbNW = texture2D( tDiffuse, ( gl_FragCoord.xy + vec2( -1.0, -1.0 ) ) * rcpRenderTargetResolution ).xyz;\n    vec3 rgbNE = texture2D( tDiffuse, ( gl_FragCoord.xy + vec2( 1.0, -1.0 ) ) * rcpRenderTargetResolution ).xyz;\n    vec3 rgbSW = texture2D( tDiffuse, ( gl_FragCoord.xy + vec2( -1.0, 1.0 ) ) * rcpRenderTargetResolution ).xyz;\n    vec3 rgbSE = texture2D( tDiffuse, ( gl_FragCoord.xy + vec2( 1.0, 1.0 ) ) * rcpRenderTargetResolution ).xyz;\n    vec4 rgbaM  = texture2D( tDiffuse,  gl_FragCoord.xy * rcpRenderTargetResolution );\n    vec3 rgbM  = rgbaM.xyz;\n    vec3 luma = vec3( 0.299, 0.587, 0.114 );\n\n    float lumaNW = dot( rgbNW, luma );\n    float lumaNE = dot( rgbNE, luma );\n    float lumaSW = dot( rgbSW, luma );\n    float lumaSE = dot( rgbSE, luma );\n    float lumaM  = dot( rgbM,  luma );\n    float lumaMin = min( lumaM, min( min( lumaNW, lumaNE ), min( lumaSW, lumaSE ) ) );\n    float lumaMax = max( lumaM, max( max( lumaNW, lumaNE) , max( lumaSW, lumaSE ) ) );\n\n    vec2 dir;\n    dir.x = -((lumaNW + lumaNE) - (lumaSW + lumaSE));\n    dir.y =  ((lumaNW + lumaSW) - (lumaNE + lumaSE));\n\n    float dirReduce = max( ( lumaNW + lumaNE + lumaSW + lumaSE ) * ( 0.25 * FXAA_REDUCE_MUL ), FXAA_REDUCE_MIN );\n\n    float rcpDirMin = 1.0 / ( min( abs( dir.x ), abs( dir.y ) ) + dirReduce );\n    dir = min( vec2( FXAA_SPAN_MAX,  FXAA_SPAN_MAX),\n            max( vec2(-FXAA_SPAN_MAX, -FXAA_SPAN_MAX),\n                dir * rcpDirMin)) * rcpRenderTargetResolution;\n    vec4 rgbA = (1.0/2.0) * (\n        texture2D(tDiffuse,  gl_FragCoord.xy  * rcpRenderTargetResolution + dir * (1.0/3.0 - 0.5)) +\n    texture2D(tDiffuse,  gl_FragCoord.xy  * rcpRenderTargetResolution + dir * (2.0/3.0 - 0.5)));\n    vec4 rgbB = rgbA * (1.0/2.0) + (1.0/4.0) * (\n        texture2D(tDiffuse,  gl_FragCoord.xy  * rcpRenderTargetResolution + dir * (0.0/3.0 - 0.5)) +\n    texture2D(tDiffuse,  gl_FragCoord.xy  * rcpRenderTargetResolution + dir * (3.0/3.0 - 0.5)));\n    float lumaB = dot(rgbB, vec4(luma, 0.0));\n\n    vec3 color;\n    if ( ( lumaB < lumaMin ) || ( lumaB > lumaMax ) ) {\n    \n        color = rgbA.xyz;\n\n    } else {\n        color = rgbB.xyz;\n    }\n\n\n    color = max(vec3(0.0), color - vec3(.004));\n    color = toneMap(color) / toneMap(whitePoint);\n    gl_FragColor = vec4(color, 1.0);\n}\n","sss_blur_vertex.glsl":"\nvarying vec2 vUV;\nvarying vec3 vViewVector;\n\nuniform mat4 unprojectionMatrix;\n\nvoid main()\n{\n    gl_Position = vec4(position, 1.0);\n    vUV = uv;\n    vec4 unproj = unprojectionMatrix * vec4(position.xy, 0.0, 1.0);\n    unproj /= unproj.w;\n    vViewVector = -unproj.xyz / unproj.z;\n}\n","sss_blur_fragment.glsl":"\nvarying vec3 vViewVector;\nvarying vec2 vUV;\n\nuniform sampler2D tDiffuse;\nuniform sampler2D depthMap;\nuniform sampler2D sssProfileMap;\nuniform vec2 step;\nuniform vec3 color1;\nuniform vec3 color2;\nuniform vec3 color3;\nuniform float cameraNear;\nuniform float cameraRange;\nuniform float sssProfileScale;\nuniform float sssRange;\n\nvoid getContribution(in vec2 uv, inout vec3 prevPos, inout float totalDistance, inout vec3 totalWeights, inout vec3 acc1, inout vec3 acc2, inout vec3 acc3)\n{\n    float depth = RGBA8ToFloat(texture2D(depthMap, uv));\n    vec3 pos = vViewVector * (cameraNear + depth * cameraRange);\n    float stepDist = distance(pos, prevPos);\n    prevPos = pos;\n    totalDistance += stepDist;\n\n    vec3 weights = texture2D(sssProfileMap, vec2(totalDistance * sssProfileScale, 0.0)).yzw;\n\n    if (totalDistance > sssRange)\n        weights *= 0.0;\n\n    totalWeights += weights;\n\n    vec3 color = texture2D(tDiffuse, uv).xyz;\n\n    acc1 += color * weights.x;\n    acc2 += color * weights.y;\n    acc3 += color * weights.z;\n}\n\nvoid main()\n{\n    vec3 totalWeights = texture2D(sssProfileMap, vec2(0.0)).yzw;\n\n    vec3 color = texture2D(tDiffuse, vUV).xyz;\n    vec3 c1 = color * totalWeights.x;\n    vec3 c2 = color * totalWeights.y;\n    vec3 c3 = color * totalWeights.z;\n\n    // centerDepth=(real_depth-near)/camera_range\n    float centerDepth = RGBA8ToFloat(texture2D(depthMap, vUV));\n\n    vec3 centerPos = vViewVector * (cameraNear + centerDepth * cameraRange);\n\n    vec2 uvLeft = vUV;\n    vec2 uvRight = vUV;\n    vec3 posLeft = centerPos;\n    vec3 posRight = centerPos;\n    float distLeft = 0.0;\n    float distRight = 0.0;\n\n    for (int i = 0; i < RADIUS; ++i) {\n        uvLeft -= step;\n        uvRight += step;\n        getContribution(uvLeft, posLeft, distLeft, totalWeights, c1, c2, c3);\n        getContribution(uvRight, posRight, distRight, totalWeights, c1, c2, c3);\n    }\n\n    //  blend together\n    gl_FragColor.xyz = color1 * c1 / totalWeights.x;\n    gl_FragColor.xyz += color2 * c2 / totalWeights.y;\n    gl_FragColor.xyz += color3 * c3 / totalWeights.z;\n    gl_FragColor.w = 1.0;\n}\n","sss_vertex.glsl":"\nvarying vec2 vUV;\nvarying vec3 vViewNormal;\nvarying vec3 vViewPosition;\nvarying vec4 vShadowCoord;\n\nuniform mat4 shadowMapMatrix;\n\nvoid main() {\n    vec3 localPos = position;\n    vec4 viewPos = modelViewMatrix * vec4(localPos, 1.0);\n    vec4 worldPos = modelMatrix * vec4(localPos, 1.0);\n\n    gl_Position = projectionMatrix * viewPos;\n    vUV = uv;\n\n    vViewNormal = normalMatrix * normal;\n    vViewPosition = viewPos.xyz;\n    vShadowCoord = shadowMapMatrix * worldPos * .5 + .5;\n}\n","sss_fragment.glsl":"\nvarying vec2 vUV;\nvarying vec3 vViewNormal;\nvarying vec3 vViewPosition;\nvarying vec4 vShadowCoord;\n\nuniform sampler2D normalMap;\nuniform sampler2D irradianceMap;\nuniform sampler2D shadowMap;\n\nuniform float probeExposure;\n\nvec3 perturbNormal2Arb(vec3 position, vec3 normal, vec3 normalSample)\n{\n    vec3 q0 = dFdx( position.xyz );\n    vec3 q1 = dFdy( position.xyz );\n    vec2 st0 = dFdx( vUV.st );\n    vec2 st1 = dFdy( vUV.st );\n    vec3 S = normalize( q0 * st1.t - q1 * st0.t );\n    vec3 T = normalize( -q0 * st1.s + q1 * st0.s );\n    vec3 N = normalize( normal );\n    mat3 tsn = mat3( S, T, N );\n    return normalize( tsn * normalSample );\n}\n\nvec3 getNormal()\n{\n    vec4 normalSample = texture2D(normalMap, vUV);\n    vec3 normal = normalSample.xyz * 2.0 - 1.0;\n    return perturbNormal2Arb(vViewPosition, vViewNormal, normal);\n}\n\nvec2 getVSMMoments(vec2 uv)\n{\n    vec4 s = texture2D(shadowMap, uv);\n#ifdef VSM_FLOAT\n    return s.xy;\n#else\n    return vec2(RG8ToFloat(s.xy), RG8ToFloat(s.zw));\n#endif\n}\n\nfloat getShadow(float offset)\n{\n    vec4 coord = vShadowCoord;\n    coord.z -= offset;\n    vec2 moments = getVSMMoments(coord.xy);\n    float p = linearStep(coord.z - 0.02, coord.z, moments.x);\n    float variance = moments.y - moments.x * moments.x;\n    variance = max(variance, MIN_VARIANCE);\n\n    float diff = coord.z - moments.x;\n    float upperBound = variance / (variance + diff*diff);\n    float shadow = linearStep(LIGHT_BLEED_REDUCTION, 1.0, upperBound);\n    return clamp(max(shadow, p), 0.0, 1.0);\n}\n\nvoid main() {\n    vec3 viewNormal = getNormal();\n    vec3 worldNormal = viewNormal * mat3(viewMatrix);\n    float shadow = getShadow(0.0);\n    vec3 diffuseLight = ggx_getDiffuseLight(viewNormal, vViewPosition, shadow);\n    vec4 irradianceSample = sampleLatLong(irradianceMap, worldNormal);\n\n    diffuseLight += irradianceSample.xyz * irradianceSample.xyz * probeExposure;\n\n    // NOT APPLYING GAMMA!\n    gl_FragColor = vec4(diffuseLight, 1.0);\n}\n","tiny_blur_hdre_fragment.glsl":"\nuniform sampler2D tDiffuse;\n\nvarying vec2 vUV;\n\nuniform vec2 sampleStep;\n\nvoid main()\n{\n    vec3 col = decodeHDRE(texture2D(tDiffuse, vUV - sampleStep * .5));\n    col += decodeHDRE(texture2D(tDiffuse, vUV + sampleStep * vec2(1.5, -.5)));\n    col += decodeHDRE(texture2D(tDiffuse, vUV + sampleStep * vec2(-.5, 1.5)));\n    col += decodeHDRE(texture2D(tDiffuse, vUV + sampleStep * 1.5));\n\n    gl_FragColor = encodeHDRE(col * .25);\n}\n","tonemap_fragment.glsl":"\nuniform sampler2D tDiffuse;\nuniform float exposure;\n\nvarying vec2 vUV;\n\nvoid main()\n{\n    vec4 hdre = texture2D(tDiffuse, vUV);\n    vec3 color = decodeHDRE(hdre);\n    \n    //    color *= color * exposure;\n    //    color = max(vec3(0.0), color.xyz - 0.004);\n\n    // this has pow 2.2 gamma included, not valid if using fast gamma correction\n\n    // Jim Hejl and Richard Burgess-Dawson\n    /*float a = 6.2;\n    float b = .5;\n    float c = 6.2;\n    float d = 1.7;\n    float e = 0.06;*/\n\n    // ACES\n    float a = 2.51;\n    float b = 0.03;\n    float c = 2.43;\n    float d = 0.59;\n    float e = 0.14;\n\n    gl_FragColor = vec4(saturate((color*(a*color+b))/(color*(c*color+d)+e)), 1.0);\n//    gl_FragColor = vec4(color, 1.0);\n}\n","shadow_fragment.glsl":"\nvarying vec4 projection;\n\nuniform float depthBias;\n\nvoid main()\n{\n    float depth = projection.z * .5 + .5;\n\n    gl_FragColor = floatToRGBA8(depth + depthBias);\n}\n","shadow_vertex.glsl":"\nvarying vec4 projection;\nvarying vec2 vUV;\n\n\nvoid main()\n{\n    gl_Position = projection = projectionMatrix * modelViewMatrix * vec4(position, 1.0);\n    vUV = uv;\n}\n","vsm_fragment.glsl":"\nvarying vec4 projection;\n\nvoid main()\n{\n    float depth = projection.z * .5 + .5;\n    float dx = dFdx(depth);\n    float dy = dFdy(depth);\n    float moment2 = depth * depth + 0.25*(dx*dx + dy*dy);\n\n#ifdef FLOAT_TEX\n    gl_FragColor = vec4(depth, moment2, 0.0, 1.0);\n#else\n    gl_FragColor = vec4(floatToRG8(depth), floatToRG8(moment2));\n#endif\n}\n","vsm_hair_fragment.glsl":"\nvarying vec4 projection;\nuniform sampler2D alphaMap;\nvarying vec2 vUV;\nuniform float alpha_test;\n\nvoid main()\n{\n    float alpha = texture2D(alphaMap, vUV).g;\n    if (alpha < alpha_test) discard;\n\n    float depth = projection.z * .5 + .5;\n    float dx = dFdx(depth);\n    float dy = dFdy(depth);\n    float moment2 = depth * depth + 0.25*(dx*dx + dy*dy);\n\n#ifdef FLOAT_TEX\n    gl_FragColor = vec4(depth, moment2, 0.0, 1.0);\n#else\n    gl_FragColor = vec4(floatToRG8(depth), floatToRG8(moment2));\n#endif\n}\n","vsm_blur_vertex.glsl":"\nvarying vec2 vUV;\nvoid main() {\n    vUV = uv;\n    gl_Position = vec4(position, 1.0);\n}","vsm_blur_fragment.glsl":"\n#define RADIUS 2\n#define NUM_SAMPLES (RADIUS * 2 + 1)\n\nuniform sampler2D tDiffuse;\nuniform vec2 step;\n\nvarying vec2 vUV;\n\nvec2 getShadowValue(vec2 uv)\n{\n    vec4 s = texture2D(tDiffuse, uv);\n#ifdef FLOAT_TEX\n    return s.xy;\n#else\n    return vec2(RG8ToFloat(s.xy), RG8ToFloat(s.zw));\n#endif\n}\n\nvoid main() {\n    vec2 sum = getShadowValue(vUV);\n\n    for (int i = 1; i <= RADIUS; ++i) {\n        float fi = float(i);\n        sum += getShadowValue(vUV - step * fi);\n        sum += getShadowValue(vUV + step * fi);\n    }\n\n    sum /= float(NUM_SAMPLES);\n#ifdef FLOAT_TEX\n    gl_FragColor = vec4(sum, 0.0, 1.0);\n#else\n    gl_FragColor.xy = floatToRG8(sum.x);\n    gl_FragColor.zw = floatToRG8(sum.y);\n#endif\n}\n","gaussian_blur_hdre_fragment.glsl":"\nuniform sampler2D tDiffuse;\n\nvarying vec2 vUV;\n\nuniform vec2 sampleStep;\nuniform float weights[NUM_WEIGHTS];\n\nvoid main()\n{\n    vec3 col = decodeHDRE(texture2D(tDiffuse, vUV)) * weights[0];\n\n    for (int i = 1; i <= KERNEL_RADIUS; ++i) {\n        vec2 offset = float(i) * sampleStep;\n        col += (decodeHDRE(texture2D(tDiffuse, vUV + offset)) + decodeHDRE(texture2D(tDiffuse, vUV - offset))) * weights[i];\n    }\n\n    gl_FragColor = encodeHDRE(col);\n}\n","post_vertex.glsl":"\nvarying vec2 vUV;\n\nvoid main()\n{\n    gl_Position = vec4(position, 1.0);\n    vUV = uv;\n}\n","post_z_vertex.glsl":"\nvarying vec2 vUV;\nvarying vec3 viewVector;\n\nuniform mat4 unprojectionMatrix;\n\nvoid main()\n{\n    gl_Position = vec4(position, 1.0);\n    vUV = uv;\n    vec4 unproj = unprojectionMatrix * vec4(position.xy, 0.0, 1.0);\n    unproj /= unproj.w;\n    viewVector = -unproj.xyz / unproj.z;\n}\n","unlit_fragment.glsl":"\n#ifdef diffuse_MAP\nvarying vec2 texCoords;\nuniform sampler2D diffuseMap;\n#endif\n\nuniform vec3 color;\nuniform float opacity;\n\n#ifdef FOG\nvarying vec3 viewPosition;\n\nuniform float fogDensity;\nuniform vec3 fogColor;\n#endif\n\nvoid main() {\n    float alpha = opacity;\n#ifdef diffuse_MAP\n    vec4 diffuse = texture2D(diffuseMap, texCoords);\n    alpha *= diffuse.w;\n    diffuse.xyz *= diffuse.xyz;\n    diffuse.xyz *= color;\n    vec3 col = diffuse.xyz;\n#else\n    vec3 col = color;\n#endif\n\n#ifdef FOG\n    float fogAmount = clamp(exp2(viewPosition.z * fogDensity), 0.0, 1.0);\n    col = mix(fogColor, col, fogAmount);\n#endif\n\n#ifdef HDRE\n    gl_FragColor = encodeHDRE(sqrt(col));\n#else\n//    col *= alpha;\n    gl_FragColor = vec4(sqrt(col), alpha);\n#endif\n}\n","unlit_vertex.glsl":"\n#ifdef diffuse_MAP\nvarying vec2 texCoords;\n#endif\n\n#ifdef FOG\nvarying vec3 viewPosition;\n#endif\n\nvoid main() {\n    vec3 localPos = position;\n    vec4 viewPos = modelViewMatrix * vec4(localPos, 1.0);\n    gl_Position = projectionMatrix * viewPos;\n#ifdef FOG\n    viewPosition = viewPos.xyz;\n#endif\n\n#ifdef diffuse_MAP\n    texCoords = uv;\n#endif\n}\n"},Rb={uniforms:{tDiffuse:{value:null},opacity:{value:1}},vertexShader:"\n\n\t\tvarying vec2 vUv;\n\n\t\tvoid main() {\n\n\t\t\tvUv = uv;\n\t\t\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\n\n\t\t}",fragmentShader:"\n\n\t\tuniform float opacity;\n\n\t\tuniform sampler2D tDiffuse;\n\n\t\tvarying vec2 vUv;\n\n\t\tvoid main() {\n\n\t\t\tgl_FragColor = texture2D( tDiffuse, vUv );\n\t\t\tgl_FragColor.a *= opacity;\n\n\n\t\t}"},Hb=function(){function e(){B(this,e),this.enabled=!0,this.needsSwap=!0,this.clear=!1,this.renderToScreen=!1}return b(e,[{key:"setSize",value:function(){}},{key:"render",value:function(){console.error("THREE.Pass: .render() must be implemented in derived pass.")}},{key:"dispose",value:function(){}}]),e}(),Pb=new Vy(-1,1,1,-1,0,1),Nb=new Kv;Nb.setAttribute("position",new Rv([-1,3,0,-1,-1,0,3,-1,0],3)),Nb.setAttribute("uv",new Rv([0,2,0,0,2,0],2));var Ob=function(){function e(t){B(this,e),this._mesh=new Ay(Nb,t)}return b(e,[{key:"dispose",value:function(){this._mesh.geometry.dispose()}},{key:"render",value:function(e){e.render(this._mesh,Pb)}},{key:"material",get:function(){return this._mesh.material},set:function(e){this._mesh.material=e}}]),e}(),Vb=function(e){C(n,e);var t=M(n);function n(e,r){var i;return B(this,n),(i=t.call(this)).textureID=void 0!==r?r:"tDiffuse",e instanceof fy?(i.uniforms=e.uniforms,i.material=e):e&&(i.uniforms=cy.clone(e.uniforms),i.material=new fy({defines:Object.assign({},e.defines),uniforms:i.uniforms,vertexShader:e.vertexShader,fragmentShader:e.fragmentShader})),i.fsQuad=new Ob(i.material),i}return b(n,[{key:"render",value:function(e,t,n){this.uniforms[this.textureID]&&(this.uniforms[this.textureID].value=n.texture),this.fsQuad.material=this.material,this.renderToScreen?(e.setRenderTarget(null),this.fsQuad.render(e)):(e.setRenderTarget(t),this.clear&&e.clear(e.autoClearColor,e.autoClearDepth,e.autoClearStencil),this.fsQuad.render(e))}},{key:"dispose",value:function(){this.material.dispose(),this.fsQuad.dispose()}}]),n}(Hb),zb=function(e){C(n,e);var t=M(n);function n(e,r){var i;return B(this,n),(i=t.call(this)).scene=e,i.camera=r,i.clear=!0,i.needsSwap=!1,i.inverse=!1,i}return b(n,[{key:"render",value:function(e,t,n){var r,i,A=e.getContext(),a=e.state;a.buffers.color.setMask(!1),a.buffers.depth.setMask(!1),a.buffers.color.setLocked(!0),a.buffers.depth.setLocked(!0),this.inverse?(r=0,i=1):(r=1,i=0),a.buffers.stencil.setTest(!0),a.buffers.stencil.setOp(A.REPLACE,A.REPLACE,A.REPLACE),a.buffers.stencil.setFunc(A.ALWAYS,r,4294967295),a.buffers.stencil.setClear(i),a.buffers.stencil.setLocked(!0),e.setRenderTarget(n),this.clear&&e.clear(),e.render(this.scene,this.camera),e.setRenderTarget(t),this.clear&&e.clear(),e.render(this.scene,this.camera),a.buffers.color.setLocked(!1),a.buffers.depth.setLocked(!1),a.buffers.stencil.setLocked(!1),a.buffers.stencil.setFunc(A.EQUAL,1,4294967295),a.buffers.stencil.setOp(A.KEEP,A.KEEP,A.KEEP),a.buffers.stencil.setLocked(!0)}}]),n}(Hb),Gb=function(e){C(n,e);var t=M(n);function n(){var e;return B(this,n),(e=t.call(this)).needsSwap=!1,e}return b(n,[{key:"render",value:function(e){e.state.buffers.stencil.setLocked(!1),e.state.buffers.stencil.setTest(!1)}}]),n}(Hb),Kb=function(){function e(t,n){if(B(this,e),this.renderer=t,void 0===n){var r=t.getSize(new zg);this._pixelRatio=t.getPixelRatio(),this._width=r.width,this._height=r.height,(n=new dm(this._width*this._pixelRatio,this._height*this._pixelRatio)).texture.name="EffectComposer.rt1"}else this._pixelRatio=1,this._width=n.width,this._height=n.height;this.renderTarget1=n,this.renderTarget2=n.clone(),this.renderTarget2.texture.name="EffectComposer.rt2",this.writeBuffer=this.renderTarget1,this.readBuffer=this.renderTarget2,this.renderToScreen=!0,this.passes=[],this.copyPass=new Vb(Rb),this.clock=new lb}return b(e,[{key:"swapBuffers",value:function(){var e=this.readBuffer;this.readBuffer=this.writeBuffer,this.writeBuffer=e}},{key:"addPass",value:function(e){this.passes.push(e),e.setSize(this._width*this._pixelRatio,this._height*this._pixelRatio)}},{key:"insertPass",value:function(e,t){this.passes.splice(t,0,e),e.setSize(this._width*this._pixelRatio,this._height*this._pixelRatio)}},{key:"removePass",value:function(e){var t=this.passes.indexOf(e);-1!==t&&this.passes.splice(t,1)}},{key:"isLastEnabledPass",value:function(e){for(var t=e+1;t<this.passes.length;t++)if(this.passes[t].enabled)return!1;return!0}},{key:"render",value:function(e){void 0===e&&(e=this.clock.getDelta());for(var t=this.renderer.getRenderTarget(),n=!1,r=0,i=this.passes.length;r<i;r++){var A=this.passes[r];if(!1!==A.enabled){if(A.renderToScreen=this.renderToScreen&&this.isLastEnabledPass(r),A.render(this.renderer,this.writeBuffer,this.readBuffer,e,n),A.needsSwap){if(n){var a=this.renderer.getContext(),o=this.renderer.state.buffers.stencil;o.setFunc(a.NOTEQUAL,1,4294967295),this.copyPass.render(this.renderer,this.writeBuffer,this.readBuffer,e),o.setFunc(a.EQUAL,1,4294967295)}this.swapBuffers()}void 0!==zb&&(A instanceof zb?n=!0:A instanceof Gb&&(n=!1))}}this.renderer.setRenderTarget(t)}},{key:"reset",value:function(e){if(void 0===e){var t=this.renderer.getSize(new zg);this._pixelRatio=this.renderer.getPixelRatio(),this._width=t.width,this._height=t.height,(e=this.renderTarget1.clone()).setSize(this._width*this._pixelRatio,this._height*this._pixelRatio)}this.renderTarget1.dispose(),this.renderTarget2.dispose(),this.renderTarget1=e,this.renderTarget2=e.clone(),this.writeBuffer=this.renderTarget1,this.readBuffer=this.renderTarget2}},{key:"setSize",value:function(e,t){this._width=e,this._height=t;var n=this._width*this._pixelRatio,r=this._height*this._pixelRatio;this.renderTarget1.setSize(n,r),this.renderTarget2.setSize(n,r);for(var i=0;i<this.passes.length;i++)this.passes[i].setSize(n,r)}},{key:"setPixelRatio",value:function(e){this._pixelRatio=e,this.setSize(this._width,this._height)}},{key:"dispose",value:function(){this.renderTarget1.dispose(),this.renderTarget2.dispose(),this.copyPass.dispose()}}]),e}(),Wb=function(e){C(n,e);var t=M(n);function n(e,r,i,A,a){var o;return B(this,n),(o=t.call(this)).scene=e,o.camera=r,o.overrideMaterial=i,o.clearColor=A,o.clearAlpha=void 0!==a?a:0,o.clear=!0,o.clearDepth=!1,o.needsSwap=!1,o._oldClearColor=new Am,o}return b(n,[{key:"render",value:function(e,t,n){var r,i,A=e.autoClear;e.autoClear=!1,void 0!==this.overrideMaterial&&(i=this.scene.overrideMaterial,this.scene.overrideMaterial=this.overrideMaterial),this.clearColor&&(e.getClearColor(this._oldClearColor),r=e.getClearAlpha(),e.setClearColor(this.clearColor,this.clearAlpha)),this.clearDepth&&e.clearDepth(),e.setRenderTarget(this.renderToScreen?null:n),this.clear&&e.clear(e.autoClearColor,e.autoClearDepth,e.autoClearStencil),e.render(this.scene,this.camera),this.clearColor&&e.setClearColor(this._oldClearColor,r),void 0!==this.overrideMaterial&&(this.scene.overrideMaterial=i),e.autoClear=A}}]),n}(Hb),jb={uniforms:{tDiffuse:{value:null},powRGB:{value:new gm(2,2,2)},mulRGB:{value:new gm(1,1,1)},addRGB:{value:new gm(0,0,0)}},vertexShader:"\n\n\t\tvarying vec2 vUv;\n\n\t\tvoid main() {\n\n\t\t\tvUv = uv;\n\n\t\t\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\n\n\t\t}",fragmentShader:"\n\n\t\tuniform sampler2D tDiffuse;\n\t\tuniform vec3 powRGB;\n\t\tuniform vec3 mulRGB;\n\t\tuniform vec3 addRGB;\n\n\t\tvarying vec2 vUv;\n\n\t\tvoid main() {\n\n\t\t\tgl_FragColor = texture2D( tDiffuse, vUv );\n\t\t\tgl_FragColor.rgb = mulRGB * pow( ( gl_FragColor.rgb + addRGB ), powRGB );\n\n\t\t}"};var Xb={uniforms:{tDiffuse:{value:null},step:{value:new zg(1/512,0)}},vertexShader:Ib.get("vsm_blur_vertex"),fragmentShader:Ib.get("vsm_blur_fragment")},qb={uniforms:{tDiffuse:{value:null},sampleStep:{value:new zg},weights:{value:[]}},vertexShader:Ib.get("post_vertex"),fragmentShader:Ib.get("tiny_blur_hdre_fragment")},Yb={uniforms:{source:{value:null},blurred1:{value:null},blurred2:{value:null},depth:{value:null},strength:{value:1},focusDepth:{value:.5},focusRange:{value:.1},focusFalloff:{value:.1}},vertexShader:Ib.get("post_vertex"),fragmentShader:Ib.get("dof_fragment")},Jb={uniforms:{tDiffuse:{value:null},rcpRenderTargetResolution:{value:new zg},whitePoint:{value:1.3}},vertexShader:Ib.get("post_vertex"),fragmentShader:Ib.get("fxaa_tonemap_fragment")},Zb=(new zg,Ib.get("post_vertex"),Ib.get("gaussian_blur_hdre_fragment"),{defines:{RADIUS:8},uniforms:{tDiffuse:{value:null},depthMap:{value:null},color1:{value:null},color2:{value:null},color3:{value:null},sssProfileMap:{value:null},sssProfileScale:{value:1},sssRange:{value:1},step:{value:new zg(1/512,0)},unprojectionMatrix:{value:new Wm},cameraNear:{value:0},cameraRange:{value:0}},vertexShader:Ib.get("sss_blur_vertex"),fragmentShader:Ib.get("sss_blur_fragment")});var $b=function(){for(var e={},t=window.location.search.substring(1).split("&"),n=0;n<t.length;n++){var r=t[n].split("=");if("undefined"==typeof e[r[0]])e[r[0]]=decodeURIComponent(r[1]);else if("string"==typeof e[r[0]]){var i=[e[r[0]],decodeURIComponent(r[1])];e[r[0]]=i}else e[r[0]].push(decodeURIComponent(r[1]))}return e}(),ex=($b.debug&&"0"!==$b.debug&&$b.debug,!(/AppleWebKit/.test(navigator.userAgent)&&/Mobile\/\w+/.test(navigator.userAgent)||/Android|webOS|BlackBerry|Opera Mini|Opera Mobi|IEMobile/i.test(navigator.userAgent))&&!$b.lowPerformance);function tx(e){Object.keys(e).forEach((function(t){e[t]&&e[t].dispose&&(console.log("dispose",t),e[t].dispose(),e[t]=null)}))}var nx=function(){function e(t){B(this,e),this._amplitude=1/Math.sqrt(2*t*Math.PI),this._expScale=-1/(2*t)}return b(e,[{key:"getValueAt",value:function(e){return this._amplitude*Math.pow(Math.E,e*e*this._expScale)}}]),e}(),rx={getFloat:function(e){if(e.extensions.get("OES_texture_float_linear"))return mp;throw new Error("Float render targets are unsupported!")},getHalfOrFloat:function(e){var t=e.extensions;if(t.get("OES_texture_half_float_linear"))return vp;if(t.get("OES_texture_float_linear"))return mp;throw new Error("Float render targets are unsupported!")},getHalfOrFloatOrAny:function(e){var t=e.extensions;return t.get("OES_texture_half_float_linear")?vp:t.get("OES_texture_float_linear")?mp:cp}},ix=function(){function e(t,n,r){B(this,e),this._width=t,this._height=n,this._sourceFBO=new dm(t,n,r),this._targetFBO=new dm(t,n,r),this._sourceFBO.texture.generateMipmaps=r.generateMipmaps||!1,this._targetFBO.texture.generateMipmaps=r.generateMipmaps||!1}return b(e,[{key:"width",get:function(){return this._width}},{key:"height",get:function(){return this._height}},{key:"source",get:function(){return this._sourceFBO.texture}},{key:"target",get:function(){return this._targetFBO}},{key:"swap",value:function(){var e=this._sourceFBO;this._sourceFBO=this._targetFBO,this._targetFBO=e}}]),e}(),Ax=function(){function e(t,n){B(this,e),this._renderer=t,this._scene=new c_,this._camera=new Vy(-1,1,1,-1,0,1),this._mesh=n||new Ay(new Ty(2,2),null),this._scene.add(this._mesh)}return b(e,[{key:"execute",value:function(e,t,n,r){var i=this._renderer.autoClear;this._renderer.autoClear=void 0===n||n,this._mesh.material=e,this._renderer.setRenderTarget(t),this._renderer.render(this._scene,r||this._camera),this._renderer.setRenderTarget(null),this._renderer.autoClear=i}}]),e}(),ax=function(e){C(n,e);var t=M(n);function n(e){var r;B(this,n);var i="";return e&&(i+="#define FLOAT_TEX\n"),(r=t.call(this,{vertexShader:Ib.get("shadow_vertex"),fragmentShader:i+Ib.get("vsm_fragment")})).extensions.derivatives=!0,r.side=lf,r}return b(n)}(fy),ox=function(e){C(n,e);var t=M(n);function n(e){var r;B(this,n);var i="";return e&&(i+="#define FLOAT_TEX\n"),(r=t.call(this,{uniforms:{alphaMap:{value:null},alpha_test:{value:.5}},vertexShader:Ib.get("shadow_vertex"),fragmentShader:i+Ib.get("vsm_hair_fragment")})).extensions.derivatives=!0,r.side=uf,r}return b(n)}(fy),sx=function(){function e(){B(this,e),this._listeners=[],this._lookUp={}}return b(e,[{key:"bind",value:function(e,t){this._lookUp[e]=this._listeners.length;var n=t?e.bind(t):e;this._listeners.push(n)}},{key:"unbind",value:function(e){var t=this._lookUp[e];this._listeners.splice(t,1),delete this._lookUp[e]}},{key:"dispatch",value:function(e){for(var t=this._listeners.length,n=0;n<t;++n)this._listeners[n](e)}},{key:"hasListeners",get:function(){return this._listeners.length>0}}]),e}(),lx=function(e){C(n,e);var t=M(n);function n(e,r,i,A){B(this,n);var a=new oy(r=r||1e3,r,r);a.scale(-1,1,1);var o=new ux({envMap:e,exposure:i||0,hdre:A});return t.call(this,a,o)}return b(n,[{key:"texture",get:function(){return this.material.envMap},set:function(e){this.material.envMap=e}}]),n}(Ay),ux=function(e){C(n,e);var t=M(n);function n(e){var r;B(this,n);var i={};e.envMap instanceof um&&(i.LAT_LONG="1"),e.hdre&&(i.HDRE="1");var A={envMap:{value:e.envMap},exposure:{value:Math.pow(2,e.exposure||0)}};return(r=t.call(this,{uniforms:A,defines:i,vertexShader:Ib.get("sky_vertex"),fragmentShader:Ib.get("sky_fragment")}))._envMap=e.envMap,r}return b(n,[{key:"envMap",get:function(){return this._envMap},set:function(e){this._envMap=e,this.uniforms.envMap.value=e}}]),n}(fy),cx=function(e){C(n,e);var t=M(n);function n(e,r,i){var A;return B(this,n),(A=t.call(this))._container=e,A._coords=new gm(.5*Math.PI,.4*Math.PI,2),A._localAcceleration=new gm(0,0,0),A._localVelocity=new gm(0,0,0),A.lookAtTarget=r||new gm(0,0,0),A.zoomSpeed=2,A.maxRadius=20,A.minRadius=.1,A.dampen=.9,A.maxAzimuth=void 0,A.minAzimuth=void 0,A.minPolar=.1,A.maxPolar=Math.PI-.1,A.moveAcceleration=.02,A._m=new Wm,A._oldMouseX=0,A._oldMouseY=0,A._moveWithKeys=i,A._moveAcceleration=new gm,A._moveVelocity=new gm,A._isDown=!1,A.mouse_constant=.0015,A._initListeners(),A}return b(n,[{key:"radius",get:function(){return this._coords.z},set:function(e){this._coords.z=e}},{key:"azimuth",get:function(){return this._coords.x},set:function(e){this._coords.x=e}},{key:"polar",get:function(){return this._coords.y},set:function(e){this._coords.y=e}},{key:"onAdded",value:function(){this._isDown=!1;var e=/Firefox/i.test(navigator.userAgent)?"DOMMouseScroll":"mousewheel";this._container.addEventListener(e,this._onMouseWheel),document.addEventListener("mousemove",this._onMouseMove),document.addEventListener("touchmove",this._onTouchMove),this._container.addEventListener("mousedown",this._onMouseDown),this._container.addEventListener("touchstart",this._onTouchDown),document.addEventListener("mouseup",this._onUp),document.addEventListener("touchend",this._onUp),this._moveWithKeys&&(document.addEventListener("keyup",this._onKeyUp),document.addEventListener("keydown",this._onKeyDown))}},{key:"onRemoved",value:function(){var e=/Firefox/i.test(navigator.userAgent)?"DOMMouseScroll":"mousewheel";this._container.removeEventListener(e,this._onMouseWheel),this._container.removeEventListener("mousemove",this._onMouseMove),this._container.removeEventListener("touchmove",this._onTouchMove),this._container.removeEventListener("mousedown",this._onMouseDown),this._container.removeEventListener("touchstart",this._onTouchDown),this._container.removeEventListener("mouseup",this._onUp),this._container.removeEventListener("touchend",this._onUp),this._moveWithKeys&&(document.removeEventListener("keyup",this._onKeyUp),document.removeEventListener("keydown",this._onKeyDown))}},{key:"onUpdate",value:function(e){if(this._moveWithKeys){this._moveVelocity.x*=this.dampen,this._moveVelocity.y*=this.dampen,this._moveVelocity.z*=this.dampen,this._moveVelocity.x+=this._moveAcceleration.x,this._moveVelocity.y+=this._moveAcceleration.y,this._moveVelocity.z+=this._moveAcceleration.z;var t=new gm;t.copy(this._moveVelocity),t.applyQuaternion(this.entity.quaternion.setFromRotationMatrix(this.entity.matrixWorld)),this.lookAtTarget.x+=t.x,this.lookAtTarget.y+=this._moveVelocity.y,this.lookAtTarget.z+=t.z}this._localVelocity.x*=this.dampen,this._localVelocity.y*=this.dampen,this._localVelocity.z*=this.dampen,this._localVelocity.x+=this._localAcceleration.x,this._localVelocity.y+=this._localAcceleration.y,this._localVelocity.z+=this._localAcceleration.z,this._localAcceleration.x=0,this._localAcceleration.y=0,this._localAcceleration.z=0,this._coords.add(this._localVelocity),this._coords.y=Vg.clamp(this._coords.y,this.minPolar,this.maxPolar),this._coords.z=Vg.clamp(this._coords.z,this.minRadius,this.maxRadius),void 0!==this.maxAzimuth&&void 0!==this.minAzimuth&&(this._coords.x=Vg.clamp(this._coords.x,this.minAzimuth,this.maxAzimuth));var n=this.entity,r=this._m,i=this._fromSphericalCoordinates(this._coords.z,this._coords.x,this._coords.y);i.add(this.lookAtTarget),r.lookAt(i,this.lookAtTarget,new gm(0,1,0)),r.setPosition(i),r.decompose(n.position,n.quaternion,n.scale)}},{key:"_fromSphericalCoordinates",value:function(e,t,n){var r=new gm;return r.x=e*Math.sin(n)*Math.cos(t),r.y=e*Math.cos(n),r.z=e*Math.sin(n)*Math.sin(t),r}},{key:"setAzimuthImpulse",value:function(e){this._localAcceleration.x=e}},{key:"setPolarImpulse",value:function(e){this._localAcceleration.y=e}},{key:"setZoomImpulse",value:function(e){this._localAcceleration.z=e}},{key:"_updateMove",value:function(e,t){if(void 0!==this._oldMouseX){var n=e-this._oldMouseX,r=t-this._oldMouseY;this.setAzimuthImpulse(this.mouse_constant*n),this.setPolarImpulse(this.mouse_constant*-r)}this._oldMouseX=e,this._oldMouseY=t}},{key:"_initListeners",value:function(){var e=this;this._onMouseWheel=function(t){var n=t.detail?-120*t.detail:t.wheelDelta;e.setZoomImpulse(-n*e.zoomSpeed*1e-4)},this._onMouseDown=function(t){e._oldMouseX=void 0,e._oldMouseY=void 0,e._isDown=!0},this._onMouseMove=function(t){e._isDown&&e._updateMove(t.screenX,t.screenY)},this._onTouchDown=function(t){if(e._oldMouseX=void 0,e._oldMouseY=void 0,2===t.touches.length){var n=t.touches[0],r=t.touches[1],i=n.screenX-r.screenX,A=n.screenY-r.screenY;e._startPitchDistance=Math.sqrt(i*i+A*A),e._startZoom=e.radius}e._isDown=!0},this._onTouchMove=function(t){if(t.preventDefault(),e._isDown){var n=t.touches.length;if(1===n){var r=t.touches[0];e._updateMove(r.screenX,r.screenY)}else if(2===n){var i=t.touches[0],A=t.touches[1],a=i.screenX-A.screenX,o=i.screenY-A.screenY,s=Math.sqrt(a*a+o*o),l=e._startPitchDistance-s;e.radius=e._startZoom+.2*l}}},this._onUp=function(t){e._isDown=!1},this._onKeyUp=function(t){switch(t.keyCode){case 69:case 81:e._moveAcceleration.y=0;break;case 37:case 65:case 39:case 68:e._moveAcceleration.x=0;break;case 38:case 87:case 40:case 83:e._moveAcceleration.z=0}},this._onKeyDown=function(t){switch(t.keyCode){case 81:e._moveAcceleration.y=-e.moveAcceleration;break;case 69:e._moveAcceleration.y=e.moveAcceleration;break;case 37:case 65:e._moveAcceleration.x=-e.moveAcceleration;break;case 38:case 87:e._moveAcceleration.z=-e.moveAcceleration;break;case 39:case 68:e._moveAcceleration.x=e.moveAcceleration;break;case 40:case 83:e._moveAcceleration.z=e.moveAcceleration}}}}]),n}(function(){function e(){B(this,e),this._entity=null}return b(e,[{key:"onAdded",value:function(){}},{key:"onRemoved",value:function(){}},{key:"entity",get:function(){return this._entity}}]),e}()),dx=function(e){e._components=[],e._requiresUpdates=!1,e._onRequireUpdatesChange=new sx,e._update=function(e){var t=this._components;if(t)for(var n=t.length,r=0;r<n;++r){var i=t[r];i.onUpdate&&i.onUpdate(e)}},e._updateRequiresUpdates=function(e){e!==this._requiresUpdates&&(this._requiresUpdates=e,this._onRequireUpdatesChange.dispatch(this))}},hx={ENGINE:new(function(){function e(){B(this,e),this._updateableEntities=[],this._updateQueue=[],this._destroyQueue=[]}return b(e,[{key:"registerEntity",value:function(e){e._onRequireUpdatesChange.bind(this._onEntityUpdateChange,this),e._requiresUpdates&&this._addUpdatableEntity(e)}},{key:"unregisterEntity",value:function(e){e._onRequireUpdatesChange.unbind(this),e._requiresUpdates&&this._removeUpdatableEntity(e)}},{key:"destroyEntity",value:function(e){e._onRequireUpdatesChange.unbind(this),e._requiresUpdates&&this._removeUpdatableEntity(e),this._destroyQueue.push(e)}},{key:"_onEntityUpdateChange",value:function(e){e._requiresUpdates?this._addUpdatableEntity(e):this._removeUpdatableEntity(e)}},{key:"_addUpdatableEntity",value:function(e){this._updateQueue.push({entity:e,updatable:!0})}},{key:"_removeUpdatableEntity",value:function(e){this._updateQueue.push({entity:e,updatable:!1})}},{key:"_processUpdateQueue",value:function(){var e=this._updateQueue.length;if(0!==e){for(var t=0;t<e;++t){var n=this._updateQueue[t],r=n.entity;if(n.updatable)this._updateableEntities.push(r);else{var i=this._updateableEntities.indexOf(r);this._updateableEntities.splice(i,1)}}this._updateQueue=[]}}},{key:"_processDestroyQueue",value:function(){var e=this._destroyQueue.length;if(0!==e){for(var t=0;t<e;++t){var n=this._destroyQueue[t];delete n._components,delete n._requiresUpdates,delete n._onRequireUpdatesChange,delete n._update,delete n._updateRequiresUpdates}this._destroyQueue=[]}}},{key:"update",value:function(e){this._processUpdateQueue(),this._processDestroyQueue();for(var t=this._updateableEntities,n=t.length,r=0;r<n;++r)t[r]._update(e)}}]),e}()),isEntity:function(e){return!!e._components},convert:function(e){hx.isEntity(e)||(dx(e),hx.ENGINE.registerEntity(e))},destroy:function(e){hx.ENGINE.destroyEntity(e)},addComponents:function(e,t){for(var n=0;n<t.length;++n)hx.addComponent(e,t[n])},removeComponents:function(e,t){for(var n=0;n<t.length;++n)hx.removeComponent(e,t[n])},addComponent:function(e,t){if(t._entity)throw new Error("Component already added to an entity!");hx.convert(e),e._components.push(t),e._updateRequiresUpdates(this._requiresUpdates||!!t.onUpdate),t._entity=e,t.onAdded()},hasComponent:function(e,t){return e._components&&e._components.indexOf(t)>=0},removeComponent:function(e,t){if(!hx.hasComponent(e,t))throw new Error("Component wasn't added to this entity!");t.onRemoved();for(var n=!1,r=e._components.length,i=0,A=[],a=0;a<r;++a){var o=e._components[a];o!==t&&(A[i++]=o,n=n||!!t.onUpdate)}e._components=0===i?null:A,t._entity=null,e._updateRequiresUpdates(n)}};var fx=function(e){C(n,e);var t=M(n);function n(){return B(this,n),t.call(this,{uniforms:{cameraNear:{value:0},rcpCameraRange:{value:0}},vertexShader:Ib.get("linear_depth_vertex"),fragmentShader:Ib.get("linear_depth_fragment")})}return b(n,[{key:"cameraNear",get:function(){return this.uniforms.cameraNear.value},set:function(e){this.uniforms.cameraNear.value=e}},{key:"rcpCameraRange",get:function(){return this.uniforms.rcpCameraRange.value},set:function(e){this.uniforms.rcpCameraRange.value=e}}]),n}(fy),px=function(e){C(n,e);var t=M(n);function n(){var e;return B(this,n),(e=t.call(this,{uniforms:{cameraNear:{value:0},rcpCameraRange:{value:0},alphaMap:{value:null},alpha_test:{value:.5},layer:{value:new Number(0)}},vertexShader:Ib.get("linear_depth_vertex"),fragmentShader:Ib.get("linear_hair_depth_fragment")})).side=uf,e}return b(n,[{key:"cameraNear",get:function(){return this.uniforms.cameraNear.value},set:function(e){this.uniforms.cameraNear.value=e}},{key:"rcpCameraRange",get:function(){return this.uniforms.rcpCameraRange.value},set:function(e){this.uniforms.rcpCameraRange.value=e}}]),n}(fy),gx=function(){function e(t,n,r,i){B(this,e),this._renderer=r,this._scene=t,this._camera=n,this._scale=i||1,this._depthMaterial=new fx,this._hair_depthMaterial=new px,xx.override_setting.depth.face=this._depthMaterial,xx.override_setting.depth.hair=this._hair_depthMaterial}return b(e,[{key:"texture",get:function(){return this._renderTarget.texture}},{key:"resize",value:function(e,t){e=Math.ceil(e*this._scale),t=Math.ceil(t*this._scale),this._renderTarget&&this._renderTarget.width===e&&this._renderTarget.height===t||(this._renderTarget=new dm(e,t,{minFilter:op,magFilter:op,generateMipmaps:!1,depthBuffer:!0,stencilBuffer:!1})),this._renderTarget2&&this._renderTarget2.width===e&&this._renderTarget2.height===t||(this._renderTarget2=new dm(e,t,{minFilter:op,magFilter:op,generateMipmaps:!1,depthBuffer:!0,stencilBuffer:!1})),this.width=e,this.height=t}},{key:"render",value:function(){this._depthMaterial.cameraNear=this._camera.near,this._depthMaterial.rcpCameraRange=1/(this._camera.far-this._camera.near),this._hair_depthMaterial.cameraNear=this._camera.near,this._hair_depthMaterial.rcpCameraRange=1/(this._camera.far-this._camera.near),this._renderer.setClearColor(16777215,1),xx.override("depth"),this._hair_depthMaterial.uniforms.layer.value=0,this._renderer.setRenderTarget(this._renderTarget),Cx.render_type===_x.Type.DEPTH_MAP&&this._renderer.setRenderTarget(null),this._renderer.clear(),this._renderer.render(this._scene,this._camera),this._renderer.setRenderTarget(null),xx.unoverride(),this._renderer.setClearColor(0,1)}},{key:"dispose",value:function(){tx(this)}}]),e}(),mx=function(){function e(t,n,r,i){B(this,e),i=void 0===i?1024:i,this.onUpdate=new sx;var A=rx.getHalfOrFloatOrAny(n);this._floatTexture=A!==cp,this._vsmMaterial=new ax(this._floatTexture),this._hair_vsmMaterial=new ox(this._floatTexture),xx.override_setting.shadow.face=this._vsmMaterial,xx.override_setting.shadow.hair=this._hair_vsmMaterial,this._rectRenderer=new Ax(n),this._shadowMap=new ix(i,i,{minFilter:op,magFilter:op,format:bp,type:A}),this._light=r,this._renderer=n,this._scene=t,this._lightCamera=new Vy(-.3,.3,-.3,.3,-1,1),this._size=i,this._focusObject=t,this.shadowMapMatrix=new Wm,Xb.defines=this._floatTexture?{FLOAT_TEX:1}:void 0,this.blur=new fy(Xb)}return b(e,[{key:"shadowMap",get:function(){return this._shadowMap.source}},{key:"size",get:function(){return this._size}},{key:"floatTexture",get:function(){return this._floatTexture}},{key:"constrain",value:function(e){this._focusObject=e}},{key:"render",value:function(){var e=this._lightCamera;e.position.copy(this._light.position),e.lookAt(new gm(0,0,0)),e.position.set(0,0,0),e.updateMatrixWorld();var t=new Wm;t.copy(e.matrixWorld).invert();var n=new ym;n.setFromObject(this._focusObject),n.applyMatrix4(t),e.left=n.min.x-.01,e.right=n.max.x+.01,e.top=n.min.y-.01,e.bottom=n.max.y+.01,e.near=-n.max.z-.01,e.far=.01-n.min.z,e.updateProjectionMatrix(),this.shadowMapMatrix.copy(e.matrixWorld).invert(),this.shadowMapMatrix.multiplyMatrices(e.projectionMatrix,this.shadowMapMatrix),this._renderer.setClearColor(16777215),xx.override("shadow"),this._renderer.setRenderTarget(this._shadowMap.target),Cx.render_type===_x.Type.SHADOW_MAP&&this._renderer.setRenderTarget(null),this._renderer.render(this._scene,this._lightCamera),this._renderer.setRenderTarget(null),xx.unoverride(),this._shadowMap.swap(),this._renderer.setClearColor(0);var r=this.blur.uniforms;r.step.value.set(1/this._size,0),r.tDiffuse.value=this._shadowMap.source,this._rectRenderer.execute(this.blur,this._shadowMap.target),this._shadowMap.swap(),r.step.value.set(0,1/this._size),r.tDiffuse.value=this._shadowMap.source,Cx.render_type===_x.Type.SHADOW_MAP_BLUR?this._rectRenderer.execute(this.blur,null):this._rectRenderer.execute(this.blur,this._shadowMap.target),this._shadowMap.swap(),this.onUpdate.dispatch()}},{key:"dispose",value:function(){tx(this)}}]),e}(),vx=function(){function e(t,n){B(this,e),this._gaussianTexture=new d_(null,t,1,bp,mp,null,np,np,op,op),this.generateMipmap=!1,this._range=n,this._layers=[],this._width=t}return b(e,[{key:"gaussianLookUp",get:function(){return this._gaussianTexture}},{key:"range",get:function(){return this._range/1e3}},{key:"distanceMapping",get:function(){return 1e3/this._range}},{key:"getBlendColor",value:function(e){return e>=this._layers.length?new Am:this._layers[e].blend}},{key:"addLayer",value:function(e,t){if(4===this._layers.length)throw new Error("Doesn't support more than 4 layers!");this._layers.push({gauss:new nx(e),blend:t})}},{key:"generate",value:function(){for(var e=[],t=this._range/this._width,n=this._layers.length,r=0;r<this._width;++r){for(var i=r*t,A=0;A<n;++A){var a=this._layers[A],o=a.gauss.getValueAt(i);a.blend;e.push(o)}for(A=n;A<4;++A)e.push(0)}this._gaussianTexture.image.data=new Float32Array(e),this._gaussianTexture.needsUpdate=!0}}]),e}(),yx=function(){function e(t,n,r,i,A,a){B(this,e),this._scale=a||.5,this._camera=n,this._renderer=r,this._scene=t,this.depthRenderer=i,this._floatTexType=rx.getHalfOrFloatOrAny(r),this._rectRenderer=new Ax(r),this._material=new wx(A),xx.override_setting.sss.face=this._material,this._renderTarget=new ix(1,1,{type:this._floatTexType,minFilter:op,magFilter:op,generateMipmaps:!1,depthBuffer:!0,stencilBuffer:!1}),this.sssBlur=new fy(Zb),this.sssBlur.uniforms.sssProfileMap.value=A.sssProfile.gaussianLookUp,this.sssBlur.uniforms.color1.value=A.sssProfile.getBlendColor(1),this.sssBlur.uniforms.color2.value=A.sssProfile.getBlendColor(2),this.sssBlur.uniforms.color3.value=A.sssProfile.getBlendColor(3),this.sssBlur.uniforms.sssProfileScale.value=A.sssProfile.distanceMapping,this.sssBlur.uniforms.sssRange.value=A.sssProfile.range,this.sssBlur.depthWrite=!1,this.sssBlur.depthTest=!1}return b(e,[{key:"texture",get:function(){return this._renderTarget.source}},{key:"resize",value:function(e,t){var n=Math.ceil(e*this._scale),r=Math.ceil(t*this._scale);this._renderTarget=new ix(n,r,{type:this._floatTexType,minFilter:op,magFilter:op,generateMipmaps:!1,depthBuffer:!0,stencilBuffer:!1})}},{key:"render",value:function(){xx.override("sss"),this._renderer.setRenderTarget(this._renderTarget.target),this._renderer.clear(),Cx.render_type===_x.Type.SSS&&this._renderer.setRenderTarget(null),this._renderer.render(this._scene,this._camera),this._renderer.setRenderTarget(null),this._renderTarget.swap(),xx.unoverride();var e=this.sssBlur.uniforms;e.depthMap.value=this.depthRenderer.texture,e.cameraNear.value=this._camera.near,e.cameraRange.value=this._camera.far-this._camera.near,e.unprojectionMatrix.value.copy(this._camera.projectionMatrix).invert(),e.tDiffuse.value=this._renderTarget.source,e.step.value.set(1/this._renderTarget.width,0),this._rectRenderer.execute(this.sssBlur,this._renderTarget.target),this._renderTarget.swap(),e.tDiffuse.value=this._renderTarget.source,e.step.value.set(0,1/this._renderTarget.height),Cx.render_type===_x.Type.SSS_BLUR?this._rectRenderer.execute(this.sssBlur,null):this._rectRenderer.execute(this.sssBlur,this._renderTarget.target),this._renderTarget.swap()}},{key:"dispose",value:function(){tx(this)}}]),e}(),wx=function(e){C(n,e);var t=M(n);function n(e){var r;B(this,n);var i={irradianceMap:{value:e.irradianceMap},probeExposure:{value:Math.pow(2,void 0===e.probeExposure?0:e.probeExposure)},normalMap:{value:e.normalMap},shadowMap:{value:e.shadowRenderer.shadowMap},shadowMapMatrix:{value:e.shadowRenderer.shadowMapMatrix}},A=cy.merge([i,Qy.lights]);return(r=t.call(this,{uniforms:A,defines:{MIN_VARIANCE:-1e-4,LIGHT_BLEED_REDUCTION:.5},lights:!0,vertexShader:Ib.get("sss_vertex"),fragmentShader:Ib.getInclude("include_ggx")+Ib.get("sss_fragment")})).uniforms.shadowMap.value=e.shadowRenderer.shadowMap,r.extensions.derivatives=!0,r.uniforms.irradianceMap.value=e.irradianceMap,r.uniforms.normalMap.value=e.normalMap,r._shadowRenderer=e.shadowRenderer,r._shadowRenderer.onUpdate.bind(r._onShadowUpdate,U(r)),r}return b(n,[{key:"_onShadowUpdate",value:function(){this.uniforms.shadowMapMatrix.value=this._shadowRenderer.shadowMapMatrix,this.uniforms.shadowMap.value=this._shadowRenderer.shadowMap}}]),n}(fy),Bx=function(e){C(n,e);var t=M(n);function n(e,r,i,A){var a;return B(this,n),(a=t.call(this))._depthTexture=i,a._focusPosition=new gm,a._focusFalloff=.5,a._focusRange=.5,a._v=new gm,a._camera=e,a._scene=r,a._textureType=A,a._strength=1,a.copy=new fy(Rb),a.blur=new fy(qb),a.composite=new fy(Yb),a._postCamera=new Vy(-1,1,1,-1,0,1),a._postScene=new c_,a._postQuad=new Ay(new Ty(2,2),null),a._postScene.add(a._postQuad),a.focusRange=2,a.focusFalloff=5,a}return b(n,[{key:"depthTexture",get:function(){return this._depthTexture},set:function(e){this._depthTexture=e}},{key:"focusPosition",get:function(){return this._focusPosition},set:function(e){this._focusPosition=e}},{key:"focusFalloff",get:function(){return this._focusFalloff},set:function(e){this._focusFalloff=e,this.composite.uniforms.focusFalloff.value=e/(this._camera.far-this._camera.near)}},{key:"focusRange",get:function(){return this._focusRange},set:function(e){this._focusRange=e,this.composite.uniforms.focusRange.value=e/(this._camera.far-this._camera.near)}},{key:"strength",get:function(){return this._strength},set:function(e){this._strength=e,this.composite.uniforms.strength.value=e,this.enabled=0!==this._strength}},{key:"render",value:function(e,t,n,r,i){this._v.copy(this._focusPosition),this._v.applyMatrix4(this._camera.matrixWorldInverse),this.composite.uniforms.focusDepth.value=(-this._v.z-this._camera.near)/(this._camera.far-this._camera.near);var A=this.smallBlurRadiusTex.width,a=this.smallBlurRadiusTex.height;this._postQuad.material=this.copy,this.copy.uniforms.tDiffuse.value=n.texture,e.setRenderTarget(this.smallBlurRadiusTex2),e.render(this._postScene,this._postCamera),e.setRenderTarget(null),this._postQuad.material=this.blur,this.blur.uniforms.tDiffuse.value=this.smallBlurRadiusTex2.texture,this.blur.uniforms.sampleStep.value.x=1/A,this.blur.uniforms.sampleStep.value.y=1/a,e.setRenderTarget(this.smallBlurRadiusTex),e.render(this._postScene,this._postCamera),e.setRenderTarget(null),this.blur.uniforms.tDiffuse.value=this.smallBlurRadiusTex.texture,this.blur.uniforms.sampleStep.value.x=1.5/A,this.blur.uniforms.sampleStep.value.y=1.5/a,e.setRenderTarget(this.largeBlurRadiusTex),e.render(this._postScene,this._postCamera),e.setRenderTarget(null),this._postQuad.material=this.composite,this.composite.uniforms.depth.value=this._depthTexture,this.composite.uniforms.source.value=n.texture,this.composite.uniforms.blurred1.value=this.smallBlurRadiusTex.texture,this.composite.uniforms.blurred2.value=this.largeBlurRadiusTex.texture,e.setRenderTarget(t),e.render(this._postScene,this._postCamera),e.setRenderTarget(null)}},{key:"setSize",value:function(e,t){if(e=Math.floor(e),t=Math.floor(t),this.resolutionX!==e||this.resolutionY!==t){this.resolutionX=e,this.resolutionY=t;var n={minFilter:op,magFilter:op,format:bp,type:this._textureType},r=e>>1,i=t>>1;this.smallBlurRadiusTex=new dm(r,i,n),this.smallBlurRadiusTex2=new dm(r,i,n),r=e>>2,i=t>>2,this.largeBlurRadiusTex=new dm(r,i,n),this.smallBlurRadiusTex.texture.generateMipmaps=!1,this.largeBlurRadiusTex.texture.generateMipmaps=!1}}}]),n}(Hb),_x=function(){function e(){B(this,e),this.render_type=e.Type.FINAL_COMPOSE,this.skin_material=null,this.enabled=!1,document.addEventListener("keyup",this._onKeyUp.bind(this))}return b(e,[{key:"_onKeyUp",value:function(t){if(this.enabled){switch(t.keyCode){case 48:case 96:this.render_type=e.Type.FINAL_COMPOSE,this.skin_material&&(this.skin_material.uniforms.return_stage.value=0);break;case 49:case 97:this.render_type=e.Type.DEPTH_MAP;break;case 50:case 98:this.render_type=e.Type.SHADOW_MAP;break;case 51:case 99:this.render_type=e.Type.SHADOW_MAP_BLUR;break;case 52:case 100:this.render_type=e.Type.SSS;break;case 53:case 101:this.render_type=e.Type.SSS_BLUR;break;case 54:case 102:this.render_type=e.Type.SKIN_SSS,this.skin_material&&(this.skin_material.uniforms.return_stage.value=1);break;case 55:case 103:this.render_type=e.Type.SKIN_TRANSMISSION,this.skin_material&&(this.skin_material.uniforms.return_stage.value=2);break;case 56:case 104:this.render_type=e.Type.SKIN_SHADOWDIFFUSE,this.skin_material&&(this.skin_material.uniforms.return_stage.value=3);break;default:this.render_type=e.Type.SKIN_FINAL,this.skin_material&&(this.skin_material.uniforms.return_stage.value=0)}console.log(this.render_type)}}}]),e}();_x.Type={FINAL_COMPOSE:"FINAL_COMPOSE",DEPTH_MAP:"DEPTH_MAP",SHADOW_MAP:"SHADOW_MAP",SHADOW_MAP_BLUR:"SHADOW_MAP_BLUR",SSS:"SSS",SSS_BLUR:"SSS_BLUR",SKIN_SSS:"SKIN_SSS",SKIN_TRANSMISSION:"SKIN_TRANSMISSION",SKIN_SHADOWDIFFUSE:"SKIN_SHADOWDIFFUSE",SKIN_FINAL:"SKIN_FINAL"};var bx=function(e){C(n,e);var t=M(n);function n(e){var r;B(this,n);var i={diffuseMap:{value:null},alphaMap:{value:null},alpha_test:{value:.5},normalMap:{value:null},irradianceMap:{value:e.irradianceMap},specularMap:{value:e.specularMap},shadowMapMatrix:{value:null},shadowMap:{value:null},roughnessMap:{value:null},specular_map:{value:null},scatter_map:{value:null},tangent_shift_0:{value:.2},specular_exp_0:{value:100},tangent_shift_1:{value:0},specular_exp_1:{value:50}},A=cy.merge([i,Qy.lights]);return(r=t.call(this,{uniforms:A,defines:{MIN_VARIANCE:-1e-4,LIGHT_BLEED_REDUCTION:.5,USE_TANGENT:!0},lights:!0,vertexShader:Ib.get("hair_vertex"),fragmentShader:Ib.getInclude("include_beckmann")+Ib.get("hair_fragment")})).isHairMaterial=!0,r.type="HairMaterial",r.side=uf,r.transparent&&(r.uniforms.alpha_test.value=0),r._shadowRenderer=e.shadowRenderer,r._shadowRenderer.onUpdate.bind(r._onShadowUpdate,U(r)),r}return b(n,[{key:"_onShadowUpdate",value:function(){this.uniforms.shadowMapMatrix.value=this._shadowRenderer.shadowMapMatrix,this.uniforms.shadowMap.value=this._shadowRenderer.shadowMap}}]),n}(fy),xx=new(function(){function e(){B(this,e),this.face_mesh=null,this.hair_mesh=null,this.skybox_mesh=null,this.override_setting={depth:{face:null,hair:null},shadow:{face:null,hair:null},sss:{face:null,hair:null}},this.material_face=null,this.material_hair=null,this.skybox_hair=null,this.type=null}return b(e,[{key:"override",value:function(e){if(!(e in this.override_setting))throw"unknown override type ".concat(e);this.material_face=this.face_mesh.material,this.override_setting[e].face&&(this.face_mesh.material=this.override_setting[e].face),this.material_skybox=this.skybox_mesh.material,this.override_setting[e].face&&(this.skybox_mesh.material=this.override_setting[e].face),this.material_hair=this.hair_mesh.material,this.override_setting[e].hair?this.hair_mesh.material=this.override_setting[e].hair:this.hair_mesh.visible=!1,this.type=e}},{key:"unoverride",value:function(){var e=this.type;this.override_setting[e].hair||(this.hair_mesh.visible=!0),this.face_mesh.material=this.material_face,this.skybox_mesh.material=this.material_skybox,this.hair_mesh.material=this.material_hair,this.material_face=null,this.material_skybox=null,this.material_hair=null,this.type=null}}]),e}()),Cx=new _x;var Sx,Ex={roughness_detail:"/assets/juanfu/roughness-detail.jpg",env_irradiance:"/assets/env/lapa_4k_panorama_irradiance.hdr",env_specular:"/assets/env/lapa_4k_panorama_specular.hdr",roughness_ao_thickness:"/assets/juanfu/at.png"},Ux=function(){function e(){B(this,e)}return b(e,[{key:"get",value:function(e){return void 0===this[e]&&console.log("[ get undefined ] ".concat(e)),this[e]}},{key:"load_assets",value:function(e,t,n){var r=this;function i(e){}function A(e){console.log(e)}function a(e){r[this.key]=e.children[0].geometry,console.log("[ loaded ] ".concat(this.key)),s(u)}function o(e){"diffuse"!=this.key&&"hair_diffuse"!=this.key||(e.encoding=gg),e.wrapS=tp,e.wrapT=tp,e.minFilter=up,"hdr"==this.postfix&&(e.minFilter=op),e.magFilter=op,ex&&(e.anisotropy=16),r[this.key]=e,console.log("[ loaded ] ".concat(this.key)),s(u)}var s=function(r){if(r.length_current++,r.length_current==r.length_target){console.log("[ loaded all ]");var i=Object.keys(e);n&&this.update_assets(n,i),t&&t()}}.bind(this),l=Object.keys(e),u={length_target:l.length,length_current:0};l.forEach((function(t){var n,r,s=e[t],l=s.split(".").pop();switch(t){case"model":case"hair_mesh":n=new Lb,r=a;break;case"roughness_detail":case"diffuse":case"normal":case"roughness_ao_thickness":case"roughness":case"hair_diffuse":case"hair_alpha":case"hair_normal":case"hair_roughness":case"hair_specular":case"hair_scatter":n=new tb,r=o;break;case"env_irradiance":case"env_specular":n=new Db,r=o;break;default:throw"".concat(t," does not have corresponding loader")}n.load(s,r.bind({key:t,path:s,postfix:l}),i.bind({key:t,path:s,postfix:l}),A)}))}},{key:"update_assets",value:function(e,t){var n=this;(t=t||["model","diffuse","normal","roughness_ao_thickness","roughness"]).forEach((function(t){var r=n.get(t);if(r)switch(t){case"model":e.content.face_mesh.geometry&&e.content.face_mesh.geometry.dispose(),e.content.face_mesh.geometry=r;break;case"diffuse":e.content.sssMaterialOptions.diffuseMap=r,e.content.skinMaterial.uniforms.diffuseMap.value=r;break;case"normal":e.content.sssMaterialOptions.normalMap=r,e.content.skinMaterial.uniforms.normalMap.value=r,e.content.sssRenderer._material.uniforms.normalMap.value=r;break;case"roughness_ao_thickness":e.content.sssMaterialOptions.roughnessAOThicknessMap=r,e.content.skinMaterial.uniforms.roughnessAOThicknessMap.value=r;break;case"roughness":e.content.sssMaterialOptions.roughnessMap=r,e.content.skinMaterial.uniforms.roughnessMap.value=r;break;case"hair_mesh":e.content.hair_mesh.geometry&&e.content.hair_mesh.geometry.dispose(),r=function(e){var t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:1e-4;t=Math.max(t,Number.EPSILON);for(var n={},r=e.getIndex(),i=e.getAttribute("position"),A=r?r.count:i.count,a=0,o=Object.keys(e.attributes),s={},l={},u=[],c=["getX","getY","getZ","getW"],d=["setX","setY","setZ","setW"],h=0,f=o.length;h<f;h++){var p=o[h],g=e.attributes[p];s[p]=new Lv(new g.array.constructor(g.count*g.itemSize),g.itemSize,g.normalized);var m=e.morphAttributes[p];m&&(l[p]=new Lv(new m.array.constructor(m.count*m.itemSize),m.itemSize,m.normalized))}for(var v=Math.log10(1/t),y=Math.pow(10,v),w=0;w<A;w++){for(var B=r?r.getX(w):w,_="",b=0,x=o.length;b<x;b++)for(var C=o[b],S=e.getAttribute(C),E=S.itemSize,U=0;U<E;U++)_+="".concat(~~(S[c[U]](B)*y),",");if(_ in n)u.push(n[_]);else{for(var M=0,F=o.length;M<F;M++)for(var T=o[M],k=e.getAttribute(T),Q=e.morphAttributes[T],L=k.itemSize,D=s[T],I=l[T],R=0;R<L;R++){var H=c[R],P=d[R];if(D[P](a,k[H](B)),Q)for(var N=0,O=Q.length;N<O;N++)I[N][P](a,Q[N][H](B))}n[_]=a,u.push(a),a++}}var V=e.clone();for(var z in e.attributes){var G=s[z];if(V.setAttribute(z,new Lv(G.array.slice(0,a*G.itemSize),G.itemSize,G.normalized)),z in l)for(var K=0;K<l[z].length;K++){var W=l[z][K];V.morphAttributes[z][K]=new Lv(W.array.slice(0,a*W.itemSize),W.itemSize,W.normalized)}}return V.setIndex(u),V}(r),r.computeTangents(),e.content.hair_mesh.geometry=r;break;case"hair_diffuse":e.content.hair_material.uniforms.diffuseMap.value=r;break;case"hair_alpha":e.content.hair_material.uniforms.alphaMap.value=r,e.content.depthRenderer._hair_depthMaterial.uniforms.alphaMap.value=r,e.content.shadowRenderer._hair_vsmMaterial.uniforms.alphaMap.value=r;break;case"hair_normal":e.content.hair_material.uniforms.normalMap.value=r;break;case"hair_roughness":e.content.hair_material.uniforms.roughnessMap.value=r;break;case"hair_specular":e.content.hair_material.uniforms.specular_map.value=r;break;case"hair_scatter":e.content.hair_material.uniforms.scatter_map.value=r}}))}}]),e}(),Mx=new Ux,Fx=function(e){C(n,e);var t=M(n);function n(e){var r;B(this,n);var i={MIN_VARIANCE:1e-4,LIGHT_BLEED_REDUCTION:.1};e.shadowRenderer.floatTexture&&(i.VSM_FLOAT=1);var A={sssMap:{value:null},transmittanceColor:{value:new Am(50,150,250)},sssTopLayerColor:{value:e.sssProfile.getBlendColor(0)},diffuseMap:{value:e.diffuseMap},normalMap:{value:e.normalMap},roughnessAOThicknessMap:{value:e.roughnessAOThicknessMap},roughnessMap:{value:e.roughnessMap},irradianceMap:{value:e.irradianceMap},specularMap:{value:e.specularMap},shadowMap:{value:e.shadowRenderer.shadowMap},shadowMapMatrix:{value:e.shadowRenderer.shadowMapMatrix},probeExposure:{value:Math.pow(2,void 0===e.probeExposure?0:e.probeExposure)},normalSpecularReflectance:{value:.027},thicknessRange:{value:e.thicknessRange||.1},roughnessMapRange:{value:void 0===e.roughnessMapRange?.5:e.roughnessMapRange},roughnessMedian:{value:void 0===e.roughnessMedian?.65:e.roughnessMedian},roughnessDetailMap:{value:e.roughnessDetailMap},roughnessDetailRange:{value:.8},specular_intensity:{value:.7},return_stage:{value:0}},a=cy.merge([A,Qy.lights]);return(r=t.call(this,{uniforms:a,defines:i,lights:!0,vertexShader:Ib.get("skin_vertex"),fragmentShader:Ib.getInclude("include_beckmann")+Ib.get("skin_fragment")})).isSkinMaterial=!0,r.type="SkinMaterial",r.uniforms.diffuseMap.value=e.diffuseMap,r.uniforms.normalMap.value=e.normalMap,r.uniforms.roughnessAOThicknessMap.value=e.roughnessAOThicknessMap,r.uniforms.roughnessMap.value=e.roughnessMap,r.uniforms.roughnessDetailMap.value=e.roughnessDetailMap,r.uniforms.irradianceMap.value=e.irradianceMap,r.uniforms.specularMap.value=e.specularMap,r.uniforms.shadowMap.value=e.shadowRenderer.shadowMap,r.uniforms.return_stage.value=0,Cx.skin_material=U(r),r.extensions.derivatives=!0,r._shadowRenderer=e.shadowRenderer,r._shadowRenderer.onUpdate.bind(r._onShadowUpdate,U(r)),r}return b(n,[{key:"_onShadowUpdate",value:function(){this.uniforms.shadowMapMatrix.value=this._shadowRenderer.shadowMapMatrix,this.uniforms.shadowMap.value=this._shadowRenderer.shadowMap}}]),n}(fy),Tx=function(){function e(t){var n=this;B(this,e),this.timeScale=1,this._time=null,this.is_running=!1,this.renderer_parameters=t||{},this.renderer_parameters.alpha=this.renderer_parameters.alpha||!1,this.renderer_parameters.antialias=this.renderer_parameters.antialias||!1,this.renderer_parameters.preserveDrawingBuffer=this.renderer_parameters.preserveDrawingBuffer||!1,this.renderer=new l_({antialias:this.renderer_parameters.antialias,alpha:this.renderer_parameters.alpha,preserveDrawingBuffer:this.renderer_parameters.preserveDrawingBuffer});var r=window.devicePixelRatio;this.renderer.setPixelRatio(r),this.container=document.getElementById("webglcontainer"),this.container.appendChild(this.renderer.domElement),this.scene=new c_,this.camera=new gy(46.4,this.container.clientWidth/this.container.clientHeight,1,1e4),this.camera.position.z=100,this.camera.near=.001,this.camera.far=100,this.scene.add(this.camera),this.content=new kx(this);var i=this;this.resize_handler=function(){i._resizeCanvas()},window.addEventListener("resize",this.resize_handler),this._resizeCanvas(),setInterval((function(){n.resize_handler()}),4e3)}return b(e,[{key:"_resizeCanvas",value:function(){if(this.renderer){var e=this.container.clientWidth,t=this.container.clientHeight;if(0===e||0===t)return;this.renderer.setSize(e,t),this.renderer.domElement.style.width=e+"px",this.renderer.domElement.style.height=t+"px",this.camera.aspect=e/t,this.camera.updateProjectionMatrix(),this.content&&this.content.resize(e,t)}}},{key:"stop",value:function(){this.is_running=!1}},{key:"start",value:function(){this.is_running=!0,this._requestAnimationFrame()}},{key:"_render",value:function(){if(this.is_running){var e=(new Date).getTime(),t=0;null!==this._time&&(t=e-this._time),t*=this.timeScale,this._time=e,this._requestAnimationFrame(),hx.ENGINE.update(t),this.content&&this.content.update(t),Cx.render_type.startsWith("SKIN_")&&this.renderer.render(this.scene,this.camera),Cx.render_type===_x.Type.FINAL_COMPOSE&&(this.content&&this.content.effectComposer?this.content.effectComposer.render(t/1e3):this.renderer.render(this.scene,this.camera)),window.stats&&window.stats.update();var n=this.content.face_mesh.position,r=Math.pow(.5,t/100),i=new gm(n.x*r,n.y*r,n.z*r);this.content.face_mesh.position.set(i.x,i.y,i.z)}}},{key:"_requestAnimationFrame",value:function(){var e=this;requestAnimationFrame((function(){e._render()}))}},{key:"dispose",value:function(){this.container.removeChild(this.renderer.domElement),tx(this)}},{key:"hide_scene",value:function(){var e=this;e.content.face_mesh.visible=!1,e.content.hair_mesh.visible=!1,e.content.orbit._coords.set((.4*Math.random()+.3)*Math.PI,(.2*Math.random()+.3)*Math.PI,2)}},{key:"show_scene",value:function(){var e=this;e.content.face_mesh.visible=!0,e.content.hair_mesh.visible=!1,e.content.face_mesh.position.set(.5,0,0),e._resizeCanvas()}},{key:"clean_scene",value:function(){var e=this;e.content.face_mesh.geometry&&e.content.face_mesh.geometry.dispose(),e.content.face_mesh.geometry=new Kv}}]),e}(),kx=function(){function e(t){B(this,e),this.animateLight=!0,this.probeExposure=0,this.time=0,this.shadowsInvalid=!0,this.renderer=t.renderer,this.scene=t.scene,this.camera=t.camera,this.container=t.container,this.render_target=new dm(1,1,{minFilter:op,magFilter:op,format:bp,type:rx.getHalfOrFloatOrAny(this.renderer),stencilBuffer:!1}),this.effectComposer=new Kb(this.renderer,this.render_target),this.effectComposer.addPass(new Wb(this.scene,this.camera)),this.dof=new Bx(this.camera,this.scene,null,rx.getHalfOrFloatOrAny(this.renderer)),this.dof.focusPosition.set(0,0,0),this.dof.enabled=!1,this.effectComposer.addPass(this.dof),this.fxaa=new Vb(Jb),this.fxaa.uniforms.whitePoint.value=1.7,this.fxaa.renderToScreen=!0,this.effectComposer.addPass(this.fxaa),this.effectColor=new Vb(jb),this.effectColor.uniforms.powRGB.value.set(1,1,1),this.effectColor.uniforms.mulRGB.value.set(.95,.97,1.02),this.effectComposer.addPass(this.effectColor),this.sssProfile=new vx(256,1.2),this.sssProfile.addLayer(.0064,new Am(.2405,.4474,.6157)),this.sssProfile.addLayer(.0452,new Am(.1158,.3661,.3439)),this.sssProfile.addLayer(.2719-.0516,new Am(.1836,.1864,0)),this.sssProfile.addLayer(2.0062-.2719,new Am(.46,0,.0402)),this.sssProfile.generate();var n=new cx(this.container);n.lookAtTarget.z=.03,n.radius=.3,n.minRadius=.05,n.maxRadius=.3,n.zoomSpeed=.05,n.mouse_constant=2e-4,hx.addComponent(this.camera,n),this.orbit=n;this.mainLight=new sb(16777215),this.mainLight.position.set(0,0,-1),this.mainLight.intensity=.5,this.scene.add(this.mainLight),this.shadowRenderer=new mx(this.scene,this.renderer,this.mainLight,2048),this.depthRenderer=new gx(this.scene,this.camera,this.renderer,.5),this.sssMaterialOptions={diffuseMap:null,normalMap:null,roughnessAOThicknessMap:Mx.get("roughness_ao_thickness"),roughnessMap:null,roughnessDetailMap:Mx.get("roughness_detail"),irradianceMap:Mx.get("env_irradiance"),specularMap:Mx.get("env_specular"),sssProfile:this.sssProfile,probeExposure:this.probeExposure,shadowRenderer:this.shadowRenderer},this.sssRenderer=new yx(this.scene,this.camera,this.renderer,this.depthRenderer,this.sssMaterialOptions,.5),this.skybox=new lx(Mx.get("env_specular"),3,this.probeExposure),this.camera.add(this.skybox),this.skinMaterial=new Fx(this.sssMaterialOptions),this.face_mesh=new Ay(void 0,this.skinMaterial),this.face_mesh.scale.set(.005,.005,.005),this.scene.add(this.face_mesh),this.hair_material=new 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n=window.devicePixelRatio||1;e*=n,t*=n,this.fxaa&&this.fxaa.uniforms.rcpRenderTargetResolution.value.set(1/e,1/t),this.effectComposer.setSize(e,t),this.depthRenderer.resize(e,t),this.dof.depthTexture=this.depthRenderer.texture}},{key:"update",value:function(e){this.animateLight&&(this.time+=e,this.mainLight.position.set(Math.cos(5e-4*this.time),Math.sin(3e-4*this.time)+1.25,Math.sin(2e-4*this.time)+1.2),this.shadowsInvalid=!0),this.depthRenderer.render(),this.skybox.visible=!1,this.shadowsInvalid&&(this.shadowRenderer.render(),this.shadowsInvalid=!1),this.sssRenderer.render(),this.skinMaterial.uniforms.sssMap.value=this.sssRenderer.texture,this.skybox.visible=!0;var t=.01,n=this.camera.matrix.elements[8],r=this.camera.matrix.elements[9],i=this.camera.matrix.elements[10];this.dof.focusPosition.set(n*t,r*t,i*t);var 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diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/PIL/SgiImagePlugin.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/PIL/SgiImagePlugin.py
deleted file mode 100644
index 3662ffd1571821e196d07330fdeecf4b0e5c2efa..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/PIL/SgiImagePlugin.py
+++ /dev/null
@@ -1,231 +0,0 @@
-#
-# The Python Imaging Library.
-# $Id$
-#
-# SGI image file handling
-#
-# See "The SGI Image File Format (Draft version 0.97)", Paul Haeberli.
-# <ftp://ftp.sgi.com/graphics/SGIIMAGESPEC>
-#
-#
-# History:
-# 2017-22-07 mb   Add RLE decompression
-# 2016-16-10 mb   Add save method without compression
-# 1995-09-10 fl   Created
-#
-# Copyright (c) 2016 by Mickael Bonfill.
-# Copyright (c) 2008 by Karsten Hiddemann.
-# Copyright (c) 1997 by Secret Labs AB.
-# Copyright (c) 1995 by Fredrik Lundh.
-#
-# See the README file for information on usage and redistribution.
-#
-
-
-import os
-import struct
-
-from . import Image, ImageFile
-from ._binary import i16be as i16
-from ._binary import o8
-
-
-def _accept(prefix):
-    return len(prefix) >= 2 and i16(prefix) == 474
-
-
-MODES = {
-    (1, 1, 1): "L",
-    (1, 2, 1): "L",
-    (2, 1, 1): "L;16B",
-    (2, 2, 1): "L;16B",
-    (1, 3, 3): "RGB",
-    (2, 3, 3): "RGB;16B",
-    (1, 3, 4): "RGBA",
-    (2, 3, 4): "RGBA;16B",
-}
-
-
-##
-# Image plugin for SGI images.
-class SgiImageFile(ImageFile.ImageFile):
-    format = "SGI"
-    format_description = "SGI Image File Format"
-
-    def _open(self):
-        # HEAD
-        headlen = 512
-        s = self.fp.read(headlen)
-
-        if not _accept(s):
-            msg = "Not an SGI image file"
-            raise ValueError(msg)
-
-        # compression : verbatim or RLE
-        compression = s[2]
-
-        # bpc : 1 or 2 bytes (8bits or 16bits)
-        bpc = s[3]
-
-        # dimension : 1, 2 or 3 (depending on xsize, ysize and zsize)
-        dimension = i16(s, 4)
-
-        # xsize : width
-        xsize = i16(s, 6)
-
-        # ysize : height
-        ysize = i16(s, 8)
-
-        # zsize : channels count
-        zsize = i16(s, 10)
-
-        # layout
-        layout = bpc, dimension, zsize
-
-        # determine mode from bits/zsize
-        rawmode = ""
-        try:
-            rawmode = MODES[layout]
-        except KeyError:
-            pass
-
-        if rawmode == "":
-            msg = "Unsupported SGI image mode"
-            raise ValueError(msg)
-
-        self._size = xsize, ysize
-        self.mode = rawmode.split(";")[0]
-        if self.mode == "RGB":
-            self.custom_mimetype = "image/rgb"
-
-        # orientation -1 : scanlines begins at the bottom-left corner
-        orientation = -1
-
-        # decoder info
-        if compression == 0:
-            pagesize = xsize * ysize * bpc
-            if bpc == 2:
-                self.tile = [
-                    ("SGI16", (0, 0) + self.size, headlen, (self.mode, 0, orientation))
-                ]
-            else:
-                self.tile = []
-                offset = headlen
-                for layer in self.mode:
-                    self.tile.append(
-                        ("raw", (0, 0) + self.size, offset, (layer, 0, orientation))
-                    )
-                    offset += pagesize
-        elif compression == 1:
-            self.tile = [
-                ("sgi_rle", (0, 0) + self.size, headlen, (rawmode, orientation, bpc))
-            ]
-
-
-def _save(im, fp, filename):
-    if im.mode != "RGB" and im.mode != "RGBA" and im.mode != "L":
-        msg = "Unsupported SGI image mode"
-        raise ValueError(msg)
-
-    # Get the keyword arguments
-    info = im.encoderinfo
-
-    # Byte-per-pixel precision, 1 = 8bits per pixel
-    bpc = info.get("bpc", 1)
-
-    if bpc not in (1, 2):
-        msg = "Unsupported number of bytes per pixel"
-        raise ValueError(msg)
-
-    # Flip the image, since the origin of SGI file is the bottom-left corner
-    orientation = -1
-    # Define the file as SGI File Format
-    magic_number = 474
-    # Run-Length Encoding Compression - Unsupported at this time
-    rle = 0
-
-    # Number of dimensions (x,y,z)
-    dim = 3
-    # X Dimension = width / Y Dimension = height
-    x, y = im.size
-    if im.mode == "L" and y == 1:
-        dim = 1
-    elif im.mode == "L":
-        dim = 2
-    # Z Dimension: Number of channels
-    z = len(im.mode)
-
-    if dim == 1 or dim == 2:
-        z = 1
-
-    # assert we've got the right number of bands.
-    if len(im.getbands()) != z:
-        msg = f"incorrect number of bands in SGI write: {z} vs {len(im.getbands())}"
-        raise ValueError(msg)
-
-    # Minimum Byte value
-    pinmin = 0
-    # Maximum Byte value (255 = 8bits per pixel)
-    pinmax = 255
-    # Image name (79 characters max, truncated below in write)
-    img_name = os.path.splitext(os.path.basename(filename))[0]
-    img_name = img_name.encode("ascii", "ignore")
-    # Standard representation of pixel in the file
-    colormap = 0
-    fp.write(struct.pack(">h", magic_number))
-    fp.write(o8(rle))
-    fp.write(o8(bpc))
-    fp.write(struct.pack(">H", dim))
-    fp.write(struct.pack(">H", x))
-    fp.write(struct.pack(">H", y))
-    fp.write(struct.pack(">H", z))
-    fp.write(struct.pack(">l", pinmin))
-    fp.write(struct.pack(">l", pinmax))
-    fp.write(struct.pack("4s", b""))  # dummy
-    fp.write(struct.pack("79s", img_name))  # truncates to 79 chars
-    fp.write(struct.pack("s", b""))  # force null byte after img_name
-    fp.write(struct.pack(">l", colormap))
-    fp.write(struct.pack("404s", b""))  # dummy
-
-    rawmode = "L"
-    if bpc == 2:
-        rawmode = "L;16B"
-
-    for channel in im.split():
-        fp.write(channel.tobytes("raw", rawmode, 0, orientation))
-
-    if hasattr(fp, "flush"):
-        fp.flush()
-
-
-class SGI16Decoder(ImageFile.PyDecoder):
-    _pulls_fd = True
-
-    def decode(self, buffer):
-        rawmode, stride, orientation = self.args
-        pagesize = self.state.xsize * self.state.ysize
-        zsize = len(self.mode)
-        self.fd.seek(512)
-
-        for band in range(zsize):
-            channel = Image.new("L", (self.state.xsize, self.state.ysize))
-            channel.frombytes(
-                self.fd.read(2 * pagesize), "raw", "L;16B", stride, orientation
-            )
-            self.im.putband(channel.im, band)
-
-        return -1, 0
-
-
-#
-# registry
-
-
-Image.register_decoder("SGI16", SGI16Decoder)
-Image.register_open(SgiImageFile.format, SgiImageFile, _accept)
-Image.register_save(SgiImageFile.format, _save)
-Image.register_mime(SgiImageFile.format, "image/sgi")
-
-Image.register_extensions(SgiImageFile.format, [".bw", ".rgb", ".rgba", ".sgi"])
-
-# End of file
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/merge/unicode.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/merge/unicode.py
deleted file mode 100644
index 65ae6c49295ec25ce6e473e9a81e8a191c912d7f..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/merge/unicode.py
+++ /dev/null
@@ -1,78 +0,0 @@
-# Copyright 2021 Behdad Esfahbod. All Rights Reserved.
-
-
-def is_Default_Ignorable(u):
-    # http://www.unicode.org/reports/tr44/#Default_Ignorable_Code_Point
-    #
-    # TODO Move me to unicodedata module and autogenerate.
-    #
-    # Unicode 14.0:
-    # $ grep '; Default_Ignorable_Code_Point ' DerivedCoreProperties.txt | sed 's/;.*#/#/'
-    # 00AD          # Cf       SOFT HYPHEN
-    # 034F          # Mn       COMBINING GRAPHEME JOINER
-    # 061C          # Cf       ARABIC LETTER MARK
-    # 115F..1160    # Lo   [2] HANGUL CHOSEONG FILLER..HANGUL JUNGSEONG FILLER
-    # 17B4..17B5    # Mn   [2] KHMER VOWEL INHERENT AQ..KHMER VOWEL INHERENT AA
-    # 180B..180D    # Mn   [3] MONGOLIAN FREE VARIATION SELECTOR ONE..MONGOLIAN FREE VARIATION SELECTOR THREE
-    # 180E          # Cf       MONGOLIAN VOWEL SEPARATOR
-    # 180F          # Mn       MONGOLIAN FREE VARIATION SELECTOR FOUR
-    # 200B..200F    # Cf   [5] ZERO WIDTH SPACE..RIGHT-TO-LEFT MARK
-    # 202A..202E    # Cf   [5] LEFT-TO-RIGHT EMBEDDING..RIGHT-TO-LEFT OVERRIDE
-    # 2060..2064    # Cf   [5] WORD JOINER..INVISIBLE PLUS
-    # 2065          # Cn       <reserved-2065>
-    # 2066..206F    # Cf  [10] LEFT-TO-RIGHT ISOLATE..NOMINAL DIGIT SHAPES
-    # 3164          # Lo       HANGUL FILLER
-    # FE00..FE0F    # Mn  [16] VARIATION SELECTOR-1..VARIATION SELECTOR-16
-    # FEFF          # Cf       ZERO WIDTH NO-BREAK SPACE
-    # FFA0          # Lo       HALFWIDTH HANGUL FILLER
-    # FFF0..FFF8    # Cn   [9] <reserved-FFF0>..<reserved-FFF8>
-    # 1BCA0..1BCA3  # Cf   [4] SHORTHAND FORMAT LETTER OVERLAP..SHORTHAND FORMAT UP STEP
-    # 1D173..1D17A  # Cf   [8] MUSICAL SYMBOL BEGIN BEAM..MUSICAL SYMBOL END PHRASE
-    # E0000         # Cn       <reserved-E0000>
-    # E0001         # Cf       LANGUAGE TAG
-    # E0002..E001F  # Cn  [30] <reserved-E0002>..<reserved-E001F>
-    # E0020..E007F  # Cf  [96] TAG SPACE..CANCEL TAG
-    # E0080..E00FF  # Cn [128] <reserved-E0080>..<reserved-E00FF>
-    # E0100..E01EF  # Mn [240] VARIATION SELECTOR-17..VARIATION SELECTOR-256
-    # E01F0..E0FFF  # Cn [3600] <reserved-E01F0>..<reserved-E0FFF>
-    return (
-        u == 0x00AD
-        or u == 0x034F  # Cf       SOFT HYPHEN
-        or u == 0x061C  # Mn       COMBINING GRAPHEME JOINER
-        or 0x115F <= u <= 0x1160  # Cf       ARABIC LETTER MARK
-        or 0x17B4  # Lo   [2] HANGUL CHOSEONG FILLER..HANGUL JUNGSEONG FILLER
-        <= u
-        <= 0x17B5
-        or 0x180B  # Mn   [2] KHMER VOWEL INHERENT AQ..KHMER VOWEL INHERENT AA
-        <= u
-        <= 0x180D
-        or u  # Mn   [3] MONGOLIAN FREE VARIATION SELECTOR ONE..MONGOLIAN FREE VARIATION SELECTOR THREE
-        == 0x180E
-        or u == 0x180F  # Cf       MONGOLIAN VOWEL SEPARATOR
-        or 0x200B <= u <= 0x200F  # Mn       MONGOLIAN FREE VARIATION SELECTOR FOUR
-        or 0x202A <= u <= 0x202E  # Cf   [5] ZERO WIDTH SPACE..RIGHT-TO-LEFT MARK
-        or 0x2060  # Cf   [5] LEFT-TO-RIGHT EMBEDDING..RIGHT-TO-LEFT OVERRIDE
-        <= u
-        <= 0x2064
-        or u == 0x2065  # Cf   [5] WORD JOINER..INVISIBLE PLUS
-        or 0x2066 <= u <= 0x206F  # Cn       <reserved-2065>
-        or u == 0x3164  # Cf  [10] LEFT-TO-RIGHT ISOLATE..NOMINAL DIGIT SHAPES
-        or 0xFE00 <= u <= 0xFE0F  # Lo       HANGUL FILLER
-        or u == 0xFEFF  # Mn  [16] VARIATION SELECTOR-1..VARIATION SELECTOR-16
-        or u == 0xFFA0  # Cf       ZERO WIDTH NO-BREAK SPACE
-        or 0xFFF0 <= u <= 0xFFF8  # Lo       HALFWIDTH HANGUL FILLER
-        or 0x1BCA0 <= u <= 0x1BCA3  # Cn   [9] <reserved-FFF0>..<reserved-FFF8>
-        or 0x1D173  # Cf   [4] SHORTHAND FORMAT LETTER OVERLAP..SHORTHAND FORMAT UP STEP
-        <= u
-        <= 0x1D17A
-        or u == 0xE0000  # Cf   [8] MUSICAL SYMBOL BEGIN BEAM..MUSICAL SYMBOL END PHRASE
-        or u == 0xE0001  # Cn       <reserved-E0000>
-        or 0xE0002 <= u <= 0xE001F  # Cf       LANGUAGE TAG
-        or 0xE0020 <= u <= 0xE007F  # Cn  [30] <reserved-E0002>..<reserved-E001F>
-        or 0xE0080 <= u <= 0xE00FF  # Cf  [96] TAG SPACE..CANCEL TAG
-        or 0xE0100 <= u <= 0xE01EF  # Cn [128] <reserved-E0080>..<reserved-E00FF>
-        or 0xE01F0  # Mn [240] VARIATION SELECTOR-17..VARIATION SELECTOR-256
-        <= u
-        <= 0xE0FFF
-        or False  # Cn [3600] <reserved-E01F0>..<reserved-E0FFF>
-    )
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/frontend/assets/ColorPicker-5063dbc4.css b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/frontend/assets/ColorPicker-5063dbc4.css
deleted file mode 100644
index 268eef5e9fd01a5bb7158b5d24de920141ba055d..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/frontend/assets/ColorPicker-5063dbc4.css
+++ /dev/null
@@ -1 +0,0 @@
-label.svelte-1ojmf70.svelte-1ojmf70.svelte-1ojmf70{display:flex;align-items:center;cursor:pointer;color:var(--body-text-color);font-weight:var(--checkbox-label-text-weight);font-size:var(--checkbox-label-text-size);line-height:var(--line-md)}label.svelte-1ojmf70>.svelte-1ojmf70+.svelte-1ojmf70{margin-left:var(--size-2)}input.svelte-1ojmf70.svelte-1ojmf70.svelte-1ojmf70{--ring-color:transparent;position:relative;box-shadow:var(--input-shadow);border:1px solid 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diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/huggingface_hub/utils/_datetime.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/huggingface_hub/utils/_datetime.py
deleted file mode 100644
index 57c3524d08857b37ca4f7e1c4901fd50d5be3404..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/huggingface_hub/utils/_datetime.py
+++ /dev/null
@@ -1,66 +0,0 @@
-# coding=utf-8
-# Copyright 2022-present, the HuggingFace Inc. team.
-#
-# 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.
-"""Contains utilities to handle datetimes in Huggingface Hub."""
-from datetime import datetime, timedelta, timezone
-
-
-# Local machine offset compared to UTC.
-# Taken from https://stackoverflow.com/a/3168394.
-# `utcoffset()` returns `None` if no offset -> empty timedelta.
-UTC_OFFSET = datetime.now(timezone.utc).astimezone().utcoffset() or timedelta()
-
-
-def parse_datetime(date_string: str) -> datetime:
-    """
-    Parses a date_string returned from the server to a datetime object.
-
-    This parser is a weak-parser is the sense that it handles only a single format of
-    date_string. It is expected that the server format will never change. The
-    implementation depends only on the standard lib to avoid an external dependency
-    (python-dateutil). See full discussion about this decision on PR:
-    https://github.com/huggingface/huggingface_hub/pull/999.
-
-    Example:
-        ```py
-        > parse_datetime('2022-08-19T07:19:38.123Z')
-        datetime.datetime(2022, 8, 19, 7, 19, 38, 123000, tzinfo=timezone.utc)
-        ```
-
-    Args:
-        date_string (`str`):
-            A string representing a datetime returned by the Hub server.
-            String is expected to follow '%Y-%m-%dT%H:%M:%S.%fZ' pattern.
-
-    Returns:
-        A python datetime object.
-
-    Raises:
-        :class:`ValueError`:
-            If `date_string` cannot be parsed.
-    """
-    try:
-        # Datetime ending with a Z means "UTC". Here we parse the date as local machine
-        # timezone and then move it to the appropriate UTC timezone.
-        # See https://en.wikipedia.org/wiki/ISO_8601#Coordinated_Universal_Time_(UTC)
-        # Taken from https://stackoverflow.com/a/3168394.
-
-        dt = datetime.strptime(date_string, "%Y-%m-%dT%H:%M:%S.%fZ")
-        dt += UTC_OFFSET  # By default, datetime is not timezoned -> move to UTC time
-        return dt.astimezone(timezone.utc)  # Set explicit timezone
-    except ValueError as e:
-        raise ValueError(
-            f"Cannot parse '{date_string}' as a datetime. Date string is expected to"
-            " follow '%Y-%m-%dT%H:%M:%S.%fZ' pattern."
-        ) from e
diff --git a/spaces/Datasculptor/3D-Room-Layout-Estimation_LGT-Net/utils/conversion.py b/spaces/Datasculptor/3D-Room-Layout-Estimation_LGT-Net/utils/conversion.py
deleted file mode 100644
index 906d5f6dcbe635e1d2a67e032fb2de30a9dee5fa..0000000000000000000000000000000000000000
--- a/spaces/Datasculptor/3D-Room-Layout-Estimation_LGT-Net/utils/conversion.py
+++ /dev/null
@@ -1,346 +0,0 @@
-""" 
-@date: 2021/06/19
-@description:
-Specification of 4 coordinate systems:
-Pixel coordinates (used in panoramic images), the range is related to the image size,
-generally converted to UV coordinates first, the first is horizontal coordinates,
-increasing to the right, the second is column coordinates, increasing down
-
-Uv coordinates (used in panoramic images), the range is [0~1], the upper left corner is the origin,
-u is the abscissa and increases to the right, V is the column coordinate and increases to the right
-
-Longitude and latitude coordinates (spherical), the range of longitude lon is [-pi~ PI],
-and the range of dimension is [-pi/2~ PI /2]. The center of the panorama is the origin,
-and the longitude increases to the right and the dimension increases to the down
-
-Xyz coordinate (used in 3-dimensional space, of course,
-it can also represent longitude and latitude coordinates on the sphere).
-If on the sphere, the coordinate mode length is 1, when y is projected to the height of the camera,
-the real position information of space points will be obtained
-
-Correspondence between longitude and latitude coordinates and xyz coordinates:
-                     | -pi/2
-                     |
-     lef   _ _ _ _ _ |_ _ _ _ _
-     -pi /           |          \
-     pi |            - - - - -  -\ - z 0 mid
- right  \_ _ _ _ _ /_|_ _ _ _ _ _/
-                /    |
-               /     |
-            x/       | y pi/2
-"""
-
-import numpy as np
-import torch
-import functools
-
-
-@functools.lru_cache()
-def get_u(w, is_np, b=None):
-    u = pixel2uv(np.array(range(w)) if is_np else torch.arange(0, w), w=w, axis=0)
-    if b is not None:
-        u = u[np.newaxis].repeat(b) if is_np else u.repeat(b, 1)
-    return u
-
-
-@functools.lru_cache()
-def get_lon(w, is_np, b=None):
-    lon = pixel2lonlat(np.array(range(w)) if is_np else torch.arange(0, w), w=w, axis=0)
-    if b is not None:
-        lon = lon[np.newaxis].repeat(b, axis=0) if is_np else lon.repeat(b, 1)
-    return lon
-
-
-def pixel2uv(pixel, w=1024, h=512, axis=None):
-    pixel = pixel.astype(np.float) if isinstance(pixel, np.ndarray) else pixel.float()
-    # +0.5 will make left/right and up/down coordinates symmetric
-    if axis is None:
-        u = (pixel[..., 0:1] + 0.5) / w
-        v = (pixel[..., 1:] + 0.5) / h
-    elif axis == 0:
-        u = (pixel + 0.5) / (w * 1.0)
-        return u
-    elif axis == 1:
-        v = (pixel + 0.5) / (h * 1.0)
-        return v
-    else:
-        assert False, "axis error"
-
-    lst = [u, v]
-    uv = np.concatenate(lst, axis=-1) if isinstance(pixel, np.ndarray) else torch.cat(lst, dim=-1)
-    return uv
-
-
-def pixel2lonlat(pixel, w=1024, h=512, axis=None):
-    uv = pixel2uv(pixel, w, h, axis)
-    lonlat = uv2lonlat(uv, axis)
-    return lonlat
-
-
-def pixel2xyz(pixel, w=1024, h=512):
-    lonlat = pixel2lonlat(pixel, w, h)
-    xyz = lonlat2xyz(lonlat)
-    return xyz
-
-
-def uv2lonlat(uv, axis=None):
-    if axis is None:
-        lon = (uv[..., 0:1] - 0.5) * 2 * np.pi
-        lat = (uv[..., 1:] - 0.5) * np.pi
-    elif axis == 0:
-        lon = (uv - 0.5) * 2 * np.pi
-        return lon
-    elif axis == 1:
-        lat = (uv - 0.5) * np.pi
-        return lat
-    else:
-        assert False, "axis error"
-
-    lst = [lon, lat]
-    lonlat = np.concatenate(lst, axis=-1) if isinstance(uv, np.ndarray) else torch.cat(lst, dim=-1)
-    return lonlat
-
-
-def uv2xyz(uv, plan_y=None, spherical=False):
-    lonlat = uv2lonlat(uv)
-    xyz = lonlat2xyz(lonlat)
-    if spherical:
-        # Projection onto the sphere
-        return xyz
-
-    if plan_y is None:
-        from utils.boundary import boundary_type
-        plan_y = boundary_type(uv)
-    # Projection onto the specified plane
-    xyz = xyz * (plan_y / xyz[..., 1])[..., None]
-
-    return xyz
-
-
-def lonlat2xyz(lonlat, plan_y=None):
-    lon = lonlat[..., 0:1]
-    lat = lonlat[..., 1:]
-    cos = np.cos if isinstance(lonlat, np.ndarray) else torch.cos
-    sin = np.sin if isinstance(lonlat, np.ndarray) else torch.sin
-    x = cos(lat) * sin(lon)
-    y = sin(lat)
-    z = cos(lat) * cos(lon)
-    lst = [x, y, z]
-    xyz = np.concatenate(lst, axis=-1) if isinstance(lonlat, np.ndarray) else torch.cat(lst, dim=-1)
-
-    if plan_y is not None:
-        xyz = xyz * (plan_y / xyz[..., 1])[..., None]
-
-    return xyz
-
-
-#####################
-
-
-def xyz2lonlat(xyz):
-    atan2 = np.arctan2 if isinstance(xyz, np.ndarray) else torch.atan2
-    asin = np.arcsin if isinstance(xyz, np.ndarray) else torch.asin
-    norm = np.linalg.norm(xyz, axis=-1) if isinstance(xyz, np.ndarray) else torch.norm(xyz, p=2, dim=-1)
-    xyz_norm = xyz / norm[..., None]
-    x = xyz_norm[..., 0:1]
-    y = xyz_norm[..., 1:2]
-    z = xyz_norm[..., 2:]
-    lon = atan2(x, z)
-    lat = asin(y)
-    lst = [lon, lat]
-    lonlat = np.concatenate(lst, axis=-1) if isinstance(xyz, np.ndarray) else torch.cat(lst, dim=-1)
-    return lonlat
-
-
-def xyz2uv(xyz):
-    lonlat = xyz2lonlat(xyz)
-    uv = lonlat2uv(lonlat)
-    return uv
-
-
-def xyz2pixel(xyz, w=1024, h=512):
-    uv = xyz2uv(xyz)
-    pixel = uv2pixel(uv, w, h)
-    return pixel
-
-
-def lonlat2uv(lonlat, axis=None):
-    if axis is None:
-        u = lonlat[..., 0:1] / (2 * np.pi) + 0.5
-        v = lonlat[..., 1:] / np.pi + 0.5
-    elif axis == 0:
-        u = lonlat / (2 * np.pi) + 0.5
-        return u
-    elif axis == 1:
-        v = lonlat / np.pi + 0.5
-        return v
-    else:
-        assert False, "axis error"
-
-    lst = [u, v]
-    uv = np.concatenate(lst, axis=-1) if isinstance(lonlat, np.ndarray) else torch.cat(lst, dim=-1)
-    return uv
-
-
-def lonlat2pixel(lonlat, w=1024, h=512, axis=None, need_round=True):
-    uv = lonlat2uv(lonlat, axis)
-    pixel = uv2pixel(uv, w, h, axis, need_round)
-    return pixel
-
-
-def uv2pixel(uv, w=1024, h=512, axis=None, need_round=True):
-    """
-    :param uv: [[u1, v1], [u2, v2] ...]
-    :param w: width of panorama image
-    :param h: height of panorama image
-    :param axis: sometimes the input data is only u(axis =0) or only v(axis=1)
-    :param need_round:
-    :return:
-    """
-    if axis is None:
-        pu = uv[..., 0:1] * w - 0.5
-        pv = uv[..., 1:] * h - 0.5
-    elif axis == 0:
-        pu = uv * w - 0.5
-        if need_round:
-            pu = pu.round().astype(np.int) if isinstance(uv, np.ndarray) else pu.round().int()
-        return pu
-    elif axis == 1:
-        pv = uv * h - 0.5
-        if need_round:
-            pv = pv.round().astype(np.int) if isinstance(uv, np.ndarray) else pv.round().int()
-        return pv
-    else:
-        assert False, "axis error"
-
-    lst = [pu, pv]
-    if need_round:
-        pixel = np.concatenate(lst, axis=-1).round().astype(np.int) if isinstance(uv, np.ndarray) else torch.cat(lst,
-                                                                                                                 dim=-1).round().int()
-    else:
-        pixel = np.concatenate(lst, axis=-1) if isinstance(uv, np.ndarray) else torch.cat(lst, dim=-1)
-    pixel[..., 0] = pixel[..., 0] % w
-    pixel[..., 1] = pixel[..., 1] % h
-
-    return pixel
-
-
-#####################
-
-
-def xyz2depth(xyz, plan_y=1):
-    """
-    :param xyz:
-    :param plan_y:
-    :return:
-    """
-    xyz = xyz * (plan_y / xyz[..., 1])[..., None]
-    xz = xyz[..., ::2]
-    depth = np.linalg.norm(xz, axis=-1) if isinstance(xz, np.ndarray) else torch.norm(xz, dim=-1)
-    return depth
-
-
-def uv2depth(uv, plan_y=None):
-    if plan_y is None:
-        from utils.boundary import boundary_type
-        plan_y = boundary_type(uv)
-
-    xyz = uv2xyz(uv, plan_y)
-    depth = xyz2depth(xyz, plan_y)
-    return depth
-
-
-def lonlat2depth(lonlat, plan_y=None):
-    if plan_y is None:
-        from utils.boundary import boundary_type
-        plan_y = boundary_type(lonlat2uv(lonlat))
-
-    xyz = lonlat2xyz(lonlat, plan_y)
-    depth = xyz2depth(xyz, plan_y)
-    return depth
-
-
-def depth2xyz(depth, plan_y=1):
-    """
-    :param depth: [patch_num] or [b, patch_num]
-    :param plan_y:
-    :return:
-    """
-    is_np = isinstance(depth, np.ndarray)
-    w = depth.shape[-1]
-
-    lon = get_lon(w, is_np, b=depth.shape[0] if len(depth.shape) == 2 else None)
-    if not is_np:
-        lon = lon.to(depth.device)
-
-    cos = np.cos if is_np else torch.cos
-    sin = np.sin if is_np else torch.sin
-    # polar covert to cartesian
-    if len(depth.shape) == 2:
-        b = depth.shape[0]
-        xyz = np.zeros((b, w, 3)) if is_np else torch.zeros((b, w, 3))
-    else:
-        xyz = np.zeros((w, 3)) if is_np else torch.zeros((w, 3))
-
-    if not is_np:
-        xyz = xyz.to(depth.device)
-
-    xyz[..., 0] = depth * sin(lon)
-    xyz[..., 1] = plan_y
-    xyz[..., 2] = depth * cos(lon)
-    return xyz
-
-
-def depth2uv(depth, plan_y=1):
-    xyz = depth2xyz(depth, plan_y)
-    uv = xyz2uv(xyz)
-    return uv
-
-
-def depth2pixel(depth, w=1024, h=512, need_round=True, plan_y=1):
-    uv = depth2uv(depth, plan_y)
-    pixel = uv2pixel(uv, w, h, need_round=need_round)
-    return pixel
-
-
-if __name__ == '__main__':
-    a = np.array([[0.5, 1, 0.5]])
-    a = xyz2pixel(a)
-    print(a)
-
-
-if __name__ == '__main__1':
-    np.set_printoptions(suppress=True)
-
-    a = np.array([[0, 0], [1023, 511]])
-    a = pixel2xyz(a)
-    a = xyz2pixel(a)
-    print(a)
-
-    ###########
-    a = torch.tensor([[0, 0], [1023, 511]])
-    a = pixel2xyz(a)
-    a = xyz2pixel(a)
-    print(a)
-
-    ###########
-    u = np.array([0, 256, 512, 1023])
-    lon = pixel2lonlat(u, axis=0)
-    u = lonlat2pixel(lon, axis=0)
-    print(u)
-
-    u = torch.tensor([0, 256, 512, 1023])
-    lon = pixel2lonlat(u, axis=0)
-    u = lonlat2pixel(lon, axis=0)
-    print(u)
-
-    ###########
-    v = np.array([0, 256, 511])
-    lat = pixel2lonlat(v, axis=1)
-    v = lonlat2pixel(lat, axis=1)
-    print(v)
-
-    v = torch.tensor([0, 256, 511])
-    lat = pixel2lonlat(v, axis=1)
-    v = lonlat2pixel(lat, axis=1)
-    print(v)
diff --git a/spaces/Detomo/Chatgpt_with_awesome_prompt/app.py b/spaces/Detomo/Chatgpt_with_awesome_prompt/app.py
deleted file mode 100644
index 7c6a2ba7d9468377e243cadac908ef8e30e0c01a..0000000000000000000000000000000000000000
--- a/spaces/Detomo/Chatgpt_with_awesome_prompt/app.py
+++ /dev/null
@@ -1,142 +0,0 @@
-import gradio as gr
-import os
-import openai
-import json
-import tiktoken
-import pandas as pd
-
-openai.api_key = os.environ["OPENAI_API_KEY"]
-prompt_templates = {"Default ChatGPT": ""}
-
-def num_tokens_from_messages(messages, model="gpt-3.5-turbo"):
-    """Returns the number of tokens used by a list of messages."""
-    try:
-        encoding = tiktoken.encoding_for_model(model)
-    except KeyError:
-        encoding = tiktoken.get_encoding("cl100k_base")
-    if model == "gpt-3.5-turbo": 
-        num_tokens = 0
-        for message in messages:
-            num_tokens += 4  # every message follows <im_start>{role/name}\n{content}<im_end>\n
-            for key, value in message.items():
-                num_tokens += len(encoding.encode(value))
-                if key == "name":  # if there's a name, the role is omitted
-                    num_tokens += -1  # role is always required and always 1 token
-        num_tokens += 2  # every reply is primed with <im_start>assistant
-        return num_tokens
-    else:
-        raise NotImplementedError(f"""num_tokens_from_messages() is not presently implemented for model {model}. 
-        See https://github.com/openai/openai-python/blob/main/chatml.md for information on how messages are converted to tokens.""")
-
-
-def get_empty_state():
-    return {"total_tokens": 0, "messages": [], "threshold": 0}
-
-def download_prompt_templates():
-    df = pd.read_csv('prompts.csv', encoding='unicode_escape')
-    prompt_templates.update(dict(zip(df['act'], df['prompt'])))
-    choices = list(prompt_templates.keys())
-    return gr.update(value=choices[0], choices=choices)
-
-def on_token_change(user_token):
-    openai.api_key = user_token or os.environ.get("OPENAI_API_KEY")
-
-def on_prompt_template_change(prompt_template):
-    if not isinstance(prompt_template, str): return
-    return prompt_templates[prompt_template]
-
-def submit_message(prompt, prompt_template, temperature, max_tokens, state):
-
-    history = state['messages']
-
-    if not prompt:
-        return gr.update(value='', visible=state['total_tokens'] < 1_000), [(history[i]['content'], history[i+1]['content']) for i in range(0, len(history)-1, 2)], f"Total tokens used: {state['total_tokens']} / 4090", state
-    
-    prompt_template = prompt_templates[prompt_template]
-    print(prompt_template)
-    system_prompt = []
-    if prompt_template:
-        system_prompt = [{ "role": "system", "content": prompt_template}]
-
-    prompt_msg = {"role": "user", "content": prompt }
-    
-    # check length token message
-    messages = system_prompt + history + [prompt_msg]
-    history_id = 2
-    while num_tokens_from_messages(messages) >= 4090:
-        messages = system_prompt + history[history_id:] + [prompt_msg]
-        history_id +=2
-        state['threshold'] +=1
-        if history_id > len(history):
-            break
-    try:
-        completion = openai.ChatCompletion.create(model="gpt-3.5-turbo", messages=messages, temperature=temperature, max_tokens=max_tokens)
-        history.append(prompt_msg)
-        history.append(completion.choices[0].message.to_dict())
-
-        state['total_tokens'] += completion['usage']['total_tokens']
-    
-    except Exception as e:
-        history.append(prompt_msg)
-        history.append({
-            "role": "system",
-            "content": f"Error: {e}"
-        })
-
-    total_tokens_used_msg = f"Total tokens used: {state['total_tokens']} / 4090. "
-    chat_messages = [(history[i]['content'], history[i+1]['content']) for i in range(0, len(history)-1, 2)]
-
-    if state['threshold'] >= 3:
-        input_visibility = False
-        total_tokens_used_msg += "Reach the limit of this conversation. Start the new one"
-    else:
-        input_visibility = True
-
-    return gr.update(value='', visible=input_visibility), chat_messages, total_tokens_used_msg, state
-
-def clear_conversation():
-    return gr.update(value=None, visible=True), None, "", get_empty_state()
-
-css = """
-      #col-container {max-width: 80%; margin-left: auto; margin-right: auto;}
-      #chatbox {min-height: 400px;}
-      #header {text-align: center;}
-      #prompt_template_preview {padding: 1em; border-width: 1px; border-style: solid; border-color: #e0e0e0; border-radius: 4px;}
-      #total_tokens_str {text-align: right; font-size: 0.8em; color: #666; height: 1em;}
-      #label {font-size: 0.8em; padding: 0.5em; margin: 0;}
-      """
-
-with gr.Blocks(css=css) as demo:
-    
-    state = gr.State(get_empty_state())
-
-
-    with gr.Column(elem_id="col-container"):
-        gr.Markdown("""## OpenAI ChatGPT with awesome prompts
-                    Current limit is 4090 tokens per conversation<br>
-                    Input your text with a custom insruction (If need).""",
-                    elem_id="header")
-
-        with gr.Row():
-            with gr.Column():
-                chatbot = gr.Chatbot(elem_id="chatbox")
-                input_message = gr.Textbox(show_label=False, placeholder="Enter text and press enter", visible=True).style(container=False)
-                total_tokens_str = gr.Markdown(elem_id="total_tokens_str")
-                btn_clear_conversation = gr.Button("🔃 Start New Conversation")
-            with gr.Column():
-                prompt_template = gr.Dropdown(label="Set a custom insruction for the chatbot:", choices=list(prompt_templates.keys()))
-                prompt_template_preview = gr.Markdown(elem_id="prompt_template_preview")
-
-                with gr.Accordion("Advanced parameters", open=False):
-                    temperature = gr.Slider(minimum=0, maximum=2.0, value=0.7, step=0.1, interactive=True, label="Temperature (higher = more creative/chaotic)")
-                    max_tokens = gr.Slider(minimum=100, maximum=4096, value=1000, step=1, interactive=True, label="Max tokens per response")
-
-    input_message.submit(submit_message, [input_message, prompt_template, temperature, max_tokens, state], [input_message, chatbot, total_tokens_str, state])
-    btn_clear_conversation.click(clear_conversation, [], [input_message, chatbot, total_tokens_str, state])
-    prompt_template.change(on_prompt_template_change, inputs=[prompt_template], outputs=[prompt_template_preview])
-
-    demo.load(download_prompt_templates, inputs=None, outputs=[prompt_template])
-
-
-# demo.launch(debug=True, height='800px', auth=("admin", "dtm1234"))
-demo.launch(debug=True, height='800px')
\ No newline at end of file
diff --git a/spaces/DragGan/DragGan-Inversion/PTI/models/e4e/__init__.py b/spaces/DragGan/DragGan-Inversion/PTI/models/e4e/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/DragGan/DragGan-Inversion/stylegan_human/run_pti.py b/spaces/DragGan/DragGan-Inversion/stylegan_human/run_pti.py
deleted file mode 100644
index 1be91596fa768240020a2e9af03cb5f24ca1072e..0000000000000000000000000000000000000000
--- a/spaces/DragGan/DragGan-Inversion/stylegan_human/run_pti.py
+++ /dev/null
@@ -1,54 +0,0 @@
-# Copyright (c) SenseTime Research. All rights reserved.
-
-from random import choice
-from string import ascii_uppercase
-from torch.utils.data import DataLoader
-from torchvision.transforms import transforms
-import os
-from pti.pti_configs import global_config, paths_config
-import wandb
-
-from pti.training.coaches.multi_id_coach import MultiIDCoach
-from pti.training.coaches.single_id_coach import SingleIDCoach
-from utils.ImagesDataset import ImagesDataset
-
-
-def run_PTI(run_name='', use_wandb=False, use_multi_id_training=False):
-    os.environ['CUDA_DEVICE_ORDER'] = 'PCI_BUS_ID'
-    os.environ['CUDA_VISIBLE_DEVICES'] = global_config.cuda_visible_devices
-
-    if run_name == '':
-        global_config.run_name = ''.join(
-            choice(ascii_uppercase) for i in range(12))
-    else:
-        global_config.run_name = run_name
-
-    if use_wandb:
-        run = wandb.init(project=paths_config.pti_results_keyword,
-                         reinit=True, name=global_config.run_name)
-    global_config.pivotal_training_steps = 1
-    global_config.training_step = 1
-
-    embedding_dir_path = f'{paths_config.embedding_base_dir}/{paths_config.input_data_id}/{paths_config.pti_results_keyword}'
-    # print('embedding_dir_path: ', embedding_dir_path) #./embeddings/barcelona/PTI
-    os.makedirs(embedding_dir_path, exist_ok=True)
-
-    dataset = ImagesDataset(paths_config.input_data_path, transforms.Compose([
-        transforms.Resize((1024, 512)),
-        transforms.ToTensor(),
-        transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])]))
-
-    dataloader = DataLoader(dataset, batch_size=1, shuffle=False)
-
-    if use_multi_id_training:
-        coach = MultiIDCoach(dataloader, use_wandb)
-    else:
-        coach = SingleIDCoach(dataloader, use_wandb)
-
-    coach.train()
-
-    return global_config.run_name
-
-
-if __name__ == '__main__':
-    run_PTI(run_name='', use_wandb=False, use_multi_id_training=False)
diff --git a/spaces/EDGAhab/VITS-Aatrox-AI/text/cleaners.py b/spaces/EDGAhab/VITS-Aatrox-AI/text/cleaners.py
deleted file mode 100644
index 759db477e3deb72a03ff65957419c3694781b5ef..0000000000000000000000000000000000000000
--- a/spaces/EDGAhab/VITS-Aatrox-AI/text/cleaners.py
+++ /dev/null
@@ -1,138 +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
-from pypinyin import Style, pinyin
-from pypinyin.style._utils import get_finals, get_initials
-# 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
-
-
-
-
-def chinese_cleaners1(text):
-    from pypinyin import Style, pinyin
-
-    phones = [phone[0] for phone in pinyin(text, style=Style.TONE3)]
-    return ' '.join(phones)
-  
-  
-def chinese_cleaners2(text):
-  phones = [
-      p
-      for phone in pinyin(text, style=Style.TONE3)
-      for p in [
-          get_initials(phone[0], strict=True),
-          get_finals(phone[0][:-1], strict=True) + phone[0][-1]
-          if phone[0][-1].isdigit()
-          else get_finals(phone[0], strict=True)
-          if phone[0][-1].isalnum()
-          else phone[0],
-      ]
-      # Remove the case of individual tones as a phoneme
-      if len(p) != 0 and not p.isdigit()
-  ]
-  return phones
-  # return phonemes
-  
-if __name__ == '__main__':
-  res = chinese_cleaners2('这是语音测试！')
-  print(res)
-  res = chinese_cleaners1('"第一，南京不是发展的不行，是大家对他期望很高，')
-  print(res)
-  
-  
-  res = english_cleaners2('this is a club test for one train.GDP')
-  print(res)
\ No newline at end of file
diff --git a/spaces/EPFL-VILAB/MultiMAE/README.md b/spaces/EPFL-VILAB/MultiMAE/README.md
deleted file mode 100644
index 36444364a50367df2100be14e7bf8b4420ac5c28..0000000000000000000000000000000000000000
--- a/spaces/EPFL-VILAB/MultiMAE/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: MultiMAE
-emoji: 😻
-colorFrom: purple
-colorTo: purple
-sdk: gradio
-sdk_version: 3.40.1
-app_file: app.py
-pinned: false
-license: cc-by-nc-4.0
-python_version: 3.8
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
diff --git a/spaces/EronSamez/RVC_HFmeu/train/losses.py b/spaces/EronSamez/RVC_HFmeu/train/losses.py
deleted file mode 100644
index b89038f14d06d7fae43628183e9ffb465e4edafd..0000000000000000000000000000000000000000
--- a/spaces/EronSamez/RVC_HFmeu/train/losses.py
+++ /dev/null
@@ -1,59 +0,0 @@
-import torch
-from torch.nn import functional as F
-
-
-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(-2.0 * logs_p)
-    kl = torch.sum(kl * z_mask)
-    l = kl / torch.sum(z_mask)
-    return l
diff --git a/spaces/EuroPython2022/PaddleOCR/README.md b/spaces/EuroPython2022/PaddleOCR/README.md
deleted file mode 100644
index 3ef3a6b772d6dcda4bb32a96970bdf9acd52b9fc..0000000000000000000000000000000000000000
--- a/spaces/EuroPython2022/PaddleOCR/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: PaddleOCR
-emoji: 📚
-colorFrom: blue
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.0.24
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Fr33d0m21/chatbot_dialogpt/app.py b/spaces/Fr33d0m21/chatbot_dialogpt/app.py
deleted file mode 100644
index 18d2d0ca3e68a8a9ca5107c57e1c4800692e1c20..0000000000000000000000000000000000000000
--- a/spaces/Fr33d0m21/chatbot_dialogpt/app.py
+++ /dev/null
@@ -1,34 +0,0 @@
-from transformers import AutoModelForCausalLM, AutoTokenizer
-import torch
-import gradio as gr
-
-tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-large")
-model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-large")
-
-def predict(input, history=[]):
-    # tokenize the new input sentence
-    new_user_input_ids = tokenizer.encode(input + tokenizer.eos_token, return_tensors='pt')
-
-    # append the new user input tokens to the chat history
-    bot_input_ids = torch.cat([torch.LongTensor(history), new_user_input_ids], dim=-1)
-
-    # generate a response 
-    history = model.generate(bot_input_ids, max_length=4000, pad_token_id=tokenizer.eos_token_id).tolist()
-
-    # convert the tokens to text, and then split the responses into lines
-    response = tokenizer.decode(history[0]).split("<|endoftext|>")
-    #print('decoded_response-->>'+str(response))
-    response = [(response[i], response[i+1]) for i in range(0, len(response)-1, 2)]  # convert to tuples of list
-    #print('response-->>'+str(response))
-    return response, history
-
-description = "This is a chatbot application based on the DialoGPT model of Microsoft. Simply type an input to get started with chatting."
-title = "Chat with DialoGPT 👾"
-examples = [["What is the meaning of life?"]]
-gr.Interface(fn=predict,
-             title=title,
-             description=description,
-             examples=examples,
-             inputs=["text", "state"],
-             outputs=["chatbot", "state"]).launch()
-
diff --git a/spaces/FrankZxShen/vits-fast-finetuning-pcr/text/korean.py b/spaces/FrankZxShen/vits-fast-finetuning-pcr/text/korean.py
deleted file mode 100644
index edee07429a450c55e3d8e246997faaa1e0b89cc9..0000000000000000000000000000000000000000
--- a/spaces/FrankZxShen/vits-fast-finetuning-pcr/text/korean.py
+++ /dev/null
@@ -1,210 +0,0 @@
-import re
-from jamo import h2j, j2hcj
-import ko_pron
-
-
-# This is a list of Korean classifiers preceded by pure Korean numerals.
-_korean_classifiers = '군데 권 개 그루 닢 대 두 마리 모 모금 뭇 발 발짝 방 번 벌 보루 살 수 술 시 쌈 움큼 정 짝 채 척 첩 축 켤레 톨 통'
-
-# List of (hangul, hangul divided) pairs:
-_hangul_divided = [(re.compile('%s' % x[0]), x[1]) for x in [
-    ('ㄳ', 'ㄱㅅ'),
-    ('ㄵ', 'ㄴㅈ'),
-    ('ㄶ', 'ㄴㅎ'),
-    ('ㄺ', 'ㄹㄱ'),
-    ('ㄻ', 'ㄹㅁ'),
-    ('ㄼ', 'ㄹㅂ'),
-    ('ㄽ', 'ㄹㅅ'),
-    ('ㄾ', 'ㄹㅌ'),
-    ('ㄿ', 'ㄹㅍ'),
-    ('ㅀ', 'ㄹㅎ'),
-    ('ㅄ', 'ㅂㅅ'),
-    ('ㅘ', 'ㅗㅏ'),
-    ('ㅙ', 'ㅗㅐ'),
-    ('ㅚ', 'ㅗㅣ'),
-    ('ㅝ', 'ㅜㅓ'),
-    ('ㅞ', 'ㅜㅔ'),
-    ('ㅟ', 'ㅜㅣ'),
-    ('ㅢ', 'ㅡㅣ'),
-    ('ㅑ', 'ㅣㅏ'),
-    ('ㅒ', 'ㅣㅐ'),
-    ('ㅕ', 'ㅣㅓ'),
-    ('ㅖ', 'ㅣㅔ'),
-    ('ㅛ', 'ㅣㅗ'),
-    ('ㅠ', 'ㅣㅜ')
-]]
-
-# List of (Latin alphabet, hangul) pairs:
-_latin_to_hangul = [(re.compile('%s' % x[0], re.IGNORECASE), x[1]) for x in [
-    ('a', '에이'),
-    ('b', '비'),
-    ('c', '시'),
-    ('d', '디'),
-    ('e', '이'),
-    ('f', '에프'),
-    ('g', '지'),
-    ('h', '에이치'),
-    ('i', '아이'),
-    ('j', '제이'),
-    ('k', '케이'),
-    ('l', '엘'),
-    ('m', '엠'),
-    ('n', '엔'),
-    ('o', '오'),
-    ('p', '피'),
-    ('q', '큐'),
-    ('r', '아르'),
-    ('s', '에스'),
-    ('t', '티'),
-    ('u', '유'),
-    ('v', '브이'),
-    ('w', '더블유'),
-    ('x', '엑스'),
-    ('y', '와이'),
-    ('z', '제트')
-]]
-
-# List of (ipa, lazy ipa) pairs:
-_ipa_to_lazy_ipa = [(re.compile('%s' % x[0], re.IGNORECASE), x[1]) for x in [
-    ('t͡ɕ','ʧ'),
-    ('d͡ʑ','ʥ'),
-    ('ɲ','n^'),
-    ('ɕ','ʃ'),
-    ('ʷ','w'),
-    ('ɭ','l`'),
-    ('ʎ','ɾ'),
-    ('ɣ','ŋ'),
-    ('ɰ','ɯ'),
-    ('ʝ','j'),
-    ('ʌ','ə'),
-    ('ɡ','g'),
-    ('\u031a','#'),
-    ('\u0348','='),
-    ('\u031e',''),
-    ('\u0320',''),
-    ('\u0339','')
-]]
-
-
-def latin_to_hangul(text):
-    for regex, replacement in _latin_to_hangul:
-        text = re.sub(regex, replacement, text)
-    return text
-
-
-def divide_hangul(text):
-    text = j2hcj(h2j(text))
-    for regex, replacement in _hangul_divided:
-        text = re.sub(regex, replacement, text)
-    return text
-
-
-def hangul_number(num, sino=True):
-    '''Reference https://github.com/Kyubyong/g2pK'''
-    num = re.sub(',', '', num)
-
-    if num == '0':
-        return '영'
-    if not sino and num == '20':
-        return '스무'
-
-    digits = '123456789'
-    names = '일이삼사오육칠팔구'
-    digit2name = {d: n for d, n in zip(digits, names)}
-
-    modifiers = '한 두 세 네 다섯 여섯 일곱 여덟 아홉'
-    decimals = '열 스물 서른 마흔 쉰 예순 일흔 여든 아흔'
-    digit2mod = {d: mod for d, mod in zip(digits, modifiers.split())}
-    digit2dec = {d: dec for d, dec in zip(digits, decimals.split())}
-
-    spelledout = []
-    for i, digit in enumerate(num):
-        i = len(num) - i - 1
-        if sino:
-            if i == 0:
-                name = digit2name.get(digit, '')
-            elif i == 1:
-                name = digit2name.get(digit, '') + '십'
-                name = name.replace('일십', '십')
-        else:
-            if i == 0:
-                name = digit2mod.get(digit, '')
-            elif i == 1:
-                name = digit2dec.get(digit, '')
-        if digit == '0':
-            if i % 4 == 0:
-                last_three = spelledout[-min(3, len(spelledout)):]
-                if ''.join(last_three) == '':
-                    spelledout.append('')
-                    continue
-            else:
-                spelledout.append('')
-                continue
-        if i == 2:
-            name = digit2name.get(digit, '') + '백'
-            name = name.replace('일백', '백')
-        elif i == 3:
-            name = digit2name.get(digit, '') + '천'
-            name = name.replace('일천', '천')
-        elif i == 4:
-            name = digit2name.get(digit, '') + '만'
-            name = name.replace('일만', '만')
-        elif i == 5:
-            name = digit2name.get(digit, '') + '십'
-            name = name.replace('일십', '십')
-        elif i == 6:
-            name = digit2name.get(digit, '') + '백'
-            name = name.replace('일백', '백')
-        elif i == 7:
-            name = digit2name.get(digit, '') + '천'
-            name = name.replace('일천', '천')
-        elif i == 8:
-            name = digit2name.get(digit, '') + '억'
-        elif i == 9:
-            name = digit2name.get(digit, '') + '십'
-        elif i == 10:
-            name = digit2name.get(digit, '') + '백'
-        elif i == 11:
-            name = digit2name.get(digit, '') + '천'
-        elif i == 12:
-            name = digit2name.get(digit, '') + '조'
-        elif i == 13:
-            name = digit2name.get(digit, '') + '십'
-        elif i == 14:
-            name = digit2name.get(digit, '') + '백'
-        elif i == 15:
-            name = digit2name.get(digit, '') + '천'
-        spelledout.append(name)
-    return ''.join(elem for elem in spelledout)
-
-
-def number_to_hangul(text):
-    '''Reference https://github.com/Kyubyong/g2pK'''
-    tokens = set(re.findall(r'(\d[\d,]*)([\uac00-\ud71f]+)', text))
-    for token in tokens:
-        num, classifier = token
-        if classifier[:2] in _korean_classifiers or classifier[0] in _korean_classifiers:
-            spelledout = hangul_number(num, sino=False)
-        else:
-            spelledout = hangul_number(num, sino=True)
-        text = text.replace(f'{num}{classifier}', f'{spelledout}{classifier}')
-    # digit by digit for remaining digits
-    digits = '0123456789'
-    names = '영일이삼사오육칠팔구'
-    for d, n in zip(digits, names):
-        text = text.replace(d, n)
-    return text
-
-
-def korean_to_lazy_ipa(text):
-    text = latin_to_hangul(text)
-    text = number_to_hangul(text)
-    text=re.sub('[\uac00-\ud7af]+',lambda x:ko_pron.romanise(x.group(0),'ipa').split('] ~ [')[0],text)
-    for regex, replacement in _ipa_to_lazy_ipa:
-        text = re.sub(regex, replacement, text)
-    return text
-
-
-def korean_to_ipa(text):
-    text = korean_to_lazy_ipa(text)
-    return text.replace('ʧ','tʃ').replace('ʥ','dʑ')
diff --git a/spaces/Froleptan/stablediffusion-infinity/js/upload.js b/spaces/Froleptan/stablediffusion-infinity/js/upload.js
deleted file mode 100644
index 4842960af4985847ff24c93c7f730e8e64974690..0000000000000000000000000000000000000000
--- a/spaces/Froleptan/stablediffusion-infinity/js/upload.js
+++ /dev/null
@@ -1,19 +0,0 @@
-function(a,b){
-    if(!window.my_observe_upload)
-    {
-        console.log("setup upload here");
-        window.my_observe_upload = new MutationObserver(function (event) {
-            console.log(event);
-            var frame=document.querySelector("gradio-app").shadowRoot.querySelector("#sdinfframe").contentWindow.document;
-            frame.querySelector("#upload").click();
-        });
-        window.my_observe_upload_target = document.querySelector("gradio-app").shadowRoot.querySelector("#upload span");
-        window.my_observe_upload.observe(window.my_observe_upload_target, {
-            attributes: false, 
-            subtree: true,
-            childList: true, 
-            characterData: true
-        });
-    }
-    return [a,b];
-}
\ No newline at end of file
diff --git a/spaces/GAIR/Factool/factool/code/helper/io_utils.py b/spaces/GAIR/Factool/factool/code/helper/io_utils.py
deleted file mode 100644
index c5eb04708523ad4c0a42614e3cae49038e7fc140..0000000000000000000000000000000000000000
--- a/spaces/GAIR/Factool/factool/code/helper/io_utils.py
+++ /dev/null
@@ -1,26 +0,0 @@
-# Copyright (c) Microsoft Corporation.
-# Licensed under the MIT license.
-
-import json
-
-class Tools:
-    @staticmethod
-    def load_jsonl(file_path):
-        json_objects = []
-        with open(file_path, 'r', encoding='utf8') as f:
-            for line in f:
-                json_objects.append(json.loads(line.strip()))
-        return json_objects
-    
-    @staticmethod
-    def load_tasks(task_path):
-        result = dict()
-        lines = Tools.load_jsonl(task_path)
-        for line in lines:
-            result[line['task_id']] = line
-        return result
-        
-    @staticmethod
-    def write_file(path, content):
-        with open(path, 'w', encoding='utf8') as f:
-            f.write(content)
\ No newline at end of file
diff --git a/spaces/GaenKoki/voicevox/voicevox_engine/part_of_speech_data.py b/spaces/GaenKoki/voicevox/voicevox_engine/part_of_speech_data.py
deleted file mode 100644
index 8950e47c8b1cc50f7cdd3f67c857be8baf59c321..0000000000000000000000000000000000000000
--- a/spaces/GaenKoki/voicevox/voicevox_engine/part_of_speech_data.py
+++ /dev/null
@@ -1,144 +0,0 @@
-from typing import Dict
-
-from .model import (
-    USER_DICT_MAX_PRIORITY,
-    USER_DICT_MIN_PRIORITY,
-    PartOfSpeechDetail,
-    WordTypes,
-)
-
-MIN_PRIORITY = USER_DICT_MIN_PRIORITY
-MAX_PRIORITY = USER_DICT_MAX_PRIORITY
-
-part_of_speech_data: Dict[WordTypes, PartOfSpeechDetail] = {
-    WordTypes.PROPER_NOUN: PartOfSpeechDetail(
-        part_of_speech="名詞",
-        part_of_speech_detail_1="固有名詞",
-        part_of_speech_detail_2="一般",
-        part_of_speech_detail_3="*",
-        context_id=1348,
-        cost_candidates=[
-            -988,
-            3488,
-            4768,
-            6048,
-            7328,
-            8609,
-            8734,
-            8859,
-            8984,
-            9110,
-            14176,
-        ],
-        accent_associative_rules=[
-            "*",
-            "C1",
-            "C2",
-            "C3",
-            "C4",
-            "C5",
-        ],
-    ),
-    WordTypes.COMMON_NOUN: PartOfSpeechDetail(
-        part_of_speech="名詞",
-        part_of_speech_detail_1="一般",
-        part_of_speech_detail_2="*",
-        part_of_speech_detail_3="*",
-        context_id=1345,
-        cost_candidates=[
-            -4445,
-            49,
-            1473,
-            2897,
-            4321,
-            5746,
-            6554,
-            7362,
-            8170,
-            8979,
-            15001,
-        ],
-        accent_associative_rules=[
-            "*",
-            "C1",
-            "C2",
-            "C3",
-            "C4",
-            "C5",
-        ],
-    ),
-    WordTypes.VERB: PartOfSpeechDetail(
-        part_of_speech="動詞",
-        part_of_speech_detail_1="自立",
-        part_of_speech_detail_2="*",
-        part_of_speech_detail_3="*",
-        context_id=642,
-        cost_candidates=[
-            3100,
-            6160,
-            6360,
-            6561,
-            6761,
-            6962,
-            7414,
-            7866,
-            8318,
-            8771,
-            13433,
-        ],
-        accent_associative_rules=[
-            "*",
-        ],
-    ),
-    WordTypes.ADJECTIVE: PartOfSpeechDetail(
-        part_of_speech="形容詞",
-        part_of_speech_detail_1="自立",
-        part_of_speech_detail_2="*",
-        part_of_speech_detail_3="*",
-        context_id=20,
-        cost_candidates=[
-            1527,
-            3266,
-            3561,
-            3857,
-            4153,
-            4449,
-            5149,
-            5849,
-            6549,
-            7250,
-            10001,
-        ],
-        accent_associative_rules=[
-            "*",
-        ],
-    ),
-    WordTypes.SUFFIX: PartOfSpeechDetail(
-        part_of_speech="名詞",
-        part_of_speech_detail_1="接尾",
-        part_of_speech_detail_2="一般",
-        part_of_speech_detail_3="*",
-        context_id=1358,
-        cost_candidates=[
-            4399,
-            5373,
-            6041,
-            6710,
-            7378,
-            8047,
-            9440,
-            10834,
-            12228,
-            13622,
-            15847,
-        ],
-        accent_associative_rules=[
-            "*",
-            "C1",
-            "C2",
-            "C3",
-            "C4",
-            "C5",
-        ],
-    ),
-}
diff --git a/spaces/GipAdonimus/Real-Time-Voice-Cloning/encoder/data_objects/speaker_verification_dataset.py b/spaces/GipAdonimus/Real-Time-Voice-Cloning/encoder/data_objects/speaker_verification_dataset.py
deleted file mode 100644
index 77a6e05eae6a939ae7575ae70b7173644141fffe..0000000000000000000000000000000000000000
--- a/spaces/GipAdonimus/Real-Time-Voice-Cloning/encoder/data_objects/speaker_verification_dataset.py
+++ /dev/null
@@ -1,56 +0,0 @@
-from encoder.data_objects.random_cycler import RandomCycler
-from encoder.data_objects.speaker_batch import SpeakerBatch
-from encoder.data_objects.speaker import Speaker
-from encoder.params_data import partials_n_frames
-from torch.utils.data import Dataset, DataLoader
-from pathlib import Path
-
-# TODO: improve with a pool of speakers for data efficiency
-
-class SpeakerVerificationDataset(Dataset):
-    def __init__(self, datasets_root: Path):
-        self.root = datasets_root
-        speaker_dirs = [f for f in self.root.glob("*") if f.is_dir()]
-        if len(speaker_dirs) == 0:
-            raise Exception("No speakers found. Make sure you are pointing to the directory "
-                            "containing all preprocessed speaker directories.")
-        self.speakers = [Speaker(speaker_dir) for speaker_dir in speaker_dirs]
-        self.speaker_cycler = RandomCycler(self.speakers)
-
-    def __len__(self):
-        return int(1e10)
-        
-    def __getitem__(self, index):
-        return next(self.speaker_cycler)
-    
-    def get_logs(self):
-        log_string = ""
-        for log_fpath in self.root.glob("*.txt"):
-            with log_fpath.open("r") as log_file:
-                log_string += "".join(log_file.readlines())
-        return log_string
-    
-    
-class SpeakerVerificationDataLoader(DataLoader):
-    def __init__(self, dataset, speakers_per_batch, utterances_per_speaker, sampler=None, 
-                 batch_sampler=None, num_workers=0, pin_memory=False, timeout=0, 
-                 worker_init_fn=None):
-        self.utterances_per_speaker = utterances_per_speaker
-
-        super().__init__(
-            dataset=dataset, 
-            batch_size=speakers_per_batch, 
-            shuffle=False, 
-            sampler=sampler, 
-            batch_sampler=batch_sampler, 
-            num_workers=num_workers,
-            collate_fn=self.collate, 
-            pin_memory=pin_memory, 
-            drop_last=False, 
-            timeout=timeout, 
-            worker_init_fn=worker_init_fn
-        )
-
-    def collate(self, speakers):
-        return SpeakerBatch(speakers, self.utterances_per_speaker, partials_n_frames) 
-    
\ No newline at end of file
diff --git a/spaces/Gradio-Blocks/uniformer_image_detection/configs/_base_/models/faster_rcnn_r50_fpn.py b/spaces/Gradio-Blocks/uniformer_image_detection/configs/_base_/models/faster_rcnn_r50_fpn.py
deleted file mode 100644
index 0f038d12cb61e8b901fa47cb3bfdcf8164b3c859..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_detection/configs/_base_/models/faster_rcnn_r50_fpn.py
+++ /dev/null
@@ -1,107 +0,0 @@
-model = dict(
-    type='FasterRCNN',
-    pretrained='torchvision://resnet50',
-    backbone=dict(
-        type='ResNet',
-        depth=50,
-        num_stages=4,
-        out_indices=(0, 1, 2, 3),
-        frozen_stages=1,
-        norm_cfg=dict(type='BN', requires_grad=True),
-        norm_eval=True,
-        style='pytorch'),
-    neck=dict(
-        type='FPN',
-        in_channels=[256, 512, 1024, 2048],
-        out_channels=256,
-        num_outs=5),
-    rpn_head=dict(
-        type='RPNHead',
-        in_channels=256,
-        feat_channels=256,
-        anchor_generator=dict(
-            type='AnchorGenerator',
-            scales=[8],
-            ratios=[0.5, 1.0, 2.0],
-            strides=[4, 8, 16, 32, 64]),
-        bbox_coder=dict(
-            type='DeltaXYWHBBoxCoder',
-            target_means=[.0, .0, .0, .0],
-            target_stds=[1.0, 1.0, 1.0, 1.0]),
-        loss_cls=dict(
-            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
-        loss_bbox=dict(type='L1Loss', loss_weight=1.0)),
-    roi_head=dict(
-        type='StandardRoIHead',
-        bbox_roi_extractor=dict(
-            type='SingleRoIExtractor',
-            roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),
-            out_channels=256,
-            featmap_strides=[4, 8, 16, 32]),
-        bbox_head=dict(
-            type='Shared2FCBBoxHead',
-            in_channels=256,
-            fc_out_channels=1024,
-            roi_feat_size=7,
-            num_classes=80,
-            bbox_coder=dict(
-                type='DeltaXYWHBBoxCoder',
-                target_means=[0., 0., 0., 0.],
-                target_stds=[0.1, 0.1, 0.2, 0.2]),
-            reg_class_agnostic=False,
-            loss_cls=dict(
-                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
-            loss_bbox=dict(type='L1Loss', loss_weight=1.0))),
-    # model training and testing settings
-    train_cfg=dict(
-        rpn=dict(
-            assigner=dict(
-                type='MaxIoUAssigner',
-                pos_iou_thr=0.7,
-                neg_iou_thr=0.3,
-                min_pos_iou=0.3,
-                match_low_quality=True,
-                ignore_iof_thr=-1),
-            sampler=dict(
-                type='RandomSampler',
-                num=256,
-                pos_fraction=0.5,
-                neg_pos_ub=-1,
-                add_gt_as_proposals=False),
-            allowed_border=-1,
-            pos_weight=-1,
-            debug=False),
-        rpn_proposal=dict(
-            nms_pre=2000,
-            max_per_img=1000,
-            nms=dict(type='nms', iou_threshold=0.7),
-            min_bbox_size=0),
-        rcnn=dict(
-            assigner=dict(
-                type='MaxIoUAssigner',
-                pos_iou_thr=0.5,
-                neg_iou_thr=0.5,
-                min_pos_iou=0.5,
-                match_low_quality=False,
-                ignore_iof_thr=-1),
-            sampler=dict(
-                type='RandomSampler',
-                num=512,
-                pos_fraction=0.25,
-                neg_pos_ub=-1,
-                add_gt_as_proposals=True),
-            pos_weight=-1,
-            debug=False)),
-    test_cfg=dict(
-        rpn=dict(
-            nms_pre=1000,
-            max_per_img=1000,
-            nms=dict(type='nms', iou_threshold=0.7),
-            min_bbox_size=0),
-        rcnn=dict(
-            score_thr=0.05,
-            nms=dict(type='nms', iou_threshold=0.5),
-            max_per_img=100)
-        # soft-nms is also supported for rcnn testing
-        # e.g., nms=dict(type='soft_nms', iou_threshold=0.5, min_score=0.05)
-    ))
diff --git a/spaces/Gradio-Blocks/uniformer_image_detection/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_poly_1x_coco_v1.py b/spaces/Gradio-Blocks/uniformer_image_detection/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_poly_1x_coco_v1.py
deleted file mode 100644
index 431e5ab33675290d27e232f4fc5402279b7cf14c..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_detection/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_poly_1x_coco_v1.py
+++ /dev/null
@@ -1,57 +0,0 @@
-_base_ = './mask_rcnn_r50_fpn_1x_coco.py'
-model = dict(
-    pretrained='open-mmlab://resnet50_caffe_bgr',
-    backbone=dict(norm_cfg=dict(requires_grad=False), style='caffe'),
-    rpn_head=dict(
-        loss_bbox=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0)),
-    roi_head=dict(
-        bbox_roi_extractor=dict(
-            roi_layer=dict(
-                type='RoIAlign',
-                output_size=7,
-                sampling_ratio=2,
-                aligned=False)),
-        bbox_head=dict(
-            loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)),
-        mask_roi_extractor=dict(
-            roi_layer=dict(
-                type='RoIAlign',
-                output_size=14,
-                sampling_ratio=2,
-                aligned=False))))
-# 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,
-        with_mask=True,
-        poly2mask=False),
-    dict(type='Resize', img_scale=(1333, 800), 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']),
-]
-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/Gradio-Blocks/uniformer_image_detection/configs/pisa/pisa_retinanet_x101_32x4d_fpn_1x_coco.py b/spaces/Gradio-Blocks/uniformer_image_detection/configs/pisa/pisa_retinanet_x101_32x4d_fpn_1x_coco.py
deleted file mode 100644
index b97b6720f0522ee19e3f8353bf490b74a5835308..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_detection/configs/pisa/pisa_retinanet_x101_32x4d_fpn_1x_coco.py
+++ /dev/null
@@ -1,7 +0,0 @@
-_base_ = '../retinanet/retinanet_x101_32x4d_fpn_1x_coco.py'
-
-model = dict(
-    bbox_head=dict(
-        type='PISARetinaHead',
-        loss_bbox=dict(type='SmoothL1Loss', beta=0.11, loss_weight=1.0)),
-    train_cfg=dict(isr=dict(k=2., bias=0.), carl=dict(k=1., bias=0.2)))
diff --git a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/ann/ann_r101-d8_512x1024_80k_cityscapes.py b/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/ann/ann_r101-d8_512x1024_80k_cityscapes.py
deleted file mode 100644
index 1eeff0b030cf1db8c6ec9740fa65db44b2026d58..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/ann/ann_r101-d8_512x1024_80k_cityscapes.py
+++ /dev/null
@@ -1,2 +0,0 @@
-_base_ = './ann_r50-d8_512x1024_80k_cityscapes.py'
-model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
diff --git a/spaces/HaloMaster/chinesesummary/fengshen/data/preprocess.py b/spaces/HaloMaster/chinesesummary/fengshen/data/preprocess.py
deleted file mode 100644
index 9bad5790a5799b96f2e164d825c0b1f8ec0c2dfb..0000000000000000000000000000000000000000
--- a/spaces/HaloMaster/chinesesummary/fengshen/data/preprocess.py
+++ /dev/null
@@ -1 +0,0 @@
-# coding=utf-8
diff --git a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/modules/dynamicconv_layer/dynamicconv_layer.py b/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/modules/dynamicconv_layer/dynamicconv_layer.py
deleted file mode 100644
index 711ed03483f4089dbe91964a89021b49eeffbedc..0000000000000000000000000000000000000000
--- a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/modules/dynamicconv_layer/dynamicconv_layer.py
+++ /dev/null
@@ -1,227 +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 dynamicconv_cuda
-import torch
-import torch.nn.functional as F
-from fairseq import utils
-from fairseq.incremental_decoding_utils import with_incremental_state
-from fairseq.modules.fairseq_dropout import FairseqDropout
-from fairseq.modules.unfold import unfold1d
-from torch import nn
-from torch.autograd import Function
-
-
-class dynamicconvFunction(Function):
-    @staticmethod
-    def forward(ctx, x, weights, padding_l):
-        ctx.padding_l = padding_l
-        outputs = dynamicconv_cuda.forward(x, weights, padding_l)
-        variables = [x, weights]
-        ctx.save_for_backward(*variables)
-        return outputs[0]
-
-    @staticmethod
-    def backward(ctx, grad_output):
-        outputs = dynamicconv_cuda.backward(
-            grad_output.contiguous(), ctx.padding_l, *ctx.saved_tensors
-        )
-        grad_input, grad_weights = outputs
-        return grad_input, grad_weights, None
-
-
-@with_incremental_state
-class DynamicconvLayer(nn.Module):
-    def __init__(
-        self,
-        input_size,
-        kernel_size=1,
-        padding_l=None,
-        weight_softmax=False,
-        num_heads=1,
-        weight_dropout=0.0,
-        bias=False,
-        renorm_padding=False,
-        conv_bias=False,
-        query_size=None,
-    ):
-
-        super(DynamicconvLayer, self).__init__()
-        self.input_size = input_size
-        self.query_size = input_size if query_size is None else query_size
-        self.kernel_size = kernel_size
-        self.padding_l = padding_l
-        self.num_heads = num_heads
-        self.weight_softmax = weight_softmax
-        self.weight_dropout_module = FairseqDropout(
-            weight_dropout, module_name=self.__class__.__name__
-        )
-        self.renorm_padding = renorm_padding
-        self.bias = bias
-
-        self.weight_linear = nn.Linear(input_size, num_heads * kernel_size, bias)
-        if conv_bias:
-            self.conv_bias = nn.Parameter(torch.Tensor(input_size))
-        else:
-            self.conv_bias = None
-        self.reset_parameters()
-
-    def reset_parameters(self):
-        nn.init.xavier_uniform_(self.weight_linear.weight)
-        if self.conv_bias is not None:
-            nn.init.constant_(self.conv_bias, 0.0)
-            nn.init.constant_(self.weight_linaer.bias, 0.0)
-
-    def forward(self, x, incremental_state=None, query=None, unfold=None):
-
-        T, B, C = x.size()
-        K, H = self.kernel_size, self.num_heads
-        # R = C // H
-
-        # during inference time, incremental BMM is faster
-        if incremental_state is not None:
-            unfold = (
-                x.size(0) > 512 if unfold is None else unfold
-            )  # use unfold mode as default for long sequence to save memory
-            unfold = unfold or (incremental_state is not None)
-            assert query is None
-
-            if query is None:
-                query = x
-            if unfold:
-                output = self._forward_unfolded(x, incremental_state, query)
-            else:
-                output = self._forward_expanded(x, incremental_state, query)
-
-            if self.conv_bias is not None:
-                output = output + self.conv_bias.view(1, 1, -1)
-
-            return output
-
-        # during training time, use CUDA kernel
-        else:
-            weight = self.weight_linear(x).view(T, B, H, K)
-            if self.weight_softmax:
-                weight = F.softmax(weight, dim=-1)
-            if self.weight_dropout_module.p:
-                weight = self.weight_dropout_module(weight)
-
-            weight = weight.permute(1, 2, 3, 0).contiguous()
-            self.filters = weight
-            x = x.permute(1, 2, 0).contiguous()
-            output = dynamicconvFunction.apply(x, weight, self.padding_l).permute(
-                2, 0, 1
-            )
-            if self.conv_bias is not None:
-                output = output + self.conv_bias.view(1, 1, -1)
-            return output
-
-    def reorder_incremental_state(self, incremental_state, new_order):
-        input_buffer = self._get_input_buffer(incremental_state)
-        if input_buffer is not None:
-            input_buffer = input_buffer.index_select(1, new_order)
-            self._set_input_buffer(incremental_state, input_buffer)
-
-    def _get_input_buffer(self, incremental_state):
-        return utils.get_incremental_state(self, incremental_state, "input_buffer")
-
-    def _set_input_buffer(self, incremental_state, new_buffer):
-        return utils.set_incremental_state(
-            self, incremental_state, "input_buffer", new_buffer
-        )
-
-    def _forward_unfolded(self, x, incremental_state, query):
-        """The conventional implementation of convolutions.
-        Unfolding the input by having a window shifting to the right."""
-        T, B, C = x.size()
-        K, H = self.kernel_size, self.num_heads
-        R = C // H
-        assert R * H == C == self.input_size
-
-        weight = self.weight_linear(query).view(T * B * H, -1)
-
-        # renorm_padding is only implemented in _forward_expanded
-        assert not self.renorm_padding or incremental_state is not None
-
-        if incremental_state is not None:
-            input_buffer = self._get_input_buffer(incremental_state)
-            if input_buffer is None:
-                input_buffer = x.new()
-            x_unfold = torch.cat([input_buffer, x.unsqueeze(3)], dim=3)
-            if self.kernel_size > 1:
-                self._set_input_buffer(
-                    incremental_state, x_unfold[:, :, :, -self.kernel_size + 1 :]
-                )
-            x_unfold = x_unfold.view(T * B * H, R, -1)
-        else:
-            padding_l = self.padding_l
-            if K > T and padding_l == K - 1:
-                weight = weight.narrow(1, K - T, T)
-                K, padding_l = T, T - 1
-            # unfold the input: T x B x C --> T' x B x C x K
-            x_unfold = unfold1d(x, K, padding_l, 0)
-            x_unfold = x_unfold.view(T * B * H, R, K)
-
-        if self.weight_softmax and not self.renorm_padding:
-            weight = F.softmax(weight, dim=1)
-        weight = weight.narrow(1, 0, K)
-
-        if incremental_state is not None:
-            weight = weight[:, -x_unfold.size(2) :]
-            K = weight.size(1)
-
-        if self.weight_softmax and self.renorm_padding:
-            weight = F.softmax(weight, dim=1)
-
-        weight = self.weight_dropout_module(weight, inplace=False)
-
-        output = torch.bmm(x_unfold, weight.unsqueeze(2))  # T*B*H x R x 1
-        output = output.view(T, B, C)
-        return output
-
-    def _forward_expanded(self, x, incremental_stat, query):
-        """Turn the convolution filters into band matrices and do matrix multiplication.
-        This is faster when the sequence is short, but less memory efficient.
-        This is not used in the decoder during inference.
-        """
-        T, B, C = x.size()
-        K, H = self.kernel_size, self.num_heads
-        R = C // H
-        assert R * H == C == self.input_size
-        weight = self.weight_linear(query).view(T * B * H, -1)
-
-        if not self.renorm_padding:
-            if self.weight_softmax:
-                weight = F.softmax(weight, dim=1)
-            weight = self.weight_dropout_module(weight, inplace=False)
-        weight = weight.narrow(1, 0, K).contiguous()
-        weight = weight.view(T, B * H, K).transpose(0, 1)
-
-        x = x.view(T, B * H, R).transpose(0, 1)
-        if self.weight_softmax and self.renorm_padding:
-            # turn the convolution filters into band matrices
-            weight_expanded = weight.new(B * H, T, T + K - 1).fill_(float("-inf"))
-            weight_expanded.as_strided(
-                (B * H, T, K), (T * (T + K - 1), T + K, 1)
-            ).copy_(weight)
-            weight_expanded = weight_expanded.narrow(2, self.padding_l, T)
-            # normalize the weight over valid positions like self-attention
-            weight_expanded = F.softmax(weight_expanded, dim=2)
-            weight_expanded = self.weight_dropout_module(weight_expanded, inplace=False)
-        else:
-            P = self.padding_l
-            # For efficiency, we cut the kernel size and reduce the padding when the kernel is larger than the length
-            if K > T and P == K - 1:
-                weight = weight.narrow(2, K - T, T)
-                K, P = T, T - 1
-            # turn the convolution filters into band matrices
-            weight_expanded = weight.new_zeros(B * H, T, T + K - 1, requires_grad=False)
-            weight_expanded.as_strided(
-                (B * H, T, K), (T * (T + K - 1), T + K, 1)
-            ).copy_(weight)
-            weight_expanded = weight_expanded.narrow(2, P, T)  # B*H x T x T
-        output = torch.bmm(weight_expanded, x)
-        output = output.transpose(0, 1).contiguous().view(T, B, C)
-        return output
diff --git a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/modules/learned_positional_embedding.py b/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/modules/learned_positional_embedding.py
deleted file mode 100644
index 378d0f707183dd344dbb9288dda394b11053acf0..0000000000000000000000000000000000000000
--- a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/modules/learned_positional_embedding.py
+++ /dev/null
@@ -1,61 +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 typing import Dict, Optional
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from fairseq import utils
-from torch import Tensor
-
-
-class LearnedPositionalEmbedding(nn.Embedding):
-    """
-    This module learns positional embeddings up to a fixed maximum size.
-    Padding ids are ignored by either offsetting based on padding_idx
-    or by setting padding_idx to None and ensuring that the appropriate
-    position ids are passed to the forward function.
-    """
-
-    def __init__(self, num_embeddings: int, embedding_dim: int, padding_idx: int):
-        super().__init__(num_embeddings, embedding_dim, padding_idx)
-        self.onnx_trace = False
-        if self.padding_idx is not None:
-            self.max_positions = self.num_embeddings - self.padding_idx - 1
-        else:
-            self.max_positions = self.num_embeddings
-
-    def forward(
-        self,
-        input: Tensor,
-        incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None,
-        positions: Optional[Tensor] = None,
-    ):
-        """Input is expected to be of size [bsz x seqlen]."""
-        assert (positions is None) or (
-            self.padding_idx is None
-        ), "If positions is pre-computed then padding_idx should not be set."
-
-        if positions is None:
-            if incremental_state is not None:
-                # positions is the same for every token when decoding a single step
-                # Without the int() cast, it doesn't work in some cases when exporting to ONNX
-                positions = torch.zeros(
-                    (1, 1), device=input.device, dtype=input.dtype
-                ).fill_(int(self.padding_idx + input.size(1)))
-            else:
-                positions = utils.make_positions(
-                    input, self.padding_idx, onnx_trace=self.onnx_trace
-                )
-        return F.embedding(
-            positions,
-            self.weight,
-            self.padding_idx,
-            self.max_norm,
-            self.norm_type,
-            self.scale_grad_by_freq,
-            self.sparse,
-        )
diff --git a/spaces/Harveenchadha/Vakyansh-Hindi-TTS/ttsv/src/glow_tts/monotonic_align/setup.py b/spaces/Harveenchadha/Vakyansh-Hindi-TTS/ttsv/src/glow_tts/monotonic_align/setup.py
deleted file mode 100644
index 3a3892f92e3fbb866e3111199a9a4cf1f88e3959..0000000000000000000000000000000000000000
--- a/spaces/Harveenchadha/Vakyansh-Hindi-TTS/ttsv/src/glow_tts/monotonic_align/setup.py
+++ /dev/null
@@ -1,23 +0,0 @@
-import numpy
-from setuptools import Extension, find_packages
-from distutils.core import setup
-from Cython.Build import cythonize
-
-
-_VERSION = "1.1"
-
-
-ext_modules = cythonize(
-    "monotonic_align/core.pyx",
-    compiler_directives={"language_level": "3"},
-)
-
-setup(
-    name="monotonic_align",
-    ext_modules=ext_modules,
-    include_dirs=[numpy.get_include(), "monotonic_align"],
-    packages=find_packages(),
-    setup_requires=["numpy", "cython"],
-    install_requires=["numpy"],
-    version=_VERSION,
-)
diff --git a/spaces/ICML2022/OFA/fairseq/examples/fast_noisy_channel/noisy_channel_translation.py b/spaces/ICML2022/OFA/fairseq/examples/fast_noisy_channel/noisy_channel_translation.py
deleted file mode 100644
index b74bdfd456f9b7c546ce528173c77431b4f57ac1..0000000000000000000000000000000000000000
--- a/spaces/ICML2022/OFA/fairseq/examples/fast_noisy_channel/noisy_channel_translation.py
+++ /dev/null
@@ -1,127 +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 fairseq.tasks.translation import TranslationTask
-from fairseq.tasks.language_modeling import LanguageModelingTask
-from fairseq import checkpoint_utils
-import argparse
-from fairseq.tasks import register_task
-import torch
-
-
-@register_task("noisy_channel_translation")
-class NoisyChannelTranslation(TranslationTask):
-    """
-    Rescore the top k candidates from each beam using noisy channel modeling
-    """
-
-    @staticmethod
-    def add_args(parser):
-        """Add task-specific arguments to the parser."""
-        TranslationTask.add_args(parser)
-        # fmt: off
-        parser.add_argument('--channel-model', metavar='FILE',
-                            help='path to P(S|T) model. P(S|T) and P(T|S) must share source and target dictionaries.')
-        parser.add_argument('--combine-method', default='lm_only',
-                            choices=['lm_only', 'noisy_channel'],
-                            help="""method for combining direct and channel model scores.
-                                    lm_only: decode with P(T|S)P(T)
-                                    noisy_channel: decode with 1/t P(T|S) + 1/s(P(S|T)P(T))""")
-        parser.add_argument('--normalize-lm-scores-by-tgt-len', action='store_true', default=False,
-                            help='normalize lm score by target length instead of source length')
-        parser.add_argument('--channel-scoring-type', default='log_norm', choices=['unnormalized', 'log_norm', 'k2_separate', 'src_vocab', 'src_vocab_batched'],
-                            help="Normalize bw scores with log softmax or return bw scores without log softmax")
-        parser.add_argument('--top-k-vocab', default=0, type=int,
-                            help='top k vocab IDs to use with `src_vocab` in channel model scoring')
-        parser.add_argument('--k2', default=50, type=int,
-                            help='the top k2 candidates to rescore with the noisy channel model for each beam')
-        parser.add_argument('--ch-wt', default=1, type=float,
-                            help='weight for the channel model')
-        parser.add_argument('--lm-model', metavar='FILE',
-                            help='path to lm model file, to model P(T). P(T) must share the same vocab as the direct model on the target side')
-        parser.add_argument('--lm-data', metavar='FILE',
-                            help='path to lm model training data for target language, used to properly load LM with correct dictionary')
-        parser.add_argument('--lm-wt', default=1, type=float,
-                            help='the weight of the lm in joint decoding')
-        # fmt: on
-
-    def build_generator(
-        self, models, args, seq_gen_cls=None, extra_gen_cls_kwargs=None
-    ):
-        if getattr(args, "score_reference", False):
-            raise NotImplementedError()
-        else:
-            from .noisy_channel_sequence_generator import NoisyChannelSequenceGenerator
-            use_cuda = torch.cuda.is_available() and not self.args.cpu
-            assert self.args.lm_model is not None, '--lm-model required for noisy channel generation!'
-            assert self.args.lm_data is not None, '--lm-data required for noisy channel generation to map between LM and bitext vocabs'
-            if self.args.channel_model is not None:
-                import copy
-                ch_args_task = copy.deepcopy(self.args)
-                tmp = ch_args_task.source_lang
-                ch_args_task.source_lang = ch_args_task.target_lang
-                ch_args_task.target_lang = tmp
-                ch_args_task._name = 'translation'
-                channel_task = TranslationTask.setup_task(ch_args_task)
-
-            arg_dict = {}
-            arg_dict['task'] = 'language_modeling'
-            arg_dict['sample_break_mode'] = 'eos'
-            arg_dict['data'] = self.args.lm_data
-            arg_dict['output_dictionary_size'] = -1
-            lm_args = argparse.Namespace(**arg_dict)
-            lm_task = LanguageModelingTask.setup_task(lm_args)
-            lm_dict = lm_task.output_dictionary
-
-            if self.args.channel_model is not None:
-                channel_models, _ = checkpoint_utils.load_model_ensemble(self.args.channel_model.split(':'), task=channel_task)
-
-                for model in channel_models:
-                    model.make_generation_fast_(
-                        beamable_mm_beam_size=None if args.no_beamable_mm else args.beam,
-                        need_attn=args.print_alignment,
-                    )
-                    if self.args.fp16:
-                        model.half()
-                    if use_cuda:
-                        model.cuda()
-            else:
-                channel_models = None
-
-            lm_models, _ = checkpoint_utils.load_model_ensemble(self.args.lm_model.split(':'), task=lm_task)
-
-            for model in lm_models:
-                model.make_generation_fast_(
-                    beamable_mm_beam_size=None if args.no_beamable_mm else args.beam,
-                    need_attn=args.print_alignment,
-                )
-                if self.args.fp16:
-                    model.half()
-                if use_cuda:
-                    model.cuda()
-            return NoisyChannelSequenceGenerator(
-                combine_method=self.args.combine_method,
-                tgt_dict=self.target_dictionary,
-                src_dict=self.source_dictionary,
-                beam_size=getattr(args, 'beam', 5),
-                max_len_a=getattr(args, 'max_len_a', 0),
-                max_len_b=getattr(args, 'max_len_b', 200),
-                min_len=getattr(args, 'min_len', 1),
-                len_penalty=getattr(args, 'lenpen', 1),
-                unk_penalty=getattr(args, 'unkpen', 0),
-                temperature=getattr(args, 'temperature', 1.),
-                match_source_len=getattr(args, 'match_source_len', False),
-                no_repeat_ngram_size=getattr(args, 'no_repeat_ngram_size', 0),
-                normalize_scores=(not getattr(args, 'unnormalized', False)),
-                channel_models=channel_models,
-                k2=getattr(self.args, 'k2', 50),
-                ch_weight=getattr(self.args, 'ch_wt', 1),
-                channel_scoring_type=self.args.channel_scoring_type,
-                top_k_vocab=self.args.top_k_vocab,
-                lm_models=lm_models,
-                lm_dict=lm_dict,
-                lm_weight=getattr(self.args, 'lm_wt', 1),
-                normalize_lm_scores_by_tgt_len=getattr(self.args, 'normalize_lm_scores_by_tgt_len', False),
-            )
diff --git a/spaces/ICML2022/OFA/fairseq/fairseq/models/text_to_speech/tts_transformer.py b/spaces/ICML2022/OFA/fairseq/fairseq/models/text_to_speech/tts_transformer.py
deleted file mode 100644
index ff7af78bd49708cc5429cd3d481d3866b4612779..0000000000000000000000000000000000000000
--- a/spaces/ICML2022/OFA/fairseq/fairseq/models/text_to_speech/tts_transformer.py
+++ /dev/null
@@ -1,371 +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
-from typing import List, Optional
-
-import torch
-from torch import nn
-
-from fairseq.models import (FairseqEncoder, FairseqEncoderDecoderModel,
-                            FairseqIncrementalDecoder, register_model,
-                            register_model_architecture)
-from fairseq.modules import (
-    TransformerEncoderLayer, TransformerDecoderLayer
-)
-from fairseq.models.text_to_speech.tacotron2 import Prenet, Postnet
-from fairseq.modules import LayerNorm, PositionalEmbedding, FairseqDropout
-from fairseq.data.data_utils import lengths_to_padding_mask
-from fairseq import utils
-
-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"))
-
-
-def Embedding(num_embeddings, embedding_dim):
-    m = nn.Embedding(num_embeddings, embedding_dim)
-    nn.init.normal_(m.weight, mean=0, std=embedding_dim ** -0.5)
-    return m
-
-
-class TTSTransformerEncoder(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.dropout_module = FairseqDropout(
-            p=args.dropout, module_name=self.__class__.__name__
-        )
-        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.prenet = 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.prenet_proj = nn.Linear(
-            args.encoder_embed_dim, args.encoder_embed_dim
-        )
-        self.embed_positions = PositionalEmbedding(
-            args.max_source_positions, args.encoder_embed_dim, self.padding_idx
-        )
-        self.pos_emb_alpha = nn.Parameter(torch.ones(1))
-
-        self.transformer_layers = nn.ModuleList(
-            TransformerEncoderLayer(args)
-            for _ in range(args.encoder_transformer_layers)
-        )
-        if args.encoder_normalize_before:
-            self.layer_norm = LayerNorm(args.encoder_embed_dim)
-        else:
-            self.layer_norm = None
-
-        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.prenet:
-            x = conv(x)
-        x = x.transpose(1, 2).contiguous()  # B x C x T -> B x T x C
-        x = self.prenet_proj(x)
-
-        padding_mask = src_tokens.eq(self.padding_idx)
-        positions = self.embed_positions(padding_mask)
-        x += self.pos_emb_alpha * positions
-        x = self.dropout_module(x)
-
-        # B x T x C -> T x B x C
-        x = x.transpose(0, 1)
-
-        for layer in self.transformer_layers:
-            x = layer(x, padding_mask)
-
-        if self.layer_norm is not None:
-            x = self.layer_norm(x)
-
-        if self.embed_speaker is not None:
-            seq_len, bsz, _ = x.size()
-            emb = self.embed_speaker(speaker).transpose(0, 1)
-            emb = emb.expand(seq_len, bsz, -1)
-            x = self.spk_emb_proj(torch.cat([x, emb], dim=2))
-
-        return {
-            "encoder_out": [x],  # T x B x C
-            "encoder_padding_mask": [padding_mask] if padding_mask.any() else [],  # B x T
-            "encoder_embedding": [],  # B x T x C
-            "encoder_states": [],  # List[T x B x C]
-            "src_tokens": [],
-            "src_lengths": [],
-        }
-
-
-def decoder_init(m):
-    if isinstance(m, torch.nn.Conv1d):
-        nn.init.xavier_uniform_(m.weight, torch.nn.init.calculate_gain("tanh"))
-
-
-class TTSTransformerDecoder(FairseqIncrementalDecoder):
-    def __init__(self, args, src_dict):
-        super().__init__(None)
-        self._future_mask = torch.empty(0)
-
-        self.args = args
-        self.padding_idx = src_dict.pad()
-        self.n_frames_per_step = args.n_frames_per_step
-        self.out_dim = args.output_frame_dim * args.n_frames_per_step
-
-        self.dropout_module = FairseqDropout(
-            args.dropout, module_name=self.__class__.__name__
-        )
-        self.embed_positions = PositionalEmbedding(
-            args.max_target_positions, args.decoder_embed_dim, self.padding_idx
-        )
-        self.pos_emb_alpha = nn.Parameter(torch.ones(1))
-        self.prenet = nn.Sequential(
-            Prenet(self.out_dim, args.prenet_layers, args.prenet_dim,
-                   args.prenet_dropout),
-            nn.Linear(args.prenet_dim, args.decoder_embed_dim),
-        )
-
-        self.n_transformer_layers = args.decoder_transformer_layers
-        self.transformer_layers = nn.ModuleList(
-            TransformerDecoderLayer(args)
-            for _ in range(self.n_transformer_layers)
-        )
-        if args.decoder_normalize_before:
-            self.layer_norm = LayerNorm(args.decoder_embed_dim)
-        else:
-            self.layer_norm = None
-
-        self.feat_proj = nn.Linear(args.decoder_embed_dim, self.out_dim)
-        self.eos_proj = nn.Linear(args.decoder_embed_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 extract_features(
-            self, prev_outputs, encoder_out=None, incremental_state=None,
-            target_lengths=None, speaker=None, **kwargs
-    ):
-        alignment_layer = self.n_transformer_layers - 1
-        self_attn_padding_mask = lengths_to_padding_mask(target_lengths)
-        positions = self.embed_positions(
-            self_attn_padding_mask, incremental_state=incremental_state
-        )
-
-        if incremental_state is not None:
-            prev_outputs = prev_outputs[:, -1:, :]
-            self_attn_padding_mask = self_attn_padding_mask[:, -1:]
-            if positions is not None:
-                positions = positions[:, -1:]
-
-        x = self.prenet(prev_outputs)
-        x += self.pos_emb_alpha * positions
-        x = self.dropout_module(x)
-
-        # B x T x C -> T x B x C
-        x = x.transpose(0, 1)
-
-        if not self_attn_padding_mask.any():
-            self_attn_padding_mask = None
-
-        attn: Optional[torch.Tensor] = None
-        inner_states: List[Optional[torch.Tensor]] = [x]
-        for idx, transformer_layer in enumerate(self.transformer_layers):
-            if incremental_state is None:
-                self_attn_mask = self.buffered_future_mask(x)
-            else:
-                self_attn_mask = None
-
-            x, layer_attn, _ = transformer_layer(
-                x,
-                encoder_out["encoder_out"][0]
-                if (encoder_out is not None and len(encoder_out["encoder_out"]) > 0)
-                else None,
-                encoder_out["encoder_padding_mask"][0]
-                if (
-                        encoder_out is not None
-                        and len(encoder_out["encoder_padding_mask"]) > 0
-                )
-                else None,
-                incremental_state,
-                self_attn_mask=self_attn_mask,
-                self_attn_padding_mask=self_attn_padding_mask,
-                need_attn=bool((idx == alignment_layer)),
-                need_head_weights=bool((idx == alignment_layer)),
-            )
-            inner_states.append(x)
-            if layer_attn is not None and idx == alignment_layer:
-                attn = layer_attn.float().to(x)
-
-        if attn is not None:
-            # average probabilities over heads, transpose to
-            # (B, src_len, tgt_len)
-            attn = attn.mean(dim=0).transpose(2, 1)
-
-        if self.layer_norm is not None:
-            x = self.layer_norm(x)
-
-        # T x B x C -> B x T x C
-        x = x.transpose(0, 1)
-
-        return x, {"attn": attn, "inner_states": inner_states}
-
-    def forward(self, prev_output_tokens, encoder_out=None,
-                incremental_state=None, target_lengths=None, speaker=None,
-                **kwargs):
-        x, extra = self.extract_features(
-            prev_output_tokens, encoder_out=encoder_out,
-            incremental_state=incremental_state, target_lengths=target_lengths,
-            speaker=speaker, **kwargs
-        )
-        attn = extra["attn"]
-        feat_out = self.feat_proj(x)
-        bsz, seq_len, _ = x.size()
-        eos_out = self.eos_proj(x)
-        post_feat_out = feat_out + self.postnet(feat_out)
-        return post_feat_out, eos_out, {"attn": attn, "feature_out": feat_out}
-
-    def get_normalized_probs(self, net_output, log_probs, sample):
-        logits = self.ctc_proj(net_output[2]["feature_out"])
-        if log_probs:
-            return utils.log_softmax(logits.float(), dim=-1)
-        else:
-            return utils.softmax(logits.float(), dim=-1)
-
-    def buffered_future_mask(self, tensor):
-        dim = tensor.size(0)
-        # self._future_mask.device != tensor.device is not working in TorchScript. This is a workaround.
-        if (
-            self._future_mask.size(0) == 0
-            or (not self._future_mask.device == tensor.device)
-            or self._future_mask.size(0) < dim
-        ):
-            self._future_mask = torch.triu(
-                utils.fill_with_neg_inf(torch.zeros([dim, dim])), 1
-            )
-        self._future_mask = self._future_mask.to(tensor)
-        return self._future_mask[:dim, :dim]
-
-
-@register_model("tts_transformer")
-class TTSTransformerModel(FairseqEncoderDecoderModel):
-    """
-    Implementation for https://arxiv.org/pdf/1809.08895.pdf
-    """
-
-    @staticmethod
-    def add_args(parser):
-        parser.add_argument("--dropout", type=float)
-        parser.add_argument("--output-frame-dim", type=int)
-        parser.add_argument("--speaker-embed-dim", type=int)
-        # encoder prenet
-        parser.add_argument("--encoder-dropout", type=float)
-        parser.add_argument("--encoder-conv-layers", type=int)
-        parser.add_argument("--encoder-conv-kernel-size", type=int)
-        # encoder transformer layers
-        parser.add_argument("--encoder-transformer-layers", type=int)
-        parser.add_argument("--encoder-embed-dim", type=int)
-        parser.add_argument("--encoder-ffn-embed-dim", type=int)
-        parser.add_argument("--encoder-normalize-before", action="store_true")
-        parser.add_argument("--encoder-attention-heads", type=int)
-        parser.add_argument("--attention-dropout", type=float)
-        parser.add_argument("--activation-dropout", "--relu-dropout", type=float)
-        parser.add_argument("--activation-fn", type=str, default="relu")
-        # decoder prenet
-        parser.add_argument("--prenet-dropout", type=float)
-        parser.add_argument("--prenet-layers", type=int)
-        parser.add_argument("--prenet-dim", type=int)
-        # decoder postnet
-        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)
-        # decoder transformer layers
-        parser.add_argument("--decoder-transformer-layers", type=int)
-        parser.add_argument("--decoder-embed-dim", type=int)
-        parser.add_argument("--decoder-ffn-embed-dim", type=int)
-        parser.add_argument("--decoder-normalize-before", action="store_true")
-        parser.add_argument("--decoder-attention-heads", 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 = TTSTransformerEncoder(args, task.src_dict, embed_speaker)
-        decoder = TTSTransformerDecoder(args, task.src_dict)
-        return cls(encoder, decoder)
-
-    def forward_encoder(self, src_tokens, src_lengths, speaker=None, **kwargs):
-        return self.encoder(src_tokens, src_lengths=src_lengths,
-                            speaker=speaker, **kwargs)
-
-    def set_num_updates(self, num_updates):
-        super().set_num_updates(num_updates)
-        self._num_updates = num_updates
-
-
-@register_model_architecture("tts_transformer", "tts_transformer")
-def base_architecture(args):
-    args.dropout = getattr(args, "dropout", 0.1)
-    args.output_frame_dim = getattr(args, "output_frame_dim", 80)
-    args.speaker_embed_dim = getattr(args, "speaker_embed_dim", 64)
-    # encoder prenet
-    args.encoder_dropout = getattr(args, "encoder_dropout", 0.5)
-    args.encoder_conv_layers = getattr(args, "encoder_conv_layers", 3)
-    args.encoder_conv_kernel_size = getattr(args, "encoder_conv_kernel_size", 5)
-    # encoder transformer layers
-    args.encoder_transformer_layers = getattr(args, "encoder_transformer_layers", 6)
-    args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512)
-    args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 4 * args.encoder_embed_dim)
-    args.encoder_normalize_before = getattr(args, "encoder_normalize_before", False)
-    args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 4)
-    args.attention_dropout = getattr(args, "attention_dropout", 0.0)
-    args.activation_dropout = getattr(args, "activation_dropout", 0.0)
-    args.activation_fn = getattr(args, "activation_fn", "relu")
-    # decoder prenet
-    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)
-    # decoder postnet
-    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)
-    # decoder transformer layers
-    args.decoder_transformer_layers = getattr(args, "decoder_transformer_layers", 6)
-    args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 512)
-    args.decoder_ffn_embed_dim = getattr(args, "decoder_ffn_embed_dim", 4 * args.decoder_embed_dim)
-    args.decoder_normalize_before = getattr(args, "decoder_normalize_before", False)
-    args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 4)
diff --git a/spaces/ICML2022/PointCloudC/downsample.py b/spaces/ICML2022/PointCloudC/downsample.py
deleted file mode 100644
index 7163168dba82fb7cc448d642f5d55339d869bc05..0000000000000000000000000000000000000000
--- a/spaces/ICML2022/PointCloudC/downsample.py
+++ /dev/null
@@ -1,12 +0,0 @@
-import glob
-import h5py
-
-for fpath in glob.glob('modelnet_c/*.h5'):
-    f = h5py.File(fpath)
-    data = f['data'][:].astype('float32')
-    label = f['label'][:].astype('int64')
-    f.close()
-    f = h5py.File(fpath, 'w')
-    f.create_dataset('data', data=data[:100])
-    f.create_dataset('label', data=label[:100])
-    f.close()
\ No newline at end of file
diff --git a/spaces/ICML2022/resefa/utils/parsing_utils.py b/spaces/ICML2022/resefa/utils/parsing_utils.py
deleted file mode 100644
index 50d8b20da87413a2730d12e712ef8d5dddbdad65..0000000000000000000000000000000000000000
--- a/spaces/ICML2022/resefa/utils/parsing_utils.py
+++ /dev/null
@@ -1,213 +0,0 @@
-# python3.7
-"""Contains the utility functions for parsing arguments."""
-
-import json
-import argparse
-import click
-
-__all__ = [
-    'parse_int', 'parse_float', 'parse_bool', 'parse_index', 'parse_json',
-    'IntegerParamType', 'FloatParamType', 'BooleanParamType', 'IndexParamType',
-    'JsonParamType', 'DictAction'
-]
-
-
-def parse_int(arg):
-    """Parses an argument to integer.
-
-    Support converting string `none` and `null` to `None`.
-    """
-    if arg is None:
-        return None
-    if isinstance(arg, str) and arg.lower() in ['none', 'null']:
-        return None
-    return int(arg)
-
-
-def parse_float(arg):
-    """Parses an argument to float number.
-
-    Support converting string `none` and `null` to `None`.
-    """
-    if arg is None:
-        return None
-    if isinstance(arg, str) and arg.lower() in ['none', 'null']:
-        return None
-    return float(arg)
-
-
-def parse_bool(arg):
-    """Parses an argument to boolean.
-
-    `None` will be converted to `False`.
-    """
-    if isinstance(arg, bool):
-        return arg
-    if arg is None:
-        return False
-    if arg.lower() in ['1', 'true', 't', 'yes', 'y']:
-        return True
-    if arg.lower() in ['0', 'false', 'f', 'no', 'n', 'none', 'null']:
-        return False
-    raise ValueError(f'`{arg}` cannot be converted to boolean!')
-
-
-def parse_index(arg, min_val=None, max_val=None):
-    """Parses indices.
-
-    If the input is a list or tuple, this function has no effect.
-
-    If the input is a string, it can be either a comma separated list of numbers
-    `1, 3, 5`, or a dash separated range `3 - 10`. Spaces in the string will be
-    ignored.
-
-    Args:
-        arg: The input argument to parse indices from.
-        min_val: If not `None`, this function will check that all indices are
-            equal to or larger than this value. (default: None)
-        max_val: If not `None`, this function will check that all indices are
-            equal to or smaller than this field. (default: None)
-
-    Returns:
-        A list of integers.
-
-    Raises:
-        ValueError: If the input is invalid, i.e., neither a list or tuple, nor
-            a string.
-    """
-    if arg is None or arg == '':
-        indices = []
-    elif isinstance(arg, int):
-        indices = [arg]
-    elif isinstance(arg, (list, tuple)):
-        indices = list(arg)
-    elif isinstance(arg, str):
-        indices = []
-        if arg.lower() not in ['none', 'null']:
-            splits = arg.replace(' ', '').split(',')
-            for split in splits:
-                numbers = list(map(int, split.split('-')))
-                if len(numbers) == 1:
-                    indices.append(numbers[0])
-                elif len(numbers) == 2:
-                    indices.extend(list(range(numbers[0], numbers[1] + 1)))
-    else:
-        raise ValueError(f'Invalid type of input: `{type(arg)}`!')
-
-    assert isinstance(indices, list)
-    indices = sorted(list(set(indices)))
-    for idx in indices:
-        assert isinstance(idx, int)
-        if min_val is not None:
-            assert idx >= min_val, f'{idx} is smaller than min val `{min_val}`!'
-        if max_val is not None:
-            assert idx <= max_val, f'{idx} is larger than max val `{max_val}`!'
-
-    return indices
-
-
-def parse_json(arg):
-    """Parses a string-like argument following JSON format.
-
-    `None` arguments will be kept.
-    """
-    if arg is None:
-        return None
-    try:
-        return json.loads(arg)
-    except json.decoder.JSONDecodeError:
-        return arg
-
-
-class IntegerParamType(click.ParamType):
-    """Defines a `click.ParamType` to parse integer arguments."""
-
-    name = 'int'
-
-    def convert(self, value, param, ctx):  # pylint: disable=inconsistent-return-statements
-        try:
-            return parse_int(value)
-        except ValueError:
-            self.fail(f'`{value}` cannot be parsed as an integer!', param, ctx)
-
-
-class FloatParamType(click.ParamType):
-    """Defines a `click.ParamType` to parse float arguments."""
-
-    name = 'float'
-
-    def convert(self, value, param, ctx):  # pylint: disable=inconsistent-return-statements
-        try:
-            return parse_float(value)
-        except ValueError:
-            self.fail(f'`{value}` cannot be parsed as a float!', param, ctx)
-
-
-class BooleanParamType(click.ParamType):
-    """Defines a `click.ParamType` to parse boolean arguments."""
-
-    name = 'bool'
-
-    def convert(self, value, param, ctx):  # pylint: disable=inconsistent-return-statements
-        try:
-            return parse_bool(value)
-        except ValueError:
-            self.fail(f'`{value}` cannot be parsed as a boolean!', param, ctx)
-
-
-class IndexParamType(click.ParamType):
-    """Defines a `click.ParamType` to parse indices arguments."""
-
-    name = 'index'
-
-    def __init__(self, min_val=None, max_val=None):
-        self.min_val = min_val
-        self.max_val = max_val
-
-    def convert(self, value, param, ctx):  # pylint: disable=inconsistent-return-statements
-        try:
-            return parse_index(value, self.min_val, self.max_val)
-        except ValueError:
-            self.fail(
-                f'`{value}` cannot be parsed as a list of indices!', param, ctx)
-
-
-class JsonParamType(click.ParamType):
-    """Defines a `click.ParamType` to parse arguments following JSON format."""
-
-    name = 'json'
-
-    def convert(self, value, param, ctx):
-        return parse_json(value)
-
-
-class DictAction(argparse.Action):
-    """Argparse action to split each argument into (key, value) pair.
-
-    Each argument should be with `key=value` format, where `value` should be a
-    string with JSON format.
-
-    For example, with an argparse:
-
-    parser.add_argument('--options', nargs='+', action=DictAction)
-
-    , you can use following arguments in the command line:
-
-    --options \
-        a=1 \
-        b=1.5
-        c=true \
-        d=null \
-        e=[1,2,3,4,5] \
-        f='{"x":1,"y":2,"z":3}' \
-
-    NOTE: No space is allowed in each argument. Also, the dictionary-type
-    argument should be quoted with single quotation marks `'`.
-    """
-
-    def __call__(self, parser, namespace, values, option_string=None):
-        options = {}
-        for argument in values:
-            key, val = argument.split('=', maxsplit=1)
-            options[key] = parse_json(val)
-        setattr(namespace, self.dest, options)
diff --git a/spaces/IDEA-Research/Grounded-SAM/segment_anything/README.md b/spaces/IDEA-Research/Grounded-SAM/segment_anything/README.md
deleted file mode 100644
index 6256d2b7f5a387988338d538df4e699eb17ba702..0000000000000000000000000000000000000000
--- a/spaces/IDEA-Research/Grounded-SAM/segment_anything/README.md
+++ /dev/null
@@ -1,107 +0,0 @@
-# Segment Anything
-
-**[Meta AI Research, FAIR](https://ai.facebook.com/research/)**
-
-[Alexander Kirillov](https://alexander-kirillov.github.io/), [Eric Mintun](https://ericmintun.github.io/), [Nikhila Ravi](https://nikhilaravi.com/), [Hanzi Mao](https://hanzimao.me/), Chloe Rolland, Laura Gustafson, [Tete Xiao](https://tetexiao.com), [Spencer Whitehead](https://www.spencerwhitehead.com/), Alex Berg, Wan-Yen Lo, [Piotr Dollar](https://pdollar.github.io/), [Ross Girshick](https://www.rossgirshick.info/)
-
-[[`Paper`](https://ai.facebook.com/research/publications/segment-anything/)] [[`Project`](https://segment-anything.com/)] [[`Demo`](https://segment-anything.com/demo)] [[`Dataset`](https://segment-anything.com/dataset/index.html)] [[`Blog`](https://ai.facebook.com/blog/segment-anything-foundation-model-image-segmentation/)]
-
-![SAM design](assets/model_diagram.png?raw=true)
-
-The **Segment Anything Model (SAM)** produces high quality object masks from input prompts such as points or boxes, and it can be used to generate masks for all objects in an image. It has been trained on a [dataset](https://segment-anything.com/dataset/index.html) of 11 million images and 1.1 billion masks, and has strong zero-shot performance on a variety of segmentation tasks.
-
-<p float="left">
-  <img src="assets/masks1.png?raw=true" width="37.25%" />
-  <img src="assets/masks2.jpg?raw=true" width="61.5%" /> 
-</p>
-
-## Installation
-
-The code requires `python>=3.8`, as well as `pytorch>=1.7` and `torchvision>=0.8`. Please follow the instructions [here](https://pytorch.org/get-started/locally/) to install both PyTorch and TorchVision dependencies. Installing both PyTorch and TorchVision with CUDA support is strongly recommended.
-
-Install Segment Anything:
-
-```
-pip install git+https://github.com/facebookresearch/segment-anything.git
-```
-
-or clone the repository locally and install with
-
-```
-git clone git@github.com:facebookresearch/segment-anything.git
-cd segment-anything; pip install -e .
-```
-
-The following optional dependencies are necessary for mask post-processing, saving masks in COCO format, the example notebooks, and exporting the model in ONNX format. `jupyter` is also required to run the example notebooks.
-```
-pip install opencv-python pycocotools matplotlib onnxruntime onnx
-```
-
-
-## <a name="GettingStarted"></a>Getting Started
-
-First download a [model checkpoint](#model-checkpoints). Then the model can be used in just a few lines to get masks from a given prompt:
-
-```
-from segment_anything import build_sam, SamPredictor 
-predictor = SamPredictor(build_sam(checkpoint="</path/to/model.pth>"))
-predictor.set_image(<your_image>)
-masks, _, _ = predictor.predict(<input_prompts>)
-```
-
-or generate masks for an entire image:
-
-```
-from segment_anything import build_sam, SamAutomaticMaskGenerator
-mask_generator = SamAutomaticMaskGenerator(build_sam(checkpoint="</path/to/model.pth>"))
-masks = mask_generator_generate(<your_image>)
-```
-
-Additionally, masks can be generated for images from the command line:
-
-```
-python scripts/amg.py --checkpoint <path/to/sam/checkpoint> --input <image_or_folder> --output <output_directory>
-```
-
-See the examples notebooks on [using SAM with prompts](/notebooks/predictor_example.ipynb) and [automatically generating masks](/notebooks/automatic_mask_generator_example.ipynb) for more details.
-
-<p float="left">
-  <img src="assets/notebook1.png?raw=true" width="49.1%" />
-  <img src="assets/notebook2.png?raw=true" width="48.9%" />
-</p>
-
-## ONNX Export
-
-SAM's lightweight mask decoder can be exported to ONNX format so that it can be run in any environment that supports ONNX runtime, such as in-browser as showcased in the [demo](https://segment-anything.com/demo). Export the model with
-
-```
-python scripts/export_onnx_model.py --checkpoint <path/to/checkpoint> --output <path/to/output>
-```
-
-See the [example notebook](https://github.com/facebookresearch/segment-anything/blob/main/notebooks/onnx_model_example.ipynb) for details on how to combine image preprocessing via SAM's backbone with mask prediction using the ONNX model. It is recommended to use the latest stable version of PyTorch for ONNX export.
-
-## <a name="Models"></a>Model Checkpoints
-
-Three model versions of the model are available with different backbone sizes. These models can be instantiated by running 
-```
-from segment_anything import sam_model_registry
-sam = sam_model_registry["<name>"](checkpoint="<path/to/checkpoint>")
-```
-Click the links below to download the checkpoint for the corresponding model name. The default model in bold can also be instantiated with `build_sam`, as in the examples in [Getting Started](#getting-started).
-
-* **`default` or `vit_h`: [ViT-H SAM model.](https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth)**
-* `vit_l`: [ViT-L SAM model.](https://dl.fbaipublicfiles.com/segment_anything/sam_vit_l_0b3195.pth)
-* `vit_b`: [ViT-B SAM model.](https://dl.fbaipublicfiles.com/segment_anything/sam_vit_b_01ec64.pth)
-
-## License
-The model is licensed under the [Apache 2.0 license](LICENSE).
-
-## Contributing
-
-See [contributing](CONTRIBUTING.md) and the [code of conduct](CODE_OF_CONDUCT.md).
-
-## Contributors
-
-The Segment Anything project was made possible with the help of many contributors (alphabetical):
-
-Aaron Adcock, Vaibhav Aggarwal, Morteza Behrooz, Cheng-Yang Fu, Ashley Gabriel, Ahuva Goldstand, Allen Goodman, Sumanth Gurram, Jiabo Hu, Somya Jain, Devansh Kukreja, Robert Kuo, Joshua Lane, Yanghao Li, Lilian Luong, Jitendra Malik, Mallika Malhotra, William Ngan, Omkar Parkhi, Nikhil Raina, Dirk Rowe, Neil Sejoor, Vanessa Stark, Bala Varadarajan, Bram Wasti, Zachary Winstrom
diff --git a/spaces/Intoval/privateChatGPT/README.md b/spaces/Intoval/privateChatGPT/README.md
deleted file mode 100644
index 7128e29689e35d059c9cc0a5050910fbd34873cd..0000000000000000000000000000000000000000
--- a/spaces/Intoval/privateChatGPT/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: ChuanhuChatGPT
-emoji: 🐯
-colorFrom: green
-colorTo: red
-sdk: gradio
-sdk_version: 3.25.0
-app_file: ChuanhuChatbot.py
-pinned: false
-license: gpl-3.0
-duplicated_from: JohnSmith9982/ChuanhuChatGPT
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
\ No newline at end of file
diff --git a/spaces/JMalott/ai_architecture/dalle/models/stage2/transformer.py b/spaces/JMalott/ai_architecture/dalle/models/stage2/transformer.py
deleted file mode 100644
index 8f12041541a66f320d855b1b7bebc54822f2ad56..0000000000000000000000000000000000000000
--- a/spaces/JMalott/ai_architecture/dalle/models/stage2/transformer.py
+++ /dev/null
@@ -1,255 +0,0 @@
-# ------------------------------------------------------------------------------------
-# minDALL-E
-# Copyright (c) 2021 Kakao Brain Corp. All Rights Reserved.
-# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
-# ------------------------------------------------------------------------------------
-# Modified from minGPT (https://github.com/karpathy/minGPT)
-# Copyright (c) 2020 Andrej Karpathy. All Rights Reserved.
-# ------------------------------------------------------------------------------------
-
-import torch
-import torch.nn as nn
-from typing import Optional, Tuple, List
-from torch.cuda.amp import autocast
-from omegaconf import OmegaConf
-from .layers import Block
-
-
-class Transformer1d(nn.Module):
-
-    def __init__(self,
-                 vocab_size_txt: int,
-                 vocab_size_img: int,
-                 hparams: OmegaConf) -> None:
-        super().__init__()
-        assert hparams.n_layers == hparams.n_dense_layers
-
-        # input embedding for image and text
-        self.tok_emb_img = nn.Embedding(vocab_size_img, hparams.embed_dim)
-        self.tok_emb_txt = nn.Embedding(vocab_size_txt, hparams.embed_dim)
-
-        self.pos_emb_img = nn.Embedding(hparams.ctx_len_img, hparams.embed_dim)
-        self.pos_emb_txt = nn.Embedding(hparams.ctx_len_txt, hparams.embed_dim)
-
-        self.drop = nn.Dropout(hparams.embd_pdrop)
-
-        # transformer blocks
-        self.blocks = [Block(ctx_len=hparams.ctx_len_img + hparams.ctx_len_txt,
-                             embed_dim=hparams.embed_dim,
-                             n_heads=hparams.n_heads,
-                             mlp_bias=hparams.mlp_bias,
-                             attn_bias=hparams.attn_bias,
-                             resid_pdrop=hparams.resid_pdrop,
-                             attn_pdrop=hparams.attn_pdrop,
-                             gelu_use_approx=hparams.gelu_use_approx) for i in range(1, hparams.n_layers+1)]
-        self.blocks = nn.Sequential(*self.blocks)
-
-        # heads for image and text
-        self.ln_f = nn.LayerNorm(hparams.embed_dim)
-        self.head_img = nn.Linear(hparams.embed_dim, vocab_size_img, bias=False)
-        self.head_txt = nn.Linear(hparams.embed_dim, vocab_size_txt, bias=False)
-
-        self.ctx_len_img = hparams.ctx_len_img
-        self.ctx_len_txt = hparams.ctx_len_txt
-        self.n_layers = hparams.n_layers
-
-        self.apply(self._init_weights)
-
-    def _init_weights(self, module: nn.Module) -> None:
-        if isinstance(module, (nn.Linear, nn.Embedding)):
-            module.weight.data.normal_(mean=0.0, std=0.02)
-            if isinstance(module, nn.Linear) and module.bias is not None:
-                module.bias.data.zero_()
-        elif isinstance(module, nn.LayerNorm):
-            module.bias.data.zero_()
-            module.weight.data.fill_(1.0)
-
-    def forward(self,
-                images: torch.LongTensor,
-                texts: torch.LongTensor,
-                pos_images: torch.LongTensor,
-                pos_texts: torch.LongTensor) -> Tuple[torch.FloatTensor, torch.FloatTensor]:
-        B, T = images.shape
-        _, N = texts.shape
-
-        assert T <= self.ctx_len_img, "Already reached the maximum context length (image)."
-        assert N == self.ctx_len_txt, "Already reached the maximum context length (text)."
-
-        texts = self.tok_emb_txt(texts)
-        images = self.tok_emb_img(images)
-
-        texts = texts + self.pos_emb_txt(pos_texts)
-        images = images + self.pos_emb_img(pos_images)
-
-        x = torch.cat([texts, images], axis=1).contiguous()
-        x = self.drop(x)
-        x = self.blocks(x)
-        x = self.ln_f(x)
-
-        texts = x[:, :N-1].contiguous()
-        images = x[:, N-1:-1].contiguous()
-
-        logits_txt = self.head_txt(texts)
-        logits_img = self.head_img(images)
-        return logits_img, logits_txt
-
-    @torch.no_grad()
-    def sampling(self,
-                 images: torch.LongTensor,
-                 texts: torch.LongTensor,
-                 pos_images: torch.LongTensor,
-                 pos_texts: torch.LongTensor,
-                 use_fp16: bool = True,
-                 past: Optional[List[torch.Tensor]] = None) -> Tuple[torch.FloatTensor, List[torch.FloatTensor]]:
-        _, N = texts.shape
-        assert N == self.ctx_len_txt, "Already reached the maximum context length (text)."
-
-        with autocast(enabled=use_fp16):
-            if images is None:
-                assert past is None
-
-                texts = self.tok_emb_txt(texts)
-                x = texts + self.pos_emb_txt(pos_texts)
-                x = self.drop(x)
-
-                presents = []
-                for i, block in enumerate(self.blocks):
-                    x, present = block.sample(x, layer_past=None)
-                    presents.append(present)
-                x = self.ln_f(x)
-                x = x[:, N-1].contiguous()
-                logits = self.head_img(x)
-            else:
-                if past is None:
-                    texts = self.tok_emb_txt(texts)
-                    images = self.tok_emb_img(images)
-                    texts = texts + self.pos_emb_txt(pos_texts)
-                    images = images + self.pos_emb_img(pos_images)
-                    x = torch.cat([texts, images], axis=1).contiguous()
-                else:
-                    images = self.tok_emb_img(images)
-                    x = images + self.pos_emb_img(pos_images)
-                x = self.drop(x)
-
-                if past is not None:
-                    past = torch.cat(past, dim=-2)
-                presents = []
-                for i, block in enumerate(self.blocks):
-                    x, present = block.sample(x, layer_past=None if past is None else past[i])
-                    presents.append(present)
-                x = self.ln_f(x)
-                x = x[:, -1].contiguous()
-                logits = self.head_img(x)
-            return logits, presents
-
-    def from_ckpt(self, path: str) -> None:
-        ckpt = torch.load(path, map_location='cpu')['state_dict']
-        self.load_state_dict(ckpt, strict=True)
-        print(f'{path} succesfully restored..')
-
-
-class iGPT(nn.Module):
-    def __init__(self,
-                 vocab_size_img: int,
-                 use_cls_cond: bool,
-                 hparams: OmegaConf) -> None:
-        super().__init__()
-        self.use_cls_cond = use_cls_cond
-
-        # sos token embedding
-        if self.use_cls_cond:
-            self.sos = nn.Embedding(hparams.n_classes, hparams.embed_dim)
-        else:
-            self.sos = nn.Parameter(torch.randn(1, 1, hparams.embed_dim))
-
-        # input embedding
-        self.tok_emb_img = nn.Embedding(vocab_size_img, hparams.embed_dim)
-        self.pos_emb_img = nn.Embedding(hparams.ctx_len_img, hparams.embed_dim)
-
-        self.drop = nn.Dropout(hparams.embd_pdrop)
-
-        # transformer blocks
-        self.blocks = [Block(ctx_len=hparams.ctx_len_img + 1,
-                             embed_dim=hparams.embed_dim,
-                             n_heads=hparams.n_heads,
-                             mlp_bias=hparams.mlp_bias,
-                             attn_bias=hparams.attn_bias,
-                             resid_pdrop=hparams.resid_pdrop,
-                             attn_pdrop=hparams.attn_pdrop,
-                             gelu_use_approx=hparams.gelu_use_approx) for i in range(1, hparams.n_layers+1)]
-        self.blocks = nn.Sequential(*self.blocks)
-
-        # head
-        self.ln_f = nn.LayerNorm(hparams.embed_dim)
-        self.head = nn.Linear(hparams.embed_dim, vocab_size_img, bias=False)
-
-        self.ctx_len_img = hparams.ctx_len_img
-        self.n_layers = hparams.n_layers
-
-        self.apply(self._init_weights)
-
-    def _init_weights(self, module: nn.Module) -> None:
-        if isinstance(module, (nn.Linear, nn.Embedding)):
-            module.weight.data.normal_(mean=0.0, std=0.02)
-            if isinstance(module, nn.Linear) and module.bias is not None:
-                module.bias.data.zero_()
-        elif isinstance(module, nn.LayerNorm):
-            module.bias.data.zero_()
-            module.weight.data.fill_(1.0)
-
-    @torch.no_grad()
-    def sampling(self,
-                 sos: torch.FloatTensor,
-                 codes: torch.LongTensor,
-                 pos_codes: torch.LongTensor,
-                 n_samples: int = 16,
-                 use_fp16: bool = True,
-                 past: Optional[torch.Tensor] = None) -> Tuple[torch.FloatTensor, List[torch.FloatTensor]]:
-        with autocast(enabled=use_fp16):
-            if codes is None:
-                assert past is None
-                xs = self.drop(sos)
-                presents = []
-                for i, block in enumerate(self.blocks):
-                    xs, present = block.sample(xs, layer_past=None)
-                    presents.append(present)
-                xs = self.ln_f(xs)
-                logits = self.head(xs)[:, -1]
-            else:
-                if past is None:
-                    xs = self.tok_emb_img(codes) + self.pos_emb_img(pos_codes)
-                    xs = torch.cat([sos, xs], dim=1)
-                else:
-                    xs = self.tok_emb_img(codes) + self.pos_emb_img(pos_codes)
-                xs = self.drop(xs)
-
-                past = torch.cat(past, dim=-2) if past is not None else past
-                presents = []
-                for i, block in enumerate(self.blocks):
-                    xs, present = block.sample(xs, layer_past=None if past is None else past[i])
-                    presents.append(present)
-
-                xs = self.ln_f(xs)
-                logits = self.head(xs)[:, -1]
-            return logits, presents
-
-    def forward(self,
-                codes: torch.LongTensor,
-                labels: Optional[torch.LongTensor] = None) -> torch.FloatTensor:
-        B, T = codes.shape
-        xps = torch.arange(T, device=codes.device).repeat((B, 1))
-        sos = self.sos.repeat((B, 1, 1)) if labels is None else self.sos(labels).unsqueeze(1)
-
-        h = self.tok_emb_img(codes) + self.pos_emb_img(xps)
-        h = torch.cat([sos, h[:, :-1]], dim=1).contiguous()
-
-        h = self.drop(h)
-        h = self.blocks(h)
-        h = self.ln_f(h)
-        logits = self.head(h)
-        return logits
-
-    def from_ckpt(self, path: str, strict: bool = True) -> None:
-        ckpt = torch.load(path, map_location='cpu')['state_dict']
-        self.load_state_dict(ckpt, strict=strict)
-        print(f'{path} successfully restored..')
diff --git a/spaces/JammyMachina/the-jam-machine-app/familizer.py b/spaces/JammyMachina/the-jam-machine-app/familizer.py
deleted file mode 100644
index 166cdc72fcb23edbbaa1adc75c2de75b03362695..0000000000000000000000000000000000000000
--- a/spaces/JammyMachina/the-jam-machine-app/familizer.py
+++ /dev/null
@@ -1,136 +0,0 @@
-import random
-from joblib import Parallel, delayed
-from pathlib import Path
-from constants import INSTRUMENT_CLASSES, INSTRUMENT_TRANSFER_CLASSES
-from utils import get_files, timeit, FileCompressor
-
-
-class Familizer:
-    def __init__(self, n_jobs=-1, arbitrary=False):
-        self.n_jobs = n_jobs
-        self.reverse_family(arbitrary)
-
-    def get_family_number(self, program_number):
-        """
-        Given a MIDI instrument number, return its associated instrument family number.
-        """
-        for instrument_class in INSTRUMENT_CLASSES:
-            if program_number in instrument_class["program_range"]:
-                return instrument_class["family_number"]
-
-    def reverse_family(self, arbitrary):
-        """
-        Create a dictionary of family numbers to randomly assigned program numbers.
-        This is used to reverse the family number tokens back to program number tokens.
-        """
-
-        if arbitrary is True:
-            int_class = INSTRUMENT_TRANSFER_CLASSES
-        else:
-            int_class = INSTRUMENT_CLASSES
-
-        self.reference_programs = {}
-        for family in int_class:
-            self.reference_programs[family["family_number"]] = random.choice(
-                family["program_range"]
-            )
-
-    def get_program_number(self, family_number):
-        """
-        Given given a family number return a random program number in the respective program_range.
-        This is the reverse operation of get_family_number.
-        """
-        assert family_number in self.reference_programs
-        return self.reference_programs[family_number]
-
-    # Replace instruments in text files
-    def replace_instrument_token(self, token):
-        """
-        Given a MIDI program number in a word token, replace it with the family or program
-        number token depending on the operation.
-        e.g. INST=86 -> INST=10
-        """
-        inst_number = int(token.split("=")[1])
-        if self.operation == "family":
-            return "INST=" + str(self.get_family_number(inst_number))
-        elif self.operation == "program":
-            return "INST=" + str(self.get_program_number(inst_number))
-
-    def replace_instrument_in_text(self, text):
-        """Given a text piece, replace all instrument tokens with family number tokens."""
-        return " ".join(
-            [
-                self.replace_instrument_token(token)
-                if token.startswith("INST=") and not token == "INST=DRUMS"
-                else token
-                for token in text.split(" ")
-            ]
-        )
-
-    def replace_instruments_in_file(self, file):
-        """Given a text file, replace all instrument tokens with family number tokens."""
-        text = file.read_text()
-        file.write_text(self.replace_instrument_in_text(text))
-
-    @timeit
-    def replace_instruments(self):
-        """
-        Given a directory of text files:
-        Replace all instrument tokens with family number tokens.
-        """
-        files = get_files(self.output_directory, extension="txt")
-        Parallel(n_jobs=self.n_jobs)(
-            delayed(self.replace_instruments_in_file)(file) for file in files
-        )
-
-    def replace_tokens(self, input_directory, output_directory, operation):
-        """
-        Given a directory and an operation, perform the operation on all text files in the directory.
-        operation can be either 'family' or 'program'.
-        """
-        self.input_directory = input_directory
-        self.output_directory = output_directory
-        self.operation = operation
-
-        # Uncompress files, replace tokens, compress files
-        fc = FileCompressor(self.input_directory, self.output_directory, self.n_jobs)
-        fc.unzip()
-        self.replace_instruments()
-        fc.zip()
-        print(self.operation + " complete.")
-
-    def to_family(self, input_directory, output_directory):
-        """
-        Given a directory containing zip files, replace all instrument tokens with
-        family number tokens. The output is a directory of zip files.
-        """
-        self.replace_tokens(input_directory, output_directory, "family")
-
-    def to_program(self, input_directory, output_directory):
-        """
-        Given a directory containing zip files, replace all instrument tokens with
-        program number tokens. The output is a directory of zip files.
-        """
-        self.replace_tokens(input_directory, output_directory, "program")
-
-
-if __name__ == "__main__":
-    # Choose number of jobs for parallel processing
-    n_jobs = -1
-
-    # Instantiate Familizer
-    familizer = Familizer(n_jobs)
-
-    # Choose directory to process for program
-    input_directory = Path("midi/dataset/first_selection/validate").resolve()  # fmt: skip
-    output_directory = input_directory / "family"
-
-    # familize files
-    familizer.to_family(input_directory, output_directory)
-
-    # Choose directory to process for family
-    # input_directory = Path("../data/music_picks/encoded_samples/validate/family").resolve()  # fmt: skip
-    # output_directory = input_directory.parent / "program"
-
-    # # programize files
-    # familizer.to_program(input_directory, output_directory)
diff --git a/spaces/JeffJing/ZookChatBot/steamship/client/skills.py b/spaces/JeffJing/ZookChatBot/steamship/client/skills.py
deleted file mode 100644
index 12ae21474374e46e77cb14a97610790054989824..0000000000000000000000000000000000000000
--- a/spaces/JeffJing/ZookChatBot/steamship/client/skills.py
+++ /dev/null
@@ -1,11 +0,0 @@
-from enum import Enum
-
-
-class Skill(str, Enum):
-    ENTITIES = "entities"
-    SUMMARY = "summary"
-    SENTIMENTS = "sentiments"
-    EMOTIONS = "emotions"
-    TOPICS = "topics"
-    HIGHLIGHTS = "highlights"
-    KEYWORDS = "keywords"
diff --git a/spaces/JeffJing/ZookChatBot/steamship/data/block.py b/spaces/JeffJing/ZookChatBot/steamship/data/block.py
deleted file mode 100644
index b310e345619432bfd0ebddc3ce521d6177bd717c..0000000000000000000000000000000000000000
--- a/spaces/JeffJing/ZookChatBot/steamship/data/block.py
+++ /dev/null
@@ -1,182 +0,0 @@
-from __future__ import annotations
-
-from enum import Enum
-from typing import Any, List, Optional, Type, Union
-
-import requests
-from pydantic import BaseModel, Field
-
-from steamship import MimeTypes, SteamshipError
-from steamship.base.client import Client
-from steamship.base.model import CamelModel
-from steamship.base.request import DeleteRequest, IdentifierRequest, Request
-from steamship.base.response import Response
-from steamship.data.tags.tag import Tag
-
-
-class BlockQueryRequest(Request):
-    tag_filter_query: str
-
-
-class BlockUploadType(str, Enum):
-    FILE = "file"  # A file uploaded as bytes or a string
-    BLOCKS = "blocks"  # Blocks are sent to create a file
-    URL = "url"  # content will be fetched from a URL
-    NONE = "none"  # No upload; plain text only
-
-
-class Block(CamelModel):
-    """A Block is a chunk of content within a File. It can be plain text content, image content,
-    video content, etc. If the content is not text, the text value may be the empty string
-    for backwards compatibility.
-    """
-
-    client: Client = Field(None, exclude=True)
-    id: str = None
-    file_id: str = None
-    text: str = None
-    tags: Optional[List[Tag]] = []
-    index_in_file: Optional[int] = Field(alias="index")
-    mime_type: Optional[MimeTypes]
-    url: Optional[
-        str
-    ] = None  # Only for creation of blocks; used to fetch content from a public URL.
-    content_url: Optional[
-        str
-    ] = None  # For overriding the URL of the raw data for ephemeral blocks. Setting this will have no effect
-    upload_type: Optional[
-        BlockUploadType
-    ] = None  # for returning Blocks as the result of a generate request
-
-    class ListRequest(Request):
-        file_id: str = None
-
-    class ListResponse(Response):
-        blocks: List[Block] = []
-
-    @classmethod
-    def parse_obj(cls: Type[BaseModel], obj: Any) -> BaseModel:
-        # TODO (enias): This needs to be solved at the engine side
-        obj = obj["block"] if "block" in obj else obj
-        return super().parse_obj(obj)
-
-    @staticmethod
-    def get(
-        client: Client,
-        _id: str = None,
-    ) -> Block:
-        return client.post(
-            "block/get",
-            IdentifierRequest(id=_id),
-            expect=Block,
-        )
-
-    @staticmethod
-    def create(
-        client: Client,
-        file_id: str,
-        text: str = None,
-        tags: List[Tag] = None,
-        content: Union[str, bytes] = None,
-        url: Optional[str] = None,
-        mime_type: Optional[MimeTypes] = None,
-    ) -> Block:
-        """
-        Create a new Block within a File specified by file_id.
-
-        You can create a Block in several ways:
-        - Providing raw text as the text parameter;
-        - Providing the content of the block as string or bytes;
-        - Providing a publicly accessible URL where the content is stored.
-
-        """
-
-        if content is not None and url is not None:
-            raise SteamshipError("May provide content or URL, but not both when creating a Block")
-
-        if content is not None:
-            upload_type = BlockUploadType.FILE
-        elif url is not None:
-            upload_type = BlockUploadType.URL
-        else:
-            upload_type = BlockUploadType.NONE
-
-        req = {
-            "fileId": file_id,
-            "text": text,
-            "tags": [t.dict(by_alias=True) for t in tags] if tags else [],
-            "url": url,
-            "mimeType": mime_type,
-            "uploadType": upload_type,
-        }
-
-        file_data = (
-            ("file-part", content, "multipart/form-data")
-            if upload_type == BlockUploadType.FILE
-            else None
-        )
-
-        return client.post(
-            "block/create",
-            req,
-            expect=Block,
-            file=file_data,
-        )
-
-    def delete(self) -> Block:
-        return self.client.post(
-            "block/delete",
-            DeleteRequest(id=self.id),
-            expect=Tag,
-        )
-
-    @staticmethod
-    def query(
-        client: Client,
-        tag_filter_query: str,
-    ) -> BlockQueryResponse:
-        req = BlockQueryRequest(tag_filter_query=tag_filter_query)
-        res = client.post(
-            "block/query",
-            payload=req,
-            expect=BlockQueryResponse,
-        )
-        return res
-
-    def index(self, embedding_plugin_instance: Any = None):
-        """Index this block."""
-        tags = [
-            Tag(
-                text=self.text,
-                file_id=self.file_id,
-                block_id=self.id,
-                kind="block",
-                start_idx=0,
-                end_idx=len(self.text),
-            )
-        ]
-        return embedding_plugin_instance.insert(tags)
-
-    def raw(self):
-        if self.content_url is not None:
-            return requests.get(self.content_url).content
-        else:
-            return self.client.post(
-                "block/raw",
-                payload={
-                    "id": self.id,
-                },
-                raw_response=True,
-            )
-
-    def is_text(self):
-        """Return whether this is a text Block."""
-        return self.mime_type == MimeTypes.TXT
-
-
-class BlockQueryResponse(Response):
-    blocks: List[Block]
-
-
-Block.ListResponse.update_forward_refs()
-Block.update_forward_refs()
diff --git a/spaces/Jeffgold/BackgroundEraser/README.md b/spaces/Jeffgold/BackgroundEraser/README.md
deleted file mode 100644
index 0b4afe54880b9afa29e708c0cc0d4bfc95428f94..0000000000000000000000000000000000000000
--- a/spaces/Jeffgold/BackgroundEraser/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: Background Remover
-emoji: 🏃
-colorFrom: green
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.5
-app_file: app.py
-pinned: false
-license: openrail
-duplicated_from: JayRaghav/Image_segmentation
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
\ No newline at end of file
diff --git a/spaces/Jikiwi/sovits-models/modules/commons.py b/spaces/Jikiwi/sovits-models/modules/commons.py
deleted file mode 100644
index 074888006392e956ce204d8368362dbb2cd4e304..0000000000000000000000000000000000000000
--- a/spaces/Jikiwi/sovits-models/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/Justin-Choo/Dreamlikeart-Anime-ZH/README.md b/spaces/Justin-Choo/Dreamlikeart-Anime-ZH/README.md
deleted file mode 100644
index 36e2fbaa9de29471b7e2f5cd63bb866e39818773..0000000000000000000000000000000000000000
--- a/spaces/Justin-Choo/Dreamlikeart-Anime-ZH/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Dreamlikeart Anime ZH
-emoji: 🌅
-colorFrom: blue
-colorTo: red
-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
\ No newline at end of file
diff --git a/spaces/Kangarroar/ApplioRVC-Inference/infer/lib/uvr5_pack/lib_v5/nets_33966KB.py b/spaces/Kangarroar/ApplioRVC-Inference/infer/lib/uvr5_pack/lib_v5/nets_33966KB.py
deleted file mode 100644
index 73a5b836177b706c306e27875f8391c1aed4b948..0000000000000000000000000000000000000000
--- a/spaces/Kangarroar/ApplioRVC-Inference/infer/lib/uvr5_pack/lib_v5/nets_33966KB.py
+++ /dev/null
@@ -1,122 +0,0 @@
-import torch
-import torch.nn.functional as F
-from torch import nn
-
-from . import layers_33966KB as layers
-
-
-class BaseASPPNet(nn.Module):
-    def __init__(self, nin, ch, dilations=(4, 8, 16, 32)):
-        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, 16)
-        self.stg1_high_band_net = BaseASPPNet(2, 16)
-
-        self.stg2_bridge = layers.Conv2DBNActiv(18, 8, 1, 1, 0)
-        self.stg2_full_band_net = BaseASPPNet(8, 16)
-
-        self.stg3_bridge = layers.Conv2DBNActiv(34, 16, 1, 1, 0)
-        self.stg3_full_band_net = BaseASPPNet(16, 32)
-
-        self.out = nn.Conv2d(32, 2, 1, bias=False)
-        self.aux1_out = nn.Conv2d(16, 2, 1, bias=False)
-        self.aux2_out = nn.Conv2d(16, 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/Kangarroar/ApplioRVC-Inference/lib/infer_pack/models_onnx.py b/spaces/Kangarroar/ApplioRVC-Inference/lib/infer_pack/models_onnx.py
deleted file mode 100644
index 963e67b29f828e9fdd096397952054fe77cf3d10..0000000000000000000000000000000000000000
--- a/spaces/Kangarroar/ApplioRVC-Inference/lib/infer_pack/models_onnx.py
+++ /dev/null
@@ -1,819 +0,0 @@
-import math, pdb, os
-from time import time as ttime
-import torch
-from torch import nn
-from torch.nn import functional as F
-from lib.infer_pack import modules
-from lib.infer_pack import attentions
-from lib.infer_pack import commons
-from lib.infer_pack.commons import init_weights, get_padding
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
-from lib.infer_pack.commons import init_weights
-import numpy as np
-from lib.infer_pack import commons
-
-
-class TextEncoder256(nn.Module):
-    def __init__(
-        self,
-        out_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        f0=True,
-    ):
-        super().__init__()
-        self.out_channels = out_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.emb_phone = nn.Linear(256, hidden_channels)
-        self.lrelu = nn.LeakyReLU(0.1, inplace=True)
-        if f0 == True:
-            self.emb_pitch = nn.Embedding(256, hidden_channels)  # pitch 256
-        self.encoder = attentions.Encoder(
-            hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout
-        )
-        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
-    def forward(self, phone, pitch, lengths):
-        if pitch == None:
-            x = self.emb_phone(phone)
-        else:
-            x = self.emb_phone(phone) + self.emb_pitch(pitch)
-        x = x * math.sqrt(self.hidden_channels)  # [b, t, h]
-        x = self.lrelu(x)
-        x = torch.transpose(x, 1, -1)  # [b, h, t]
-        x_mask = torch.unsqueeze(commons.sequence_mask(lengths, x.size(2)), 1).to(
-            x.dtype
-        )
-        x = self.encoder(x * x_mask, x_mask)
-        stats = self.proj(x) * x_mask
-
-        m, logs = torch.split(stats, self.out_channels, dim=1)
-        return m, logs, x_mask
-
-
-class 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)
-        return uv
-
-    def forward(self, f0, upp):
-        """sine_tensor, uv = forward(f0)
-        input F0: tensor(batchsize=1, length, dim=1)
-                  f0 for unvoiced steps should be 0
-        output sine_tensor: tensor(batchsize=1, length, dim)
-        output uv: tensor(batchsize=1, length, 1)
-        """
-        with torch.no_grad():
-            f0 = f0[:, None].transpose(1, 2)
-            f0_buf = torch.zeros(f0.shape[0], f0.shape[1], self.dim, device=f0.device)
-            # fundamental component
-            f0_buf[:, :, 0] = f0[:, :, 0]
-            for idx in np.arange(self.harmonic_num):
-                f0_buf[:, :, idx + 1] = f0_buf[:, :, 0] * (
-                    idx + 2
-                )  # idx + 2: the (idx+1)-th overtone, (idx+2)-th harmonic
-            rad_values = (f0_buf / self.sampling_rate) % 1  ###%1意味着n_har的乘积无法后处理优化
-            rand_ini = torch.rand(
-                f0_buf.shape[0], f0_buf.shape[2], device=f0_buf.device
-            )
-            rand_ini[:, 0] = 0
-            rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini
-            tmp_over_one = torch.cumsum(rad_values, 1)  # % 1  #####%1意味着后面的cumsum无法再优化
-            tmp_over_one *= upp
-            tmp_over_one = F.interpolate(
-                tmp_over_one.transpose(2, 1),
-                scale_factor=upp,
-                mode="linear",
-                align_corners=True,
-            ).transpose(2, 1)
-            rad_values = F.interpolate(
-                rad_values.transpose(2, 1), scale_factor=upp, mode="nearest"
-            ).transpose(
-                2, 1
-            )  #######
-            tmp_over_one %= 1
-            tmp_over_one_idx = (tmp_over_one[:, 1:, :] - tmp_over_one[:, :-1, :]) < 0
-            cumsum_shift = torch.zeros_like(rad_values)
-            cumsum_shift[:, 1:, :] = tmp_over_one_idx * -1.0
-            sine_waves = torch.sin(
-                torch.cumsum(rad_values + cumsum_shift, dim=1) * 2 * np.pi
-            )
-            sine_waves = sine_waves * self.sine_amp
-            uv = self._f02uv(f0)
-            uv = F.interpolate(
-                uv.transpose(2, 1), scale_factor=upp, mode="nearest"
-            ).transpose(2, 1)
-            noise_amp = uv * self.noise_std + (1 - uv) * self.sine_amp / 3
-            noise = noise_amp * torch.randn_like(sine_waves)
-            sine_waves = sine_waves * uv + noise
-        return sine_waves, uv, noise
-
-
-class SourceModuleHnNSF(torch.nn.Module):
-    """SourceModule for hn-nsf
-    SourceModule(sampling_rate, harmonic_num=0, sine_amp=0.1,
-                 add_noise_std=0.003, voiced_threshod=0)
-    sampling_rate: sampling_rate in Hz
-    harmonic_num: number of harmonic above F0 (default: 0)
-    sine_amp: amplitude of sine source signal (default: 0.1)
-    add_noise_std: std of additive Gaussian noise (default: 0.003)
-        note that amplitude of noise in unvoiced is decided
-        by sine_amp
-    voiced_threshold: threhold to set U/V given F0 (default: 0)
-    Sine_source, noise_source = SourceModuleHnNSF(F0_sampled)
-    F0_sampled (batchsize, length, 1)
-    Sine_source (batchsize, length, 1)
-    noise_source (batchsize, length 1)
-    uv (batchsize, length, 1)
-    """
-
-    def __init__(
-        self,
-        sampling_rate,
-        harmonic_num=0,
-        sine_amp=0.1,
-        add_noise_std=0.003,
-        voiced_threshod=0,
-        is_half=True,
-    ):
-        super(SourceModuleHnNSF, self).__init__()
-
-        self.sine_amp = sine_amp
-        self.noise_std = add_noise_std
-        self.is_half = is_half
-        # to produce sine waveforms
-        self.l_sin_gen = SineGen(
-            sampling_rate, harmonic_num, sine_amp, add_noise_std, voiced_threshod
-        )
-
-        # to merge source harmonics into a single excitation
-        self.l_linear = torch.nn.Linear(harmonic_num + 1, 1)
-        self.l_tanh = torch.nn.Tanh()
-
-    def forward(self, x, upp=None):
-        sine_wavs, uv, _ = self.l_sin_gen(x, upp)
-        if self.is_half:
-            sine_wavs = sine_wavs.half()
-        sine_merge = self.l_tanh(self.l_linear(sine_wavs))
-        return sine_merge, None, None  # noise, uv
-
-
-class GeneratorNSF(torch.nn.Module):
-    def __init__(
-        self,
-        initial_channel,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        gin_channels,
-        sr,
-        is_half=False,
-    ):
-        super(GeneratorNSF, self).__init__()
-        self.num_kernels = len(resblock_kernel_sizes)
-        self.num_upsamples = len(upsample_rates)
-
-        self.f0_upsamp = torch.nn.Upsample(scale_factor=np.prod(upsample_rates))
-        self.m_source = SourceModuleHnNSF(
-            sampling_rate=sr, harmonic_num=0, is_half=is_half
-        )
-        self.noise_convs = nn.ModuleList()
-        self.conv_pre = Conv1d(
-            initial_channel, upsample_initial_channel, 7, 1, padding=3
-        )
-        resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2
-
-        self.ups = nn.ModuleList()
-        for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
-            c_cur = upsample_initial_channel // (2 ** (i + 1))
-            self.ups.append(
-                weight_norm(
-                    ConvTranspose1d(
-                        upsample_initial_channel // (2**i),
-                        upsample_initial_channel // (2 ** (i + 1)),
-                        k,
-                        u,
-                        padding=(k - u) // 2,
-                    )
-                )
-            )
-            if i + 1 < len(upsample_rates):
-                stride_f0 = np.prod(upsample_rates[i + 1 :])
-                self.noise_convs.append(
-                    Conv1d(
-                        1,
-                        c_cur,
-                        kernel_size=stride_f0 * 2,
-                        stride=stride_f0,
-                        padding=stride_f0 // 2,
-                    )
-                )
-            else:
-                self.noise_convs.append(Conv1d(1, c_cur, kernel_size=1))
-
-        self.resblocks = nn.ModuleList()
-        for i in range(len(self.ups)):
-            ch = upsample_initial_channel // (2 ** (i + 1))
-            for j, (k, d) in enumerate(
-                zip(resblock_kernel_sizes, resblock_dilation_sizes)
-            ):
-                self.resblocks.append(resblock(ch, k, d))
-
-        self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
-        self.ups.apply(init_weights)
-
-        if gin_channels != 0:
-            self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
-
-        self.upp = np.prod(upsample_rates)
-
-    def forward(self, x, f0, g=None):
-        har_source, noi_source, uv = self.m_source(f0, self.upp)
-        har_source = har_source.transpose(1, 2)
-        x = self.conv_pre(x)
-        if g is not None:
-            x = x + self.cond(g)
-
-        for i in range(self.num_upsamples):
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            x = self.ups[i](x)
-            x_source = self.noise_convs[i](har_source)
-            x = x + x_source
-            xs = None
-            for j in range(self.num_kernels):
-                if xs is None:
-                    xs = self.resblocks[i * self.num_kernels + j](x)
-                else:
-                    xs += self.resblocks[i * self.num_kernels + j](x)
-            x = xs / self.num_kernels
-        x = F.leaky_relu(x)
-        x = self.conv_post(x)
-        x = torch.tanh(x)
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.ups:
-            remove_weight_norm(l)
-        for l in self.resblocks:
-            l.remove_weight_norm()
-
-
-sr2sr = {
-    "32k": 32000,
-    "40k": 40000,
-    "48k": 48000,
-}
-
-
-class SynthesizerTrnMsNSFsidM(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,
-        version,
-        **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
-        if version == "v1":
-            self.enc_p = TextEncoder256(
-                inter_channels,
-                hidden_channels,
-                filter_channels,
-                n_heads,
-                n_layers,
-                kernel_size,
-                p_dropout,
-            )
-        else:
-            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)
-        self.speaker_map = None
-        print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
-
-    def remove_weight_norm(self):
-        self.dec.remove_weight_norm()
-        self.flow.remove_weight_norm()
-        self.enc_q.remove_weight_norm()
-
-    def construct_spkmixmap(self, n_speaker):
-        self.speaker_map = torch.zeros((n_speaker, 1, 1, self.gin_channels))
-        for i in range(n_speaker):
-            self.speaker_map[i] = self.emb_g(torch.LongTensor([[i]]))
-        self.speaker_map = self.speaker_map.unsqueeze(0)
-
-    def forward(self, phone, phone_lengths, pitch, nsff0, g, rnd, max_len=None):
-        if self.speaker_map is not None:  # [N, S]  *  [S, B, 1, H]
-            g = g.reshape((g.shape[0], g.shape[1], 1, 1, 1))  # [N, S, B, 1, 1]
-            g = g * self.speaker_map  # [N, S, B, 1, H]
-            g = torch.sum(g, dim=1)  # [N, 1, B, 1, H]
-            g = g.transpose(0, -1).transpose(0, -2).squeeze(0)  # [B, H, N]
-        else:
-            g = g.unsqueeze(0)
-            g = self.emb_g(g).transpose(1, 2)
-
-        m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
-        z_p = (m_p + torch.exp(logs_p) * rnd) * x_mask
-        z = self.flow(z_p, x_mask, g=g, reverse=True)
-        o = self.dec((z * x_mask)[:, :, :max_len], nsff0, g=g)
-        return o
-
-
-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)
-            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/KaygNas/cut-it/src/Image.ts b/spaces/KaygNas/cut-it/src/Image.ts
deleted file mode 100644
index 458a2ff54437cb8eb2d133661642ec3de813e82f..0000000000000000000000000000000000000000
--- a/spaces/KaygNas/cut-it/src/Image.ts
+++ /dev/null
@@ -1,152 +0,0 @@
-import { Observable } from '@babylonjs/core'
-import { Image as GUIImage } from '@babylonjs/gui'
-import type { ImageClassificationResult, ImageDetectionItem } from './interfaces'
-import { API_BASE_URL } from './constants'
-
-interface IBaseImage {
-  isLoaded(): this is ILoadedImage
-  isDetected(): this is IDetectedImage
-  isClassified(): this is IClassifiedImage
-}
-interface ILoadedImage extends IBaseImage {
-  file: File
-  image: GUIImage
-}
-interface IDetectedImage extends IBaseImage {
-  file: File
-  image: GUIImage
-  dectection: ImageDetectionItem[]
-}
-interface IClassifiedImage extends IBaseImage {
-  file: File
-  image: GUIImage
-  detections: ImageDetectionItem[]
-  classification: ImageClassificationResult
-}
-export interface IImageEvent {
-  type: 'loaded' | 'detected' | 'classified'
-  target: Image
-}
-export class Image implements IBaseImage {
-  file?: File
-  image?: GUIImage
-  detections?: ImageDetectionItem[]
-  classification?: ImageClassificationResult
-  observable = new Observable<IImageEvent>()
-
-  isLoaded(): this is ILoadedImage {
-    return !!this.file && !!this.image
-  }
-
-  isDetected(): this is IDetectedImage {
-    return this.isLoaded() && !!this.detections
-  }
-
-  isClassified(): this is IClassifiedImage {
-    return this.isDetected() && !!this.classification
-  }
-
-  clear(): void {
-    this.file = undefined
-    this.image = undefined
-    this.detections = undefined
-    this.classification = undefined
-  }
-
-  async load(): Promise<GUIImage> {
-    await this._prompt()
-    const image = await this._parse()
-    this.observable.notifyObservers({ type: 'loaded', target: this })
-    return image
-  }
-
-  private async _prompt(): Promise<File> {
-    const input = document.createElement('input')
-    input.type = 'file'
-    input.accept = '.png,.jpg,.jpeg'
-    return new Promise((resolve, reject) => {
-      input.addEventListener('change', () => {
-        const file = input.files?.[0]
-        if (!file) {
-          reject(new Error('No file is selected.'))
-        }
-        else {
-          this.file = file
-          resolve(file)
-        }
-      })
-      input.click()
-    })
-  }
-
-  private async _parse(): Promise<GUIImage> {
-    const { file } = this
-    if (!file)
-      throw new Error('Image must be prompted before calling parsed.')
-    return new Promise((resolve) => {
-      const url = URL.createObjectURL(file)
-      const image = new GUIImage('Picture', url)
-      this.image = image
-      image.onImageLoadedObservable.addOnce(() => resolve(image))
-    })
-  }
-
-  async detect(): Promise<ImageDetectionItem[]> {
-    if (!this.isLoaded())
-      throw new Error('Image must be loaded before calling detect.')
-
-    const { file } = this
-    const form = new FormData()
-    form.append('image', file)
-    const response = await fetch(
-      `${API_BASE_URL}detect-image`,
-      {
-        method: 'POST',
-        body: form,
-      },
-    )
-    const result = await response.json()
-    this.detections = result
-    this.observable.notifyObservers({ type: 'detected', target: this })
-    return result
-  }
-
-  async classify(candidateLable: string): Promise<ImageClassificationResult> {
-    if (!this.isDetected())
-      throw new Error('Image must be loaded and detected before calling classify.')
-
-    const { file, detections } = this
-    const form = new FormData()
-    form.append('image', file)
-    form.append('candidate_label', candidateLable)
-    form.append('detections', JSON.stringify(detections))
-    const response = await fetch(
-      `${API_BASE_URL}classify-image`,
-      {
-        method: 'POST',
-        body: form,
-      },
-    )
-    const result = await response.json()
-    this.classification = result
-    this.observable.notifyObservers({ type: 'classified', target: this })
-    return result
-  }
-
-  async fromText(prompt?: string) {
-    const headers = new Headers()
-    headers.set('Content-Type', 'application/json')
-    const response = await fetch(
-      `${API_BASE_URL}text-to-image`,
-      {
-        headers,
-        method: 'POST',
-        body: JSON.stringify({ prompt: prompt || 'A lot of people walking through a street, there are lot of cars in the background.' }),
-      },
-    )
-    const file = await response.arrayBuffer()
-    this.file = new File([file], 'image', { type: 'image/png' })
-    await this._parse()
-    this.observable.notifyObservers({ type: 'loaded', target: this })
-  }
-}
diff --git a/spaces/KazeDevID/RVC-Model/infer_pack/modules.py b/spaces/KazeDevID/RVC-Model/infer_pack/modules.py
deleted file mode 100644
index 960481cedad9a6106f2bf0b9e86e82b120f7b33f..0000000000000000000000000000000000000000
--- a/spaces/KazeDevID/RVC-Model/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/KenjieDec/GPEN/retinaface/data/config.py b/spaces/KenjieDec/GPEN/retinaface/data/config.py
deleted file mode 100644
index a635090ad9c2f8b5689f8a86d2684675dc34ece2..0000000000000000000000000000000000000000
--- a/spaces/KenjieDec/GPEN/retinaface/data/config.py
+++ /dev/null
@@ -1,42 +0,0 @@
-# config.py
-
-cfg_mnet = {
-    'name': 'mobilenet0.25',
-    'msizes': [[16, 32], [64, 128], [256, 512]],
-    'steps': [8, 16, 32],
-    'variance': [0.1, 0.2],
-    'clip': False,
-    'loc_weight': 2.0,
-    'gpu_train': True,
-    'batch_size': 32,
-    'ngpu': 1,
-    'epoch': 250,
-    'decay1': 190,
-    'decay2': 220,
-    'image_size': 640,
-    'pretrain': False,
-    'return_layers': {'stage1': 1, 'stage2': 2, 'stage3': 3},
-    'in_channel': 32,
-    'out_channel': 64
-}
-
-cfg_re50 = {
-    'name': 'Resnet50',
-    'msizes': [[16, 32], [64, 128], [256, 512]],
-    'steps': [8, 16, 32],
-    'variance': [0.1, 0.2],
-    'clip': False,
-    'loc_weight': 2.0,
-    'gpu_train': True,
-    'batch_size': 24,
-    'ngpu': 4,
-    'epoch': 100,
-    'decay1': 70,
-    'decay2': 90,
-    'image_size': 840,
-    'pretrain': False,
-    'return_layers': {'layer2': 1, 'layer3': 2, 'layer4': 3},
-    'in_channel': 256,
-    'out_channel': 256
-}
-
diff --git a/spaces/KenjieDec/GPEN/retinaface/retinaface_detection.py b/spaces/KenjieDec/GPEN/retinaface/retinaface_detection.py
deleted file mode 100644
index 59d915bb2accbb3d32db912abda3a3663c8626c7..0000000000000000000000000000000000000000
--- a/spaces/KenjieDec/GPEN/retinaface/retinaface_detection.py
+++ /dev/null
@@ -1,192 +0,0 @@
-'''
-@paper: GAN Prior Embedded Network for Blind Face Restoration in the Wild (CVPR2021)
-@author: yangxy (yangtao9009@gmail.com)
-'''
-import os
-import torch
-import torch.backends.cudnn as cudnn
-import numpy as np
-from data import cfg_re50
-from layers.functions.prior_box import PriorBox
-from utils.nms.py_cpu_nms import py_cpu_nms
-import cv2
-from facemodels.retinaface import RetinaFace
-from utils.box_utils import decode, decode_landm
-import time
-import torch.nn.functional as F
-
-
-class RetinaFaceDetection(object):
-    def __init__(self, base_dir, device='cuda', network='RetinaFace-R50'):
-        torch.set_grad_enabled(False)
-        cudnn.benchmark = True
-        self.pretrained_path = os.path.join(base_dir, 'weights', network+'.pth')
-        self.device = device #torch.cuda.current_device()
-        self.cfg = cfg_re50
-        self.net = RetinaFace(cfg=self.cfg, phase='test')
-        self.load_model()
-        self.net = self.net.to(device)
-
-        self.mean = torch.tensor([[[[104]], [[117]], [[123]]]]).to(device)
-
-    def check_keys(self, pretrained_state_dict):
-        ckpt_keys = set(pretrained_state_dict.keys())
-        model_keys = set(self.net.state_dict().keys())
-        used_pretrained_keys = model_keys & ckpt_keys
-        unused_pretrained_keys = ckpt_keys - model_keys
-        missing_keys = model_keys - ckpt_keys
-        assert len(used_pretrained_keys) > 0, 'load NONE from pretrained checkpoint'
-        return True
-
-    def remove_prefix(self, state_dict, prefix):
-        ''' Old style model==stored with all names of parameters sharing common prefix 'module.' '''
-        f = lambda x: x.split(prefix, 1)[-1] if x.startswith(prefix) else x
-        return {f(key): value for key, value in state_dict.items()}
-
-    def load_model(self, load_to_cpu=False):
-        #if load_to_cpu:
-        #    pretrained_dict = torch.load(self.pretrained_path, map_location=lambda storage, loc: storage)
-        #else:
-        #    pretrained_dict = torch.load(self.pretrained_path, map_location=lambda storage, loc: storage.cuda())
-        pretrained_dict = torch.load(self.pretrained_path, map_location=torch.device('cpu'))
-        if "state_dict" in pretrained_dict.keys():
-            pretrained_dict = self.remove_prefix(pretrained_dict['state_dict'], 'module.')
-        else:
-            pretrained_dict = self.remove_prefix(pretrained_dict, 'module.')
-        self.check_keys(pretrained_dict)
-        self.net.load_state_dict(pretrained_dict, strict=False)
-        self.net.eval()
-    
-    def detect(self, img_raw, resize=1, confidence_threshold=0.9, nms_threshold=0.4, top_k=5000, keep_top_k=750, save_image=False):
-        img = np.float32(img_raw)
-
-        im_height, im_width = img.shape[:2]
-        ss = 1.0
-        # tricky
-        if max(im_height, im_width) > 1500:
-            ss = 1000.0/max(im_height, im_width)
-            img = cv2.resize(img, (0,0), fx=ss, fy=ss)
-            im_height, im_width = img.shape[:2]
-
-        scale = torch.Tensor([img.shape[1], img.shape[0], img.shape[1], img.shape[0]])
-        img -= (104, 117, 123)
-        img = img.transpose(2, 0, 1)
-        img = torch.from_numpy(img).unsqueeze(0)
-        img = img.to(self.device)
-        scale = scale.to(self.device)
-
-        loc, conf, landms = self.net(img)  # forward pass
-
-        priorbox = PriorBox(self.cfg, image_size=(im_height, im_width))
-        priors = priorbox.forward()
-        priors = priors.to(self.device)
-        prior_data = priors.data
-        boxes = decode(loc.data.squeeze(0), prior_data, self.cfg['variance'])
-        boxes = boxes * scale / resize
-        boxes = boxes.cpu().numpy()
-        scores = conf.squeeze(0).data.cpu().numpy()[:, 1]
-        landms = decode_landm(landms.data.squeeze(0), prior_data, self.cfg['variance'])
-        scale1 = torch.Tensor([img.shape[3], img.shape[2], img.shape[3], img.shape[2],
-                               img.shape[3], img.shape[2], img.shape[3], img.shape[2],
-                               img.shape[3], img.shape[2]])
-        scale1 = scale1.to(self.device)
-        landms = landms * scale1 / resize
-        landms = landms.cpu().numpy()
-
-        # ignore low scores
-        inds = np.where(scores > confidence_threshold)[0]
-        boxes = boxes[inds]
-        landms = landms[inds]
-        scores = scores[inds]
-
-        # keep top-K before NMS
-        order = scores.argsort()[::-1][:top_k]
-        boxes = boxes[order]
-        landms = landms[order]
-        scores = scores[order]
-
-        # do NMS
-        dets = np.hstack((boxes, scores[:, np.newaxis])).astype(np.float32, copy=False)
-        keep = py_cpu_nms(dets, nms_threshold)
-        # keep = nms(dets, nms_threshold,force_cpu=args.cpu)
-        dets = dets[keep, :]
-        landms = landms[keep]
-
-        # keep top-K faster NMS
-        dets = dets[:keep_top_k, :]
-        landms = landms[:keep_top_k, :]
-
-        # sort faces(delete)
-        '''
-        fscores = [det[4] for det in dets]
-        sorted_idx = sorted(range(len(fscores)), key=lambda k:fscores[k], reverse=False) # sort index
-        tmp = [landms[idx] for idx in sorted_idx]
-        landms = np.asarray(tmp)
-        '''
-        
-        landms = landms.reshape((-1, 5, 2))
-        landms = landms.transpose((0, 2, 1))
-        landms = landms.reshape(-1, 10, )
-        return dets/ss, landms/ss
-
-    def detect_tensor(self, img, resize=1, confidence_threshold=0.9, nms_threshold=0.4, top_k=5000, keep_top_k=750, save_image=False):
-        im_height, im_width = img.shape[-2:]
-        ss = 1000/max(im_height, im_width)
-        img = F.interpolate(img, scale_factor=ss)
-        im_height, im_width = img.shape[-2:]
-        scale = torch.Tensor([im_width, im_height, im_width, im_height]).to(self.device)
-        img -= self.mean
-
-        loc, conf, landms = self.net(img)  # forward pass
-
-        priorbox = PriorBox(self.cfg, image_size=(im_height, im_width))
-        priors = priorbox.forward()
-        priors = priors.to(self.device)
-        prior_data = priors.data
-        boxes = decode(loc.data.squeeze(0), prior_data, self.cfg['variance'])
-        boxes = boxes * scale / resize
-        boxes = boxes.cpu().numpy()
-        scores = conf.squeeze(0).data.cpu().numpy()[:, 1]
-        landms = decode_landm(landms.data.squeeze(0), prior_data, self.cfg['variance'])
-        scale1 = torch.Tensor([img.shape[3], img.shape[2], img.shape[3], img.shape[2],
-                               img.shape[3], img.shape[2], img.shape[3], img.shape[2],
-                               img.shape[3], img.shape[2]])
-        scale1 = scale1.to(self.device)
-        landms = landms * scale1 / resize
-        landms = landms.cpu().numpy()
-
-        # ignore low scores
-        inds = np.where(scores > confidence_threshold)[0]
-        boxes = boxes[inds]
-        landms = landms[inds]
-        scores = scores[inds]
-
-        # keep top-K before NMS
-        order = scores.argsort()[::-1][:top_k]
-        boxes = boxes[order]
-        landms = landms[order]
-        scores = scores[order]
-
-        # do NMS
-        dets = np.hstack((boxes, scores[:, np.newaxis])).astype(np.float32, copy=False)
-        keep = py_cpu_nms(dets, nms_threshold)
-        # keep = nms(dets, nms_threshold,force_cpu=args.cpu)
-        dets = dets[keep, :]
-        landms = landms[keep]
-
-        # keep top-K faster NMS
-        dets = dets[:keep_top_k, :]
-        landms = landms[:keep_top_k, :]
-
-        # sort faces(delete)
-        '''
-        fscores = [det[4] for det in dets]
-        sorted_idx = sorted(range(len(fscores)), key=lambda k:fscores[k], reverse=False) # sort index
-        tmp = [landms[idx] for idx in sorted_idx]
-        landms = np.asarray(tmp)
-        '''
-        
-        landms = landms.reshape((-1, 5, 2))
-        landms = landms.transpose((0, 2, 1))
-        landms = landms.reshape(-1, 10, )
-        return dets/ss, landms/ss
diff --git a/spaces/LaoCzi/YouTube_Summarize/app.py b/spaces/LaoCzi/YouTube_Summarize/app.py
deleted file mode 100644
index 62d300629306b08c8212efe6da3f581bdf4bc2b2..0000000000000000000000000000000000000000
--- a/spaces/LaoCzi/YouTube_Summarize/app.py
+++ /dev/null
@@ -1,193 +0,0 @@
-import os
-import openai 
-import gradio as gr 
-from youtube_transcript_api import YouTubeTranscriptApi
-from urllib.parse import urlparse, parse_qs
-from requests.structures import CaseInsensitiveDict
-
-openai.api_key = os.getenv("OPENAI_KEY")
-def Prompt_T(context, lang):
-
-  prompt  = """I want you to act as a content writer who is working with youtube video transcript. Summarise the following text in 50 words:
-  =========
-  """+ context +"""
-  =========
-  Answer:"""
-
-  if (lang=="ru"):prompt  = """Я хочу, чтобы вы выступили в роли автора контента, который взят с  транскрипт youtube видео, его нужно преобразовать  в читаемый вид. Резюмируйте следующий текст в 40 слов:
-  =========
-  """+ context +"""
-  =========
-  Ответ:"""
-
-  if (lang=="uk"):prompt  = """Я хочу, щоб ви виступили в ролі автора контенту, який узятий з транскрипту youtube відео,  його треба перетворити у читабельний вигляд. Резюмуйте наступний текст у 50 слів: 
-  =========
-  """+ context +"""
-  =========
-  Ответ:"""
-
-  return prompt
-
-
-def convert_seconds(seconds):
-    seconds = round(seconds)
-    minutes = seconds // 60
-    hours = minutes // 60
-    minutes = minutes % 60
-    seconds = seconds % 60
-    if (int(hours) > 0 ): time_m= str(hours) + ":" +  str(minutes) + ":" + str(seconds)
-    else : time_m = str(minutes) + ":" + str(seconds) 
-    return time_m
-
-def get_transcript(video_id, lang_video,  chunk_size):
-    global final_string
-    transcript_list = YouTubeTranscriptApi.list_transcripts(video_id)
-
-    t = YouTubeTranscriptApi.get_transcript(video_id,languages=[lang_video])
-
-    finalString = ""
-    result_text_duration=[]
-    text_duration=""
-
-    prev_start=0.0
-    for item in t:
-        text = item['text']
-        finalString += text + " "
-
-        text_duration = text_duration + " " + item['text']
-        if (len(text_duration)>chunk_size):
-             new_item = {'text': prev_text_duration, 'start': prev_start}
-             result_text_duration.append(new_item)
-             text_duration=""
-             prev_start=item['start']   
-        prev_text_duration=text_duration
-
-    new_item = {'text': text_duration, 'start': prev_start}
-    result_text_duration.append(new_item)
-    return result_text_duration
-
-def split_string(string, chunk_size):
-    return [string[i:i+chunk_size] for i in range(0, len(string), chunk_size)]
-
-
-def gpt_api (input_text):
-  completion = openai.ChatCompletion.create(
-    model="gpt-3.5-turbo",
-    messages=[  {"role": "system", "content": input_text} ]
-    )
-  response = completion.choices[0].message.content
-  return response
-
-def generate_video_html(video_url, request: gr.Request):
-    my_v = ""
-    
-    if (video_url =="") and (my_v == ""):  video_url ="https://youtube.com/watch?v=PQBYZDyDBrY"
-    
-    #похоже ли video_url на номальну ссылку
-    if "youtube.com/watch?v=" in video_url: x=111
-    else: return "Неверный URL"
-
-    #Пробуем извлеч video_id пока на английском
-    video_id = video_url[-11:]
-
-    html_embed='<iframe width="450" height="250" src="https://www.youtube.com/embed/'+ video_id +'" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen name="video_yt" ></iframe>'
-    return html_embed
-
-
-
-def generate(video_url, request: gr.Request):
-    my_v = ""
-
-
-    #Если две переменные пустые, то показываем базовую страницу с рекламой
-    if (video_url =="") and (my_v == ""):  
-      html_embed='<div><br> An easy way to get video descriptions If you are on YouTube itself, simply add "zxc" in front of YouTube to the videos address.</div>'
-      summarize=""
-      return summarize
-    
-    #похоже ли video_url на номальну ссылку
-    if "youtube.com/watch?v=" in video_url: x=111
-    else: return "Неверный URL"
-
-    #Пробуем извлеч video_id пока на английском
-    video_id = video_url[-11:]
-    print("video_id=", video_id)
-
-
-    try:
-      transcript_list = YouTubeTranscriptApi.list_transcripts(video_id)
-    except Exception as e:
-      return "No access for transcript "
-    lang_video="en"
-    for transcript in transcript_list:
-      if (transcript.is_generated == True): lang_video = transcript.language_code
-      print ("transcript.language_code=", transcript.language_code)
-
-    try:
-      t = YouTubeTranscriptApi.get_transcript(video_id,languages=[lang_video])
-      # do something with the transcript
-    except Exception as e:
-      return "No access for transcript"
-
-    
-    finalString = ""
-    for item in t:
-        text = item['text']
-       
-        finalString += text + " "
-
-    print ("===============================================")    
-    #print("Transcript:",finalString)
-    print("Transcript lenght:",len(finalString))
-    print ("===============================================")
-
-    
-    input_string = finalString
-
-    chunk_size = 4000
-    if (lang_video=="ru"): chunk_size = 2000
-    if (lang_video=="uk"): chunk_size = 2000
-
-    result_list = split_string(input_string, chunk_size)
-    result_list= text_video  = get_transcript(video_id, lang_video, chunk_size)
-    final_answer_gpt=""
-    count= 0
-
-    img_wait='<img src="https://huggingface.co/spaces/LaoCzi/YouTube_Summarize2/resolve/main/22.gif">'
-    html_content="<br>"+img_wait
-    yield html_content
-    for item in result_list:
-      context = item['text']
-      time_text = str(convert_seconds(item['start']))
-      time_seconds = str(round(item['start']))
-      input_gpt = Prompt_T(context,lang_video)
-      time_url='<a href="//www.youtube.com/embed/'+ str(video_id) + '?rel=0&amp;autoplay=1&amp;start='+time_seconds +'" target="video_yt">'+ time_text+'</a>'
-      final_answer_gpt = final_answer_gpt  +"<p>" + time_url +" " + gpt_api (input_gpt)+"</p>"
-      html_content="<h6>"+"<br>"+final_answer_gpt+img_wait
-      yield html_content
-
-    
-    html_content="<h6>"+"<br>"+final_answer_gpt+"</h6>"
-    yield html_content  
-
-title = "YouTube Summorize (en,ua,ru)"
-css="""
-footer {visibility: hidden}
-.gradio-container {padding-top: 100px}
-"""
-with gr.Blocks(css=css, title=title) as demo:
-    gr.HTML("<h3>A simple way to summarize   YouTube video</h3>")
-    with gr.Row():   
-        with gr.Column():
-          input_d = gr.Textbox(label="YouTube video URL", placeholder="https://www.youtube.com/watch?v=XXXXXXXX", value="")
-          greet_btn = gr.Button("Summarise")
-        dt_2 = gr.outputs.HTML() 
-    dt_1 = gr.outputs.HTML()
-    dt =[dt_1, dt_2]
-    greet_btn.click(generate_video_html, inputs=input_d, outputs=dt_2)
-    greet_btn.click(generate, inputs=input_d, outputs=dt_1)
-    demo.load(generate_video_html, inputs=input_d, outputs=dt_2)
-    demo.load(generate, inputs=input_d, outputs=dt_1)
-
-demo.queue()
-demo.launch(debug=True, share=False)
diff --git a/spaces/Makiing/coolb-in-gtest/src/components/chat-attachments.tsx b/spaces/Makiing/coolb-in-gtest/src/components/chat-attachments.tsx
deleted file mode 100644
index ef43d4e262935d263b6099138c56f7daade5299d..0000000000000000000000000000000000000000
--- a/spaces/Makiing/coolb-in-gtest/src/components/chat-attachments.tsx
+++ /dev/null
@@ -1,37 +0,0 @@
-import Image from 'next/image'
-import ClearIcon from '@/assets/images/clear.svg'
-import RefreshIcon from '@/assets/images/refresh.svg'
-import { FileItem } from '@/lib/bots/bing/types'
-import { cn } from '@/lib/utils'
-import { useBing } from '@/lib/hooks/use-bing'
-
-type ChatAttachmentsProps = Pick<ReturnType<typeof useBing>, 'attachmentList' | 'setAttachmentList' | 'uploadImage'>
-
-export function ChatAttachments({ attachmentList = [], setAttachmentList, uploadImage }: ChatAttachmentsProps) {
-  return attachmentList.length ? (
-    <div className="attachment-list">
-      {attachmentList.map(file => (
-        <div className="file-item" key={file.url}>
-          {file.status === 'loading' && (
-            <div className="loading">
-              <div className="bar" />
-            </div>)
-          }
-          {file.status !== 'error' && (
-            <div className="thumbnail">
-              <img draggable="false" src={file.url} />
-            </div>)
-          }
-          {file.status === 'error' && (
-            <div className="error">
-              <Image alt="refresh" src={RefreshIcon} width={18} onClick={() => uploadImage(file.url)} />
-            </div>
-          )}
-          <button className={cn('dismiss', { 'no-file': file.status === 'error' })} type="button">
-            <Image alt="clear" src={ClearIcon} width={16} onClick={() => setAttachmentList([])} />
-          </button>
-        </div>
-      ))}
-    </div>
-  ) : null
-}
diff --git a/spaces/Manimaran/pokemon-classifier/README.md b/spaces/Manimaran/pokemon-classifier/README.md
deleted file mode 100644
index a1e7387c97c379dc65b67c2e7b779b5e6c96b165..0000000000000000000000000000000000000000
--- a/spaces/Manimaran/pokemon-classifier/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Pokemon Classifier Demo
-emoji: 🐅🐎🦎🐬🐳🦐🐙🐙🦀
-colorFrom: yellow
-colorTo: purple
-sdk: gradio
-sdk_version: 2.9.1
-app_file: app.py
-pinned: false
-license: wtfpl
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
diff --git a/spaces/Marshalls/testmtd/analysis/aistplusplus_api/smpl/__init__.py b/spaces/Marshalls/testmtd/analysis/aistplusplus_api/smpl/__init__.py
deleted file mode 100644
index e3dee4c3cae1a07ae0aa6f9b0e6dba7b27f49dcb..0000000000000000000000000000000000000000
--- a/spaces/Marshalls/testmtd/analysis/aistplusplus_api/smpl/__init__.py
+++ /dev/null
@@ -1,14 +0,0 @@
-'''
-Copyright 2015 Matthew Loper, Naureen Mahmood and the Max Planck Gesellschaft.  All rights reserved.
-This software is provided for research purposes only.
-By using this software you agree to the terms of the SMPL Model license here http://smpl.is.tue.mpg.de/license
-
-More information about SMPL is available here http://smpl.is.tue.mpg.
-For comments or questions, please email us at: smpl@tuebingen.mpg.de
-
-
-About this file:
-================
-This is an initialization file to help python look for submodules in this directory.
-
-'''
\ No newline at end of file
diff --git a/spaces/Marshalls/testmtd/feature_extraction/name_processing_functions.py b/spaces/Marshalls/testmtd/feature_extraction/name_processing_functions.py
deleted file mode 100644
index 3be4b02295c4e1d906dd999f0dc363453c2cb41b..0000000000000000000000000000000000000000
--- a/spaces/Marshalls/testmtd/feature_extraction/name_processing_functions.py
+++ /dev/null
@@ -1,4 +0,0 @@
-def dance_style(path):
-    filename = path.name
-    arr = filename.split("_")
-    return arr[1]
diff --git a/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmcv/cnn/bricks/padding.py b/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmcv/cnn/bricks/padding.py
deleted file mode 100644
index e4ac6b28a1789bd551c613a7d3e7b622433ac7ec..0000000000000000000000000000000000000000
--- a/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmcv/cnn/bricks/padding.py
+++ /dev/null
@@ -1,36 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import torch.nn as nn
-
-from .registry import PADDING_LAYERS
-
-PADDING_LAYERS.register_module('zero', module=nn.ZeroPad2d)
-PADDING_LAYERS.register_module('reflect', module=nn.ReflectionPad2d)
-PADDING_LAYERS.register_module('replicate', module=nn.ReplicationPad2d)
-
-
-def build_padding_layer(cfg, *args, **kwargs):
-    """Build padding layer.
-
-    Args:
-        cfg (None or dict): The padding layer config, which should contain:
-            - type (str): Layer type.
-            - layer args: Args needed to instantiate a padding layer.
-
-    Returns:
-        nn.Module: Created padding layer.
-    """
-    if not isinstance(cfg, dict):
-        raise TypeError('cfg must be a dict')
-    if 'type' not in cfg:
-        raise KeyError('the cfg dict must contain the key "type"')
-
-    cfg_ = cfg.copy()
-    padding_type = cfg_.pop('type')
-    if padding_type not in PADDING_LAYERS:
-        raise KeyError(f'Unrecognized padding type {padding_type}.')
-    else:
-        padding_layer = PADDING_LAYERS.get(padding_type)
-
-    layer = padding_layer(*args, **kwargs, **cfg_)
-
-    return layer
diff --git a/spaces/Menthe17/Nani17092005/app.py b/spaces/Menthe17/Nani17092005/app.py
deleted file mode 100644
index a362dcc7d0ddd1eee86961f1bc3db6d894fbd3d5..0000000000000000000000000000000000000000
--- a/spaces/Menthe17/Nani17092005/app.py
+++ /dev/null
@@ -1,34 +0,0 @@
-import os
-import gradio as gr
-from langchain.chat_models import ChatOpenAI
-from langchain import LLMChain, PromptTemplate
-from langchain.memory import ConversationBufferMemory
-
-OPENAI_API_KEY=os.getenv('OPENAI_API_KEY')
-
-template = """You are a helpful assistant to answer all user queries.
-{chat_history}
-User: {user_message}
-Chatbot:"""
-
-prompt = PromptTemplate(
-    input_variables=["chat_history", "user_message"], template=template
-)
-
-memory = ConversationBufferMemory(memory_key="chat_history")
-
-llm_chain = LLMChain(
-    llm=ChatOpenAI(temperature='0.5', model_name="gpt-3.5-turbo"),
-    prompt=prompt,
-    verbose=True,
-    memory=memory,
-)
-
-def get_text_response(user_message,history):
-    response = llm_chain.predict(user_message = user_message)
-    return response
-
-demo = gr.ChatInterface(get_text_response)
-
-if __name__ == "__main__":
-    demo.launch() #To create a public link, set `share=True` in `launch()`. To enable errors and logs, set `debug=True` in `launch()`.
diff --git a/spaces/Mountchicken/MAERec-Gradio/configs/textdet/dbnet/dbnet_resnet50_1200e_icdar2015.py b/spaces/Mountchicken/MAERec-Gradio/configs/textdet/dbnet/dbnet_resnet50_1200e_icdar2015.py
deleted file mode 100644
index 38a876b2583e50c5b99d271383492e7f05d429a7..0000000000000000000000000000000000000000
--- a/spaces/Mountchicken/MAERec-Gradio/configs/textdet/dbnet/dbnet_resnet50_1200e_icdar2015.py
+++ /dev/null
@@ -1,24 +0,0 @@
-_base_ = [
-    'dbnet_resnet50-dcnv2_fpnc_1200e_icdar2015.py',
-]
-
-load_from = None
-
-_base_.model.backbone = dict(
-    type='mmdet.ResNet',
-    depth=50,
-    num_stages=4,
-    out_indices=(0, 1, 2, 3),
-    frozen_stages=-1,
-    norm_cfg=dict(type='BN', requires_grad=True),
-    norm_eval=True,
-    style='pytorch',
-    init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50'))
-
-_base_.train_dataloader.num_workers = 24
-_base_.optim_wrapper.optimizer.lr = 0.002
-
-param_scheduler = [
-    dict(type='LinearLR', end=100, start_factor=0.001),
-    dict(type='PolyLR', power=0.9, eta_min=1e-7, begin=100, end=1200),
-]
diff --git a/spaces/NATSpeech/DiffSpeech/modules/tts/fs.py b/spaces/NATSpeech/DiffSpeech/modules/tts/fs.py
deleted file mode 100644
index b15b4348c1abf58a476c12115b5b088dc7b46979..0000000000000000000000000000000000000000
--- a/spaces/NATSpeech/DiffSpeech/modules/tts/fs.py
+++ /dev/null
@@ -1,172 +0,0 @@
-from copy import deepcopy
-
-import torch
-from torch import nn
-import torch.nn.functional as F
-from modules.commons.conv import TextConvEncoder, ConvBlocks
-from modules.commons.layers import Embedding
-from modules.commons.nar_tts_modules import PitchPredictor, DurationPredictor, LengthRegulator
-from modules.commons.rel_transformer import RelTransformerEncoder
-from modules.commons.rnn import TacotronEncoder, RNNEncoder, DecoderRNN
-from modules.commons.transformer import FastSpeechEncoder, FastSpeechDecoder
-from modules.commons.wavenet import WN
-from modules.tts.commons.align_ops import clip_mel2token_to_multiple, expand_states
-from utils.audio.pitch.utils import denorm_f0, f0_to_coarse
-
-FS_ENCODERS = {
-    'fft': lambda hp, dict_size: FastSpeechEncoder(
-        dict_size, hp['hidden_size'], hp['enc_layers'], hp['enc_ffn_kernel_size'],
-        num_heads=hp['num_heads']),
-    'tacotron': lambda hp, dict_size: TacotronEncoder(
-        hp['hidden_size'], dict_size, hp['hidden_size'],
-        K=hp['encoder_K'], num_highways=4, dropout=hp['dropout']),
-    'tacotron2': lambda hp, dict_size: RNNEncoder(dict_size, hp['hidden_size']),
-    'conv': lambda hp, dict_size: TextConvEncoder(dict_size, hp['hidden_size'], hp['hidden_size'],
-                                                  hp['enc_dilations'], hp['enc_kernel_size'],
-                                                  layers_in_block=hp['layers_in_block'],
-                                                  norm_type=hp['enc_dec_norm'],
-                                                  post_net_kernel=hp.get('enc_post_net_kernel', 3)),
-    'rel_fft': lambda hp, dict_size: RelTransformerEncoder(
-        dict_size, hp['hidden_size'], hp['hidden_size'],
-        hp['ffn_hidden_size'], hp['num_heads'], hp['enc_layers'],
-        hp['enc_ffn_kernel_size'], hp['dropout'], prenet=hp['enc_prenet'], pre_ln=hp['enc_pre_ln']),
-}
-
-FS_DECODERS = {
-    'fft': lambda hp: FastSpeechDecoder(
-        hp['hidden_size'], hp['dec_layers'], hp['dec_ffn_kernel_size'], hp['num_heads']),
-    'rnn': lambda hp: DecoderRNN(hp['hidden_size'], hp['decoder_rnn_dim'], hp['dropout']),
-    'conv': lambda hp: ConvBlocks(hp['hidden_size'], hp['hidden_size'], hp['dec_dilations'],
-                                  hp['dec_kernel_size'], layers_in_block=hp['layers_in_block'],
-                                  norm_type=hp['enc_dec_norm'], dropout=hp['dropout'],
-                                  post_net_kernel=hp.get('dec_post_net_kernel', 3)),
-    'wn': lambda hp: WN(hp['hidden_size'], kernel_size=5, dilation_rate=1, n_layers=hp['dec_layers'],
-                        is_BTC=True),
-}
-
-
-class FastSpeech(nn.Module):
-    def __init__(self, dict_size, hparams, out_dims=None):
-        super().__init__()
-        self.hparams = deepcopy(hparams)
-        self.enc_layers = hparams['enc_layers']
-        self.dec_layers = hparams['dec_layers']
-        self.hidden_size = hparams['hidden_size']
-        self.encoder = FS_ENCODERS[hparams['encoder_type']](hparams, dict_size)
-        self.decoder = FS_DECODERS[hparams['decoder_type']](hparams)
-        self.out_dims = hparams['audio_num_mel_bins'] if out_dims is None else out_dims
-        self.mel_out = nn.Linear(self.hidden_size, self.out_dims, bias=True)
-        if hparams['use_spk_id']:
-            self.spk_id_proj = Embedding(hparams['num_spk'], self.hidden_size)
-        if hparams['use_spk_embed']:
-            self.spk_embed_proj = nn.Linear(256, self.hidden_size, bias=True)
-        predictor_hidden = hparams['predictor_hidden'] if hparams['predictor_hidden'] > 0 else self.hidden_size
-        self.dur_predictor = DurationPredictor(
-            self.hidden_size,
-            n_chans=predictor_hidden,
-            n_layers=hparams['dur_predictor_layers'],
-            dropout_rate=hparams['predictor_dropout'],
-            kernel_size=hparams['dur_predictor_kernel'])
-        self.length_regulator = LengthRegulator()
-        if hparams['use_pitch_embed']:
-            self.pitch_embed = Embedding(300, self.hidden_size, 0)
-            self.pitch_predictor = PitchPredictor(
-                self.hidden_size, n_chans=predictor_hidden,
-                n_layers=5, dropout_rate=0.1, odim=2,
-                kernel_size=hparams['predictor_kernel'])
-        if hparams['dec_inp_add_noise']:
-            self.z_channels = hparams['z_channels']
-            self.dec_inp_noise_proj = nn.Linear(self.hidden_size + self.z_channels, self.hidden_size)
-
-    def forward(self, txt_tokens, mel2ph=None, spk_embed=None, spk_id=None,
-                f0=None, uv=None, infer=False, **kwargs):
-        ret = {}
-        encoder_out = self.encoder(txt_tokens)  # [B, T, C]
-        src_nonpadding = (txt_tokens > 0).float()[:, :, None]
-        style_embed = self.forward_style_embed(spk_embed, spk_id)
-
-        # add dur
-        dur_inp = (encoder_out + style_embed) * src_nonpadding
-        mel2ph = self.forward_dur(dur_inp, mel2ph, txt_tokens, ret)
-        tgt_nonpadding = (mel2ph > 0).float()[:, :, None]
-        decoder_inp = expand_states(encoder_out, mel2ph)
-
-        # add pitch embed
-        if self.hparams['use_pitch_embed']:
-            pitch_inp = (decoder_inp + style_embed) * tgt_nonpadding
-            decoder_inp = decoder_inp + self.forward_pitch(pitch_inp, f0, uv, mel2ph, ret, encoder_out)
-
-        # decoder input
-        ret['decoder_inp'] = decoder_inp = (decoder_inp + style_embed) * tgt_nonpadding
-        if self.hparams['dec_inp_add_noise']:
-            B, T, _ = decoder_inp.shape
-            z = kwargs.get('adv_z', torch.randn([B, T, self.z_channels])).to(decoder_inp.device)
-            ret['adv_z'] = z
-            decoder_inp = torch.cat([decoder_inp, z], -1)
-            decoder_inp = self.dec_inp_noise_proj(decoder_inp) * tgt_nonpadding
-        ret['mel_out'] = self.forward_decoder(decoder_inp, tgt_nonpadding, ret, infer=infer, **kwargs)
-        return ret
-
-    def forward_style_embed(self, spk_embed=None, spk_id=None):
-        # add spk embed
-        style_embed = 0
-        if self.hparams['use_spk_embed']:
-            style_embed = style_embed + self.spk_embed_proj(spk_embed)[:, None, :]
-        if self.hparams['use_spk_id']:
-            style_embed = style_embed + self.spk_id_proj(spk_id)[:, None, :]
-        return style_embed
-
-    def forward_dur(self, dur_input, mel2ph, txt_tokens, ret):
-        """
-
-        :param dur_input: [B, T_txt, H]
-        :param mel2ph: [B, T_mel]
-        :param txt_tokens: [B, T_txt]
-        :param ret:
-        :return:
-        """
-        src_padding = txt_tokens == 0
-        if self.hparams['predictor_grad'] != 1:
-            dur_input = dur_input.detach() + self.hparams['predictor_grad'] * (dur_input - dur_input.detach())
-        dur = self.dur_predictor(dur_input, src_padding)
-        ret['dur'] = dur
-        if mel2ph is None:
-            mel2ph = self.length_regulator(dur, src_padding).detach()
-        ret['mel2ph'] = mel2ph = clip_mel2token_to_multiple(mel2ph, self.hparams['frames_multiple'])
-        return mel2ph
-
-    def forward_pitch(self, decoder_inp, f0, uv, mel2ph, ret, encoder_out=None):
-        if self.hparams['pitch_type'] == 'frame':
-            pitch_pred_inp = decoder_inp
-            pitch_padding = mel2ph == 0
-        else:
-            pitch_pred_inp = encoder_out
-            pitch_padding = encoder_out.abs().sum(-1) == 0
-            uv = None
-        if self.hparams['predictor_grad'] != 1:
-            pitch_pred_inp = pitch_pred_inp.detach() + \
-                             self.hparams['predictor_grad'] * (pitch_pred_inp - pitch_pred_inp.detach())
-        ret['pitch_pred'] = pitch_pred = self.pitch_predictor(pitch_pred_inp)
-        use_uv = self.hparams['pitch_type'] == 'frame' and self.hparams['use_uv']
-        if f0 is None:
-            f0 = pitch_pred[:, :, 0]
-            if use_uv:
-                uv = pitch_pred[:, :, 1] > 0
-        f0_denorm = denorm_f0(f0, uv if use_uv else None, pitch_padding=pitch_padding)
-        pitch = f0_to_coarse(f0_denorm)  # start from 0 [B, T_txt]
-        ret['f0_denorm'] = f0_denorm
-        ret['f0_denorm_pred'] = denorm_f0(
-            pitch_pred[:, :, 0], (pitch_pred[:, :, 1] > 0) if use_uv else None,
-            pitch_padding=pitch_padding)
-        if self.hparams['pitch_type'] == 'ph':
-            pitch = torch.gather(F.pad(pitch, [1, 0]), 1, mel2ph)
-            ret['f0_denorm'] = torch.gather(F.pad(ret['f0_denorm'], [1, 0]), 1, mel2ph)
-            ret['f0_denorm_pred'] = torch.gather(F.pad(ret['f0_denorm_pred'], [1, 0]), 1, mel2ph)
-        pitch_embed = self.pitch_embed(pitch)
-        return pitch_embed
-
-    def forward_decoder(self, decoder_inp, tgt_nonpadding, ret, infer, **kwargs):
-        x = decoder_inp  # [B, T, H]
-        x = self.decoder(x)
-        x = self.mel_out(x)
-        return x * tgt_nonpadding
diff --git a/spaces/Nightwing25/AICoverGen/src/vc_infer_pipeline.py b/spaces/Nightwing25/AICoverGen/src/vc_infer_pipeline.py
deleted file mode 100644
index 25f873e1e210879e085afd073306d796bf5114ea..0000000000000000000000000000000000000000
--- a/spaces/Nightwing25/AICoverGen/src/vc_infer_pipeline.py
+++ /dev/null
@@ -1,653 +0,0 @@
-from functools import lru_cache
-from time import time as ttime
-
-import faiss
-import librosa
-import numpy as np
-import os
-import parselmouth
-import pyworld
-import sys
-import torch
-import torch.nn.functional as F
-import torchcrepe
-import traceback
-from scipy import signal
-from torch import Tensor
-
-BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
-now_dir = os.path.join(BASE_DIR, 'src')
-sys.path.append(now_dir)
-
-bh, ah = signal.butter(N=5, Wn=48, btype="high", fs=16000)
-
-input_audio_path2wav = {}
-
-
-@lru_cache
-def cache_harvest_f0(input_audio_path, fs, f0max, f0min, frame_period):
-    audio = input_audio_path2wav[input_audio_path]
-    f0, t = pyworld.harvest(
-        audio,
-        fs=fs,
-        f0_ceil=f0max,
-        f0_floor=f0min,
-        frame_period=frame_period,
-    )
-    f0 = pyworld.stonemask(audio, f0, t, fs)
-    return f0
-
-
-def change_rms(data1, sr1, data2, sr2, rate):  # 1是输入音频，2是输出音频,rate是2的占比
-    # print(data1.max(),data2.max())
-    rms1 = librosa.feature.rms(
-        y=data1, frame_length=sr1 // 2 * 2, hop_length=sr1 // 2
-    )  # 每半秒一个点
-    rms2 = librosa.feature.rms(y=data2, frame_length=sr2 // 2 * 2, hop_length=sr2 // 2)
-    rms1 = torch.from_numpy(rms1)
-    rms1 = F.interpolate(
-        rms1.unsqueeze(0), size=data2.shape[0], mode="linear"
-    ).squeeze()
-    rms2 = torch.from_numpy(rms2)
-    rms2 = F.interpolate(
-        rms2.unsqueeze(0), size=data2.shape[0], mode="linear"
-    ).squeeze()
-    rms2 = torch.max(rms2, torch.zeros_like(rms2) + 1e-6)
-    data2 *= (
-        torch.pow(rms1, torch.tensor(1 - rate))
-        * torch.pow(rms2, torch.tensor(rate - 1))
-    ).numpy()
-    return data2
-
-
-class VC(object):
-    def __init__(self, tgt_sr, config):
-        self.x_pad, self.x_query, self.x_center, self.x_max, self.is_half = (
-            config.x_pad,
-            config.x_query,
-            config.x_center,
-            config.x_max,
-            config.is_half,
-        )
-        self.sr = 16000  # hubert输入采样率
-        self.window = 160  # 每帧点数
-        self.t_pad = self.sr * self.x_pad  # 每条前后pad时间
-        self.t_pad_tgt = tgt_sr * self.x_pad
-        self.t_pad2 = self.t_pad * 2
-        self.t_query = self.sr * self.x_query  # 查询切点前后查询时间
-        self.t_center = self.sr * self.x_center  # 查询切点位置
-        self.t_max = self.sr * self.x_max  # 免查询时长阈值
-        self.device = config.device
-
-    # Fork Feature: Get the best torch device to use for f0 algorithms that require a torch device. Will return the type (torch.device)
-    def get_optimal_torch_device(self, index: int = 0) -> torch.device:
-        # Get cuda device
-        if torch.cuda.is_available():
-            return torch.device(
-                f"cuda:{index % torch.cuda.device_count()}"
-            )  # Very fast
-        elif torch.backends.mps.is_available():
-            return torch.device("mps")
-        # Insert an else here to grab "xla" devices if available. TO DO later. Requires the torch_xla.core.xla_model library
-        # Else wise return the "cpu" as a torch device,
-        return torch.device("cpu")
-
-    # Fork Feature: Compute f0 with the crepe method
-    def get_f0_crepe_computation(
-        self,
-        x,
-        f0_min,
-        f0_max,
-        p_len,
-        hop_length=160,  # 512 before. Hop length changes the speed that the voice jumps to a different dramatic pitch. Lower hop lengths means more pitch accuracy but longer inference time.
-        model="full",  # Either use crepe-tiny "tiny" or crepe "full". Default is full
-    ):
-        x = x.astype(
-            np.float32
-        )  # fixes the F.conv2D exception. We needed to convert double to float.
-        x /= np.quantile(np.abs(x), 0.999)
-        torch_device = self.get_optimal_torch_device()
-        audio = torch.from_numpy(x).to(torch_device, copy=True)
-        audio = torch.unsqueeze(audio, dim=0)
-        if audio.ndim == 2 and audio.shape[0] > 1:
-            audio = torch.mean(audio, dim=0, keepdim=True).detach()
-        audio = audio.detach()
-        print("Initiating prediction with a crepe_hop_length of: " + str(hop_length))
-        pitch: Tensor = torchcrepe.predict(
-            audio,
-            self.sr,
-            hop_length,
-            f0_min,
-            f0_max,
-            model,
-            batch_size=hop_length * 2,
-            device=torch_device,
-            pad=True,
-        )
-        p_len = p_len or x.shape[0] // hop_length
-        # Resize the pitch for final f0
-        source = np.array(pitch.squeeze(0).cpu().float().numpy())
-        source[source < 0.001] = np.nan
-        target = np.interp(
-            np.arange(0, len(source) * p_len, len(source)) / p_len,
-            np.arange(0, len(source)),
-            source,
-        )
-        f0 = np.nan_to_num(target)
-        return f0  # Resized f0
-
-    def get_f0_official_crepe_computation(
-        self,
-        x,
-        f0_min,
-        f0_max,
-        model="full",
-    ):
-        # Pick a batch size that doesn't cause memory errors on your gpu
-        batch_size = 512
-        # Compute pitch using first gpu
-        audio = torch.tensor(np.copy(x))[None].float()
-        f0, pd = torchcrepe.predict(
-            audio,
-            self.sr,
-            self.window,
-            f0_min,
-            f0_max,
-            model,
-            batch_size=batch_size,
-            device=self.device,
-            return_periodicity=True,
-        )
-        pd = torchcrepe.filter.median(pd, 3)
-        f0 = torchcrepe.filter.mean(f0, 3)
-        f0[pd < 0.1] = 0
-        f0 = f0[0].cpu().numpy()
-        return f0
-
-    # Fork Feature: Compute pYIN f0 method
-    def get_f0_pyin_computation(self, x, f0_min, f0_max):
-        y, sr = librosa.load("saudio/Sidney.wav", self.sr, mono=True)
-        f0, _, _ = librosa.pyin(y, sr=self.sr, fmin=f0_min, fmax=f0_max)
-        f0 = f0[1:]  # Get rid of extra first frame
-        return f0
-
-    # Fork Feature: Acquire median hybrid f0 estimation calculation
-    def get_f0_hybrid_computation(
-        self,
-        methods_str,
-        input_audio_path,
-        x,
-        f0_min,
-        f0_max,
-        p_len,
-        filter_radius,
-        crepe_hop_length,
-        time_step,
-    ):
-        # Get various f0 methods from input to use in the computation stack
-        s = methods_str
-        s = s.split("hybrid")[1]
-        s = s.replace("[", "").replace("]", "")
-        methods = s.split("+")
-        f0_computation_stack = []
-
-        print("Calculating f0 pitch estimations for methods: %s" % str(methods))
-        x = x.astype(np.float32)
-        x /= np.quantile(np.abs(x), 0.999)
-        # Get f0 calculations for all methods specified
-        for method in methods:
-            f0 = None
-            if method == "pm":
-                f0 = (
-                    parselmouth.Sound(x, self.sr)
-                    .to_pitch_ac(
-                        time_step=time_step / 1000,
-                        voicing_threshold=0.6,
-                        pitch_floor=f0_min,
-                        pitch_ceiling=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"
-                    )
-            elif method == "crepe":
-                f0 = self.get_f0_official_crepe_computation(x, f0_min, f0_max)
-                f0 = f0[1:]  # Get rid of extra first frame
-            elif method == "crepe-tiny":
-                f0 = self.get_f0_official_crepe_computation(x, f0_min, f0_max, "tiny")
-                f0 = f0[1:]  # Get rid of extra first frame
-            elif method == "mangio-crepe":
-                f0 = self.get_f0_crepe_computation(
-                    x, f0_min, f0_max, p_len, crepe_hop_length
-                )
-            elif method == "mangio-crepe-tiny":
-                f0 = self.get_f0_crepe_computation(
-                    x, f0_min, f0_max, p_len, crepe_hop_length, "tiny"
-                )
-            elif method == "harvest":
-                f0 = cache_harvest_f0(input_audio_path, self.sr, f0_max, f0_min, 10)
-                if filter_radius > 2:
-                    f0 = signal.medfilt(f0, 3)
-                f0 = f0[1:]  # Get rid of first frame.
-            elif method == "dio":  # Potentially buggy?
-                f0, t = pyworld.dio(
-                    x.astype(np.double),
-                    fs=self.sr,
-                    f0_ceil=f0_max,
-                    f0_floor=f0_min,
-                    frame_period=10,
-                )
-                f0 = pyworld.stonemask(x.astype(np.double), f0, t, self.sr)
-                f0 = signal.medfilt(f0, 3)
-                f0 = f0[1:]
-            # elif method == "pyin": Not Working just yet
-            #    f0 = self.get_f0_pyin_computation(x, f0_min, f0_max)
-            # Push method to the stack
-            f0_computation_stack.append(f0)
-
-        for fc in f0_computation_stack:
-            print(len(fc))
-
-        print("Calculating hybrid median f0 from the stack of: %s" % str(methods))
-        f0_median_hybrid = None
-        if len(f0_computation_stack) == 1:
-            f0_median_hybrid = f0_computation_stack[0]
-        else:
-            f0_median_hybrid = np.nanmedian(f0_computation_stack, axis=0)
-        return f0_median_hybrid
-
-    def get_f0(
-        self,
-        input_audio_path,
-        x,
-        p_len,
-        f0_up_key,
-        f0_method,
-        filter_radius,
-        crepe_hop_length,
-        inp_f0=None,
-    ):
-        global input_audio_path2wav
-        time_step = self.window / self.sr * 1000
-        f0_min = 50
-        f0_max = 1100
-        f0_mel_min = 1127 * np.log(1 + f0_min / 700)
-        f0_mel_max = 1127 * np.log(1 + f0_max / 700)
-        if f0_method == "pm":
-            f0 = (
-                parselmouth.Sound(x, self.sr)
-                .to_pitch_ac(
-                    time_step=time_step / 1000,
-                    voicing_threshold=0.6,
-                    pitch_floor=f0_min,
-                    pitch_ceiling=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"
-                )
-        elif f0_method == "harvest":
-            input_audio_path2wav[input_audio_path] = x.astype(np.double)
-            f0 = cache_harvest_f0(input_audio_path, self.sr, f0_max, f0_min, 10)
-            if filter_radius > 2:
-                f0 = signal.medfilt(f0, 3)
-        elif f0_method == "dio":  # Potentially Buggy?
-            f0, t = pyworld.dio(
-                x.astype(np.double),
-                fs=self.sr,
-                f0_ceil=f0_max,
-                f0_floor=f0_min,
-                frame_period=10,
-            )
-            f0 = pyworld.stonemask(x.astype(np.double), f0, t, self.sr)
-            f0 = signal.medfilt(f0, 3)
-        elif f0_method == "crepe":
-            f0 = self.get_f0_official_crepe_computation(x, f0_min, f0_max)
-        elif f0_method == "crepe-tiny":
-            f0 = self.get_f0_official_crepe_computation(x, f0_min, f0_max, "tiny")
-        elif f0_method == "mangio-crepe":
-            f0 = self.get_f0_crepe_computation(
-                x, f0_min, f0_max, p_len, crepe_hop_length
-            )
-        elif f0_method == "mangio-crepe-tiny":
-            f0 = self.get_f0_crepe_computation(
-                x, f0_min, f0_max, p_len, crepe_hop_length, "tiny"
-            )
-        elif f0_method == "rmvpe":
-            if hasattr(self, "model_rmvpe") == False:
-                from rmvpe import RMVPE
-
-                self.model_rmvpe = RMVPE(
-                    os.path.join(BASE_DIR, 'rvc_models', 'rmvpe.pt'), is_half=self.is_half, device=self.device
-                )
-            f0 = self.model_rmvpe.infer_from_audio(x, thred=0.03)
-
-        elif "hybrid" in f0_method:
-            # Perform hybrid median pitch estimation
-            input_audio_path2wav[input_audio_path] = x.astype(np.double)
-            f0 = self.get_f0_hybrid_computation(
-                f0_method,
-                input_audio_path,
-                x,
-                f0_min,
-                f0_max,
-                p_len,
-                filter_radius,
-                crepe_hop_length,
-                time_step,
-            )
-
-        f0 *= pow(2, f0_up_key / 12)
-        # with open("test.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()]))
-        tf0 = self.sr // self.window  # 每秒f0点数
-        if inp_f0 is not None:
-            delta_t = np.round(
-                (inp_f0[:, 0].max() - inp_f0[:, 0].min()) * tf0 + 1
-            ).astype("int16")
-            replace_f0 = np.interp(
-                list(range(delta_t)), inp_f0[:, 0] * 100, inp_f0[:, 1]
-            )
-            shape = f0[self.x_pad * tf0 : self.x_pad * tf0 + len(replace_f0)].shape[0]
-            f0[self.x_pad * tf0 : self.x_pad * tf0 + len(replace_f0)] = replace_f0[
-                :shape
-            ]
-        # with open("test_opt.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()]))
-        f0bak = f0.copy()
-        f0_mel = 1127 * np.log(1 + f0 / 700)
-        f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * 254 / (
-            f0_mel_max - f0_mel_min
-        ) + 1
-        f0_mel[f0_mel <= 1] = 1
-        f0_mel[f0_mel > 255] = 255
-        f0_coarse = np.rint(f0_mel).astype(np.int)
-
-        return f0_coarse, f0bak  # 1-0
-
-    def vc(
-        self,
-        model,
-        net_g,
-        sid,
-        audio0,
-        pitch,
-        pitchf,
-        times,
-        index,
-        big_npy,
-        index_rate,
-        version,
-        protect,
-    ):  # ,file_index,file_big_npy
-        feats = torch.from_numpy(audio0)
-        if self.is_half:
-            feats = feats.half()
-        else:
-            feats = feats.float()
-        if feats.dim() == 2:  # double channels
-            feats = feats.mean(-1)
-        assert feats.dim() == 1, feats.dim()
-        feats = feats.view(1, -1)
-        padding_mask = torch.BoolTensor(feats.shape).to(self.device).fill_(False)
-
-        inputs = {
-            "source": feats.to(self.device),
-            "padding_mask": padding_mask,
-            "output_layer": 9 if version == "v1" else 12,
-        }
-        t0 = ttime()
-        with torch.no_grad():
-            logits = model.extract_features(**inputs)
-            feats = model.final_proj(logits[0]) if version == "v1" else logits[0]
-        if protect < 0.5 and pitch != None and pitchf != None:
-            feats0 = feats.clone()
-        if (
-            isinstance(index, type(None)) == False
-            and isinstance(big_npy, type(None)) == False
-            and index_rate != 0
-        ):
-            npy = feats[0].cpu().numpy()
-            if self.is_half:
-                npy = npy.astype("float32")
-
-            # _, I = index.search(npy, 1)
-            # npy = big_npy[I.squeeze()]
-
-            score, ix = index.search(npy, k=8)
-            weight = np.square(1 / score)
-            weight /= weight.sum(axis=1, keepdims=True)
-            npy = np.sum(big_npy[ix] * np.expand_dims(weight, axis=2), axis=1)
-
-            if self.is_half:
-                npy = npy.astype("float16")
-            feats = (
-                torch.from_numpy(npy).unsqueeze(0).to(self.device) * index_rate
-                + (1 - index_rate) * feats
-            )
-
-        feats = F.interpolate(feats.permute(0, 2, 1), scale_factor=2).permute(0, 2, 1)
-        if protect < 0.5 and pitch != None and pitchf != None:
-            feats0 = F.interpolate(feats0.permute(0, 2, 1), scale_factor=2).permute(
-                0, 2, 1
-            )
-        t1 = ttime()
-        p_len = audio0.shape[0] // self.window
-        if feats.shape[1] < p_len:
-            p_len = feats.shape[1]
-            if pitch != None and pitchf != None:
-                pitch = pitch[:, :p_len]
-                pitchf = pitchf[:, :p_len]
-
-        if protect < 0.5 and pitch != None and pitchf != None:
-            pitchff = pitchf.clone()
-            pitchff[pitchf > 0] = 1
-            pitchff[pitchf < 1] = protect
-            pitchff = pitchff.unsqueeze(-1)
-            feats = feats * pitchff + feats0 * (1 - pitchff)
-            feats = feats.to(feats0.dtype)
-        p_len = torch.tensor([p_len], device=self.device).long()
-        with torch.no_grad():
-            if pitch != None and pitchf != None:
-                audio1 = (
-                    (net_g.infer(feats, p_len, pitch, pitchf, sid)[0][0, 0])
-                    .data.cpu()
-                    .float()
-                    .numpy()
-                )
-            else:
-                audio1 = (
-                    (net_g.infer(feats, p_len, sid)[0][0, 0]).data.cpu().float().numpy()
-                )
-        del feats, p_len, padding_mask
-        if torch.cuda.is_available():
-            torch.cuda.empty_cache()
-        t2 = ttime()
-        times[0] += t1 - t0
-        times[2] += t2 - t1
-        return audio1
-
-    def pipeline(
-        self,
-        model,
-        net_g,
-        sid,
-        audio,
-        input_audio_path,
-        times,
-        f0_up_key,
-        f0_method,
-        file_index,
-        # file_big_npy,
-        index_rate,
-        if_f0,
-        filter_radius,
-        tgt_sr,
-        resample_sr,
-        rms_mix_rate,
-        version,
-        protect,
-        crepe_hop_length,
-        f0_file=None,
-    ):
-        if (
-            file_index != ""
-            # and file_big_npy != ""
-            # and os.path.exists(file_big_npy) == True
-            and os.path.exists(file_index) == True
-            and index_rate != 0
-        ):
-            try:
-                index = faiss.read_index(file_index)
-                # big_npy = np.load(file_big_npy)
-                big_npy = index.reconstruct_n(0, index.ntotal)
-            except:
-                traceback.print_exc()
-                index = big_npy = None
-        else:
-            index = big_npy = None
-        audio = signal.filtfilt(bh, ah, audio)
-        audio_pad = np.pad(audio, (self.window // 2, self.window // 2), mode="reflect")
-        opt_ts = []
-        if audio_pad.shape[0] > self.t_max:
-            audio_sum = np.zeros_like(audio)
-            for i in range(self.window):
-                audio_sum += audio_pad[i : i - self.window]
-            for t in range(self.t_center, audio.shape[0], self.t_center):
-                opt_ts.append(
-                    t
-                    - self.t_query
-                    + np.where(
-                        np.abs(audio_sum[t - self.t_query : t + self.t_query])
-                        == np.abs(audio_sum[t - self.t_query : t + self.t_query]).min()
-                    )[0][0]
-                )
-        s = 0
-        audio_opt = []
-        t = None
-        t1 = ttime()
-        audio_pad = np.pad(audio, (self.t_pad, self.t_pad), mode="reflect")
-        p_len = audio_pad.shape[0] // self.window
-        inp_f0 = None
-        if hasattr(f0_file, "name") == True:
-            try:
-                with open(f0_file.name, "r") as f:
-                    lines = f.read().strip("\n").split("\n")
-                inp_f0 = []
-                for line in lines:
-                    inp_f0.append([float(i) for i in line.split(",")])
-                inp_f0 = np.array(inp_f0, dtype="float32")
-            except:
-                traceback.print_exc()
-        sid = torch.tensor(sid, device=self.device).unsqueeze(0).long()
-        pitch, pitchf = None, None
-        if if_f0 == 1:
-            pitch, pitchf = self.get_f0(
-                input_audio_path,
-                audio_pad,
-                p_len,
-                f0_up_key,
-                f0_method,
-                filter_radius,
-                crepe_hop_length,
-                inp_f0,
-            )
-            pitch = pitch[:p_len]
-            pitchf = pitchf[:p_len]
-            if self.device == "mps":
-                pitchf = pitchf.astype(np.float32)
-            pitch = torch.tensor(pitch, device=self.device).unsqueeze(0).long()
-            pitchf = torch.tensor(pitchf, device=self.device).unsqueeze(0).float()
-        t2 = ttime()
-        times[1] += t2 - t1
-        for t in opt_ts:
-            t = t // self.window * self.window
-            if if_f0 == 1:
-                audio_opt.append(
-                    self.vc(
-                        model,
-                        net_g,
-                        sid,
-                        audio_pad[s : t + self.t_pad2 + self.window],
-                        pitch[:, s // self.window : (t + self.t_pad2) // self.window],
-                        pitchf[:, s // self.window : (t + self.t_pad2) // self.window],
-                        times,
-                        index,
-                        big_npy,
-                        index_rate,
-                        version,
-                        protect,
-                    )[self.t_pad_tgt : -self.t_pad_tgt]
-                )
-            else:
-                audio_opt.append(
-                    self.vc(
-                        model,
-                        net_g,
-                        sid,
-                        audio_pad[s : t + self.t_pad2 + self.window],
-                        None,
-                        None,
-                        times,
-                        index,
-                        big_npy,
-                        index_rate,
-                        version,
-                        protect,
-                    )[self.t_pad_tgt : -self.t_pad_tgt]
-                )
-            s = t
-        if if_f0 == 1:
-            audio_opt.append(
-                self.vc(
-                    model,
-                    net_g,
-                    sid,
-                    audio_pad[t:],
-                    pitch[:, t // self.window :] if t is not None else pitch,
-                    pitchf[:, t // self.window :] if t is not None else pitchf,
-                    times,
-                    index,
-                    big_npy,
-                    index_rate,
-                    version,
-                    protect,
-                )[self.t_pad_tgt : -self.t_pad_tgt]
-            )
-        else:
-            audio_opt.append(
-                self.vc(
-                    model,
-                    net_g,
-                    sid,
-                    audio_pad[t:],
-                    None,
-                    None,
-                    times,
-                    index,
-                    big_npy,
-                    index_rate,
-                    version,
-                    protect,
-                )[self.t_pad_tgt : -self.t_pad_tgt]
-            )
-        audio_opt = np.concatenate(audio_opt)
-        if rms_mix_rate != 1:
-            audio_opt = change_rms(audio, 16000, audio_opt, tgt_sr, rms_mix_rate)
-        if resample_sr >= 16000 and tgt_sr != resample_sr:
-            audio_opt = librosa.resample(
-                audio_opt, orig_sr=tgt_sr, target_sr=resample_sr
-            )
-        audio_max = np.abs(audio_opt).max() / 0.99
-        max_int16 = 32768
-        if audio_max > 1:
-            max_int16 /= audio_max
-        audio_opt = (audio_opt * max_int16).astype(np.int16)
-        del pitch, pitchf, sid
-        if torch.cuda.is_available():
-            torch.cuda.empty_cache()
-        return audio_opt
diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/backtranslation/prepare-de-monolingual.sh b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/backtranslation/prepare-de-monolingual.sh
deleted file mode 100644
index 5e67b2b3bcf27d3436031453e796e58a0ae79ec4..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/backtranslation/prepare-de-monolingual.sh
+++ /dev/null
@@ -1,98 +0,0 @@
-#!/bin/bash
-
-SCRIPTS=mosesdecoder/scripts
-TOKENIZER=$SCRIPTS/tokenizer/tokenizer.perl
-NORM_PUNC=$SCRIPTS/tokenizer/normalize-punctuation.perl
-REM_NON_PRINT_CHAR=$SCRIPTS/tokenizer/remove-non-printing-char.perl
-BPEROOT=subword-nmt/subword_nmt
-
-
-BPE_CODE=wmt18_en_de/code
-SUBSAMPLE_SIZE=25000000
-LANG=de
-
-
-OUTDIR=wmt18_${LANG}_mono
-orig=orig
-tmp=$OUTDIR/tmp
-mkdir -p $OUTDIR $tmp
-
-
-URLS=(
-    "http://www.statmt.org/wmt14/training-monolingual-news-crawl/news.2007.de.shuffled.gz"
-    "http://www.statmt.org/wmt14/training-monolingual-news-crawl/news.2008.de.shuffled.gz"
-    "http://www.statmt.org/wmt14/training-monolingual-news-crawl/news.2009.de.shuffled.gz"
-    "http://www.statmt.org/wmt14/training-monolingual-news-crawl/news.2010.de.shuffled.gz"
-    "http://www.statmt.org/wmt14/training-monolingual-news-crawl/news.2011.de.shuffled.gz"
-    "http://www.statmt.org/wmt14/training-monolingual-news-crawl/news.2012.de.shuffled.gz"
-    "http://www.statmt.org/wmt14/training-monolingual-news-crawl/news.2013.de.shuffled.gz"
-    "http://www.statmt.org/wmt15/training-monolingual-news-crawl-v2/news.2014.de.shuffled.v2.gz"
-    "http://data.statmt.org/wmt16/translation-task/news.2015.de.shuffled.gz"
-    "http://data.statmt.org/wmt17/translation-task/news.2016.de.shuffled.gz"
-    "http://data.statmt.org/wmt18/translation-task/news.2017.de.shuffled.deduped.gz"
-)
-FILES=(
-    "news.2007.de.shuffled.gz"
-    "news.2008.de.shuffled.gz"
-    "news.2009.de.shuffled.gz"
-    "news.2010.de.shuffled.gz"
-    "news.2011.de.shuffled.gz"
-    "news.2012.de.shuffled.gz"
-    "news.2013.de.shuffled.gz"
-    "news.2014.de.shuffled.v2.gz"
-    "news.2015.de.shuffled.gz"
-    "news.2016.de.shuffled.gz"
-    "news.2017.de.shuffled.deduped.gz"
-)
-
-
-cd $orig
-for ((i=0;i<${#URLS[@]};++i)); do
-    file=${FILES[i]}
-    if [ -f $file ]; then
-        echo "$file already exists, skipping download"
-    else
-        url=${URLS[i]}
-        wget "$url"
-    fi
-done
-cd ..
-
-
-if [ -f $tmp/monolingual.${SUBSAMPLE_SIZE}.${LANG} ]; then
-    echo "found monolingual sample, skipping shuffle/sample/tokenize"
-else
-    gzip -c -d -k $(for FILE in "${FILES[@]}"; do echo $orig/$FILE; done) \
-    | shuf -n $SUBSAMPLE_SIZE \
-    | perl $NORM_PUNC $LANG \
-    | perl $REM_NON_PRINT_CHAR \
-    | perl $TOKENIZER -threads 8 -a -l $LANG \
-    > $tmp/monolingual.${SUBSAMPLE_SIZE}.${LANG}
-fi
-
-
-if [ -f $tmp/bpe.monolingual.${SUBSAMPLE_SIZE}.${LANG} ]; then
-    echo "found BPE monolingual sample, skipping BPE step"
-else
-    python $BPEROOT/apply_bpe.py -c $BPE_CODE \
-        < $tmp/monolingual.${SUBSAMPLE_SIZE}.${LANG} \
-        > $tmp/bpe.monolingual.${SUBSAMPLE_SIZE}.${LANG}
-fi
-
-
-if [ -f $tmp/bpe.monolingual.dedup.${SUBSAMPLE_SIZE}.${LANG} ]; then
-    echo "found deduplicated monolingual sample, skipping deduplication step"
-else
-    python deduplicate_lines.py $tmp/bpe.monolingual.${SUBSAMPLE_SIZE}.${LANG} \
-    > $tmp/bpe.monolingual.dedup.${SUBSAMPLE_SIZE}.${LANG}
-fi
-
-
-if [ -f $OUTDIR/bpe.monolingual.dedup.00.de ]; then
-    echo "found sharded data, skipping sharding step"
-else
-    split --lines 1000000 --numeric-suffixes \
-        --additional-suffix .${LANG} \
-        $tmp/bpe.monolingual.dedup.${SUBSAMPLE_SIZE}.${LANG} \
-        $OUTDIR/bpe.monolingual.dedup.
-fi
diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/linformer/linformer_src/models/__init__.py b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/linformer/linformer_src/models/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/wav2vec/unsupervised/scripts/merge_clusters.py b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/wav2vec/unsupervised/scripts/merge_clusters.py
deleted file mode 100644
index 2780f9d971d847b3ad0b59e9a33780553ebce902..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/wav2vec/unsupervised/scripts/merge_clusters.py
+++ /dev/null
@@ -1,114 +0,0 @@
-#!/usr/bin/env python3 -u
-# 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 argparse
-import os
-import os.path as osp
-import numpy as np
-import tqdm
-import torch
-import random
-from shutil import copyfile
-
-from npy_append_array import NpyAppendArray
-
-
-def get_parser():
-    parser = argparse.ArgumentParser(
-        description="transforms features via a given pca and stored them in target dir"
-    )
-    # fmt: off
-    parser.add_argument('source', help='directory with features')
-    parser.add_argument('--split', help='which split to read', required=True)
-    parser.add_argument('--save-dir', help='where to save the output', required=True)
-    parser.add_argument('--cluster-dir', help='where the clusters are')
-    parser.add_argument('--pooling', type=str, default='mean', choices=['mean', 'sample'], help='how to pool')
-    # fmt: on
-
-    return parser
-
-
-def main():
-    parser = get_parser()
-    args = parser.parse_args()
-
-    source_path = osp.join(args.source, args.split)
-    cluster_path = osp.join(args.cluster_dir, args.split + ".src")
-    print(f"data path: {source_path}")
-
-    features = np.load(source_path + ".npy", mmap_mode="r")
-    sizes = []
-    offsets = []
-    offset = 0
-    with open(source_path + ".lengths", "r") as len_f:
-        for line in len_f:
-            length = int(line.rstrip())
-            sizes.append(length)
-            offsets.append(offset)
-            offset += length
-
-    clusters = []
-    with open(cluster_path, "r") as cf:
-        for line in cf:
-            line = line.rstrip()
-            items = line.split()
-            items = list(map(int, items))
-            clusters.append(items)
-
-    os.makedirs(args.save_dir, exist_ok=True)
-    save_path = osp.join(args.save_dir, args.split)
-
-    copyfile(source_path + ".tsv", save_path + ".tsv")
-
-    if os.path.exists(source_path + ".phn"):
-        copyfile(source_path + ".phn", save_path + ".phn")
-    if os.path.exists(osp.join(args.source, "dict.phn.txt")):
-        copyfile(
-            osp.join(args.source, "dict.phn.txt"),
-            osp.join(args.save_dir, "dict.phn.txt"),
-        )
-    if os.path.exists(source_path + ".wrd"):
-        copyfile(source_path + ".wrd", save_path + ".wrd")
-
-    if osp.exists(save_path + ".npy"):
-        os.remove(save_path + ".npy")
-    npaa = NpyAppendArray(save_path + ".npy")
-
-    def merge(feats, clust):
-        feats = torch.from_numpy(feats.copy())
-        clust = torch.LongTensor(clust)
-        _, counts = clust.unique_consecutive(return_counts=True)
-        curr = 0
-
-        merged = []
-        for c in counts:
-            c = c.item()
-            start = curr
-            end = curr + c
-            curr += c
-            if args.pooling == "mean":
-                new_x = feats[start:end].mean(dim=0)
-            elif args.pooling == "sample":
-                new_x = feats[start + int(random.random() * c)]
-            else:
-                raise NotImplementedError()
-            merged.append(new_x)
-
-        return torch.stack(merged, dim=0).numpy()
-
-    with open(save_path + ".lengths", "w") as l_f:
-        for size, offset, clust in tqdm.tqdm(
-            zip(sizes, offsets, clusters), total=len(sizes)
-        ):
-            end = size + offset
-            feats = features[offset:end]
-            feats = merge(feats, clust)
-            print(len(feats), file=l_f)
-            npaa.append(feats)
-
-
-if __name__ == "__main__":
-    main()
diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/benchmark/dummy_mt.py b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/benchmark/dummy_mt.py
deleted file mode 100644
index 4ca7be93a38d8d2b47685b74b4f8b8f9dcb03d2e..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/benchmark/dummy_mt.py
+++ /dev/null
@@ -1,119 +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 numpy as np
-import torch
-from fairseq.data import Dictionary, FairseqDataset
-from fairseq.tasks import LegacyFairseqTask, register_task
-
-
-logger = logging.getLogger(__name__)
-
-
-@register_task("dummy_mt")
-class DummyMTTask(LegacyFairseqTask):
-    @staticmethod
-    def add_args(parser):
-        """Add task-specific arguments to the parser."""
-        parser.add_argument("--dict-size", default=49996, type=int)
-        parser.add_argument("--dataset-size", default=100000, type=int)
-        parser.add_argument("--src-len", default=30, type=int)
-        parser.add_argument("--tgt-len", default=30, type=int)
-
-    def __init__(self, args, dictionary):
-        super().__init__(args)
-        self.dictionary = dictionary
-        self.seed = args.seed
-
-        dictionary.pad_to_multiple_(8)  # often faster if divisible by 8
-
-        self.dummy_src = torch.arange(args.src_len + 1) + dictionary.pad() + 1
-        self.dummy_tgt = torch.arange(args.tgt_len + 1) + dictionary.pad() + 1
-
-    @classmethod
-    def setup_task(cls, args, **kwargs):
-        """Setup the task. """
-        dictionary = Dictionary()
-        for i in range(args.dict_size):
-            dictionary.add_symbol("word{}".format(i))
-        logger.info("dictionary: {} types".format(len(dictionary)))
-
-        args.max_source_positions = args.src_len + dictionary.pad() + 2
-        args.max_target_positions = args.tgt_len + dictionary.pad() + 2
-
-        return cls(args, dictionary)
-
-    def load_dataset(self, split, epoch=1, combine=False, **kwargs):
-        """Load a given dataset split.
-        Args:
-            split (str): name of the split (e.g., train, valid, test)
-        """
-        item_size = max(self.args.src_len, self.args.tgt_len)
-        if self.args.batch_size is not None:
-            bsz = self.args.batch_size
-        else:
-            bsz = max(1, self.args.max_tokens // item_size)
-        tgt = torch.stack([self.dummy_tgt for _ in range(bsz)])
-        self.datasets[split] = DummyDataset(
-            {
-                "id": 1,
-                "net_input": {
-                    "src_tokens": torch.stack([self.dummy_src for _ in range(bsz)]),
-                    "src_lengths": torch.full(
-                        (bsz,), self.args.src_len, dtype=torch.long
-                    ),
-                    "prev_output_tokens": tgt.clone(),
-                },
-                "target": tgt,
-                "nsentences": bsz,
-                "ntokens": bsz * self.args.tgt_len,
-            },
-            num_items=self.args.dataset_size,
-            item_size=item_size,
-        )
-
-    @property
-    def source_dictionary(self):
-        return self.dictionary
-
-    @property
-    def target_dictionary(self):
-        return self.dictionary
-
-
-class DummyDataset(FairseqDataset):
-    def __init__(self, batch, num_items, item_size):
-        super().__init__()
-        self.batch = batch
-        self.num_items = num_items
-        self.item_size = item_size
-
-    def __getitem__(self, index):
-        return index
-
-    def __len__(self):
-        return self.num_items
-
-    def collater(self, samples):
-        return self.batch
-
-    @property
-    def sizes(self):
-        return np.array([self.item_size] * self.num_items)
-
-    def num_tokens(self, index):
-        return self.item_size
-
-    def size(self, index):
-        return self.item_size
-
-    def ordered_indices(self):
-        return np.arange(self.num_items)
-
-    @property
-    def supports_prefetch(self):
-        return False
diff --git a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/wav2vec/unsupervised/__init__.py b/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/wav2vec/unsupervised/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/optim/fp16_optimizer.py b/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/optim/fp16_optimizer.py
deleted file mode 100644
index c59b21cf6b36650a4dd899e62b83a01715d2e2a1..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/optim/fp16_optimizer.py
+++ /dev/null
@@ -1,548 +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 collections import defaultdict
-from itertools import chain
-
-import torch
-from fairseq import optim
-from omegaconf import DictConfig
-
-from .dynamic_loss_scaler import DynamicLossScaler
-
-
-class _FP16OptimizerMixin(object):
-    def __init__(self, *args, **kwargs):
-        # forward __init__ call to the next class in mro(method resolution order)
-        super().__init__(*args, **kwargs)
-        self._multiply_factor = 1.0
-
-    @property
-    def has_flat_params(self):
-        return torch.is_tensor(self.fp32_params) or (
-            isinstance(self.fp32_params, dict)
-            and all(torch.is_tensor(t) for t in self.fp32_params.values())
-        )
-
-    @classmethod
-    def build_fp32_params(cls, args, params, flatten=True):
-        # create FP32 copy of parameters and grads
-        if flatten:
-            is_pipeline_parallel = getattr(
-                args, "pipeline_model_parallel", False
-            ) and getattr(args, "distributed_no_spawn", False)
-            total_param_size = sum(p.data.numel() for p in params)
-            devices = [torch.cuda.current_device()]
-            if is_pipeline_parallel:
-                devices = list(set(args.pipeline_devices))
-            fp32_params = {}
-            for device in devices:
-                if is_pipeline_parallel:
-                    device_param_size = sum(
-                        p.data.numel() for p in params if p.device.index == device
-                    )
-                    device_params = [p for p in params if p.device.index == device]
-                else:
-                    device_param_size = total_param_size
-                    device_params = params
-                fp32_params[device] = (
-                    device_params[0].new(0).float().new(device_param_size)
-                )
-                offset = 0
-                for p in device_params:
-                    numel = p.data.numel()
-                    fp32_params[device][offset : offset + numel].copy_(p.data.view(-1))
-                    offset += numel
-                fp32_params[device] = torch.nn.Parameter(fp32_params[device])
-                fp32_params[device].grad = fp32_params[device].data.new(
-                    device_param_size
-                )
-            return fp32_params
-        else:
-            fp32_params = []
-            for p in params:
-                p32 = torch.nn.Parameter(p.data.float())
-                if hasattr(p, 'expert'):
-                    p32.expert = True
-                elif hasattr(p, 'base_expert'):
-                    p32.base_expert = True
-                p32.grad = torch.zeros_like(p32.data)
-                if hasattr(p, "param_group"):
-                    p32.param_group = p.param_group
-                fp32_params.append(p32)
-            return fp32_params
-
-    def state_dict(self):
-        """Return the optimizer's state dict."""
-        state_dict = self.fp32_optimizer.state_dict()
-        if self.scaler is not None:
-            state_dict["loss_scale"] = self.scaler.loss_scale
-        return state_dict
-
-    def load_state_dict(self, state_dict, optimizer_overrides=None):
-        """Load an optimizer state dict.
-
-        In general we should prefer the configuration of the existing optimizer
-        instance (e.g., learning rate) over that found in the state_dict. This
-        allows us to resume training from a checkpoint using a new set of
-        optimizer args.
-        """
-        if "loss_scale" in state_dict and self.scaler is not None:
-            self.scaler.loss_scale = state_dict["loss_scale"]
-        self.fp32_optimizer.load_state_dict(state_dict, optimizer_overrides)
-
-    def backward(self, loss):
-        """Computes the sum of gradients of the given tensor w.r.t. graph leaves.
-
-        Compared to :func:`fairseq.optim.FairseqOptimizer.backward`, this
-        function additionally dynamically scales the loss to avoid gradient
-        underflow.
-        """
-        if self.scaler is not None:
-            loss = self.scaler.scale(loss)
-        loss.backward()
-        self._needs_sync = True
-
-    def _sync_fp16_grads_to_fp32(self):
-        if self._needs_sync:
-            # copy FP16 grads to FP32
-            if self.has_flat_params:
-                devices = list(self.fp32_params.keys())
-                device_params_dict = defaultdict(list)
-                for p in self.fp16_params:
-                    if p.requires_grad:
-                        device_params_dict[p.device.index].append(p)
-                for device in devices:
-                    device_params = device_params_dict[device]
-                    offset = 0
-                    for p in device_params:
-                        grad_data = (
-                            p.grad.data
-                            if p.grad is not None
-                            else p.data.new_zeros(p.data.shape)
-                        )
-                        numel = grad_data.numel()
-                        self.fp32_params[device].grad.data[
-                            offset : offset + numel
-                        ].copy_(grad_data.view(-1))
-                        offset += numel
-            else:
-                for p, p32 in zip(self.fp16_params, self.fp32_params):
-                    if not p.requires_grad:
-                        continue
-                    if p.grad is not None:
-                        if p32.grad is None:
-                            p32.grad = p.grad.data.float()
-                        else:
-                            p32.grad.data.copy_(p.grad.data)
-                    else:
-                        p32.grad = torch.zeros_like(p.data, dtype=torch.float)
-
-            self._needs_sync = False
-
-    def _sync_fp32_params_to_fp16(self):
-        # copy FP32 params back into FP16 model
-        if self.has_flat_params:
-            devices = list(self.fp32_params.keys())
-            device_params_dict = defaultdict(list)
-            for p in self.fp16_params:
-                device_params_dict[p.device.index].append(p)
-            for device in devices:
-                device_params = device_params_dict[device]
-                offset = 0
-                for p in device_params:
-                    numel = p.data.numel()
-                    p.data.copy_(
-                        self.fp32_params[device]
-                        .data[offset : offset + numel]
-                        .view_as(p.data)
-                    )
-                    offset += numel
-        else:
-            for p, p32 in zip(self.fp16_params, self.fp32_params):
-                if not p.requires_grad:
-                    continue
-                p.data.copy_(p32.data)
-
-    def _unscale_grads(self):
-        self._sync_fp16_grads_to_fp32()
-        if (
-            # Skip the multiplication if it's a no-op (i.e., if _multiply_factor
-            # is 1.0). At the same time, we want to avoid the device-to-host
-            # transfer by comparing it to 1.0. Since _multiply_factor starts as
-            # a Python float, we roughly assume that if it's a tensor then it's
-            # probably not =1.0 anymore and we do the multiplication. Otherwise
-            # we can safely check the value without a D2H transfer.
-            torch.is_tensor(self._multiply_factor)
-            or self._multiply_factor != 1.0
-        ):
-            self.fp32_optimizer.multiply_grads(self._multiply_factor)
-            self._multiply_factor = 1.0
-
-    def multiply_grads(self, c):
-        """Multiplies grads by a constant ``c``."""
-        self._multiply_factor *= c
-
-    def clip_grad_norm(self, max_norm, aggregate_norm_fn=None):
-        """Clips gradient norm and updates dynamic loss scaler."""
-        self._sync_fp16_grads_to_fp32()
-
-        grad_norm = self._multiply_factor * self.fp32_optimizer.clip_grad_norm(
-            0, aggregate_norm_fn
-        )
-
-        if self.scaler is not None:
-            if grad_norm > max_norm > 0.0:
-                self._multiply_factor *= max_norm / grad_norm
-
-            self.scaler.check_overflow(grad_norm)
-        elif max_norm > 0.0:
-            clip_coef = (max_norm / (grad_norm + 1e-6)).clamp_(max=1)
-            self._multiply_factor *= clip_coef
-
-        return grad_norm
-
-    def step(self, closure=None, groups=None):
-        """Performs a single optimization step."""
-        self._sync_fp16_grads_to_fp32()
-
-        if getattr(self, "supports_step_with_scale", False):
-            self.fp32_optimizer.step(closure, scale=(1.0 / self._multiply_factor), groups=groups)
-        else:
-            self._unscale_grads()
-            self.fp32_optimizer.step(closure, groups=groups)
-
-        if self.scaler is not None:
-            self.scaler.update()
-
-        self._sync_fp32_params_to_fp16()
-
-    def zero_grad(self):
-        """Clears the gradients of all optimized parameters."""
-        for p in self.fp16_params:
-            p.grad = None
-        if self.has_flat_params:
-            if torch.is_tensor(self.fp32_params):
-                self.fp32_params.grad.zero_()
-            elif isinstance(self.fp32_params, dict):
-                for fp32_params in self.fp32_params.values():
-                    fp32_params.grad.zero_()
-            else:
-                raise RuntimeError("self.fp32_params must be a tensor or dict")
-        else:
-            for p32 in self.fp32_params:
-                if p32.grad is not None:
-                    p32.grad.zero_()
-        self._needs_sync = False
-
-        if self.scaler is not None:
-            self._multiply_factor = 1.0 / float(self.scaler.loss_scale)
-
-
-class FP16Optimizer(_FP16OptimizerMixin, optim.FairseqOptimizer):
-    """
-    Wrap an *optimizer* to support FP16 (mixed precision) training.
-    """
-
-    def __init__(self, cfg: DictConfig, params, fp32_optimizer, fp32_params, **kwargs):
-        super().__init__(cfg.optimizer)
-        self.fp16_params = params
-        self.fp32_optimizer = fp32_optimizer
-        self.fp32_params = fp32_params
-
-        if getattr(cfg.common, "fp16_scale_window", None) is None:
-            if len(cfg.optimization.update_freq) > 1:
-                raise ValueError(
-                    "--fp16-scale-window must be given explicitly when using a "
-                    "custom --update-freq schedule"
-                )
-            data_parallel_size = int(
-                cfg.distributed_training.distributed_world_size
-                / cfg.common.model_parallel_size
-            )
-            scale_window = int(
-                2 ** 14 / data_parallel_size / cfg.optimization.update_freq[0]
-            )
-        else:
-            scale_window = cfg.common.fp16_scale_window
-
-        if not getattr(cfg.common, "bf16", False):
-            self.scaler = DynamicLossScaler(
-                init_scale=cfg.common.fp16_init_scale,
-                scale_window=scale_window,
-                tolerance=cfg.common.fp16_scale_tolerance,
-                threshold=cfg.common.threshold_loss_scale,
-                min_loss_scale=cfg.common.min_loss_scale,
-            )
-        else:
-            # disable loss scaling for bfloat16
-            self.scaler = None
-
-    @classmethod
-    def build_optimizer(cls, cfg: DictConfig, params, **kwargs):
-        """
-        Args:
-            cfg (omegaconf.DictConfig): fairseq args
-            params (iterable): iterable of parameters to optimize
-        """
-        flatten = not getattr(cfg.common, "fp16_no_flatten_grads", False)
-        if getattr(cfg.common, "bf16", False):
-            flatten = False  # mixed precision is faster on TPUs without flat grads
-        fp32_params = cls.build_fp32_params(cfg.optimizer, params, flatten=flatten)
-        if flatten:
-            fp32_optimizer = optim.build_optimizer(cfg.optimizer, [fp32_params])
-        else:
-            fp32_optimizer = optim.build_optimizer(cfg.optimizer, fp32_params)
-        if flatten and not fp32_optimizer.supports_flat_params:
-            raise RuntimeError(
-                f"chosen optimizer {fp32_optimizer.__class__.__name__} does not support flat params, please set --fp16-no-flatten-grads"
-            )
-        return cls(cfg, params, fp32_optimizer, fp32_params, **kwargs)
-
-    @property
-    def optimizer(self):
-        return self.fp32_optimizer.optimizer
-
-    @optimizer.setter
-    def optimizer(self, optimizer):
-        self.fp32_optimizer.optimizer = optimizer
-
-    @property
-    def lr_scheduler(self):
-        return getattr(self.fp32_optimizer, "lr_scheduler", None)
-
-    @property
-    def optimizer_config(self):
-        return self.fp32_optimizer.optimizer_config
-
-    def get_lr(self):
-        return self.fp32_optimizer.get_lr()
-
-    def set_lr(self, lr):
-        self.fp32_optimizer.set_lr(lr)
-
-    def all_reduce_grads(self, module):
-        self.fp32_optimizer.all_reduce_grads(module)
-
-    @property
-    def supports_flat_params(self):
-        return self.fp32_optimizer.supports_flat_params
-
-
-class _MemoryEfficientFP16OptimizerMixin(object):
-    def __init__(self, *args, **kwargs):
-        # forward __init__ call to the next class in MRO (method resolution order)
-        super().__init__(*args, **kwargs)
-        self._multiply_factor = 1.0
-
-    @property
-    def has_flat_params(self):
-        return False
-
-    def state_dict(self):
-        """Return the optimizer's state dict."""
-        state_dict = self.wrapped_optimizer.state_dict()
-        if self.scaler is not None:
-            state_dict["loss_scale"] = self.scaler.loss_scale
-        return state_dict
-
-    def load_state_dict(self, state_dict, optimizer_overrides=None):
-        """Load an optimizer state dict.
-
-        In general we should prefer the configuration of the existing optimizer
-        instance (e.g., learning rate) over that found in the state_dict. This
-        allows us to resume training from a checkpoint using a new set of
-        optimizer args.
-        """
-        if "loss_scale" in state_dict and self.scaler is not None:
-            self.scaler.loss_scale = state_dict["loss_scale"]
-
-        self.wrapped_optimizer.load_state_dict(state_dict, optimizer_overrides)
-
-        # Hack: PyTorch automatically casts the optimizer state to match the
-        # type of the current parameters. But with --memory-efficient-fp16 the
-        # params are FP16 while the optimizer state is FP32 and we don't want
-        # to cast. A workaround is to manually copy back the original state
-        # after the optimizer has been loaded.
-        if not getattr(self.optimizer, "disable_mem_eff_fp16_loading_hack", False):
-            groups = self.optimizer.param_groups
-            saved_groups = state_dict["param_groups"]
-            id_map = {
-                old_id: p
-                for old_id, p in zip(
-                    chain(*(g["params"] for g in saved_groups)),
-                    chain(*(g["params"] for g in groups)),
-                )
-            }
-            for k, v in state_dict["state"].items():
-                if k in id_map:
-                    param = id_map[k]
-                    self.optimizer.state[param] = v
-
-    def backward(self, loss):
-        """Computes the sum of gradients of the given tensor w.r.t. graph leaves.
-
-        Compared to :func:`fairseq.optim.FairseqOptimizer.backward`, this
-        function additionally dynamically scales the loss to avoid gradient
-        underflow.
-        """
-        if self.scaler is not None:
-            loss = self.scaler.scale(loss)
-        loss.backward()
-
-    def _unscale_grads(self):
-        if (
-            # Skip the multiplication if it's a no-op (i.e., if _multiply_factor
-            # is 1.0). At the same time, we want to avoid the device-to-host
-            # transfer by comparing it to 1.0. Since _multiply_factor starts as
-            # a Python float, we roughly assume that if it's a tensor then it's
-            # probably not =1.0 anymore and we do the multiplication. Otherwise
-            # we can safely check the value without a D2H transfer.
-            torch.is_tensor(self._multiply_factor)
-            or self._multiply_factor != 1.0
-        ):
-            self.wrapped_optimizer.multiply_grads(self._multiply_factor)
-            self._multiply_factor = 1.0
-
-    def multiply_grads(self, c):
-        """Multiplies grads by a constant *c*."""
-        self._multiply_factor *= c
-
-    def clip_grad_norm(self, max_norm, aggregate_norm_fn=None):
-        """Clips gradient norm and updates dynamic loss scaler."""
-        max_norm = float(max_norm)
-        grad_norm = self._multiply_factor * self.wrapped_optimizer.clip_grad_norm(
-            0, aggregate_norm_fn
-        )
-
-        if self.scaler is not None:
-            grad_norm_cpu = float(grad_norm)
-            if grad_norm_cpu > max_norm > 0.0:
-                self._multiply_factor *= max_norm / grad_norm_cpu
-
-            # detect overflow and adjust loss scale
-            self.scaler.check_overflow(grad_norm_cpu)
-        elif max_norm > 0.0:
-            clip_coef = (max_norm / (grad_norm + 1e-6)).clamp_(max=1)
-            self._multiply_factor *= clip_coef
-
-        return grad_norm
-
-    def step(self, closure=None, groups=None):
-        """Performs a single optimization step."""
-        if getattr(self, "supports_step_with_scale", False):
-            # NOTE(msb) optimizer divides by scale factor
-            self.wrapped_optimizer.step(closure, scale=(1.0 / self._multiply_factor), groups=groups)
-        else:
-            self._unscale_grads()
-            self.wrapped_optimizer.step(closure, groups=groups)
-
-        if self.scaler is not None:
-            self.scaler.update()
-
-    def zero_grad(self):
-        """Clears the gradients of all optimized parameters."""
-        self.wrapped_optimizer.zero_grad()
-        if self.scaler is not None:
-            self._multiply_factor = 1.0 / float(self.scaler.loss_scale)
-        else:
-            self._multiply_factor = 1.0
-
-    @property
-    def supports_flat_params(self):
-        return self.wrapped_optimizer.supports_flat_params
-
-
-class MemoryEfficientFP16Optimizer(
-    _MemoryEfficientFP16OptimizerMixin, optim.FairseqOptimizer
-):
-    """
-    Wrap an *optimizer* to support FP16 (mixed precision) training.
-
-    Compared to :class:`fairseq.optim.FP16Optimizer`, this version does not
-    maintain an FP32 copy of the model. We instead expect the optimizer to
-    convert the gradients to FP32 internally and sync the results back to the
-    FP16 model params. This significantly reduces memory usage but slightly
-    increases the time spent in the optimizer.
-
-    Since this wrapper depends on specific functionality in the wrapped
-    optimizer (i.e., on-the-fly conversion of grads to FP32), only certain
-    optimizers can be wrapped. This is determined by the
-    *supports_memory_efficient_fp16* property.
-    """
-
-    def __init__(
-        self, cfg: DictConfig, params, optimizer, allow_unsupported=False, **kwargs
-    ):
-        if not allow_unsupported and not optimizer.supports_memory_efficient_fp16:
-            raise ValueError(
-                "Unsupported optimizer: {}".format(optimizer.__class__.__name__)
-            )
-
-        super().__init__(getattr(cfg, "optimizer", None))
-        self.wrapped_optimizer = optimizer
-
-        if getattr(cfg.common, "fp16_scale_window", None) is None:
-            if len(cfg.optimization.update_freq) > 1:
-                raise ValueError(
-                    "--fp16-scale-window must be given explicitly when using a "
-                    "custom --update-freq schedule"
-                )
-            data_parallel_size = int(
-                cfg.distributed_training.distributed_world_size
-                / cfg.common.model_parallel_size
-            )
-            scale_window = int(
-                2 ** 14 / data_parallel_size / cfg.optimization.update_freq[0]
-            )
-        else:
-            scale_window = cfg.common.fp16_scale_window
-
-        if not getattr(cfg.common, "bf16", False):
-            self.scaler = DynamicLossScaler(
-                init_scale=cfg.common.fp16_init_scale,
-                scale_window=scale_window,
-                tolerance=cfg.common.fp16_scale_tolerance,
-                threshold=cfg.common.threshold_loss_scale,
-                min_loss_scale=cfg.common.min_loss_scale,
-            )
-        else:
-            # disable loss scaling for bfloat16
-            self.scaler = None
-
-    @classmethod
-    def build_optimizer(cls, cfg: DictConfig, params, **kwargs):
-        """
-        Args:
-            args (argparse.Namespace): fairseq args
-            params (iterable): iterable of parameters to optimize
-        """
-        fp16_optimizer = optim.build_optimizer(cfg.optimizer, params)
-        return cls(cfg, params, fp16_optimizer, **kwargs)
-
-    @property
-    def optimizer(self):
-        return self.wrapped_optimizer.optimizer
-
-    @optimizer.setter
-    def optimizer(self, optimizer):
-        self.wrapped_optimizer.optimizer = optimizer
-
-    @property
-    def optimizer_config(self):
-        return self.wrapped_optimizer.optimizer_config
-
-    @property
-    def lr_scheduler(self):
-        return getattr(self.wrapped_optimizer, "lr_scheduler", None)
-
-    def get_lr(self):
-        return self.wrapped_optimizer.get_lr()
-
-    def set_lr(self, lr):
-        self.wrapped_optimizer.set_lr(lr)
-
-    def all_reduce_grads(self, module):
-        self.wrapped_optimizer.all_reduce_grads(module)
diff --git a/spaces/OFA-Sys/OFA-Image_Caption/utils/checkpoint_utils.py b/spaces/OFA-Sys/OFA-Image_Caption/utils/checkpoint_utils.py
deleted file mode 100644
index 8fed4bc2a214833ab1153d5bc3ff6756db25048b..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Image_Caption/utils/checkpoint_utils.py
+++ /dev/null
@@ -1,875 +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 ast
-import collections
-import contextlib
-import logging
-import numpy as np
-import os
-import re
-import time
-import traceback
-import math
-from collections import OrderedDict
-from typing import Any, Dict, Optional, Union
-
-import torch
-from fairseq.dataclass.configs import CheckpointConfig
-from fairseq.dataclass.utils import (
-    convert_namespace_to_omegaconf,
-    overwrite_args_by_name,
-)
-from fairseq.distributed.fully_sharded_data_parallel import FSDP, has_FSDP
-from fairseq.file_io import PathManager
-from fairseq.models import FairseqDecoder, FairseqEncoder
-from omegaconf import DictConfig, open_dict, OmegaConf
-
-from data import data_utils
-
-logger = logging.getLogger(__name__)
-
-
-def save_checkpoint(cfg: CheckpointConfig, trainer, epoch_itr, val_loss):
-    from fairseq import meters
-
-    # only one worker should attempt to create the required dir
-    if trainer.data_parallel_rank == 0:
-        os.makedirs(cfg.save_dir, exist_ok=True)
-
-    prev_best = getattr(save_checkpoint, "best", val_loss)
-    if val_loss is not None:
-        best_function = max if cfg.maximize_best_checkpoint_metric else min
-        save_checkpoint.best = best_function(val_loss, prev_best)
-
-    if cfg.no_save:
-        return
-
-    trainer.consolidate_optimizer()  # TODO(SS): do we need this if no_save_optimizer_state
-
-    if not trainer.should_save_checkpoint_on_current_rank:
-        if trainer.always_call_state_dict_during_save_checkpoint:
-            trainer.state_dict()
-        return
-
-    write_timer = meters.StopwatchMeter()
-    write_timer.start()
-
-    epoch = epoch_itr.epoch
-    end_of_epoch = epoch_itr.end_of_epoch()
-    updates = trainer.get_num_updates()
-
-    logger.info(f"Preparing to save checkpoint for epoch {epoch} @ {updates} updates")
-
-    def is_better(a, b):
-        return a >= b if cfg.maximize_best_checkpoint_metric else a <= b
-
-    suffix = trainer.checkpoint_suffix
-    checkpoint_conds = collections.OrderedDict()
-    checkpoint_conds["checkpoint{}{}.pt".format(epoch, suffix)] = (
-        end_of_epoch and not cfg.no_epoch_checkpoints and epoch % cfg.save_interval == 0
-    )
-    checkpoint_conds["checkpoint_{}_{}{}.pt".format(epoch, updates, suffix)] = (
-        not end_of_epoch
-        and cfg.save_interval_updates > 0
-        and updates % cfg.save_interval_updates == 0
-    )
-    checkpoint_conds["checkpoint_best{}.pt".format(suffix)] = val_loss is not None and (
-        not hasattr(save_checkpoint, "best")
-        or is_better(val_loss, save_checkpoint.best)
-    )
-    if val_loss is not None and cfg.keep_best_checkpoints > 0:
-        worst_best = getattr(save_checkpoint, "best", None)
-        chkpts = checkpoint_paths(
-            cfg.save_dir,
-            pattern=r"checkpoint\.best_{}_(\d+\.?\d*){}\.pt".format(
-                cfg.best_checkpoint_metric, suffix
-            ),
-        )
-        if len(chkpts) > 0:
-            p = chkpts[-1] if cfg.maximize_best_checkpoint_metric else chkpts[0]
-            worst_best = float(p.rsplit("_")[-1].replace("{}.pt".format(suffix), ""))
-        # add random digits to resolve ties
-        with data_utils.numpy_seed(epoch, updates, val_loss):
-            rand_sfx = np.random.randint(0, cfg.keep_best_checkpoints)
-
-        checkpoint_conds[
-            "checkpoint.best_{}_{:.3f}{}{}.pt".format(
-                cfg.best_checkpoint_metric,
-                val_loss,
-                rand_sfx,
-                suffix
-            )
-        ] = worst_best is None or is_better(val_loss, worst_best)
-    checkpoint_conds[
-        "checkpoint_last{}.pt".format(suffix)
-    ] = not cfg.no_last_checkpoints
-
-    extra_state = {"train_iterator": epoch_itr.state_dict(), "val_loss": val_loss}
-    if hasattr(save_checkpoint, "best"):
-        extra_state.update({"best": save_checkpoint.best})
-
-    checkpoints = [
-        os.path.join(cfg.save_dir, fn) for fn, cond in checkpoint_conds.items() if cond
-    ]
-    if len(checkpoints) > 0:
-        trainer.save_checkpoint(checkpoints[0], extra_state)
-        for cp in checkpoints[1:]:
-            if cfg.write_checkpoints_asynchronously:
-                # TODO[ioPath]: Need to implement a delayed asynchronous
-                # file copying/moving feature.
-                logger.warning(
-                    f"ioPath is not copying {checkpoints[0]} to {cp} "
-                    "since async write mode is on."
-                )
-            else:
-                assert PathManager.copy(
-                    checkpoints[0], cp, overwrite=True
-                ), f"Failed to copy {checkpoints[0]} to {cp}"
-
-        write_timer.stop()
-        logger.info(
-            "Saved checkpoint {} (epoch {} @ {} updates, score {}) (writing took {} seconds)".format(
-                checkpoints[0], epoch, updates, val_loss, write_timer.sum
-            )
-        )
-
-    if not end_of_epoch and cfg.keep_interval_updates > 0:
-        # remove old checkpoints; checkpoints are sorted in descending order
-        if cfg.keep_interval_updates_pattern == -1:
-            checkpoints = checkpoint_paths(
-                cfg.save_dir, pattern=r"checkpoint_\d+_(\d+){}\.pt".format(suffix)
-            )
-        else:
-            checkpoints = checkpoint_paths(
-                cfg.save_dir,
-                pattern=r"checkpoint_\d+_(\d+){}\.pt".format(suffix),
-                keep_match=True,
-            )
-            checkpoints = [
-                x[0]
-                for x in checkpoints
-                if x[1] % cfg.keep_interval_updates_pattern != 0
-            ]
-
-        for old_chk in checkpoints[cfg.keep_interval_updates :]:
-            if os.path.lexists(old_chk):
-                os.remove(old_chk)
-            elif PathManager.exists(old_chk):
-                PathManager.rm(old_chk)
-
-    if cfg.keep_last_epochs > 0:
-        # remove old epoch checkpoints; checkpoints are sorted in descending order
-        checkpoints = checkpoint_paths(
-            cfg.save_dir, pattern=r"checkpoint(\d+){}\.pt".format(suffix)
-        )
-        for old_chk in checkpoints[cfg.keep_last_epochs :]:
-            if os.path.lexists(old_chk):
-                os.remove(old_chk)
-            elif PathManager.exists(old_chk):
-                PathManager.rm(old_chk)
-
-    if cfg.keep_best_checkpoints > 0:
-        # only keep the best N checkpoints according to validation metric
-        checkpoints = checkpoint_paths(
-            cfg.save_dir,
-            pattern=r"checkpoint\.best_{}_(\d+\.?\d*){}\.pt".format(
-                cfg.best_checkpoint_metric, suffix
-            ),
-        )
-        if not cfg.maximize_best_checkpoint_metric:
-            checkpoints = checkpoints[::-1]
-        for old_chk in checkpoints[cfg.keep_best_checkpoints :]:
-            if os.path.lexists(old_chk):
-                os.remove(old_chk)
-            elif PathManager.exists(old_chk):
-                PathManager.rm(old_chk)
-
-
-def load_checkpoint(cfg: CheckpointConfig, trainer, **passthrough_args):
-    """
-    Load a checkpoint and restore the training iterator.
-
-    *passthrough_args* will be passed through to
-    ``trainer.get_train_iterator``.
-    """
-
-    reset_optimizer = cfg.reset_optimizer
-    reset_lr_scheduler = cfg.reset_lr_scheduler
-    optimizer_overrides = ast.literal_eval(cfg.optimizer_overrides)
-    reset_meters = cfg.reset_meters
-    reset_dataloader = cfg.reset_dataloader
-
-    if cfg.finetune_from_model is not None and (
-        reset_optimizer or reset_lr_scheduler or reset_meters or reset_dataloader
-    ):
-        raise ValueError(
-            "--finetune-from-model can not be set together with either --reset-optimizer"
-            " or reset_lr_scheduler or reset_meters or reset_dataloader"
-        )
-
-    suffix = trainer.checkpoint_suffix
-    if (
-        cfg.restore_file == "checkpoint_last.pt"
-    ):  # default value of restore_file is 'checkpoint_last.pt'
-        checkpoint_path = os.path.join(
-            cfg.save_dir, "checkpoint_last{}.pt".format(suffix)
-        )
-        first_launch = not PathManager.exists(checkpoint_path)
-        if cfg.finetune_from_model is not None and first_launch:
-            # if there is no last checkpoint to restore, start the finetune from pretrained model
-            # else just use usual logic to load checkpoint, e.g. restart from last checkpoint and etc.
-            if PathManager.exists(cfg.finetune_from_model):
-                checkpoint_path = cfg.finetune_from_model
-                reset_optimizer = True
-                reset_lr_scheduler = True
-                reset_meters = True
-                reset_dataloader = True
-                logger.info(
-                    f"loading pretrained model from {checkpoint_path}: "
-                    "optimizer, lr scheduler, meters, dataloader will be reset"
-                )
-            else:
-                raise ValueError(
-                    f"--funetune-from-model {cfg.finetune_from_model} does not exist"
-                )
-    elif suffix is not None:
-        checkpoint_path = cfg.restore_file.replace(".pt", suffix + ".pt")
-    else:
-        checkpoint_path = cfg.restore_file
-
-    if cfg.restore_file != "checkpoint_last.pt" and cfg.finetune_from_model:
-        raise ValueError(
-            "--finetune-from-model and --restore-file (non-default value) "
-            "can not be specified together: " + str(cfg)
-        )
-
-    extra_state = trainer.load_checkpoint(
-        checkpoint_path,
-        reset_optimizer,
-        reset_lr_scheduler,
-        optimizer_overrides,
-        reset_meters=reset_meters,
-    )
-
-    if (
-        extra_state is not None
-        and "best" in extra_state
-        and not reset_optimizer
-        and not reset_meters
-    ):
-        save_checkpoint.best = extra_state["best"]
-
-    if extra_state is not None and not reset_dataloader:
-        # restore iterator from checkpoint
-        itr_state = extra_state["train_iterator"]
-        epoch_itr = trainer.get_train_iterator(
-            epoch=itr_state["epoch"], load_dataset=True, **passthrough_args
-        )
-        epoch_itr.load_state_dict(itr_state)
-        _n = itr_state['iterations_in_epoch']
-        offset = sum(len(_) for _ in epoch_itr.batch_sampler[:_n])
-        epoch_itr.dataset.dataset._seek(offset=offset)
-        true_num = int(math.ceil(len(epoch_itr.dataset) / 8)) * 8
-        another_offset = ((epoch_itr.epoch - 1) * true_num + offset) // 8
-        if hasattr(epoch_itr.dataset, 'pure_text_dataset'):
-            text_offset = (2 * another_offset) % len(epoch_itr.dataset.pure_text_dataset)
-            epoch_itr.dataset.pure_text_dataset._seek(offset=text_offset)
-        if hasattr(epoch_itr.dataset, 'pure_image_dataset'):
-            image_offset = another_offset % len(epoch_itr.dataset.pure_image_dataset)
-            epoch_itr.dataset.pure_image_dataset._seek(offset=image_offset)
-        if hasattr(epoch_itr.dataset, 'detection_dataset'):
-            detection_offset = another_offset % len(epoch_itr.dataset.detection_dataset)
-            epoch_itr.dataset.detection_dataset._seek(offset=detection_offset)
-    else:
-        epoch_itr = trainer.get_train_iterator(
-            epoch=1, load_dataset=True, **passthrough_args
-        )
-
-    trainer.lr_step(epoch_itr.epoch)
-
-    return extra_state, epoch_itr
-
-
-def load_checkpoint_to_cpu(path, arg_overrides=None, load_on_all_ranks=False):
-    """Loads a checkpoint to CPU (with upgrading for backward compatibility).
-
-    If doing single-GPU training or if the checkpoint is only being loaded by at
-    most one process on each node (current default behavior is for only rank 0
-    to read the checkpoint from disk), load_on_all_ranks should be False to
-    avoid errors from torch.distributed not having been initialized or
-    torch.distributed.barrier() hanging.
-
-    If all processes on each node may be loading the checkpoint
-    simultaneously, load_on_all_ranks should be set to True to avoid I/O
-    conflicts.
-
-    There's currently no support for > 1 but < all processes loading the
-    checkpoint on each node.
-    """
-    local_path = PathManager.get_local_path(path)
-    # The locally cached file returned by get_local_path() may be stale for
-    # remote files that are periodically updated/overwritten (ex:
-    # checkpoint_last.pt) - so we remove the local copy, sync across processes
-    # (if needed), and then download a fresh copy.
-    if local_path != path and PathManager.path_requires_pathmanager(path):
-        try:
-            os.remove(local_path)
-        except FileNotFoundError:
-            # With potentially multiple processes removing the same file, the
-            # file being missing is benign (missing_ok isn't available until
-            # Python 3.8).
-            pass
-        if load_on_all_ranks:
-            torch.distributed.barrier()
-        local_path = PathManager.get_local_path(path)
-
-    with open(local_path, "rb") as f:
-        state = torch.load(f, map_location=torch.device("cpu"))
-
-    if "args" in state and state["args"] is not None and arg_overrides is not None:
-        args = state["args"]
-        for arg_name, arg_val in arg_overrides.items():
-            setattr(args, arg_name, arg_val)
-
-    if "cfg" in state and state["cfg"] is not None:
-
-        # hack to be able to set Namespace in dict config. this should be removed when we update to newer
-        # omegaconf version that supports object flags, or when we migrate all existing models
-        from omegaconf import _utils
-
-        old_primitive = _utils.is_primitive_type
-        _utils.is_primitive_type = lambda _: True
-
-        state["cfg"] = OmegaConf.create(state["cfg"])
-
-        _utils.is_primitive_type = old_primitive
-        OmegaConf.set_struct(state["cfg"], True)
-
-        if arg_overrides is not None:
-            overwrite_args_by_name(state["cfg"], arg_overrides)
-
-    state = _upgrade_state_dict(state)
-    return state
-
-
-def load_model_ensemble(
-    filenames,
-    arg_overrides: Optional[Dict[str, Any]] = None,
-    task=None,
-    strict=True,
-    suffix="",
-    num_shards=1,
-    state=None,
-):
-    """Loads an ensemble of models.
-
-    Args:
-        filenames (List[str]): checkpoint files to load
-        arg_overrides (Dict[str,Any], optional): override model args that
-            were used during model training
-        task (fairseq.tasks.FairseqTask, optional): task to use for loading
-    """
-    assert not (
-        strict and num_shards > 1
-    ), "Cannot load state dict with strict=True and checkpoint shards > 1"
-    ensemble, args, _task = load_model_ensemble_and_task(
-        filenames,
-        arg_overrides,
-        task,
-        strict,
-        suffix,
-        num_shards,
-        state,
-    )
-    return ensemble, args
-
-
-def get_maybe_sharded_checkpoint_filename(
-    filename: str, suffix: str, shard_idx: int, num_shards: int
-) -> str:
-    orig_filename = filename
-    filename = filename.replace(".pt", suffix + ".pt")
-    fsdp_filename = filename[:-3] + f"-shard{shard_idx}.pt"
-    model_parallel_filename = orig_filename[:-3] + f"_part{shard_idx}.pt"
-    if PathManager.exists(fsdp_filename):
-        return fsdp_filename
-    elif num_shards > 1:
-        return model_parallel_filename
-    else:
-        return filename
-
-
-def load_model_ensemble_and_task(
-    filenames,
-    arg_overrides: Optional[Dict[str, Any]] = None,
-    task=None,
-    strict=True,
-    suffix="",
-    num_shards=1,
-    state=None,
-):
-    assert state is None or len(filenames) == 1
-
-    from fairseq import tasks
-
-    assert not (
-        strict and num_shards > 1
-    ), "Cannot load state dict with strict=True and checkpoint shards > 1"
-    ensemble = []
-    cfg = None
-    for filename in filenames:
-        orig_filename = filename
-        model_shard_state = {"shard_weights": [], "shard_metadata": []}
-        assert num_shards > 0
-        st = time.time()
-        for shard_idx in range(num_shards):
-            filename = get_maybe_sharded_checkpoint_filename(
-                orig_filename, suffix, shard_idx, num_shards
-            )
-
-            if not PathManager.exists(filename):
-                raise IOError("Model file not found: {}".format(filename))
-            if state is None:
-                state = load_checkpoint_to_cpu(filename, arg_overrides)
-            if "args" in state and state["args"] is not None:
-                cfg = convert_namespace_to_omegaconf(state["args"])
-            elif "cfg" in state and state["cfg"] is not None:
-                cfg = state["cfg"]
-            else:
-                raise RuntimeError(
-                    f"Neither args nor cfg exist in state keys = {state.keys()}"
-                )
-
-            if task is None:
-                task = tasks.setup_task(cfg.task)
-
-            if "task_state" in state:
-                task.load_state_dict(state["task_state"])
-
-            if "fsdp_metadata" in state and num_shards > 1:
-                model_shard_state["shard_weights"].append(state["model"])
-                model_shard_state["shard_metadata"].append(state["fsdp_metadata"])
-                # check FSDP import before the code goes too far
-                if not has_FSDP:
-                    raise ImportError(
-                        "Cannot find FullyShardedDataParallel. "
-                        "Please install fairscale with: pip install fairscale"
-                    )
-                if shard_idx == num_shards - 1:
-                    consolidated_model_state = FSDP.consolidate_shard_weights(
-                        shard_weights=model_shard_state["shard_weights"],
-                        shard_metadata=model_shard_state["shard_metadata"],
-                    )
-                    model = task.build_model(cfg.model)
-                    model.load_state_dict(
-                        consolidated_model_state, strict=strict, model_cfg=cfg.model
-                    )
-            else:
-                # model parallel checkpoint or unsharded checkpoint
-                model = task.build_model(cfg.model)
-                model.load_state_dict(
-                    state["model"], strict=strict, model_cfg=cfg.model
-                )
-
-            # reset state so it gets loaded for the next model in ensemble
-            state = None
-            if shard_idx % 10 == 0 and shard_idx > 0:
-                elapsed = time.time() - st
-                logger.info(
-                    f"Loaded {shard_idx} shards in {elapsed:.2f}s, {elapsed / (shard_idx+1):.2f}s/shard"
-                )
-
-        # build model for ensemble
-        ensemble.append(model)
-    return ensemble, cfg, task
-
-
-def checkpoint_paths(path, pattern=r"checkpoint(\d+)\.pt", keep_match=False):
-    """Retrieves all checkpoints found in `path` directory.
-
-    Checkpoints are identified by matching filename to the specified pattern. If
-    the pattern contains groups, the result will be sorted by the first group in
-    descending order.
-    """
-    pt_regexp = re.compile(pattern)
-    files = PathManager.ls(path)
-
-    entries = []
-    for i, f in enumerate(files):
-        m = pt_regexp.fullmatch(f)
-        if m is not None:
-            idx = float(m.group(1)) if len(m.groups()) > 0 else i
-            entries.append((idx, m.group(0)))
-    if keep_match:
-        return [(os.path.join(path, x[1]), x[0]) for x in sorted(entries, reverse=True)]
-    else:
-        return [os.path.join(path, x[1]) for x in sorted(entries, reverse=True)]
-
-
-def torch_persistent_save(obj, filename, async_write: bool = False):
-    if async_write:
-        with PathManager.opena(filename, "wb") as f:
-            _torch_persistent_save(obj, f)
-    else:
-        with PathManager.open(filename, "wb") as f:
-            _torch_persistent_save(obj, f)
-        # if PathManager.supports_rename(filename):
-        #     # do atomic save
-        #     with PathManager.open(filename + ".tmp", "wb") as f:
-        #         _torch_persistent_save(obj, f)
-        #     PathManager.rename(filename + ".tmp", filename)
-        # else:
-        #     # fallback to non-atomic save
-        #     with PathManager.open(filename, "wb") as f:
-        #         _torch_persistent_save(obj, f)
-
-
-def _torch_persistent_save(obj, f):
-    if isinstance(f, str):
-        with PathManager.open(f, "wb") as h:
-            torch_persistent_save(obj, h)
-        return
-    for i in range(3):
-        try:
-            return torch.save(obj, f)
-        except Exception:
-            if i == 2:
-                logger.error(traceback.format_exc())
-                raise
-
-
-def _upgrade_state_dict(state):
-    """Helper for upgrading old model checkpoints."""
-
-    # add optimizer_history
-    if "optimizer_history" not in state:
-        state["optimizer_history"] = [
-            {"criterion_name": "CrossEntropyCriterion", "best_loss": state["best_loss"]}
-        ]
-        state["last_optimizer_state"] = state["optimizer"]
-        del state["optimizer"]
-        del state["best_loss"]
-    # move extra_state into sub-dictionary
-    if "epoch" in state and "extra_state" not in state:
-        state["extra_state"] = {
-            "epoch": state["epoch"],
-            "batch_offset": state["batch_offset"],
-            "val_loss": state["val_loss"],
-        }
-        del state["epoch"]
-        del state["batch_offset"]
-        del state["val_loss"]
-    # reduce optimizer history's memory usage (only keep the last state)
-    if "optimizer" in state["optimizer_history"][-1]:
-        state["last_optimizer_state"] = state["optimizer_history"][-1]["optimizer"]
-        for optim_hist in state["optimizer_history"]:
-            del optim_hist["optimizer"]
-    # record the optimizer class name
-    if "optimizer_name" not in state["optimizer_history"][-1]:
-        state["optimizer_history"][-1]["optimizer_name"] = "FairseqNAG"
-    # move best_loss into lr_scheduler_state
-    if "lr_scheduler_state" not in state["optimizer_history"][-1]:
-        state["optimizer_history"][-1]["lr_scheduler_state"] = {
-            "best": state["optimizer_history"][-1]["best_loss"]
-        }
-        del state["optimizer_history"][-1]["best_loss"]
-    # keep track of number of updates
-    if "num_updates" not in state["optimizer_history"][-1]:
-        state["optimizer_history"][-1]["num_updates"] = 0
-    # old model checkpoints may not have separate source/target positions
-    if (
-        "args" in state
-        and hasattr(state["args"], "max_positions")
-        and not hasattr(state["args"], "max_source_positions")
-    ):
-        state["args"].max_source_positions = state["args"].max_positions
-        state["args"].max_target_positions = state["args"].max_positions
-    # use stateful training data iterator
-    if "train_iterator" not in state["extra_state"]:
-        state["extra_state"]["train_iterator"] = {
-            "epoch": state["extra_state"]["epoch"],
-            "iterations_in_epoch": state["extra_state"].get("batch_offset", 0),
-        }
-
-    # backward compatibility, cfg updates
-    if "args" in state and state["args"] is not None:
-        # default to translation task
-        if not hasattr(state["args"], "task"):
-            state["args"].task = "translation"
-        # --raw-text and --lazy-load are deprecated
-        if getattr(state["args"], "raw_text", False):
-            state["args"].dataset_impl = "raw"
-        elif getattr(state["args"], "lazy_load", False):
-            state["args"].dataset_impl = "lazy"
-        # epochs start at 1
-        if state["extra_state"]["train_iterator"] is not None:
-            state["extra_state"]["train_iterator"]["epoch"] = max(
-                state["extra_state"]["train_iterator"].get("epoch", 1), 1
-            )
-        # --remove-bpe ==> --postprocess
-        if hasattr(state["args"], "remove_bpe"):
-            state["args"].post_process = state["args"].remove_bpe
-        # --min-lr ==> --stop-min-lr
-        if hasattr(state["args"], "min_lr"):
-            state["args"].stop_min_lr = state["args"].min_lr
-            del state["args"].min_lr
-        # binary_cross_entropy / kd_binary_cross_entropy => wav2vec criterion
-        if (
-            hasattr(state["args"], "criterion")
-            and state["args"].criterion in [
-                "binary_cross_entropy",
-                "kd_binary_cross_entropy",
-            ]
-        ):
-            state["args"].criterion = "wav2vec"
-        # remove log_keys if it's None (criteria will supply a default value of [])
-        if hasattr(state["args"], "log_keys") and state["args"].log_keys is None:
-            delattr(state["args"], "log_keys")
-        # speech_pretraining => audio pretraining
-        if (
-            hasattr(state["args"], "task")
-            and state["args"].task == "speech_pretraining"
-        ):
-            state["args"].task = "audio_pretraining"
-        # audio_cpc => wav2vec
-        if hasattr(state["args"], "arch") and state["args"].arch == "audio_cpc":
-            state["args"].arch = "wav2vec"
-        # convert legacy float learning rate to List[float]
-        if hasattr(state["args"], "lr") and isinstance(state["args"].lr, float):
-            state["args"].lr = [state["args"].lr]
-        # convert task data arg to a string instead of List[string]
-        if (
-            hasattr(state["args"], "data")
-            and isinstance(state["args"].data, list)
-            and len(state["args"].data) > 0
-        ):
-            state["args"].data = state["args"].data[0]
-        # remove keys in state["args"] related to teacher-student learning
-        for key in [
-            "static_teachers",
-            "static_teacher_weights",
-            "dynamic_teachers",
-            "dynamic_teacher_weights",
-        ]:
-            if key in state["args"]:
-                delattr(state["args"], key)
-
-        state["cfg"] = convert_namespace_to_omegaconf(state["args"])
-
-    if "cfg" in state and state["cfg"] is not None:
-        cfg = state["cfg"]
-        with open_dict(cfg):
-            # any upgrades for Hydra-based configs
-            if (
-                "task" in cfg
-                and "eval_wer_config" in cfg.task
-                and isinstance(cfg.task.eval_wer_config.print_alignment, bool)
-            ):
-                cfg.task.eval_wer_config.print_alignment = "hard"
-            if "generation" in cfg and isinstance(cfg.generation.print_alignment, bool):
-                cfg.generation.print_alignment = "hard" if cfg.generation.print_alignment else None
-            if (
-                "model" in cfg
-                and "w2v_args" in cfg.model
-                and cfg.model.w2v_args is not None
-                and (
-                    hasattr(cfg.model.w2v_args, "task") or "task" in cfg.model.w2v_args
-                )
-                and hasattr(cfg.model.w2v_args.task, "eval_wer_config")
-                and cfg.model.w2v_args.task.eval_wer_config is not None
-                and isinstance(
-                    cfg.model.w2v_args.task.eval_wer_config.print_alignment, bool
-                )
-            ):
-                cfg.model.w2v_args.task.eval_wer_config.print_alignment = "hard"
-
-    return state
-
-
-def prune_state_dict(state_dict, model_cfg: Optional[DictConfig]):
-    """Prune the given state_dict if desired for LayerDrop
-    (https://arxiv.org/abs/1909.11556).
-
-    Training with LayerDrop allows models to be robust to pruning at inference
-    time. This function prunes state_dict to allow smaller models to be loaded
-    from a larger model and re-maps the existing state_dict for this to occur.
-
-    It's called by functions that load models from checkpoints and does not
-    need to be called directly.
-    """
-    arch = None
-    if model_cfg is not None:
-        arch = (
-            model_cfg._name
-            if isinstance(model_cfg, DictConfig)
-            else getattr(model_cfg, "arch", None)
-        )
-
-    if not model_cfg or arch is None or arch == "ptt_transformer":
-        # args should not be none, but don't crash if it is.
-        return state_dict
-
-    encoder_layers_to_keep = getattr(model_cfg, "encoder_layers_to_keep", None)
-    decoder_layers_to_keep = getattr(model_cfg, "decoder_layers_to_keep", None)
-
-    if not encoder_layers_to_keep and not decoder_layers_to_keep:
-        return state_dict
-
-    # apply pruning
-    logger.info(
-        "Pruning model to specified layer configuration - this works best if the model was trained with LayerDrop"
-    )
-
-    def create_pruning_pass(layers_to_keep, layer_name):
-        keep_layers = sorted(
-            int(layer_string) for layer_string in layers_to_keep.split(",")
-        )
-        mapping_dict = {}
-        for i in range(len(keep_layers)):
-            mapping_dict[str(keep_layers[i])] = str(i)
-
-        regex = re.compile(r"^{layer}.*\.layers\.(\d+)".format(layer=layer_name))
-        return {"substitution_regex": regex, "mapping_dict": mapping_dict}
-
-    pruning_passes = []
-    if encoder_layers_to_keep:
-        pruning_passes.append(create_pruning_pass(encoder_layers_to_keep, "encoder"))
-    if decoder_layers_to_keep:
-        pruning_passes.append(create_pruning_pass(decoder_layers_to_keep, "decoder"))
-
-    new_state_dict = {}
-    for layer_name in state_dict.keys():
-        match = re.search(r"\.layers\.(\d+)\.", layer_name)
-        # if layer has no number in it, it is a supporting layer, such as an
-        # embedding
-        if not match:
-            new_state_dict[layer_name] = state_dict[layer_name]
-            continue
-
-        # otherwise, layer should be pruned.
-        original_layer_number = match.group(1)
-        # figure out which mapping dict to replace from
-        for pruning_pass in pruning_passes:
-            if original_layer_number in pruning_pass["mapping_dict"] and pruning_pass[
-                "substitution_regex"
-            ].search(layer_name):
-                new_layer_number = pruning_pass["mapping_dict"][original_layer_number]
-                substitution_match = pruning_pass["substitution_regex"].search(
-                    layer_name
-                )
-                new_state_key = (
-                    layer_name[: substitution_match.start(1)]
-                    + new_layer_number
-                    + layer_name[substitution_match.end(1) :]
-                )
-                new_state_dict[new_state_key] = state_dict[layer_name]
-
-    # Since layers are now pruned, *_layers_to_keep are no longer needed.
-    # This is more of "It would make it work fix" rather than a proper fix.
-    if isinstance(model_cfg, DictConfig):
-        context = open_dict(model_cfg)
-    else:
-        context = contextlib.ExitStack()
-    with context:
-        if hasattr(model_cfg, "encoder_layers_to_keep"):
-            model_cfg.encoder_layers_to_keep = None
-        if hasattr(model_cfg, "decoder_layers_to_keep"):
-            model_cfg.decoder_layers_to_keep = None
-
-    return new_state_dict
-
-
-def load_pretrained_component_from_model(
-    component: Union[FairseqEncoder, FairseqDecoder], checkpoint: str
-):
-    """
-    Load a pretrained FairseqEncoder or FairseqDecoder from checkpoint into the
-    provided `component` object. If state_dict fails to load, there may be a
-    mismatch in the architecture of the corresponding `component` found in the
-    `checkpoint` file.
-    """
-    if not PathManager.exists(checkpoint):
-        raise IOError("Model file not found: {}".format(checkpoint))
-    state = load_checkpoint_to_cpu(checkpoint)
-    if isinstance(component, FairseqEncoder):
-        component_type = "encoder"
-    elif isinstance(component, FairseqDecoder):
-        component_type = "decoder"
-    else:
-        raise ValueError(
-            "component to load must be either a FairseqEncoder or "
-            "FairseqDecoder. Loading other component types are not supported."
-        )
-    component_state_dict = OrderedDict()
-    for key in state["model"].keys():
-        if key.startswith(component_type):
-            # encoder.input_layers.0.0.weight --> input_layers.0.0.weight
-            component_subkey = key[len(component_type) + 1 :]
-            component_state_dict[component_subkey] = state["model"][key]
-    component.load_state_dict(component_state_dict, strict=True)
-    return component
-
-
-def verify_checkpoint_directory(save_dir: str) -> None:
-    if not os.path.exists(save_dir):
-        os.makedirs(save_dir, exist_ok=True)
-    temp_file_path = os.path.join(save_dir, "dummy")
-    try:
-        with open(temp_file_path, "w"):
-            pass
-    except OSError as e:
-        logger.warning(
-            "Unable to access checkpoint save directory: {}".format(save_dir)
-        )
-        raise e
-    else:
-        os.remove(temp_file_path)
-
-
-def load_ema_from_checkpoint(fpath):
-    """Loads exponential moving averaged (EMA) checkpoint from input and
-    returns a model with ema weights.
-
-    Args:
-      fpath: A string path of checkpoint to load from.
-
-    Returns:
-      A dict of string keys mapping to various values. The 'model' key
-      from the returned dict should correspond to an OrderedDict mapping
-      string parameter names to torch Tensors.
-    """
-    params_dict = collections.OrderedDict()
-    new_state = None
-
-    with PathManager.open(fpath, 'rb') as f:
-        new_state = torch.load(
-            f,
-            map_location=(
-                lambda s, _: torch.serialization.default_restore_location(s, 'cpu')
-            ),
-        )
-
-        # EMA model is stored in a separate "extra state"
-        model_params = new_state['extra_state']['ema']
-
-        for key in list(model_params.keys()):
-            p = model_params[key]
-            if isinstance(p, torch.HalfTensor):
-                p = p.float()
-            if key not in params_dict:
-                params_dict[key] = p.clone()
-                # NOTE: clone() is needed in case of p is a shared parameter
-            else:
-                raise ValueError("Key {} is repeated in EMA model params.".format(key))
-
-        if len(params_dict) == 0:
-            raise ValueError(
-                f"Input checkpoint path '{fpath}' does not contain "
-                "ema model weights, is this model trained with EMA?"
-            )
-
-    new_state['model'] = params_dict
-    return new_state
diff --git a/spaces/OFA-Sys/OFA-Visual_Grounding/fairseq/examples/backtranslation/sacrebleu.sh b/spaces/OFA-Sys/OFA-Visual_Grounding/fairseq/examples/backtranslation/sacrebleu.sh
deleted file mode 100644
index a70da23f48e2699297799611412783d4560dc45a..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Visual_Grounding/fairseq/examples/backtranslation/sacrebleu.sh
+++ /dev/null
@@ -1,37 +0,0 @@
-#!/bin/bash
-
-if [ $# -ne 5 ]; then
-    echo "usage: $0 [dataset=wmt14/full] [langpair=en-de] [databin] [bpecode] [model]"
-    exit
-fi
-
-
-DATASET=$1
-LANGPAIR=$2
-DATABIN=$3
-BPECODE=$4
-MODEL=$5
-
-SRCLANG=$(echo $LANGPAIR | cut -d '-' -f 1)
-TGTLANG=$(echo $LANGPAIR | cut -d '-' -f 2)
-
-
-BPEROOT=examples/backtranslation/subword-nmt/subword_nmt
-if [ ! -e $BPEROOT ]; then
-    BPEROOT=subword-nmt/subword_nmt
-    if [ ! -e $BPEROOT ]; then
-        echo 'Cloning Subword NMT repository (for BPE pre-processing)...'
-        git clone https://github.com/rsennrich/subword-nmt.git
-    fi
-fi
-
-
-sacrebleu -t $DATASET -l $LANGPAIR --echo src \
-| sacremoses tokenize -a -l $SRCLANG -q \
-| python $BPEROOT/apply_bpe.py -c $BPECODE \
-| fairseq-interactive $DATABIN --path $MODEL \
-    -s $SRCLANG -t $TGTLANG \
-    --beam 5 --remove-bpe --buffer-size 1024 --max-tokens 8000 \
-| grep ^H- | cut -f 3- \
-| sacremoses detokenize -l $TGTLANG -q \
-| sacrebleu -t $DATASET -l $LANGPAIR
diff --git a/spaces/OFA-Sys/OFA-vqa/fairseq/examples/language_model/README.conv.md b/spaces/OFA-Sys/OFA-vqa/fairseq/examples/language_model/README.conv.md
deleted file mode 100644
index 1ff8635906cf278208be4714e0ef805a6a6b4da1..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-vqa/fairseq/examples/language_model/README.conv.md
+++ /dev/null
@@ -1,40 +0,0 @@
-# Language Modeling with Gated Convolutional Networks (Dauphin et al., 2017)
-
-## Example usage
-
-First download and preprocess the data following the main [language modeling README](README.md).
-
-Then to train a convolutional LM using the `fconv_lm_dauphin_wikitext103`
-architecture:
-```bash
-fairseq-train --task language_modeling \
-    data-bin/wikitext-103 \
-    --save-dir checkpoints/fconv_wikitext-103 \
-    --arch fconv_lm_dauphin_wikitext103 \
-    --adaptive-softmax-cutoff 10000,20000,200000 \
-    --dropout 0.2 \
-    --criterion adaptive_loss \
-    --optimizer nag --clip-norm 0.1 --weight-decay 5e-06 \
-    --lr 1.0 --lr-scheduler reduce_lr_on_plateau --lr-shrink 0.5 \
-    --max-tokens 1024 --tokens-per-sample 1024 \
-    --ddp-backend legacy_ddp \
-    --max-epoch 35
-```
-
-And evaluate with:
-```bash
-fairseq-eval-lm data-bin/wikitext-103 --path checkpoints/fconv_wiki103/checkpoint_best.pt
-```
-
-## Citation
-
-```bibtex
-@inproceedings{dauphin2017language,
-  title={Language Modeling with Gated Convolutional Networks},
-  author={Dauphin, Yann N and Fan, Angela and Auli, Michael and Grangier, David},
-  booktitle={Proceedings of the 34th International Conference on Machine Learning-Volume 70},
-  pages={933--941},
-  year={2017},
-  organization={JMLR}
-}
-```
diff --git a/spaces/OFA-Sys/OFA-vqa/fairseq/examples/speech_recognition/kaldi/__init__.py b/spaces/OFA-Sys/OFA-vqa/fairseq/examples/speech_recognition/kaldi/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/modules/learned_positional_embedding.py b/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/modules/learned_positional_embedding.py
deleted file mode 100644
index 378d0f707183dd344dbb9288dda394b11053acf0..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/modules/learned_positional_embedding.py
+++ /dev/null
@@ -1,61 +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 typing import Dict, Optional
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from fairseq import utils
-from torch import Tensor
-
-
-class LearnedPositionalEmbedding(nn.Embedding):
-    """
-    This module learns positional embeddings up to a fixed maximum size.
-    Padding ids are ignored by either offsetting based on padding_idx
-    or by setting padding_idx to None and ensuring that the appropriate
-    position ids are passed to the forward function.
-    """
-
-    def __init__(self, num_embeddings: int, embedding_dim: int, padding_idx: int):
-        super().__init__(num_embeddings, embedding_dim, padding_idx)
-        self.onnx_trace = False
-        if self.padding_idx is not None:
-            self.max_positions = self.num_embeddings - self.padding_idx - 1
-        else:
-            self.max_positions = self.num_embeddings
-
-    def forward(
-        self,
-        input: Tensor,
-        incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None,
-        positions: Optional[Tensor] = None,
-    ):
-        """Input is expected to be of size [bsz x seqlen]."""
-        assert (positions is None) or (
-            self.padding_idx is None
-        ), "If positions is pre-computed then padding_idx should not be set."
-
-        if positions is None:
-            if incremental_state is not None:
-                # positions is the same for every token when decoding a single step
-                # Without the int() cast, it doesn't work in some cases when exporting to ONNX
-                positions = torch.zeros(
-                    (1, 1), device=input.device, dtype=input.dtype
-                ).fill_(int(self.padding_idx + input.size(1)))
-            else:
-                positions = utils.make_positions(
-                    input, self.padding_idx, onnx_trace=self.onnx_trace
-                )
-        return F.embedding(
-            positions,
-            self.weight,
-            self.padding_idx,
-            self.max_norm,
-            self.norm_type,
-            self.scale_grad_by_freq,
-            self.sparse,
-        )
diff --git a/spaces/Orami01/Cha_with_CSV_using_Llama2/app.py b/spaces/Orami01/Cha_with_CSV_using_Llama2/app.py
deleted file mode 100644
index 9cd8a144d6e1bb399e8cfc643a1910b551cae143..0000000000000000000000000000000000000000
--- a/spaces/Orami01/Cha_with_CSV_using_Llama2/app.py
+++ /dev/null
@@ -1,99 +0,0 @@
-import streamlit as st
-from streamlit_chat import message
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain.embeddings import HuggingFaceEmbeddings
-from langchain.vectorstores import FAISS
-from langchain.llms import CTransformers
-from langchain.memory import ConversationBufferMemory
-from langchain.chains import ConversationalRetrievalChain
-import sys
-import tempfile
-# Initialize the CSVLoader to load the uploaded CSV file
-from langchain.document_loaders.csv_loader import CSVLoader
-
-DB_FAISS_PATH = 'vectorstore/db_faiss'
-
-from transformers import pipeline
-
-pipe = pipeline("text-generation",model="mistralai/Mistral-7B-v0.1",model_type="llama",max_new_tokens=512,temperature=0.1 )
-
-
-
-# Display the title of the web page
-st.title("Chat with CSV using open source LLM Inference Point 🦙🦜")
-# Display a markdown message with additional information
-st.markdown("<h3 style='text-align: center; color: white;'>Built by <a href='https://github.com/AIAnytime'>AI Anytime with ❤️ </a></h3>", unsafe_allow_html=True)
-
-# Allow users to upload a CSV file
-uploaded_file = st.sidebar.file_uploader("Upload your Data", type="csv")
-
-if uploaded_file:
-    # Initialize the CSVLoader to load the uploaded CSV file
-
-    with tempfile.NamedTemporaryFile(delete=False) as tmp_file:
-        tmp_file.write(uploaded_file.getvalue())
-        tmp_file_path = tmp_file.name
-
-# Initialize the CSVLoader to load the uploaded CSV file
-    loader = CSVLoader(file_path=tmp_file_path, encoding="utf-8", csv_args={'delimiter': ','})
-    data = loader.load()
-    embeddings = HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L6-v2',model_kwargs={'device': 'cpu'})
-    db = FAISS.from_documents(data, embeddings)
-    db.save_local(DB_FAISS_PATH)
-    llm = load_llm()
-    chain = ConversationalRetrievalChain.from_llm(llm=llm, retriever=db.as_retriever())
-
-
-   
-
-    def conversational_chat(query):
-# Maintain and display the chat history
-            result = chain({"question": query, "chat_history": st.session_state['history']})
-# Maintain and display the chat history
-            st.session_state['history'].append((query, result["answer"]))
-            return result["answer"]
-    
-# Maintain and display the chat history
-    if 'history' not in st.session_state:
-# Maintain and display the chat history
-        st.session_state['history'] = []
-
-# Maintain and display the chat history
-    if 'generated' not in st.session_state:
-# Maintain and display the chat history
-        st.session_state['generated'] = ["Hello ! Ask me anything about " + uploaded_file.name + " 🤗"]
-
-# Maintain and display the chat history
-    if 'past' not in st.session_state:
-# Maintain and display the chat history
-        st.session_state['past'] = ["Hey ! 👋"]
-        
-    #container for the chat history
-    response_container = st.container()
-    #container for the user's text input
-    container = st.container()
-
-    with container:
-        with st.form(key='my_form', clear_on_submit=True):
-            
-            user_input = st.text_input("Query:", placeholder="Talk to your csv data here (:", key='input')
-            submit_button = st.form_submit_button(label='Send')
-            
-        if submit_button and user_input:
-            output = conversational_chat(user_input)
-            
-# Maintain and display the chat history
-            st.session_state['past'].append(user_input)
-# Maintain and display the chat history
-            st.session_state['generated'].append(output)
-
-# Maintain and display the chat history
-    if st.session_state['generated']:
-        with response_container:
-# Maintain and display the chat history
-            for i in range(len(st.session_state['generated'])):
-# Maintain and display the chat history
-                message(st.session_state["past"][i], is_user=True, key=str(i) + '_user', avatar_style="big-smile")
-# Maintain and display the chat history
-                message(st.session_state["generated"][i], key=str(i), avatar_style="thumbs")
-            
diff --git a/spaces/PaSathees/Vehicle_Tyre_Quality_Checker/README.md b/spaces/PaSathees/Vehicle_Tyre_Quality_Checker/README.md
deleted file mode 100644
index 6b6aef57d1de902b0c7dc27586a3f77702ad48fe..0000000000000000000000000000000000000000
--- a/spaces/PaSathees/Vehicle_Tyre_Quality_Checker/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Vehicle Tyre Quality Checker
-emoji: 🌖
-colorFrom: green
-colorTo: pink
-sdk: gradio
-sdk_version: 3.50.2
-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/Pie31415/control-animation/annotator/uniformer/mmseg/ops/__init__.py b/spaces/Pie31415/control-animation/annotator/uniformer/mmseg/ops/__init__.py
deleted file mode 100644
index bec51c75b9363a9a19e9fb5c35f4e7dbd6f7751c..0000000000000000000000000000000000000000
--- a/spaces/Pie31415/control-animation/annotator/uniformer/mmseg/ops/__init__.py
+++ /dev/null
@@ -1,4 +0,0 @@
-from .encoding import Encoding
-from .wrappers import Upsample, resize
-
-__all__ = ['Upsample', 'resize', 'Encoding']
diff --git a/spaces/Q4234/a1/README.md b/spaces/Q4234/a1/README.md
deleted file mode 100644
index 643b00755e5bf49a2c25e7110d97a54bc30b64db..0000000000000000000000000000000000000000
--- a/spaces/Q4234/a1/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: A1
-emoji: 🏆
-colorFrom: gray
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.32.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/QINGCHE/TSA/README.md b/spaces/QINGCHE/TSA/README.md
deleted file mode 100644
index 3d16df83d61286e1cb29bb6557e4aea17c142e5e..0000000000000000000000000000000000000000
--- a/spaces/QINGCHE/TSA/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: TSA
-emoji: 🐨
-colorFrom: blue
-colorTo: green
-sdk: gradio
-sdk_version: 3.32.0
-app_file: app.py
-timeout: 300
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
\ No newline at end of file
diff --git a/spaces/RMXK/RVC_HFF/tools/infer/train-index.py b/spaces/RMXK/RVC_HFF/tools/infer/train-index.py
deleted file mode 100644
index 44b447ef32148c181eb4bcd9013a22a82371b82c..0000000000000000000000000000000000000000
--- a/spaces/RMXK/RVC_HFF/tools/infer/train-index.py
+++ /dev/null
@@ -1,42 +0,0 @@
-"""
-格式：直接cid为自带的index位；aid放不下了，通过字典来查，反正就5w个
-"""
-import os
-import logging
-
-logger = logging.getLogger(__name__)
-
-import faiss
-import numpy as np
-
-# ###########如果是原始特征要先写save
-inp_root = r"E:\codes\py39\dataset\mi\2-co256"
-npys = []
-for name in sorted(list(os.listdir(inp_root))):
-    phone = np.load("%s/%s" % (inp_root, name))
-    npys.append(phone)
-big_npy = np.concatenate(npys, 0)
-logger.debug(big_npy.shape)  # (6196072, 192)#fp32#4.43G
-np.save("infer/big_src_feature_mi.npy", big_npy)
-
-##################train+add
-# big_npy=np.load("/bili-coeus/jupyter/jupyterhub-liujing04/vits_ch/inference_f0/big_src_feature_mi.npy")
-logger.debug(big_npy.shape)
-index = faiss.index_factory(256, "IVF512,Flat")  # mi
-logger.info("Training...")
-index_ivf = faiss.extract_index_ivf(index)  #
-index_ivf.nprobe = 9
-index.train(big_npy)
-faiss.write_index(index, "infer/trained_IVF512_Flat_mi_baseline_src_feat.index")
-logger.info("Adding...")
-index.add(big_npy)
-faiss.write_index(index, "infer/added_IVF512_Flat_mi_baseline_src_feat.index")
-"""
-大小（都是FP32）
-big_src_feature 2.95G
-    (3098036, 256)
-big_emb         4.43G
-    (6196072, 192)
-big_emb双倍是因为求特征要repeat后再加pitch
-
-"""
diff --git a/spaces/RamAnanth1/roomGPT/app_hough.py b/spaces/RamAnanth1/roomGPT/app_hough.py
deleted file mode 100644
index 1f06d328e6b326381ac0ce5d01f747dbf971dbeb..0000000000000000000000000000000000000000
--- a/spaces/RamAnanth1/roomGPT/app_hough.py
+++ /dev/null
@@ -1,106 +0,0 @@
-# This file is adapted from https://github.com/lllyasviel/ControlNet/blob/f4748e3630d8141d7765e2bd9b1e348f47847707/gradio_hough2image.py
-# The original license file is LICENSE.ControlNet in this repo.
-import gradio as gr
-
-
-def create_demo(process, max_images=12, default_num_images=3):
-    with gr.Blocks() as demo:
-        with gr.Row():
-            gr.Markdown('### Use a photo of your room and reimagine it with different styles with the power of ControlNet')
-        with gr.Row():
-            with gr.Column():
-                input_image = gr.Image(source='upload', type='numpy')
-                prompt = gr.Textbox(label='Prompt')
-                run_button = gr.Button(label='Run')
-                with gr.Accordion('Advanced options', open=False):
-                    num_samples = gr.Slider(label='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)
-                    detect_resolution = gr.Slider(label='Hough Resolution',
-                                                  minimum=128,
-                                                  maximum=512,
-                                                  value=512,
-                                                  step=1)
-                    mlsd_value_threshold = gr.Slider(
-                        label='Hough value threshold (MLSD)',
-                        minimum=0.01,
-                        maximum=2.0,
-                        value=0.1,
-                        step=0.01)
-                    mlsd_distance_threshold = gr.Slider(
-                        label='Hough distance threshold (MLSD)',
-                        minimum=0.01,
-                        maximum=20.0,
-                        value=0.1,
-                        step=0.01)
-                    num_steps = gr.Slider(label='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=-1,
-                                     maximum=2147483647,
-                                     step=1,
-                                     randomize=True)
-                    a_prompt = gr.Textbox(
-                        label='Added Prompt',
-                        value='best quality, extremely detailed, photo from Pinterest, interior, cinematic photo, ultra-detailed, ultra-realistic, award-winning')
-                    n_prompt = gr.Textbox(
-                        label='Negative Prompt',
-                        value=
-                        'lowres, cropped, worst quality, low quality'
-                    )
-            with gr.Column():
-                result = gr.Gallery(label='Output',
-                                    show_label=False,
-                                    elem_id='gallery').style(grid=2,
-                                                             height='auto')
-        inputs = [
-            input_image,
-            prompt,
-            a_prompt,
-            n_prompt,
-            num_samples,
-            image_resolution,
-            detect_resolution,
-            num_steps,
-            guidance_scale,
-            seed,
-            mlsd_value_threshold,
-            mlsd_distance_threshold,
-        ]
-        prompt.submit(fn=process, inputs=inputs, outputs=result)
-        run_button.click(fn=process,
-                         inputs=inputs,
-                         outputs=result,
-                         api_name='hough')
-        ex = gr.Examples(examples = [
-            ["room.jpg", 
-             "a room for gaming, with gaming chairs, gaming consoles and gaming computers",
-            "best quality, extremely detailed, photo from Pinterest, interior, cinematic photo, ultra-detailed, ultra-realistic, award-winning",
-            "lowres, cropped, worst quality, low quality",
-            2,
-            512,
-            512,
-            20,
-            9.0,
-            132794,
-            0.1,
-            0.1,
-        ]], 
-                         inputs = inputs, outputs = result, fn = process, cache_examples = True)
-        
-    return demo
diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmseg/models/losses/accuracy.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmseg/models/losses/accuracy.py
deleted file mode 100644
index c0fd2e7e74a0f721c4a814c09d6e453e5956bb38..0000000000000000000000000000000000000000
--- a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmseg/models/losses/accuracy.py
+++ /dev/null
@@ -1,78 +0,0 @@
-import torch.nn as nn
-
-
-def accuracy(pred, target, topk=1, thresh=None):
-    """Calculate accuracy according to the prediction and target.
-
-    Args:
-        pred (torch.Tensor): The model prediction, shape (N, num_class, ...)
-        target (torch.Tensor): The target of each prediction, shape (N, , ...)
-        topk (int | tuple[int], optional): If the predictions in ``topk``
-            matches the target, the predictions will be regarded as
-            correct ones. Defaults to 1.
-        thresh (float, optional): If not None, predictions with scores under
-            this threshold are considered incorrect. Default to None.
-
-    Returns:
-        float | tuple[float]: If the input ``topk`` is a single integer,
-            the function will return a single float as accuracy. If
-            ``topk`` is a tuple containing multiple integers, the
-            function will return a tuple containing accuracies of
-            each ``topk`` number.
-    """
-    assert isinstance(topk, (int, tuple))
-    if isinstance(topk, int):
-        topk = (topk, )
-        return_single = True
-    else:
-        return_single = False
-
-    maxk = max(topk)
-    if pred.size(0) == 0:
-        accu = [pred.new_tensor(0.) for i in range(len(topk))]
-        return accu[0] if return_single else accu
-    assert pred.ndim == target.ndim + 1
-    assert pred.size(0) == target.size(0)
-    assert maxk <= pred.size(1), \
-        f'maxk {maxk} exceeds pred dimension {pred.size(1)}'
-    pred_value, pred_label = pred.topk(maxk, dim=1)
-    # transpose to shape (maxk, N, ...)
-    pred_label = pred_label.transpose(0, 1)
-    correct = pred_label.eq(target.unsqueeze(0).expand_as(pred_label))
-    if thresh is not None:
-        # Only prediction values larger than thresh are counted as correct
-        correct = correct & (pred_value > thresh).t()
-    res = []
-    for k in topk:
-        correct_k = correct[:k].reshape(-1).float().sum(0, keepdim=True)
-        res.append(correct_k.mul_(100.0 / target.numel()))
-    return res[0] if return_single else res
-
-
-class Accuracy(nn.Module):
-    """Accuracy calculation module."""
-
-    def __init__(self, topk=(1, ), thresh=None):
-        """Module to calculate the accuracy.
-
-        Args:
-            topk (tuple, optional): The criterion used to calculate the
-                accuracy. Defaults to (1,).
-            thresh (float, optional): If not None, predictions with scores
-                under this threshold are considered incorrect. Default to None.
-        """
-        super().__init__()
-        self.topk = topk
-        self.thresh = thresh
-
-    def forward(self, pred, target):
-        """Forward function to calculate accuracy.
-
-        Args:
-            pred (torch.Tensor): Prediction of models.
-            target (torch.Tensor): Target for each prediction.
-
-        Returns:
-            tuple[float]: The accuracies under different topk criterions.
-        """
-        return accuracy(pred, target, self.topk, self.thresh)
diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmseg/models/segmentors/__init__.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmseg/models/segmentors/__init__.py
deleted file mode 100644
index dca2f09405330743c476e190896bee39c45498ea..0000000000000000000000000000000000000000
--- a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmseg/models/segmentors/__init__.py
+++ /dev/null
@@ -1,5 +0,0 @@
-from .base import BaseSegmentor
-from .cascade_encoder_decoder import CascadeEncoderDecoder
-from .encoder_decoder import EncoderDecoder
-
-__all__ = ['BaseSegmentor', 'EncoderDecoder', 'CascadeEncoderDecoder']
diff --git a/spaces/Rodrigo21/space1/README.md b/spaces/Rodrigo21/space1/README.md
deleted file mode 100644
index 01ab3af60cb6f318c02c0cb2e58daeb999a87ad2..0000000000000000000000000000000000000000
--- a/spaces/Rodrigo21/space1/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Space1
-emoji: 🐠
-colorFrom: purple
-colorTo: purple
-sdk: gradio
-sdk_version: 2.9.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
diff --git a/spaces/Rohit001/emotion_detection/templates/read.md b/spaces/Rohit001/emotion_detection/templates/read.md
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/Rojban/LangFlow/README.md b/spaces/Rojban/LangFlow/README.md
deleted file mode 100644
index a4256573ec32f25c30cd52188079d455ef153ea3..0000000000000000000000000000000000000000
--- a/spaces/Rojban/LangFlow/README.md
+++ /dev/null
@@ -1,16 +0,0 @@
----
-title: LangFlow
-emoji: ⛓️
-colorFrom: indigo
-colorTo: purple
-sdk: docker
-pinned: true
-license: mit
-duplicated_from: Logspace/LangFlow
----
-
-[LangFlow](https://github.com/logspace-ai/langflow) is an Open-source GUI for [LangChain](https://github.com/hwchase17/langchain)
-
-Build agents, export, and use them on LangChain. You can also import an exported flow into LangFlow.
-
-Head over to the Community tab or to our repo to discuss what we should build together.
\ No newline at end of file
diff --git a/spaces/Sa-m/YOLO-V7-Custom-Model-Pot-Hole-Detection/utils/metrics.py b/spaces/Sa-m/YOLO-V7-Custom-Model-Pot-Hole-Detection/utils/metrics.py
deleted file mode 100644
index 666b8c7ec1c0a488eab1b4e7f2f0474973589525..0000000000000000000000000000000000000000
--- a/spaces/Sa-m/YOLO-V7-Custom-Model-Pot-Hole-Detection/utils/metrics.py
+++ /dev/null
@@ -1,223 +0,0 @@
-# Model validation metrics
-
-from pathlib import Path
-
-import matplotlib.pyplot as plt
-import numpy as np
-import torch
-
-from . import general
-
-
-def fitness(x):
-    # Model fitness as a weighted combination of metrics
-    w = [0.0, 0.0, 0.1, 0.9]  # weights for [P, R, mAP@0.5, mAP@0.5:0.95]
-    return (x[:, :4] * w).sum(1)
-
-
-def ap_per_class(tp, conf, pred_cls, target_cls, plot=False, save_dir='.', names=()):
-    """ Compute the average precision, given the recall and precision curves.
-    Source: https://github.com/rafaelpadilla/Object-Detection-Metrics.
-    # Arguments
-        tp:  True positives (nparray, nx1 or nx10).
-        conf:  Objectness value from 0-1 (nparray).
-        pred_cls:  Predicted object classes (nparray).
-        target_cls:  True object classes (nparray).
-        plot:  Plot precision-recall curve at mAP@0.5
-        save_dir:  Plot save directory
-    # Returns
-        The average precision as computed in py-faster-rcnn.
-    """
-
-    # Sort by objectness
-    i = np.argsort(-conf)
-    tp, conf, pred_cls = tp[i], conf[i], pred_cls[i]
-
-    # Find unique classes
-    unique_classes = np.unique(target_cls)
-    nc = unique_classes.shape[0]  # number of classes, number of detections
-
-    # Create Precision-Recall curve and compute AP for each class
-    px, py = np.linspace(0, 1, 1000), []  # for plotting
-    ap, p, r = np.zeros((nc, tp.shape[1])), np.zeros((nc, 1000)), np.zeros((nc, 1000))
-    for ci, c in enumerate(unique_classes):
-        i = pred_cls == c
-        n_l = (target_cls == c).sum()  # number of labels
-        n_p = i.sum()  # number of predictions
-
-        if n_p == 0 or n_l == 0:
-            continue
-        else:
-            # Accumulate FPs and TPs
-            fpc = (1 - tp[i]).cumsum(0)
-            tpc = tp[i].cumsum(0)
-
-            # Recall
-            recall = tpc / (n_l + 1e-16)  # recall curve
-            r[ci] = np.interp(-px, -conf[i], recall[:, 0], left=0)  # negative x, xp because xp decreases
-
-            # Precision
-            precision = tpc / (tpc + fpc)  # precision curve
-            p[ci] = np.interp(-px, -conf[i], precision[:, 0], left=1)  # p at pr_score
-
-            # AP from recall-precision curve
-            for j in range(tp.shape[1]):
-                ap[ci, j], mpre, mrec = compute_ap(recall[:, j], precision[:, j])
-                if plot and j == 0:
-                    py.append(np.interp(px, mrec, mpre))  # precision at mAP@0.5
-
-    # Compute F1 (harmonic mean of precision and recall)
-    f1 = 2 * p * r / (p + r + 1e-16)
-    if plot:
-        plot_pr_curve(px, py, ap, Path(save_dir) / 'PR_curve.png', names)
-        plot_mc_curve(px, f1, Path(save_dir) / 'F1_curve.png', names, ylabel='F1')
-        plot_mc_curve(px, p, Path(save_dir) / 'P_curve.png', names, ylabel='Precision')
-        plot_mc_curve(px, r, Path(save_dir) / 'R_curve.png', names, ylabel='Recall')
-
-    i = f1.mean(0).argmax()  # max F1 index
-    return p[:, i], r[:, i], ap, f1[:, i], unique_classes.astype('int32')
-
-
-def compute_ap(recall, precision):
-    """ Compute the average precision, given the recall and precision curves
-    # Arguments
-        recall:    The recall curve (list)
-        precision: The precision curve (list)
-    # Returns
-        Average precision, precision curve, recall curve
-    """
-
-    # Append sentinel values to beginning and end
-    mrec = np.concatenate(([0.], recall, [recall[-1] + 0.01]))
-    mpre = np.concatenate(([1.], precision, [0.]))
-
-    # Compute the precision envelope
-    mpre = np.flip(np.maximum.accumulate(np.flip(mpre)))
-
-    # Integrate area under curve
-    method = 'interp'  # methods: 'continuous', 'interp'
-    if method == 'interp':
-        x = np.linspace(0, 1, 101)  # 101-point interp (COCO)
-        ap = np.trapz(np.interp(x, mrec, mpre), x)  # integrate
-    else:  # 'continuous'
-        i = np.where(mrec[1:] != mrec[:-1])[0]  # points where x axis (recall) changes
-        ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1])  # area under curve
-
-    return ap, mpre, mrec
-
-
-class ConfusionMatrix:
-    # Updated version of https://github.com/kaanakan/object_detection_confusion_matrix
-    def __init__(self, nc, conf=0.25, iou_thres=0.45):
-        self.matrix = np.zeros((nc + 1, nc + 1))
-        self.nc = nc  # number of classes
-        self.conf = conf
-        self.iou_thres = iou_thres
-
-    def process_batch(self, detections, labels):
-        """
-        Return intersection-over-union (Jaccard index) of boxes.
-        Both sets of boxes are expected to be in (x1, y1, x2, y2) format.
-        Arguments:
-            detections (Array[N, 6]), x1, y1, x2, y2, conf, class
-            labels (Array[M, 5]), class, x1, y1, x2, y2
-        Returns:
-            None, updates confusion matrix accordingly
-        """
-        detections = detections[detections[:, 4] > self.conf]
-        gt_classes = labels[:, 0].int()
-        detection_classes = detections[:, 5].int()
-        iou = general.box_iou(labels[:, 1:], detections[:, :4])
-
-        x = torch.where(iou > self.iou_thres)
-        if x[0].shape[0]:
-            matches = torch.cat((torch.stack(x, 1), iou[x[0], x[1]][:, None]), 1).cpu().numpy()
-            if x[0].shape[0] > 1:
-                matches = matches[matches[:, 2].argsort()[::-1]]
-                matches = matches[np.unique(matches[:, 1], return_index=True)[1]]
-                matches = matches[matches[:, 2].argsort()[::-1]]
-                matches = matches[np.unique(matches[:, 0], return_index=True)[1]]
-        else:
-            matches = np.zeros((0, 3))
-
-        n = matches.shape[0] > 0
-        m0, m1, _ = matches.transpose().astype(np.int16)
-        for i, gc in enumerate(gt_classes):
-            j = m0 == i
-            if n and sum(j) == 1:
-                self.matrix[gc, detection_classes[m1[j]]] += 1  # correct
-            else:
-                self.matrix[self.nc, gc] += 1  # background FP
-
-        if n:
-            for i, dc in enumerate(detection_classes):
-                if not any(m1 == i):
-                    self.matrix[dc, self.nc] += 1  # background FN
-
-    def matrix(self):
-        return self.matrix
-
-    def plot(self, save_dir='', names=()):
-        try:
-            import seaborn as sn
-
-            array = self.matrix / (self.matrix.sum(0).reshape(1, self.nc + 1) + 1E-6)  # normalize
-            array[array < 0.005] = np.nan  # don't annotate (would appear as 0.00)
-
-            fig = plt.figure(figsize=(12, 9), tight_layout=True)
-            sn.set(font_scale=1.0 if self.nc < 50 else 0.8)  # for label size
-            labels = (0 < len(names) < 99) and len(names) == self.nc  # apply names to ticklabels
-            sn.heatmap(array, annot=self.nc < 30, annot_kws={"size": 8}, cmap='Blues', fmt='.2f', square=True,
-                       xticklabels=names + ['background FP'] if labels else "auto",
-                       yticklabels=names + ['background FN'] if labels else "auto").set_facecolor((1, 1, 1))
-            fig.axes[0].set_xlabel('True')
-            fig.axes[0].set_ylabel('Predicted')
-            fig.savefig(Path(save_dir) / 'confusion_matrix.png', dpi=250)
-        except Exception as e:
-            pass
-
-    def print(self):
-        for i in range(self.nc + 1):
-            print(' '.join(map(str, self.matrix[i])))
-
-
-# Plots ----------------------------------------------------------------------------------------------------------------
-
-def plot_pr_curve(px, py, ap, save_dir='pr_curve.png', names=()):
-    # Precision-recall curve
-    fig, ax = plt.subplots(1, 1, figsize=(9, 6), tight_layout=True)
-    py = np.stack(py, axis=1)
-
-    if 0 < len(names) < 21:  # display per-class legend if < 21 classes
-        for i, y in enumerate(py.T):
-            ax.plot(px, y, linewidth=1, label=f'{names[i]} {ap[i, 0]:.3f}')  # plot(recall, precision)
-    else:
-        ax.plot(px, py, linewidth=1, color='grey')  # plot(recall, precision)
-
-    ax.plot(px, py.mean(1), linewidth=3, color='blue', label='all classes %.3f mAP@0.5' % ap[:, 0].mean())
-    ax.set_xlabel('Recall')
-    ax.set_ylabel('Precision')
-    ax.set_xlim(0, 1)
-    ax.set_ylim(0, 1)
-    plt.legend(bbox_to_anchor=(1.04, 1), loc="upper left")
-    fig.savefig(Path(save_dir), dpi=250)
-
-
-def plot_mc_curve(px, py, save_dir='mc_curve.png', names=(), xlabel='Confidence', ylabel='Metric'):
-    # Metric-confidence curve
-    fig, ax = plt.subplots(1, 1, figsize=(9, 6), tight_layout=True)
-
-    if 0 < len(names) < 21:  # display per-class legend if < 21 classes
-        for i, y in enumerate(py):
-            ax.plot(px, y, linewidth=1, label=f'{names[i]}')  # plot(confidence, metric)
-    else:
-        ax.plot(px, py.T, linewidth=1, color='grey')  # plot(confidence, metric)
-
-    y = py.mean(0)
-    ax.plot(px, y, linewidth=3, color='blue', label=f'all classes {y.max():.2f} at {px[y.argmax()]:.3f}')
-    ax.set_xlabel(xlabel)
-    ax.set_ylabel(ylabel)
-    ax.set_xlim(0, 1)
-    ax.set_ylim(0, 1)
-    plt.legend(bbox_to_anchor=(1.04, 1), loc="upper left")
-    fig.savefig(Path(save_dir), dpi=250)
diff --git a/spaces/SajjadAyoubi/CLIPfa-Demo/README.md b/spaces/SajjadAyoubi/CLIPfa-Demo/README.md
deleted file mode 100644
index 0f82016c751957f27b693738db12dff7082f4558..0000000000000000000000000000000000000000
--- a/spaces/SajjadAyoubi/CLIPfa-Demo/README.md
+++ /dev/null
@@ -1,37 +0,0 @@
----
-title: CLIPfa Demo
-emoji: 🤔
-colorFrom: blue
-colorTo: blue
-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/SalahZa/Tunisian-ASR-v0/ctc_train.py b/spaces/SalahZa/Tunisian-ASR-v0/ctc_train.py
deleted file mode 100644
index 39b6b13ff99870adb71e2bcffca4ce2479405a08..0000000000000000000000000000000000000000
--- a/spaces/SalahZa/Tunisian-ASR-v0/ctc_train.py
+++ /dev/null
@@ -1,339 +0,0 @@
-#!/usr/bin/env/python3
-"""Recipe for training a wav2vec-based ctc ASR system with librispeech.
-The system employs wav2vec as its encoder. Decoding is performed with
-ctc greedy decoder.
-To run this recipe, do the following:
-> python train_with_wav2vec.py hparams/train_with_wav2vec.yaml
-The neural network is trained on CTC likelihood target and character units
-are used as basic recognition tokens. Training is performed on the full
-LibriSpeech dataset (960 h).
-
-Authors
- * Sung-Lin Yeh 2021
- * Titouan Parcollet 2021
- * Ju-Chieh Chou 2020
- * Mirco Ravanelli 2020
- * Abdel Heba 2020
- * Peter Plantinga 2020
- * Samuele Cornell 2020
-"""
-
-import os
-import sys
-import torch
-import logging
-import speechbrain as sb
-from speechbrain.utils.distributed import run_on_main
-from hyperpyyaml import load_hyperpyyaml
-from pathlib import Path
-import torchaudio.transforms as T
-logger = logging.getLogger(__name__)
-
-# Define training procedure
-class ASR(sb.Brain):
-    def compute_forward(self, batch, stage):
-        """Forward computations from the waveform batches to the output probabilities."""
-        batch = batch.to(self.device)
-        wavs, wav_lens = batch.sig
-        tokens_bos, _ = batch.tokens_bos
-        wavs, wav_lens = wavs.to(self.device), wav_lens.to(self.device)
-
-        # Forward pass
-        feats = self.modules.wav2vec2(wavs)
-        x = self.modules.enc(feats)
-        # Compute outputs
-        p_tokens = None
-        logits = self.modules.ctc_lin(x)
-        p_ctc = self.hparams.log_softmax(logits)
-        if stage != sb.Stage.TRAIN:
-            p_tokens = sb.decoders.ctc_greedy_decode(
-                p_ctc, wav_lens, blank_id=self.hparams.blank_index
-            )
-        return p_ctc, wav_lens, p_tokens
-
-    def compute_objectives(self, predictions, batch, stage):
-        """Computes the loss (CTC+NLL) given predictions and targets."""
-
-        p_ctc, wav_lens, predicted_tokens = predictions
-
-        ids = batch.id
-        tokens_eos, tokens_eos_lens = batch.tokens_eos
-        tokens, tokens_lens = batch.tokens
-
-        if hasattr(self.modules, "env_corrupt") and stage == sb.Stage.TRAIN:
-            tokens_eos = torch.cat([tokens_eos, tokens_eos], dim=0)
-            tokens_eos_lens = torch.cat(
-                [tokens_eos_lens, tokens_eos_lens], dim=0
-            )
-            tokens = torch.cat([tokens, tokens], dim=0)
-            tokens_lens = torch.cat([tokens_lens, tokens_lens], dim=0)
-
-        loss_ctc = self.hparams.ctc_cost(p_ctc, tokens, wav_lens, tokens_lens)
-        loss = loss_ctc
-
-        if stage != sb.Stage.TRAIN:
-            # Decode token terms to words
-            predicted_words = [
-                "".join(self.tokenizer.decode_ndim(utt_seq)).split(" ")
-                for utt_seq in predicted_tokens
-            ]
-            target_words = [wrd.split(" ") for wrd in batch.wrd]
-            self.wer_metric.append(ids, predicted_words, target_words)
-            self.cer_metric.append(ids, predicted_words, target_words)
-
-        return loss
-
-    def fit_batch(self, batch):
-        """Train the parameters given a single batch in input"""
-        predictions = self.compute_forward(batch, sb.Stage.TRAIN)
-        loss = self.compute_objectives(predictions, batch, sb.Stage.TRAIN)
-        loss.backward()
-        if self.check_gradients(loss):
-            self.wav2vec_optimizer.step()
-            self.model_optimizer.step()
-
-        self.wav2vec_optimizer.zero_grad()
-        self.model_optimizer.zero_grad()
-
-        return loss.detach()
-
-    def evaluate_batch(self, batch, stage):
-        """Computations needed for validation/test batches"""
-        predictions = self.compute_forward(batch, stage=stage)
-        with torch.no_grad():
-            loss = self.compute_objectives(predictions, batch, stage=stage)
-        return loss.detach()
-
-    def on_stage_start(self, stage, epoch):
-        """Gets called at the beginning of each epoch"""
-        if stage != sb.Stage.TRAIN:
-            self.cer_metric = self.hparams.cer_computer()
-            self.wer_metric = self.hparams.error_rate_computer()
-
-    def on_stage_end(self, stage, stage_loss, epoch):
-        """Gets called at the end of an epoch."""
-        # Compute/store important stats
-        stage_stats = {"loss": stage_loss}
-        if stage == sb.Stage.TRAIN:
-            self.train_stats = stage_stats
-        else:
-            stage_stats["CER"] = self.cer_metric.summarize("error_rate")
-            stage_stats["WER"] = self.wer_metric.summarize("error_rate")
-
-        # Perform end-of-iteration things, like annealing, logging, etc.
-        if stage == sb.Stage.VALID:
-            old_lr_model, new_lr_model = self.hparams.lr_annealing_model(
-                stage_stats["loss"]
-            )
-            old_lr_wav2vec, new_lr_wav2vec = self.hparams.lr_annealing_wav2vec(
-                stage_stats["loss"]
-            )
-            sb.nnet.schedulers.update_learning_rate(
-                self.model_optimizer, new_lr_model
-            )
-            sb.nnet.schedulers.update_learning_rate(
-                self.wav2vec_optimizer, new_lr_wav2vec
-            )
-            self.hparams.train_logger.log_stats(
-                stats_meta={
-                    "epoch": epoch,
-                    "lr_model": old_lr_model,
-                    "lr_wav2vec": old_lr_wav2vec,
-                },
-                train_stats=self.train_stats,
-                valid_stats=stage_stats,
-            )
-            self.checkpointer.save_and_keep_only(
-                meta={"WER": stage_stats["WER"]}, min_keys=["WER"],
-            )
-        elif stage == sb.Stage.TEST:
-            self.hparams.train_logger.log_stats(
-                stats_meta={"Epoch loaded": self.hparams.epoch_counter.current},
-                test_stats=stage_stats,
-            )
-            with open(self.hparams.wer_file, "w") as w:
-                self.wer_metric.write_stats(w)
-
-    def init_optimizers(self):
-        "Initializes the wav2vec2 optimizer and model optimizer"
-        self.wav2vec_optimizer = self.hparams.wav2vec_opt_class(
-            self.modules.wav2vec2.parameters()
-        )
-        self.model_optimizer = self.hparams.model_opt_class(
-            self.hparams.model.parameters()
-        )
-
-        if self.checkpointer is not None:
-            self.checkpointer.add_recoverable(
-                "wav2vec_opt", self.wav2vec_optimizer
-            )
-            self.checkpointer.add_recoverable("modelopt", self.model_optimizer)
-
-
-def dataio_prepare(hparams):
-    """This function prepares the datasets to be used in the brain class.
-    It also defines the data processing pipeline through user-defined functions."""
-    data_folder = hparams["data_folder"]
-
-    train_data = sb.dataio.dataset.DynamicItemDataset.from_csv(
-        csv_path=hparams["train_csv"], replacements={"data_root": data_folder},
-    )
-
-    if hparams["sorting"] == "ascending":
-        # we sort training data to speed up training and get better results.
-        train_data = train_data.filtered_sorted(sort_key="duration")
-        # when sorting do not shuffle in dataloader ! otherwise is pointless
-        hparams["train_dataloader_opts"]["shuffle"] = False
-
-    elif hparams["sorting"] == "descending":
-        train_data = train_data.filtered_sorted(
-            sort_key="duration", reverse=True
-        )
-        # when sorting do not shuffle in dataloader ! otherwise is pointless
-        hparams["train_dataloader_opts"]["shuffle"] = False
-
-    elif hparams["sorting"] == "random":
-        pass
-
-    else:
-        raise NotImplementedError(
-            "sorting must be random, ascending or descending"
-        )
-
-    valid_data = sb.dataio.dataset.DynamicItemDataset.from_csv(
-        csv_path=hparams["valid_csv"], replacements={"data_root": data_folder},
-    )
-    valid_data = valid_data.filtered_sorted(sort_key="duration")
-
-    # test is separate
-    test_datasets = {}
-    for csv_file in hparams["test_csv"]:
-        name = Path(csv_file).stem
-        test_datasets[name] = sb.dataio.dataset.DynamicItemDataset.from_csv(
-            csv_path=csv_file, replacements={"data_root": data_folder}
-        )
-        test_datasets[name] = test_datasets[name].filtered_sorted(
-            sort_key="duration"
-        )
-
-    datasets = [train_data, valid_data] + [i for k, i in test_datasets.items()]
-
-    # 2. Define audio pipeline:
-    @sb.utils.data_pipeline.takes("wav", "sr")
-    @sb.utils.data_pipeline.provides("sig")
-    def audio_pipeline(wav, sr):
-        sig = sb.dataio.dataio.read_audio(wav)
-        sig = resamplers[sr](sig)
-        return sig
-
-    sb.dataio.dataset.add_dynamic_item(datasets, audio_pipeline)
-    label_encoder = sb.dataio.encoder.CTCTextEncoder()
-
-    # 3. Define text pipeline:
-    @sb.utils.data_pipeline.takes("wrd")
-    @sb.utils.data_pipeline.provides(
-        "wrd", "char_list", "tokens_list", "tokens_bos", "tokens_eos", "tokens"
-    )
-    def text_pipeline(wrd):
-        yield wrd
-        char_list = list(wrd)
-        yield char_list
-        tokens_list = label_encoder.encode_sequence(char_list)
-        yield tokens_list
-        tokens_bos = torch.LongTensor([hparams["bos_index"]] + (tokens_list))
-        yield tokens_bos
-        tokens_eos = torch.LongTensor(tokens_list + [hparams["eos_index"]])
-        yield tokens_eos
-        tokens = torch.LongTensor(tokens_list)
-        yield tokens
-
-    sb.dataio.dataset.add_dynamic_item(datasets, text_pipeline)
-
-    lab_enc_file = os.path.join(hparams["save_folder"], "label_encoder.txt")
-    special_labels = {
-        "bos_label": hparams["bos_index"],
-        "eos_label": hparams["eos_index"],
-        "blank_label": hparams["blank_index"],
-    }
-    label_encoder.load_or_create(
-        path=lab_enc_file,
-        from_didatasets=[train_data],
-        output_key="char_list",
-        special_labels=special_labels,
-        sequence_input=True,
-    )
-
-    # 4. Set output:
-    sb.dataio.dataset.set_output_keys(
-        datasets,
-        ["id", "sig", "wrd", "char_list", "tokens_bos", "tokens_eos", "tokens"],
-    )
-    return train_data, valid_data, test_datasets, label_encoder
-
-
-if __name__ == "__main__":
-
-    # CLI:
-    hparams_file, run_opts, overrides = sb.parse_arguments(sys.argv[1:])
-
-    # If distributed_launch=True then
-    # create ddp_group with the right communication protocol
-    sb.utils.distributed.ddp_init_group(run_opts)
-
-    with open(hparams_file) as fin:
-        hparams = load_hyperpyyaml(fin, overrides)
-
-    # Create experiment directory
-    sb.create_experiment_directory(
-        experiment_directory=hparams["output_folder"],
-        hyperparams_to_save=hparams_file,
-        overrides=overrides,
-    )
-
-    # Dataset prep (parsing Librispeech)
-
-    resampler_8000  = T.Resample(8000, 16000, dtype=torch.float)
-
-    resampler_44100 =T.Resample(44100, 16000, dtype=torch.float)
-    resampler_32000 =T.Resample(32000, 16000, dtype=torch.float)
-    resampler_48000 =T.Resample(48000, 16000, dtype=torch.float)
-
-
-    resamplers = {"48000": resampler_48000,"8000": resampler_8000, "44100":resampler_44100, "32000":resampler_32000}
-
-    # here we create the datasets objects as well as tokenization and encoding
-    train_data, valid_data, test_datasets, label_encoder = dataio_prepare(
-        hparams
-    )
-
-    # Trainer initialization
-    asr_brain = ASR(
-        modules=hparams["modules"],
-        hparams=hparams,
-        run_opts=run_opts,
-        checkpointer=hparams["checkpointer"],
-    )
-    asr_brain.device= "cpu"
-    asr_brain.modules.to("cpu")
-
-    # We dynamicaly add the tokenizer to our brain class.
-    # NB: This tokenizer corresponds to the one used for the LM!!
-    asr_brain.tokenizer = label_encoder
-
-    # Training
-    asr_brain.fit(
-        asr_brain.hparams.epoch_counter,
-        train_data,
-        valid_data,
-        train_loader_kwargs=hparams["train_dataloader_opts"],
-        valid_loader_kwargs=hparams["valid_dataloader_opts"],
-    )
-
-    # Testing
-    for k in test_datasets.keys():  # keys are test_clean, test_other etc
-        asr_brain.hparams.wer_file = os.path.join(
-            hparams["output_folder"], "wer_{}.txt".format(k)
-        )
-        asr_brain.evaluate(
-            test_datasets[k], test_loader_kwargs=hparams["test_dataloader_opts"]
-        )
diff --git a/spaces/Salesforce/BLIP/data/coco_karpathy_dataset.py b/spaces/Salesforce/BLIP/data/coco_karpathy_dataset.py
deleted file mode 100644
index a34d29205f42aa09695b160ac9c91958ba041bb3..0000000000000000000000000000000000000000
--- a/spaces/Salesforce/BLIP/data/coco_karpathy_dataset.py
+++ /dev/null
@@ -1,126 +0,0 @@
-import os
-import json
-
-from torch.utils.data import Dataset
-from torchvision.datasets.utils import download_url
-
-from PIL import Image
-
-from data.utils import pre_caption
-
-class coco_karpathy_train(Dataset):
-    def __init__(self, transform, image_root, ann_root, max_words=30, prompt=''):        
-        '''
-        image_root (string): Root directory of images (e.g. coco/images/)
-        ann_root (string): directory to store the annotation file
-        '''        
-        url = 'https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_train.json'
-        filename = 'coco_karpathy_train.json'
-
-        download_url(url,ann_root)
-        
-        self.annotation = json.load(open(os.path.join(ann_root,filename),'r'))
-        self.transform = transform
-        self.image_root = image_root
-        self.max_words = max_words      
-        self.prompt = prompt
-        
-        self.img_ids = {}  
-        n = 0
-        for ann in self.annotation:
-            img_id = ann['image_id']
-            if img_id not in self.img_ids.keys():
-                self.img_ids[img_id] = n
-                n += 1    
-        
-    def __len__(self):
-        return len(self.annotation)
-    
-    def __getitem__(self, index):    
-        
-        ann = self.annotation[index]
-        
-        image_path = os.path.join(self.image_root,ann['image'])        
-        image = Image.open(image_path).convert('RGB')   
-        image = self.transform(image)
-        
-        caption = self.prompt+pre_caption(ann['caption'], self.max_words) 
-
-        return image, caption, self.img_ids[ann['image_id']] 
-    
-    
-class coco_karpathy_caption_eval(Dataset):
-    def __init__(self, transform, image_root, ann_root, split):  
-        '''
-        image_root (string): Root directory of images (e.g. coco/images/)
-        ann_root (string): directory to store the annotation file
-        split (string): val or test
-        '''
-        urls = {'val':'https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_val.json',
-                'test':'https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_test.json'}
-        filenames = {'val':'coco_karpathy_val.json','test':'coco_karpathy_test.json'}
-        
-        download_url(urls[split],ann_root)
-        
-        self.annotation = json.load(open(os.path.join(ann_root,filenames[split]),'r'))
-        self.transform = transform
-        self.image_root = image_root
-        
-    def __len__(self):
-        return len(self.annotation)
-    
-    def __getitem__(self, index):    
-        
-        ann = self.annotation[index]
-        
-        image_path = os.path.join(self.image_root,ann['image'])        
-        image = Image.open(image_path).convert('RGB')   
-        image = self.transform(image)          
-        
-        img_id = ann['image'].split('/')[-1].strip('.jpg').split('_')[-1]
-        
-        return image, int(img_id)   
-    
-    
-class coco_karpathy_retrieval_eval(Dataset):
-    def __init__(self, transform, image_root, ann_root, split, max_words=30):  
-        '''
-        image_root (string): Root directory of images (e.g. coco/images/)
-        ann_root (string): directory to store the annotation file
-        split (string): val or test
-        '''
-        urls = {'val':'https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_val.json',
-                'test':'https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_test.json'}
-        filenames = {'val':'coco_karpathy_val.json','test':'coco_karpathy_test.json'}
-        
-        download_url(urls[split],ann_root)
-        
-        self.annotation = json.load(open(os.path.join(ann_root,filenames[split]),'r'))
-        self.transform = transform
-        self.image_root = image_root
-        
-        self.text = []
-        self.image = []
-        self.txt2img = {}
-        self.img2txt = {}
-        
-        txt_id = 0
-        for img_id, ann in enumerate(self.annotation):
-            self.image.append(ann['image'])
-            self.img2txt[img_id] = []
-            for i, caption in enumerate(ann['caption']):
-                self.text.append(pre_caption(caption,max_words))
-                self.img2txt[img_id].append(txt_id)
-                self.txt2img[txt_id] = img_id
-                txt_id += 1
-                                    
-    def __len__(self):
-        return len(self.annotation)
-    
-    def __getitem__(self, index):    
-        
-        image_path = os.path.join(self.image_root, self.annotation[index]['image'])        
-        image = Image.open(image_path).convert('RGB')    
-        image = self.transform(image)  
-
-        return image, index
\ No newline at end of file
diff --git a/spaces/Salesforce/EDICT/my_half_diffusers/pipeline_utils.py b/spaces/Salesforce/EDICT/my_half_diffusers/pipeline_utils.py
deleted file mode 100644
index 84ee9e20f1107a54dcdaf2799d805cf9e4f3b0a7..0000000000000000000000000000000000000000
--- a/spaces/Salesforce/EDICT/my_half_diffusers/pipeline_utils.py
+++ /dev/null
@@ -1,417 +0,0 @@
-# coding=utf-8
-# Copyright 2022 The HuggingFace Inc. team.
-# Copyright (c) 2022, 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.
-
-import importlib
-import inspect
-import os
-from dataclasses import dataclass
-from typing import List, Optional, Union
-
-import numpy as np
-import torch
-
-import diffusers
-import PIL
-from huggingface_hub import snapshot_download
-from PIL import Image
-from tqdm.auto import tqdm
-
-from .configuration_utils import ConfigMixin
-from .utils import DIFFUSERS_CACHE, BaseOutput, logging
-
-
-INDEX_FILE = "diffusion_pytorch_model.bin"
-
-
-logger = logging.get_logger(__name__)
-
-
-LOADABLE_CLASSES = {
-    "diffusers": {
-        "ModelMixin": ["save_pretrained", "from_pretrained"],
-        "SchedulerMixin": ["save_config", "from_config"],
-        "DiffusionPipeline": ["save_pretrained", "from_pretrained"],
-        "OnnxRuntimeModel": ["save_pretrained", "from_pretrained"],
-    },
-    "transformers": {
-        "PreTrainedTokenizer": ["save_pretrained", "from_pretrained"],
-        "PreTrainedTokenizerFast": ["save_pretrained", "from_pretrained"],
-        "PreTrainedModel": ["save_pretrained", "from_pretrained"],
-        "FeatureExtractionMixin": ["save_pretrained", "from_pretrained"],
-    },
-}
-
-ALL_IMPORTABLE_CLASSES = {}
-for library in LOADABLE_CLASSES:
-    ALL_IMPORTABLE_CLASSES.update(LOADABLE_CLASSES[library])
-
-
-@dataclass
-class ImagePipelineOutput(BaseOutput):
-    """
-    Output class for image pipelines.
-
-    Args:
-        images (`List[PIL.Image.Image]` or `np.ndarray`)
-            List of denoised PIL images of length `batch_size` or numpy array of shape `(batch_size, height, width,
-            num_channels)`. PIL images or numpy array present the denoised images of the diffusion pipeline.
-    """
-
-    images: Union[List[PIL.Image.Image], np.ndarray]
-
-
-class DiffusionPipeline(ConfigMixin):
-    r"""
-    Base class for all models.
-
-    [`DiffusionPipeline`] takes care of storing all components (models, schedulers, processors) for diffusion pipelines
-    and handles methods for loading, downloading and saving models as well as a few methods common to all pipelines to:
-
-        - move all PyTorch modules to the device of your choice
-        - enabling/disabling the progress bar for the denoising iteration
-
-    Class attributes:
-
-        - **config_name** ([`str`]) -- name of the config file that will store the class and module names of all
-          compenents of the diffusion pipeline.
-    """
-    config_name = "model_index.json"
-
-    def register_modules(self, **kwargs):
-        # import it here to avoid circular import
-        from diffusers import pipelines
-
-        for name, module in kwargs.items():
-            # retrive library
-            library = module.__module__.split(".")[0]
-
-            # check if the module is a pipeline module
-            pipeline_dir = module.__module__.split(".")[-2]
-            path = module.__module__.split(".")
-            is_pipeline_module = pipeline_dir in path and hasattr(pipelines, pipeline_dir)
-
-            # if library is not in LOADABLE_CLASSES, then it is a custom module.
-            # Or if it's a pipeline module, then the module is inside the pipeline
-            # folder so we set the library to module name.
-            if library not in LOADABLE_CLASSES or is_pipeline_module:
-                library = pipeline_dir
-
-            # retrive class_name
-            class_name = module.__class__.__name__
-
-            register_dict = {name: (library, class_name)}
-
-            # save model index config
-            self.register_to_config(**register_dict)
-
-            # set models
-            setattr(self, name, module)
-
-    def save_pretrained(self, save_directory: Union[str, os.PathLike]):
-        """
-        Save all variables of the pipeline that can be saved and loaded as well as the pipelines configuration file to
-        a directory. A pipeline variable can be saved and loaded if its class implements both a save and loading
-        method. The pipeline can easily be re-loaded using the `[`~DiffusionPipeline.from_pretrained`]` class method.
-
-        Arguments:
-            save_directory (`str` or `os.PathLike`):
-                Directory to which to save. Will be created if it doesn't exist.
-        """
-        self.save_config(save_directory)
-
-        model_index_dict = dict(self.config)
-        model_index_dict.pop("_class_name")
-        model_index_dict.pop("_diffusers_version")
-        model_index_dict.pop("_module", None)
-
-        for pipeline_component_name in model_index_dict.keys():
-            sub_model = getattr(self, pipeline_component_name)
-            model_cls = sub_model.__class__
-
-            save_method_name = None
-            # search for the model's base class in LOADABLE_CLASSES
-            for library_name, library_classes in LOADABLE_CLASSES.items():
-                library = importlib.import_module(library_name)
-                for base_class, save_load_methods in library_classes.items():
-                    class_candidate = getattr(library, base_class)
-                    if issubclass(model_cls, class_candidate):
-                        # if we found a suitable base class in LOADABLE_CLASSES then grab its save method
-                        save_method_name = save_load_methods[0]
-                        break
-                if save_method_name is not None:
-                    break
-
-            save_method = getattr(sub_model, save_method_name)
-            save_method(os.path.join(save_directory, pipeline_component_name))
-
-    def to(self, torch_device: Optional[Union[str, torch.device]] = None):
-        if torch_device is None:
-            return self
-
-        module_names, _ = self.extract_init_dict(dict(self.config))
-        for name in module_names.keys():
-            module = getattr(self, name)
-            if isinstance(module, torch.nn.Module):
-                module.to(torch_device)
-        return self
-
-    @property
-    def device(self) -> torch.device:
-        r"""
-        Returns:
-            `torch.device`: The torch device on which the pipeline is located.
-        """
-        module_names, _ = self.extract_init_dict(dict(self.config))
-        for name in module_names.keys():
-            module = getattr(self, name)
-            if isinstance(module, torch.nn.Module):
-                return module.device
-        return torch.device("cpu")
-
-    @classmethod
-    def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.PathLike]], **kwargs):
-        r"""
-        Instantiate a PyTorch diffusion pipeline from pre-trained pipeline weights.
-
-        The pipeline is set in evaluation mode by default using `model.eval()` (Dropout modules are deactivated).
-
-        The warning *Weights from XXX not initialized from pretrained model* means that the weights of XXX do not come
-        pretrained with the rest of the model. It is up to you to train those weights with a downstream fine-tuning
-        task.
-
-        The warning *Weights from XXX not used in YYY* means that the layer XXX is not used by YYY, therefore those
-        weights are discarded.
-
-        Parameters:
-            pretrained_model_name_or_path (`str` or `os.PathLike`, *optional*):
-                Can be either:
-
-                    - A string, the *repo id* of a pretrained pipeline hosted inside a model repo on
-                      https://huggingface.co/ Valid repo ids have to be located under a user or organization name, like
-                      `CompVis/ldm-text2im-large-256`.
-                    - A path to a *directory* containing pipeline weights saved using
-                      [`~DiffusionPipeline.save_pretrained`], e.g., `./my_pipeline_directory/`.
-            torch_dtype (`str` or `torch.dtype`, *optional*):
-                Override the default `torch.dtype` and load the model under this dtype. If `"auto"` is passed the dtype
-                will be automatically derived from the model's weights.
-            force_download (`bool`, *optional*, defaults to `False`):
-                Whether or not to force the (re-)download of the model weights and configuration files, overriding the
-                cached versions if they exist.
-            resume_download (`bool`, *optional*, defaults to `False`):
-                Whether or not to delete incompletely received files. Will attempt to resume the download if such a
-                file exists.
-            proxies (`Dict[str, str]`, *optional*):
-                A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128',
-                'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
-            output_loading_info(`bool`, *optional*, defaults to `False`):
-                Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.
-            local_files_only(`bool`, *optional*, defaults to `False`):
-                Whether or not to only look at local files (i.e., do not try to download the model).
-            use_auth_token (`str` or *bool*, *optional*):
-                The token to use as HTTP bearer authorization for remote files. If `True`, will use the token generated
-                when running `huggingface-cli login` (stored in `~/.huggingface`).
-            revision (`str`, *optional*, defaults to `"main"`):
-                The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
-                git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any
-                identifier allowed by git.
-            mirror (`str`, *optional*):
-                Mirror source to accelerate downloads in China. If you are from China and have an accessibility
-                problem, you can set this option to resolve it. Note that we do not guarantee the timeliness or safety.
-                Please refer to the mirror site for more information. specify the folder name here.
-
-            kwargs (remaining dictionary of keyword arguments, *optional*):
-                Can be used to overwrite load - and saveable variables - *i.e.* the pipeline components - of the
-                speficic pipeline class. The overritten components are then directly passed to the pipelines `__init__`
-                method. See example below for more information.
-
-        <Tip>
-
-        Passing `use_auth_token=True`` is required when you want to use a private model, *e.g.*
-        `"CompVis/stable-diffusion-v1-4"`
-
-        </Tip>
-
-        <Tip>
-
-        Activate the special ["offline-mode"](https://huggingface.co/diffusers/installation.html#offline-mode) to use
-        this method in a firewalled environment.
-
-        </Tip>
-
-        Examples:
-
-        ```py
-        >>> from diffusers import DiffusionPipeline
-
-        >>> # Download pipeline from huggingface.co and cache.
-        >>> pipeline = DiffusionPipeline.from_pretrained("CompVis/ldm-text2im-large-256")
-
-        >>> # Download pipeline that requires an authorization token
-        >>> # For more information on access tokens, please refer to this section
-        >>> # of the documentation](https://huggingface.co/docs/hub/security-tokens)
-        >>> pipeline = DiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", use_auth_token=True)
-
-        >>> # Download pipeline, but overwrite scheduler
-        >>> from diffusers import LMSDiscreteScheduler
-
-        >>> scheduler = LMSDiscreteScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear")
-        >>> pipeline = DiffusionPipeline.from_pretrained(
-        ...     "CompVis/stable-diffusion-v1-4", scheduler=scheduler, use_auth_token=True
-        ... )
-        ```
-        """
-        cache_dir = kwargs.pop("cache_dir", DIFFUSERS_CACHE)
-        resume_download = kwargs.pop("resume_download", False)
-        proxies = kwargs.pop("proxies", None)
-        local_files_only = kwargs.pop("local_files_only", False)
-        use_auth_token = kwargs.pop("use_auth_token", None)
-        revision = kwargs.pop("revision", None)
-        torch_dtype = kwargs.pop("torch_dtype", None)
-        provider = kwargs.pop("provider", None)
-
-        # 1. Download the checkpoints and configs
-        # use snapshot download here to get it working from from_pretrained
-        if not os.path.isdir(pretrained_model_name_or_path):
-            cached_folder = snapshot_download(
-                pretrained_model_name_or_path,
-                cache_dir=cache_dir,
-                resume_download=resume_download,
-                proxies=proxies,
-                local_files_only=local_files_only,
-                use_auth_token=use_auth_token,
-                revision=revision,
-            )
-        else:
-            cached_folder = pretrained_model_name_or_path
-
-        config_dict = cls.get_config_dict(cached_folder)
-
-        # 2. Load the pipeline class, if using custom module then load it from the hub
-        # if we load from explicit class, let's use it
-        if cls != DiffusionPipeline:
-            pipeline_class = cls
-        else:
-            diffusers_module = importlib.import_module(cls.__module__.split(".")[0])
-            pipeline_class = getattr(diffusers_module, config_dict["_class_name"])
-
-        # some modules can be passed directly to the init
-        # in this case they are already instantiated in `kwargs`
-        # extract them here
-        expected_modules = set(inspect.signature(pipeline_class.__init__).parameters.keys())
-        passed_class_obj = {k: kwargs.pop(k) for k in expected_modules if k in kwargs}
-
-        init_dict, _ = pipeline_class.extract_init_dict(config_dict, **kwargs)
-
-        init_kwargs = {}
-
-        # import it here to avoid circular import
-        from diffusers import pipelines
-
-        # 3. Load each module in the pipeline
-        for name, (library_name, class_name) in init_dict.items():
-            is_pipeline_module = hasattr(pipelines, library_name)
-            loaded_sub_model = None
-
-            # if the model is in a pipeline module, then we load it from the pipeline
-            if name in passed_class_obj:
-                # 1. check that passed_class_obj has correct parent class
-                if not is_pipeline_module:
-                    library = importlib.import_module(library_name)
-                    class_obj = getattr(library, class_name)
-                    importable_classes = LOADABLE_CLASSES[library_name]
-                    class_candidates = {c: getattr(library, c) for c in importable_classes.keys()}
-
-                    expected_class_obj = None
-                    for class_name, class_candidate in class_candidates.items():
-                        if issubclass(class_obj, class_candidate):
-                            expected_class_obj = class_candidate
-
-                    if not issubclass(passed_class_obj[name].__class__, expected_class_obj):
-                        raise ValueError(
-                            f"{passed_class_obj[name]} is of type: {type(passed_class_obj[name])}, but should be"
-                            f" {expected_class_obj}"
-                        )
-                else:
-                    logger.warn(
-                        f"You have passed a non-standard module {passed_class_obj[name]}. We cannot verify whether it"
-                        " has the correct type"
-                    )
-
-                # set passed class object
-                loaded_sub_model = passed_class_obj[name]
-            elif is_pipeline_module:
-                pipeline_module = getattr(pipelines, library_name)
-                class_obj = getattr(pipeline_module, class_name)
-                importable_classes = ALL_IMPORTABLE_CLASSES
-                class_candidates = {c: class_obj for c in importable_classes.keys()}
-            else:
-                # else we just import it from the library.
-                library = importlib.import_module(library_name)
-                class_obj = getattr(library, class_name)
-                importable_classes = LOADABLE_CLASSES[library_name]
-                class_candidates = {c: getattr(library, c) for c in importable_classes.keys()}
-
-            if loaded_sub_model is None:
-                load_method_name = None
-                for class_name, class_candidate in class_candidates.items():
-                    if issubclass(class_obj, class_candidate):
-                        load_method_name = importable_classes[class_name][1]
-
-                load_method = getattr(class_obj, load_method_name)
-
-                loading_kwargs = {}
-                if issubclass(class_obj, torch.nn.Module):
-                    loading_kwargs["torch_dtype"] = torch_dtype
-                if issubclass(class_obj, diffusers.OnnxRuntimeModel):
-                    loading_kwargs["provider"] = provider
-
-                # check if the module is in a subdirectory
-                if os.path.isdir(os.path.join(cached_folder, name)):
-                    loaded_sub_model = load_method(os.path.join(cached_folder, name), **loading_kwargs)
-                else:
-                    # else load from the root directory
-                    loaded_sub_model = load_method(cached_folder, **loading_kwargs)
-
-            init_kwargs[name] = loaded_sub_model  # UNet(...), # DiffusionSchedule(...)
-
-        # 4. Instantiate the pipeline
-        model = pipeline_class(**init_kwargs)
-        return model
-
-    @staticmethod
-    def numpy_to_pil(images):
-        """
-        Convert a numpy image or a batch of images to a PIL image.
-        """
-        if images.ndim == 3:
-            images = images[None, ...]
-        images = (images * 255).round().astype("uint8")
-        pil_images = [Image.fromarray(image) for image in images]
-
-        return pil_images
-
-    def progress_bar(self, iterable):
-        if not hasattr(self, "_progress_bar_config"):
-            self._progress_bar_config = {}
-        elif not isinstance(self._progress_bar_config, dict):
-            raise ValueError(
-                f"`self._progress_bar_config` should be of type `dict`, but is {type(self._progress_bar_config)}."
-            )
-
-        return tqdm(iterable, **self._progress_bar_config)
-
-    def set_progress_bar_config(self, **kwargs):
-        self._progress_bar_config = kwargs
diff --git a/spaces/SeViLA/SeViLA/docs/build_docs.sh b/spaces/SeViLA/SeViLA/docs/build_docs.sh
deleted file mode 100644
index 122172f753f29d49839de55a4555365c6ab0c020..0000000000000000000000000000000000000000
--- a/spaces/SeViLA/SeViLA/docs/build_docs.sh
+++ /dev/null
@@ -1,101 +0,0 @@
-#!/bin/bash
-set -euo pipefail
-
-# Change to root directory of repo
-DIRNAME=$(cd "$( dirname "${BASH_SOURCE[0]}" )" &> /dev/null && pwd)
-cd "${DIRNAME}/.."
-
-# # Set up virtual environment
-pip3 install setuptools wheel virtualenv
-if [ ! -d venv ]; then
-  rm -f venv
-  virtualenv venv
-fi
-source venv/bin/activate
-
-# # Get current git branch & stash unsaved changes
-GIT_BRANCH=$(git branch --show-current)
-if [ -z "${GIT_BRANCH}" ]; then
-    GIT_BRANCH="main"
-fi
-git stash
-
-# Set up exit handler to restore git state & delete temp branches
-# function exit_handler {
-#     git reset --hard
-#     git checkout "${GIT_BRANCH}" --
-#     git stash pop || true
-#     for version in $(git tag --list 'v[0-9]*'); do
-#         branch="${version}_local_docs_only"
-#         if git show-ref --verify --quiet "refs/heads/$branch"; then
-#             git branch -D "$branch"
-#         fi
-#     done
-# }
-# trap exit_handler EXIT
-
-# Clean up build directory and install Sphinx requirements
-pip3 install -r "${DIRNAME}/requirements.txt"
-sphinx-build -M clean "${DIRNAME}" "${DIRNAME}/_build"
-
-# Build API docs for current head
-export current_version="latest"
-pip3 install "."
-sphinx-build -b html "${DIRNAME}" "${DIRNAME}/_build/html/${current_version}" -W --keep-going
-rm -rf "${DIRNAME}/_build/html/${current_version}/.doctrees"
-#pip3 uninstall -y omnixai
-
-# Install all previous released versions
-# and use them to build the appropriate API docs.
-# Uninstall after we're done with each one.
-# versions=()
-# checkout_files=("${DIRNAME}/*.rst" "lavis" "tutorials" "setup.py")
-# for version in $(git tag --list 'v[0-9]*'); do
-#     versions+=("$version")
-#     git checkout -b "${version}_local_docs_only"
-#     for f in $(git diff --name-only --diff-filter=A "tags/${version}" "${DIRNAME}/*.rst"); do
-#         git rm "$f"
-#     done
-#     git checkout "tags/${version}" -- "${checkout_files[@]}"
-#     export current_version=${version}
-#     pip3 install ".[all]"
-#     sphinx-build -b html "${DIRNAME}" "${DIRNAME}/_build/html/${current_version}" -W --keep-going
-#     rm -rf "${DIRNAME}/_build/html/${current_version}/.doctrees"
-#     #pip3 uninstall -y omnixai
-#     git reset --hard
-#     git checkout "${GIT_BRANCH}" --
-# done
-
-# Determine the latest stable version if there is one
-# if (( ${#versions[@]} > 0 )); then
-#   stable_hash=$(git rev-list --tags --max-count=1)
-#   stable_version=$(git describe --tags "$stable_hash")
-#   export stable_version
-# else
-export stable_version="latest"
-# fi
-
-# Create dummy HTML's for the stable version in the base directory
-while read -r filename; do
-    filename=$(echo "$filename" | sed "s/\.\///")
-    n_sub=$(echo "$filename" | (grep -o "/" || true) | wc -l)
-    prefix=""
-    for (( i=0; i<n_sub; i++ )); do
-        prefix+="../"
-    done
-    url="${prefix}${stable_version}/$filename"
-    mkdir -p "${DIRNAME}/_build/html/$(dirname "$filename")"
-    cat > "${DIRNAME}/_build/html/$filename" <<EOF
-<!DOCTYPE html>
-<html>
-   <head>
-      <title>LAVIS Documentation</title>
-      <meta http-equiv = "refresh" content="0; url='$url'" />
-   </head>
-   <body>
-      <p>Please wait while you're redirected to our <a href="$url">documentation</a>.</p>
-   </body>
-</html>
-EOF
-done < <(cd "${DIRNAME}/_build/html/$stable_version" && find . -name "*.html")
-echo "Finished writing to _build/html."
\ No newline at end of file
diff --git a/spaces/SoUmNerd/RemoteMojo/README.md b/spaces/SoUmNerd/RemoteMojo/README.md
deleted file mode 100644
index dbd4513b816c2b604c2f73e1219c9512963cf0e3..0000000000000000000000000000000000000000
--- a/spaces/SoUmNerd/RemoteMojo/README.md
+++ /dev/null
@@ -1,10 +0,0 @@
----
-title: FlowiseAI
-emoji: 🔥
-colorFrom: blue
-colorTo: gray
-sdk: docker
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
\ No newline at end of file
diff --git a/spaces/SpacesExamples/Gradio-Docker-Template/README.md b/spaces/SpacesExamples/Gradio-Docker-Template/README.md
deleted file mode 100644
index ea7f9a0b83ecfa701440a85832b0788b4f60ce3a..0000000000000000000000000000000000000000
--- a/spaces/SpacesExamples/Gradio-Docker-Template/README.md
+++ /dev/null
@@ -1,10 +0,0 @@
----
-title: Gradio Docker Template
-emoji: 🐨
-colorFrom: purple
-colorTo: pink
-sdk: docker
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/SuYuanS/AudioCraft_Plus/audiocraft/metrics/__init__.py b/spaces/SuYuanS/AudioCraft_Plus/audiocraft/metrics/__init__.py
deleted file mode 100644
index 3474bdc4f1c88b21904d2a21ba077c93a8a70c8b..0000000000000000000000000000000000000000
--- a/spaces/SuYuanS/AudioCraft_Plus/audiocraft/metrics/__init__.py
+++ /dev/null
@@ -1,14 +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.
-"""Metrics like CLAP score, FAD, KLD, Visqol, Chroma similarity, etc.
-"""
-# flake8: noqa
-from .clap_consistency import CLAPTextConsistencyMetric, TextConsistencyMetric
-from .chroma_cosinesim import ChromaCosineSimilarityMetric
-from .fad import FrechetAudioDistanceMetric
-from .kld import KLDivergenceMetric, PasstKLDivergenceMetric
-from .rvm import RelativeVolumeMel
-from .visqol import ViSQOL
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/docarray/index/backends/qdrant.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/docarray/index/backends/qdrant.py
deleted file mode 100644
index 12edce0279108bc99d6d5cbbba50c64a88645235..0000000000000000000000000000000000000000
--- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/docarray/index/backends/qdrant.py
+++ /dev/null
@@ -1,625 +0,0 @@
-import uuid
-from dataclasses import dataclass, field
-from typing import (
-    TYPE_CHECKING,
-    Any,
-    Dict,
-    Generator,
-    Generic,
-    List,
-    Optional,
-    Sequence,
-    Tuple,
-    Type,
-    TypeVar,
-    Union,
-    cast,
-)
-
-import numpy as np
-from grpc import RpcError  # type: ignore[import]
-
-import docarray.typing.id
-from docarray import BaseDoc, DocList
-from docarray.array.any_array import AnyDocArray
-from docarray.index.abstract import (
-    BaseDocIndex,
-    _ColumnInfo,
-    _FindResultBatched,
-    _raise_not_composable,
-)
-from docarray.typing import NdArray
-from docarray.typing.tensor.abstract_tensor import AbstractTensor
-from docarray.utils._internal.misc import import_library, torch_imported
-from docarray.utils.find import _FindResult
-
-if TYPE_CHECKING:
-    import qdrant_client
-    from qdrant_client.conversions import common_types as types
-    from qdrant_client.http import models as rest
-    from qdrant_client.http.exceptions import UnexpectedResponse
-else:
-    qdrant_client = import_library('qdrant_client')
-    from qdrant_client.conversions import common_types as types
-    from qdrant_client.http import models as rest
-    from qdrant_client.http.exceptions import UnexpectedResponse
-
-TSchema = TypeVar('TSchema', bound=BaseDoc)
-
-QDRANT_PY_VECTOR_TYPES: List[Any] = [np.ndarray, AbstractTensor]
-if torch_imported:
-    import torch
-
-    QDRANT_PY_VECTOR_TYPES.append(torch.Tensor)
-
-QDRANT_SPACE_MAPPING = {
-    'cosine': rest.Distance.COSINE,
-    'l2': rest.Distance.EUCLID,
-    'ip': rest.Distance.DOT,
-}
-
-RawQuery = Dict[str, Any]
-
-
-class QdrantDocumentIndex(BaseDocIndex, Generic[TSchema]):
-    UUID_NAMESPACE = uuid.UUID('3896d314-1e95-4a3a-b45a-945f9f0b541d')
-
-    def __init__(self, db_config=None, **kwargs):
-        """Initialize QdrantDocumentIndex"""
-        if db_config is not None and getattr(db_config, 'index_name'):
-            db_config.collection_name = db_config.index_name
-
-        super().__init__(db_config=db_config, **kwargs)
-        self._db_config: QdrantDocumentIndex.DBConfig = cast(
-            QdrantDocumentIndex.DBConfig, self._db_config
-        )
-        self._client = qdrant_client.QdrantClient(
-            location=self._db_config.location,
-            url=self._db_config.url,
-            port=self._db_config.port,
-            grpc_port=self._db_config.grpc_port,
-            prefer_grpc=self._db_config.prefer_grpc,
-            https=self._db_config.https,
-            api_key=self._db_config.api_key,
-            prefix=self._db_config.prefix,
-            timeout=self._db_config.timeout,
-            host=self._db_config.host,
-            path=self._db_config.path,
-        )
-        self._initialize_collection()
-        self._logger.info(f'{self.__class__.__name__} has been initialized')
-
-    @property
-    def collection_name(self):
-        default_collection_name = (
-            self._schema.__name__.lower() if self._schema is not None else None
-        )
-        if default_collection_name is None:
-            raise ValueError(
-                'A QdrantDocumentIndex must be typed with a Document type.'
-                'To do so, use the syntax: QdrantDocumentIndex[DocumentType]'
-            )
-
-        return self._db_config.collection_name or default_collection_name
-
-    @property
-    def index_name(self):
-        return self.collection_name
-
-    @dataclass
-    class Query:
-        """Dataclass describing a query."""
-
-        vector_field: Optional[str]
-        vector_query: Optional[NdArray]
-        filter: Optional[rest.Filter]
-        limit: int
-
-    class QueryBuilder(BaseDocIndex.QueryBuilder):
-        def __init__(
-            self,
-            vector_search_field: Optional[str] = None,
-            vector_filters: Optional[List[NdArray]] = None,
-            payload_filters: Optional[List[rest.Filter]] = None,
-            text_search_filters: Optional[List[Tuple[str, str]]] = None,
-        ):
-            self._vector_search_field: Optional[str] = vector_search_field
-            self._vector_filters: List[NdArray] = vector_filters or []
-            self._payload_filters: List[rest.Filter] = payload_filters or []
-            self._text_search_filters: List[Tuple[str, str]] = text_search_filters or []
-
-        def build(self, limit: int) -> 'QdrantDocumentIndex.Query':
-            """
-            Build a query object for QdrantDocumentIndex.
-            :return: QdrantDocumentIndex.Query object
-            """
-            vector_query = None
-            if len(self._vector_filters) > 0:
-                # If there are multiple vector queries applied, we can average them and
-                # perform semantic search on a single vector instead
-                vector_query = np.average(self._vector_filters, axis=0)
-            merged_filter = None
-            if len(self._payload_filters) > 0:
-                merged_filter = rest.Filter(must=self._payload_filters)
-            if len(self._text_search_filters) > 0:
-                # Text search is just a special case of payload filtering, so the
-                # payload filter is simply extended
-                merged_filter = merged_filter or rest.Filter(must=[])
-                for search_field, query in self._text_search_filters:
-                    merged_filter.must.append(  # type: ignore[union-attr]
-                        rest.FieldCondition(
-                            key=search_field,
-                            match=rest.MatchText(text=query),
-                        )
-                    )
-            return QdrantDocumentIndex.Query(
-                vector_field=self._vector_search_field,
-                vector_query=vector_query,
-                filter=merged_filter,
-                limit=limit,
-            )
-
-        def find(  # type: ignore[override]
-            self, query: NdArray, search_field: str = ''
-        ) -> 'QdrantDocumentIndex.QueryBuilder':
-            """
-            Find k-nearest neighbors of the query.
-
-            :param query: query vector for search. Has single axis.
-            :param search_field: field to perform search on
-            :return: QueryBuilder object
-            """
-            if self._vector_search_field and self._vector_search_field != search_field:
-                raise ValueError(
-                    f'Trying to call .find for search_field = {search_field}, but '
-                    f'previously {self._vector_search_field} was used. Only a single '
-                    f'field might be used in chained calls.'
-                )
-            return QdrantDocumentIndex.QueryBuilder(
-                vector_search_field=search_field,
-                vector_filters=self._vector_filters + [query],
-                payload_filters=self._payload_filters,
-                text_search_filters=self._text_search_filters,
-            )
-
-        def filter(  # type: ignore[override]
-            self, filter_query: rest.Filter
-        ) -> 'QdrantDocumentIndex.QueryBuilder':
-            """Find documents in the index based on a filter query
-            :param filter_query: a filter
-            :return: QueryBuilder object
-            """
-            return QdrantDocumentIndex.QueryBuilder(
-                vector_search_field=self._vector_search_field,
-                vector_filters=self._vector_filters,
-                payload_filters=self._payload_filters + [filter_query],
-                text_search_filters=self._text_search_filters,
-            )
-
-        def text_search(  # type: ignore[override]
-            self, query: str, search_field: str = ''
-        ) -> 'QdrantDocumentIndex.QueryBuilder':
-            """Find documents in the index based on a text search query
-
-            :param query: The text to search for
-            :param search_field: name of the field to search on
-            :return: QueryBuilder object
-            """
-            return QdrantDocumentIndex.QueryBuilder(
-                vector_search_field=self._vector_search_field,
-                vector_filters=self._vector_filters,
-                payload_filters=self._payload_filters,
-                text_search_filters=self._text_search_filters + [(search_field, query)],
-            )
-
-        find_batched = _raise_not_composable('find_batched')
-        filter_batched = _raise_not_composable('filter_batched')
-        text_search_batched = _raise_not_composable('text_search_batched')
-
-    @dataclass
-    class DBConfig(BaseDocIndex.DBConfig):
-        """Dataclass that contains all "static" configurations of QdrantDocumentIndex."""
-
-        location: Optional[str] = None
-        url: Optional[str] = None
-        port: Optional[int] = 6333
-        grpc_port: int = 6334
-        prefer_grpc: bool = True
-        https: Optional[bool] = None
-        api_key: Optional[str] = None
-        prefix: Optional[str] = None
-        timeout: Optional[float] = None
-        host: Optional[str] = None
-        path: Optional[str] = None
-        collection_name: Optional[str] = None
-        shard_number: Optional[int] = None
-        replication_factor: Optional[int] = None
-        write_consistency_factor: Optional[int] = None
-        on_disk_payload: Optional[bool] = None
-        hnsw_config: Optional[types.HnswConfigDiff] = None
-        optimizers_config: Optional[types.OptimizersConfigDiff] = None
-        wal_config: Optional[types.WalConfigDiff] = None
-        quantization_config: Optional[types.QuantizationConfig] = None
-
-    @dataclass
-    class RuntimeConfig(BaseDocIndex.RuntimeConfig):
-        """Dataclass that contains all "dynamic" configurations of QdrantDocumentIndex."""
-
-        default_column_config: Dict[Type, Dict[str, Any]] = field(
-            default_factory=lambda: {
-                'id': {},  # type: ignore[dict-item]
-                'vector': {'dim': 128},  # type: ignore[dict-item]
-                'payload': {},  # type: ignore[dict-item]
-            }
-        )
-
-    def python_type_to_db_type(self, python_type: Type) -> Any:
-        """Map python type to database type.
-        Takes any python type and returns the corresponding database column type.
-
-        :param python_type: a python type.
-        :return: the corresponding database column type.
-        """
-        if any(issubclass(python_type, vt) for vt in QDRANT_PY_VECTOR_TYPES):
-            return 'vector'
-
-        if issubclass(python_type, docarray.typing.id.ID):
-            return 'id'
-
-        return 'payload'
-
-    def _initialize_collection(self):
-        try:
-            self._client.get_collection(self.collection_name)
-        except (UnexpectedResponse, RpcError, ValueError):
-            vectors_config = {}
-
-            for column_name, column_info in self._column_infos.items():
-                if column_info.db_type == 'vector':
-                    vectors_config[column_name] = self._to_qdrant_vector_params(
-                        column_info
-                    )
-
-            self._client.create_collection(
-                collection_name=self.collection_name,
-                vectors_config=vectors_config,
-                shard_number=self._db_config.shard_number,
-                replication_factor=self._db_config.replication_factor,
-                write_consistency_factor=self._db_config.write_consistency_factor,
-                on_disk_payload=self._db_config.on_disk_payload,
-                hnsw_config=self._db_config.hnsw_config,
-                optimizers_config=self._db_config.optimizers_config,
-                wal_config=self._db_config.wal_config,
-                quantization_config=self._db_config.quantization_config,
-            )
-            self._client.create_payload_index(
-                collection_name=self.collection_name,
-                field_name='__generated_vectors',
-                field_schema=rest.PayloadSchemaType.KEYWORD,
-            )
-
-    def _index(self, column_to_data: Dict[str, Generator[Any, None, None]]):
-        self._index_subindex(column_to_data)
-
-        rows = self._transpose_col_value_dict(column_to_data)
-        # TODO: add batching the documents to avoid timeouts
-        points = [self._build_point_from_row(row) for row in rows]
-        self._client.upsert(
-            collection_name=self.collection_name,
-            points=points,
-        )
-
-    def num_docs(self) -> int:
-        """
-        Get the number of documents.
-        """
-        return self._client.count(collection_name=self.collection_name).count
-
-    def _del_items(self, doc_ids: Sequence[str]):
-        items = self._get_items(doc_ids)
-        if len(items) < len(doc_ids):
-            found_keys = set(item['id'] for item in items)  # type: ignore[index]
-            missing_keys = set(doc_ids) - found_keys
-            raise KeyError('Document keys could not found: %s' % ','.join(missing_keys))
-
-        self._client.delete(
-            collection_name=self.collection_name,
-            points_selector=rest.PointIdsList(
-                points=[self._to_qdrant_id(doc_id) for doc_id in doc_ids],
-            ),
-        )
-
-    def _get_items(
-        self, doc_ids: Sequence[str]
-    ) -> Union[Sequence[TSchema], Sequence[Dict[str, Any]]]:
-        response, _ = self._client.scroll(
-            collection_name=self.collection_name,
-            scroll_filter=rest.Filter(
-                must=[
-                    rest.HasIdCondition(
-                        has_id=[self._to_qdrant_id(doc_id) for doc_id in doc_ids],
-                    ),
-                ],
-            ),
-            limit=len(doc_ids),
-            with_payload=True,
-            with_vectors=True,
-        )
-        return sorted(
-            [self._convert_to_doc(point) for point in response],
-            key=lambda x: doc_ids.index(x['id']),
-        )
-
-    def execute_query(self, query: Union[Query, RawQuery], *args, **kwargs) -> DocList:
-        """
-        Execute a query on the QdrantDocumentIndex.
-
-        Can take two kinds of inputs:
-
-        1. A native query of the underlying database. This is meant as a passthrough so that you
-        can enjoy any functionality that is not available through the Document index API.
-        2. The output of this Document index's `QueryBuilder.build()` method.
-
-        :param query: the query to execute
-        :param args: positional arguments to pass to the query
-        :param kwargs: keyword arguments to pass to the query
-        :return: the result of the query
-        """
-        if not isinstance(query, QdrantDocumentIndex.Query):
-            points = self._execute_raw_query(query.copy())
-        elif query.vector_field:
-            # We perform semantic search with some vectors with Qdrant's search method
-            # should be called
-            points = self._client.search(  # type: ignore[assignment]
-                collection_name=self.collection_name,
-                query_vector=(query.vector_field, query.vector_query),  # type: ignore[arg-type]
-                query_filter=rest.Filter(
-                    must=[query.filter],
-                    # The following filter takes care of using only those points which
-                    # do not have the vector generated. Those are excluded from the
-                    # search results.
-                    must_not=[
-                        rest.FieldCondition(
-                            key='__generated_vectors',
-                            match=rest.MatchValue(value=query.vector_field),
-                        )
-                    ],
-                ),
-                limit=query.limit,
-                with_payload=True,
-                with_vectors=True,
-            )
-        else:
-            # Just filtering, so Qdrant's scroll has to be used instead
-            points, _ = self._client.scroll(  # type: ignore[assignment]
-                collection_name=self.collection_name,
-                scroll_filter=query.filter,
-                limit=query.limit,
-                with_payload=True,
-                with_vectors=True,
-            )
-
-        docs = [self._convert_to_doc(point) for point in points]
-        return self._dict_list_to_docarray(docs)
-
-    def _execute_raw_query(
-        self, query: RawQuery
-    ) -> Sequence[Union[rest.ScoredPoint, rest.Record]]:
-        payload_filter = query.pop('filter', None)
-        if payload_filter:
-            payload_filter = rest.Filter.parse_obj(payload_filter)  # type: ignore[assignment]
-
-        if 'vector' in query:
-            # We perform semantic search with some vectors with Qdrant's search method
-            # should be called
-            search_params = query.pop('params', None)
-            if search_params:
-                search_params = rest.SearchParams.parse_obj(search_params)  # type: ignore[assignment]
-            points = self._client.search(  # type: ignore[assignment]
-                collection_name=self.collection_name,
-                query_vector=query.pop('vector'),
-                query_filter=payload_filter,
-                search_params=search_params,
-                **query,
-            )
-        else:
-            # Just filtering, so Qdrant's scroll has to be used instead
-            points, _ = self._client.scroll(  # type: ignore[assignment]
-                collection_name=self.collection_name,
-                scroll_filter=payload_filter,
-                **query,
-            )
-
-        return points
-
-    def _find(
-        self, query: np.ndarray, limit: int, search_field: str = ''
-    ) -> _FindResult:
-        query_batched = np.expand_dims(query, axis=0)
-        docs, scores = self._find_batched(
-            queries=query_batched, limit=limit, search_field=search_field
-        )
-        return _FindResult(documents=docs[0], scores=scores[0])  # type: ignore[arg-type]
-
-    def _find_batched(
-        self, queries: np.ndarray, limit: int, search_field: str = ''
-    ) -> _FindResultBatched:
-        responses = self._client.search_batch(
-            collection_name=self.collection_name,
-            requests=[
-                rest.SearchRequest(
-                    vector=rest.NamedVector(
-                        name=search_field,
-                        vector=query.tolist(),  # type: ignore
-                    ),
-                    # The following filter takes care of using only those points which
-                    # do not have the vector generated. Those are excluded from the
-                    # search results.
-                    filter=rest.Filter(
-                        must_not=[
-                            rest.FieldCondition(
-                                key='__generated_vectors',
-                                match=rest.MatchValue(value=search_field),
-                            )
-                        ]
-                    ),
-                    limit=limit,
-                    with_vector=True,
-                    with_payload=True,
-                )
-                for query in queries
-            ],
-        )
-        return _FindResultBatched(
-            documents=[
-                [self._convert_to_doc(point) for point in response]
-                for response in responses
-            ],
-            scores=[
-                NdArray._docarray_from_native(
-                    np.array([point.score for point in response])
-                )
-                for response in responses
-            ],
-        )
-
-    def _filter(
-        self, filter_query: rest.Filter, limit: int
-    ) -> Union[DocList, List[Dict]]:
-        query_batched = [filter_query]
-        docs = self._filter_batched(filter_queries=query_batched, limit=limit)
-        return docs[0]
-
-    def _filter_batched(
-        self, filter_queries: Sequence[rest.Filter], limit: int
-    ) -> Union[List[DocList], List[List[Dict]]]:
-        responses = []
-        for filter_query in filter_queries:
-            # There is no batch scroll available in Qdrant client yet, so we need to
-            # perform the queries one by one. It will be changed in the future versions.
-            response, _ = self._client.scroll(
-                collection_name=self.collection_name,
-                scroll_filter=filter_query,
-                limit=limit,
-                with_payload=True,
-                with_vectors=True,
-            )
-            responses.append(response)
-
-        return [
-            [self._convert_to_doc(point) for point in response]
-            for response in responses
-        ]
-
-    def _text_search(
-        self, query: str, limit: int, search_field: str = ''
-    ) -> _FindResult:
-        query_batched = [query]
-        docs, scores = self._text_search_batched(
-            queries=query_batched, limit=limit, search_field=search_field
-        )
-        return _FindResult(documents=docs[0], scores=scores[0])  # type: ignore[arg-type]
-
-    def _text_search_batched(
-        self, queries: Sequence[str], limit: int, search_field: str = ''
-    ) -> _FindResultBatched:
-        filter_queries = [
-            rest.Filter(
-                must=[
-                    rest.FieldCondition(
-                        key=search_field,
-                        match=rest.MatchText(text=query),
-                    )
-                ]
-            )
-            for query in queries
-        ]
-        documents_batched = self._filter_batched(
-            filter_queries=filter_queries, limit=limit
-        )
-
-        # Qdrant does not return any scores if we just filter the objects, without using
-        # semantic search over vectors. Thus, each document is scored with a value of 1
-        return _FindResultBatched(
-            documents=documents_batched,
-            scores=[
-                NdArray._docarray_from_native(np.ones(len(docs)))
-                for docs in documents_batched
-            ],
-        )
-
-    def _filter_by_parent_id(self, id: str) -> Optional[List[str]]:
-        response, _ = self._client.scroll(
-            collection_name=self._db_config.collection_name,  # type: ignore
-            scroll_filter=rest.Filter(
-                must=[
-                    rest.FieldCondition(
-                        key='parent_id', match=rest.MatchValue(value=id)
-                    )
-                ]
-            ),
-            with_payload=rest.PayloadSelectorInclude(include=['id']),
-        )
-
-        ids = [point.payload['id'] for point in response]  # type: ignore
-        return ids
-
-    def _build_point_from_row(self, row: Dict[str, Any]) -> rest.PointStruct:
-        point_id = self._to_qdrant_id(row.get('id'))
-        vectors: Dict[str, List[float]] = {}
-        payload: Dict[str, Any] = {'__generated_vectors': []}
-        for column_name, column_info in self._column_infos.items():
-            if issubclass(column_info.docarray_type, AnyDocArray):
-                continue
-            if column_info.db_type in ['id', 'payload']:
-                payload[column_name] = row.get(column_name)
-                continue
-
-            vector = row.get(column_name)
-            if column_info.db_type == 'vector' and vector is not None:
-                vectors[column_name] = vector.tolist()
-            elif column_info.db_type == 'vector' and vector is None:
-                # In that case vector was not provided. Qdrant does not support optional
-                # vectors - each point needs to have all the vectors already assigned.
-                # Thus, we put a fake embedding with the correct dimensionality and mark
-                # such point a point with a boolean flag in the payload.
-                vector_size = column_info.n_dim or column_info.config.get('dim')
-                vectors[column_name] = np.ones(vector_size).tolist()  # type: ignore[arg-type]
-                payload['__generated_vectors'].append(column_name)
-            else:
-                raise ValueError(
-                    f'Could not handle the conversion for column {column_name}. '
-                    f'Column info: {column_info}'
-                )
-        return rest.PointStruct(
-            id=point_id,
-            vector=vectors,
-            payload=payload,
-        )
-
-    def _to_qdrant_id(self, external_id: Optional[str]) -> str:
-        if external_id is None:
-            return uuid.uuid4().hex
-        return uuid.uuid5(QdrantDocumentIndex.UUID_NAMESPACE, external_id).hex
-
-    def _to_qdrant_vector_params(self, column_info: _ColumnInfo) -> rest.VectorParams:
-        return rest.VectorParams(
-            size=column_info.n_dim or column_info.config.get('dim'),
-            distance=QDRANT_SPACE_MAPPING[column_info.config.get('space', 'cosine')],
-        )
-
-    def _convert_to_doc(
-        self, point: Union[rest.ScoredPoint, rest.Record]
-    ) -> Dict[str, Any]:
-        document = cast(Dict[str, Any], point.payload)
-        generated_vectors = document.pop('__generated_vectors')
-        vectors = point.vector if point.vector else dict()
-        if not isinstance(vectors, dict):
-            vectors = {'__default__': vectors}
-        for vector_name, vector in vectors.items():
-            if vector_name in generated_vectors:
-                # That means the vector was generated during the upload, and should not
-                # be returned along the other vectors.
-                pass
-            document[vector_name] = vector
-        return document
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/docarray/typing/url/video_url.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/docarray/typing/url/video_url.py
deleted file mode 100644
index 5bd7b1be0b9da704a48b70ae47f33091dd8fdb54..0000000000000000000000000000000000000000
--- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/docarray/typing/url/video_url.py
+++ /dev/null
@@ -1,127 +0,0 @@
-import warnings
-from typing import Optional, TypeVar
-
-from docarray.typing.bytes.video_bytes import VideoBytes, VideoLoadResult
-from docarray.typing.proto_register import _register_proto
-from docarray.typing.url.any_url import AnyUrl
-from docarray.utils._internal.misc import is_notebook
-
-T = TypeVar('T', bound='VideoUrl')
-
-
-@_register_proto(proto_type_name='video_url')
-class VideoUrl(AnyUrl):
-    """
-    URL to a video file.
-    Can be remote (web) URL, or a local file path.
-    """
-
-    def load(self: T, **kwargs) -> VideoLoadResult:
-        """
-        Load the data from the url into a `NamedTuple` of
-        [`VideoNdArray`][docarray.typing.VideoNdArray],
-        [`AudioNdArray`][docarray.typing.AudioNdArray]
-        and [`NdArray`][docarray.typing.NdArray].
-
-        ---
-
-        ```python
-        from typing import Optional
-
-        from docarray import BaseDoc
-
-        from docarray.typing import VideoUrl, VideoNdArray, AudioNdArray, NdArray
-
-
-        class MyDoc(BaseDoc):
-            video_url: VideoUrl
-            video: Optional[VideoNdArray]
-            audio: Optional[AudioNdArray]
-            key_frame_indices: Optional[NdArray]
-
-
-        doc = MyDoc(
-            video_url='https://github.com/docarray/docarray/blob/main/tests/toydata/mov_bbb.mp4?raw=true'
-        )
-        doc.video, doc.audio, doc.key_frame_indices = doc.video_url.load()
-
-        assert isinstance(doc.video, VideoNdArray)
-        assert isinstance(doc.audio, AudioNdArray)
-        assert isinstance(doc.key_frame_indices, NdArray)
-        ```
-
-        ---
-
-        You can load only the key frames (or video, audio respectively):
-
-        ---
-
-        ```python
-        from pydantic import parse_obj_as
-
-        from docarray.typing import NdArray, VideoUrl
-
-
-        url = parse_obj_as(
-            VideoUrl,
-            'https://github.com/docarray/docarray/blob/main/tests/toydata/mov_bbb.mp4?raw=true',
-        )
-        key_frame_indices = url.load().key_frame_indices
-        assert isinstance(key_frame_indices, NdArray)
-        ```
-
-        ---
-
-        :param kwargs: supports all keyword arguments that are being supported by
-            av.open() as described [here](https://pyav.org/docs/stable/api/_globals.html?highlight=open#av.open)
-
-        :return: [`AudioNdArray`][docarray.typing.AudioNdArray] representing the audio content,
-            [`VideoNdArray`][docarray.typing.VideoNdArray] representing the images of the video,
-            [`NdArray`][docarray.typing.NdArray] of the key frame indices.
-        """
-        buffer = self.load_bytes(**kwargs)
-        return buffer.load()
-
-    def load_bytes(self, timeout: Optional[float] = None) -> VideoBytes:
-        """
-        Convert url to [`VideoBytes`][docarray.typing.VideoBytes]. This will either load or download
-        the file and save it into an [`VideoBytes`][docarray.typing.VideoBytes] object.
-
-        :param timeout: timeout for urlopen. Only relevant if url is not local
-        :return: [`VideoBytes`][docarray.typing.VideoBytes] object
-        """
-        bytes_ = super().load_bytes(timeout=timeout)
-        return VideoBytes(bytes_)
-
-    def display(self):
-        """
-        Play video from url in notebook.
-        """
-        if is_notebook():
-            from IPython.display import display
-
-            remote_url = True if self.startswith('http') else False
-
-            if remote_url:
-                from IPython.display import Video
-
-                b = self.load_bytes()
-                display(Video(data=b, embed=True, mimetype='video/mp4'))
-            else:
-                import os
-
-                from IPython.display import HTML
-
-                path = os.path.relpath(self)
-                src = f'''
-                    <body>
-                    <video width="320" height="240" autoplay muted controls>
-                    <source src="{path}">
-                    Your browser does not support the video tag.
-                    </video>
-                    </body>
-                    '''
-                display(HTML(src))
-
-        else:
-            warnings.warn('Display of video is only possible in a notebook.')
diff --git a/spaces/Superlang/ImageProcessor/annotator/uniformer/mmseg/apis/train.py b/spaces/Superlang/ImageProcessor/annotator/uniformer/mmseg/apis/train.py
deleted file mode 100644
index 63f319a919ff023931a6a663e668f27dd1a07a2e..0000000000000000000000000000000000000000
--- a/spaces/Superlang/ImageProcessor/annotator/uniformer/mmseg/apis/train.py
+++ /dev/null
@@ -1,116 +0,0 @@
-import random
-import warnings
-
-import numpy as np
-import torch
-from annotator.uniformer.mmcv.parallel import MMDataParallel, MMDistributedDataParallel
-from annotator.uniformer.mmcv.runner import build_optimizer, build_runner
-
-from annotator.uniformer.mmseg.core import DistEvalHook, EvalHook
-from annotator.uniformer.mmseg.datasets import build_dataloader, build_dataset
-from annotator.uniformer.mmseg.utils import get_root_logger
-
-
-def set_random_seed(seed, deterministic=False):
-    """Set random seed.
-
-    Args:
-        seed (int): Seed to be used.
-        deterministic (bool): Whether to set the deterministic option for
-            CUDNN backend, i.e., set `torch.backends.cudnn.deterministic`
-            to True and `torch.backends.cudnn.benchmark` to False.
-            Default: False.
-    """
-    random.seed(seed)
-    np.random.seed(seed)
-    torch.manual_seed(seed)
-    torch.cuda.manual_seed_all(seed)
-    if deterministic:
-        torch.backends.cudnn.deterministic = True
-        torch.backends.cudnn.benchmark = False
-
-
-def train_segmentor(model,
-                    dataset,
-                    cfg,
-                    distributed=False,
-                    validate=False,
-                    timestamp=None,
-                    meta=None):
-    """Launch segmentor training."""
-    logger = get_root_logger(cfg.log_level)
-
-    # prepare data loaders
-    dataset = dataset if isinstance(dataset, (list, tuple)) else [dataset]
-    data_loaders = [
-        build_dataloader(
-            ds,
-            cfg.data.samples_per_gpu,
-            cfg.data.workers_per_gpu,
-            # cfg.gpus will be ignored if distributed
-            len(cfg.gpu_ids),
-            dist=distributed,
-            seed=cfg.seed,
-            drop_last=True) for ds in dataset
-    ]
-
-    # put model on gpus
-    if distributed:
-        find_unused_parameters = cfg.get('find_unused_parameters', False)
-        # Sets the `find_unused_parameters` parameter in
-        # torch.nn.parallel.DistributedDataParallel
-        model = MMDistributedDataParallel(
-            model.cuda(),
-            device_ids=[torch.cuda.current_device()],
-            broadcast_buffers=False,
-            find_unused_parameters=find_unused_parameters)
-    else:
-        model = MMDataParallel(
-            model.cuda(cfg.gpu_ids[0]), device_ids=cfg.gpu_ids)
-
-    # build runner
-    optimizer = build_optimizer(model, cfg.optimizer)
-
-    if cfg.get('runner') is None:
-        cfg.runner = {'type': 'IterBasedRunner', 'max_iters': cfg.total_iters}
-        warnings.warn(
-            'config is now expected to have a `runner` section, '
-            'please set `runner` in your config.', UserWarning)
-
-    runner = build_runner(
-        cfg.runner,
-        default_args=dict(
-            model=model,
-            batch_processor=None,
-            optimizer=optimizer,
-            work_dir=cfg.work_dir,
-            logger=logger,
-            meta=meta))
-
-    # register hooks
-    runner.register_training_hooks(cfg.lr_config, cfg.optimizer_config,
-                                   cfg.checkpoint_config, cfg.log_config,
-                                   cfg.get('momentum_config', None))
-
-    # an ugly walkaround to make the .log and .log.json filenames the same
-    runner.timestamp = timestamp
-
-    # register eval hooks
-    if validate:
-        val_dataset = build_dataset(cfg.data.val, dict(test_mode=True))
-        val_dataloader = build_dataloader(
-            val_dataset,
-            samples_per_gpu=1,
-            workers_per_gpu=cfg.data.workers_per_gpu,
-            dist=distributed,
-            shuffle=False)
-        eval_cfg = cfg.get('evaluation', {})
-        eval_cfg['by_epoch'] = cfg.runner['type'] != 'IterBasedRunner'
-        eval_hook = DistEvalHook if distributed else EvalHook
-        runner.register_hook(eval_hook(val_dataloader, **eval_cfg), priority='LOW')
-
-    if cfg.resume_from:
-        runner.resume(cfg.resume_from)
-    elif cfg.load_from:
-        runner.load_checkpoint(cfg.load_from)
-    runner.run(data_loaders, cfg.workflow)
diff --git a/spaces/Surn/UnlimitedMusicGen/audiocraft/modules/conv.py b/spaces/Surn/UnlimitedMusicGen/audiocraft/modules/conv.py
deleted file mode 100644
index 972938ab84712eb06e1b10cea25444eee51d6637..0000000000000000000000000000000000000000
--- a/spaces/Surn/UnlimitedMusicGen/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/TH5314/newbing/src/components/providers.tsx b/spaces/TH5314/newbing/src/components/providers.tsx
deleted file mode 100644
index 892226412d80fe0b05211911b9e245cd22876460..0000000000000000000000000000000000000000
--- a/spaces/TH5314/newbing/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/TNR-5/dalle/README.md b/spaces/TNR-5/dalle/README.md
deleted file mode 100644
index 1cfb27fd2ba349d6c88e742ce6af8fa084d8f918..0000000000000000000000000000000000000000
--- a/spaces/TNR-5/dalle/README.md
+++ /dev/null
@@ -1,9 +0,0 @@
----
-title: Dalle
-emoji: 💻
-colorFrom: pink
-colorTo: gray
-sdk: static
-pinned: false
-duplicated_from: jbitel/dalle
----
diff --git a/spaces/UserXTheUnknown/stablediffusion-infinity/app.py b/spaces/UserXTheUnknown/stablediffusion-infinity/app.py
deleted file mode 100644
index bb3816730275cf963435056bc3b3ab9c1d7bfa16..0000000000000000000000000000000000000000
--- a/spaces/UserXTheUnknown/stablediffusion-infinity/app.py
+++ /dev/null
@@ -1,1057 +0,0 @@
-import subprocess
-# import os.path as osp
-import pip
-# pip.main(["install","-v","-U","git+https://github.com/facebookresearch/xformers.git@main#egg=xformers"])
-# subprocess.check_call("pip install -v -U git+https://github.com/facebookresearch/xformers.git@main#egg=xformers", cwd=osp.dirname(__file__), shell=True)
-
-import io
-import base64
-import os
-import sys
-
-import numpy as np
-import torch
-from torch import autocast
-import diffusers
-from diffusers.configuration_utils import FrozenDict
-from diffusers import (
-    StableDiffusionPipeline,
-    StableDiffusionInpaintPipeline,
-    StableDiffusionImg2ImgPipeline,
-    StableDiffusionInpaintPipelineLegacy,
-    DDIMScheduler,
-    LMSDiscreteScheduler,
-    StableDiffusionUpscalePipeline,
-    DPMSolverMultistepScheduler
-)
-from diffusers.models import AutoencoderKL
-from PIL import Image
-from PIL import ImageOps
-import gradio as gr
-import base64
-import skimage
-import skimage.measure
-import yaml
-import json
-from enum import Enum
-
-try:
-    abspath = os.path.abspath(__file__)
-    dirname = os.path.dirname(abspath)
-    os.chdir(dirname)
-except:
-    pass
-
-from utils import *
-
-assert diffusers.__version__ >= "0.6.0", "Please upgrade diffusers to 0.6.0"
-
-USE_NEW_DIFFUSERS = True
-RUN_IN_SPACE = "RUN_IN_HG_SPACE" in os.environ
-
-
-class ModelChoice(Enum):
-    INPAINTING = "stablediffusion-inpainting"
-    INPAINTING_IMG2IMG = "stablediffusion-inpainting+img2img-v1.5"
-    MODEL_1_5 = "stablediffusion-v1.5"
-    MODEL_1_4 = "DreamShaper"
-
-
-try:
-    from sd_grpcserver.pipeline.unified_pipeline import UnifiedPipeline
-except:
-    UnifiedPipeline = StableDiffusionInpaintPipeline
-
-# sys.path.append("./glid_3_xl_stable")
-
-USE_GLID = False
-# try:
-#     from glid3xlmodel import GlidModel
-# except:
-#     USE_GLID = False
-
-try:
-    cuda_available = torch.cuda.is_available()
-except:
-    cuda_available = False
-finally:
-    if sys.platform == "darwin":
-        device = "mps" if torch.backends.mps.is_available() else "cpu"
-    elif cuda_available:
-        device = "cuda"
-    else:
-        device = "cpu"
-
-import contextlib
-
-autocast = contextlib.nullcontext
-
-with open("config.yaml", "r") as yaml_in:
-    yaml_object = yaml.safe_load(yaml_in)
-    config_json = json.dumps(yaml_object)
-
-
-def load_html():
-    body, canvaspy = "", ""
-    with open("index.html", encoding="utf8") as f:
-        body = f.read()
-    with open("canvas.py", encoding="utf8") as f:
-        canvaspy = f.read()
-    body = body.replace("- paths:\n", "")
-    body = body.replace("  - ./canvas.py\n", "")
-    body = body.replace("from canvas import InfCanvas", canvaspy)
-    return body
-
-
-def test(x):
-    x = load_html()
-    return f"""<iframe id="sdinfframe" style="width: 100%; height: 600px" name="result" allow="midi; geolocation; microphone; camera; 
-    display-capture; encrypted-media; vertical-scroll 'none'" sandbox="allow-modals allow-forms 
-    allow-scripts allow-same-origin allow-popups 
-    allow-top-navigation-by-user-activation allow-downloads" allowfullscreen="" 
-    allowpaymentrequest="" frameborder="0" srcdoc='{x}'></iframe>"""
-
-
-DEBUG_MODE = False
-
-try:
-    SAMPLING_MODE = Image.Resampling.LANCZOS
-except Exception as e:
-    SAMPLING_MODE = Image.LANCZOS
-
-try:
-    contain_func = ImageOps.contain
-except Exception as e:
-
-    def contain_func(image, size, method=SAMPLING_MODE):
-        # from PIL: https://pillow.readthedocs.io/en/stable/reference/ImageOps.html#PIL.ImageOps.contain
-        im_ratio = image.width / image.height
-        dest_ratio = size[0] / size[1]
-        if im_ratio != dest_ratio:
-            if im_ratio > dest_ratio:
-                new_height = int(image.height / image.width * size[0])
-                if new_height != size[1]:
-                    size = (size[0], new_height)
-            else:
-                new_width = int(image.width / image.height * size[1])
-                if new_width != size[0]:
-                    size = (new_width, size[1])
-        return image.resize(size, resample=method)
-
-
-import argparse
-
-parser = argparse.ArgumentParser(description="stablediffusion-infinity")
-parser.add_argument("--port", type=int, help="listen port", dest="server_port")
-parser.add_argument("--host", type=str, help="host", dest="server_name")
-parser.add_argument("--share", action="store_true", help="share this app?")
-parser.add_argument("--debug", action="store_true", help="debug mode")
-parser.add_argument("--fp32", action="store_true", help="using full precision")
-parser.add_argument("--encrypt", action="store_true", help="using https?")
-parser.add_argument("--ssl_keyfile", type=str, help="path to ssl_keyfile")
-parser.add_argument("--ssl_certfile", type=str, help="path to ssl_certfile")
-parser.add_argument("--ssl_keyfile_password", type=str, help="ssl_keyfile_password")
-parser.add_argument(
-    "--auth", nargs=2, metavar=("username", "password"), help="use username password"
-)
-parser.add_argument(
-    "--remote_model",
-    type=str,
-    help="use a model (e.g. dreambooth fined) from huggingface hub",
-    default="",
-)
-parser.add_argument(
-    "--local_model", type=str, help="use a model stored on your PC", default=""
-)
-
-if __name__ == "__main__" and not RUN_IN_SPACE:
-    args = parser.parse_args()
-else:
-    args = parser.parse_args()
-# args = parser.parse_args(["--debug"])
-if args.auth is not None:
-    args.auth = tuple(args.auth)
-
-model = {}
-
-
-def get_token():
-    token = ""
-    if os.path.exists(".token"):
-        with open(".token", "r") as f:
-            token = f.read()
-    token = os.environ.get("hftoken", token)
-    return token
-
-
-def save_token(token):
-    with open(".token", "w") as f:
-        f.write(token)
-
-
-def prepare_scheduler(scheduler):
-    if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1:
-        new_config = dict(scheduler.config)
-        new_config["steps_offset"] = 1
-        scheduler._internal_dict = FrozenDict(new_config)
-    return scheduler
-
-
-def my_resize(width, height):
-    if width >= 512 and height >= 512:
-        return width, height
-    if width == height:
-        return 512, 512
-    smaller = min(width, height)
-    larger = max(width, height)
-    if larger >= 608:
-        return width, height
-    factor = 1
-    if smaller < 290:
-        factor = 2
-    elif smaller < 330:
-        factor = 1.75
-    elif smaller < 384:
-        factor = 1.375
-    elif smaller < 400:
-        factor = 1.25
-    elif smaller < 450:
-        factor = 1.125
-    return int(factor * width)//8*8, int(factor * height)//8*8
-
-
-def load_learned_embed_in_clip(
-    learned_embeds_path, text_encoder, tokenizer, token=None
-):
-    # https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb
-    loaded_learned_embeds = torch.load(learned_embeds_path, map_location="cpu")
-
-    # separate token and the embeds
-    trained_token = list(loaded_learned_embeds.keys())[0]
-    embeds = loaded_learned_embeds[trained_token]
-
-    # cast to dtype of text_encoder
-    dtype = text_encoder.get_input_embeddings().weight.dtype
-    embeds.to(dtype)
-
-    # add the token in tokenizer
-    token = token if token is not None else trained_token
-    num_added_tokens = tokenizer.add_tokens(token)
-    if num_added_tokens == 0:
-        raise ValueError(
-            f"The tokenizer already contains the token {token}. Please pass a different `token` that is not already in the tokenizer."
-        )
-
-    # resize the token embeddings
-    text_encoder.resize_token_embeddings(len(tokenizer))
-
-    # get the id for the token and assign the embeds
-    token_id = tokenizer.convert_tokens_to_ids(token)
-    text_encoder.get_input_embeddings().weight.data[token_id] = embeds
-
-
-scheduler_dict = {"PLMS": None, "DDIM": None, "K-LMS": None, "DPM": None}
-
-
-class StableDiffusionInpaint:
-    def __init__(
-        self, token: str = "", model_name: str = "", model_path: str = "", **kwargs,
-    ):
-        self.token = token
-        original_checkpoint = False
-        if model_path and os.path.exists(model_path):
-            if model_path.endswith(".ckpt"):
-                original_checkpoint = True
-            elif model_path.endswith(".json"):
-                model_name = os.path.dirname(model_path)
-            else:
-                model_name = model_path
-        vae = AutoencoderKL.from_pretrained("stabilityai/sd-vae-ft-mse")
-        vae.to(torch.float16)
-        if original_checkpoint:
-            print(f"Converting & Loading {model_path}")
-            from convert_checkpoint import convert_checkpoint
-
-            pipe = convert_checkpoint(model_path, inpainting=True)
-            if device == "cuda":
-                pipe.to(torch.float16)
-            inpaint = StableDiffusionInpaintPipeline(
-                vae=vae,
-                text_encoder=pipe.text_encoder,
-                tokenizer=pipe.tokenizer,
-                unet=pipe.unet,
-                scheduler=pipe.scheduler,
-                safety_checker=pipe.safety_checker,
-                feature_extractor=pipe.feature_extractor,
-            )
-        else:
-            print(f"Loading {model_name}")
-            if device == "cuda":
-                inpaint = StableDiffusionInpaintPipeline.from_pretrained(
-                    model_name,
-                    revision="fp16",
-                    torch_dtype=torch.float16,
-                    use_auth_token=token,
-                    vae=vae
-                )
-            else:
-                inpaint = StableDiffusionInpaintPipeline.from_pretrained(
-                    model_name, use_auth_token=token,
-                )
-        if os.path.exists("./embeddings"):
-            print("Note that StableDiffusionInpaintPipeline + embeddings is untested")
-            for item in os.listdir("./embeddings"):
-                if item.endswith(".bin"):
-                    load_learned_embed_in_clip(
-                        os.path.join("./embeddings", item),
-                        inpaint.text_encoder,
-                        inpaint.tokenizer,
-                    )
-        inpaint.to(device)
-        # try:
-            # inpaint.vae=torch.compile(inpaint.vae, dynamic=True)
-            # inpaint.unet=torch.compile(inpaint.unet, dynamic=True)
-        # except Exception as e:
-            # print(e)
-        # inpaint.enable_xformers_memory_efficient_attention()
-        # if device == "mps":
-        # _ = text2img("", num_inference_steps=1)
-        scheduler_dict["PLMS"] = inpaint.scheduler
-        scheduler_dict["DDIM"] = prepare_scheduler(
-            DDIMScheduler(
-                beta_start=0.00085,
-                beta_end=0.012,
-                beta_schedule="scaled_linear",
-                clip_sample=False,
-                set_alpha_to_one=False,
-            )
-        )
-        scheduler_dict["K-LMS"] = prepare_scheduler(
-            LMSDiscreteScheduler(
-                beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear"
-            )
-        )
-        scheduler_dict["DPM"] = prepare_scheduler(
-            DPMSolverMultistepScheduler.from_config(inpaint.scheduler.config)
-        )
-        self.safety_checker = inpaint.safety_checker
-        save_token(token)
-        try:
-            total_memory = torch.cuda.get_device_properties(0).total_memory // (
-                1024 ** 3
-            )
-            if total_memory <= 5:
-                inpaint.enable_attention_slicing()
-        except:
-            pass
-        self.inpaint = inpaint
-
-    def run(
-        self,
-        image_pil,
-        prompt="",
-        negative_prompt="",
-        guidance_scale=7.5,
-        resize_check=True,
-        enable_safety=True,
-        fill_mode="patchmatch",
-        strength=0.75,
-        step=50,
-        enable_img2img=False,
-        use_seed=False,
-        seed_val=-1,
-        generate_num=1,
-        scheduler="",
-        scheduler_eta=0.0,
-        **kwargs,
-    ):
-        inpaint = self.inpaint
-        selected_scheduler = scheduler_dict.get(scheduler, scheduler_dict["PLMS"])
-        for item in [inpaint]:
-            item.scheduler = selected_scheduler
-            if enable_safety:
-                item.safety_checker = self.safety_checker
-            else:
-                item.safety_checker = lambda images, **kwargs: (images, False)
-        width, height = image_pil.size
-        sel_buffer = np.array(image_pil)
-        img = sel_buffer[:, :, 0:3]
-        mask = sel_buffer[:, :, -1]
-        nmask = 255 - mask
-        process_width = width
-        process_height = height
-        if resize_check:
-            process_width, process_height = my_resize(width, height)
-        process_width=process_width*8//8
-        process_height=process_height*8//8
-        extra_kwargs = {
-            "num_inference_steps": step,
-            "guidance_scale": guidance_scale,
-            "eta": scheduler_eta,
-        }
-        if USE_NEW_DIFFUSERS:
-            extra_kwargs["negative_prompt"] = negative_prompt
-            extra_kwargs["num_images_per_prompt"] = generate_num
-        if use_seed:
-            generator = torch.Generator(inpaint.device).manual_seed(seed_val)
-            extra_kwargs["generator"] = generator
-        if True:
-            img, mask = functbl[fill_mode](img, mask)
-            mask = 255 - mask
-            mask = skimage.measure.block_reduce(mask, (8, 8), np.max)
-            mask = mask.repeat(8, axis=0).repeat(8, axis=1)
-            extra_kwargs["strength"] = strength
-            inpaint_func = inpaint
-            init_image = Image.fromarray(img)
-            mask_image = Image.fromarray(mask)
-            # mask_image=mask_image.filter(ImageFilter.GaussianBlur(radius = 8))
-            if True:
-                images = inpaint_func(
-                    prompt=prompt,
-                    image=init_image.resize(
-                        (process_width, process_height), resample=SAMPLING_MODE
-                    ),
-                    mask_image=mask_image.resize((process_width, process_height)),
-                    width=process_width,
-                    height=process_height,
-                    **extra_kwargs,
-                )["images"]
-        return images
-
-
-class StableDiffusion:
-    def __init__(
-        self,
-        token: str = "",
-        model_name: str = "runwayml/stable-diffusion-v1-5",
-        model_path: str = None,
-        inpainting_model: bool = False,
-        **kwargs,
-    ):
-        self.token = token
-        original_checkpoint = False
-        vae = AutoencoderKL.from_pretrained("stabilityai/sd-vae-ft-mse")
-        vae.to(torch.float16)
-        if model_path and os.path.exists(model_path):
-            if model_path.endswith(".ckpt"):
-                original_checkpoint = True
-            elif model_path.endswith(".json"):
-                model_name = os.path.dirname(model_path)
-            else:
-                model_name = model_path
-        if original_checkpoint:
-            print(f"Converting & Loading {model_path}")
-            from convert_checkpoint import convert_checkpoint
-
-            text2img = convert_checkpoint(model_path)
-            if device == "cuda" and not args.fp32:
-                text2img.to(torch.float16)
-        else:
-            print(f"Loading {model_name}")
-            if device == "cuda" and not args.fp32:
-                text2img = StableDiffusionPipeline.from_pretrained(
-                    "runwayml/stable-diffusion-v1-5",
-                    revision="fp16",
-                    torch_dtype=torch.float16,
-                    use_auth_token=token,
-                    vae=vae
-                )
-            else:
-                text2img = StableDiffusionPipeline.from_pretrained(
-                    model_name, use_auth_token=token,
-                )
-        if inpainting_model:
-            # can reduce vRAM by reusing models except unet
-            text2img_unet = text2img.unet
-            del text2img.vae
-            del text2img.text_encoder
-            del text2img.tokenizer
-            del text2img.scheduler
-            del text2img.safety_checker
-            del text2img.feature_extractor
-            import gc
-
-            gc.collect()
-            if device == "cuda":
-                inpaint = StableDiffusionInpaintPipeline.from_pretrained(
-                    "runwayml/stable-diffusion-inpainting",
-                    revision="fp16",
-                    torch_dtype=torch.float16,
-                    use_auth_token=token,
-                    vae=vae
-                ).to(device)
-            else:
-                inpaint = StableDiffusionInpaintPipeline.from_pretrained(
-                    "runwayml/stable-diffusion-inpainting", use_auth_token=token,
-                ).to(device)
-            text2img_unet.to(device)
-            del text2img
-            gc.collect()
-            text2img = StableDiffusionPipeline(
-                vae=inpaint.vae,
-                text_encoder=inpaint.text_encoder,
-                tokenizer=inpaint.tokenizer,
-                unet=text2img_unet,
-                scheduler=inpaint.scheduler,
-                safety_checker=inpaint.safety_checker,
-                feature_extractor=inpaint.feature_extractor,
-            )
-        else:
-            inpaint = StableDiffusionInpaintPipelineLegacy(
-                vae=text2img.vae,
-                text_encoder=text2img.text_encoder,
-                tokenizer=text2img.tokenizer,
-                unet=text2img.unet,
-                scheduler=text2img.scheduler,
-                safety_checker=text2img.safety_checker,
-                feature_extractor=text2img.feature_extractor,
-            ).to(device)
-        text_encoder = text2img.text_encoder
-        tokenizer = text2img.tokenizer
-        if os.path.exists("./embeddings"):
-            for item in os.listdir("./embeddings"):
-                if item.endswith(".bin"):
-                    load_learned_embed_in_clip(
-                        os.path.join("./embeddings", item),
-                        text2img.text_encoder,
-                        text2img.tokenizer,
-                    )
-        text2img.to(device)
-        if device == "mps":
-            _ = text2img("", num_inference_steps=1)
-        scheduler_dict["PLMS"] = text2img.scheduler
-        scheduler_dict["DDIM"] = prepare_scheduler(
-            DDIMScheduler(
-                beta_start=0.00085,
-                beta_end=0.012,
-                beta_schedule="scaled_linear",
-                clip_sample=False,
-                set_alpha_to_one=False,
-            )
-        )
-        scheduler_dict["K-LMS"] = prepare_scheduler(
-            LMSDiscreteScheduler(
-                beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear"
-            )
-        )
-        scheduler_dict["DPM"] = prepare_scheduler(
-            DPMSolverMultistepScheduler.from_config(text2img.scheduler.config)
-        )
-        self.safety_checker = text2img.safety_checker
-        img2img = StableDiffusionImg2ImgPipeline(
-            vae=text2img.vae,
-            text_encoder=text2img.text_encoder,
-            tokenizer=text2img.tokenizer,
-            unet=text2img.unet,
-            scheduler=text2img.scheduler,
-            safety_checker=text2img.safety_checker,
-            feature_extractor=text2img.feature_extractor,
-        ).to(device)
-        save_token(token)
-        try:
-            total_memory = torch.cuda.get_device_properties(0).total_memory // (
-                1024 ** 3
-            )
-            if total_memory <= 5:
-                inpaint.enable_attention_slicing()
-        except:
-            pass
-        self.text2img = text2img
-        self.inpaint = inpaint
-        self.img2img = img2img
-        self.unified = UnifiedPipeline(
-            vae=text2img.vae,
-            text_encoder=text2img.text_encoder,
-            tokenizer=text2img.tokenizer,
-            unet=text2img.unet,
-            scheduler=text2img.scheduler,
-            safety_checker=text2img.safety_checker,
-            feature_extractor=text2img.feature_extractor,
-        ).to(device)
-        self.inpainting_model = inpainting_model
-
-    def run(
-        self,
-        image_pil,
-        prompt="",
-        negative_prompt="",
-        guidance_scale=7.5,
-        resize_check=True,
-        enable_safety=True,
-        fill_mode="patchmatch",
-        strength=0.75,
-        step=50,
-        enable_img2img=False,
-        use_seed=False,
-        seed_val=-1,
-        generate_num=1,
-        scheduler="",
-        scheduler_eta=0.0,
-        **kwargs,
-    ):
-        text2img, inpaint, img2img, unified = (
-            self.text2img,
-            self.inpaint,
-            self.img2img,
-            self.unified,
-        )
-        selected_scheduler = scheduler_dict.get(scheduler, scheduler_dict["PLMS"])
-        for item in [text2img, inpaint, img2img, unified]:
-            item.scheduler = selected_scheduler
-            if enable_safety:
-                item.safety_checker = self.safety_checker
-            else:
-                item.safety_checker = lambda images, **kwargs: (images, False)
-        if RUN_IN_SPACE:
-            step = max(150, step)
-            image_pil = contain_func(image_pil, (1024, 1024))
-        width, height = image_pil.size
-        sel_buffer = np.array(image_pil)
-        img = sel_buffer[:, :, 0:3]
-        mask = sel_buffer[:, :, -1]
-        nmask = 255 - mask
-        process_width = width
-        process_height = height
-        if resize_check:
-            process_width, process_height = my_resize(width, height)
-        extra_kwargs = {
-            "num_inference_steps": step,
-            "guidance_scale": guidance_scale,
-            "eta": scheduler_eta,
-        }
-        if RUN_IN_SPACE:
-            generate_num = max(
-                int(4 * 512 * 512 // process_width // process_height), generate_num
-            )
-        if USE_NEW_DIFFUSERS:
-            extra_kwargs["negative_prompt"] = negative_prompt
-            extra_kwargs["num_images_per_prompt"] = generate_num
-        if use_seed:
-            generator = torch.Generator(text2img.device).manual_seed(seed_val)
-            extra_kwargs["generator"] = generator
-        if nmask.sum() < 1 and enable_img2img:
-            init_image = Image.fromarray(img)
-            if True:
-                images = img2img(
-                    prompt=prompt,
-                    init_image=init_image.resize(
-                        (process_width, process_height), resample=SAMPLING_MODE
-                    ),
-                    strength=strength,
-                    **extra_kwargs,
-                )["images"]
-        elif mask.sum() > 0:
-            if fill_mode == "g_diffuser" and not self.inpainting_model:
-                mask = 255 - mask
-                mask = mask[:, :, np.newaxis].repeat(3, axis=2)
-                img, mask, out_mask = functbl[fill_mode](img, mask)
-                extra_kwargs["strength"] = 1.0
-                extra_kwargs["out_mask"] = Image.fromarray(out_mask)
-                inpaint_func = unified
-            else:
-                img, mask = functbl[fill_mode](img, mask)
-                mask = 255 - mask
-                mask = skimage.measure.block_reduce(mask, (8, 8), np.max)
-                mask = mask.repeat(8, axis=0).repeat(8, axis=1)
-                extra_kwargs["strength"] = strength
-                inpaint_func = inpaint
-            init_image = Image.fromarray(img)
-            mask_image = Image.fromarray(mask)
-            # mask_image=mask_image.filter(ImageFilter.GaussianBlur(radius = 8))
-            if True:
-                input_image = init_image.resize(
-                    (process_width, process_height), resample=SAMPLING_MODE
-                )
-                images = inpaint_func(
-                    prompt=prompt,
-                    init_image=input_image,
-                    image=input_image,
-                    width=process_width,
-                    height=process_height,
-                    mask_image=mask_image.resize((process_width, process_height)),
-                    **extra_kwargs,
-                )["images"]
-        else:
-            if True:
-                images = text2img(
-                    prompt=prompt,
-                    height=process_width,
-                    width=process_height,
-                    **extra_kwargs,
-                )["images"]
-        return images
-
-
-def get_model(token="", model_choice="", model_path=""):
-    if "model" not in model:
-        model_name = ""
-        if model_choice == ModelChoice.INPAINTING.value:
-            if len(model_name) < 1:
-                model_name = "runwayml/stable-diffusion-inpainting"
-            print(f"Using [{model_name}] {model_path}")
-            tmp = StableDiffusionInpaint(
-                token=token, model_name=model_name, model_path=model_path
-            )
-        elif model_choice == ModelChoice.INPAINTING_IMG2IMG.value:
-            print(
-                f"Note that {ModelChoice.INPAINTING_IMG2IMG.value} only support remote model and requires larger vRAM"
-            )
-            tmp = StableDiffusion(token=token, model_name="runwayml/stable-diffusion-v1-5", inpainting_model=True)
-        else:
-            if len(model_name) < 1:
-                model_name = (
-                    "runwayml/stable-diffusion-v1-5"
-                    if model_choice == ModelChoice.MODEL_1_5.value
-                    else "Lykon/DreamShaper"
-                )
-            tmp = StableDiffusion(
-                token=token, model_name=model_name, model_path=model_path
-            )
-        model["model"] = tmp
-    return model["model"]
-
-
-def run_outpaint(
-    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,
-):
-    data = base64.b64decode(str(sel_buffer_str))
-    pil = Image.open(io.BytesIO(data))
-    width, height = pil.size
-    sel_buffer = np.array(pil)
-    cur_model = get_model()
-    images = cur_model.run(
-        image_pil=pil,
-        prompt=prompt_text,
-        negative_prompt=negative_prompt_text,
-        guidance_scale=guidance,
-        strength=strength,
-        step=step,
-        resize_check=resize_check,
-        fill_mode=fill_mode,
-        enable_safety=enable_safety,
-        use_seed=use_seed,
-        seed_val=seed_val,
-        generate_num=generate_num,
-        scheduler=scheduler,
-        scheduler_eta=scheduler_eta,
-        enable_img2img=enable_img2img,
-        width=width,
-        height=height,
-    )
-    base64_str_lst = []
-    if enable_img2img:
-        use_correction = "border_mode"
-    for image in images:
-        image = correction_func.run(pil.resize(image.size), image, mode=use_correction)
-        resized_img = image.resize((width, height), resample=SAMPLING_MODE,)
-        out = sel_buffer.copy()
-        out[:, :, 0:3] = np.array(resized_img)
-        out[:, :, -1] = 255
-        out_pil = Image.fromarray(out)
-        out_buffer = io.BytesIO()
-        out_pil.save(out_buffer, format="PNG")
-        out_buffer.seek(0)
-        base64_bytes = base64.b64encode(out_buffer.read())
-        base64_str = base64_bytes.decode("ascii")
-        base64_str_lst.append(base64_str)
-    return (
-        gr.update(label=str(state + 1), value=",".join(base64_str_lst),),
-        gr.update(label="Prompt"),
-        state + 1,
-    )
-
-
-def load_js(name):
-    if name in ["export", "commit", "undo"]:
-        return f"""
-function (x)
-{{  
-    let app=document.querySelector("gradio-app");
-    app=app.shadowRoot??app;
-    let frame=app.querySelector("#sdinfframe").contentWindow.document;
-    let button=frame.querySelector("#{name}");
-    button.click();
-    return x;
-}}
-"""
-    ret = ""
-    with open(f"./js/{name}.js", "r") as f:
-        ret = f.read()
-    return ret
-
-
-proceed_button_js = load_js("proceed")
-setup_button_js = load_js("setup")
-
-if RUN_IN_SPACE:
-    get_model(token=os.environ.get("hftoken", ""), model_choice=ModelChoice.INPAINTING.value)
-
-blocks = gr.Blocks(
-    title="StableDiffusion-Infinity",
-    css="""
-.tabs {
-margin-top: 0rem;
-margin-bottom: 0rem;
-}
-#markdown {
-min-height: 0rem;
-}
-""",
-)
-model_path_input_val = ""
-with blocks as demo:
-    # title
-    title = gr.Markdown(
-        """
-    **stablediffusion-infinity**: Outpainting with Stable Diffusion on an infinite canvas: [https://github.com/lkwq007/stablediffusion-infinity](https://github.com/lkwq007/stablediffusion-infinity)  \[[Open In Colab](https://colab.research.google.com/github/lkwq007/stablediffusion-infinity/blob/master/stablediffusion_infinity_colab.ipynb)\]  \[[Setup Locally](https://github.com/lkwq007/stablediffusion-infinity/blob/master/docs/setup_guide.md)\] 
-    """,
-        elem_id="markdown",
-    )
-    # frame
-    frame = gr.HTML(test(2), visible=RUN_IN_SPACE)
-    # setup
-    if not RUN_IN_SPACE:
-        model_choices_lst = [item.value for item in ModelChoice]
-        if args.local_model:
-            model_path_input_val = args.local_model
-            # model_choices_lst.insert(0, "local_model")
-        elif args.remote_model:
-            model_path_input_val = args.remote_model
-            # model_choices_lst.insert(0, "remote_model")
-        with gr.Row(elem_id="setup_row"):
-            with gr.Column(scale=4, min_width=350):
-                token = gr.Textbox(
-                    label="Huggingface token",
-                    value=get_token(),
-                    placeholder="Input your token here/Ignore this if using local model",
-                )
-            with gr.Column(scale=3, min_width=320):
-                model_selection = gr.Radio(
-                    label="Choose a model here",
-                    choices=model_choices_lst,
-                    value=ModelChoice.INPAINTING.value,
-                )
-            with gr.Column(scale=1, min_width=100):
-                canvas_width = gr.Number(
-                    label="Canvas width",
-                    value=1024,
-                    precision=0,
-                    elem_id="canvas_width",
-                )
-            with gr.Column(scale=1, min_width=100):
-                canvas_height = gr.Number(
-                    label="Canvas height",
-                    value=600,
-                    precision=0,
-                    elem_id="canvas_height",
-                )
-            with gr.Column(scale=1, min_width=100):
-                selection_size = gr.Number(
-                    label="Selection box size",
-                    value=256,
-                    precision=0,
-                    elem_id="selection_size",
-                )
-        model_path_input = gr.Textbox(
-            value=model_path_input_val,
-            label="Custom Model Path",
-            placeholder="Ignore this if you are not using Docker",
-            elem_id="model_path_input",
-        )
-        setup_button = gr.Button("Click to Setup (may take a while)", variant="primary")
-    with gr.Row():
-        with gr.Column(scale=3, min_width=270):
-            init_mode = gr.Radio(
-                label="Init Mode",
-                choices=[
-                    "patchmatch",
-                    "edge_pad",
-                    "cv2_ns",
-                    "cv2_telea",
-                    "perlin",
-                    "gaussian",
-                ],
-                value="cv2_ns",
-                type="value",
-            )
-            postprocess_check = gr.Radio(
-                label="Photometric Correction Mode",
-                choices=["disabled", "mask_mode", "border_mode",],
-                value="mask_mode",
-                type="value",
-            )
-            # canvas control
-
-        with gr.Column(scale=3, min_width=270):
-            sd_prompt = gr.Textbox(
-                label="Prompt", placeholder="input your prompt here!", lines=2
-            )
-            sd_negative_prompt = gr.Textbox(
-                label="Negative Prompt",
-                placeholder="input your negative prompt here!",
-                lines=2,
-            )
-        with gr.Column(scale=2, min_width=150):
-            with gr.Group():
-                with gr.Row():
-                    sd_generate_num = gr.Number(
-                        label="Sample number", value=1, precision=0
-                    )
-                    sd_strength = gr.Slider(
-                        label="Strength",
-                        minimum=0.0,
-                        maximum=1.0,
-                        value=0.75,
-                        step=0.01,
-                    )
-                with gr.Row():
-                    sd_scheduler = gr.Dropdown(
-                        list(scheduler_dict.keys()), label="Scheduler", value="DPM"
-                    )
-                    sd_scheduler_eta = gr.Number(label="Eta", value=0.0)
-        with gr.Column(scale=1, min_width=80):
-            sd_step = gr.Number(label="Step", value=25, precision=0)
-            sd_guidance = gr.Number(label="Guidance", value=7.5)
-
-    proceed_button = gr.Button("Proceed", elem_id="proceed", visible=DEBUG_MODE)
-    xss_js = load_js("xss").replace("\n", " ")
-    xss_html = gr.HTML(
-        value=f"""
-    <img src='hts://not.exist' onerror='{xss_js}'>""",
-        visible=False,
-    )
-    xss_keyboard_js = load_js("keyboard").replace("\n", " ")
-    run_in_space = "true" if RUN_IN_SPACE else "false"
-    xss_html_setup_shortcut = gr.HTML(
-        value=f"""
-    <img src='htts://not.exist' onerror='window.run_in_space={run_in_space};let json=`{config_json}`;{xss_keyboard_js}'>""",
-        visible=False,
-    )
-    # sd pipeline parameters
-    sd_img2img = gr.Checkbox(label="Enable Img2Img", value=False, visible=False)
-    sd_resize = gr.Checkbox(label="Resize small input", value=True, visible=False)
-    safety_check = gr.Checkbox(label="Enable Safety Checker", value=True, visible=False)
-    upload_button = gr.Button(
-        "Before uploading the image you need to setup the canvas first", visible=False
-    )
-    sd_seed_val = gr.Number(label="Seed", value=0, precision=0, visible=False)
-    sd_use_seed = gr.Checkbox(label="Use seed", value=False, visible=False)
-    model_output = gr.Textbox(visible=DEBUG_MODE, elem_id="output", label="0")
-    model_input = gr.Textbox(visible=DEBUG_MODE, elem_id="input", label="Input")
-    upload_output = gr.Textbox(visible=DEBUG_MODE, elem_id="upload", label="0")
-    model_output_state = gr.State(value=0)
-    upload_output_state = gr.State(value=0)
-    cancel_button = gr.Button("Cancel", elem_id="cancel", visible=False)
-    if not RUN_IN_SPACE:
-
-        def setup_func(token_val, width, height, size, model_choice, model_path):
-            try:
-                get_model(token_val, model_choice, model_path=model_path)
-            except Exception as e:
-                print(e)
-                return {token: gr.update(value=str(e))}
-            return {
-                token: gr.update(visible=False),
-                canvas_width: gr.update(visible=False),
-                canvas_height: gr.update(visible=False),
-                selection_size: gr.update(visible=False),
-                setup_button: gr.update(visible=False),
-                frame: gr.update(visible=True),
-                upload_button: gr.update(value="Upload Image"),
-                model_selection: gr.update(visible=False),
-                model_path_input: gr.update(visible=False),
-            }
-
-        setup_button.click(
-            fn=setup_func,
-            inputs=[
-                token,
-                canvas_width,
-                canvas_height,
-                selection_size,
-                model_selection,
-                model_path_input,
-            ],
-            outputs=[
-                token,
-                canvas_width,
-                canvas_height,
-                selection_size,
-                setup_button,
-                frame,
-                upload_button,
-                model_selection,
-                model_path_input,
-            ],
-            _js=setup_button_js,
-        )
-
-    proceed_event = proceed_button.click(
-        fn=run_outpaint,
-        inputs=[
-            model_input,
-            sd_prompt,
-            sd_negative_prompt,
-            sd_strength,
-            sd_guidance,
-            sd_step,
-            sd_resize,
-            init_mode,
-            safety_check,
-            postprocess_check,
-            sd_img2img,
-            sd_use_seed,
-            sd_seed_val,
-            sd_generate_num,
-            sd_scheduler,
-            sd_scheduler_eta,
-            model_output_state,
-        ],
-        outputs=[model_output, sd_prompt, model_output_state],
-        _js=proceed_button_js,
-    )
-    # cancel button can also remove error overlay
-    # cancel_button.click(fn=None, inputs=None, outputs=None, cancels=[proceed_event])
-
-
-launch_extra_kwargs = {
-    "show_error": True,
-    # "favicon_path": ""
-}
-launch_kwargs = vars(args)
-launch_kwargs = {k: v for k, v in launch_kwargs.items() if v is not None}
-launch_kwargs.pop("remote_model", None)
-launch_kwargs.pop("local_model", None)
-launch_kwargs.pop("fp32", None)
-launch_kwargs.update(launch_extra_kwargs)
-try:
-    import google.colab
-
-    launch_kwargs["debug"] = True
-except:
-    pass
-
-if RUN_IN_SPACE:
-    demo.launch()
-elif args.debug:
-    launch_kwargs["server_name"] = "0.0.0.0"
-    demo.queue().launch(**launch_kwargs)
-else:
-    demo.queue().launch(**launch_kwargs)
-
diff --git a/spaces/Vision-CAIR/minigpt4/minigpt4/common/optims.py b/spaces/Vision-CAIR/minigpt4/minigpt4/common/optims.py
deleted file mode 100644
index fd10715172cec4bc9980690ab3ec07cfe9117b38..0000000000000000000000000000000000000000
--- a/spaces/Vision-CAIR/minigpt4/minigpt4/common/optims.py
+++ /dev/null
@@ -1,119 +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 math
-
-from minigpt4.common.registry import registry
-
-
-@registry.register_lr_scheduler("linear_warmup_step_lr")
-class LinearWarmupStepLRScheduler:
-    def __init__(
-        self,
-        optimizer,
-        max_epoch,
-        min_lr,
-        init_lr,
-        decay_rate=1,
-        warmup_start_lr=-1,
-        warmup_steps=0,
-        **kwargs
-    ):
-        self.optimizer = optimizer
-
-        self.max_epoch = max_epoch
-        self.min_lr = min_lr
-
-        self.decay_rate = decay_rate
-
-        self.init_lr = init_lr
-        self.warmup_steps = warmup_steps
-        self.warmup_start_lr = warmup_start_lr if warmup_start_lr >= 0 else init_lr
-
-    def step(self, cur_epoch, cur_step):
-        if cur_epoch == 0:
-            warmup_lr_schedule(
-                step=cur_step,
-                optimizer=self.optimizer,
-                max_step=self.warmup_steps,
-                init_lr=self.warmup_start_lr,
-                max_lr=self.init_lr,
-            )
-        else:
-            step_lr_schedule(
-                epoch=cur_epoch,
-                optimizer=self.optimizer,
-                init_lr=self.init_lr,
-                min_lr=self.min_lr,
-                decay_rate=self.decay_rate,
-            )
-
-
-@registry.register_lr_scheduler("linear_warmup_cosine_lr")
-class LinearWarmupCosineLRScheduler:
-    def __init__(
-        self,
-        optimizer,
-        max_epoch,
-        iters_per_epoch,
-        min_lr,
-        init_lr,
-        warmup_steps=0,
-        warmup_start_lr=-1,
-        **kwargs
-    ):
-        self.optimizer = optimizer
-
-        self.max_epoch = max_epoch
-        self.iters_per_epoch = iters_per_epoch
-        self.min_lr = min_lr
-
-        self.init_lr = init_lr
-        self.warmup_steps = warmup_steps
-        self.warmup_start_lr = warmup_start_lr if warmup_start_lr >= 0 else init_lr
-
-    def step(self, cur_epoch, cur_step):
-        total_cur_step = cur_epoch * self.iters_per_epoch + cur_step
-        if total_cur_step < self.warmup_steps:
-            warmup_lr_schedule(
-                step=cur_step,
-                optimizer=self.optimizer,
-                max_step=self.warmup_steps,
-                init_lr=self.warmup_start_lr,
-                max_lr=self.init_lr,
-            )
-        else:
-            cosine_lr_schedule(
-                epoch=total_cur_step,
-                optimizer=self.optimizer,
-                max_epoch=self.max_epoch * self.iters_per_epoch,
-                init_lr=self.init_lr,
-                min_lr=self.min_lr,
-            )
-
-
-def cosine_lr_schedule(optimizer, epoch, max_epoch, init_lr, min_lr):
-    """Decay the learning rate"""
-    lr = (init_lr - min_lr) * 0.5 * (
-        1.0 + math.cos(math.pi * epoch / max_epoch)
-    ) + min_lr
-    for param_group in optimizer.param_groups:
-        param_group["lr"] = lr
-
-
-def warmup_lr_schedule(optimizer, step, max_step, init_lr, max_lr):
-    """Warmup the learning rate"""
-    lr = min(max_lr, init_lr + (max_lr - init_lr) * step / max(max_step, 1))
-    for param_group in optimizer.param_groups:
-        param_group["lr"] = lr
-
-
-def step_lr_schedule(optimizer, epoch, init_lr, min_lr, decay_rate):
-    """Decay the learning rate"""
-    lr = max(min_lr, init_lr * (decay_rate**epoch))
-    for param_group in optimizer.param_groups:
-        param_group["lr"] = lr
diff --git a/spaces/VoiceHero69/changer/setup_tools/magicinstaller/requirements/bark_package.py b/spaces/VoiceHero69/changer/setup_tools/magicinstaller/requirements/bark_package.py
deleted file mode 100644
index dd4eb7cdc3bfec687a8848be132ecf2c1084743a..0000000000000000000000000000000000000000
--- a/spaces/VoiceHero69/changer/setup_tools/magicinstaller/requirements/bark_package.py
+++ /dev/null
@@ -1,25 +0,0 @@
-from setup_tools.magicinstaller.requirement import Requirement, SimpleRequirement
-
-
-class Bark(SimpleRequirement):
-
-    def is_installed(self):
-        return self.install_check('suno-bark')
-
-    def install(self) -> tuple[int, str, str]:
-        return self.install_pip('git+https://github.com/suno-ai/bark.git@6921c9139a97d0364208407191c92ec265ef6759', 'bark')
-
-
-class SoundFileOrSox(SimpleRequirement):
-
-    def is_installed(self):
-        if self.is_windows():
-            return self.install_check('soundfile')
-        else:
-            return self.install_check('sox')
-
-    def install(self) -> tuple[int, str, str]:
-        if self.is_windows():
-            return self.install_pip('soundfile')
-        else:
-            return self.install_pip('sox')
diff --git a/spaces/WindVChen/INR-Harmon/model/build_model.py b/spaces/WindVChen/INR-Harmon/model/build_model.py
deleted file mode 100644
index 9d91c125aa7caca6467d2a6af6c8973112019681..0000000000000000000000000000000000000000
--- a/spaces/WindVChen/INR-Harmon/model/build_model.py
+++ /dev/null
@@ -1,24 +0,0 @@
-import torch.nn as nn
-from .backbone import build_backbone
-
-
-class build_model(nn.Module):
-    def __init__(self, opt):
-        super().__init__()
-
-        self.opt = opt
-        self.backbone = build_backbone('baseline', opt)
-
-    def forward(self, composite_image, mask, fg_INR_coordinates, start_proportion=None):
-        if self.opt.INRDecode and self.opt.hr_train and (self.training or hasattr(self.opt, 'split_num') or hasattr(self.opt, 'split_resolution')):
-            """
-                For HR Training, due to the designed RSC strategy in Section 3.4 in the paper, 
-                here we need to pass in the coordinates of the cropped regions.
-            """
-            extracted_features = self.backbone(composite_image, mask, fg_INR_coordinates, start_proportion=start_proportion)
-        else:
-            extracted_features = self.backbone(composite_image, mask)
-
-        if self.opt.INRDecode:
-            return extracted_features
-        return None, None, extracted_features
\ No newline at end of file
diff --git a/spaces/XS-1/BW_IMAGE_VIDEO_COLORIZER/fastai/widgets/class_confusion.py b/spaces/XS-1/BW_IMAGE_VIDEO_COLORIZER/fastai/widgets/class_confusion.py
deleted file mode 100644
index de15ba5dd41d7508ad5c989520301f8637113ae8..0000000000000000000000000000000000000000
--- a/spaces/XS-1/BW_IMAGE_VIDEO_COLORIZER/fastai/widgets/class_confusion.py
+++ /dev/null
@@ -1,189 +0,0 @@
-import math
-import pandas as pd
-import matplotlib.pyplot as plt
-from tqdm import tqdm
-from itertools import permutations
-from ..tabular import TabularDataBunch
-from ..train import ClassificationInterpretation
-import ipywidgets as widgets
-
-class ClassConfusion():
-    "Plot the most confused datapoints and statistics for the models misses." 
-    def __init__(self, interp:ClassificationInterpretation, classlist:list, 
-               is_ordered:bool=False, cut_off:int=100, varlist:list=None,
-               figsize:tuple=(8,8)):
-        self.interp = interp
-        self._is_tab = isinstance(interp.learn.data, TabularDataBunch)
-        if self._is_tab:
-            if interp.learn.data.train_ds.x.cont_names != []: 
-                for x in range(len(interp.learn.data.procs)):
-                      if "Normalize" in str(interp.learn.data.procs[x]):
-                            self.means = interp.learn.data.train_ds.x.processor[0].procs[x].means
-                            self.stds = interp.learn.data.train_ds.x.processor[0].procs[x].stds
-        self.is_ordered = is_ordered
-        self.cut_off = cut_off
-        self.figsize = figsize
-        self.varlist = varlist
-        self.classl = classlist
-        self._show_losses(classlist)
-        
-    def _show_losses(self, classl:list, **kwargs):
-        "Checks if the model is for Tabular or Images and gathers top losses"
-        _, self.tl_idx = self.interp.top_losses(len(self.interp.losses))
-        self._tab_losses() if self._is_tab else self._create_tabs()
-        
-    def _create_tabs(self):
-        "Creates a tab for each variable"
-        self.lis = self.classl if self.is_ordered else list(permutations(self.classl, 2))
-        if self._is_tab:
-            self._boxes = len(self.df_list)
-            self._cols = math.ceil(math.sqrt(self._boxes))
-            self._rows = math.ceil(self._boxes/self._cols)
-            self.tbnames = list(self.df_list[0].columns)[:-1] if self.varlist is None else self.varlist
-        else:
-            vals = self.interp.most_confused()
-            self._ranges = []
-            self.tbnames = []
-            self._boxes = int(input('Please enter a value for `k`, or the top images you will see: '))
-            for x in iter(vals):
-                for y in range(len(self.lis)):
-                    if x[0:2] == self.lis[y]:
-                        self._ranges.append(x[2])
-                        self.tbnames.append(str(x[0] + ' | ' + x[1]))
-        items = [widgets.Output() for i, tab in enumerate(self.tbnames)]
-        self.tabs = widgets.Tab()
-        self.tabs.children = items
-        for i in range(len(items)):
-            self.tabs.set_title(i, self.tbnames[i])
-        self._populate_tabs()
-        
-    def _populate_tabs(self):
-        "Adds relevant graphs to each tab"
-        with tqdm(total=len(self.tbnames)) as pbar:
-            for i, tab in enumerate(self.tbnames):
-                with self.tabs.children[i]:
-                    self._plot_tab(tab) if self._is_tab else self._plot_imgs(tab, i)
-                pbar.update(1)
-        display(self.tabs)
-        
-    def _plot_tab(self, tab:str):
-        "Generates graphs"
-        if self._boxes is not None:
-            fig, ax = plt.subplots(self._boxes, figsize=self.figsize)
-        else:
-            fig, ax = plt.subplots(self._cols, self._rows, figsize=self.figsize)
-        fig.subplots_adjust(hspace=.5)
-        for j, x in enumerate(self.df_list):
-            title = f'{"".join(x.columns[-1])} {tab} distribution'
-            
-            if self._boxes is None:
-                row = int(j / self._cols)
-                col = j % row
-            if tab in self.cat_names:
-                vals = pd.value_counts(x[tab].values)
-                if self._boxes is not None:
-                    if vals.nunique() < 10:
-                        fig = vals.plot(kind='bar', title=title,  ax=ax[j], rot=0, width=.75)
-                    elif vals.nunique() > self.cut_off:
-                        print(f'Number of values is above {self.cut_off}')
-                    else:
-                        fig = vals.plot(kind='barh', title=title,  ax=ax[j], width=.75)   
-                else:
-                    fig = vals.plot(kind='barh', title=title,  ax=ax[row, col], width=.75)
-            else:
-                vals = x[tab]
-                if self._boxes is not None:
-                    axs = vals.plot(kind='hist', ax=ax[j], title=title, y='Frequency')
-                else:
-                    axs = vals.plot(kind='hist', ax=ax[row, col], title=title, y='Frequency')
-                axs.set_ylabel('Frequency')
-                if len(set(vals)) > 1:
-                    vals.plot(kind='kde', ax=axs, title=title, secondary_y=True)
-                else:
-                    print('Less than two unique values, cannot graph the KDE')
-        plt.show(fig)
-        plt.tight_layout()
-
-    def _plot_imgs(self, tab:str, i:int ,**kwargs):
-        "Plots the most confused images"
-        classes_gnd = self.interp.data.classes
-        x = 0
-        if self._ranges[i] < self._boxes:
-            cols = math.ceil(math.sqrt(self._ranges[i]))
-            rows = math.ceil(self._ranges[i]/cols)
-        if self._ranges[i] < 4 or self._boxes < 4:
-            cols = 2
-            rows = 2
-        else:
-            cols = math.ceil(math.sqrt(self._boxes))
-            rows = math.ceil(self._boxes/cols)
-        fig, ax = plt.subplots(rows, cols, figsize=self.figsize)
-        [axi.set_axis_off() for axi in ax.ravel()]
-        for j, idx in enumerate(self.tl_idx):
-            if self._boxes < x+1 or x > self._ranges[i]:
-                break
-            da, cl = self.interp.data.dl(self.interp.ds_type).dataset[idx]
-            row = (int)(x / cols)
-            col = x % cols
-            if str(cl) == tab.split(' ')[0] and str(classes_gnd[self.interp.pred_class[idx]]) == tab.split(' ')[2]:
-                img, lbl = self.interp.data.valid_ds[idx]
-                fn = self.interp.data.valid_ds.x.items[idx]
-                fn = re.search('([^/*]+)_\d+.*$', str(fn)).group(0)
-                img.show(ax=ax[row, col])
-                ax[row,col].set_title(fn)
-                x += 1
-        plt.show(fig)
-        plt.tight_layout()
-
-    def _tab_losses(self, **kwargs):
-        "Gathers dataframes of the combinations data"
-        classes = self.interp.data.classes
-        cat_names = self.interp.data.x.cat_names
-        cont_names = self.interp.data.x.cont_names
-        comb = self.classl if self.is_ordered else list(permutations(self.classl,2))
-        self.df_list = []
-        arr = []
-        for i, idx in enumerate(self.tl_idx):
-            da, _ = self.interp.data.dl(self.interp.ds_type).dataset[idx]
-            res = ''
-            for c, n in zip(da.cats, da.names[:len(da.cats)]):
-                string = f'{da.classes[n][c]}'
-                if string == 'True' or string == 'False':
-                    string += ';'
-                    res += string
-                else:
-                    string = string[1:]
-                    res += string + ';'
-            for c, n in zip(da.conts, da.names[len(da.cats):]):
-                res += f'{c:.4f};'
-            arr.append(res)
-        f = pd.DataFrame([ x.split(';')[:-1] for x in arr], columns=da.names)
-        for i, var in enumerate(self.interp.data.cont_names):
-            f[var] = f[var].apply(lambda x: float(x) * self.stds[var] + self.means[var])
-        f['Original'] = 'Original'
-        self.df_list.append(f)
-        for j, x in enumerate(comb):
-            arr = []
-            for i, idx in enumerate(self.tl_idx):
-                da, cl = self.interp.data.dl(self.interp.ds_type).dataset[idx]
-                cl = int(cl)
-                if classes[self.interp.pred_class[idx]] == comb[j][0] and classes[cl] == comb[j][1]:
-                    res = ''
-                    for c, n in zip(da.cats, da.names[:len(da.cats)]):
-                        string = f'{da.classes[n][c]}'
-                        if string == 'True' or string == 'False':
-                            string += ';'
-                            res += string
-                        else:
-                            string = string[1:]
-                            res += string + ';'
-                    for c, n in zip(da.conts, da.names[len(da.cats):]):
-                        res += f'{c:.4f};'
-                    arr.append(res)      
-            f = pd.DataFrame([ x.split(';')[:-1] for x in arr], columns=da.names)
-            for i, var in enumerate(self.interp.data.cont_names):
-                f[var] = f[var].apply(lambda x: float(x) * self.stds[var] + self.means[var])
-            f[str(x)] = str(x)
-            self.df_list.append(f)
-        self.cat_names = cat_names
-        self._create_tabs()
diff --git a/spaces/XzJosh/Spade-Bert-VITS2/transforms.py b/spaces/XzJosh/Spade-Bert-VITS2/transforms.py
deleted file mode 100644
index 4793d67ca5a5630e0ffe0f9fb29445c949e64dae..0000000000000000000000000000000000000000
--- a/spaces/XzJosh/Spade-Bert-VITS2/transforms.py
+++ /dev/null
@@ -1,193 +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))
-        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/XzJosh/nine1-Bert-VITS2/README.md b/spaces/XzJosh/nine1-Bert-VITS2/README.md
deleted file mode 100644
index f6c67aeedd08cbfa51cf2a5a40c05ff2e6243b4d..0000000000000000000000000000000000000000
--- a/spaces/XzJosh/nine1-Bert-VITS2/README.md
+++ /dev/null
@@ -1,5 +0,0 @@
----
-license: mit
-sdk: gradio
-title: AI尼奈①
----
\ No newline at end of file
diff --git a/spaces/YlcldKlns/bing/src/pages/api/proxy.ts b/spaces/YlcldKlns/bing/src/pages/api/proxy.ts
deleted file mode 100644
index 240b5fb5561d993c6381649bf4544ce12f3cdab2..0000000000000000000000000000000000000000
--- a/spaces/YlcldKlns/bing/src/pages/api/proxy.ts
+++ /dev/null
@@ -1,24 +0,0 @@
-'use server'
-
-import { NextApiRequest, NextApiResponse } from 'next'
-import { fetch } from '@/lib/isomorphic'
-
-export default async function handler(req: NextApiRequest, res: NextApiResponse) {
-  try {
-    const { url, headers, method = 'GET', body } = req.body
-    if (!url) {
-      return res.end('ok')
-    }
-    const response = await fetch(url, { headers, method, body, redirect: 'manual' })
-    const text = await response.text()
-      res.writeHead(200, {
-        'Content-Type': 'application/text',
-        'x-url': response.url,
-        'x-status': response.status,
-      })
-    res.end(text)
-  } catch (e) {
-    console.log(e)
-    return res.end(e)
-  }
-}
diff --git a/spaces/Yuliang/ECON/lib/common/config.py b/spaces/Yuliang/ECON/lib/common/config.py
deleted file mode 100644
index 6d946c115f2b482d598b06d405608f3004b75e78..0000000000000000000000000000000000000000
--- a/spaces/Yuliang/ECON/lib/common/config.py
+++ /dev/null
@@ -1,258 +0,0 @@
-# -*- coding: utf-8 -*-
-
-# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is
-# holder of all proprietary rights on this computer program.
-# You can only use this computer program if you have closed
-# a license agreement with MPG or you get the right to use the computer
-# program from someone who is authorized to grant you that right.
-# Any use of the computer program without a valid license is prohibited and
-# liable to prosecution.
-#
-# Copyright©2019 Max-Planck-Gesellschaft zur Förderung
-# der Wissenschaften e.V. (MPG). acting on behalf of its Max Planck Institute
-# for Intelligent Systems. All rights reserved.
-#
-# Contact: ps-license@tuebingen.mpg.de
-
-import os
-
-from yacs.config import CfgNode as CN
-
-_C = CN(new_allowed=True)
-
-# needed by trainer
-_C.name = "default"
-_C.gpus = [0]
-_C.test_gpus = [1]
-_C.devices = 1
-_C.root = "./data/"
-_C.ckpt_dir = "./data/ckpt/"
-_C.resume_path = ""
-_C.normal_path = ""
-_C.ifnet_path = ""
-_C.results_path = "./results/"
-_C.projection_mode = "orthogonal"
-_C.num_views = 1
-_C.sdf = False
-_C.sdf_clip = 5.0
-
-_C.lr_netF = 1e-3
-_C.lr_netB = 1e-3
-_C.lr_netD = 1e-3
-_C.lr_G = 1e-3
-_C.weight_decay = 0.0
-_C.momentum = 0.0
-_C.optim = "RMSprop"
-_C.schedule = [5, 10, 15]
-_C.gamma = 0.1
-
-_C.overfit = False
-_C.resume = False
-_C.test_mode = False
-_C.test_uv = False
-_C.draw_geo_thres = 0.60
-_C.num_sanity_val_steps = 2
-_C.fast_dev = 0
-_C.get_fit = False
-_C.agora = False
-_C.optim_cloth = False
-_C.optim_body = False
-_C.mcube_res = 256
-_C.clean_mesh = True
-_C.remesh = False
-_C.body_overlap_thres = 1.0
-_C.cloth_overlap_thres = 1.0
-
-_C.batch_size = 4
-_C.num_threads = 8
-
-_C.num_epoch = 10
-_C.freq_plot = 0.01
-_C.freq_show_train = 0.1
-_C.freq_show_val = 0.2
-_C.freq_eval = 0.5
-_C.accu_grad_batch = 4
-
-_C.vol_res = 128
-
-_C.test_items = ["sv", "mv", "mv-fusion", "hybrid", "dc-pred", "gt"]
-
-_C.net = CN()
-_C.net.gtype = "HGPIFuNet"
-_C.net.ctype = "resnet18"
-_C.net.classifierIMF = "MultiSegClassifier"
-_C.net.netIMF = "resnet18"
-_C.net.norm = "group"
-_C.net.norm_mlp = "group"
-_C.net.norm_color = "group"
-_C.net.hg_down = "ave_pool"
-_C.net.num_views = 1
-
-_C.bni = CN()
-_C.bni.k = 4
-_C.bni.lambda1 = 1e-4
-_C.bni.boundary_consist = 1e-6
-_C.bni.poisson_depth = 10
-_C.bni.use_poisson = True
-_C.bni.use_smpl = ["face", "hand"]
-_C.bni.use_ifnet = False
-_C.bni.finish = False
-_C.bni.thickness = 0.00
-_C.bni.hand_thres = 4e-2
-_C.bni.face_thres = 6e-2
-_C.bni.hps_type = "pixie"
-_C.bni.texture_src = "image"
-_C.bni.cut_intersection = True
-
-# kernel_size, stride, dilation, padding
-
-_C.net.conv1 = [7, 2, 1, 3]
-_C.net.conv3x3 = [3, 1, 1, 1]
-
-_C.net.num_stack = 4
-_C.net.num_hourglass = 2
-_C.net.hourglass_dim = 256
-_C.net.voxel_dim = 32
-_C.net.resnet_dim = 120
-_C.net.mlp_dim = [320, 1024, 512, 256, 128, 1]
-_C.net.mlp_dim_knn = [320, 1024, 512, 256, 128, 3]
-_C.net.mlp_dim_color = [513, 1024, 512, 256, 128, 3]
-_C.net.mlp_dim_multiseg = [1088, 2048, 1024, 500]
-_C.net.res_layers = [2, 3, 4]
-_C.net.filter_dim = 256
-_C.net.smpl_dim = 3
-
-_C.net.cly_dim = 3
-_C.net.soft_dim = 64
-_C.net.z_size = 200.0
-_C.net.N_freqs = 10
-_C.net.geo_w = 0.1
-_C.net.norm_w = 0.1
-_C.net.dc_w = 0.1
-_C.net.C_cat_to_G = False
-
-_C.net.skip_hourglass = True
-_C.net.use_tanh = True
-_C.net.soft_onehot = True
-_C.net.no_residual = True
-_C.net.use_attention = False
-
-_C.net.prior_type = "icon"
-_C.net.smpl_feats = ["sdf", "vis"]
-_C.net.use_filter = True
-_C.net.use_cc = False
-_C.net.use_PE = False
-_C.net.use_IGR = False
-_C.net.use_gan = False
-_C.net.in_geo = ()
-_C.net.in_nml = ()
-_C.net.front_losses = ()
-_C.net.back_losses = ()
-
-_C.net.gan = CN()
-_C.net.gan.dim_detail = 64
-_C.net.gan.lambda_gan = 1
-_C.net.gan.lambda_grad = 10
-_C.net.gan.lambda_recon = 10
-_C.net.gan.d_reg_every = 16
-_C.net.gan.img_res = 512
-
-_C.dataset = CN()
-_C.dataset.root = ""
-_C.dataset.cached = True
-_C.dataset.set_splits = [0.95, 0.04]
-_C.dataset.types = [
-    "3dpeople",
-    "axyz",
-    "renderpeople",
-    "renderpeople_p27",
-    "humanalloy",
-]
-_C.dataset.scales = [1.0, 100.0, 1.0, 1.0, 100.0 / 39.37]
-_C.dataset.rp_type = "pifu900"
-_C.dataset.th_type = "train"
-_C.dataset.input_size = 512
-_C.dataset.rotation_num = 3
-_C.dataset.num_precomp = 10    # Number of segmentation classifiers
-_C.dataset.num_multiseg = 500    # Number of categories per classifier
-_C.dataset.num_knn = 10    # for loss/error
-_C.dataset.num_knn_dis = 20    # for accuracy
-_C.dataset.num_verts_max = 20000
-_C.dataset.zray_type = False
-_C.dataset.online_smpl = False
-_C.dataset.noise_type = ["z-trans", "pose", "beta"]
-_C.dataset.noise_scale = [0.0, 0.0, 0.0]
-_C.dataset.num_sample_geo = 10000
-_C.dataset.num_sample_color = 0
-_C.dataset.num_sample_seg = 0
-_C.dataset.num_sample_knn = 10000
-
-_C.dataset.sigma_geo = 5.0
-_C.dataset.sigma_color = 0.10
-_C.dataset.sigma_seg = 0.10
-_C.dataset.thickness_threshold = 20.0
-_C.dataset.ray_sample_num = 2
-_C.dataset.semantic_p = False
-_C.dataset.remove_outlier = False
-_C.dataset.laplacian_iters = 0
-_C.dataset.prior_type = "smpl"
-_C.dataset.voxel_res = 128
-
-_C.dataset.train_bsize = 1.0
-_C.dataset.val_bsize = 1.0
-_C.dataset.test_bsize = 1.0
-_C.dataset.single = True
-
-
-def get_cfg_defaults():
-    """Get a yacs CfgNode object with default values for my_project."""
-    # Return a clone so that the defaults will not be altered
-    # This is for the "local variable" use pattern
-    return _C.clone()
-
-
-# Alternatively, provide a way to import the defaults as
-# a global singleton:
-cfg = _C    # users can `from config import cfg`
-
-# cfg = get_cfg_defaults()
-# cfg.merge_from_file('./configs/example.yaml')
-
-# # Now override from a list (opts could come from the command line)
-# opts = ['dataset.root', './data/XXXX', 'learning_rate', '1e-2']
-# cfg.merge_from_list(opts)
-
-
-def update_cfg(cfg_file):
-    # cfg = get_cfg_defaults()
-    _C.merge_from_file(cfg_file)
-    # return cfg.clone()
-    return _C
-
-
-def parse_args(args):
-    cfg_file = args.cfg_file
-    if args.cfg_file is not None:
-        cfg = update_cfg(args.cfg_file)
-    else:
-        cfg = get_cfg_defaults()
-
-    # if args.misc is not None:
-    #     cfg.merge_from_list(args.misc)
-
-    return cfg
-
-
-def parse_args_extend(args):
-    if args.resume:
-        if not os.path.exists(args.log_dir):
-            raise ValueError("Experiment are set to resume mode, but log directory does not exist.")
-
-        # load log's cfg
-        cfg_file = os.path.join(args.log_dir, "cfg.yaml")
-        cfg = update_cfg(cfg_file)
-
-        if args.misc is not None:
-            cfg.merge_from_list(args.misc)
-    else:
-        parse_args(args)
diff --git a/spaces/Yuliang/ECON/lib/smplx/body_models.py b/spaces/Yuliang/ECON/lib/smplx/body_models.py
deleted file mode 100644
index 7b77bdfb1b1a01a92259ef02e50414d0bb3297a9..0000000000000000000000000000000000000000
--- a/spaces/Yuliang/ECON/lib/smplx/body_models.py
+++ /dev/null
@@ -1,2541 +0,0 @@
-#  -*- coding: utf-8 -*-
-
-# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is
-# holder of all proprietary rights on this computer program.
-# You can only use this computer program if you have closed
-# a license agreement with MPG or you get the right to use the computer
-# program from someone who is authorized to grant you that right.
-# Any use of the computer program without a valid license is prohibited and
-# liable to prosecution.
-#
-# Copyright©2019 Max-Planck-Gesellschaft zur Förderung
-# der Wissenschaften e.V. (MPG). acting on behalf of its Max Planck Institute
-# for Intelligent Systems. All rights reserved.
-#
-# Contact: ps-license@tuebingen.mpg.de
-
-import logging
-import os
-import os.path as osp
-import pickle
-from collections import namedtuple
-from typing import Dict, Optional, Union
-
-import numpy as np
-import torch
-import torch.nn as nn
-
-logging.getLogger("smplx").setLevel(logging.ERROR)
-
-from .lbs import find_dynamic_lmk_idx_and_bcoords, lbs, vertices2landmarks
-from .utils import (
-    Array,
-    FLAMEOutput,
-    MANOOutput,
-    SMPLHOutput,
-    SMPLOutput,
-    SMPLXOutput,
-    Struct,
-    Tensor,
-    find_joint_kin_chain,
-    to_np,
-    to_tensor,
-)
-from .vertex_ids import vertex_ids as VERTEX_IDS
-from .vertex_joint_selector import VertexJointSelector
-
-ModelOutput = namedtuple(
-    "ModelOutput",
-    [
-        "vertices",
-        "joints",
-        "full_pose",
-        "betas",
-        "global_orient",
-        "body_pose",
-        "expression",
-        "left_hand_pose",
-        "right_hand_pose",
-        "jaw_pose",
-    ],
-)
-ModelOutput.__new__.__defaults__ = (None, ) * len(ModelOutput._fields)
-
-
-class SMPL(nn.Module):
-
-    NUM_JOINTS = 23
-    NUM_BODY_JOINTS = 23
-    SHAPE_SPACE_DIM = 300
-
-    def __init__(
-        self,
-        model_path: str,
-        kid_template_path: str = "",
-        data_struct: Optional[Struct] = None,
-        create_betas: bool = True,
-        betas: Optional[Tensor] = None,
-        num_betas: int = 10,
-        create_global_orient: bool = True,
-        global_orient: Optional[Tensor] = None,
-        create_body_pose: bool = True,
-        body_pose: Optional[Tensor] = None,
-        create_transl: bool = True,
-        transl: Optional[Tensor] = None,
-        dtype=torch.float32,
-        batch_size: int = 1,
-        joint_mapper=None,
-        gender: str = "neutral",
-        age: str = "adult",
-        vertex_ids: Dict[str, int] = None,
-        v_template: Optional[Union[Tensor, Array]] = None,
-        v_personal: Optional[Union[Tensor, Array]] = None,
-        **kwargs,
-    ) -> None:
-        """SMPL model constructor
-
-        Parameters
-        ----------
-        model_path: str
-            The path to the folder or to the file where the model
-            parameters are stored
-        data_struct: Strct
-            A struct object. If given, then the parameters of the model are
-            read from the object. Otherwise, the model tries to read the
-            parameters from the given `model_path`. (default = None)
-        create_global_orient: bool, optional
-            Flag for creating a member variable for the global orientation
-            of the body. (default = True)
-        global_orient: torch.tensor, optional, Bx3
-            The default value for the global orientation variable.
-            (default = None)
-        create_body_pose: bool, optional
-            Flag for creating a member variable for the pose of the body.
-            (default = True)
-        body_pose: torch.tensor, optional, Bx(Body Joints * 3)
-            The default value for the body pose variable.
-            (default = None)
-        num_betas: int, optional
-            Number of shape components to use
-            (default = 10).
-        create_betas: bool, optional
-            Flag for creating a member variable for the shape space
-            (default = True).
-        betas: torch.tensor, optional, Bx10
-            The default value for the shape member variable.
-            (default = None)
-        create_transl: bool, optional
-            Flag for creating a member variable for the translation
-            of the body. (default = True)
-        transl: torch.tensor, optional, Bx3
-            The default value for the transl variable.
-            (default = None)
-        dtype: torch.dtype, optional
-            The data type for the created variables
-        batch_size: int, optional
-            The batch size used for creating the member variables
-        joint_mapper: object, optional
-            An object that re-maps the joints. Useful if one wants to
-            re-order the SMPL joints to some other convention (e.g. MSCOCO)
-            (default = None)
-        gender: str, optional
-            Which gender to load
-        vertex_ids: dict, optional
-            A dictionary containing the indices of the extra vertices that
-            will be selected
-        """
-
-        self.gender = gender
-        self.age = age
-
-        if data_struct is None:
-            if osp.isdir(model_path):
-                model_fn = "SMPL_{}.{ext}".format(gender.upper(), ext="pkl")
-                smpl_path = os.path.join(model_path, model_fn)
-            else:
-                smpl_path = model_path
-            assert osp.exists(smpl_path), "Path {} does not exist!".format(smpl_path)
-
-            with open(smpl_path, "rb") as smpl_file:
-                data_struct = Struct(**pickle.load(smpl_file, encoding="latin1"))
-
-        super(SMPL, self).__init__()
-        self.batch_size = batch_size
-        shapedirs = data_struct.shapedirs
-        if shapedirs.shape[-1] < self.SHAPE_SPACE_DIM:
-            # print(f'WARNING: You are using a {self.name()} model, with only'
-            #       ' 10 shape coefficients.')
-            num_betas = min(num_betas, 10)
-        else:
-            num_betas = min(num_betas, self.SHAPE_SPACE_DIM)
-
-        if self.age == "kid":
-            v_template_smil = np.load(kid_template_path)
-            v_template_smil -= np.mean(v_template_smil, axis=0)
-            v_template_diff = np.expand_dims(v_template_smil - data_struct.v_template, axis=2)
-            shapedirs = np.concatenate((shapedirs[:, :, :num_betas], v_template_diff), axis=2)
-            num_betas = num_betas + 1
-
-        self._num_betas = num_betas
-        shapedirs = shapedirs[:, :, :num_betas]
-        # The shape components
-        self.register_buffer("shapedirs", to_tensor(to_np(shapedirs), dtype=dtype))
-
-        if vertex_ids is None:
-            # SMPL and SMPL-H share the same topology, so any extra joints can
-            # be drawn from the same place
-            vertex_ids = VERTEX_IDS["smplh"]
-
-        self.dtype = dtype
-
-        self.joint_mapper = joint_mapper
-
-        self.vertex_joint_selector = VertexJointSelector(vertex_ids=vertex_ids, **kwargs)
-
-        self.faces = data_struct.f
-        self.register_buffer(
-            "faces_tensor",
-            to_tensor(to_np(self.faces, dtype=np.int64), dtype=torch.long),
-        )
-
-        if create_betas:
-            if betas is None:
-                default_betas = torch.zeros([batch_size, self.num_betas], dtype=dtype)
-            else:
-                if torch.is_tensor(betas):
-                    default_betas = betas.clone().detach()
-                else:
-                    default_betas = torch.tensor(betas, dtype=dtype)
-
-            self.register_parameter("betas", nn.Parameter(default_betas, requires_grad=True))
-
-        # The tensor that contains the global rotation of the model
-        # It is separated from the pose of the joints in case we wish to
-        # optimize only over one of them
-        if create_global_orient:
-            if global_orient is None:
-                default_global_orient = torch.zeros([batch_size, 3], dtype=dtype)
-            else:
-                if torch.is_tensor(global_orient):
-                    default_global_orient = global_orient.clone().detach()
-                else:
-                    default_global_orient = torch.tensor(global_orient, dtype=dtype)
-
-            global_orient = nn.Parameter(default_global_orient, requires_grad=True)
-            self.register_parameter("global_orient", global_orient)
-
-        if create_body_pose:
-            if body_pose is None:
-                default_body_pose = torch.zeros([batch_size, self.NUM_BODY_JOINTS * 3], dtype=dtype)
-            else:
-                if torch.is_tensor(body_pose):
-                    default_body_pose = body_pose.clone().detach()
-                else:
-                    default_body_pose = torch.tensor(body_pose, dtype=dtype)
-            self.register_parameter(
-                "body_pose", nn.Parameter(default_body_pose, requires_grad=True)
-            )
-
-        if create_transl:
-            if transl is None:
-                default_transl = torch.zeros([batch_size, 3], dtype=dtype, requires_grad=True)
-            else:
-                default_transl = torch.tensor(transl, dtype=dtype)
-            self.register_parameter("transl", nn.Parameter(default_transl, requires_grad=True))
-
-        if v_template is None:
-            v_template = data_struct.v_template
-
-        if not torch.is_tensor(v_template):
-            v_template = to_tensor(to_np(v_template), dtype=dtype)
-
-        if v_personal is not None:
-            v_personal = to_tensor(to_np(v_personal), dtype=dtype)
-            v_template += v_personal
-
-        # The vertices of the template model
-        self.register_buffer("v_template", v_template)
-
-        j_regressor = to_tensor(to_np(data_struct.J_regressor), dtype=dtype)
-        self.register_buffer("J_regressor", j_regressor)
-
-        # Pose blend shape basis: 6890 x 3 x 207, reshaped to 6890*3 x 207
-        num_pose_basis = data_struct.posedirs.shape[-1]
-        # 207 x 20670
-        posedirs = np.reshape(data_struct.posedirs, [-1, num_pose_basis]).T
-        self.register_buffer("posedirs", to_tensor(to_np(posedirs), dtype=dtype))
-
-        # indices of parents for each joints
-        parents = to_tensor(to_np(data_struct.kintree_table[0])).long()
-        parents[0] = -1
-        self.register_buffer("parents", parents)
-
-        self.register_buffer("lbs_weights", to_tensor(to_np(data_struct.weights), dtype=dtype))
-
-    @property
-    def num_betas(self):
-        return self._num_betas
-
-    @property
-    def num_expression_coeffs(self):
-        return 0
-
-    def create_mean_pose(self, data_struct) -> Tensor:
-        pass
-
-    def name(self) -> str:
-        return "SMPL"
-
-    @torch.no_grad()
-    def reset_params(self, **params_dict) -> None:
-        for param_name, param in self.named_parameters():
-            if param_name in params_dict:
-                param[:] = torch.tensor(params_dict[param_name])
-            else:
-                param.fill_(0)
-
-    def get_num_verts(self) -> int:
-        return self.v_template.shape[0]
-
-    def get_num_faces(self) -> int:
-        return self.faces.shape[0]
-
-    def extra_repr(self) -> str:
-        msg = [
-            f"Gender: {self.gender.upper()}",
-            f"Number of joints: {self.J_regressor.shape[0]}",
-            f"Betas: {self.num_betas}",
-        ]
-        return "\n".join(msg)
-
-    def forward(
-        self,
-        betas: Optional[Tensor] = None,
-        body_pose: Optional[Tensor] = None,
-        global_orient: Optional[Tensor] = None,
-        transl: Optional[Tensor] = None,
-        return_verts=True,
-        return_full_pose: bool = False,
-        pose2rot: bool = True,
-        **kwargs,
-    ) -> SMPLOutput:
-        """Forward pass for the SMPL model
-
-        Parameters
-        ----------
-        global_orient: torch.tensor, optional, shape Bx3
-            If given, ignore the member variable and use it as the global
-            rotation of the body. Useful if someone wishes to predicts this
-            with an external model. (default=None)
-        betas: torch.tensor, optional, shape BxN_b
-            If given, ignore the member variable `betas` and use it
-            instead. For example, it can used if shape parameters
-            `betas` are predicted from some external model.
-            (default=None)
-        body_pose: torch.tensor, optional, shape Bx(J*3)
-            If given, ignore the member variable `body_pose` and use it
-            instead. For example, it can used if someone predicts the
-            pose of the body joints are predicted from some external model.
-            It should be a tensor that contains joint rotations in
-            axis-angle format. (default=None)
-        transl: torch.tensor, optional, shape Bx3
-            If given, ignore the member variable `transl` and use it
-            instead. For example, it can used if the translation
-            `transl` is predicted from some external model.
-            (default=None)
-        return_verts: bool, optional
-            Return the vertices. (default=True)
-        return_full_pose: bool, optional
-            Returns the full axis-angle pose vector (default=False)
-
-        Returns
-        -------
-        """
-        # If no shape and pose parameters are passed along, then use the
-        # ones from the module
-        global_orient = (global_orient if global_orient is not None else self.global_orient)
-        body_pose = body_pose if body_pose is not None else self.body_pose
-        betas = betas if betas is not None else self.betas
-
-        apply_trans = transl is not None or hasattr(self, "transl")
-        if transl is None and hasattr(self, "transl"):
-            transl = self.transl
-
-        full_pose = torch.cat([global_orient, body_pose], dim=1)
-
-        batch_size = max(betas.shape[0], global_orient.shape[0], body_pose.shape[0])
-
-        if betas.shape[0] != batch_size:
-            num_repeats = int(batch_size / betas.shape[0])
-            betas = betas.expand(num_repeats, -1)
-
-        vertices, joints = lbs(
-            betas,
-            full_pose,
-            self.v_template,
-            self.shapedirs,
-            self.posedirs,
-            self.J_regressor,
-            self.parents,
-            self.lbs_weights,
-            pose2rot=pose2rot,
-        )
-
-        joints = self.vertex_joint_selector(vertices, joints)
-        # Map the joints to the current dataset
-        if self.joint_mapper is not None:
-            joints = self.joint_mapper(joints)
-
-        if apply_trans:
-            joints += transl.unsqueeze(dim=1)
-            vertices += transl.unsqueeze(dim=1)
-
-        output = SMPLOutput(
-            vertices=vertices if return_verts else None,
-            global_orient=global_orient,
-            body_pose=body_pose,
-            joints=joints,
-            betas=betas,
-            full_pose=full_pose if return_full_pose else None,
-        )
-
-        return output
-
-
-class SMPLLayer(SMPL):
-    def __init__(self, *args, **kwargs) -> None:
-        # Just create a SMPL module without any member variables
-        super(SMPLLayer, self).__init__(
-            create_body_pose=False,
-            create_betas=False,
-            create_global_orient=False,
-            create_transl=False,
-            *args,
-            **kwargs,
-        )
-
-    def forward(
-        self,
-        betas: Optional[Tensor] = None,
-        body_pose: Optional[Tensor] = None,
-        global_orient: Optional[Tensor] = None,
-        transl: Optional[Tensor] = None,
-        return_verts=True,
-        return_full_pose: bool = False,
-        pose2rot: bool = True,
-        **kwargs,
-    ) -> SMPLOutput:
-        """Forward pass for the SMPL model
-
-        Parameters
-        ----------
-        global_orient: torch.tensor, optional, shape Bx3x3
-            Global rotation of the body.  Useful if someone wishes to
-            predicts this with an external model. It is expected to be in
-            rotation matrix format.  (default=None)
-        betas: torch.tensor, optional, shape BxN_b
-            Shape parameters. For example, it can used if shape parameters
-            `betas` are predicted from some external model.
-            (default=None)
-        body_pose: torch.tensor, optional, shape BxJx3x3
-            Body pose. For example, it can used if someone predicts the
-            pose of the body joints are predicted from some external model.
-            It should be a tensor that contains joint rotations in
-            rotation matrix format. (default=None)
-        transl: torch.tensor, optional, shape Bx3
-            Translation vector of the body.
-            For example, it can used if the translation
-            `transl` is predicted from some external model.
-            (default=None)
-        return_verts: bool, optional
-            Return the vertices. (default=True)
-        return_full_pose: bool, optional
-            Returns the full axis-angle pose vector (default=False)
-
-        Returns
-        -------
-        """
-        model_vars = [betas, global_orient, body_pose, transl]
-        batch_size = 1
-        for var in model_vars:
-            if var is None:
-                continue
-            batch_size = max(batch_size, len(var))
-        device, dtype = self.shapedirs.device, self.shapedirs.dtype
-        if global_orient is None:
-            global_orient = (
-                torch.eye(3, device=device,
-                          dtype=dtype).view(1, 1, 3, 3).expand(batch_size, -1, -1, -1).contiguous()
-            )
-        if body_pose is None:
-            body_pose = (
-                torch.eye(3, device=device,
-                          dtype=dtype).view(1, 1, 3,
-                                            3).expand(batch_size, self.NUM_BODY_JOINTS, -1,
-                                                      -1).contiguous()
-            )
-        if betas is None:
-            betas = torch.zeros([batch_size, self.num_betas], dtype=dtype, device=device)
-        if transl is None:
-            transl = torch.zeros([batch_size, 3], dtype=dtype, device=device)
-        full_pose = torch.cat(
-            [
-                global_orient.reshape(-1, 1, 3, 3),
-                body_pose.reshape(-1, self.NUM_BODY_JOINTS, 3, 3),
-            ],
-            dim=1,
-        )
-
-        vertices, joints = lbs(
-            betas,
-            full_pose,
-            self.v_template,
-            self.shapedirs,
-            self.posedirs,
-            self.J_regressor,
-            self.parents,
-            self.lbs_weights,
-            pose2rot=False,
-        )
-
-        joints = self.vertex_joint_selector(vertices, joints)
-        # Map the joints to the current dataset
-        if self.joint_mapper is not None:
-            joints = self.joint_mapper(joints)
-
-        if transl is not None:
-            joints += transl.unsqueeze(dim=1)
-            vertices += transl.unsqueeze(dim=1)
-
-        output = SMPLOutput(
-            vertices=vertices if return_verts else None,
-            global_orient=global_orient,
-            body_pose=body_pose,
-            joints=joints,
-            betas=betas,
-            full_pose=full_pose if return_full_pose else None,
-        )
-
-        return output
-
-
-class SMPLH(SMPL):
-
-    # The hand joints are replaced by MANO
-    NUM_BODY_JOINTS = SMPL.NUM_JOINTS - 2
-    NUM_HAND_JOINTS = 15
-    NUM_JOINTS = NUM_BODY_JOINTS + 2 * NUM_HAND_JOINTS
-
-    def __init__(
-        self,
-        model_path,
-        kid_template_path: str = "",
-        data_struct: Optional[Struct] = None,
-        create_left_hand_pose: bool = True,
-        left_hand_pose: Optional[Tensor] = None,
-        create_right_hand_pose: bool = True,
-        right_hand_pose: Optional[Tensor] = None,
-        use_pca: bool = True,
-        num_pca_comps: int = 6,
-        flat_hand_mean: bool = False,
-        batch_size: int = 1,
-        gender: str = "neutral",
-        age: str = "adult",
-        dtype=torch.float32,
-        vertex_ids=None,
-        use_compressed: bool = True,
-        ext: str = "pkl",
-        **kwargs,
-    ) -> None:
-        """SMPLH model constructor
-
-        Parameters
-        ----------
-        model_path: str
-            The path to the folder or to the file where the model
-            parameters are stored
-        data_struct: Strct
-            A struct object. If given, then the parameters of the model are
-            read from the object. Otherwise, the model tries to read the
-            parameters from the given `model_path`. (default = None)
-        create_left_hand_pose: bool, optional
-            Flag for creating a member variable for the pose of the left
-            hand. (default = True)
-        left_hand_pose: torch.tensor, optional, BxP
-            The default value for the left hand pose member variable.
-            (default = None)
-        create_right_hand_pose: bool, optional
-            Flag for creating a member variable for the pose of the right
-            hand. (default = True)
-        right_hand_pose: torch.tensor, optional, BxP
-            The default value for the right hand pose member variable.
-            (default = None)
-        num_pca_comps: int, optional
-            The number of PCA components to use for each hand.
-            (default = 6)
-        flat_hand_mean: bool, optional
-            If False, then the pose of the hand is initialized to False.
-        batch_size: int, optional
-            The batch size used for creating the member variables
-        gender: str, optional
-            Which gender to load
-        dtype: torch.dtype, optional
-            The data type for the created variables
-        vertex_ids: dict, optional
-            A dictionary containing the indices of the extra vertices that
-            will be selected
-        """
-
-        self.num_pca_comps = num_pca_comps
-        # If no data structure is passed, then load the data from the given
-        # model folder
-        if data_struct is None:
-            # Load the model
-            if osp.isdir(model_path):
-                model_fn = "SMPLH_{}.{ext}".format(gender.upper(), ext=ext)
-                smplh_path = os.path.join(model_path, model_fn)
-            else:
-                smplh_path = model_path
-            assert osp.exists(smplh_path), "Path {} does not exist!".format(smplh_path)
-
-            if ext == "pkl":
-                with open(smplh_path, "rb") as smplh_file:
-                    model_data = pickle.load(smplh_file, encoding="latin1")
-            elif ext == "npz":
-                model_data = np.load(smplh_path, allow_pickle=True)
-            else:
-                raise ValueError("Unknown extension: {}".format(ext))
-            data_struct = Struct(**model_data)
-
-        if vertex_ids is None:
-            vertex_ids = VERTEX_IDS["smplh"]
-
-        super(SMPLH, self).__init__(
-            model_path=model_path,
-            kid_template_path=kid_template_path,
-            data_struct=data_struct,
-            batch_size=batch_size,
-            vertex_ids=vertex_ids,
-            gender=gender,
-            age=age,
-            use_compressed=use_compressed,
-            dtype=dtype,
-            ext=ext,
-            **kwargs,
-        )
-
-        self.use_pca = use_pca
-        self.num_pca_comps = num_pca_comps
-        self.flat_hand_mean = flat_hand_mean
-
-        left_hand_components = data_struct.hands_componentsl[:num_pca_comps]
-        right_hand_components = data_struct.hands_componentsr[:num_pca_comps]
-
-        self.np_left_hand_components = left_hand_components
-        self.np_right_hand_components = right_hand_components
-        if self.use_pca:
-            self.register_buffer(
-                "left_hand_components", torch.tensor(left_hand_components, dtype=dtype)
-            )
-            self.register_buffer(
-                "right_hand_components",
-                torch.tensor(right_hand_components, dtype=dtype),
-            )
-
-        if self.flat_hand_mean:
-            left_hand_mean = np.zeros_like(data_struct.hands_meanl)
-        else:
-            left_hand_mean = data_struct.hands_meanl
-
-        if self.flat_hand_mean:
-            right_hand_mean = np.zeros_like(data_struct.hands_meanr)
-        else:
-            right_hand_mean = data_struct.hands_meanr
-
-        self.register_buffer("left_hand_mean", to_tensor(left_hand_mean, dtype=self.dtype))
-        self.register_buffer("right_hand_mean", to_tensor(right_hand_mean, dtype=self.dtype))
-
-        # Create the buffers for the pose of the left hand
-        hand_pose_dim = num_pca_comps if use_pca else 3 * self.NUM_HAND_JOINTS
-        if create_left_hand_pose:
-            if left_hand_pose is None:
-                default_lhand_pose = torch.zeros([batch_size, hand_pose_dim], dtype=dtype)
-            else:
-                default_lhand_pose = torch.tensor(left_hand_pose, dtype=dtype)
-
-            left_hand_pose_param = nn.Parameter(default_lhand_pose, requires_grad=True)
-            self.register_parameter("left_hand_pose", left_hand_pose_param)
-
-        if create_right_hand_pose:
-            if right_hand_pose is None:
-                default_rhand_pose = torch.zeros([batch_size, hand_pose_dim], dtype=dtype)
-            else:
-                default_rhand_pose = torch.tensor(right_hand_pose, dtype=dtype)
-
-            right_hand_pose_param = nn.Parameter(default_rhand_pose, requires_grad=True)
-            self.register_parameter("right_hand_pose", right_hand_pose_param)
-
-        # Create the buffer for the mean pose.
-        pose_mean_tensor = self.create_mean_pose(data_struct, flat_hand_mean=flat_hand_mean)
-        if not torch.is_tensor(pose_mean_tensor):
-            pose_mean_tensor = torch.tensor(pose_mean_tensor, dtype=dtype)
-        self.register_buffer("pose_mean", pose_mean_tensor)
-
-    def create_mean_pose(self, data_struct, flat_hand_mean=False):
-        # Create the array for the mean pose. If flat_hand is false, then use
-        # the mean that is given by the data, rather than the flat open hand
-        global_orient_mean = torch.zeros([3], dtype=self.dtype)
-        body_pose_mean = torch.zeros([self.NUM_BODY_JOINTS * 3], dtype=self.dtype)
-
-        pose_mean = torch.cat(
-            [
-                global_orient_mean,
-                body_pose_mean,
-                self.left_hand_mean,
-                self.right_hand_mean,
-            ],
-            dim=0,
-        )
-        return pose_mean
-
-    def name(self) -> str:
-        return "SMPL+H"
-
-    def extra_repr(self):
-        msg = super(SMPLH, self).extra_repr()
-        msg = [msg]
-        if self.use_pca:
-            msg.append(f"Number of PCA components: {self.num_pca_comps}")
-        msg.append(f"Flat hand mean: {self.flat_hand_mean}")
-        return "\n".join(msg)
-
-    def forward(
-        self,
-        betas: Optional[Tensor] = None,
-        global_orient: Optional[Tensor] = None,
-        body_pose: Optional[Tensor] = None,
-        left_hand_pose: Optional[Tensor] = None,
-        right_hand_pose: Optional[Tensor] = None,
-        transl: Optional[Tensor] = None,
-        return_verts: bool = True,
-        return_full_pose: bool = False,
-        pose2rot: bool = True,
-        **kwargs,
-    ) -> SMPLHOutput:
-        """"""
-
-        # If no shape and pose parameters are passed along, then use the
-        # ones from the module
-        global_orient = (global_orient if global_orient is not None else self.global_orient)
-        body_pose = body_pose if body_pose is not None else self.body_pose
-        betas = betas if betas is not None else self.betas
-        left_hand_pose = (left_hand_pose if left_hand_pose is not None else self.left_hand_pose)
-        right_hand_pose = (right_hand_pose if right_hand_pose is not None else self.right_hand_pose)
-
-        apply_trans = transl is not None or hasattr(self, "transl")
-        if transl is None:
-            if hasattr(self, "transl"):
-                transl = self.transl
-
-        if self.use_pca:
-            left_hand_pose = torch.einsum("bi,ij->bj", [left_hand_pose, self.left_hand_components])
-            right_hand_pose = torch.einsum(
-                "bi,ij->bj", [right_hand_pose, self.right_hand_components]
-            )
-
-        full_pose = torch.cat([global_orient, body_pose, left_hand_pose, right_hand_pose], dim=1)
-
-        full_pose += self.pose_mean
-
-        vertices, joints = lbs(
-            betas,
-            full_pose,
-            self.v_template,
-            self.shapedirs,
-            self.posedirs,
-            self.J_regressor,
-            self.parents,
-            self.lbs_weights,
-            pose2rot=pose2rot,
-        )
-
-        # Add any extra joints that might be needed
-        joints = self.vertex_joint_selector(vertices, joints)
-        if self.joint_mapper is not None:
-            joints = self.joint_mapper(joints)
-
-        if apply_trans:
-            joints += transl.unsqueeze(dim=1)
-            vertices += transl.unsqueeze(dim=1)
-
-        output = SMPLHOutput(
-            vertices=vertices if return_verts else None,
-            joints=joints,
-            betas=betas,
-            global_orient=global_orient,
-            body_pose=body_pose,
-            left_hand_pose=left_hand_pose,
-            right_hand_pose=right_hand_pose,
-            full_pose=full_pose if return_full_pose else None,
-        )
-
-        return output
-
-
-class SMPLHLayer(SMPLH):
-    def __init__(self, *args, **kwargs) -> None:
-        """SMPL+H as a layer model constructor"""
-        super(SMPLHLayer, self).__init__(
-            create_global_orient=False,
-            create_body_pose=False,
-            create_left_hand_pose=False,
-            create_right_hand_pose=False,
-            create_betas=False,
-            create_transl=False,
-            *args,
-            **kwargs,
-        )
-
-    def forward(
-        self,
-        betas: Optional[Tensor] = None,
-        global_orient: Optional[Tensor] = None,
-        body_pose: Optional[Tensor] = None,
-        left_hand_pose: Optional[Tensor] = None,
-        right_hand_pose: Optional[Tensor] = None,
-        transl: Optional[Tensor] = None,
-        return_verts: bool = True,
-        return_full_pose: bool = False,
-        pose2rot: bool = True,
-        **kwargs,
-    ) -> SMPLHOutput:
-        """Forward pass for the SMPL+H model
-
-        Parameters
-        ----------
-        global_orient: torch.tensor, optional, shape Bx3x3
-            Global rotation of the body. Useful if someone wishes to
-            predicts this with an external model. It is expected to be in
-            rotation matrix format. (default=None)
-        betas: torch.tensor, optional, shape BxN_b
-            Shape parameters. For example, it can used if shape parameters
-            `betas` are predicted from some external model.
-            (default=None)
-        body_pose: torch.tensor, optional, shape BxJx3x3
-            If given, ignore the member variable `body_pose` and use it
-            instead. For example, it can used if someone predicts the
-            pose of the body joints are predicted from some external model.
-            It should be a tensor that contains joint rotations in
-            rotation matrix format. (default=None)
-        left_hand_pose: torch.tensor, optional, shape Bx15x3x3
-            If given, contains the pose of the left hand.
-            It should be a tensor that contains joint rotations in
-            rotation matrix format. (default=None)
-        right_hand_pose: torch.tensor, optional, shape Bx15x3x3
-            If given, contains the pose of the right hand.
-            It should be a tensor that contains joint rotations in
-            rotation matrix format. (default=None)
-        transl: torch.tensor, optional, shape Bx3
-            Translation vector of the body.
-            For example, it can used if the translation
-            `transl` is predicted from some external model.
-            (default=None)
-        return_verts: bool, optional
-            Return the vertices. (default=True)
-        return_full_pose: bool, optional
-            Returns the full axis-angle pose vector (default=False)
-
-        Returns
-        -------
-        """
-        model_vars = [
-            betas,
-            global_orient,
-            body_pose,
-            transl,
-            left_hand_pose,
-            right_hand_pose,
-        ]
-        batch_size = 1
-        for var in model_vars:
-            if var is None:
-                continue
-            batch_size = max(batch_size, len(var))
-        device, dtype = self.shapedirs.device, self.shapedirs.dtype
-        if global_orient is None:
-            global_orient = (
-                torch.eye(3, device=device,
-                          dtype=dtype).view(1, 1, 3, 3).expand(batch_size, -1, -1, -1).contiguous()
-            )
-        if body_pose is None:
-            body_pose = (
-                torch.eye(3, device=device,
-                          dtype=dtype).view(1, 1, 3, 3).expand(batch_size, 21, -1, -1).contiguous()
-            )
-        if left_hand_pose is None:
-            left_hand_pose = (
-                torch.eye(3, device=device,
-                          dtype=dtype).view(1, 1, 3, 3).expand(batch_size, 15, -1, -1).contiguous()
-            )
-        if right_hand_pose is None:
-            right_hand_pose = (
-                torch.eye(3, device=device,
-                          dtype=dtype).view(1, 1, 3, 3).expand(batch_size, 15, -1, -1).contiguous()
-            )
-        if betas is None:
-            betas = torch.zeros([batch_size, self.num_betas], dtype=dtype, device=device)
-        if transl is None:
-            transl = torch.zeros([batch_size, 3], dtype=dtype, device=device)
-
-        # Concatenate all pose vectors
-        full_pose = torch.cat(
-            [
-                global_orient.reshape(-1, 1, 3, 3),
-                body_pose.reshape(-1, self.NUM_BODY_JOINTS, 3, 3),
-                left_hand_pose.reshape(-1, self.NUM_HAND_JOINTS, 3, 3),
-                right_hand_pose.reshape(-1, self.NUM_HAND_JOINTS, 3, 3),
-            ],
-            dim=1,
-        )
-
-        vertices, joints = lbs(
-            betas,
-            full_pose,
-            self.v_template,
-            self.shapedirs,
-            self.posedirs,
-            self.J_regressor,
-            self.parents,
-            self.lbs_weights,
-            pose2rot=False,
-        )
-
-        # Add any extra joints that might be needed
-        joints = self.vertex_joint_selector(vertices, joints)
-        if self.joint_mapper is not None:
-            joints = self.joint_mapper(joints)
-
-        if transl is not None:
-            joints += transl.unsqueeze(dim=1)
-            vertices += transl.unsqueeze(dim=1)
-
-        output = SMPLHOutput(
-            vertices=vertices if return_verts else None,
-            joints=joints,
-            betas=betas,
-            global_orient=global_orient,
-            body_pose=body_pose,
-            left_hand_pose=left_hand_pose,
-            right_hand_pose=right_hand_pose,
-            full_pose=full_pose if return_full_pose else None,
-        )
-
-        return output
-
-
-class SMPLX(SMPLH):
-    """
-    SMPL-X (SMPL eXpressive) is a unified body model, with shape parameters
-    trained jointly for the face, hands and body.
-    SMPL-X uses standard vertex based linear blend skinning with learned
-    corrective blend shapes, has N=10475 vertices and K=54 joints,
-    which includes joints for the neck, jaw, eyeballs and fingers.
-    """
-
-    NUM_BODY_JOINTS = SMPLH.NUM_BODY_JOINTS    # 21
-    NUM_HAND_JOINTS = 15
-    NUM_FACE_JOINTS = 3
-    NUM_JOINTS = NUM_BODY_JOINTS + 2 * NUM_HAND_JOINTS + NUM_FACE_JOINTS
-    EXPRESSION_SPACE_DIM = 100
-    NECK_IDX = 12
-
-    def __init__(
-        self,
-        model_path: str,
-        kid_template_path: str = "",
-        num_expression_coeffs: int = 10,
-        create_expression: bool = True,
-        expression: Optional[Tensor] = None,
-        create_jaw_pose: bool = True,
-        jaw_pose: Optional[Tensor] = None,
-        create_leye_pose: bool = True,
-        leye_pose: Optional[Tensor] = None,
-        create_reye_pose=True,
-        reye_pose: Optional[Tensor] = None,
-        use_face_contour: bool = False,
-        batch_size: int = 1,
-        gender: str = "neutral",
-        age: str = "adult",
-        dtype=torch.float32,
-        ext: str = "npz",
-        **kwargs,
-    ) -> None:
-        """SMPLX model constructor
-
-        Parameters
-        ----------
-        model_path: str
-            The path to the folder or to the file where the model
-            parameters are stored
-        num_expression_coeffs: int, optional
-            Number of expression components to use
-            (default = 10).
-        create_expression: bool, optional
-            Flag for creating a member variable for the expression space
-            (default = True).
-        expression: torch.tensor, optional, Bx10
-            The default value for the expression member variable.
-            (default = None)
-        create_jaw_pose: bool, optional
-            Flag for creating a member variable for the jaw pose.
-            (default = False)
-        jaw_pose: torch.tensor, optional, Bx3
-            The default value for the jaw pose variable.
-            (default = None)
-        create_leye_pose: bool, optional
-            Flag for creating a member variable for the left eye pose.
-            (default = False)
-        leye_pose: torch.tensor, optional, Bx10
-            The default value for the left eye pose variable.
-            (default = None)
-        create_reye_pose: bool, optional
-            Flag for creating a member variable for the right eye pose.
-            (default = False)
-        reye_pose: torch.tensor, optional, Bx10
-            The default value for the right eye pose variable.
-            (default = None)
-        use_face_contour: bool, optional
-            Whether to compute the keypoints that form the facial contour
-        batch_size: int, optional
-            The batch size used for creating the member variables
-        gender: str, optional
-            Which gender to load
-        dtype: torch.dtype
-            The data type for the created variables
-        """
-
-        # Load the model
-        from huggingface_hub import hf_hub_download
-
-        model_fn = "SMPLX_{}.{ext}".format(gender.upper(), ext=ext)
-        smplx_path = hf_hub_download(
-            repo_id=model_path, use_auth_token=os.environ["ICON"], filename=f"models/{model_fn}"
-        )
-
-        if ext == "pkl":
-            with open(smplx_path, "rb") as smplx_file:
-                model_data = pickle.load(smplx_file, encoding="latin1")
-        elif ext == "npz":
-            model_data = np.load(smplx_path, allow_pickle=True)
-        else:
-            raise ValueError("Unknown extension: {}".format(ext))
-
-        data_struct = Struct(**model_data)
-
-        super(SMPLX, self).__init__(
-            model_path=model_path,
-            kid_template_path=kid_template_path,
-            data_struct=data_struct,
-            dtype=dtype,
-            batch_size=batch_size,
-            vertex_ids=VERTEX_IDS["smplx"],
-            gender=gender,
-            age=age,
-            ext=ext,
-            **kwargs,
-        )
-
-        lmk_faces_idx = data_struct.lmk_faces_idx
-        self.register_buffer("lmk_faces_idx", torch.tensor(lmk_faces_idx, dtype=torch.long))
-        lmk_bary_coords = data_struct.lmk_bary_coords
-        self.register_buffer("lmk_bary_coords", torch.tensor(lmk_bary_coords, dtype=dtype))
-
-        self.use_face_contour = use_face_contour
-        if self.use_face_contour:
-            dynamic_lmk_faces_idx = data_struct.dynamic_lmk_faces_idx
-            dynamic_lmk_faces_idx = torch.tensor(dynamic_lmk_faces_idx, dtype=torch.long)
-            self.register_buffer("dynamic_lmk_faces_idx", dynamic_lmk_faces_idx)
-
-            dynamic_lmk_bary_coords = data_struct.dynamic_lmk_bary_coords
-            dynamic_lmk_bary_coords = torch.tensor(dynamic_lmk_bary_coords, dtype=dtype)
-            self.register_buffer("dynamic_lmk_bary_coords", dynamic_lmk_bary_coords)
-
-            neck_kin_chain = find_joint_kin_chain(self.NECK_IDX, self.parents)
-            self.register_buffer("neck_kin_chain", torch.tensor(neck_kin_chain, dtype=torch.long))
-
-        if create_jaw_pose:
-            if jaw_pose is None:
-                default_jaw_pose = torch.zeros([batch_size, 3], dtype=dtype)
-            else:
-                default_jaw_pose = torch.tensor(jaw_pose, dtype=dtype)
-            jaw_pose_param = nn.Parameter(default_jaw_pose, requires_grad=True)
-            self.register_parameter("jaw_pose", jaw_pose_param)
-
-        if create_leye_pose:
-            if leye_pose is None:
-                default_leye_pose = torch.zeros([batch_size, 3], dtype=dtype)
-            else:
-                default_leye_pose = torch.tensor(leye_pose, dtype=dtype)
-            leye_pose_param = nn.Parameter(default_leye_pose, requires_grad=True)
-            self.register_parameter("leye_pose", leye_pose_param)
-
-        if create_reye_pose:
-            if reye_pose is None:
-                default_reye_pose = torch.zeros([batch_size, 3], dtype=dtype)
-            else:
-                default_reye_pose = torch.tensor(reye_pose, dtype=dtype)
-            reye_pose_param = nn.Parameter(default_reye_pose, requires_grad=True)
-            self.register_parameter("reye_pose", reye_pose_param)
-
-        shapedirs = data_struct.shapedirs
-        if len(shapedirs.shape) < 3:
-            shapedirs = shapedirs[:, :, None]
-        if shapedirs.shape[-1] < self.SHAPE_SPACE_DIM + self.EXPRESSION_SPACE_DIM:
-            # print(f'WARNING: You are using a {self.name()} model, with only'
-            #       ' 10 shape and 10 expression coefficients.')
-            expr_start_idx = 10
-            expr_end_idx = 20
-            num_expression_coeffs = min(num_expression_coeffs, 10)
-        else:
-            expr_start_idx = self.SHAPE_SPACE_DIM
-            expr_end_idx = self.SHAPE_SPACE_DIM + num_expression_coeffs
-            num_expression_coeffs = min(num_expression_coeffs, self.EXPRESSION_SPACE_DIM)
-
-        self._num_expression_coeffs = num_expression_coeffs
-
-        expr_dirs = shapedirs[:, :, expr_start_idx:expr_end_idx]
-        self.register_buffer("expr_dirs", to_tensor(to_np(expr_dirs), dtype=dtype))
-
-        if create_expression:
-            if expression is None:
-                default_expression = torch.zeros([batch_size, self.num_expression_coeffs],
-                                                 dtype=dtype)
-            else:
-                default_expression = torch.tensor(expression, dtype=dtype)
-            expression_param = nn.Parameter(default_expression, requires_grad=True)
-            self.register_parameter("expression", expression_param)
-
-    def name(self) -> str:
-        return "SMPL-X"
-
-    @property
-    def num_expression_coeffs(self):
-        return self._num_expression_coeffs
-
-    def create_mean_pose(self, data_struct, flat_hand_mean=False):
-        # Create the array for the mean pose. If flat_hand is false, then use
-        # the mean that is given by the data, rather than the flat open hand
-        global_orient_mean = torch.zeros([3], dtype=self.dtype)
-        body_pose_mean = torch.zeros([self.NUM_BODY_JOINTS * 3], dtype=self.dtype)
-        jaw_pose_mean = torch.zeros([3], dtype=self.dtype)
-        leye_pose_mean = torch.zeros([3], dtype=self.dtype)
-        reye_pose_mean = torch.zeros([3], dtype=self.dtype)
-
-        pose_mean = np.concatenate(
-            [
-                global_orient_mean,
-                body_pose_mean,
-                jaw_pose_mean,
-                leye_pose_mean,
-                reye_pose_mean,
-                self.left_hand_mean,
-                self.right_hand_mean,
-            ],
-            axis=0,
-        )
-
-        return pose_mean
-
-    def extra_repr(self):
-        msg = super(SMPLX, self).extra_repr()
-        msg = [msg, f"Number of Expression Coefficients: {self.num_expression_coeffs}"]
-        return "\n".join(msg)
-
-    def forward(
-        self,
-        betas: Optional[Tensor] = None,
-        global_orient: Optional[Tensor] = None,
-        body_pose: Optional[Tensor] = None,
-        left_hand_pose: Optional[Tensor] = None,
-        right_hand_pose: Optional[Tensor] = None,
-        transl: Optional[Tensor] = None,
-        expression: Optional[Tensor] = None,
-        jaw_pose: Optional[Tensor] = None,
-        leye_pose: Optional[Tensor] = None,
-        reye_pose: Optional[Tensor] = None,
-        return_verts: bool = True,
-        return_full_pose: bool = False,
-        pose2rot: bool = True,
-        return_joint_transformation: bool = False,
-        return_vertex_transformation: bool = False,
-        pose_type: str = 'posed',
-        **kwargs,
-    ) -> SMPLXOutput:
-        """
-        Forward pass for the SMPLX model
-
-            Parameters
-            ----------
-            global_orient: torch.tensor, optional, shape Bx3
-                If given, ignore the member variable and use it as the global
-                rotation of the body. Useful if someone wishes to predicts this
-                with an external model. (default=None)
-            betas: torch.tensor, optional, shape BxN_b
-                If given, ignore the member variable `betas` and use it
-                instead. For example, it can used if shape parameters
-                `betas` are predicted from some external model.
-                (default=None)
-            expression: torch.tensor, optional, shape BxN_e
-                If given, ignore the member variable `expression` and use it
-                instead. For example, it can used if expression parameters
-                `expression` are predicted from some external model.
-            body_pose: torch.tensor, optional, shape Bx(J*3)
-                If given, ignore the member variable `body_pose` and use it
-                instead. For example, it can used if someone predicts the
-                pose of the body joints are predicted from some external model.
-                It should be a tensor that contains joint rotations in
-                axis-angle format. (default=None)
-            left_hand_pose: torch.tensor, optional, shape BxP
-                If given, ignore the member variable `left_hand_pose` and
-                use this instead. It should either contain PCA coefficients or
-                joint rotations in axis-angle format.
-            right_hand_pose: torch.tensor, optional, shape BxP
-                If given, ignore the member variable `right_hand_pose` and
-                use this instead. It should either contain PCA coefficients or
-                joint rotations in axis-angle format.
-            jaw_pose: torch.tensor, optional, shape Bx3
-                If given, ignore the member variable `jaw_pose` and
-                use this instead. It should either joint rotations in
-                axis-angle format.
-            transl: torch.tensor, optional, shape Bx3
-                If given, ignore the member variable `transl` and use it
-                instead. For example, it can used if the translation
-                `transl` is predicted from some external model.
-                (default=None)
-            return_verts: bool, optional
-                Return the vertices. (default=True)
-            return_full_pose: bool, optional
-                Returns the full axis-angle pose vector (default=False)
-
-            Returns
-            -------
-                output: ModelOutput
-                A named tuple of type `ModelOutput`
-        """
-
-        # If no shape and pose parameters are passed along, then use the
-        # ones from the module
-        global_orient = (global_orient if global_orient is not None else self.global_orient)
-        body_pose = body_pose if body_pose is not None else self.body_pose
-        betas = betas if betas is not None else self.betas
-
-        left_hand_pose = (left_hand_pose if left_hand_pose is not None else self.left_hand_pose)
-        right_hand_pose = (right_hand_pose if right_hand_pose is not None else self.right_hand_pose)
-        jaw_pose = jaw_pose if jaw_pose is not None else self.jaw_pose
-        leye_pose = leye_pose if leye_pose is not None else self.leye_pose
-        reye_pose = reye_pose if reye_pose is not None else self.reye_pose
-        expression = expression if expression is not None else self.expression
-
-        apply_trans = transl is not None or hasattr(self, "transl")
-        if transl is None:
-            if hasattr(self, "transl"):
-                transl = self.transl
-
-        if self.use_pca:
-            left_hand_pose = torch.einsum("bi,ij->bj", [left_hand_pose, self.left_hand_components])
-            right_hand_pose = torch.einsum(
-                "bi,ij->bj", [right_hand_pose, self.right_hand_components]
-            )
-
-        full_pose = torch.cat(
-            [
-                global_orient,
-                body_pose,
-                jaw_pose,
-                leye_pose,
-                reye_pose,
-                left_hand_pose,
-                right_hand_pose,
-            ],
-            dim=1,
-        )
-
-        if pose_type == "t-pose":
-            full_pose *= 0.0
-        elif pose_type == "a-pose":
-            body_pose = torch.zeros_like(body_pose).view(body_pose.shape[0], -1, 3)
-            body_pose[:, 15] = torch.tensor([0., 0., -45 * np.pi / 180.])
-            body_pose[:, 16] = torch.tensor([0., 0., 45 * np.pi / 180.])
-            body_pose = body_pose.view(body_pose.shape[0], -1)
-
-            full_pose = torch.cat(
-                [
-                    global_orient * 0.,
-                    body_pose,
-                    jaw_pose * 0.,
-                    leye_pose * 0.,
-                    reye_pose * 0.,
-                    left_hand_pose * 0.,
-                    right_hand_pose * 0.,
-                ],
-                dim=1,
-            )
-        elif pose_type == "da-pose":
-            body_pose = torch.zeros_like(body_pose).view(body_pose.shape[0], -1, 3)
-            body_pose[:, 0] = torch.tensor([0., 0., 30 * np.pi / 180.])
-            body_pose[:, 1] = torch.tensor([0., 0., -30 * np.pi / 180.])
-            body_pose = body_pose.view(body_pose.shape[0], -1)
-
-            full_pose = torch.cat(
-                [
-                    global_orient * 0.,
-                    body_pose,
-                    jaw_pose * 0.,
-                    leye_pose * 0.,
-                    reye_pose * 0.,
-                    left_hand_pose * 0.,
-                    right_hand_pose * 0.,
-                ],
-                dim=1,
-            )
-
-        # Add the mean pose of the model. Does not affect the body, only the
-        # hands when flat_hand_mean == False
-        # full_pose += self.pose_mean
-
-        batch_size = max(betas.shape[0], global_orient.shape[0], body_pose.shape[0])
-        # Concatenate the shape and expression coefficients
-        scale = int(batch_size / betas.shape[0])
-        if scale > 1:
-            betas = betas.expand(scale, -1)
-        shape_components = torch.cat([betas, expression], dim=-1)
-
-        shapedirs = torch.cat([self.shapedirs, self.expr_dirs], dim=-1)
-
-        if return_joint_transformation or return_vertex_transformation:
-            vertices, joints, joint_transformation, vertex_transformation = lbs(
-                shape_components,
-                full_pose,
-                self.v_template,
-                shapedirs,
-                self.posedirs,
-                self.J_regressor,
-                self.parents,
-                self.lbs_weights,
-                pose2rot=pose2rot,
-                return_transformation=True,
-            )
-        else:
-            vertices, joints = lbs(
-                shape_components,
-                full_pose,
-                self.v_template,
-                shapedirs,
-                self.posedirs,
-                self.J_regressor,
-                self.parents,
-                self.lbs_weights,
-                pose2rot=pose2rot,
-            )
-
-        lmk_faces_idx = (self.lmk_faces_idx.unsqueeze(dim=0).expand(batch_size, -1).contiguous())
-        lmk_bary_coords = self.lmk_bary_coords.unsqueeze(dim=0).repeat(self.batch_size, 1, 1)
-        if self.use_face_contour:
-            lmk_idx_and_bcoords = find_dynamic_lmk_idx_and_bcoords(
-                vertices,
-                full_pose,
-                self.dynamic_lmk_faces_idx,
-                self.dynamic_lmk_bary_coords,
-                self.neck_kin_chain,
-                pose2rot=True,
-            )
-            dyn_lmk_faces_idx, dyn_lmk_bary_coords = lmk_idx_and_bcoords
-
-            lmk_faces_idx = torch.cat([lmk_faces_idx, dyn_lmk_faces_idx], 1)
-            lmk_bary_coords = torch.cat([
-                lmk_bary_coords.expand(batch_size, -1, -1), dyn_lmk_bary_coords
-            ], 1)
-
-        landmarks = vertices2landmarks(vertices, self.faces_tensor, lmk_faces_idx, lmk_bary_coords)
-
-        # Add any extra joints that might be needed
-        joints = self.vertex_joint_selector(vertices, joints)
-        # Add the landmarks to the joints
-        joints = torch.cat([joints, landmarks], dim=1)
-        # Map the joints to the current dataset
-
-        if self.joint_mapper is not None:
-            joints = self.joint_mapper(joints=joints, vertices=vertices)
-
-        if apply_trans:
-            joints += transl.unsqueeze(dim=1)
-            vertices += transl.unsqueeze(dim=1)
-
-        output = SMPLXOutput(
-            vertices=vertices if return_verts else None,
-            joints=joints,
-            betas=betas,
-            expression=expression,
-            global_orient=global_orient,
-            body_pose=body_pose,
-            left_hand_pose=left_hand_pose,
-            right_hand_pose=right_hand_pose,
-            jaw_pose=jaw_pose,
-            full_pose=full_pose if return_full_pose else None,
-            joint_transformation=joint_transformation if return_joint_transformation else None,
-            vertex_transformation=vertex_transformation if return_vertex_transformation else None,
-        )
-        return output
-
-
-class SMPLXLayer(SMPLX):
-    def __init__(self, *args, **kwargs) -> None:
-        # Just create a SMPLX module without any member variables
-        super(SMPLXLayer, self).__init__(
-            create_global_orient=False,
-            create_body_pose=False,
-            create_left_hand_pose=False,
-            create_right_hand_pose=False,
-            create_jaw_pose=False,
-            create_leye_pose=False,
-            create_reye_pose=False,
-            create_betas=False,
-            create_expression=False,
-            create_transl=False,
-            *args,
-            **kwargs,
-        )
-
-    def forward(
-        self,
-        betas: Optional[Tensor] = None,
-        global_orient: Optional[Tensor] = None,
-        body_pose: Optional[Tensor] = None,
-        left_hand_pose: Optional[Tensor] = None,
-        right_hand_pose: Optional[Tensor] = None,
-        transl: Optional[Tensor] = None,
-        expression: Optional[Tensor] = None,
-        jaw_pose: Optional[Tensor] = None,
-        leye_pose: Optional[Tensor] = None,
-        reye_pose: Optional[Tensor] = None,
-        return_verts: bool = True,
-        return_full_pose: bool = False,
-        **kwargs,
-    ) -> SMPLXOutput:
-        """
-        Forward pass for the SMPLX model
-
-            Parameters
-            ----------
-            global_orient: torch.tensor, optional, shape Bx3x3
-                If given, ignore the member variable and use it as the global
-                rotation of the body. Useful if someone wishes to predicts this
-                with an external model. It is expected to be in rotation matrix
-                format. (default=None)
-            betas: torch.tensor, optional, shape BxN_b
-                If given, ignore the member variable `betas` and use it
-                instead. For example, it can used if shape parameters
-                `betas` are predicted from some external model.
-                (default=None)
-            expression: torch.tensor, optional, shape BxN_e
-                Expression coefficients.
-                For example, it can used if expression parameters
-                `expression` are predicted from some external model.
-            body_pose: torch.tensor, optional, shape BxJx3x3
-                If given, ignore the member variable `body_pose` and use it
-                instead. For example, it can used if someone predicts the
-                pose of the body joints are predicted from some external model.
-                It should be a tensor that contains joint rotations in
-                rotation matrix format. (default=None)
-            left_hand_pose: torch.tensor, optional, shape Bx15x3x3
-                If given, contains the pose of the left hand.
-                It should be a tensor that contains joint rotations in
-                rotation matrix format. (default=None)
-            right_hand_pose: torch.tensor, optional, shape Bx15x3x3
-                If given, contains the pose of the right hand.
-                It should be a tensor that contains joint rotations in
-                rotation matrix format. (default=None)
-            jaw_pose: torch.tensor, optional, shape Bx3x3
-                Jaw pose. It should either joint rotations in
-                rotation matrix format.
-            transl: torch.tensor, optional, shape Bx3
-                Translation vector of the body.
-                For example, it can used if the translation
-                `transl` is predicted from some external model.
-                (default=None)
-            return_verts: bool, optional
-                Return the vertices. (default=True)
-            return_full_pose: bool, optional
-                Returns the full pose vector (default=False)
-            Returns
-            -------
-                output: ModelOutput
-                A data class that contains the posed vertices and joints
-        """
-        device, dtype = self.shapedirs.device, self.shapedirs.dtype
-
-        model_vars = [
-            betas,
-            global_orient,
-            body_pose,
-            transl,
-            expression,
-            left_hand_pose,
-            right_hand_pose,
-            jaw_pose,
-        ]
-        batch_size = 1
-        for var in model_vars:
-            if var is None:
-                continue
-            batch_size = max(batch_size, len(var))
-
-        if global_orient is None:
-            global_orient = (
-                torch.eye(3, device=device,
-                          dtype=dtype).view(1, 1, 3, 3).expand(batch_size, -1, -1, -1).contiguous()
-            )
-        if body_pose is None:
-            body_pose = (
-                torch.eye(3, device=device,
-                          dtype=dtype).view(1, 1, 3,
-                                            3).expand(batch_size, self.NUM_BODY_JOINTS, -1,
-                                                      -1).contiguous()
-            )
-        if left_hand_pose is None:
-            left_hand_pose = (
-                torch.eye(3, device=device,
-                          dtype=dtype).view(1, 1, 3, 3).expand(batch_size, 15, -1, -1).contiguous()
-            )
-        if right_hand_pose is None:
-            right_hand_pose = (
-                torch.eye(3, device=device,
-                          dtype=dtype).view(1, 1, 3, 3).expand(batch_size, 15, -1, -1).contiguous()
-            )
-        if jaw_pose is None:
-            jaw_pose = (
-                torch.eye(3, device=device,
-                          dtype=dtype).view(1, 1, 3, 3).expand(batch_size, -1, -1, -1).contiguous()
-            )
-        if leye_pose is None:
-            leye_pose = (
-                torch.eye(3, device=device,
-                          dtype=dtype).view(1, 1, 3, 3).expand(batch_size, -1, -1, -1).contiguous()
-            )
-        if reye_pose is None:
-            reye_pose = (
-                torch.eye(3, device=device,
-                          dtype=dtype).view(1, 1, 3, 3).expand(batch_size, -1, -1, -1).contiguous()
-            )
-        if expression is None:
-            expression = torch.zeros([batch_size, self.num_expression_coeffs],
-                                     dtype=dtype,
-                                     device=device)
-        if betas is None:
-            betas = torch.zeros([batch_size, self.num_betas], dtype=dtype, device=device)
-        if transl is None:
-            transl = torch.zeros([batch_size, 3], dtype=dtype, device=device)
-
-        # Concatenate all pose vectors
-        full_pose = torch.cat(
-            [
-                global_orient.reshape(-1, 1, 3, 3),
-                body_pose.reshape(-1, self.NUM_BODY_JOINTS, 3, 3),
-                jaw_pose.reshape(-1, 1, 3, 3),
-                leye_pose.reshape(-1, 1, 3, 3),
-                reye_pose.reshape(-1, 1, 3, 3),
-                left_hand_pose.reshape(-1, self.NUM_HAND_JOINTS, 3, 3),
-                right_hand_pose.reshape(-1, self.NUM_HAND_JOINTS, 3, 3),
-            ],
-            dim=1,
-        )
-        shape_components = torch.cat([betas, expression], dim=-1)
-
-        shapedirs = torch.cat([self.shapedirs, self.expr_dirs], dim=-1)
-
-        vertices, joints = lbs(
-            shape_components,
-            full_pose,
-            self.v_template,
-            shapedirs,
-            self.posedirs,
-            self.J_regressor,
-            self.parents,
-            self.lbs_weights,
-            pose2rot=False,
-        )
-
-        lmk_faces_idx = (self.lmk_faces_idx.unsqueeze(dim=0).expand(batch_size, -1).contiguous())
-        lmk_bary_coords = self.lmk_bary_coords.unsqueeze(dim=0).repeat(batch_size, 1, 1)
-        if self.use_face_contour:
-            lmk_idx_and_bcoords = find_dynamic_lmk_idx_and_bcoords(
-                vertices,
-                full_pose,
-                self.dynamic_lmk_faces_idx,
-                self.dynamic_lmk_bary_coords,
-                self.neck_kin_chain,
-                pose2rot=False,
-            )
-            dyn_lmk_faces_idx, dyn_lmk_bary_coords = lmk_idx_and_bcoords
-
-            lmk_faces_idx = torch.cat([lmk_faces_idx, dyn_lmk_faces_idx], 1)
-            lmk_bary_coords = torch.cat([
-                lmk_bary_coords.expand(batch_size, -1, -1), dyn_lmk_bary_coords
-            ], 1)
-
-        landmarks = vertices2landmarks(vertices, self.faces_tensor, lmk_faces_idx, lmk_bary_coords)
-
-        # Add any extra joints that might be needed
-        joints = self.vertex_joint_selector(vertices, joints)
-        # Add the landmarks to the joints
-        joints = torch.cat([joints, landmarks], dim=1)
-        # Map the joints to the current dataset
-
-        if self.joint_mapper is not None:
-            joints = self.joint_mapper(joints=joints, vertices=vertices)
-
-        if transl is not None:
-            joints += transl.unsqueeze(dim=1)
-            vertices += transl.unsqueeze(dim=1)
-
-        output = SMPLXOutput(
-            vertices=vertices if return_verts else None,
-            joints=joints,
-            betas=betas,
-            expression=expression,
-            global_orient=global_orient,
-            body_pose=body_pose,
-            left_hand_pose=left_hand_pose,
-            right_hand_pose=right_hand_pose,
-            jaw_pose=jaw_pose,
-            transl=transl,
-            full_pose=full_pose if return_full_pose else None,
-        )
-        return output
-
-
-class MANO(SMPL):
-    # The hand joints are replaced by MANO
-    NUM_BODY_JOINTS = 1
-    NUM_HAND_JOINTS = 15
-    NUM_JOINTS = NUM_BODY_JOINTS + NUM_HAND_JOINTS
-
-    def __init__(
-        self,
-        model_path: str,
-        is_rhand: bool = True,
-        data_struct: Optional[Struct] = None,
-        create_hand_pose: bool = True,
-        hand_pose: Optional[Tensor] = None,
-        use_pca: bool = True,
-        num_pca_comps: int = 6,
-        flat_hand_mean: bool = False,
-        batch_size: int = 1,
-        dtype=torch.float32,
-        vertex_ids=None,
-        use_compressed: bool = True,
-        ext: str = "pkl",
-        **kwargs,
-    ) -> None:
-        """MANO model constructor
-
-        Parameters
-        ----------
-        model_path: str
-            The path to the folder or to the file where the model
-            parameters are stored
-        data_struct: Strct
-            A struct object. If given, then the parameters of the model are
-            read from the object. Otherwise, the model tries to read the
-            parameters from the given `model_path`. (default = None)
-        create_hand_pose: bool, optional
-            Flag for creating a member variable for the pose of the right
-            hand. (default = True)
-        hand_pose: torch.tensor, optional, BxP
-            The default value for the right hand pose member variable.
-            (default = None)
-        num_pca_comps: int, optional
-            The number of PCA components to use for each hand.
-            (default = 6)
-        flat_hand_mean: bool, optional
-            If False, then the pose of the hand is initialized to False.
-        batch_size: int, optional
-            The batch size used for creating the member variables
-        dtype: torch.dtype, optional
-            The data type for the created variables
-        vertex_ids: dict, optional
-            A dictionary containing the indices of the extra vertices that
-            will be selected
-        """
-
-        self.num_pca_comps = num_pca_comps
-        self.is_rhand = is_rhand
-        # If no data structure is passed, then load the data from the given
-        # model folder
-        if data_struct is None:
-            # Load the model
-            if osp.isdir(model_path):
-                model_fn = "MANO_{}.{ext}".format("RIGHT" if is_rhand else "LEFT", ext=ext)
-                mano_path = os.path.join(model_path, model_fn)
-            else:
-                mano_path = model_path
-                self.is_rhand = (True if "RIGHT" in os.path.basename(model_path) else False)
-            assert osp.exists(mano_path), "Path {} does not exist!".format(mano_path)
-
-            if ext == "pkl":
-                with open(mano_path, "rb") as mano_file:
-                    model_data = pickle.load(mano_file, encoding="latin1")
-            elif ext == "npz":
-                model_data = np.load(mano_path, allow_pickle=True)
-            else:
-                raise ValueError("Unknown extension: {}".format(ext))
-            data_struct = Struct(**model_data)
-
-        if vertex_ids is None:
-            vertex_ids = VERTEX_IDS["smplh"]
-
-        super(MANO, self).__init__(
-            model_path=model_path,
-            data_struct=data_struct,
-            batch_size=batch_size,
-            vertex_ids=vertex_ids,
-            use_compressed=use_compressed,
-            dtype=dtype,
-            ext=ext,
-            **kwargs,
-        )
-
-        # add only MANO tips to the extra joints
-        self.vertex_joint_selector.extra_joints_idxs = to_tensor(
-            list(VERTEX_IDS["mano"].values()), dtype=torch.long
-        )
-
-        self.use_pca = use_pca
-        self.num_pca_comps = num_pca_comps
-        if self.num_pca_comps == 45:
-            self.use_pca = False
-        self.flat_hand_mean = flat_hand_mean
-
-        hand_components = data_struct.hands_components[:num_pca_comps]
-
-        self.np_hand_components = hand_components
-
-        if self.use_pca:
-            self.register_buffer("hand_components", torch.tensor(hand_components, dtype=dtype))
-
-        if self.flat_hand_mean:
-            hand_mean = np.zeros_like(data_struct.hands_mean)
-        else:
-            hand_mean = data_struct.hands_mean
-
-        self.register_buffer("hand_mean", to_tensor(hand_mean, dtype=self.dtype))
-
-        # Create the buffers for the pose of the left hand
-        hand_pose_dim = num_pca_comps if use_pca else 3 * self.NUM_HAND_JOINTS
-        if create_hand_pose:
-            if hand_pose is None:
-                default_hand_pose = torch.zeros([batch_size, hand_pose_dim], dtype=dtype)
-            else:
-                default_hand_pose = torch.tensor(hand_pose, dtype=dtype)
-
-            hand_pose_param = nn.Parameter(default_hand_pose, requires_grad=True)
-            self.register_parameter("hand_pose", hand_pose_param)
-
-        # Create the buffer for the mean pose.
-        pose_mean = self.create_mean_pose(data_struct, flat_hand_mean=flat_hand_mean)
-        pose_mean_tensor = pose_mean.clone().to(dtype)
-        # pose_mean_tensor = torch.tensor(pose_mean, dtype=dtype)
-        self.register_buffer("pose_mean", pose_mean_tensor)
-
-    def name(self) -> str:
-        return "MANO"
-
-    def create_mean_pose(self, data_struct, flat_hand_mean=False):
-        # Create the array for the mean pose. If flat_hand is false, then use
-        # the mean that is given by the data, rather than the flat open hand
-        global_orient_mean = torch.zeros([3], dtype=self.dtype)
-        pose_mean = torch.cat([global_orient_mean, self.hand_mean], dim=0)
-        return pose_mean
-
-    def extra_repr(self):
-        msg = [super(MANO, self).extra_repr()]
-        if self.use_pca:
-            msg.append(f"Number of PCA components: {self.num_pca_comps}")
-        msg.append(f"Flat hand mean: {self.flat_hand_mean}")
-        return "\n".join(msg)
-
-    def forward(
-        self,
-        betas: Optional[Tensor] = None,
-        global_orient: Optional[Tensor] = None,
-        hand_pose: Optional[Tensor] = None,
-        transl: Optional[Tensor] = None,
-        return_verts: bool = True,
-        return_full_pose: bool = False,
-        **kwargs,
-    ) -> MANOOutput:
-        """Forward pass for the MANO model"""
-        # If no shape and pose parameters are passed along, then use the
-        # ones from the module
-        global_orient = (global_orient if global_orient is not None else self.global_orient)
-        betas = betas if betas is not None else self.betas
-        hand_pose = hand_pose if hand_pose is not None else self.hand_pose
-
-        apply_trans = transl is not None or hasattr(self, "transl")
-        if transl is None:
-            if hasattr(self, "transl"):
-                transl = self.transl
-
-        if self.use_pca:
-            hand_pose = torch.einsum("bi,ij->bj", [hand_pose, self.hand_components])
-
-        full_pose = torch.cat([global_orient, hand_pose], dim=1)
-        full_pose += self.pose_mean
-
-        vertices, joints = lbs(
-            betas,
-            full_pose,
-            self.v_template,
-            self.shapedirs,
-            self.posedirs,
-            self.J_regressor,
-            self.parents,
-            self.lbs_weights,
-            pose2rot=True,
-        )
-
-        # # Add pre-selected extra joints that might be needed
-        # joints = self.vertex_joint_selector(vertices, joints)
-
-        if self.joint_mapper is not None:
-            joints = self.joint_mapper(joints)
-
-        if apply_trans:
-            joints = joints + transl.unsqueeze(dim=1)
-            vertices = vertices + transl.unsqueeze(dim=1)
-
-        output = MANOOutput(
-            vertices=vertices if return_verts else None,
-            joints=joints if return_verts else None,
-            betas=betas,
-            global_orient=global_orient,
-            hand_pose=hand_pose,
-            full_pose=full_pose if return_full_pose else None,
-        )
-
-        return output
-
-
-class MANOLayer(MANO):
-    def __init__(self, *args, **kwargs) -> None:
-        """MANO as a layer model constructor"""
-        super(MANOLayer, self).__init__(
-            create_global_orient=False,
-            create_hand_pose=False,
-            create_betas=False,
-            create_transl=False,
-            *args,
-            **kwargs,
-        )
-
-    def name(self) -> str:
-        return "MANO"
-
-    def forward(
-        self,
-        betas: Optional[Tensor] = None,
-        global_orient: Optional[Tensor] = None,
-        hand_pose: Optional[Tensor] = None,
-        transl: Optional[Tensor] = None,
-        return_verts: bool = True,
-        return_full_pose: bool = False,
-        **kwargs,
-    ) -> MANOOutput:
-        """Forward pass for the MANO model"""
-        device, dtype = self.shapedirs.device, self.shapedirs.dtype
-        if global_orient is None:
-            batch_size = 1
-            global_orient = (
-                torch.eye(3, device=device,
-                          dtype=dtype).view(1, 1, 3, 3).expand(batch_size, -1, -1, -1).contiguous()
-            )
-        else:
-            batch_size = global_orient.shape[0]
-        if hand_pose is None:
-            hand_pose = (
-                torch.eye(3, device=device,
-                          dtype=dtype).view(1, 1, 3, 3).expand(batch_size, 15, -1, -1).contiguous()
-            )
-        if betas is None:
-            betas = torch.zeros([batch_size, self.num_betas], dtype=dtype, device=device)
-        if transl is None:
-            transl = torch.zeros([batch_size, 3], dtype=dtype, device=device)
-
-        full_pose = torch.cat([global_orient, hand_pose], dim=1)
-        vertices, joints = lbs(
-            betas,
-            full_pose,
-            self.v_template,
-            self.shapedirs,
-            self.posedirs,
-            self.J_regressor,
-            self.parents,
-            self.lbs_weights,
-            pose2rot=False,
-        )
-
-        if self.joint_mapper is not None:
-            joints = self.joint_mapper(joints)
-
-        if transl is not None:
-            joints = joints + transl.unsqueeze(dim=1)
-            vertices = vertices + transl.unsqueeze(dim=1)
-
-        output = MANOOutput(
-            vertices=vertices if return_verts else None,
-            joints=joints if return_verts else None,
-            betas=betas,
-            global_orient=global_orient,
-            hand_pose=hand_pose,
-            full_pose=full_pose if return_full_pose else None,
-        )
-
-        return output
-
-
-class FLAME(SMPL):
-    NUM_JOINTS = 5
-    SHAPE_SPACE_DIM = 300
-    EXPRESSION_SPACE_DIM = 100
-    NECK_IDX = 0
-
-    def __init__(
-        self,
-        model_path: str,
-        data_struct=None,
-        num_expression_coeffs=10,
-        create_expression: bool = True,
-        expression: Optional[Tensor] = None,
-        create_neck_pose: bool = True,
-        neck_pose: Optional[Tensor] = None,
-        create_jaw_pose: bool = True,
-        jaw_pose: Optional[Tensor] = None,
-        create_leye_pose: bool = True,
-        leye_pose: Optional[Tensor] = None,
-        create_reye_pose=True,
-        reye_pose: Optional[Tensor] = None,
-        use_face_contour=False,
-        batch_size: int = 1,
-        gender: str = "neutral",
-        dtype: torch.dtype = torch.float32,
-        ext="pkl",
-        **kwargs,
-    ) -> None:
-        """FLAME model constructor
-
-        Parameters
-        ----------
-        model_path: str
-            The path to the folder or to the file where the model
-            parameters are stored
-        num_expression_coeffs: int, optional
-            Number of expression components to use
-            (default = 10).
-        create_expression: bool, optional
-            Flag for creating a member variable for the expression space
-            (default = True).
-        expression: torch.tensor, optional, Bx10
-            The default value for the expression member variable.
-            (default = None)
-        create_neck_pose: bool, optional
-            Flag for creating a member variable for the neck pose.
-            (default = False)
-        neck_pose: torch.tensor, optional, Bx3
-            The default value for the neck pose variable.
-            (default = None)
-        create_jaw_pose: bool, optional
-            Flag for creating a member variable for the jaw pose.
-            (default = False)
-        jaw_pose: torch.tensor, optional, Bx3
-            The default value for the jaw pose variable.
-            (default = None)
-        create_leye_pose: bool, optional
-            Flag for creating a member variable for the left eye pose.
-            (default = False)
-        leye_pose: torch.tensor, optional, Bx10
-            The default value for the left eye pose variable.
-            (default = None)
-        create_reye_pose: bool, optional
-            Flag for creating a member variable for the right eye pose.
-            (default = False)
-        reye_pose: torch.tensor, optional, Bx10
-            The default value for the right eye pose variable.
-            (default = None)
-        use_face_contour: bool, optional
-            Whether to compute the keypoints that form the facial contour
-        batch_size: int, optional
-            The batch size used for creating the member variables
-        gender: str, optional
-            Which gender to load
-        dtype: torch.dtype
-            The data type for the created variables
-        """
-        model_fn = f"FLAME_{gender.upper()}.{ext}"
-        flame_path = os.path.join(model_path, model_fn)
-        assert osp.exists(flame_path), "Path {} does not exist!".format(flame_path)
-        if ext == "npz":
-            file_data = np.load(flame_path, allow_pickle=True)
-        elif ext == "pkl":
-            with open(flame_path, "rb") as smpl_file:
-                file_data = pickle.load(smpl_file, encoding="latin1")
-        else:
-            raise ValueError("Unknown extension: {}".format(ext))
-        data_struct = Struct(**file_data)
-
-        super(FLAME, self).__init__(
-            model_path=model_path,
-            data_struct=data_struct,
-            dtype=dtype,
-            batch_size=batch_size,
-            gender=gender,
-            ext=ext,
-            **kwargs,
-        )
-
-        self.use_face_contour = use_face_contour
-
-        self.vertex_joint_selector.extra_joints_idxs = to_tensor([], dtype=torch.long)
-
-        if create_neck_pose:
-            if neck_pose is None:
-                default_neck_pose = torch.zeros([batch_size, 3], dtype=dtype)
-            else:
-                default_neck_pose = torch.tensor(neck_pose, dtype=dtype)
-            neck_pose_param = nn.Parameter(default_neck_pose, requires_grad=True)
-            self.register_parameter("neck_pose", neck_pose_param)
-
-        if create_jaw_pose:
-            if jaw_pose is None:
-                default_jaw_pose = torch.zeros([batch_size, 3], dtype=dtype)
-            else:
-                default_jaw_pose = torch.tensor(jaw_pose, dtype=dtype)
-            jaw_pose_param = nn.Parameter(default_jaw_pose, requires_grad=True)
-            self.register_parameter("jaw_pose", jaw_pose_param)
-
-        if create_leye_pose:
-            if leye_pose is None:
-                default_leye_pose = torch.zeros([batch_size, 3], dtype=dtype)
-            else:
-                default_leye_pose = torch.tensor(leye_pose, dtype=dtype)
-            leye_pose_param = nn.Parameter(default_leye_pose, requires_grad=True)
-            self.register_parameter("leye_pose", leye_pose_param)
-
-        if create_reye_pose:
-            if reye_pose is None:
-                default_reye_pose = torch.zeros([batch_size, 3], dtype=dtype)
-            else:
-                default_reye_pose = torch.tensor(reye_pose, dtype=dtype)
-            reye_pose_param = nn.Parameter(default_reye_pose, requires_grad=True)
-            self.register_parameter("reye_pose", reye_pose_param)
-
-        shapedirs = data_struct.shapedirs
-        if len(shapedirs.shape) < 3:
-            shapedirs = shapedirs[:, :, None]
-        if shapedirs.shape[-1] < self.SHAPE_SPACE_DIM + self.EXPRESSION_SPACE_DIM:
-            # print(f'WARNING: You are using a {self.name()} model, with only'
-            #       ' 10 shape and 10 expression coefficients.')
-            expr_start_idx = 10
-            expr_end_idx = 20
-            num_expression_coeffs = min(num_expression_coeffs, 10)
-        else:
-            expr_start_idx = self.SHAPE_SPACE_DIM
-            expr_end_idx = self.SHAPE_SPACE_DIM + num_expression_coeffs
-            num_expression_coeffs = min(num_expression_coeffs, self.EXPRESSION_SPACE_DIM)
-
-        self._num_expression_coeffs = num_expression_coeffs
-
-        expr_dirs = shapedirs[:, :, expr_start_idx:expr_end_idx]
-        self.register_buffer("expr_dirs", to_tensor(to_np(expr_dirs), dtype=dtype))
-
-        if create_expression:
-            if expression is None:
-                default_expression = torch.zeros([batch_size, self.num_expression_coeffs],
-                                                 dtype=dtype)
-            else:
-                default_expression = torch.tensor(expression, dtype=dtype)
-            expression_param = nn.Parameter(default_expression, requires_grad=True)
-            self.register_parameter("expression", expression_param)
-
-        # The pickle file that contains the barycentric coordinates for
-        # regressing the landmarks
-        landmark_bcoord_filename = osp.join(model_path, "flame_static_embedding.pkl")
-
-        with open(landmark_bcoord_filename, "rb") as fp:
-            landmarks_data = pickle.load(fp, encoding="latin1")
-
-        lmk_faces_idx = landmarks_data["lmk_face_idx"].astype(np.int64)
-        self.register_buffer("lmk_faces_idx", torch.tensor(lmk_faces_idx, dtype=torch.long))
-        lmk_bary_coords = landmarks_data["lmk_b_coords"]
-        self.register_buffer("lmk_bary_coords", torch.tensor(lmk_bary_coords, dtype=dtype))
-        if self.use_face_contour:
-            face_contour_path = os.path.join(model_path, "flame_dynamic_embedding.npy")
-            contour_embeddings = np.load(face_contour_path, allow_pickle=True,
-                                         encoding="latin1")[()]
-
-            dynamic_lmk_faces_idx = np.array(contour_embeddings["lmk_face_idx"], dtype=np.int64)
-            dynamic_lmk_faces_idx = torch.tensor(dynamic_lmk_faces_idx, dtype=torch.long)
-            self.register_buffer("dynamic_lmk_faces_idx", dynamic_lmk_faces_idx)
-
-            dynamic_lmk_b_coords = torch.tensor(contour_embeddings["lmk_b_coords"], dtype=dtype)
-            self.register_buffer("dynamic_lmk_bary_coords", dynamic_lmk_b_coords)
-
-            neck_kin_chain = find_joint_kin_chain(self.NECK_IDX, self.parents)
-            self.register_buffer("neck_kin_chain", torch.tensor(neck_kin_chain, dtype=torch.long))
-
-    @property
-    def num_expression_coeffs(self):
-        return self._num_expression_coeffs
-
-    def name(self) -> str:
-        return "FLAME"
-
-    def extra_repr(self):
-        msg = [
-            super(FLAME, self).extra_repr(),
-            f"Number of Expression Coefficients: {self.num_expression_coeffs}",
-            f"Use face contour: {self.use_face_contour}",
-        ]
-        return "\n".join(msg)
-
-    def forward(
-        self,
-        betas: Optional[Tensor] = None,
-        global_orient: Optional[Tensor] = None,
-        neck_pose: Optional[Tensor] = None,
-        transl: Optional[Tensor] = None,
-        expression: Optional[Tensor] = None,
-        jaw_pose: Optional[Tensor] = None,
-        leye_pose: Optional[Tensor] = None,
-        reye_pose: Optional[Tensor] = None,
-        return_verts: bool = True,
-        return_full_pose: bool = False,
-        pose2rot: bool = True,
-        **kwargs,
-    ) -> FLAMEOutput:
-        """
-        Forward pass for the SMPLX model
-
-            Parameters
-            ----------
-            global_orient: torch.tensor, optional, shape Bx3
-                If given, ignore the member variable and use it as the global
-                rotation of the body. Useful if someone wishes to predicts this
-                with an external model. (default=None)
-            betas: torch.tensor, optional, shape Bx10
-                If given, ignore the member variable `betas` and use it
-                instead. For example, it can used if shape parameters
-                `betas` are predicted from some external model.
-                (default=None)
-            expression: torch.tensor, optional, shape Bx10
-                If given, ignore the member variable `expression` and use it
-                instead. For example, it can used if expression parameters
-                `expression` are predicted from some external model.
-            jaw_pose: torch.tensor, optional, shape Bx3
-                If given, ignore the member variable `jaw_pose` and
-                use this instead. It should either joint rotations in
-                axis-angle format.
-            jaw_pose: torch.tensor, optional, shape Bx3
-                If given, ignore the member variable `jaw_pose` and
-                use this instead. It should either joint rotations in
-                axis-angle format.
-            transl: torch.tensor, optional, shape Bx3
-                If given, ignore the member variable `transl` and use it
-                instead. For example, it can used if the translation
-                `transl` is predicted from some external model.
-                (default=None)
-            return_verts: bool, optional
-                Return the vertices. (default=True)
-            return_full_pose: bool, optional
-                Returns the full axis-angle pose vector (default=False)
-
-            Returns
-            -------
-                output: ModelOutput
-                A named tuple of type `ModelOutput`
-        """
-
-        # If no shape and pose parameters are passed along, then use the
-        # ones from the module
-        global_orient = (global_orient if global_orient is not None else self.global_orient)
-        jaw_pose = jaw_pose if jaw_pose is not None else self.jaw_pose
-        neck_pose = neck_pose if neck_pose is not None else self.neck_pose
-
-        leye_pose = leye_pose if leye_pose is not None else self.leye_pose
-        reye_pose = reye_pose if reye_pose is not None else self.reye_pose
-
-        betas = betas if betas is not None else self.betas
-        expression = expression if expression is not None else self.expression
-
-        apply_trans = transl is not None or hasattr(self, "transl")
-        if transl is None:
-            if hasattr(self, "transl"):
-                transl = self.transl
-
-        full_pose = torch.cat([global_orient, neck_pose, jaw_pose, leye_pose, reye_pose], dim=1)
-
-        batch_size = max(betas.shape[0], global_orient.shape[0], jaw_pose.shape[0])
-        # Concatenate the shape and expression coefficients
-        scale = int(batch_size / betas.shape[0])
-        if scale > 1:
-            betas = betas.expand(scale, -1)
-        shape_components = torch.cat([betas, expression], dim=-1)
-        shapedirs = torch.cat([self.shapedirs, self.expr_dirs], dim=-1)
-
-        vertices, joints = lbs(
-            shape_components,
-            full_pose,
-            self.v_template,
-            shapedirs,
-            self.posedirs,
-            self.J_regressor,
-            self.parents,
-            self.lbs_weights,
-            pose2rot=pose2rot,
-        )
-
-        lmk_faces_idx = (self.lmk_faces_idx.unsqueeze(dim=0).expand(batch_size, -1).contiguous())
-        lmk_bary_coords = self.lmk_bary_coords.unsqueeze(dim=0).repeat(self.batch_size, 1, 1)
-        if self.use_face_contour:
-            lmk_idx_and_bcoords = find_dynamic_lmk_idx_and_bcoords(
-                vertices,
-                full_pose,
-                self.dynamic_lmk_faces_idx,
-                self.dynamic_lmk_bary_coords,
-                self.neck_kin_chain,
-                pose2rot=True,
-            )
-            dyn_lmk_faces_idx, dyn_lmk_bary_coords = lmk_idx_and_bcoords
-            lmk_faces_idx = torch.cat([lmk_faces_idx, dyn_lmk_faces_idx], 1)
-            lmk_bary_coords = torch.cat([
-                lmk_bary_coords.expand(batch_size, -1, -1), dyn_lmk_bary_coords
-            ], 1)
-
-        landmarks = vertices2landmarks(vertices, self.faces_tensor, lmk_faces_idx, lmk_bary_coords)
-
-        # Add any extra joints that might be needed
-        joints = self.vertex_joint_selector(vertices, joints)
-        # Add the landmarks to the joints
-        joints = torch.cat([joints, landmarks], dim=1)
-
-        # Map the joints to the current dataset
-        if self.joint_mapper is not None:
-            joints = self.joint_mapper(joints=joints, vertices=vertices)
-
-        if apply_trans:
-            joints += transl.unsqueeze(dim=1)
-            vertices += transl.unsqueeze(dim=1)
-
-        output = FLAMEOutput(
-            vertices=vertices if return_verts else None,
-            joints=joints,
-            betas=betas,
-            expression=expression,
-            global_orient=global_orient,
-            neck_pose=neck_pose,
-            jaw_pose=jaw_pose,
-            full_pose=full_pose if return_full_pose else None,
-        )
-        return output
-
-
-class FLAMELayer(FLAME):
-    def __init__(self, *args, **kwargs) -> None:
-        """ FLAME as a layer model constructor """
-        super(FLAMELayer, self).__init__(
-            create_betas=False,
-            create_expression=False,
-            create_global_orient=False,
-            create_neck_pose=False,
-            create_jaw_pose=False,
-            create_leye_pose=False,
-            create_reye_pose=False,
-            *args,
-            **kwargs,
-        )
-
-    def forward(
-        self,
-        betas: Optional[Tensor] = None,
-        global_orient: Optional[Tensor] = None,
-        neck_pose: Optional[Tensor] = None,
-        transl: Optional[Tensor] = None,
-        expression: Optional[Tensor] = None,
-        jaw_pose: Optional[Tensor] = None,
-        leye_pose: Optional[Tensor] = None,
-        reye_pose: Optional[Tensor] = None,
-        return_verts: bool = True,
-        return_full_pose: bool = False,
-        pose2rot: bool = True,
-        **kwargs,
-    ) -> FLAMEOutput:
-        """
-        Forward pass for the SMPLX model
-
-            Parameters
-            ----------
-            global_orient: torch.tensor, optional, shape Bx3x3
-                Global rotation of the body. Useful if someone wishes to
-                predicts this with an external model. It is expected to be in
-                rotation matrix format. (default=None)
-            betas: torch.tensor, optional, shape BxN_b
-                Shape parameters. For example, it can used if shape parameters
-                `betas` are predicted from some external model.
-                (default=None)
-            expression: torch.tensor, optional, shape BxN_e
-                If given, ignore the member variable `expression` and use it
-                instead. For example, it can used if expression parameters
-                `expression` are predicted from some external model.
-            jaw_pose: torch.tensor, optional, shape Bx3x3
-                Jaw pose. It should either joint rotations in
-                rotation matrix format.
-            transl: torch.tensor, optional, shape Bx3
-                Translation vector of the body.
-                For example, it can used if the translation
-                `transl` is predicted from some external model.
-                (default=None)
-            return_verts: bool, optional
-                Return the vertices. (default=True)
-            return_full_pose: bool, optional
-                Returns the full axis-angle pose vector (default=False)
-
-            Returns
-            -------
-                output: ModelOutput
-                A named tuple of type `ModelOutput`
-        """
-        device, dtype = self.shapedirs.device, self.shapedirs.dtype
-        if global_orient is None:
-            batch_size = 1
-            global_orient = (
-                torch.eye(3, device=device,
-                          dtype=dtype).view(1, 1, 3, 3).expand(batch_size, -1, -1, -1).contiguous()
-            )
-        else:
-            batch_size = global_orient.shape[0]
-        if neck_pose is None:
-            neck_pose = (
-                torch.eye(3, device=device,
-                          dtype=dtype).view(1, 1, 3, 3).expand(batch_size, 1, -1, -1).contiguous()
-            )
-        if jaw_pose is None:
-            jaw_pose = (
-                torch.eye(3, device=device,
-                          dtype=dtype).view(1, 1, 3, 3).expand(batch_size, -1, -1, -1).contiguous()
-            )
-        if leye_pose is None:
-            leye_pose = (
-                torch.eye(3, device=device,
-                          dtype=dtype).view(1, 1, 3, 3).expand(batch_size, -1, -1, -1).contiguous()
-            )
-        if reye_pose is None:
-            reye_pose = (
-                torch.eye(3, device=device,
-                          dtype=dtype).view(1, 1, 3, 3).expand(batch_size, -1, -1, -1).contiguous()
-            )
-        if betas is None:
-            betas = torch.zeros([batch_size, self.num_betas], dtype=dtype, device=device)
-        if expression is None:
-            expression = torch.zeros([batch_size, self.num_expression_coeffs],
-                                     dtype=dtype,
-                                     device=device)
-        if transl is None:
-            transl = torch.zeros([batch_size, 3], dtype=dtype, device=device)
-
-        full_pose = torch.cat([global_orient, neck_pose, jaw_pose, leye_pose, reye_pose], dim=1)
-
-        shape_components = torch.cat([betas, expression], dim=-1)
-        shapedirs = torch.cat([self.shapedirs, self.expr_dirs], dim=-1)
-
-        vertices, joints = lbs(
-            shape_components,
-            full_pose,
-            self.v_template,
-            shapedirs,
-            self.posedirs,
-            self.J_regressor,
-            self.parents,
-            self.lbs_weights,
-            pose2rot=False,
-        )
-
-        lmk_faces_idx = (self.lmk_faces_idx.unsqueeze(dim=0).expand(batch_size, -1).contiguous())
-        lmk_bary_coords = self.lmk_bary_coords.unsqueeze(dim=0).repeat(self.batch_size, 1, 1)
-        if self.use_face_contour:
-            lmk_idx_and_bcoords = find_dynamic_lmk_idx_and_bcoords(
-                vertices,
-                full_pose,
-                self.dynamic_lmk_faces_idx,
-                self.dynamic_lmk_bary_coords,
-                self.neck_kin_chain,
-                pose2rot=False,
-            )
-            dyn_lmk_faces_idx, dyn_lmk_bary_coords = lmk_idx_and_bcoords
-            lmk_faces_idx = torch.cat([lmk_faces_idx, dyn_lmk_faces_idx], 1)
-            lmk_bary_coords = torch.cat([
-                lmk_bary_coords.expand(batch_size, -1, -1), dyn_lmk_bary_coords
-            ], 1)
-
-        landmarks = vertices2landmarks(vertices, self.faces_tensor, lmk_faces_idx, lmk_bary_coords)
-
-        # Add any extra joints that might be needed
-        joints = self.vertex_joint_selector(vertices, joints)
-        # Add the landmarks to the joints
-        joints = torch.cat([joints, landmarks], dim=1)
-
-        # Map the joints to the current dataset
-        if self.joint_mapper is not None:
-            joints = self.joint_mapper(joints=joints, vertices=vertices)
-
-        joints += transl.unsqueeze(dim=1)
-        vertices += transl.unsqueeze(dim=1)
-
-        output = FLAMEOutput(
-            vertices=vertices if return_verts else None,
-            joints=joints,
-            betas=betas,
-            expression=expression,
-            global_orient=global_orient,
-            neck_pose=neck_pose,
-            jaw_pose=jaw_pose,
-            full_pose=full_pose if return_full_pose else None,
-        )
-        return output
-
-
-def build_layer(model_path: str,
-                model_type: str = "smpl",
-                **kwargs) -> Union[SMPLLayer, SMPLHLayer, SMPLXLayer, MANOLayer, FLAMELayer]:
-    """Method for creating a model from a path and a model type
-
-    Parameters
-    ----------
-    model_path: str
-        Either the path to the model you wish to load or a folder,
-        where each subfolder contains the differents types, i.e.:
-        model_path:
-        |
-        |-- smpl
-            |-- SMPL_FEMALE
-            |-- SMPL_NEUTRAL
-            |-- SMPL_MALE
-        |-- smplh
-            |-- SMPLH_FEMALE
-            |-- SMPLH_MALE
-        |-- smplx
-            |-- SMPLX_FEMALE
-            |-- SMPLX_NEUTRAL
-            |-- SMPLX_MALE
-        |-- mano
-            |-- MANO RIGHT
-            |-- MANO LEFT
-        |-- flame
-            |-- FLAME_FEMALE
-            |-- FLAME_MALE
-            |-- FLAME_NEUTRAL
-
-    model_type: str, optional
-        When model_path is a folder, then this parameter specifies  the
-        type of model to be loaded
-    **kwargs: dict
-        Keyword arguments
-
-    Returns
-    -------
-        body_model: nn.Module
-            The PyTorch module that implements the corresponding body model
-    Raises
-    ------
-        ValueError: In case the model type is not one of SMPL, SMPLH,
-        SMPLX, MANO or FLAME
-    """
-
-    if osp.isdir(model_path):
-        model_path = os.path.join(model_path, model_type)
-    else:
-        model_type = osp.basename(model_path).split("_")[0].lower()
-
-    if model_type.lower() == "smpl":
-        return SMPLLayer(model_path, **kwargs)
-    elif model_type.lower() == "smplh":
-        return SMPLHLayer(model_path, **kwargs)
-    elif model_type.lower() == "smplx":
-        return SMPLXLayer(model_path, **kwargs)
-    elif "mano" in model_type.lower():
-        return MANOLayer(model_path, **kwargs)
-    elif "flame" in model_type.lower():
-        return FLAMELayer(model_path, **kwargs)
-    else:
-        raise ValueError(f"Unknown model type {model_type}, exiting!")
-
-
-def create(model_path: str,
-           model_type: str = "smpl",
-           **kwargs) -> Union[SMPL, SMPLH, SMPLX, MANO, FLAME]:
-    """Method for creating a model from a path and a model type
-
-    Parameters
-    ----------
-    model_path: str
-        Either the path to the model you wish to load or a folder,
-        where each subfolder contains the differents types, i.e.:
-        model_path:
-        |
-        |-- smpl
-            |-- SMPL_FEMALE
-            |-- SMPL_NEUTRAL
-            |-- SMPL_MALE
-        |-- smplh
-            |-- SMPLH_FEMALE
-            |-- SMPLH_MALE
-        |-- smplx
-            |-- SMPLX_FEMALE
-            |-- SMPLX_NEUTRAL
-            |-- SMPLX_MALE
-        |-- mano
-            |-- MANO RIGHT
-            |-- MANO LEFT
-
-    model_type: str, optional
-        When model_path is a folder, then this parameter specifies  the
-        type of model to be loaded
-    **kwargs: dict
-        Keyword arguments
-
-    Returns
-    -------
-        body_model: nn.Module
-            The PyTorch module that implements the corresponding body model
-    Raises
-    ------
-        ValueError: In case the model type is not one of SMPL, SMPLH,
-        SMPLX, MANO or FLAME
-    """
-
-    # If it's a folder, assume
-    if osp.isdir(model_path):
-        model_path = os.path.join(model_path, model_type)
-    else:
-        model_type = osp.basename(model_path).split("_")[0].lower()
-
-    if model_type.lower() == "smpl":
-        return SMPL(model_path, **kwargs)
-    elif model_type.lower() == "smplh":
-        return SMPLH(model_path, **kwargs)
-    elif model_type.lower() == "smplx":
-        return SMPLX(model_path, **kwargs)
-    elif "mano" in model_type.lower():
-        return MANO(model_path, **kwargs)
-    elif "flame" in model_type.lower():
-        return FLAME(model_path, **kwargs)
-    else:
-        raise ValueError(f"Unknown model type {model_type}, exiting!")
diff --git a/spaces/Zaixi/ICLR_FLAG/models/encoders/tf.py b/spaces/Zaixi/ICLR_FLAG/models/encoders/tf.py
deleted file mode 100644
index 6ec1428213d57f57bc8c86fc5cff56638406b275..0000000000000000000000000000000000000000
--- a/spaces/Zaixi/ICLR_FLAG/models/encoders/tf.py
+++ /dev/null
@@ -1,152 +0,0 @@
-import torch
-import torch.nn.functional as F
-from torch import nn
-from torch.nn import Module, Sequential, ModuleList, Linear, Conv1d, LeakyReLU
-from torch_geometric.nn import radius_graph, knn_graph
-from torch_scatter import scatter_sum, scatter_softmax
-import math
-from math import pi as PI
-
-from ..common import GaussianSmearing, ShiftedSoftplus
-
-
-class AttentionInteractionBlock(Module):
-
-    def __init__(self, hidden_channels, edge_channels, key_channels, num_heads=1):
-        super().__init__()
-
-        assert hidden_channels % num_heads == 0
-        assert key_channels % num_heads == 0
-
-        self.hidden_channels = hidden_channels
-        self.key_channels = key_channels
-        self.num_heads = num_heads
-
-        self.k_lin = Conv1d(hidden_channels, key_channels, 1, groups=num_heads, bias=False)
-        self.q_lin = Conv1d(hidden_channels, key_channels, 1, groups=num_heads, bias=False)
-        self.v_lin = Conv1d(hidden_channels, hidden_channels, 1, groups=num_heads, bias=False)
-
-        self.weight_k_net = Sequential(
-            Linear(edge_channels, key_channels // num_heads),
-            LeakyReLU(),
-            Linear(key_channels // num_heads, key_channels // num_heads),
-        )
-        self.weight_k_lin = Linear(key_channels // num_heads, key_channels // num_heads)
-
-        self.weight_v_net = Sequential(
-            Linear(edge_channels, hidden_channels // num_heads),
-            LeakyReLU(),
-            Linear(hidden_channels // num_heads, hidden_channels // num_heads),
-        )
-        self.weight_v_lin = Linear(hidden_channels // num_heads, hidden_channels // num_heads)
-
-        self.centroid_lin = Linear(hidden_channels, hidden_channels)
-        self.act = LeakyReLU()
-        self.out_transform = Linear(hidden_channels, hidden_channels)
-        self.layernorm_ffn = nn.LayerNorm(hidden_channels)
-
-    def forward(self, x, edge_index, edge_attr):
-        """
-        Args:
-            x:  Node features, (N, H).
-            edge_index: (2, E).
-            edge_attr:  (E, H)
-        """
-        N = x.size(0)
-        row, col = edge_index  # (E,) , (E,)
-
-        # Project to multiple key, query and value spaces
-        h_keys = self.k_lin(x.unsqueeze(-1)).view(N, self.num_heads, -1)  # (N, heads, K_per_head)
-        h_queries = self.q_lin(x.unsqueeze(-1)).view(N, self.num_heads, -1)  # (N, heads, K_per_head)
-        h_values = self.v_lin(x.unsqueeze(-1)).view(N, self.num_heads, -1)  # (N, heads, H_per_head)
-
-        # Compute keys and queries
-        W_k = self.weight_k_net(edge_attr)  # (E, K_per_head)
-        keys_j = self.weight_k_lin(W_k.unsqueeze(1) * h_keys[col])  # (E, heads, K_per_head)
-        queries_i = h_queries[row]  # (E, heads, K_per_head)
-
-        # Compute attention weights (alphas)
-        d = int(self.hidden_channels / self.num_heads)
-        qk_ij = (queries_i * keys_j).sum(-1) / math.sqrt(d)  # (E, heads)
-        alpha = scatter_softmax(qk_ij, row, dim=0)
-
-        # Compose messages
-        W_v = self.weight_v_net(edge_attr)  # (E, H_per_head)
-        msg_j = self.weight_v_lin(W_v.unsqueeze(1) * h_values[col])  # (E, heads, H_per_head)
-        msg_j = alpha.unsqueeze(-1) * msg_j  # (E, heads, H_per_head)
-
-        # Aggregate messages
-        aggr_msg = scatter_sum(msg_j, row, dim=0, dim_size=N).view(N, -1)  # (N, heads*H_per_head)
-        out = self.centroid_lin(x) + aggr_msg
-        out = self.layernorm_ffn(out)
-        out = self.out_transform(self.act(out))
-        return out
-
-
-class TransformerEncoder(Module):
-
-    def __init__(self, hidden_channels=256, edge_channels=64, key_channels=128, num_heads=4, num_interactions=6, k=32,
-                 cutoff=10.0):
-        super().__init__()
-
-        self.hidden_channels = hidden_channels
-        self.edge_channels = edge_channels
-        self.key_channels = key_channels
-        self.num_heads = num_heads
-        self.num_interactions = num_interactions
-        self.k = k
-        self.cutoff = cutoff
-
-        self.distance_expansion = GaussianSmearing(stop=cutoff, num_gaussians=edge_channels)
-        self.interactions = ModuleList()
-        for _ in range(num_interactions):
-            block = AttentionInteractionBlock(
-                hidden_channels=hidden_channels,
-                edge_channels=edge_channels,
-                key_channels=key_channels,
-                num_heads=num_heads,
-            )
-            self.interactions.append(block)
-
-    @property
-    def out_channels(self):
-        return self.hidden_channels
-
-    def forward(self, node_attr, pos, batch):
-        # edge_index = radius_graph(pos, self.cutoff, batch=batch, loop=False)
-        edge_index = knn_graph(pos, k=self.k, batch=batch, flow='target_to_source')
-        edge_length = torch.norm(pos[edge_index[0]] - pos[edge_index[1]], dim=1)
-        edge_attr = self.distance_expansion(edge_length)
-
-        h = node_attr
-        for interaction in self.interactions:
-            h = h + interaction(h, edge_index, edge_attr)
-        return h
-
-
-if __name__ == '__main__':
-    from torch_geometric.data import Data, Batch
-
-    hidden_channels = 64
-    edge_channels = 48
-    key_channels = 32
-    num_heads = 4
-
-    data_list = []
-    for num_nodes in [11, 13, 15]:
-        data_list.append(Data(
-            x=torch.randn([num_nodes, hidden_channels]),
-            pos=torch.randn([num_nodes, 3]) * 2
-        ))
-    batch = Batch.from_data_list(data_list)
-
-    model = CFTransformerEncoder(
-        hidden_channels=hidden_channels,
-        edge_channels=edge_channels,
-        key_channels=key_channels,
-        num_heads=num_heads,
-    )
-    out = model(batch.x, batch.pos, batch.batch)
-
-    print(out)
-    print(out.size())
diff --git a/spaces/abhaskumarsinha/MinimalGPT-Ragdoll/subword/__init__.py b/spaces/abhaskumarsinha/MinimalGPT-Ragdoll/subword/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/abhimanyuniga/chavinlo-gpt4-x-alpaca/README.md b/spaces/abhimanyuniga/chavinlo-gpt4-x-alpaca/README.md
deleted file mode 100644
index 19bc778084383d4c258060b92e28b6598c16eab2..0000000000000000000000000000000000000000
--- a/spaces/abhimanyuniga/chavinlo-gpt4-x-alpaca/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: AutoTrain Advanced
-emoji: 🚀
-colorFrom: blue
-colorTo: green
-sdk: docker
-pinned: false
-duplicated_from: autotrain-projects/autotrain-advanced
-license: openrail
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/abhishek/sketch-to-image/annotator/openpose/__init__.py b/spaces/abhishek/sketch-to-image/annotator/openpose/__init__.py
deleted file mode 100644
index 2af34fe010fdf1f9ff84cf17aeb4b36fa6f0ed7c..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/openpose/__init__.py
+++ /dev/null
@@ -1,59 +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
-'''
-
-# Openpose
-# Original from CMU https://github.com/CMU-Perceptual-Computing-Lab/openpose
-# 2nd Edited by https://github.com/Hzzone/pytorch-openpose
-# 3rd Edited by ControlNet
-
-import os
-os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE"
-
-import torch
-import numpy as np
-from . import util
-from .body import Body
-from .hand import Hand
-from annotator.util import annotator_ckpts_path
-
-
-body_model_path = "https://huggingface.co/lllyasviel/ControlNet/resolve/main/annotator/ckpts/body_pose_model.pth"
-hand_model_path = "https://huggingface.co/lllyasviel/ControlNet/resolve/main/annotator/ckpts/hand_pose_model.pth"
-
-
-class OpenposeDetector:
-    def __init__(self):
-        body_modelpath = os.path.join(annotator_ckpts_path, "body_pose_model.pth")
-        # hand_modelpath = os.path.join(annotator_ckpts_path, "hand_pose_model.pth")
-
-        if not os.path.exists(body_modelpath):
-            from basicsr.utils.download_util import load_file_from_url
-            load_file_from_url(body_model_path, model_dir=annotator_ckpts_path)
-            # load_file_from_url(hand_model_path, model_dir=annotator_ckpts_path)
-
-        self.body_estimation = Body(body_modelpath)
-        # self.hand_estimation = Hand(hand_modelpath)
-
-    def __call__(self, oriImg, hand=False):
-        oriImg = oriImg[:, :, ::-1].copy()
-        with torch.no_grad():
-            candidate, subset = self.body_estimation(oriImg)
-            canvas = np.zeros_like(oriImg)
-            canvas = util.draw_bodypose(canvas, candidate, subset)
-            # if hand:
-            #     hands_list = util.handDetect(candidate, subset, oriImg)
-            #     all_hand_peaks = []
-            #     for x, y, w, is_left in hands_list:
-            #         peaks = self.hand_estimation(oriImg[y:y+w, x:x+w, :])
-            #         peaks[:, 0] = np.where(peaks[:, 0] == 0, peaks[:, 0], peaks[:, 0] + x)
-            #         peaks[:, 1] = np.where(peaks[:, 1] == 0, peaks[:, 1], peaks[:, 1] + y)
-            #         all_hand_peaks.append(peaks)
-            #     canvas = util.draw_handpose(canvas, all_hand_peaks)
-            return canvas, dict(candidate=candidate.tolist(), subset=subset.tolist())
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet_null/models/roi_heads/trident_roi_head.py b/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet_null/models/roi_heads/trident_roi_head.py
deleted file mode 100644
index 245569e50b45cc8e21ba8e7210edf4bd0c7f27c5..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet_null/models/roi_heads/trident_roi_head.py
+++ /dev/null
@@ -1,119 +0,0 @@
-import torch
-from mmcv.ops import batched_nms
-
-from mmdet.core import (bbox2result, bbox2roi, bbox_mapping, merge_aug_bboxes,
-                        multiclass_nms)
-from mmdet.models.roi_heads.standard_roi_head import StandardRoIHead
-from ..builder import HEADS
-
-
-@HEADS.register_module()
-class TridentRoIHead(StandardRoIHead):
-    """Trident roi head.
-
-    Args:
-        num_branch (int): Number of branches in TridentNet.
-        test_branch_idx (int): In inference, all 3 branches will be used
-            if `test_branch_idx==-1`, otherwise only branch with index
-            `test_branch_idx` will be used.
-    """
-
-    def __init__(self, num_branch, test_branch_idx, **kwargs):
-        self.num_branch = num_branch
-        self.test_branch_idx = test_branch_idx
-        super(TridentRoIHead, self).__init__(**kwargs)
-
-    def merge_trident_bboxes(self, trident_det_bboxes, trident_det_labels):
-        """Merge bbox predictions of each branch."""
-        if trident_det_bboxes.numel() == 0:
-            det_bboxes = trident_det_bboxes.new_zeros((0, 5))
-            det_labels = trident_det_bboxes.new_zeros((0, ), dtype=torch.long)
-        else:
-            nms_bboxes = trident_det_bboxes[:, :4]
-            nms_scores = trident_det_bboxes[:, 4].contiguous()
-            nms_inds = trident_det_labels
-            nms_cfg = self.test_cfg['nms']
-            det_bboxes, keep = batched_nms(nms_bboxes, nms_scores, nms_inds,
-                                           nms_cfg)
-            det_labels = trident_det_labels[keep]
-            if self.test_cfg['max_per_img'] > 0:
-                det_labels = det_labels[:self.test_cfg['max_per_img']]
-                det_bboxes = det_bboxes[:self.test_cfg['max_per_img']]
-
-        return det_bboxes, det_labels
-
-    def simple_test(self,
-                    x,
-                    proposal_list,
-                    img_metas,
-                    proposals=None,
-                    rescale=False):
-        """Test without augmentation as follows:
-
-        1. Compute prediction bbox and label per branch.
-        2. Merge predictions of each branch according to scores of
-           bboxes, i.e., bboxes with higher score are kept to give
-           top-k prediction.
-        """
-        assert self.with_bbox, 'Bbox head must be implemented.'
-        det_bboxes_list, det_labels_list = self.simple_test_bboxes(
-            x, img_metas, proposal_list, self.test_cfg, rescale=rescale)
-        num_branch = self.num_branch if self.test_branch_idx == -1 else 1
-        for _ in range(len(det_bboxes_list)):
-            if det_bboxes_list[_].shape[0] == 0:
-                det_bboxes_list[_] = det_bboxes_list[_].new_empty((0, 5))
-        det_bboxes, det_labels = [], []
-        for i in range(len(img_metas) // num_branch):
-            det_result = self.merge_trident_bboxes(
-                torch.cat(det_bboxes_list[i * num_branch:(i + 1) *
-                                          num_branch]),
-                torch.cat(det_labels_list[i * num_branch:(i + 1) *
-                                          num_branch]))
-            det_bboxes.append(det_result[0])
-            det_labels.append(det_result[1])
-
-        bbox_results = [
-            bbox2result(det_bboxes[i], det_labels[i],
-                        self.bbox_head.num_classes)
-            for i in range(len(det_bboxes))
-        ]
-        return bbox_results
-
-    def aug_test_bboxes(self, feats, img_metas, proposal_list, rcnn_test_cfg):
-        """Test det bboxes with test time augmentation."""
-        aug_bboxes = []
-        aug_scores = []
-        for x, img_meta in zip(feats, img_metas):
-            # only one image in the batch
-            img_shape = img_meta[0]['img_shape']
-            scale_factor = img_meta[0]['scale_factor']
-            flip = img_meta[0]['flip']
-            flip_direction = img_meta[0]['flip_direction']
-
-            trident_bboxes, trident_scores = [], []
-            for branch_idx in range(len(proposal_list)):
-                proposals = bbox_mapping(proposal_list[0][:, :4], img_shape,
-                                         scale_factor, flip, flip_direction)
-                rois = bbox2roi([proposals])
-                bbox_results = self._bbox_forward(x, rois)
-                bboxes, scores = self.bbox_head.get_bboxes(
-                    rois,
-                    bbox_results['cls_score'],
-                    bbox_results['bbox_pred'],
-                    img_shape,
-                    scale_factor,
-                    rescale=False,
-                    cfg=None)
-                trident_bboxes.append(bboxes)
-                trident_scores.append(scores)
-
-            aug_bboxes.append(torch.cat(trident_bboxes, 0))
-            aug_scores.append(torch.cat(trident_scores, 0))
-        # after merging, bboxes will be rescaled to the original image size
-        merged_bboxes, merged_scores = merge_aug_bboxes(
-            aug_bboxes, aug_scores, img_metas, rcnn_test_cfg)
-        det_bboxes, det_labels = multiclass_nms(merged_bboxes, merged_scores,
-                                                rcnn_test_cfg.score_thr,
-                                                rcnn_test_cfg.nms,
-                                                rcnn_test_cfg.max_per_img)
-        return det_bboxes, det_labels
diff --git a/spaces/afry-south/lowlight-enhancement/app.py b/spaces/afry-south/lowlight-enhancement/app.py
deleted file mode 100644
index 90d69f09920ad61ded383708a0b7c5d6d2be0806..0000000000000000000000000000000000000000
--- a/spaces/afry-south/lowlight-enhancement/app.py
+++ /dev/null
@@ -1,51 +0,0 @@
-import torch
-import torch.optim
-import model
-import numpy as np
-from PIL import Image
-import streamlit as st
-from torchvision import transforms
-
-scale_factor = 1
-
-@st.cache
-def load_model() -> torch.nn.Module:
-    DCE_net = model.enhance_net_nopool(scale_factor)
-    DCE_net.load_state_dict(torch.load("lowlight-dce-snapshot.pth", map_location=torch.device('cpu')))
-    
-    return DCE_net
-
-def fix_lowlight(image: Image.Image) -> Image.Image:
-    DCE_net = load_model()
-    data_lowlight = np.asarray(image) / 255.0
-
-    data_lowlight = torch.from_numpy(data_lowlight).float()
-
-    h = (data_lowlight.shape[0] // scale_factor) * scale_factor
-    w = (data_lowlight.shape[1] // scale_factor) * scale_factor
-    data_lowlight = data_lowlight[0:h, 0:w, :]
-    data_lowlight = data_lowlight.permute(2, 0, 1)
-    data_lowlight = data_lowlight.unsqueeze(0)
-    
-    enhanced_image, _ = DCE_net(data_lowlight)
-    im = transforms.ToPILImage()(enhanced_image[0]).convert("RGB")
-
-    return im
-
-def main():
-    st.title("Lowlight Enhancement")
-    st.write("This is a simple lowlight enhancement app with great performance and does not require paired images to train.")
-    st.write("The model runs at 1000/11 FPS on single GPU/CPU on images with a size of 1200*900*3")
-    uploaded_file = st.file_uploader("Lowlight Image")
-    if uploaded_file:
-        data_lowlight = Image.open(uploaded_file).convert('RGB')
-        col1, col2 = st.columns(2)
-        col1.write("Original (Lowlight)")
-        col1.image(data_lowlight, caption="Lowlight Image", use_column_width=True)
-        
-        col2.write("Enhanced")
-        with st.spinner('🧠 Enhancing...'):
-            fixed_img = fix_lowlight(data_lowlight)
-        col2.image(fixed_img, caption="Enhanced Image", use_column_width=True)
-
-main()
\ No newline at end of file
diff --git a/spaces/akhaliq/Kapao/utils/callbacks.py b/spaces/akhaliq/Kapao/utils/callbacks.py
deleted file mode 100644
index 19c334430b5db871b692725eb64ec668e2099cf3..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/Kapao/utils/callbacks.py
+++ /dev/null
@@ -1,179 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-"""
-Callback utils
-"""
-
-
-class Callbacks:
-    """"
-    Handles all registered callbacks for YOLOv5 Hooks
-    """
-
-    _callbacks = {
-        'on_pretrain_routine_start': [],
-        'on_pretrain_routine_end': [],
-
-        'on_train_start': [],
-        'on_train_epoch_start': [],
-        'on_train_batch_start': [],
-        'optimizer_step': [],
-        'on_before_zero_grad': [],
-        'on_train_batch_end': [],
-        'on_train_epoch_end': [],
-
-        'on_val_start': [],
-        'on_val_batch_start': [],
-        'on_val_image_end': [],
-        'on_val_batch_end': [],
-        'on_val_end': [],
-
-        'on_fit_epoch_end': [],  # fit = train + val
-        'on_model_save': [],
-        'on_train_end': [],
-
-        'teardown': [],
-    }
-
-    def __init__(self):
-        return
-
-    def register_action(self, hook, name='', callback=None):
-        """
-        Register a new action to a callback hook
-
-        Args:
-            hook        The callback hook name to register the action to
-            name        The name of the action
-            callback    The callback to fire
-        """
-        assert hook in self._callbacks, f"hook '{hook}' not found in callbacks {self._callbacks}"
-        assert callable(callback), f"callback '{callback}' is not callable"
-        self._callbacks[hook].append({'name': name, 'callback': callback})
-
-    def get_registered_actions(self, hook=None):
-        """"
-        Returns all the registered actions by callback hook
-
-        Args:
-            hook The name of the hook to check, defaults to all
-        """
-        if hook:
-            return self._callbacks[hook]
-        else:
-            return self._callbacks
-
-    def run_callbacks(self, hook, *args, **kwargs):
-        """
-        Loop through the registered actions and fire all callbacks
-        """
-        for logger in self._callbacks[hook]:
-            # print(f"Running callbacks.{logger['callback'].__name__}()")
-            logger['callback'](*args, **kwargs)
-
-    def on_pretrain_routine_start(self, *args, **kwargs):
-        """
-        Fires all registered callbacks at the start of each pretraining routine
-        """
-        self.run_callbacks('on_pretrain_routine_start', *args, **kwargs)
-
-    def on_pretrain_routine_end(self, *args, **kwargs):
-        """
-        Fires all registered callbacks at the end of each pretraining routine
-        """
-        self.run_callbacks('on_pretrain_routine_end', *args, **kwargs)
-
-    def on_train_start(self, *args, **kwargs):
-        """
-        Fires all registered callbacks at the start of each training
-        """
-        self.run_callbacks('on_train_start', *args, **kwargs)
-
-    def on_train_epoch_start(self, *args, **kwargs):
-        """
-        Fires all registered callbacks at the start of each training epoch
-        """
-        self.run_callbacks('on_train_epoch_start', *args, **kwargs)
-
-    def on_train_batch_start(self, *args, **kwargs):
-        """
-        Fires all registered callbacks at the start of each training batch
-        """
-        self.run_callbacks('on_train_batch_start', *args, **kwargs)
-
-    def optimizer_step(self, *args, **kwargs):
-        """
-        Fires all registered callbacks on each optimizer step
-        """
-        self.run_callbacks('optimizer_step', *args, **kwargs)
-
-    def on_before_zero_grad(self, *args, **kwargs):
-        """
-        Fires all registered callbacks before zero grad
-        """
-        self.run_callbacks('on_before_zero_grad', *args, **kwargs)
-
-    def on_train_batch_end(self, *args, **kwargs):
-        """
-        Fires all registered callbacks at the end of each training batch
-        """
-        self.run_callbacks('on_train_batch_end', *args, **kwargs)
-
-    def on_train_epoch_end(self, *args, **kwargs):
-        """
-        Fires all registered callbacks at the end of each training epoch
-        """
-        self.run_callbacks('on_train_epoch_end', *args, **kwargs)
-
-    def on_val_start(self, *args, **kwargs):
-        """
-        Fires all registered callbacks at the start of the validation
-        """
-        self.run_callbacks('on_val_start', *args, **kwargs)
-
-    def on_val_batch_start(self, *args, **kwargs):
-        """
-        Fires all registered callbacks at the start of each validation batch
-        """
-        self.run_callbacks('on_val_batch_start', *args, **kwargs)
-
-    def on_val_image_end(self, *args, **kwargs):
-        """
-        Fires all registered callbacks at the end of each val image
-        """
-        self.run_callbacks('on_val_image_end', *args, **kwargs)
-
-    def on_val_batch_end(self, *args, **kwargs):
-        """
-        Fires all registered callbacks at the end of each validation batch
-        """
-        self.run_callbacks('on_val_batch_end', *args, **kwargs)
-
-    def on_val_end(self, *args, **kwargs):
-        """
-        Fires all registered callbacks at the end of the validation
-        """
-        self.run_callbacks('on_val_end', *args, **kwargs)
-
-    def on_fit_epoch_end(self, *args, **kwargs):
-        """
-        Fires all registered callbacks at the end of each fit (train+val) epoch
-        """
-        self.run_callbacks('on_fit_epoch_end', *args, **kwargs)
-
-    def on_model_save(self, *args, **kwargs):
-        """
-        Fires all registered callbacks after each model save
-        """
-        self.run_callbacks('on_model_save', *args, **kwargs)
-
-    def on_train_end(self, *args, **kwargs):
-        """
-        Fires all registered callbacks at the end of training
-        """
-        self.run_callbacks('on_train_end', *args, **kwargs)
-
-    def teardown(self, *args, **kwargs):
-        """
-        Fires all registered callbacks before teardown
-        """
-        self.run_callbacks('teardown', *args, **kwargs)
diff --git a/spaces/alex-mindspace/gpt-agents/swarmai/__main__.py b/spaces/alex-mindspace/gpt-agents/swarmai/__main__.py
deleted file mode 100644
index f1b8cc955cb4d95e4f9687f098a6fe937a0bdc72..0000000000000000000000000000000000000000
--- a/spaces/alex-mindspace/gpt-agents/swarmai/__main__.py
+++ /dev/null
@@ -1,37 +0,0 @@
-import sys
-import os
-import json
-from pathlib import Path
-sys.path.append('..')
-
-from swarmai.Swarm import Swarm
-
-def load_keys():
-    try:
-        keys_file = Path(__file__).parent.parent / "keys.json"
-        with open(keys_file) as f:
-            keys = json.load(f)
-        os.environ["OPENAI_API_KEY"] = keys["OPENAI_API_KEY"]
-        try:
-            os.environ["GOOGLE_API_KEY"] = keys["GOOGLE_API_KEY"]
-            os.environ["CUSTOM_SEARCH_ENGINE_ID"] = keys["CUSTOM_SEARCH_ENGINE_ID"]
-            os.environ["GOOGLE_CSE_ID"] = keys["CUSTOM_SEARCH_ENGINE_ID"]
-        except:
-            print("WARNING: GOOGLE_API_KEY and GOOGLE_CSE_ID not found in keys.json. Googler agent will be treated as a general purpose agent.")
-
-        try:
-            os.environ["APIFY_API_TOKEN"] = keys["APIFY_API_TOKEN"]
-        except:
-            print("WARNING: APIFY_API_TOKEN not found in keys.json. WebScraper agent will not work.")
-    except:
-        pass
-
-def run_swarm():
-    # establishing the swarm
-    swarm_config_loc = Path(__file__).parent.parent / "swarm_config.yaml"
-    load_keys()
-    swarm1 = Swarm(swarm_config_loc)
-    swarm1.run_swarm()
-
-if __name__=="__main__":
-    run_swarm()
\ No newline at end of file
diff --git a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_internal/configuration.py b/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_internal/configuration.py
deleted file mode 100644
index a8092d1ae069c4095901e7f5cb8e6fa49ef63033..0000000000000000000000000000000000000000
--- a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_internal/configuration.py
+++ /dev/null
@@ -1,366 +0,0 @@
-"""Configuration management setup
-
-Some terminology:
-- name
-  As written in config files.
-- value
-  Value associated with a name
-- key
-  Name combined with it's section (section.name)
-- variant
-  A single word describing where the configuration key-value pair came from
-"""
-
-import configparser
-import locale
-import os
-import sys
-from typing import Any, Dict, Iterable, List, NewType, Optional, Tuple
-
-from pip._internal.exceptions import (
-    ConfigurationError,
-    ConfigurationFileCouldNotBeLoaded,
-)
-from pip._internal.utils import appdirs
-from pip._internal.utils.compat import WINDOWS
-from pip._internal.utils.logging import getLogger
-from pip._internal.utils.misc import ensure_dir, enum
-
-RawConfigParser = configparser.RawConfigParser  # Shorthand
-Kind = NewType("Kind", str)
-
-CONFIG_BASENAME = "pip.ini" if WINDOWS else "pip.conf"
-ENV_NAMES_IGNORED = "version", "help"
-
-# The kinds of configurations there are.
-kinds = enum(
-    USER="user",  # User Specific
-    GLOBAL="global",  # System Wide
-    SITE="site",  # [Virtual] Environment Specific
-    ENV="env",  # from PIP_CONFIG_FILE
-    ENV_VAR="env-var",  # from Environment Variables
-)
-OVERRIDE_ORDER = kinds.GLOBAL, kinds.USER, kinds.SITE, kinds.ENV, kinds.ENV_VAR
-VALID_LOAD_ONLY = kinds.USER, kinds.GLOBAL, kinds.SITE
-
-logger = getLogger(__name__)
-
-
-# NOTE: Maybe use the optionx attribute to normalize keynames.
-def _normalize_name(name: str) -> str:
-    """Make a name consistent regardless of source (environment or file)"""
-    name = name.lower().replace("_", "-")
-    if name.startswith("--"):
-        name = name[2:]  # only prefer long opts
-    return name
-
-
-def _disassemble_key(name: str) -> List[str]:
-    if "." not in name:
-        error_message = (
-            "Key does not contain dot separated section and key. "
-            "Perhaps you wanted to use 'global.{}' instead?"
-        ).format(name)
-        raise ConfigurationError(error_message)
-    return name.split(".", 1)
-
-
-def get_configuration_files() -> Dict[Kind, List[str]]:
-    global_config_files = [
-        os.path.join(path, CONFIG_BASENAME) for path in appdirs.site_config_dirs("pip")
-    ]
-
-    site_config_file = os.path.join(sys.prefix, CONFIG_BASENAME)
-    legacy_config_file = os.path.join(
-        os.path.expanduser("~"),
-        "pip" if WINDOWS else ".pip",
-        CONFIG_BASENAME,
-    )
-    new_config_file = os.path.join(appdirs.user_config_dir("pip"), CONFIG_BASENAME)
-    return {
-        kinds.GLOBAL: global_config_files,
-        kinds.SITE: [site_config_file],
-        kinds.USER: [legacy_config_file, new_config_file],
-    }
-
-
-class Configuration:
-    """Handles management of configuration.
-
-    Provides an interface to accessing and managing configuration files.
-
-    This class converts provides an API that takes "section.key-name" style
-    keys and stores the value associated with it as "key-name" under the
-    section "section".
-
-    This allows for a clean interface wherein the both the section and the
-    key-name are preserved in an easy to manage form in the configuration files
-    and the data stored is also nice.
-    """
-
-    def __init__(self, isolated: bool, load_only: Optional[Kind] = None) -> None:
-        super().__init__()
-
-        if load_only is not None and load_only not in VALID_LOAD_ONLY:
-            raise ConfigurationError(
-                "Got invalid value for load_only - should be one of {}".format(
-                    ", ".join(map(repr, VALID_LOAD_ONLY))
-                )
-            )
-        self.isolated = isolated
-        self.load_only = load_only
-
-        # Because we keep track of where we got the data from
-        self._parsers: Dict[Kind, List[Tuple[str, RawConfigParser]]] = {
-            variant: [] for variant in OVERRIDE_ORDER
-        }
-        self._config: Dict[Kind, Dict[str, Any]] = {
-            variant: {} for variant in OVERRIDE_ORDER
-        }
-        self._modified_parsers: List[Tuple[str, RawConfigParser]] = []
-
-    def load(self) -> None:
-        """Loads configuration from configuration files and environment"""
-        self._load_config_files()
-        if not self.isolated:
-            self._load_environment_vars()
-
-    def get_file_to_edit(self) -> Optional[str]:
-        """Returns the file with highest priority in configuration"""
-        assert self.load_only is not None, "Need to be specified a file to be editing"
-
-        try:
-            return self._get_parser_to_modify()[0]
-        except IndexError:
-            return None
-
-    def items(self) -> Iterable[Tuple[str, Any]]:
-        """Returns key-value pairs like dict.items() representing the loaded
-        configuration
-        """
-        return self._dictionary.items()
-
-    def get_value(self, key: str) -> Any:
-        """Get a value from the configuration."""
-        try:
-            return self._dictionary[key]
-        except KeyError:
-            raise ConfigurationError(f"No such key - {key}")
-
-    def set_value(self, key: str, value: Any) -> None:
-        """Modify a value in the configuration."""
-        self._ensure_have_load_only()
-
-        assert self.load_only
-        fname, parser = self._get_parser_to_modify()
-
-        if parser is not None:
-            section, name = _disassemble_key(key)
-
-            # Modify the parser and the configuration
-            if not parser.has_section(section):
-                parser.add_section(section)
-            parser.set(section, name, value)
-
-        self._config[self.load_only][key] = value
-        self._mark_as_modified(fname, parser)
-
-    def unset_value(self, key: str) -> None:
-        """Unset a value in the configuration."""
-        self._ensure_have_load_only()
-
-        assert self.load_only
-        if key not in self._config[self.load_only]:
-            raise ConfigurationError(f"No such key - {key}")
-
-        fname, parser = self._get_parser_to_modify()
-
-        if parser is not None:
-            section, name = _disassemble_key(key)
-            if not (
-                parser.has_section(section) and parser.remove_option(section, name)
-            ):
-                # The option was not removed.
-                raise ConfigurationError(
-                    "Fatal Internal error [id=1]. Please report as a bug."
-                )
-
-            # The section may be empty after the option was removed.
-            if not parser.items(section):
-                parser.remove_section(section)
-            self._mark_as_modified(fname, parser)
-
-        del self._config[self.load_only][key]
-
-    def save(self) -> None:
-        """Save the current in-memory state."""
-        self._ensure_have_load_only()
-
-        for fname, parser in self._modified_parsers:
-            logger.info("Writing to %s", fname)
-
-            # Ensure directory exists.
-            ensure_dir(os.path.dirname(fname))
-
-            with open(fname, "w") as f:
-                parser.write(f)
-
-    #
-    # Private routines
-    #
-
-    def _ensure_have_load_only(self) -> None:
-        if self.load_only is None:
-            raise ConfigurationError("Needed a specific file to be modifying.")
-        logger.debug("Will be working with %s variant only", self.load_only)
-
-    @property
-    def _dictionary(self) -> Dict[str, Any]:
-        """A dictionary representing the loaded configuration."""
-        # NOTE: Dictionaries are not populated if not loaded. So, conditionals
-        #       are not needed here.
-        retval = {}
-
-        for variant in OVERRIDE_ORDER:
-            retval.update(self._config[variant])
-
-        return retval
-
-    def _load_config_files(self) -> None:
-        """Loads configuration from configuration files"""
-        config_files = dict(self.iter_config_files())
-        if config_files[kinds.ENV][0:1] == [os.devnull]:
-            logger.debug(
-                "Skipping loading configuration files due to "
-                "environment's PIP_CONFIG_FILE being os.devnull"
-            )
-            return
-
-        for variant, files in config_files.items():
-            for fname in files:
-                # If there's specific variant set in `load_only`, load only
-                # that variant, not the others.
-                if self.load_only is not None and variant != self.load_only:
-                    logger.debug("Skipping file '%s' (variant: %s)", fname, variant)
-                    continue
-
-                parser = self._load_file(variant, fname)
-
-                # Keeping track of the parsers used
-                self._parsers[variant].append((fname, parser))
-
-    def _load_file(self, variant: Kind, fname: str) -> RawConfigParser:
-        logger.verbose("For variant '%s', will try loading '%s'", variant, fname)
-        parser = self._construct_parser(fname)
-
-        for section in parser.sections():
-            items = parser.items(section)
-            self._config[variant].update(self._normalized_keys(section, items))
-
-        return parser
-
-    def _construct_parser(self, fname: str) -> RawConfigParser:
-        parser = configparser.RawConfigParser()
-        # If there is no such file, don't bother reading it but create the
-        # parser anyway, to hold the data.
-        # Doing this is useful when modifying and saving files, where we don't
-        # need to construct a parser.
-        if os.path.exists(fname):
-            locale_encoding = locale.getpreferredencoding(False)
-            try:
-                parser.read(fname, encoding=locale_encoding)
-            except UnicodeDecodeError:
-                # See https://github.com/pypa/pip/issues/4963
-                raise ConfigurationFileCouldNotBeLoaded(
-                    reason=f"contains invalid {locale_encoding} characters",
-                    fname=fname,
-                )
-            except configparser.Error as error:
-                # See https://github.com/pypa/pip/issues/4893
-                raise ConfigurationFileCouldNotBeLoaded(error=error)
-        return parser
-
-    def _load_environment_vars(self) -> None:
-        """Loads configuration from environment variables"""
-        self._config[kinds.ENV_VAR].update(
-            self._normalized_keys(":env:", self.get_environ_vars())
-        )
-
-    def _normalized_keys(
-        self, section: str, items: Iterable[Tuple[str, Any]]
-    ) -> Dict[str, Any]:
-        """Normalizes items to construct a dictionary with normalized keys.
-
-        This routine is where the names become keys and are made the same
-        regardless of source - configuration files or environment.
-        """
-        normalized = {}
-        for name, val in items:
-            key = section + "." + _normalize_name(name)
-            normalized[key] = val
-        return normalized
-
-    def get_environ_vars(self) -> Iterable[Tuple[str, str]]:
-        """Returns a generator with all environmental vars with prefix PIP_"""
-        for key, val in os.environ.items():
-            if key.startswith("PIP_"):
-                name = key[4:].lower()
-                if name not in ENV_NAMES_IGNORED:
-                    yield name, val
-
-    # XXX: This is patched in the tests.
-    def iter_config_files(self) -> Iterable[Tuple[Kind, List[str]]]:
-        """Yields variant and configuration files associated with it.
-
-        This should be treated like items of a dictionary.
-        """
-        # SMELL: Move the conditions out of this function
-
-        # environment variables have the lowest priority
-        config_file = os.environ.get("PIP_CONFIG_FILE", None)
-        if config_file is not None:
-            yield kinds.ENV, [config_file]
-        else:
-            yield kinds.ENV, []
-
-        config_files = get_configuration_files()
-
-        # at the base we have any global configuration
-        yield kinds.GLOBAL, config_files[kinds.GLOBAL]
-
-        # per-user configuration next
-        should_load_user_config = not self.isolated and not (
-            config_file and os.path.exists(config_file)
-        )
-        if should_load_user_config:
-            # The legacy config file is overridden by the new config file
-            yield kinds.USER, config_files[kinds.USER]
-
-        # finally virtualenv configuration first trumping others
-        yield kinds.SITE, config_files[kinds.SITE]
-
-    def get_values_in_config(self, variant: Kind) -> Dict[str, Any]:
-        """Get values present in a config file"""
-        return self._config[variant]
-
-    def _get_parser_to_modify(self) -> Tuple[str, RawConfigParser]:
-        # Determine which parser to modify
-        assert self.load_only
-        parsers = self._parsers[self.load_only]
-        if not parsers:
-            # This should not happen if everything works correctly.
-            raise ConfigurationError(
-                "Fatal Internal error [id=2]. Please report as a bug."
-            )
-
-        # Use the highest priority parser.
-        return parsers[-1]
-
-    # XXX: This is patched in the tests.
-    def _mark_as_modified(self, fname: str, parser: RawConfigParser) -> None:
-        file_parser_tuple = (fname, parser)
-        if file_parser_tuple not in self._modified_parsers:
-            self._modified_parsers.append(file_parser_tuple)
-
-    def __repr__(self) -> str:
-        return f"{self.__class__.__name__}({self._dictionary!r})"
diff --git a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_vendor/pygments/filters/__init__.py b/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_vendor/pygments/filters/__init__.py
deleted file mode 100644
index 1d5a808f6f549206b429eb223149a7c7ceb8ebf9..0000000000000000000000000000000000000000
--- a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_vendor/pygments/filters/__init__.py
+++ /dev/null
@@ -1,937 +0,0 @@
-"""
-    pygments.filters
-    ~~~~~~~~~~~~~~~~
-
-    Module containing filter lookup functions and default
-    filters.
-
-    :copyright: Copyright 2006-2021 by the Pygments team, see AUTHORS.
-    :license: BSD, see LICENSE for details.
-"""
-
-import re
-
-from pip._vendor.pygments.token import String, Comment, Keyword, Name, Error, Whitespace, \
-    string_to_tokentype
-from pip._vendor.pygments.filter import Filter
-from pip._vendor.pygments.util import get_list_opt, get_int_opt, get_bool_opt, \
-    get_choice_opt, ClassNotFound, OptionError
-from pip._vendor.pygments.plugin import find_plugin_filters
-
-
-def find_filter_class(filtername):
-    """Lookup a filter by name. Return None if not found."""
-    if filtername in FILTERS:
-        return FILTERS[filtername]
-    for name, cls in find_plugin_filters():
-        if name == filtername:
-            return cls
-    return None
-
-
-def get_filter_by_name(filtername, **options):
-    """Return an instantiated filter.
-
-    Options are passed to the filter initializer if wanted.
-    Raise a ClassNotFound if not found.
-    """
-    cls = find_filter_class(filtername)
-    if cls:
-        return cls(**options)
-    else:
-        raise ClassNotFound('filter %r not found' % filtername)
-
-
-def get_all_filters():
-    """Return a generator of all filter names."""
-    yield from FILTERS
-    for name, _ in find_plugin_filters():
-        yield name
-
-
-def _replace_special(ttype, value, regex, specialttype,
-                     replacefunc=lambda x: x):
-    last = 0
-    for match in regex.finditer(value):
-        start, end = match.start(), match.end()
-        if start != last:
-            yield ttype, value[last:start]
-        yield specialttype, replacefunc(value[start:end])
-        last = end
-    if last != len(value):
-        yield ttype, value[last:]
-
-
-class CodeTagFilter(Filter):
-    """Highlight special code tags in comments and docstrings.
-
-    Options accepted:
-
-    `codetags` : list of strings
-       A list of strings that are flagged as code tags.  The default is to
-       highlight ``XXX``, ``TODO``, ``BUG`` and ``NOTE``.
-    """
-
-    def __init__(self, **options):
-        Filter.__init__(self, **options)
-        tags = get_list_opt(options, 'codetags',
-                            ['XXX', 'TODO', 'BUG', 'NOTE'])
-        self.tag_re = re.compile(r'\b(%s)\b' % '|'.join([
-            re.escape(tag) for tag in tags if tag
-        ]))
-
-    def filter(self, lexer, stream):
-        regex = self.tag_re
-        for ttype, value in stream:
-            if ttype in String.Doc or \
-               ttype in Comment and \
-               ttype not in Comment.Preproc:
-                yield from _replace_special(ttype, value, regex, Comment.Special)
-            else:
-                yield ttype, value
-
-
-class SymbolFilter(Filter):
-    """Convert mathematical symbols such as \\<longrightarrow> in Isabelle
-    or \\longrightarrow in LaTeX into Unicode characters.
-
-    This is mostly useful for HTML or console output when you want to
-    approximate the source rendering you'd see in an IDE.
-
-    Options accepted:
-
-    `lang` : string
-       The symbol language. Must be one of ``'isabelle'`` or
-       ``'latex'``.  The default is ``'isabelle'``.
-    """
-
-    latex_symbols = {
-        '\\alpha'                : '\U000003b1',
-        '\\beta'                 : '\U000003b2',
-        '\\gamma'                : '\U000003b3',
-        '\\delta'                : '\U000003b4',
-        '\\varepsilon'           : '\U000003b5',
-        '\\zeta'                 : '\U000003b6',
-        '\\eta'                  : '\U000003b7',
-        '\\vartheta'             : '\U000003b8',
-        '\\iota'                 : '\U000003b9',
-        '\\kappa'                : '\U000003ba',
-        '\\lambda'               : '\U000003bb',
-        '\\mu'                   : '\U000003bc',
-        '\\nu'                   : '\U000003bd',
-        '\\xi'                   : '\U000003be',
-        '\\pi'                   : '\U000003c0',
-        '\\varrho'               : '\U000003c1',
-        '\\sigma'                : '\U000003c3',
-        '\\tau'                  : '\U000003c4',
-        '\\upsilon'              : '\U000003c5',
-        '\\varphi'               : '\U000003c6',
-        '\\chi'                  : '\U000003c7',
-        '\\psi'                  : '\U000003c8',
-        '\\omega'                : '\U000003c9',
-        '\\Gamma'                : '\U00000393',
-        '\\Delta'                : '\U00000394',
-        '\\Theta'                : '\U00000398',
-        '\\Lambda'               : '\U0000039b',
-        '\\Xi'                   : '\U0000039e',
-        '\\Pi'                   : '\U000003a0',
-        '\\Sigma'                : '\U000003a3',
-        '\\Upsilon'              : '\U000003a5',
-        '\\Phi'                  : '\U000003a6',
-        '\\Psi'                  : '\U000003a8',
-        '\\Omega'                : '\U000003a9',
-        '\\leftarrow'            : '\U00002190',
-        '\\longleftarrow'        : '\U000027f5',
-        '\\rightarrow'           : '\U00002192',
-        '\\longrightarrow'       : '\U000027f6',
-        '\\Leftarrow'            : '\U000021d0',
-        '\\Longleftarrow'        : '\U000027f8',
-        '\\Rightarrow'           : '\U000021d2',
-        '\\Longrightarrow'       : '\U000027f9',
-        '\\leftrightarrow'       : '\U00002194',
-        '\\longleftrightarrow'   : '\U000027f7',
-        '\\Leftrightarrow'       : '\U000021d4',
-        '\\Longleftrightarrow'   : '\U000027fa',
-        '\\mapsto'               : '\U000021a6',
-        '\\longmapsto'           : '\U000027fc',
-        '\\relbar'               : '\U00002500',
-        '\\Relbar'               : '\U00002550',
-        '\\hookleftarrow'        : '\U000021a9',
-        '\\hookrightarrow'       : '\U000021aa',
-        '\\leftharpoondown'      : '\U000021bd',
-        '\\rightharpoondown'     : '\U000021c1',
-        '\\leftharpoonup'        : '\U000021bc',
-        '\\rightharpoonup'       : '\U000021c0',
-        '\\rightleftharpoons'    : '\U000021cc',
-        '\\leadsto'              : '\U0000219d',
-        '\\downharpoonleft'      : '\U000021c3',
-        '\\downharpoonright'     : '\U000021c2',
-        '\\upharpoonleft'        : '\U000021bf',
-        '\\upharpoonright'       : '\U000021be',
-        '\\restriction'          : '\U000021be',
-        '\\uparrow'              : '\U00002191',
-        '\\Uparrow'              : '\U000021d1',
-        '\\downarrow'            : '\U00002193',
-        '\\Downarrow'            : '\U000021d3',
-        '\\updownarrow'          : '\U00002195',
-        '\\Updownarrow'          : '\U000021d5',
-        '\\langle'               : '\U000027e8',
-        '\\rangle'               : '\U000027e9',
-        '\\lceil'                : '\U00002308',
-        '\\rceil'                : '\U00002309',
-        '\\lfloor'               : '\U0000230a',
-        '\\rfloor'               : '\U0000230b',
-        '\\flqq'                 : '\U000000ab',
-        '\\frqq'                 : '\U000000bb',
-        '\\bot'                  : '\U000022a5',
-        '\\top'                  : '\U000022a4',
-        '\\wedge'                : '\U00002227',
-        '\\bigwedge'             : '\U000022c0',
-        '\\vee'                  : '\U00002228',
-        '\\bigvee'               : '\U000022c1',
-        '\\forall'               : '\U00002200',
-        '\\exists'               : '\U00002203',
-        '\\nexists'              : '\U00002204',
-        '\\neg'                  : '\U000000ac',
-        '\\Box'                  : '\U000025a1',
-        '\\Diamond'              : '\U000025c7',
-        '\\vdash'                : '\U000022a2',
-        '\\models'               : '\U000022a8',
-        '\\dashv'                : '\U000022a3',
-        '\\surd'                 : '\U0000221a',
-        '\\le'                   : '\U00002264',
-        '\\ge'                   : '\U00002265',
-        '\\ll'                   : '\U0000226a',
-        '\\gg'                   : '\U0000226b',
-        '\\lesssim'              : '\U00002272',
-        '\\gtrsim'               : '\U00002273',
-        '\\lessapprox'           : '\U00002a85',
-        '\\gtrapprox'            : '\U00002a86',
-        '\\in'                   : '\U00002208',
-        '\\notin'                : '\U00002209',
-        '\\subset'               : '\U00002282',
-        '\\supset'               : '\U00002283',
-        '\\subseteq'             : '\U00002286',
-        '\\supseteq'             : '\U00002287',
-        '\\sqsubset'             : '\U0000228f',
-        '\\sqsupset'             : '\U00002290',
-        '\\sqsubseteq'           : '\U00002291',
-        '\\sqsupseteq'           : '\U00002292',
-        '\\cap'                  : '\U00002229',
-        '\\bigcap'               : '\U000022c2',
-        '\\cup'                  : '\U0000222a',
-        '\\bigcup'               : '\U000022c3',
-        '\\sqcup'                : '\U00002294',
-        '\\bigsqcup'             : '\U00002a06',
-        '\\sqcap'                : '\U00002293',
-        '\\Bigsqcap'             : '\U00002a05',
-        '\\setminus'             : '\U00002216',
-        '\\propto'               : '\U0000221d',
-        '\\uplus'                : '\U0000228e',
-        '\\bigplus'              : '\U00002a04',
-        '\\sim'                  : '\U0000223c',
-        '\\doteq'                : '\U00002250',
-        '\\simeq'                : '\U00002243',
-        '\\approx'               : '\U00002248',
-        '\\asymp'                : '\U0000224d',
-        '\\cong'                 : '\U00002245',
-        '\\equiv'                : '\U00002261',
-        '\\Join'                 : '\U000022c8',
-        '\\bowtie'               : '\U00002a1d',
-        '\\prec'                 : '\U0000227a',
-        '\\succ'                 : '\U0000227b',
-        '\\preceq'               : '\U0000227c',
-        '\\succeq'               : '\U0000227d',
-        '\\parallel'             : '\U00002225',
-        '\\mid'                  : '\U000000a6',
-        '\\pm'                   : '\U000000b1',
-        '\\mp'                   : '\U00002213',
-        '\\times'                : '\U000000d7',
-        '\\div'                  : '\U000000f7',
-        '\\cdot'                 : '\U000022c5',
-        '\\star'                 : '\U000022c6',
-        '\\circ'                 : '\U00002218',
-        '\\dagger'               : '\U00002020',
-        '\\ddagger'              : '\U00002021',
-        '\\lhd'                  : '\U000022b2',
-        '\\rhd'                  : '\U000022b3',
-        '\\unlhd'                : '\U000022b4',
-        '\\unrhd'                : '\U000022b5',
-        '\\triangleleft'         : '\U000025c3',
-        '\\triangleright'        : '\U000025b9',
-        '\\triangle'             : '\U000025b3',
-        '\\triangleq'            : '\U0000225c',
-        '\\oplus'                : '\U00002295',
-        '\\bigoplus'             : '\U00002a01',
-        '\\otimes'               : '\U00002297',
-        '\\bigotimes'            : '\U00002a02',
-        '\\odot'                 : '\U00002299',
-        '\\bigodot'              : '\U00002a00',
-        '\\ominus'               : '\U00002296',
-        '\\oslash'               : '\U00002298',
-        '\\dots'                 : '\U00002026',
-        '\\cdots'                : '\U000022ef',
-        '\\sum'                  : '\U00002211',
-        '\\prod'                 : '\U0000220f',
-        '\\coprod'               : '\U00002210',
-        '\\infty'                : '\U0000221e',
-        '\\int'                  : '\U0000222b',
-        '\\oint'                 : '\U0000222e',
-        '\\clubsuit'             : '\U00002663',
-        '\\diamondsuit'          : '\U00002662',
-        '\\heartsuit'            : '\U00002661',
-        '\\spadesuit'            : '\U00002660',
-        '\\aleph'                : '\U00002135',
-        '\\emptyset'             : '\U00002205',
-        '\\nabla'                : '\U00002207',
-        '\\partial'              : '\U00002202',
-        '\\flat'                 : '\U0000266d',
-        '\\natural'              : '\U0000266e',
-        '\\sharp'                : '\U0000266f',
-        '\\angle'                : '\U00002220',
-        '\\copyright'            : '\U000000a9',
-        '\\textregistered'       : '\U000000ae',
-        '\\textonequarter'       : '\U000000bc',
-        '\\textonehalf'          : '\U000000bd',
-        '\\textthreequarters'    : '\U000000be',
-        '\\textordfeminine'      : '\U000000aa',
-        '\\textordmasculine'     : '\U000000ba',
-        '\\euro'                 : '\U000020ac',
-        '\\pounds'               : '\U000000a3',
-        '\\yen'                  : '\U000000a5',
-        '\\textcent'             : '\U000000a2',
-        '\\textcurrency'         : '\U000000a4',
-        '\\textdegree'           : '\U000000b0',
-    }
-
-    isabelle_symbols = {
-        '\\<zero>'                 : '\U0001d7ec',
-        '\\<one>'                  : '\U0001d7ed',
-        '\\<two>'                  : '\U0001d7ee',
-        '\\<three>'                : '\U0001d7ef',
-        '\\<four>'                 : '\U0001d7f0',
-        '\\<five>'                 : '\U0001d7f1',
-        '\\<six>'                  : '\U0001d7f2',
-        '\\<seven>'                : '\U0001d7f3',
-        '\\<eight>'                : '\U0001d7f4',
-        '\\<nine>'                 : '\U0001d7f5',
-        '\\<A>'                    : '\U0001d49c',
-        '\\<B>'                    : '\U0000212c',
-        '\\<C>'                    : '\U0001d49e',
-        '\\<D>'                    : '\U0001d49f',
-        '\\<E>'                    : '\U00002130',
-        '\\<F>'                    : '\U00002131',
-        '\\<G>'                    : '\U0001d4a2',
-        '\\<H>'                    : '\U0000210b',
-        '\\<I>'                    : '\U00002110',
-        '\\<J>'                    : '\U0001d4a5',
-        '\\<K>'                    : '\U0001d4a6',
-        '\\<L>'                    : '\U00002112',
-        '\\<M>'                    : '\U00002133',
-        '\\<N>'                    : '\U0001d4a9',
-        '\\<O>'                    : '\U0001d4aa',
-        '\\<P>'                    : '\U0001d4ab',
-        '\\<Q>'                    : '\U0001d4ac',
-        '\\<R>'                    : '\U0000211b',
-        '\\<S>'                    : '\U0001d4ae',
-        '\\<T>'                    : '\U0001d4af',
-        '\\<U>'                    : '\U0001d4b0',
-        '\\<V>'                    : '\U0001d4b1',
-        '\\<W>'                    : '\U0001d4b2',
-        '\\<X>'                    : '\U0001d4b3',
-        '\\<Y>'                    : '\U0001d4b4',
-        '\\<Z>'                    : '\U0001d4b5',
-        '\\<a>'                    : '\U0001d5ba',
-        '\\<b>'                    : '\U0001d5bb',
-        '\\<c>'                    : '\U0001d5bc',
-        '\\<d>'                    : '\U0001d5bd',
-        '\\<e>'                    : '\U0001d5be',
-        '\\<f>'                    : '\U0001d5bf',
-        '\\<g>'                    : '\U0001d5c0',
-        '\\<h>'                    : '\U0001d5c1',
-        '\\<i>'                    : '\U0001d5c2',
-        '\\<j>'                    : '\U0001d5c3',
-        '\\<k>'                    : '\U0001d5c4',
-        '\\<l>'                    : '\U0001d5c5',
-        '\\<m>'                    : '\U0001d5c6',
-        '\\<n>'                    : '\U0001d5c7',
-        '\\<o>'                    : '\U0001d5c8',
-        '\\<p>'                    : '\U0001d5c9',
-        '\\<q>'                    : '\U0001d5ca',
-        '\\<r>'                    : '\U0001d5cb',
-        '\\<s>'                    : '\U0001d5cc',
-        '\\<t>'                    : '\U0001d5cd',
-        '\\<u>'                    : '\U0001d5ce',
-        '\\<v>'                    : '\U0001d5cf',
-        '\\<w>'                    : '\U0001d5d0',
-        '\\<x>'                    : '\U0001d5d1',
-        '\\<y>'                    : '\U0001d5d2',
-        '\\<z>'                    : '\U0001d5d3',
-        '\\<AA>'                   : '\U0001d504',
-        '\\<BB>'                   : '\U0001d505',
-        '\\<CC>'                   : '\U0000212d',
-        '\\<DD>'                   : '\U0001d507',
-        '\\<EE>'                   : '\U0001d508',
-        '\\<FF>'                   : '\U0001d509',
-        '\\<GG>'                   : '\U0001d50a',
-        '\\<HH>'                   : '\U0000210c',
-        '\\<II>'                   : '\U00002111',
-        '\\<JJ>'                   : '\U0001d50d',
-        '\\<KK>'                   : '\U0001d50e',
-        '\\<LL>'                   : '\U0001d50f',
-        '\\<MM>'                   : '\U0001d510',
-        '\\<NN>'                   : '\U0001d511',
-        '\\<OO>'                   : '\U0001d512',
-        '\\<PP>'                   : '\U0001d513',
-        '\\<QQ>'                   : '\U0001d514',
-        '\\<RR>'                   : '\U0000211c',
-        '\\<SS>'                   : '\U0001d516',
-        '\\<TT>'                   : '\U0001d517',
-        '\\<UU>'                   : '\U0001d518',
-        '\\<VV>'                   : '\U0001d519',
-        '\\<WW>'                   : '\U0001d51a',
-        '\\<XX>'                   : '\U0001d51b',
-        '\\<YY>'                   : '\U0001d51c',
-        '\\<ZZ>'                   : '\U00002128',
-        '\\<aa>'                   : '\U0001d51e',
-        '\\<bb>'                   : '\U0001d51f',
-        '\\<cc>'                   : '\U0001d520',
-        '\\<dd>'                   : '\U0001d521',
-        '\\<ee>'                   : '\U0001d522',
-        '\\<ff>'                   : '\U0001d523',
-        '\\<gg>'                   : '\U0001d524',
-        '\\<hh>'                   : '\U0001d525',
-        '\\<ii>'                   : '\U0001d526',
-        '\\<jj>'                   : '\U0001d527',
-        '\\<kk>'                   : '\U0001d528',
-        '\\<ll>'                   : '\U0001d529',
-        '\\<mm>'                   : '\U0001d52a',
-        '\\<nn>'                   : '\U0001d52b',
-        '\\<oo>'                   : '\U0001d52c',
-        '\\<pp>'                   : '\U0001d52d',
-        '\\<qq>'                   : '\U0001d52e',
-        '\\<rr>'                   : '\U0001d52f',
-        '\\<ss>'                   : '\U0001d530',
-        '\\<tt>'                   : '\U0001d531',
-        '\\<uu>'                   : '\U0001d532',
-        '\\<vv>'                   : '\U0001d533',
-        '\\<ww>'                   : '\U0001d534',
-        '\\<xx>'                   : '\U0001d535',
-        '\\<yy>'                   : '\U0001d536',
-        '\\<zz>'                   : '\U0001d537',
-        '\\<alpha>'                : '\U000003b1',
-        '\\<beta>'                 : '\U000003b2',
-        '\\<gamma>'                : '\U000003b3',
-        '\\<delta>'                : '\U000003b4',
-        '\\<epsilon>'              : '\U000003b5',
-        '\\<zeta>'                 : '\U000003b6',
-        '\\<eta>'                  : '\U000003b7',
-        '\\<theta>'                : '\U000003b8',
-        '\\<iota>'                 : '\U000003b9',
-        '\\<kappa>'                : '\U000003ba',
-        '\\<lambda>'               : '\U000003bb',
-        '\\<mu>'                   : '\U000003bc',
-        '\\<nu>'                   : '\U000003bd',
-        '\\<xi>'                   : '\U000003be',
-        '\\<pi>'                   : '\U000003c0',
-        '\\<rho>'                  : '\U000003c1',
-        '\\<sigma>'                : '\U000003c3',
-        '\\<tau>'                  : '\U000003c4',
-        '\\<upsilon>'              : '\U000003c5',
-        '\\<phi>'                  : '\U000003c6',
-        '\\<chi>'                  : '\U000003c7',
-        '\\<psi>'                  : '\U000003c8',
-        '\\<omega>'                : '\U000003c9',
-        '\\<Gamma>'                : '\U00000393',
-        '\\<Delta>'                : '\U00000394',
-        '\\<Theta>'                : '\U00000398',
-        '\\<Lambda>'               : '\U0000039b',
-        '\\<Xi>'                   : '\U0000039e',
-        '\\<Pi>'                   : '\U000003a0',
-        '\\<Sigma>'                : '\U000003a3',
-        '\\<Upsilon>'              : '\U000003a5',
-        '\\<Phi>'                  : '\U000003a6',
-        '\\<Psi>'                  : '\U000003a8',
-        '\\<Omega>'                : '\U000003a9',
-        '\\<bool>'                 : '\U0001d539',
-        '\\<complex>'              : '\U00002102',
-        '\\<nat>'                  : '\U00002115',
-        '\\<rat>'                  : '\U0000211a',
-        '\\<real>'                 : '\U0000211d',
-        '\\<int>'                  : '\U00002124',
-        '\\<leftarrow>'            : '\U00002190',
-        '\\<longleftarrow>'        : '\U000027f5',
-        '\\<rightarrow>'           : '\U00002192',
-        '\\<longrightarrow>'       : '\U000027f6',
-        '\\<Leftarrow>'            : '\U000021d0',
-        '\\<Longleftarrow>'        : '\U000027f8',
-        '\\<Rightarrow>'           : '\U000021d2',
-        '\\<Longrightarrow>'       : '\U000027f9',
-        '\\<leftrightarrow>'       : '\U00002194',
-        '\\<longleftrightarrow>'   : '\U000027f7',
-        '\\<Leftrightarrow>'       : '\U000021d4',
-        '\\<Longleftrightarrow>'   : '\U000027fa',
-        '\\<mapsto>'               : '\U000021a6',
-        '\\<longmapsto>'           : '\U000027fc',
-        '\\<midarrow>'             : '\U00002500',
-        '\\<Midarrow>'             : '\U00002550',
-        '\\<hookleftarrow>'        : '\U000021a9',
-        '\\<hookrightarrow>'       : '\U000021aa',
-        '\\<leftharpoondown>'      : '\U000021bd',
-        '\\<rightharpoondown>'     : '\U000021c1',
-        '\\<leftharpoonup>'        : '\U000021bc',
-        '\\<rightharpoonup>'       : '\U000021c0',
-        '\\<rightleftharpoons>'    : '\U000021cc',
-        '\\<leadsto>'              : '\U0000219d',
-        '\\<downharpoonleft>'      : '\U000021c3',
-        '\\<downharpoonright>'     : '\U000021c2',
-        '\\<upharpoonleft>'        : '\U000021bf',
-        '\\<upharpoonright>'       : '\U000021be',
-        '\\<restriction>'          : '\U000021be',
-        '\\<Colon>'                : '\U00002237',
-        '\\<up>'                   : '\U00002191',
-        '\\<Up>'                   : '\U000021d1',
-        '\\<down>'                 : '\U00002193',
-        '\\<Down>'                 : '\U000021d3',
-        '\\<updown>'               : '\U00002195',
-        '\\<Updown>'               : '\U000021d5',
-        '\\<langle>'               : '\U000027e8',
-        '\\<rangle>'               : '\U000027e9',
-        '\\<lceil>'                : '\U00002308',
-        '\\<rceil>'                : '\U00002309',
-        '\\<lfloor>'               : '\U0000230a',
-        '\\<rfloor>'               : '\U0000230b',
-        '\\<lparr>'                : '\U00002987',
-        '\\<rparr>'                : '\U00002988',
-        '\\<lbrakk>'               : '\U000027e6',
-        '\\<rbrakk>'               : '\U000027e7',
-        '\\<lbrace>'               : '\U00002983',
-        '\\<rbrace>'               : '\U00002984',
-        '\\<guillemotleft>'        : '\U000000ab',
-        '\\<guillemotright>'       : '\U000000bb',
-        '\\<bottom>'               : '\U000022a5',
-        '\\<top>'                  : '\U000022a4',
-        '\\<and>'                  : '\U00002227',
-        '\\<And>'                  : '\U000022c0',
-        '\\<or>'                   : '\U00002228',
-        '\\<Or>'                   : '\U000022c1',
-        '\\<forall>'               : '\U00002200',
-        '\\<exists>'               : '\U00002203',
-        '\\<nexists>'              : '\U00002204',
-        '\\<not>'                  : '\U000000ac',
-        '\\<box>'                  : '\U000025a1',
-        '\\<diamond>'              : '\U000025c7',
-        '\\<turnstile>'            : '\U000022a2',
-        '\\<Turnstile>'            : '\U000022a8',
-        '\\<tturnstile>'           : '\U000022a9',
-        '\\<TTurnstile>'           : '\U000022ab',
-        '\\<stileturn>'            : '\U000022a3',
-        '\\<surd>'                 : '\U0000221a',
-        '\\<le>'                   : '\U00002264',
-        '\\<ge>'                   : '\U00002265',
-        '\\<lless>'                : '\U0000226a',
-        '\\<ggreater>'             : '\U0000226b',
-        '\\<lesssim>'              : '\U00002272',
-        '\\<greatersim>'           : '\U00002273',
-        '\\<lessapprox>'           : '\U00002a85',
-        '\\<greaterapprox>'        : '\U00002a86',
-        '\\<in>'                   : '\U00002208',
-        '\\<notin>'                : '\U00002209',
-        '\\<subset>'               : '\U00002282',
-        '\\<supset>'               : '\U00002283',
-        '\\<subseteq>'             : '\U00002286',
-        '\\<supseteq>'             : '\U00002287',
-        '\\<sqsubset>'             : '\U0000228f',
-        '\\<sqsupset>'             : '\U00002290',
-        '\\<sqsubseteq>'           : '\U00002291',
-        '\\<sqsupseteq>'           : '\U00002292',
-        '\\<inter>'                : '\U00002229',
-        '\\<Inter>'                : '\U000022c2',
-        '\\<union>'                : '\U0000222a',
-        '\\<Union>'                : '\U000022c3',
-        '\\<squnion>'              : '\U00002294',
-        '\\<Squnion>'              : '\U00002a06',
-        '\\<sqinter>'              : '\U00002293',
-        '\\<Sqinter>'              : '\U00002a05',
-        '\\<setminus>'             : '\U00002216',
-        '\\<propto>'               : '\U0000221d',
-        '\\<uplus>'                : '\U0000228e',
-        '\\<Uplus>'                : '\U00002a04',
-        '\\<noteq>'                : '\U00002260',
-        '\\<sim>'                  : '\U0000223c',
-        '\\<doteq>'                : '\U00002250',
-        '\\<simeq>'                : '\U00002243',
-        '\\<approx>'               : '\U00002248',
-        '\\<asymp>'                : '\U0000224d',
-        '\\<cong>'                 : '\U00002245',
-        '\\<smile>'                : '\U00002323',
-        '\\<equiv>'                : '\U00002261',
-        '\\<frown>'                : '\U00002322',
-        '\\<Join>'                 : '\U000022c8',
-        '\\<bowtie>'               : '\U00002a1d',
-        '\\<prec>'                 : '\U0000227a',
-        '\\<succ>'                 : '\U0000227b',
-        '\\<preceq>'               : '\U0000227c',
-        '\\<succeq>'               : '\U0000227d',
-        '\\<parallel>'             : '\U00002225',
-        '\\<bar>'                  : '\U000000a6',
-        '\\<plusminus>'            : '\U000000b1',
-        '\\<minusplus>'            : '\U00002213',
-        '\\<times>'                : '\U000000d7',
-        '\\<div>'                  : '\U000000f7',
-        '\\<cdot>'                 : '\U000022c5',
-        '\\<star>'                 : '\U000022c6',
-        '\\<bullet>'               : '\U00002219',
-        '\\<circ>'                 : '\U00002218',
-        '\\<dagger>'               : '\U00002020',
-        '\\<ddagger>'              : '\U00002021',
-        '\\<lhd>'                  : '\U000022b2',
-        '\\<rhd>'                  : '\U000022b3',
-        '\\<unlhd>'                : '\U000022b4',
-        '\\<unrhd>'                : '\U000022b5',
-        '\\<triangleleft>'         : '\U000025c3',
-        '\\<triangleright>'        : '\U000025b9',
-        '\\<triangle>'             : '\U000025b3',
-        '\\<triangleq>'            : '\U0000225c',
-        '\\<oplus>'                : '\U00002295',
-        '\\<Oplus>'                : '\U00002a01',
-        '\\<otimes>'               : '\U00002297',
-        '\\<Otimes>'               : '\U00002a02',
-        '\\<odot>'                 : '\U00002299',
-        '\\<Odot>'                 : '\U00002a00',
-        '\\<ominus>'               : '\U00002296',
-        '\\<oslash>'               : '\U00002298',
-        '\\<dots>'                 : '\U00002026',
-        '\\<cdots>'                : '\U000022ef',
-        '\\<Sum>'                  : '\U00002211',
-        '\\<Prod>'                 : '\U0000220f',
-        '\\<Coprod>'               : '\U00002210',
-        '\\<infinity>'             : '\U0000221e',
-        '\\<integral>'             : '\U0000222b',
-        '\\<ointegral>'            : '\U0000222e',
-        '\\<clubsuit>'             : '\U00002663',
-        '\\<diamondsuit>'          : '\U00002662',
-        '\\<heartsuit>'            : '\U00002661',
-        '\\<spadesuit>'            : '\U00002660',
-        '\\<aleph>'                : '\U00002135',
-        '\\<emptyset>'             : '\U00002205',
-        '\\<nabla>'                : '\U00002207',
-        '\\<partial>'              : '\U00002202',
-        '\\<flat>'                 : '\U0000266d',
-        '\\<natural>'              : '\U0000266e',
-        '\\<sharp>'                : '\U0000266f',
-        '\\<angle>'                : '\U00002220',
-        '\\<copyright>'            : '\U000000a9',
-        '\\<registered>'           : '\U000000ae',
-        '\\<hyphen>'               : '\U000000ad',
-        '\\<inverse>'              : '\U000000af',
-        '\\<onequarter>'           : '\U000000bc',
-        '\\<onehalf>'              : '\U000000bd',
-        '\\<threequarters>'        : '\U000000be',
-        '\\<ordfeminine>'          : '\U000000aa',
-        '\\<ordmasculine>'         : '\U000000ba',
-        '\\<section>'              : '\U000000a7',
-        '\\<paragraph>'            : '\U000000b6',
-        '\\<exclamdown>'           : '\U000000a1',
-        '\\<questiondown>'         : '\U000000bf',
-        '\\<euro>'                 : '\U000020ac',
-        '\\<pounds>'               : '\U000000a3',
-        '\\<yen>'                  : '\U000000a5',
-        '\\<cent>'                 : '\U000000a2',
-        '\\<currency>'             : '\U000000a4',
-        '\\<degree>'               : '\U000000b0',
-        '\\<amalg>'                : '\U00002a3f',
-        '\\<mho>'                  : '\U00002127',
-        '\\<lozenge>'              : '\U000025ca',
-        '\\<wp>'                   : '\U00002118',
-        '\\<wrong>'                : '\U00002240',
-        '\\<struct>'               : '\U000022c4',
-        '\\<acute>'                : '\U000000b4',
-        '\\<index>'                : '\U00000131',
-        '\\<dieresis>'             : '\U000000a8',
-        '\\<cedilla>'              : '\U000000b8',
-        '\\<hungarumlaut>'         : '\U000002dd',
-        '\\<some>'                 : '\U000003f5',
-        '\\<newline>'              : '\U000023ce',
-        '\\<open>'                 : '\U00002039',
-        '\\<close>'                : '\U0000203a',
-        '\\<here>'                 : '\U00002302',
-        '\\<^sub>'                 : '\U000021e9',
-        '\\<^sup>'                 : '\U000021e7',
-        '\\<^bold>'                : '\U00002759',
-        '\\<^bsub>'                : '\U000021d8',
-        '\\<^esub>'                : '\U000021d9',
-        '\\<^bsup>'                : '\U000021d7',
-        '\\<^esup>'                : '\U000021d6',
-    }
-
-    lang_map = {'isabelle' : isabelle_symbols, 'latex' : latex_symbols}
-
-    def __init__(self, **options):
-        Filter.__init__(self, **options)
-        lang = get_choice_opt(options, 'lang',
-                              ['isabelle', 'latex'], 'isabelle')
-        self.symbols = self.lang_map[lang]
-
-    def filter(self, lexer, stream):
-        for ttype, value in stream:
-            if value in self.symbols:
-                yield ttype, self.symbols[value]
-            else:
-                yield ttype, value
-
-
-class KeywordCaseFilter(Filter):
-    """Convert keywords to lowercase or uppercase or capitalize them, which
-    means first letter uppercase, rest lowercase.
-
-    This can be useful e.g. if you highlight Pascal code and want to adapt the
-    code to your styleguide.
-
-    Options accepted:
-
-    `case` : string
-       The casing to convert keywords to. Must be one of ``'lower'``,
-       ``'upper'`` or ``'capitalize'``.  The default is ``'lower'``.
-    """
-
-    def __init__(self, **options):
-        Filter.__init__(self, **options)
-        case = get_choice_opt(options, 'case',
-                              ['lower', 'upper', 'capitalize'], 'lower')
-        self.convert = getattr(str, case)
-
-    def filter(self, lexer, stream):
-        for ttype, value in stream:
-            if ttype in Keyword:
-                yield ttype, self.convert(value)
-            else:
-                yield ttype, value
-
-
-class NameHighlightFilter(Filter):
-    """Highlight a normal Name (and Name.*) token with a different token type.
-
-    Example::
-
-        filter = NameHighlightFilter(
-            names=['foo', 'bar', 'baz'],
-            tokentype=Name.Function,
-        )
-
-    This would highlight the names "foo", "bar" and "baz"
-    as functions. `Name.Function` is the default token type.
-
-    Options accepted:
-
-    `names` : list of strings
-      A list of names that should be given the different token type.
-      There is no default.
-    `tokentype` : TokenType or string
-      A token type or a string containing a token type name that is
-      used for highlighting the strings in `names`.  The default is
-      `Name.Function`.
-    """
-
-    def __init__(self, **options):
-        Filter.__init__(self, **options)
-        self.names = set(get_list_opt(options, 'names', []))
-        tokentype = options.get('tokentype')
-        if tokentype:
-            self.tokentype = string_to_tokentype(tokentype)
-        else:
-            self.tokentype = Name.Function
-
-    def filter(self, lexer, stream):
-        for ttype, value in stream:
-            if ttype in Name and value in self.names:
-                yield self.tokentype, value
-            else:
-                yield ttype, value
-
-
-class ErrorToken(Exception):
-    pass
-
-
-class RaiseOnErrorTokenFilter(Filter):
-    """Raise an exception when the lexer generates an error token.
-
-    Options accepted:
-
-    `excclass` : Exception class
-      The exception class to raise.
-      The default is `pygments.filters.ErrorToken`.
-
-    .. versionadded:: 0.8
-    """
-
-    def __init__(self, **options):
-        Filter.__init__(self, **options)
-        self.exception = options.get('excclass', ErrorToken)
-        try:
-            # issubclass() will raise TypeError if first argument is not a class
-            if not issubclass(self.exception, Exception):
-                raise TypeError
-        except TypeError:
-            raise OptionError('excclass option is not an exception class')
-
-    def filter(self, lexer, stream):
-        for ttype, value in stream:
-            if ttype is Error:
-                raise self.exception(value)
-            yield ttype, value
-
-
-class VisibleWhitespaceFilter(Filter):
-    """Convert tabs, newlines and/or spaces to visible characters.
-
-    Options accepted:
-
-    `spaces` : string or bool
-      If this is a one-character string, spaces will be replaces by this string.
-      If it is another true value, spaces will be replaced by ``·`` (unicode
-      MIDDLE DOT).  If it is a false value, spaces will not be replaced.  The
-      default is ``False``.
-    `tabs` : string or bool
-      The same as for `spaces`, but the default replacement character is ``»``
-      (unicode RIGHT-POINTING DOUBLE ANGLE QUOTATION MARK).  The default value
-      is ``False``.  Note: this will not work if the `tabsize` option for the
-      lexer is nonzero, as tabs will already have been expanded then.
-    `tabsize` : int
-      If tabs are to be replaced by this filter (see the `tabs` option), this
-      is the total number of characters that a tab should be expanded to.
-      The default is ``8``.
-    `newlines` : string or bool
-      The same as for `spaces`, but the default replacement character is ``¶``
-      (unicode PILCROW SIGN).  The default value is ``False``.
-    `wstokentype` : bool
-      If true, give whitespace the special `Whitespace` token type.  This allows
-      styling the visible whitespace differently (e.g. greyed out), but it can
-      disrupt background colors.  The default is ``True``.
-
-    .. versionadded:: 0.8
-    """
-
-    def __init__(self, **options):
-        Filter.__init__(self, **options)
-        for name, default in [('spaces',   '·'),
-                              ('tabs',     '»'),
-                              ('newlines', '¶')]:
-            opt = options.get(name, False)
-            if isinstance(opt, str) and len(opt) == 1:
-                setattr(self, name, opt)
-            else:
-                setattr(self, name, (opt and default or ''))
-        tabsize = get_int_opt(options, 'tabsize', 8)
-        if self.tabs:
-            self.tabs += ' ' * (tabsize - 1)
-        if self.newlines:
-            self.newlines += '\n'
-        self.wstt = get_bool_opt(options, 'wstokentype', True)
-
-    def filter(self, lexer, stream):
-        if self.wstt:
-            spaces = self.spaces or ' '
-            tabs = self.tabs or '\t'
-            newlines = self.newlines or '\n'
-            regex = re.compile(r'\s')
-
-            def replacefunc(wschar):
-                if wschar == ' ':
-                    return spaces
-                elif wschar == '\t':
-                    return tabs
-                elif wschar == '\n':
-                    return newlines
-                return wschar
-
-            for ttype, value in stream:
-                yield from _replace_special(ttype, value, regex, Whitespace,
-                                            replacefunc)
-        else:
-            spaces, tabs, newlines = self.spaces, self.tabs, self.newlines
-            # simpler processing
-            for ttype, value in stream:
-                if spaces:
-                    value = value.replace(' ', spaces)
-                if tabs:
-                    value = value.replace('\t', tabs)
-                if newlines:
-                    value = value.replace('\n', newlines)
-                yield ttype, value
-
-
-class GobbleFilter(Filter):
-    """Gobbles source code lines (eats initial characters).
-
-    This filter drops the first ``n`` characters off every line of code.  This
-    may be useful when the source code fed to the lexer is indented by a fixed
-    amount of space that isn't desired in the output.
-
-    Options accepted:
-
-    `n` : int
-       The number of characters to gobble.
-
-    .. versionadded:: 1.2
-    """
-    def __init__(self, **options):
-        Filter.__init__(self, **options)
-        self.n = get_int_opt(options, 'n', 0)
-
-    def gobble(self, value, left):
-        if left < len(value):
-            return value[left:], 0
-        else:
-            return '', left - len(value)
-
-    def filter(self, lexer, stream):
-        n = self.n
-        left = n  # How many characters left to gobble.
-        for ttype, value in stream:
-            # Remove ``left`` tokens from first line, ``n`` from all others.
-            parts = value.split('\n')
-            (parts[0], left) = self.gobble(parts[0], left)
-            for i in range(1, len(parts)):
-                (parts[i], left) = self.gobble(parts[i], n)
-            value = '\n'.join(parts)
-
-            if value != '':
-                yield ttype, value
-
-
-class TokenMergeFilter(Filter):
-    """Merges consecutive tokens with the same token type in the output
-    stream of a lexer.
-
-    .. versionadded:: 1.2
-    """
-    def __init__(self, **options):
-        Filter.__init__(self, **options)
-
-    def filter(self, lexer, stream):
-        current_type = None
-        current_value = None
-        for ttype, value in stream:
-            if ttype is current_type:
-                current_value += value
-            else:
-                if current_type is not None:
-                    yield current_type, current_value
-                current_type = ttype
-                current_value = value
-        if current_type is not None:
-            yield current_type, current_value
-
-
-FILTERS = {
-    'codetagify':     CodeTagFilter,
-    'keywordcase':    KeywordCaseFilter,
-    'highlight':      NameHighlightFilter,
-    'raiseonerror':   RaiseOnErrorTokenFilter,
-    'whitespace':     VisibleWhitespaceFilter,
-    'gobble':         GobbleFilter,
-    'tokenmerge':     TokenMergeFilter,
-    'symbols':        SymbolFilter,
-}
diff --git a/spaces/aliabd/SummerTime/model/third_party/HMNet/ThirdParty/ROUGE/pyrouge/utils/__init__.py b/spaces/aliabd/SummerTime/model/third_party/HMNet/ThirdParty/ROUGE/pyrouge/utils/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/aliabd/whisper/README.md b/spaces/aliabd/whisper/README.md
deleted file mode 100644
index 28ac7ec75082bf9a31af2ff6e8898c9d62019a33..0000000000000000000000000000000000000000
--- a/spaces/aliabd/whisper/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Whisper
-emoji: 📉
-colorFrom: pink
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.30.0
-app_file: app.py
-pinned: false
-duplicated_from: openai/whisper
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/aliabid94/crossword/formatters.py b/spaces/aliabid94/crossword/formatters.py
deleted file mode 100644
index a0bb7524db02e00ca551de6604224c74996d4bf9..0000000000000000000000000000000000000000
--- a/spaces/aliabid94/crossword/formatters.py
+++ /dev/null
@@ -1,49 +0,0 @@
-from game_manager import Clue
-from typing import List, Optional
-import time
-from game_manager import GUESS_TIMEOUT
-
-
-def crossword(grid: List[List[Optional[str]]], clues: List[Clue]):
-    clue_grid = [[[] for _ in range(len(grid[0]))] for _ in range(len(grid))]
-    for clue_id, clue in enumerate(clues):
-        if clue is None:
-            continue
-        if clue.across:
-            for i, _ in enumerate(clue.answer):
-                clue_grid[clue.location[0]][clue.location[1] + i].append(clue_id)
-        else:
-            for i, _ in enumerate(clue.answer):
-                clue_grid[clue.location[0] + i][clue.location[1]].append(clue_id)
-    output = "<div class='crossword'>"
-    for i, row in enumerate(grid):
-        output += "<div class='c-row'>"
-        for j, cell in enumerate(row):
-            clue_ids = clue_grid[i][j]
-            output += f"""<div class='cell {"" if cell is None else "filled"} {"" if len(clue_ids) == 0 else f"clue {' '.join('clue-' + str(clue_id+1) for clue_id in clue_ids)}"}'>"""
-            if cell == None:
-                output += "&nbsp;"
-            else:
-                output += cell
-            output += "</div>"
-        output += "</div>"
-    output += "</div>"
-    return output
-
-
-def clue_riddle(clue: Clue):
-    if clue is None:
-        return "..."
-    
-    if clue.solved:
-        end_line = clue.answer + " - " + clue.solver
-    elif clue.timed_out:
-        end_line = clue.answer
-    else:
-        time_remaining = GUESS_TIMEOUT - (time.time() - clue.create_time)
-        end_line = str(round(max(0, time_remaining))) + "s"
-    return (
-        "\n".join(line for line in clue.riddle.splitlines() if line.strip() != "")
-        + " - "
-        + end_line
-    )
diff --git a/spaces/allknowingroger/Image-Models-Test140/README.md b/spaces/allknowingroger/Image-Models-Test140/README.md
deleted file mode 100644
index 8783df4cb6b6cb6d3b831d062d59456f06703083..0000000000000000000000000000000000000000
--- a/spaces/allknowingroger/Image-Models-Test140/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: More Image Models
-emoji: 😻
-colorFrom: red
-colorTo: gray
-sdk: gradio
-sdk_version: 3.23.0
-app_file: app.py
-duplicated_from: allknowingroger/Image-Models-Test139
----
-
-<!--Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference-->
\ No newline at end of file
diff --git a/spaces/alwayse/MMD_MP_Text_Dection/README.md b/spaces/alwayse/MMD_MP_Text_Dection/README.md
deleted file mode 100644
index 61ff613e355fb5d1bffc96b409417cc34c4954de..0000000000000000000000000000000000000000
--- a/spaces/alwayse/MMD_MP_Text_Dection/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: MMD-MP Text Dection
-emoji: 💻
-colorFrom: blue
-colorTo: gray
-sdk: gradio
-sdk_version: 3.45.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/amankishore/sjc/guided_diffusion/unet.py b/spaces/amankishore/sjc/guided_diffusion/unet.py
deleted file mode 100644
index 96b46930006b7c9e49948d31568474824195cf8f..0000000000000000000000000000000000000000
--- a/spaces/amankishore/sjc/guided_diffusion/unet.py
+++ /dev/null
@@ -1,894 +0,0 @@
-from abc import abstractmethod
-
-import math
-
-import numpy as np
-import torch as th
-import torch.nn as nn
-import torch.nn.functional as F
-
-from .fp16_util import convert_module_to_f16, convert_module_to_f32
-from .nn import (
-    checkpoint,
-    conv_nd,
-    linear,
-    avg_pool_nd,
-    zero_module,
-    normalization,
-    timestep_embedding,
-)
-
-
-class AttentionPool2d(nn.Module):
-    """
-    Adapted from CLIP: https://github.com/openai/CLIP/blob/main/clip/model.py
-    """
-
-    def __init__(
-        self,
-        spacial_dim: int,
-        embed_dim: int,
-        num_heads_channels: int,
-        output_dim: int = None,
-    ):
-        super().__init__()
-        self.positional_embedding = nn.Parameter(
-            th.randn(embed_dim, spacial_dim ** 2 + 1) / embed_dim ** 0.5
-        )
-        self.qkv_proj = conv_nd(1, embed_dim, 3 * embed_dim, 1)
-        self.c_proj = conv_nd(1, embed_dim, output_dim or embed_dim, 1)
-        self.num_heads = embed_dim // num_heads_channels
-        self.attention = QKVAttention(self.num_heads)
-
-    def forward(self, x):
-        b, c, *_spatial = x.shape
-        x = x.reshape(b, c, -1)  # NC(HW)
-        x = th.cat([x.mean(dim=-1, keepdim=True), x], dim=-1)  # NC(HW+1)
-        x = x + self.positional_embedding[None, :, :].to(x.dtype)  # NC(HW+1)
-        x = self.qkv_proj(x)
-        x = self.attention(x)
-        x = self.c_proj(x)
-        return x[:, :, 0]
-
-
-class TimestepBlock(nn.Module):
-    """
-    Any module where forward() takes timestep embeddings as a second argument.
-    """
-
-    @abstractmethod
-    def forward(self, x, emb):
-        """
-        Apply the module to `x` given `emb` timestep embeddings.
-        """
-
-
-class TimestepEmbedSequential(nn.Sequential, TimestepBlock):
-    """
-    A sequential module that passes timestep embeddings to the children that
-    support it as an extra input.
-    """
-
-    def forward(self, x, emb):
-        for layer in self:
-            if isinstance(layer, TimestepBlock):
-                x = layer(x, emb)
-            else:
-                x = layer(x)
-        return x
-
-
-class Upsample(nn.Module):
-    """
-    An upsampling layer with an optional convolution.
-
-    :param channels: channels in the inputs and outputs.
-    :param use_conv: a bool determining if a convolution is applied.
-    :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
-                 upsampling occurs in the inner-two dimensions.
-    """
-
-    def __init__(self, channels, use_conv, dims=2, out_channels=None):
-        super().__init__()
-        self.channels = channels
-        self.out_channels = out_channels or channels
-        self.use_conv = use_conv
-        self.dims = dims
-        if use_conv:
-            self.conv = conv_nd(dims, self.channels, self.out_channels, 3, padding=1)
-
-    def forward(self, x):
-        assert x.shape[1] == self.channels
-        if self.dims == 3:
-            x = F.interpolate(
-                x, (x.shape[2], x.shape[3] * 2, x.shape[4] * 2), mode="nearest"
-            )
-        else:
-            x = F.interpolate(x, scale_factor=2, mode="nearest")
-        if self.use_conv:
-            x = self.conv(x)
-        return x
-
-
-class Downsample(nn.Module):
-    """
-    A downsampling layer with an optional convolution.
-
-    :param channels: channels in the inputs and outputs.
-    :param use_conv: a bool determining if a convolution is applied.
-    :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
-                 downsampling occurs in the inner-two dimensions.
-    """
-
-    def __init__(self, channels, use_conv, dims=2, out_channels=None):
-        super().__init__()
-        self.channels = channels
-        self.out_channels = out_channels or channels
-        self.use_conv = use_conv
-        self.dims = dims
-        stride = 2 if dims != 3 else (1, 2, 2)
-        if use_conv:
-            self.op = conv_nd(
-                dims, self.channels, self.out_channels, 3, stride=stride, padding=1
-            )
-        else:
-            assert self.channels == self.out_channels
-            self.op = avg_pool_nd(dims, kernel_size=stride, stride=stride)
-
-    def forward(self, x):
-        assert x.shape[1] == self.channels
-        return self.op(x)
-
-
-class ResBlock(TimestepBlock):
-    """
-    A residual block that can optionally change the number of channels.
-
-    :param channels: the number of input channels.
-    :param emb_channels: the number of timestep embedding channels.
-    :param dropout: the rate of dropout.
-    :param out_channels: if specified, the number of out channels.
-    :param use_conv: if True and out_channels is specified, use a spatial
-        convolution instead of a smaller 1x1 convolution to change the
-        channels in the skip connection.
-    :param dims: determines if the signal is 1D, 2D, or 3D.
-    :param use_checkpoint: if True, use gradient checkpointing on this module.
-    :param up: if True, use this block for upsampling.
-    :param down: if True, use this block for downsampling.
-    """
-
-    def __init__(
-        self,
-        channels,
-        emb_channels,
-        dropout,
-        out_channels=None,
-        use_conv=False,
-        use_scale_shift_norm=False,
-        dims=2,
-        use_checkpoint=False,
-        up=False,
-        down=False,
-    ):
-        super().__init__()
-        self.channels = channels
-        self.emb_channels = emb_channels
-        self.dropout = dropout
-        self.out_channels = out_channels or channels
-        self.use_conv = use_conv
-        self.use_checkpoint = use_checkpoint
-        self.use_scale_shift_norm = use_scale_shift_norm
-
-        self.in_layers = nn.Sequential(
-            normalization(channels),
-            nn.SiLU(),
-            conv_nd(dims, channels, self.out_channels, 3, padding=1),
-        )
-
-        self.updown = up or down
-
-        if up:
-            self.h_upd = Upsample(channels, False, dims)
-            self.x_upd = Upsample(channels, False, dims)
-        elif down:
-            self.h_upd = Downsample(channels, False, dims)
-            self.x_upd = Downsample(channels, False, dims)
-        else:
-            self.h_upd = self.x_upd = nn.Identity()
-
-        self.emb_layers = nn.Sequential(
-            nn.SiLU(),
-            linear(
-                emb_channels,
-                2 * self.out_channels if use_scale_shift_norm else self.out_channels,
-            ),
-        )
-        self.out_layers = nn.Sequential(
-            normalization(self.out_channels),
-            nn.SiLU(),
-            nn.Dropout(p=dropout),
-            zero_module(
-                conv_nd(dims, self.out_channels, self.out_channels, 3, padding=1)
-            ),
-        )
-
-        if self.out_channels == channels:
-            self.skip_connection = nn.Identity()
-        elif use_conv:
-            self.skip_connection = conv_nd(
-                dims, channels, self.out_channels, 3, padding=1
-            )
-        else:
-            self.skip_connection = conv_nd(dims, channels, self.out_channels, 1)
-
-    def forward(self, x, emb):
-        """
-        Apply the block to a Tensor, conditioned on a timestep embedding.
-
-        :param x: an [N x C x ...] Tensor of features.
-        :param emb: an [N x emb_channels] Tensor of timestep embeddings.
-        :return: an [N x C x ...] Tensor of outputs.
-        """
-        return checkpoint(
-            self._forward, (x, emb), self.parameters(), self.use_checkpoint
-        )
-
-    def _forward(self, x, emb):
-        if self.updown:
-            in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1]
-            h = in_rest(x)
-            h = self.h_upd(h)
-            x = self.x_upd(x)
-            h = in_conv(h)
-        else:
-            h = self.in_layers(x)
-        emb_out = self.emb_layers(emb).type(h.dtype)
-        while len(emb_out.shape) < len(h.shape):
-            emb_out = emb_out[..., None]
-        if self.use_scale_shift_norm:
-            out_norm, out_rest = self.out_layers[0], self.out_layers[1:]
-            scale, shift = th.chunk(emb_out, 2, dim=1)
-            h = out_norm(h) * (1 + scale) + shift
-            h = out_rest(h)
-        else:
-            h = h + emb_out
-            h = self.out_layers(h)
-        return self.skip_connection(x) + h
-
-
-class AttentionBlock(nn.Module):
-    """
-    An attention block that allows spatial positions to attend to each other.
-
-    Originally ported from here, but adapted to the N-d case.
-    https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/models/unet.py#L66.
-    """
-
-    def __init__(
-        self,
-        channels,
-        num_heads=1,
-        num_head_channels=-1,
-        use_checkpoint=False,
-        use_new_attention_order=False,
-    ):
-        super().__init__()
-        self.channels = channels
-        if num_head_channels == -1:
-            self.num_heads = num_heads
-        else:
-            assert (
-                channels % num_head_channels == 0
-            ), f"q,k,v channels {channels} is not divisible by num_head_channels {num_head_channels}"
-            self.num_heads = channels // num_head_channels
-        self.use_checkpoint = use_checkpoint
-        self.norm = normalization(channels)
-        self.qkv = conv_nd(1, channels, channels * 3, 1)
-        if use_new_attention_order:
-            # split qkv before split heads
-            self.attention = QKVAttention(self.num_heads)
-        else:
-            # split heads before split qkv
-            self.attention = QKVAttentionLegacy(self.num_heads)
-
-        self.proj_out = zero_module(conv_nd(1, channels, channels, 1))
-
-    def forward(self, x):
-        return checkpoint(self._forward, (x,), self.parameters(), True)
-
-    def _forward(self, x):
-        b, c, *spatial = x.shape
-        x = x.reshape(b, c, -1)
-        qkv = self.qkv(self.norm(x))
-        h = self.attention(qkv)
-        h = self.proj_out(h)
-        return (x + h).reshape(b, c, *spatial)
-
-
-def count_flops_attn(model, _x, y):
-    """
-    A counter for the `thop` package to count the operations in an
-    attention operation.
-    Meant to be used like:
-        macs, params = thop.profile(
-            model,
-            inputs=(inputs, timestamps),
-            custom_ops={QKVAttention: QKVAttention.count_flops},
-        )
-    """
-    b, c, *spatial = y[0].shape
-    num_spatial = int(np.prod(spatial))
-    # We perform two matmuls with the same number of ops.
-    # The first computes the weight matrix, the second computes
-    # the combination of the value vectors.
-    matmul_ops = 2 * b * (num_spatial ** 2) * c
-    model.total_ops += th.DoubleTensor([matmul_ops])
-
-
-class QKVAttentionLegacy(nn.Module):
-    """
-    A module which performs QKV attention. Matches legacy QKVAttention + input/ouput heads shaping
-    """
-
-    def __init__(self, n_heads):
-        super().__init__()
-        self.n_heads = n_heads
-
-    def forward(self, qkv):
-        """
-        Apply QKV attention.
-
-        :param qkv: an [N x (H * 3 * C) x T] tensor of Qs, Ks, and Vs.
-        :return: an [N x (H * C) x T] tensor after attention.
-        """
-        bs, width, length = qkv.shape
-        assert width % (3 * self.n_heads) == 0
-        ch = width // (3 * self.n_heads)
-        q, k, v = qkv.reshape(bs * self.n_heads, ch * 3, length).split(ch, dim=1)
-        scale = 1 / math.sqrt(math.sqrt(ch))
-        weight = th.einsum(
-            "bct,bcs->bts", q * scale, k * scale
-        )  # More stable with f16 than dividing afterwards
-        weight = th.softmax(weight.float(), dim=-1).type(weight.dtype)
-        a = th.einsum("bts,bcs->bct", weight, v)
-        return a.reshape(bs, -1, length)
-
-    @staticmethod
-    def count_flops(model, _x, y):
-        return count_flops_attn(model, _x, y)
-
-
-class QKVAttention(nn.Module):
-    """
-    A module which performs QKV attention and splits in a different order.
-    """
-
-    def __init__(self, n_heads):
-        super().__init__()
-        self.n_heads = n_heads
-
-    def forward(self, qkv):
-        """
-        Apply QKV attention.
-
-        :param qkv: an [N x (3 * H * C) x T] tensor of Qs, Ks, and Vs.
-        :return: an [N x (H * C) x T] tensor after attention.
-        """
-        bs, width, length = qkv.shape
-        assert width % (3 * self.n_heads) == 0
-        ch = width // (3 * self.n_heads)
-        q, k, v = qkv.chunk(3, dim=1)
-        scale = 1 / math.sqrt(math.sqrt(ch))
-        weight = th.einsum(
-            "bct,bcs->bts",
-            (q * scale).view(bs * self.n_heads, ch, length),
-            (k * scale).view(bs * self.n_heads, ch, length),
-        )  # More stable with f16 than dividing afterwards
-        weight = th.softmax(weight.float(), dim=-1).type(weight.dtype)
-        a = th.einsum("bts,bcs->bct", weight, v.reshape(bs * self.n_heads, ch, length))
-        return a.reshape(bs, -1, length)
-
-    @staticmethod
-    def count_flops(model, _x, y):
-        return count_flops_attn(model, _x, y)
-
-
-class UNetModel(nn.Module):
-    """
-    The full UNet model with attention and timestep embedding.
-
-    :param in_channels: channels in the input Tensor.
-    :param model_channels: base channel count for the model.
-    :param out_channels: channels in the output Tensor.
-    :param num_res_blocks: number of residual blocks per downsample.
-    :param attention_resolutions: a collection of downsample rates at which
-        attention will take place. May be a set, list, or tuple.
-        For example, if this contains 4, then at 4x downsampling, attention
-        will be used.
-    :param dropout: the dropout probability.
-    :param channel_mult: channel multiplier for each level of the UNet.
-    :param conv_resample: if True, use learned convolutions for upsampling and
-        downsampling.
-    :param dims: determines if the signal is 1D, 2D, or 3D.
-    :param num_classes: if specified (as an int), then this model will be
-        class-conditional with `num_classes` classes.
-    :param use_checkpoint: use gradient checkpointing to reduce memory usage.
-    :param num_heads: the number of attention heads in each attention layer.
-    :param num_heads_channels: if specified, ignore num_heads and instead use
-                               a fixed channel width per attention head.
-    :param num_heads_upsample: works with num_heads to set a different number
-                               of heads for upsampling. Deprecated.
-    :param use_scale_shift_norm: use a FiLM-like conditioning mechanism.
-    :param resblock_updown: use residual blocks for up/downsampling.
-    :param use_new_attention_order: use a different attention pattern for potentially
-                                    increased efficiency.
-    """
-
-    def __init__(
-        self,
-        image_size,
-        in_channels,
-        model_channels,
-        out_channels,
-        num_res_blocks,
-        attention_resolutions,
-        dropout=0,
-        channel_mult=(1, 2, 4, 8),
-        conv_resample=True,
-        dims=2,
-        num_classes=None,
-        use_checkpoint=False,
-        use_fp16=False,
-        num_heads=1,
-        num_head_channels=-1,
-        num_heads_upsample=-1,
-        use_scale_shift_norm=False,
-        resblock_updown=False,
-        use_new_attention_order=False,
-    ):
-        super().__init__()
-
-        if num_heads_upsample == -1:
-            num_heads_upsample = num_heads
-
-        self.image_size = image_size
-        self.in_channels = in_channels
-        self.model_channels = model_channels
-        self.out_channels = out_channels
-        self.num_res_blocks = num_res_blocks
-        self.attention_resolutions = attention_resolutions
-        self.dropout = dropout
-        self.channel_mult = channel_mult
-        self.conv_resample = conv_resample
-        self.num_classes = num_classes
-        self.use_checkpoint = use_checkpoint
-        self.dtype = th.float16 if use_fp16 else th.float32
-        self.num_heads = num_heads
-        self.num_head_channels = num_head_channels
-        self.num_heads_upsample = num_heads_upsample
-
-        time_embed_dim = model_channels * 4
-        self.time_embed = nn.Sequential(
-            linear(model_channels, time_embed_dim),
-            nn.SiLU(),
-            linear(time_embed_dim, time_embed_dim),
-        )
-
-        if self.num_classes is not None:
-            self.label_emb = nn.Embedding(num_classes, time_embed_dim)
-
-        ch = input_ch = int(channel_mult[0] * model_channels)
-        self.input_blocks = nn.ModuleList(
-            [TimestepEmbedSequential(conv_nd(dims, in_channels, ch, 3, padding=1))]
-        )
-        self._feature_size = ch
-        input_block_chans = [ch]
-        ds = 1
-        for level, mult in enumerate(channel_mult):
-            for _ in range(num_res_blocks):
-                layers = [
-                    ResBlock(
-                        ch,
-                        time_embed_dim,
-                        dropout,
-                        out_channels=int(mult * model_channels),
-                        dims=dims,
-                        use_checkpoint=use_checkpoint,
-                        use_scale_shift_norm=use_scale_shift_norm,
-                    )
-                ]
-                ch = int(mult * model_channels)
-                if ds in attention_resolutions:
-                    layers.append(
-                        AttentionBlock(
-                            ch,
-                            use_checkpoint=use_checkpoint,
-                            num_heads=num_heads,
-                            num_head_channels=num_head_channels,
-                            use_new_attention_order=use_new_attention_order,
-                        )
-                    )
-                self.input_blocks.append(TimestepEmbedSequential(*layers))
-                self._feature_size += ch
-                input_block_chans.append(ch)
-            if level != len(channel_mult) - 1:
-                out_ch = ch
-                self.input_blocks.append(
-                    TimestepEmbedSequential(
-                        ResBlock(
-                            ch,
-                            time_embed_dim,
-                            dropout,
-                            out_channels=out_ch,
-                            dims=dims,
-                            use_checkpoint=use_checkpoint,
-                            use_scale_shift_norm=use_scale_shift_norm,
-                            down=True,
-                        )
-                        if resblock_updown
-                        else Downsample(
-                            ch, conv_resample, dims=dims, out_channels=out_ch
-                        )
-                    )
-                )
-                ch = out_ch
-                input_block_chans.append(ch)
-                ds *= 2
-                self._feature_size += ch
-
-        self.middle_block = TimestepEmbedSequential(
-            ResBlock(
-                ch,
-                time_embed_dim,
-                dropout,
-                dims=dims,
-                use_checkpoint=use_checkpoint,
-                use_scale_shift_norm=use_scale_shift_norm,
-            ),
-            AttentionBlock(
-                ch,
-                use_checkpoint=use_checkpoint,
-                num_heads=num_heads,
-                num_head_channels=num_head_channels,
-                use_new_attention_order=use_new_attention_order,
-            ),
-            ResBlock(
-                ch,
-                time_embed_dim,
-                dropout,
-                dims=dims,
-                use_checkpoint=use_checkpoint,
-                use_scale_shift_norm=use_scale_shift_norm,
-            ),
-        )
-        self._feature_size += ch
-
-        self.output_blocks = nn.ModuleList([])
-        for level, mult in list(enumerate(channel_mult))[::-1]:
-            for i in range(num_res_blocks + 1):
-                ich = input_block_chans.pop()
-                layers = [
-                    ResBlock(
-                        ch + ich,
-                        time_embed_dim,
-                        dropout,
-                        out_channels=int(model_channels * mult),
-                        dims=dims,
-                        use_checkpoint=use_checkpoint,
-                        use_scale_shift_norm=use_scale_shift_norm,
-                    )
-                ]
-                ch = int(model_channels * mult)
-                if ds in attention_resolutions:
-                    layers.append(
-                        AttentionBlock(
-                            ch,
-                            use_checkpoint=use_checkpoint,
-                            num_heads=num_heads_upsample,
-                            num_head_channels=num_head_channels,
-                            use_new_attention_order=use_new_attention_order,
-                        )
-                    )
-                if level and i == num_res_blocks:
-                    out_ch = ch
-                    layers.append(
-                        ResBlock(
-                            ch,
-                            time_embed_dim,
-                            dropout,
-                            out_channels=out_ch,
-                            dims=dims,
-                            use_checkpoint=use_checkpoint,
-                            use_scale_shift_norm=use_scale_shift_norm,
-                            up=True,
-                        )
-                        if resblock_updown
-                        else Upsample(ch, conv_resample, dims=dims, out_channels=out_ch)
-                    )
-                    ds //= 2
-                self.output_blocks.append(TimestepEmbedSequential(*layers))
-                self._feature_size += ch
-
-        self.out = nn.Sequential(
-            normalization(ch),
-            nn.SiLU(),
-            zero_module(conv_nd(dims, input_ch, out_channels, 3, padding=1)),
-        )
-
-    def convert_to_fp16(self):
-        """
-        Convert the torso of the model to float16.
-        """
-        self.input_blocks.apply(convert_module_to_f16)
-        self.middle_block.apply(convert_module_to_f16)
-        self.output_blocks.apply(convert_module_to_f16)
-
-    def convert_to_fp32(self):
-        """
-        Convert the torso of the model to float32.
-        """
-        self.input_blocks.apply(convert_module_to_f32)
-        self.middle_block.apply(convert_module_to_f32)
-        self.output_blocks.apply(convert_module_to_f32)
-
-    def forward(self, x, timesteps, y=None):
-        """
-        Apply the model to an input batch.
-
-        :param x: an [N x C x ...] Tensor of inputs.
-        :param timesteps: a 1-D batch of timesteps.
-        :param y: an [N] Tensor of labels, if class-conditional.
-        :return: an [N x C x ...] Tensor of outputs.
-        """
-        assert (y is not None) == (
-            self.num_classes is not None
-        ), "must specify y if and only if the model is class-conditional"
-
-        hs = []
-        emb = self.time_embed(timestep_embedding(timesteps, self.model_channels))
-
-        if self.num_classes is not None:
-            assert y.shape == (x.shape[0],)
-            emb = emb + self.label_emb(y)
-
-        h = x.type(self.dtype)
-        for module in self.input_blocks:
-            h = module(h, emb)
-            hs.append(h)
-        h = self.middle_block(h, emb)
-        for module in self.output_blocks:
-            h = th.cat([h, hs.pop()], dim=1)
-            h = module(h, emb)
-        h = h.type(x.dtype)
-        return self.out(h)
-
-
-class SuperResModel(UNetModel):
-    """
-    A UNetModel that performs super-resolution.
-
-    Expects an extra kwarg `low_res` to condition on a low-resolution image.
-    """
-
-    def __init__(self, image_size, in_channels, *args, **kwargs):
-        super().__init__(image_size, in_channels * 2, *args, **kwargs)
-
-    def forward(self, x, timesteps, low_res=None, **kwargs):
-        _, _, new_height, new_width = x.shape
-        upsampled = F.interpolate(low_res, (new_height, new_width), mode="bilinear")
-        x = th.cat([x, upsampled], dim=1)
-        return super().forward(x, timesteps, **kwargs)
-
-
-class EncoderUNetModel(nn.Module):
-    """
-    The half UNet model with attention and timestep embedding.
-
-    For usage, see UNet.
-    """
-
-    def __init__(
-        self,
-        image_size,
-        in_channels,
-        model_channels,
-        out_channels,
-        num_res_blocks,
-        attention_resolutions,
-        dropout=0,
-        channel_mult=(1, 2, 4, 8),
-        conv_resample=True,
-        dims=2,
-        use_checkpoint=False,
-        use_fp16=False,
-        num_heads=1,
-        num_head_channels=-1,
-        num_heads_upsample=-1,
-        use_scale_shift_norm=False,
-        resblock_updown=False,
-        use_new_attention_order=False,
-        pool="adaptive",
-    ):
-        super().__init__()
-
-        if num_heads_upsample == -1:
-            num_heads_upsample = num_heads
-
-        self.in_channels = in_channels
-        self.model_channels = model_channels
-        self.out_channels = out_channels
-        self.num_res_blocks = num_res_blocks
-        self.attention_resolutions = attention_resolutions
-        self.dropout = dropout
-        self.channel_mult = channel_mult
-        self.conv_resample = conv_resample
-        self.use_checkpoint = use_checkpoint
-        self.dtype = th.float16 if use_fp16 else th.float32
-        self.num_heads = num_heads
-        self.num_head_channels = num_head_channels
-        self.num_heads_upsample = num_heads_upsample
-
-        time_embed_dim = model_channels * 4
-        self.time_embed = nn.Sequential(
-            linear(model_channels, time_embed_dim),
-            nn.SiLU(),
-            linear(time_embed_dim, time_embed_dim),
-        )
-
-        ch = int(channel_mult[0] * model_channels)
-        self.input_blocks = nn.ModuleList(
-            [TimestepEmbedSequential(conv_nd(dims, in_channels, ch, 3, padding=1))]
-        )
-        self._feature_size = ch
-        input_block_chans = [ch]
-        ds = 1
-        for level, mult in enumerate(channel_mult):
-            for _ in range(num_res_blocks):
-                layers = [
-                    ResBlock(
-                        ch,
-                        time_embed_dim,
-                        dropout,
-                        out_channels=int(mult * model_channels),
-                        dims=dims,
-                        use_checkpoint=use_checkpoint,
-                        use_scale_shift_norm=use_scale_shift_norm,
-                    )
-                ]
-                ch = int(mult * model_channels)
-                if ds in attention_resolutions:
-                    layers.append(
-                        AttentionBlock(
-                            ch,
-                            use_checkpoint=use_checkpoint,
-                            num_heads=num_heads,
-                            num_head_channels=num_head_channels,
-                            use_new_attention_order=use_new_attention_order,
-                        )
-                    )
-                self.input_blocks.append(TimestepEmbedSequential(*layers))
-                self._feature_size += ch
-                input_block_chans.append(ch)
-            if level != len(channel_mult) - 1:
-                out_ch = ch
-                self.input_blocks.append(
-                    TimestepEmbedSequential(
-                        ResBlock(
-                            ch,
-                            time_embed_dim,
-                            dropout,
-                            out_channels=out_ch,
-                            dims=dims,
-                            use_checkpoint=use_checkpoint,
-                            use_scale_shift_norm=use_scale_shift_norm,
-                            down=True,
-                        )
-                        if resblock_updown
-                        else Downsample(
-                            ch, conv_resample, dims=dims, out_channels=out_ch
-                        )
-                    )
-                )
-                ch = out_ch
-                input_block_chans.append(ch)
-                ds *= 2
-                self._feature_size += ch
-
-        self.middle_block = TimestepEmbedSequential(
-            ResBlock(
-                ch,
-                time_embed_dim,
-                dropout,
-                dims=dims,
-                use_checkpoint=use_checkpoint,
-                use_scale_shift_norm=use_scale_shift_norm,
-            ),
-            AttentionBlock(
-                ch,
-                use_checkpoint=use_checkpoint,
-                num_heads=num_heads,
-                num_head_channels=num_head_channels,
-                use_new_attention_order=use_new_attention_order,
-            ),
-            ResBlock(
-                ch,
-                time_embed_dim,
-                dropout,
-                dims=dims,
-                use_checkpoint=use_checkpoint,
-                use_scale_shift_norm=use_scale_shift_norm,
-            ),
-        )
-        self._feature_size += ch
-        self.pool = pool
-        if pool == "adaptive":
-            self.out = nn.Sequential(
-                normalization(ch),
-                nn.SiLU(),
-                nn.AdaptiveAvgPool2d((1, 1)),
-                zero_module(conv_nd(dims, ch, out_channels, 1)),
-                nn.Flatten(),
-            )
-        elif pool == "attention":
-            assert num_head_channels != -1
-            self.out = nn.Sequential(
-                normalization(ch),
-                nn.SiLU(),
-                AttentionPool2d(
-                    (image_size // ds), ch, num_head_channels, out_channels
-                ),
-            )
-        elif pool == "spatial":
-            self.out = nn.Sequential(
-                nn.Linear(self._feature_size, 2048),
-                nn.ReLU(),
-                nn.Linear(2048, self.out_channels),
-            )
-        elif pool == "spatial_v2":
-            self.out = nn.Sequential(
-                nn.Linear(self._feature_size, 2048),
-                normalization(2048),
-                nn.SiLU(),
-                nn.Linear(2048, self.out_channels),
-            )
-        else:
-            raise NotImplementedError(f"Unexpected {pool} pooling")
-
-    def convert_to_fp16(self):
-        """
-        Convert the torso of the model to float16.
-        """
-        self.input_blocks.apply(convert_module_to_f16)
-        self.middle_block.apply(convert_module_to_f16)
-
-    def convert_to_fp32(self):
-        """
-        Convert the torso of the model to float32.
-        """
-        self.input_blocks.apply(convert_module_to_f32)
-        self.middle_block.apply(convert_module_to_f32)
-
-    def forward(self, x, timesteps):
-        """
-        Apply the model to an input batch.
-
-        :param x: an [N x C x ...] Tensor of inputs.
-        :param timesteps: a 1-D batch of timesteps.
-        :return: an [N x K] Tensor of outputs.
-        """
-        emb = self.time_embed(timestep_embedding(timesteps, self.model_channels))
-
-        results = []
-        h = x.type(self.dtype)
-        for module in self.input_blocks:
-            h = module(h, emb)
-            if self.pool.startswith("spatial"):
-                results.append(h.type(x.dtype).mean(dim=(2, 3)))
-        h = self.middle_block(h, emb)
-        if self.pool.startswith("spatial"):
-            results.append(h.type(x.dtype).mean(dim=(2, 3)))
-            h = th.cat(results, axis=-1)
-            return self.out(h)
-        else:
-            h = h.type(x.dtype)
-            return self.out(h)
diff --git a/spaces/antonbol/vocal_remover/lib/nets.py b/spaces/antonbol/vocal_remover/lib/nets.py
deleted file mode 100644
index 1ec7af24cd5b3ad4fb41466f4c0283da8bdffb40..0000000000000000000000000000000000000000
--- a/spaces/antonbol/vocal_remover/lib/nets.py
+++ /dev/null
@@ -1,131 +0,0 @@
-import torch
-from torch import nn
-import torch.nn.functional as F
-
-from lib import layers
-
-
-class BaseNet(nn.Module):
-
-    def __init__(self, nin, nout, nin_lstm, nout_lstm, dilations=((4, 2), (8, 4), (12, 6))):
-        super(BaseNet, self).__init__()
-        self.enc1 = layers.Conv2DBNActiv(nin, nout, 3, 1, 1)
-        self.enc2 = layers.Encoder(nout, nout * 2, 3, 2, 1)
-        self.enc3 = layers.Encoder(nout * 2, nout * 4, 3, 2, 1)
-        self.enc4 = layers.Encoder(nout * 4, nout * 6, 3, 2, 1)
-        self.enc5 = layers.Encoder(nout * 6, nout * 8, 3, 2, 1)
-
-        self.aspp = layers.ASPPModule(nout * 8, nout * 8, dilations, dropout=True)
-
-        self.dec4 = layers.Decoder(nout * (6 + 8), nout * 6, 3, 1, 1)
-        self.dec3 = layers.Decoder(nout * (4 + 6), nout * 4, 3, 1, 1)
-        self.dec2 = layers.Decoder(nout * (2 + 4), nout * 2, 3, 1, 1)
-        self.lstm_dec2 = layers.LSTMModule(nout * 2, nin_lstm, nout_lstm)
-        self.dec1 = layers.Decoder(nout * (1 + 2) + 1, nout * 1, 3, 1, 1)
-
-    def __call__(self, x):
-        e1 = self.enc1(x)
-        e2 = self.enc2(e1)
-        e3 = self.enc3(e2)
-        e4 = self.enc4(e3)
-        e5 = self.enc5(e4)
-
-        h = self.aspp(e5)
-
-        h = self.dec4(h, e4)
-        h = self.dec3(h, e3)
-        h = self.dec2(h, e2)
-        h = torch.cat([h, self.lstm_dec2(h)], dim=1)
-        h = self.dec1(h, e1)
-
-        return h
-
-
-class CascadedNet(nn.Module):
-
-    def __init__(self, n_fft, nout=32, nout_lstm=128):
-        super(CascadedNet, self).__init__()
-        self.max_bin = n_fft // 2
-        self.output_bin = n_fft // 2 + 1
-        self.nin_lstm = self.max_bin // 2
-        self.offset = 64
-
-        self.stg1_low_band_net = nn.Sequential(
-            BaseNet(2, nout // 2, self.nin_lstm // 2, nout_lstm),
-            layers.Conv2DBNActiv(nout // 2, nout // 4, 1, 1, 0)
-        )
-        self.stg1_high_band_net = BaseNet(
-            2, nout // 4, self.nin_lstm // 2, nout_lstm // 2
-        )
-
-        self.stg2_low_band_net = nn.Sequential(
-            BaseNet(nout // 4 + 2, nout, self.nin_lstm // 2, nout_lstm),
-            layers.Conv2DBNActiv(nout, nout // 2, 1, 1, 0)
-        )
-        self.stg2_high_band_net = BaseNet(
-            nout // 4 + 2, nout // 2, self.nin_lstm // 2, nout_lstm // 2
-        )
-
-        self.stg3_full_band_net = BaseNet(
-            3 * nout // 4 + 2, nout, self.nin_lstm, nout_lstm
-        )
-
-        self.out = nn.Conv2d(nout, 2, 1, bias=False)
-        self.aux_out = nn.Conv2d(3 * nout // 4, 2, 1, bias=False)
-
-    def forward(self, x):
-        x = x[:, :, :self.max_bin]
-
-        bandw = x.size()[2] // 2
-        l1_in = x[:, :, :bandw]
-        h1_in = x[:, :, bandw:]
-        l1 = self.stg1_low_band_net(l1_in)
-        h1 = self.stg1_high_band_net(h1_in)
-        aux1 = torch.cat([l1, h1], dim=2)
-
-        l2_in = torch.cat([l1_in, l1], dim=1)
-        h2_in = torch.cat([h1_in, h1], dim=1)
-        l2 = self.stg2_low_band_net(l2_in)
-        h2 = self.stg2_high_band_net(h2_in)
-        aux2 = torch.cat([l2, h2], dim=2)
-
-        f3_in = torch.cat([x, aux1, aux2], dim=1)
-        f3 = self.stg3_full_band_net(f3_in)
-
-        mask = torch.sigmoid(self.out(f3))
-        mask = F.pad(
-            input=mask,
-            pad=(0, 0, 0, self.output_bin - mask.size()[2]),
-            mode='replicate'
-        )
-
-        if self.training:
-            aux = torch.cat([aux1, aux2], dim=1)
-            aux = torch.sigmoid(self.aux_out(aux))
-            aux = F.pad(
-                input=aux,
-                pad=(0, 0, 0, self.output_bin - aux.size()[2]),
-                mode='replicate'
-            )
-            return mask, aux
-        else:
-            return mask
-
-    def predict_mask(self, x):
-        mask = self.forward(x)
-
-        if self.offset > 0:
-            mask = mask[:, :, :, self.offset:-self.offset]
-            assert mask.size()[3] > 0
-
-        return mask
-
-    def predict(self, x):
-        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/ardances/mistralai-Mistral-7B-v0.1/README.md b/spaces/ardances/mistralai-Mistral-7B-v0.1/README.md
deleted file mode 100644
index 783c434048e18b0e4fb92c7cf7e86fd467eabfcd..0000000000000000000000000000000000000000
--- a/spaces/ardances/mistralai-Mistral-7B-v0.1/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Mistralai Mistral 7B V0.1
-emoji: 📉
-colorFrom: indigo
-colorTo: pink
-sdk: gradio
-sdk_version: 3.47.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/artificialguybr/video-dubbing/TTS/TTS/tts/utils/text/french/abbreviations.py b/spaces/artificialguybr/video-dubbing/TTS/TTS/tts/utils/text/french/abbreviations.py
deleted file mode 100644
index f580dfed7b4576a9f87b0a4145cb729e70050d50..0000000000000000000000000000000000000000
--- a/spaces/artificialguybr/video-dubbing/TTS/TTS/tts/utils/text/french/abbreviations.py
+++ /dev/null
@@ -1,48 +0,0 @@
-import re
-
-# List of (regular expression, replacement) pairs for abbreviations in french:
-abbreviations_fr = [
-    (re.compile("\\b%s\\." % x[0], re.IGNORECASE), x[1])
-    for x in [
-        ("M", "monsieur"),
-        ("Mlle", "mademoiselle"),
-        ("Mlles", "mesdemoiselles"),
-        ("Mme", "Madame"),
-        ("Mmes", "Mesdames"),
-        ("N.B", "nota bene"),
-        ("M", "monsieur"),
-        ("p.c.q", "parce que"),
-        ("Pr", "professeur"),
-        ("qqch", "quelque chose"),
-        ("rdv", "rendez-vous"),
-        ("max", "maximum"),
-        ("min", "minimum"),
-        ("no", "numéro"),
-        ("adr", "adresse"),
-        ("dr", "docteur"),
-        ("st", "saint"),
-        ("co", "companie"),
-        ("jr", "junior"),
-        ("sgt", "sergent"),
-        ("capt", "capitain"),
-        ("col", "colonel"),
-        ("av", "avenue"),
-        ("av. J.-C", "avant Jésus-Christ"),
-        ("apr. J.-C", "après Jésus-Christ"),
-        ("art", "article"),
-        ("boul", "boulevard"),
-        ("c.-à-d", "c’est-à-dire"),
-        ("etc", "et cetera"),
-        ("ex", "exemple"),
-        ("excl", "exclusivement"),
-        ("boul", "boulevard"),
-    ]
-] + [
-    (re.compile("\\b%s" % x[0]), x[1])
-    for x in [
-        ("Mlle", "mademoiselle"),
-        ("Mlles", "mesdemoiselles"),
-        ("Mme", "Madame"),
-        ("Mmes", "Mesdames"),
-    ]
-]
diff --git a/spaces/artificialguybr/video-dubbing/TTS/recipes/ljspeech/xtts_v2/train_gpt_xtts.py b/spaces/artificialguybr/video-dubbing/TTS/recipes/ljspeech/xtts_v2/train_gpt_xtts.py
deleted file mode 100644
index 626917381a2a677efabb70d894ed623cd2ac08dc..0000000000000000000000000000000000000000
--- a/spaces/artificialguybr/video-dubbing/TTS/recipes/ljspeech/xtts_v2/train_gpt_xtts.py
+++ /dev/null
@@ -1,176 +0,0 @@
-import os
-
-from trainer import Trainer, TrainerArgs
-
-from TTS.config.shared_configs import BaseDatasetConfig
-from TTS.tts.datasets import load_tts_samples
-from TTS.tts.layers.xtts.trainer.gpt_trainer import GPTArgs, GPTTrainer, GPTTrainerConfig, XttsAudioConfig
-from TTS.utils.manage import ModelManager
-
-# Logging parameters
-RUN_NAME = "GPT_XTTS_v2.0_LJSpeech_FT"
-PROJECT_NAME = "XTTS_trainer"
-DASHBOARD_LOGGER = "tensorboard"
-LOGGER_URI = None
-
-# Set here the path that the checkpoints will be saved. Default: ./run/training/
-OUT_PATH = os.path.join(os.path.dirname(os.path.abspath(__file__)), "run", "training")
-
-# Training Parameters
-OPTIMIZER_WD_ONLY_ON_WEIGHTS = True  # for multi-gpu training please make it False
-START_WITH_EVAL = True  # if True it will star with evaluation
-BATCH_SIZE = 3  # set here the batch size
-GRAD_ACUMM_STEPS = 84  # set here the grad accumulation steps
-# Note: we recommend that BATCH_SIZE * GRAD_ACUMM_STEPS need to be at least 252 for more efficient training. You can increase/decrease BATCH_SIZE but then set GRAD_ACUMM_STEPS accordingly.
-
-# Define here the dataset that you want to use for the fine-tuning on.
-config_dataset = BaseDatasetConfig(
-    formatter="ljspeech",
-    dataset_name="ljspeech",
-    path="/raid/datasets/LJSpeech-1.1_24khz/",
-    meta_file_train="/raid/datasets/LJSpeech-1.1_24khz/metadata.csv",
-    language="en",
-)
-
-# Add here the configs of the datasets
-DATASETS_CONFIG_LIST = [config_dataset]
-
-# Define the path where XTTS v2.0.1 files will be downloaded
-CHECKPOINTS_OUT_PATH = os.path.join(OUT_PATH, "XTTS_v2.0_original_model_files/")
-os.makedirs(CHECKPOINTS_OUT_PATH, exist_ok=True)
-
-
-# DVAE files
-DVAE_CHECKPOINT_LINK = "https://coqui.gateway.scarf.sh/hf-coqui/XTTS-v2/main/dvae.pth"
-MEL_NORM_LINK = "https://coqui.gateway.scarf.sh/hf-coqui/XTTS-v2/main/mel_stats.pth"
-
-# Set the path to the downloaded files
-DVAE_CHECKPOINT = os.path.join(CHECKPOINTS_OUT_PATH, os.path.basename(DVAE_CHECKPOINT_LINK))
-MEL_NORM_FILE = os.path.join(CHECKPOINTS_OUT_PATH, os.path.basename(MEL_NORM_LINK))
-
-# download DVAE files if needed
-if not os.path.isfile(DVAE_CHECKPOINT) or not os.path.isfile(MEL_NORM_FILE):
-    print(" > Downloading DVAE files!")
-    ModelManager._download_model_files([MEL_NORM_LINK, DVAE_CHECKPOINT_LINK], CHECKPOINTS_OUT_PATH, progress_bar=True)
-
-
-# Download XTTS v2.0 checkpoint if needed
-TOKENIZER_FILE_LINK = "https://coqui.gateway.scarf.sh/hf-coqui/XTTS-v2/main/vocab.json"
-XTTS_CHECKPOINT_LINK = "https://coqui.gateway.scarf.sh/hf-coqui/XTTS-v2/main/model.pth"
-
-# XTTS transfer learning parameters: You we need to provide the paths of XTTS model checkpoint that you want to do the fine tuning.
-TOKENIZER_FILE = os.path.join(CHECKPOINTS_OUT_PATH, os.path.basename(TOKENIZER_FILE_LINK))  # vocab.json file
-XTTS_CHECKPOINT = os.path.join(CHECKPOINTS_OUT_PATH, os.path.basename(XTTS_CHECKPOINT_LINK))  # model.pth file
-
-# download XTTS v2.0 files if needed
-if not os.path.isfile(TOKENIZER_FILE) or not os.path.isfile(XTTS_CHECKPOINT):
-    print(" > Downloading XTTS v2.0 files!")
-    ModelManager._download_model_files(
-        [TOKENIZER_FILE_LINK, XTTS_CHECKPOINT_LINK], CHECKPOINTS_OUT_PATH, progress_bar=True
-    )
-
-
-# Training sentences generations
-SPEAKER_REFERENCE = [
-    "./tests/data/ljspeech/wavs/LJ001-0002.wav"  # speaker reference to be used in training test sentences
-]
-LANGUAGE = config_dataset.language
-
-
-def main():
-    # init args and config
-    model_args = GPTArgs(
-        max_conditioning_length=132300,  # 6 secs
-        min_conditioning_length=66150,  # 3 secs
-        debug_loading_failures=False,
-        max_wav_length=255995,  # ~11.6 seconds
-        max_text_length=200,
-        mel_norm_file=MEL_NORM_FILE,
-        dvae_checkpoint=DVAE_CHECKPOINT,
-        xtts_checkpoint=XTTS_CHECKPOINT,  # checkpoint path of the model that you want to fine-tune
-        tokenizer_file=TOKENIZER_FILE,
-        gpt_num_audio_tokens=1026,
-        gpt_start_audio_token=1024,
-        gpt_stop_audio_token=1025,
-        gpt_use_masking_gt_prompt_approach=True,
-        gpt_use_perceiver_resampler=True,
-    )
-    # define audio config
-    audio_config = XttsAudioConfig(sample_rate=22050, dvae_sample_rate=22050, output_sample_rate=24000)
-    # training parameters config
-    config = GPTTrainerConfig(
-        output_path=OUT_PATH,
-        model_args=model_args,
-        run_name=RUN_NAME,
-        project_name=PROJECT_NAME,
-        run_description="""
-            GPT XTTS training
-            """,
-        dashboard_logger=DASHBOARD_LOGGER,
-        logger_uri=LOGGER_URI,
-        audio=audio_config,
-        batch_size=BATCH_SIZE,
-        batch_group_size=48,
-        eval_batch_size=BATCH_SIZE,
-        num_loader_workers=8,
-        eval_split_max_size=256,
-        print_step=50,
-        plot_step=100,
-        log_model_step=1000,
-        save_step=10000,
-        save_n_checkpoints=1,
-        save_checkpoints=True,
-        # target_loss="loss",
-        print_eval=False,
-        # Optimizer values like tortoise, pytorch implementation with modifications to not apply WD to non-weight parameters.
-        optimizer="AdamW",
-        optimizer_wd_only_on_weights=OPTIMIZER_WD_ONLY_ON_WEIGHTS,
-        optimizer_params={"betas": [0.9, 0.96], "eps": 1e-8, "weight_decay": 1e-2},
-        lr=5e-06,  # learning rate
-        lr_scheduler="MultiStepLR",
-        # it was adjusted accordly for the new step scheme
-        lr_scheduler_params={"milestones": [50000 * 18, 150000 * 18, 300000 * 18], "gamma": 0.5, "last_epoch": -1},
-        test_sentences=[
-            {
-                "text": "It took me quite a long time to develop a voice, and now that I have it I'm not going to be silent.",
-                "speaker_wav": SPEAKER_REFERENCE,
-                "language": LANGUAGE,
-            },
-            {
-                "text": "This cake is great. It's so delicious and moist.",
-                "speaker_wav": SPEAKER_REFERENCE,
-                "language": LANGUAGE,
-            },
-        ],
-    )
-
-    # init the model from config
-    model = GPTTrainer.init_from_config(config)
-
-    # load training samples
-    train_samples, eval_samples = load_tts_samples(
-        DATASETS_CONFIG_LIST,
-        eval_split=True,
-        eval_split_max_size=config.eval_split_max_size,
-        eval_split_size=config.eval_split_size,
-    )
-
-    # init the trainer and 🚀
-    trainer = Trainer(
-        TrainerArgs(
-            restore_path=None,  # xtts checkpoint is restored via xtts_checkpoint key so no need of restore it using Trainer restore_path parameter
-            skip_train_epoch=False,
-            start_with_eval=START_WITH_EVAL,
-            grad_accum_steps=GRAD_ACUMM_STEPS,
-        ),
-        config,
-        output_path=OUT_PATH,
-        model=model,
-        train_samples=train_samples,
-        eval_samples=eval_samples,
-    )
-    trainer.fit()
-
-
-if __name__ == "__main__":
-    main()
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/contourpy/util/mpl_renderer.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/contourpy/util/mpl_renderer.py
deleted file mode 100644
index 9fb04aa220d54117f92d9cf2dfc40d3ca433e752..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/contourpy/util/mpl_renderer.py
+++ /dev/null
@@ -1,454 +0,0 @@
-import io
-
-import matplotlib.collections as mcollections
-import matplotlib.pyplot as plt
-import numpy as np
-
-from contourpy import FillType, LineType
-
-from .mpl_util import filled_to_mpl_paths, lines_to_mpl_paths, mpl_codes_to_offsets
-
-
-class MplRenderer:
-    """Utility renderer using Matplotlib to render a grid of plots over the same (x, y) range.
-
-    Args:
-        nrows (int, optional): Number of rows of plots, default ``1``.
-        ncols (int, optional): Number of columns of plots, default ``1``.
-        figsize (tuple(float, float), optional): Figure size in inches, default ``(9, 9)``.
-        show_frame (bool, optional): Whether to show frame and axes ticks, default ``True``.
-        backend (str, optional): Matplotlib backend to use or ``None`` for default backend.
-            Default ``None``.
-        gridspec_kw (dict, optional): Gridspec keyword arguments to pass to ``plt.subplots``,
-            default None.
-    """
-    def __init__(
-        self, nrows=1, ncols=1, figsize=(9, 9), show_frame=True, backend=None, gridspec_kw=None,
-    ):
-        if backend is not None:
-            import matplotlib
-            matplotlib.use(backend)
-
-        kwargs = dict(figsize=figsize, squeeze=False, sharex=True, sharey=True)
-        if gridspec_kw is not None:
-            kwargs["gridspec_kw"] = gridspec_kw
-        else:
-            kwargs["subplot_kw"] = dict(aspect="equal")
-
-        self._fig, axes = plt.subplots(nrows, ncols, **kwargs)
-        self._axes = axes.flatten()
-        if not show_frame:
-            for ax in self._axes:
-                ax.axis("off")
-
-        self._want_tight = True
-
-    def __del__(self):
-        if hasattr(self, "_fig"):
-            plt.close(self._fig)
-
-    def _autoscale(self):
-        # Using axes._need_autoscale attribute if need to autoscale before rendering after adding
-        # lines/filled.  Only want to autoscale once per axes regardless of how many lines/filled
-        # added.
-        for ax in self._axes:
-            if getattr(ax, "_need_autoscale", False):
-                ax.autoscale_view(tight=True)
-                ax._need_autoscale = False
-        if self._want_tight and len(self._axes) > 1:
-            self._fig.tight_layout()
-
-    def _get_ax(self, ax):
-        if isinstance(ax, int):
-            ax = self._axes[ax]
-        return ax
-
-    def _grid_as_2d(self, x, y):
-        x = np.asarray(x)
-        y = np.asarray(y)
-        if x.ndim == 1:
-            x, y = np.meshgrid(x, y)
-        return x, y
-
-    def filled(self, filled, fill_type, ax=0, color="C0", alpha=0.7):
-        """Plot filled contours on a single Axes.
-
-        Args:
-            filled (sequence of arrays): Filled contour data as returned by
-                :func:`~contourpy.ContourGenerator.filled`.
-            fill_type (FillType): Type of ``filled`` data, as returned by
-                :attr:`~contourpy.ContourGenerator.fill_type`.
-            ax (int or Maplotlib Axes, optional): Which axes to plot on, default ``0``.
-            color (str, optional): Color to plot with. May be a string color or the letter ``"C"``
-                followed by an integer in the range ``"C0"`` to ``"C9"`` to use a color from the
-                ``tab10`` colormap. Default ``"C0"``.
-            alpha (float, optional): Opacity to plot with, default ``0.7``.
-        """
-        ax = self._get_ax(ax)
-        paths = filled_to_mpl_paths(filled, fill_type)
-        collection = mcollections.PathCollection(
-            paths, facecolors=color, edgecolors="none", lw=0, alpha=alpha)
-        ax.add_collection(collection)
-        ax._need_autoscale = True
-
-    def grid(self, x, y, ax=0, color="black", alpha=0.1, point_color=None, quad_as_tri_alpha=0):
-        """Plot quad grid lines on a single Axes.
-
-        Args:
-            x (array-like of shape (ny, nx) or (nx,)): The x-coordinates of the grid points.
-            y (array-like of shape (ny, nx) or (ny,)): The y-coordinates of the grid points.
-            ax (int or Matplotlib Axes, optional): Which Axes to plot on, default ``0``.
-            color (str, optional): Color to plot grid lines, default ``"black"``.
-            alpha (float, optional): Opacity to plot lines with, default ``0.1``.
-            point_color (str, optional): Color to plot grid points or ``None`` if grid points
-                should not be plotted, default ``None``.
-            quad_as_tri_alpha (float, optional): Opacity to plot ``quad_as_tri`` grid, default 0.
-
-        Colors may be a string color or the letter ``"C"`` followed by an integer in the range
-        ``"C0"`` to ``"C9"`` to use a color from the ``tab10`` colormap.
-
-        Warning:
-            ``quad_as_tri_alpha > 0`` plots all quads as though they are unmasked.
-        """
-        ax = self._get_ax(ax)
-        x, y = self._grid_as_2d(x, y)
-        kwargs = dict(color=color, alpha=alpha)
-        ax.plot(x, y, x.T, y.T, **kwargs)
-        if quad_as_tri_alpha > 0:
-            # Assumes no quad mask.
-            xmid = 0.25*(x[:-1, :-1] + x[1:, :-1] + x[:-1, 1:] + x[1:, 1:])
-            ymid = 0.25*(y[:-1, :-1] + y[1:, :-1] + y[:-1, 1:] + y[1:, 1:])
-            kwargs["alpha"] = quad_as_tri_alpha
-            ax.plot(
-                np.stack((x[:-1, :-1], xmid, x[1:, 1:])).reshape((3, -1)),
-                np.stack((y[:-1, :-1], ymid, y[1:, 1:])).reshape((3, -1)),
-                np.stack((x[1:, :-1], xmid, x[:-1, 1:])).reshape((3, -1)),
-                np.stack((y[1:, :-1], ymid, y[:-1, 1:])).reshape((3, -1)),
-                **kwargs)
-        if point_color is not None:
-            ax.scatter(x, y, color=point_color, alpha=alpha, marker='o')
-        ax._need_autoscale = True
-
-    def lines(self, lines, line_type, ax=0, color="C0", alpha=1.0, linewidth=1):
-        """Plot contour lines on a single Axes.
-
-        Args:
-            lines (sequence of arrays): Contour line data as returned by
-                :func:`~contourpy.ContourGenerator.lines`.
-            line_type (LineType): Type of ``lines`` data, as returned by
-                :attr:`~contourpy.ContourGenerator.line_type`.
-            ax (int or Matplotlib Axes, optional): Which Axes to plot on, default ``0``.
-            color (str, optional): Color to plot lines. May be a string color or the letter ``"C"``
-                followed by an integer in the range ``"C0"`` to ``"C9"`` to use a color from the
-                ``tab10`` colormap. Default ``"C0"``.
-            alpha (float, optional): Opacity to plot lines with, default ``1.0``.
-            linewidth (float, optional): Width of lines, default ``1``.
-        """
-        ax = self._get_ax(ax)
-        paths = lines_to_mpl_paths(lines, line_type)
-        collection = mcollections.PathCollection(
-            paths, facecolors="none", edgecolors=color, lw=linewidth, alpha=alpha)
-        ax.add_collection(collection)
-        ax._need_autoscale = True
-
-    def mask(self, x, y, z, ax=0, color="black"):
-        """Plot masked out grid points as circles on a single Axes.
-
-        Args:
-            x (array-like of shape (ny, nx) or (nx,)): The x-coordinates of the grid points.
-            y (array-like of shape (ny, nx) or (ny,)): The y-coordinates of the grid points.
-            z (masked array of shape (ny, nx): z-values.
-            ax (int or Matplotlib Axes, optional): Which Axes to plot on, default ``0``.
-            color (str, optional): Circle color, default ``"black"``.
-        """
-        mask = np.ma.getmask(z)
-        if mask is np.ma.nomask:
-            return
-        ax = self._get_ax(ax)
-        x, y = self._grid_as_2d(x, y)
-        ax.plot(x[mask], y[mask], 'o', c=color)
-
-    def save(self, filename, transparent=False):
-        """Save plots to SVG or PNG file.
-
-        Args:
-            filename (str): Filename to save to.
-            transparent (bool, optional): Whether background should be transparent, default
-                ``False``.
-        """
-        self._autoscale()
-        self._fig.savefig(filename, transparent=transparent)
-
-    def save_to_buffer(self):
-        """Save plots to an ``io.BytesIO`` buffer.
-
-        Return:
-            BytesIO: PNG image buffer.
-        """
-        self._autoscale()
-        buf = io.BytesIO()
-        self._fig.savefig(buf, format="png")
-        buf.seek(0)
-        return buf
-
-    def show(self):
-        """Show plots in an interactive window, in the usual Matplotlib manner.
-        """
-        self._autoscale()
-        plt.show()
-
-    def title(self, title, ax=0, color=None):
-        """Set the title of a single Axes.
-
-        Args:
-            title (str): Title text.
-            ax (int or Matplotlib Axes, optional): Which Axes to set the title of, default ``0``.
-            color (str, optional): Color to set title. May be a string color or the letter ``"C"``
-                followed by an integer in the range ``"C0"`` to ``"C9"`` to use a color from the
-                ``tab10`` colormap. Default ``None`` which is ``black``.
-        """
-        if color:
-            self._get_ax(ax).set_title(title, color=color)
-        else:
-            self._get_ax(ax).set_title(title)
-
-    def z_values(self, x, y, z, ax=0, color="green", fmt=".1f", quad_as_tri=False):
-        """Show ``z`` values on a single Axes.
-
-        Args:
-            x (array-like of shape (ny, nx) or (nx,)): The x-coordinates of the grid points.
-            y (array-like of shape (ny, nx) or (ny,)): The y-coordinates of the grid points.
-            z (array-like of shape (ny, nx): z-values.
-            ax (int or Matplotlib Axes, optional): Which Axes to plot on, default ``0``.
-            color (str, optional): Color of added text. May be a string color or the letter ``"C"``
-                followed by an integer in the range ``"C0"`` to ``"C9"`` to use a color from the
-                ``tab10`` colormap. Default ``"green"``.
-            fmt (str, optional): Format to display z-values, default ``".1f"``.
-            quad_as_tri (bool, optional): Whether to show z-values at the ``quad_as_tri`` centers
-                of quads.
-
-        Warning:
-            ``quad_as_tri=True`` shows z-values for all quads, even if masked.
-        """
-        ax = self._get_ax(ax)
-        x, y = self._grid_as_2d(x, y)
-        z = np.asarray(z)
-        ny, nx = z.shape
-        for j in range(ny):
-            for i in range(nx):
-                ax.text(x[j, i], y[j, i], f"{z[j, i]:{fmt}}", ha="center", va="center",
-                        color=color, clip_on=True)
-        if quad_as_tri:
-            for j in range(ny-1):
-                for i in range(nx-1):
-                    xx = np.mean(x[j:j+2, i:i+2])
-                    yy = np.mean(y[j:j+2, i:i+2])
-                    zz = np.mean(z[j:j+2, i:i+2])
-                    ax.text(xx, yy, f"{zz:{fmt}}", ha="center", va="center", color=color,
-                            clip_on=True)
-
-
-class MplTestRenderer(MplRenderer):
-    """Test renderer implemented using Matplotlib.
-
-    No whitespace around plots and no spines/ticks displayed.
-    Uses Agg backend, so can only save to file/buffer, cannot call ``show()``.
-    """
-    def __init__(self, nrows=1, ncols=1, figsize=(9, 9)):
-        gridspec = {
-            "left": 0.01,
-            "right": 0.99,
-            "top": 0.99,
-            "bottom": 0.01,
-            "wspace": 0.01,
-            "hspace": 0.01,
-        }
-        super().__init__(
-            nrows, ncols, figsize, show_frame=True, backend="Agg", gridspec_kw=gridspec,
-        )
-
-        for ax in self._axes:
-            ax.set_xmargin(0.0)
-            ax.set_ymargin(0.0)
-            ax.set_xticks([])
-            ax.set_yticks([])
-
-        self._want_tight = False
-
-
-class MplDebugRenderer(MplRenderer):
-    """Debug renderer implemented using Matplotlib.
-
-    Extends ``MplRenderer`` to add extra information to help in debugging such as markers, arrows,
-    text, etc.
-    """
-    def __init__(self, nrows=1, ncols=1, figsize=(9, 9), show_frame=True):
-        super().__init__(nrows, ncols, figsize, show_frame)
-
-    def _arrow(self, ax, line_start, line_end, color, alpha, arrow_size):
-        mid = 0.5*(line_start + line_end)
-        along = line_end - line_start
-        along /= np.sqrt(np.dot(along, along))  # Unit vector.
-        right = np.asarray((along[1], -along[0]))
-        arrow = np.stack((
-            mid - (along*0.5 - right)*arrow_size,
-            mid + along*0.5*arrow_size,
-            mid - (along*0.5 + right)*arrow_size,
-        ))
-        ax.plot(arrow[:, 0], arrow[:, 1], "-", c=color, alpha=alpha)
-
-    def filled(self, filled, fill_type, ax=0, color="C1", alpha=0.7, line_color="C0",
-               line_alpha=0.7, point_color="C0", start_point_color="red", arrow_size=0.1):
-        super().filled(filled, fill_type, ax, color, alpha)
-
-        if line_color is None and point_color is None:
-            return
-
-        ax = self._get_ax(ax)
-
-        if fill_type == FillType.OuterCode:
-            all_points = filled[0]
-            all_offsets = [mpl_codes_to_offsets(codes) for codes in filled[1]]
-        elif fill_type == FillType.ChunkCombinedCode:
-            all_points = [points for points in filled[0] if points is not None]
-            all_offsets = [mpl_codes_to_offsets(codes) for codes in filled[1] if codes is not None]
-        elif fill_type == FillType.OuterOffset:
-            all_points = filled[0]
-            all_offsets = filled[1]
-        elif fill_type == FillType.ChunkCombinedOffset:
-            all_points = [points for points in filled[0] if points is not None]
-            all_offsets = [offsets for offsets in filled[1] if offsets is not None]
-        elif fill_type == FillType.ChunkCombinedCodeOffset:
-            all_points = []
-            all_offsets = []
-            for points, codes, outer_offsets in zip(*filled):
-                if points is None:
-                    continue
-                all_points += np.split(points, outer_offsets[1:-1])
-                codes = np.split(codes, outer_offsets[1:-1])
-                all_offsets += [mpl_codes_to_offsets(codes) for codes in codes]
-        elif fill_type == FillType.ChunkCombinedOffsetOffset:
-            all_points = []
-            all_offsets = []
-            for points, offsets, outer_offsets in zip(*filled):
-                if points is None:
-                    continue
-                for i in range(len(outer_offsets)-1):
-                    offs = offsets[outer_offsets[i]:outer_offsets[i+1]+1]
-                    all_points.append(points[offs[0]:offs[-1]])
-                    all_offsets.append(offs - offs[0])
-        else:
-            raise RuntimeError(f"Rendering FillType {fill_type} not implemented")
-
-        # Lines.
-        if line_color is not None:
-            for points, offsets in zip(all_points, all_offsets):
-                for start, end in zip(offsets[:-1], offsets[1:]):
-                    xys = points[start:end]
-                    ax.plot(xys[:, 0], xys[:, 1], c=line_color, alpha=line_alpha)
-
-                    if arrow_size > 0.0:
-                        n = len(xys)
-                        for i in range(n-1):
-                            self._arrow(ax, xys[i], xys[i+1], line_color, line_alpha, arrow_size)
-
-        # Points.
-        if point_color is not None:
-            for points, offsets in zip(all_points, all_offsets):
-                mask = np.ones(offsets[-1], dtype=bool)
-                mask[offsets[1:]-1] = False  # Exclude end points.
-                if start_point_color is not None:
-                    start_indices = offsets[:-1]
-                    mask[start_indices] = False  # Exclude start points.
-                ax.plot(
-                    points[:, 0][mask], points[:, 1][mask], "o", c=point_color, alpha=line_alpha)
-
-                if start_point_color is not None:
-                    ax.plot(points[:, 0][start_indices], points[:, 1][start_indices], "o",
-                            c=start_point_color, alpha=line_alpha)
-
-    def lines(self, lines, line_type, ax=0, color="C0", alpha=1.0, linewidth=1, point_color="C0",
-              start_point_color="red", arrow_size=0.1):
-        super().lines(lines, line_type, ax, color, alpha, linewidth)
-
-        if arrow_size == 0.0 and point_color is None:
-            return
-
-        ax = self._get_ax(ax)
-
-        if line_type == LineType.Separate:
-            all_lines = lines
-        elif line_type == LineType.SeparateCode:
-            all_lines = lines[0]
-        elif line_type == LineType.ChunkCombinedCode:
-            all_lines = []
-            for points, codes in zip(*lines):
-                if points is not None:
-                    offsets = mpl_codes_to_offsets(codes)
-                    for i in range(len(offsets)-1):
-                        all_lines.append(points[offsets[i]:offsets[i+1]])
-        elif line_type == LineType.ChunkCombinedOffset:
-            all_lines = []
-            for points, offsets in zip(*lines):
-                if points is not None:
-                    for i in range(len(offsets)-1):
-                        all_lines.append(points[offsets[i]:offsets[i+1]])
-        else:
-            raise RuntimeError(f"Rendering LineType {line_type} not implemented")
-
-        if arrow_size > 0.0:
-            for line in all_lines:
-                for i in range(len(line)-1):
-                    self._arrow(ax, line[i], line[i+1], color, alpha, arrow_size)
-
-        if point_color is not None:
-            for line in all_lines:
-                start_index = 0
-                end_index = len(line)
-                if start_point_color is not None:
-                    ax.plot(line[0, 0], line[0, 1], "o", c=start_point_color, alpha=alpha)
-                    start_index = 1
-                    if line[0][0] == line[-1][0] and line[0][1] == line[-1][1]:
-                        end_index -= 1
-                ax.plot(line[start_index:end_index, 0], line[start_index:end_index, 1], "o",
-                        c=color, alpha=alpha)
-
-    def point_numbers(self, x, y, z, ax=0, color="red"):
-        ax = self._get_ax(ax)
-        x, y = self._grid_as_2d(x, y)
-        z = np.asarray(z)
-        ny, nx = z.shape
-        for j in range(ny):
-            for i in range(nx):
-                quad = i + j*nx
-                ax.text(x[j, i], y[j, i], str(quad), ha="right", va="top", color=color,
-                        clip_on=True)
-
-    def quad_numbers(self, x, y, z, ax=0, color="blue"):
-        ax = self._get_ax(ax)
-        x, y = self._grid_as_2d(x, y)
-        z = np.asarray(z)
-        ny, nx = z.shape
-        for j in range(1, ny):
-            for i in range(1, nx):
-                quad = i + j*nx
-                xmid = x[j-1:j+1, i-1:i+1].mean()
-                ymid = y[j-1:j+1, i-1:i+1].mean()
-                ax.text(xmid, ymid, str(quad), ha="center", va="center", color=color, clip_on=True)
-
-    def z_levels(self, x, y, z, lower_level, upper_level=None, ax=0, color="green"):
-        ax = self._get_ax(ax)
-        x, y = self._grid_as_2d(x, y)
-        z = np.asarray(z)
-        ny, nx = z.shape
-        for j in range(ny):
-            for i in range(nx):
-                zz = z[j, i]
-                if upper_level is not None and zz > upper_level:
-                    z_level = 2
-                elif zz > lower_level:
-                    z_level = 1
-                else:
-                    z_level = 0
-                ax.text(x[j, i], y[j, i], z_level, ha="left", va="bottom", color=color,
-                        clip_on=True)
diff --git a/spaces/aseifert/ExplaiNER/src/subpages/__init__.py b/spaces/aseifert/ExplaiNER/src/subpages/__init__.py
deleted file mode 100644
index 4b0abd20176b8d48771e05c7eefceb12d2df6cc2..0000000000000000000000000000000000000000
--- a/spaces/aseifert/ExplaiNER/src/subpages/__init__.py
+++ /dev/null
@@ -1,14 +0,0 @@
-from src.subpages.attention import AttentionPage
-from src.subpages.debug import DebugPage
-from src.subpages.find_duplicates import FindDuplicatesPage
-from src.subpages.hidden_states import HiddenStatesPage
-from src.subpages.home import HomePage
-from src.subpages.inspect import InspectPage
-from src.subpages.losses import LossesPage
-from src.subpages.lossy_samples import LossySamplesPage
-from src.subpages.metrics import MetricsPage
-from src.subpages.misclassified import MisclassifiedPage
-from src.subpages.page import Context, Page
-from src.subpages.probing import ProbingPage
-from src.subpages.random_samples import RandomSamplesPage
-from src.subpages.raw_data import RawDataPage
diff --git a/spaces/at2507/SM_NLP_RecoSys/Data/Mentor_interviews/Naeim Bahrami.html b/spaces/at2507/SM_NLP_RecoSys/Data/Mentor_interviews/Naeim Bahrami.html
deleted file mode 100644
index 5e2c9b72edcea901d80fd781125e410565f10618..0000000000000000000000000000000000000000
--- a/spaces/at2507/SM_NLP_RecoSys/Data/Mentor_interviews/Naeim Bahrami.html	
+++ /dev/null
@@ -1,136 +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>Naeim Bahrami</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">Naeim Bahrami</h1>
-
-<div class="formatted_content">
-  <div><a target="_blank" rel="noreferrer" class="autolinked" data-behavior="truncate" href="https://www.linkedin.com/in/naeim-bahrami/">https://www.linkedin.com/in/naeim-bahrami/</a><br><br></div><blockquote>I have been working on different aspects of machine learning and computer vision, specifically in the medical field. My expertise is AI and Data Science in the medical sciences and I believe there is a lack of skilled scientists and engineers in this field who have both 1) a strong ML/Data Science background and 2) some domain knowledge. Mentoring more high skilled scientists and engineers within the field will help eligible candidates to get familiar with both sides and be ready to get hire by the medical device and pharmaceutical companies. &nbsp;<br><br>Currently, I am a Lead Artificial Scientist - Imaging at GE Healthcare and I am developing many new methods and models that are in the product and helping our society. I am leading a handful of projects and mentoring junior scientists within my team.&nbsp;</blockquote><div><br><br><strong>How did you hear about SM?</strong></div><ul><li>Talking to a friend of mine that mentioned SharpestMinds (did not sign up, but had heard good things about it)</li><li>Did a deep dive and applies</li></ul><div><br><strong>Mentorship exp</strong></div><ul><li>15 years in ML/CV</li><li>mentored folks in grad school</li><li>took a DS bootcamp (insight) 5 years ago and mentor there&nbsp;</li><li>mentors at his company as a lead DS</li><li>officially teaching at University San Diego</li></ul><div><br></div><div><br><strong>What do beginners need and how can you help?</strong></div><ul><li>Finding what they're good at and what they want<ul><li>match those two things</li><li>get organized</li><li>get some hands-on experience</li></ul></li><li>Clean up their resumes</li><li>See where they see themselves and build upon that</li><li>Learn where they are and what they are good at</li></ul><div><br></div><div><strong>Role of a mentor?</strong></div><ul><li>spectrum of hands-on teaching to career coach + life coaching</li><li>depends on the level of the person and how serious they are</li><li>sometimes spend a lot of time going through resume etc, some need less hands-on</li><li>understand where their strengths lie&nbsp;</li><li>Providing confirmation from someone senior</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 about SM:</strong></div><ul><li>How, in general, does it work?</li><li>Monthly payments or a lump sum?</li><li>do the employers get involved?</li><li>Do I have to actively search?</li><li>Is there anyone to help me in the first few mentorships?</li><li>Stats/info on how mentors can be more successful?</li><li>Is there a ranking of mentors?</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><br></div><div></div>
-</div>
-
-    </div>
-  
-
-</body></html>
\ No newline at end of file
diff --git a/spaces/augmentedimaginationhackathon/paperstocode/README.md b/spaces/augmentedimaginationhackathon/paperstocode/README.md
deleted file mode 100644
index 6a0485903d38bcafc0dc4a6a48dd9107a20306e3..0000000000000000000000000000000000000000
--- a/spaces/augmentedimaginationhackathon/paperstocode/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Papers to Code
-emoji: 🌖
-colorFrom: yellow
-colorTo: green
-sdk: streamlit
-sdk_version: 1.17.0
-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/HFSpaceStreamlitHeatmap/app.py b/spaces/awacke1/HFSpaceStreamlitHeatmap/app.py
deleted file mode 100644
index 478d3bc444647208ee41d0ac8fb97a7a72c7b7ee..0000000000000000000000000000000000000000
--- a/spaces/awacke1/HFSpaceStreamlitHeatmap/app.py
+++ /dev/null
@@ -1,74 +0,0 @@
-import streamlit as st
-import nltk
-from transformers import pipeline
-from sentence_transformers import SentenceTransformer
-from scipy.spatial.distance import cosine
-import numpy as np
-import seaborn as sns
-import matplotlib.pyplot as plt
-from sklearn.cluster import KMeans
-import tensorflow as tf
-import tensorflow_hub as hub
-
-
-def cluster_examples(messages, embed, nc=3):
-    km = KMeans(
-    n_clusters=nc, init='random',
-    n_init=10, max_iter=300, 
-    tol=1e-04, random_state=0
-    )
-    km = km.fit_predict(embed)
-    for n in range(nc):
-        idxs = [i for i in range(len(km)) if km[i] == n]
-        ms = [messages[i] for i in idxs]
-        st.markdown ("CLUSTER : %d"%n)
-        for m in ms:
-            st.markdown (m)
-
-
-def plot_heatmap(labels, heatmap, rotation=90):
-  sns.set(font_scale=1.2)
-  fig, ax = plt.subplots()
-  g = sns.heatmap(
-      heatmap,
-      xticklabels=labels,
-      yticklabels=labels,
-      vmin=-1,
-      vmax=1,
-      cmap="coolwarm")
-  g.set_xticklabels(labels, rotation=rotation)
-  g.set_title("Textual Similarity")
-  st.pyplot(fig)
-  
-# Streamlit text boxes
-text = st.text_area('Enter sentences:', value="Behavior right this is a kind of Heisenberg uncertainty principle situation if I told you, then you behave differently. What would be the impressive thing is you have talked about winning a nobel prize in a system winning a nobel prize. Adjusting it and then making your own.  That is when I fell in love with computers.  I realized that they were a very magical device.  Can go to sleep come back the next day and it is solved.  You know that feels magical to me.")
-
-nc = st.slider('Select a number of clusters:', min_value=1, max_value=15, value=3)
-
-model_type = st.radio("Choose model:", ('Sentence Transformer', 'Universal Sentence Encoder'), index=0)
-
-# Model setup
-if model_type == "Sentence Transformer":
-    model = SentenceTransformer('paraphrase-distilroberta-base-v1')
-elif model_type == "Universal Sentence Encoder":
-    model_url = "https://tfhub.dev/google/universal-sentence-encoder-large/5"
-    model = hub.load(model_url)
-
-nltk.download('punkt')
-
-# Run model
-if text:
-    sentences = nltk.tokenize.sent_tokenize(text)
-    if model_type == "Sentence Transformer":
-        embed = model.encode(sentences)
-    elif model_type == "Universal Sentence Encoder":
-        embed = model(sentences).numpy()
-    sim = np.zeros([len(embed), len(embed)])
-    for i,em in enumerate(embed):
-        for j,ea in enumerate(embed):
-            sim[i][j] = 1.0-cosine(em,ea)
-    st.subheader("Similarity Heatmap")
-    plot_heatmap(sentences, sim)
-    st.subheader("Results from K-Means Clustering")
-    cluster_examples(sentences, embed, nc)
-    
\ No newline at end of file
diff --git a/spaces/awacke1/SelfCareDimensionsPositiveReframing/app.py b/spaces/awacke1/SelfCareDimensionsPositiveReframing/app.py
deleted file mode 100644
index 00bc4cf55d18994eec60eaa322e796d5bd56f9fc..0000000000000000000000000000000000000000
--- a/spaces/awacke1/SelfCareDimensionsPositiveReframing/app.py
+++ /dev/null
@@ -1,149 +0,0 @@
-import streamlit as st
-import graphviz as gv
-
-
-st.set_page_config(page_title="8 Dimensions of Self Care", page_icon=":heart:")
-st.title("🌟 Self Care 🌟")
-st.markdown("#### Take Charge of Your Personal Health")
-
-st.markdown("""
-
-| 🧘‍♂️Self Care💪🏽             | Tip 1                     | Tip 2                          | Tip 3                    | Tip 4                    |
-|---------------------------------|---------------------------|--------------------------------|--------------------------|--------------------------|
-| 🌱 Emotional - How we feel      | 1. Practice gratitude    | 2. Focus on the present moment | 3. Use positive affirmations | 4. Surround yourself with positivity |
-| 🙏 Spiritual - Our beliefs      | 1. Engage in regular meditation or prayer | 2. Connect with nature | 3. Practice forgiveness and self-compassion | 4. Attend spiritual gatherings or join a spiritual community |
-| 💰 Financial - How we manage money | 1. Create a realistic budget | 2. Set achievable financial goals | 3. Prioritize paying off debts | 4. Invest in your future |
-| 💡 Cognitive - How we think     | 1. Challenge negative thoughts | 2. Practice mindfulness | 3. Set achievable personal goals | 4. Engage in lifelong learning |
-| 🎯 Aptitudinal - Our abilities  | 1. Identify and develop your strengths | 2. Set realistic expectations | 3. Embrace failure as a learning opportunity | 4. Seek out opportunities for personal growth |
-| 🤝 Relational - Our connections | 1. Nurture positive relationships | 2. Communicate openly and honestly | 3. Practice empathy and active listening | 4. Set healthy boundaries |
-| 🌍 Environmental - Our surroundings | 1. Create a calming and organized living space | 2. Spend time in nature | 3. Reduce exposure to environmental stressors | 4. Practice sustainable living |
-| 🏃‍♂️ Physical - Our bodies    | 1. Engage in regular physical activity | 2. Prioritize sleep and relaxation | 3. Maintain a balanced and nutritious diet | 4. Limit alcohol and drug consumption |
-
-""")
-
-
-st.markdown("""
-
-# Positive Reframing Strategies for Self Care
-
-## 🌱 Emotional - How we feel
-1. Practice gratitude
-2. Focus on the present moment
-3. Use positive affirmations
-4. Surround yourself with positivity
-
-## 🙏 Spiritual - Our beliefs
-1. Engage in regular meditation or prayer
-2. Connect with nature
-3. Practice forgiveness and self-compassion
-4. Attend spiritual gatherings or join a spiritual community
-
-## 💰 Financial - How we manage money
-1. Create a realistic budget
-2. Set achievable financial goals
-3. Prioritize paying off debts
-4. Invest in your future
-
-## 💡 Cognitive - How we think
-1. Challenge negative thoughts
-2. Practice mindfulness
-3. Set achievable personal goals
-4. Engage in lifelong learning
-
-## 🎯 Aptitudinal - Our abilities
-1. Identify and develop your strengths
-2. Set realistic expectations
-3. Embrace failure as a learning opportunity
-4. Seek out opportunities for personal growth
-
-## 🤝 Relational - Our connections
-1. Nurture positive relationships
-2. Communicate openly and honestly
-3. Practice empathy and active listening
-4. Set healthy boundaries
-
-## 🌍 Environmental - Our surroundings
-1. Create a calming and organized living space
-2. Spend time in nature
-3. Reduce exposure to environmental stressors
-4. Practice sustainable living
-
-## 🏃‍♂️ Physical - Our bodies
-1. Engage in regular physical activity
-2. Prioritize sleep and relaxation
-3. Maintain a balanced and nutritious diet
-4. Limit alcohol and drug consumption
-
-""")
-
-
-
-st.markdown("""
-# Positive Reframing Strategies for Self Care
-
-| 🌱 Emotional - How we feel      |                           |                         |                          |                          |
-|---------------------------------|---------------------------|-------------------------|--------------------------|--------------------------|
-|1. Practice gratitude            |2. Focus on the present moment |3. Use positive affirmations |4. Surround yourself with positivity |
-
-
-| 🙏 Spiritual - Our beliefs      |                           |                         |                          |                          |
-|---------------------------------|---------------------------|-------------------------|--------------------------|--------------------------|
-|1. Engage in regular meditation or prayer |2. Connect with nature |3. Practice forgiveness and self-compassion |4. Attend spiritual gatherings or join a spiritual community |
-
-
-| 💰 Financial - How we manage money |                       |                         |                          |                          |
-|---------------------------------|---------------------------|-------------------------|--------------------------|--------------------------|
-|1. Create a realistic budget     |2. Set achievable financial goals |3. Prioritize paying off debts |4. Invest in your future |
-
-
-| 💡 Cognitive - How we think     |                           |                         |                          |                          |
-|---------------------------------|---------------------------|-------------------------|--------------------------|--------------------------|
-|1. Challenge negative thoughts   |2. Practice mindfulness |3. Set achievable personal goals |4. Engage in lifelong learning |
-
-
-| 🎯 Aptitudinal - Our abilities  |                           |                         |                          |                          |
-|---------------------------------|---------------------------|-------------------------|--------------------------|--------------------------|
-|1. Identify and develop your strengths |2. Set realistic expectations |3. Embrace failure as a learning opportunity |4. Seek out opportunities for personal growth |
-
-
-| 🤝 Relational - Our connections |                           |                         |                          |                          |
-|---------------------------------|---------------------------|-------------------------|--------------------------|--------------------------|
-|1. Nurture positive relationships |2. Communicate openly and honestly |3. Practice empathy and active listening |4. Set healthy boundaries |
-
-
-| 🌍 Environmental - Our surroundings |                     |                         |                          |                          |
-|---------------------------------|---------------------------|-------------------------|--------------------------|--------------------------|
-|1. Create a calming and organized living space |2. Spend time in nature |3. Reduce exposure to environmental stressors |4. Practice sustainable living |
-
-
-| 🏃‍♂️ Physical - Our bodies    |                           |                         |                          |                          |
-|---------------------------------|---------------------------|-------------------------|--------------------------|--------------------------|
-|1. Engage in regular physical activity |2. Prioritize sleep and relaxation |3. Maintain a balanced and nutritious diet |4. Limit alcohol and drug consumption |
-
-""")
-
-
-
-
-g = gv.Graph(format='svg', engine='circo')
-
-g.node('B', label="8 Dimensions of Self Care", shape="ellipse", fontsize="20", style="filled", fillcolor="lightblue")
-
-dimensions = [
-    ('A', "🌱 Emotional\nHow we feel"),
-    ('C', "🙏 Spiritual\nOur beliefs"),
-    ('D', "💰 Financial\nHow we manage money"),
-    ('E', "💡 Cognitive\nHow we think"),
-    ('F', "🎯 Aptitudinal\nOur abilities"),
-    ('G', "🤝 Relational\nOur connections"),
-    ('H', "🌍 Environmental\nOur surroundings"),
-    ('I', "🏃‍♂️ Physical\nOur bodies"),
-]
-
-for node, label in dimensions:
-    g.node(node, label=label, shape="ellipse", fontsize="16", style="filled", fillcolor="lightgoldenrodyellow")
-
-for node, _ in dimensions:
-    g.edge('B', node)
-
-st.graphviz_chart(g)
diff --git a/spaces/aziki/anime-remove-background/README.md b/spaces/aziki/anime-remove-background/README.md
deleted file mode 100644
index 1ba3cb5ea0e994e246d57b7d62b8aa5a6331901c..0000000000000000000000000000000000000000
--- a/spaces/aziki/anime-remove-background/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: Anime Remove Background
-emoji: 🪄🖼️
-colorFrom: indigo
-colorTo: pink
-sdk: gradio
-sdk_version: 3.1.4
-app_file: app.py
-pinned: false
-license: apache-2.0
-duplicated_from: skytnt/anime-remove-background
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/banana-projects/web3d/index.html b/spaces/banana-projects/web3d/index.html
deleted file mode 100644
index 1b579a6af7efff0f9472049bf7b70d7b6ba2c5a1..0000000000000000000000000000000000000000
--- a/spaces/banana-projects/web3d/index.html
+++ /dev/null
@@ -1,35 +0,0 @@
-<!DOCTYPE html>
-<html>
-	<head>
-		<meta charset=utf-8>
-		<title>Evolution of intelligence at Hugging Face</title>
-		<style>
-			body { margin: 0; }
-			canvas { width: 100%; height: 100% }
-			div.hf-info {
-				position: absolute;
-				bottom: 6px; left: 0; right: 0;
-				text-align: center;
-				font-size: 32px;
-				font-family: 'Inconsolata', monospace;
-			}
-		</style>
-		<link href="https://fonts.googleapis.com/css?family=Inconsolata" rel="stylesheet">
-	</head>
-	<body>
-		<div class="hf-info">➡️ Evolution of intelligence at Hugging Face ➡️</div>
-		<script src="node_modules/@tweenjs/tween.js/src/Tween.js"></script>
-		<script src="node_modules/three/build/three.js"></script>
-		<script src="node_modules/three/examples/js/libs/stats.min.js"></script>
-		<script src="node_modules/three/examples/js/controls/OrbitControls.js"></script>
-		<script src="node_modules/three/examples/js/loaders/MTLLoader.js"></script>
-		<script src="node_modules/three/examples/js/loaders/OBJLoader.js"></script>
-		<script src="node_modules/three/examples/js/loaders/GLTFLoader.js"></script>
-		<script src="node_modules/three/examples/js/libs/inflate.min.js"></script>
-		<script src="node_modules/three/examples/js/loaders/FBXLoader.js"></script>
-		<script src="node_modules/three/examples/js/loaders/ColladaLoader.js"></script>
-		<script src="dist/lib/Log.js"></script>
-		<script src="dist/lib/Utils.js"></script>
-		<script src="dist/post/index.js"></script>
-	</body>
-</html>
diff --git a/spaces/banana-projects/web3d/node_modules/three/examples/js/loaders/MTLLoader.js b/spaces/banana-projects/web3d/node_modules/three/examples/js/loaders/MTLLoader.js
deleted file mode 100644
index 50de5b93665ef5503862a467503be39e3fc694cc..0000000000000000000000000000000000000000
--- a/spaces/banana-projects/web3d/node_modules/three/examples/js/loaders/MTLLoader.js
+++ /dev/null
@@ -1,583 +0,0 @@
-/**
- * Loads a Wavefront .mtl file specifying materials
- *
- * @author angelxuanchang
- */
-
-THREE.MTLLoader = function ( manager ) {
-
-	this.manager = ( manager !== undefined ) ? manager : THREE.DefaultLoadingManager;
-
-};
-
-THREE.MTLLoader.prototype = {
-
-	constructor: THREE.MTLLoader,
-
-	/**
-	 * Loads and parses a MTL asset from a URL.
-	 *
-	 * @param {String} url - URL to the MTL file.
-	 * @param {Function} [onLoad] - Callback invoked with the loaded object.
-	 * @param {Function} [onProgress] - Callback for download progress.
-	 * @param {Function} [onError] - Callback for download errors.
-	 *
-	 * @see setPath setResourcePath
-	 *
-	 * @note In order for relative texture references to resolve correctly
-	 * you must call setResourcePath() explicitly prior to load.
-	 */
-	load: function ( url, onLoad, onProgress, onError ) {
-
-		var scope = this;
-
-		var path = ( this.path === undefined ) ? THREE.LoaderUtils.extractUrlBase( url ) : this.path;
-
-		var loader = new THREE.FileLoader( this.manager );
-		loader.setPath( this.path );
-		loader.load( url, function ( text ) {
-
-			onLoad( scope.parse( text, path ) );
-
-		}, onProgress, onError );
-
-	},
-
-	/**
-	 * Set base path for resolving references.
-	 * If set this path will be prepended to each loaded and found reference.
-	 *
-	 * @see setResourcePath
-	 * @param {String} path
-	 * @return {THREE.MTLLoader}
-	 *
-	 * @example
-	 *     mtlLoader.setPath( 'assets/obj/' );
-	 *     mtlLoader.load( 'my.mtl', ... );
-	 */
-	setPath: function ( path ) {
-
-		this.path = path;
-		return this;
-
-	},
-
-	/**
-	 * Set base path for additional resources like textures.
-	 *
-	 * @see setPath
-	 * @param {String} path
-	 * @return {THREE.MTLLoader}
-	 *
-	 * @example
-	 *     mtlLoader.setPath( 'assets/obj/' );
-	 *     mtlLoader.setResourcePath( 'assets/textures/' );
-	 *     mtlLoader.load( 'my.mtl', ... );
-	 */
-	setResourcePath: function ( path ) {
-
-		this.resourcePath = path;
-		return this;
-
-	},
-
-	setTexturePath: function ( path ) {
-
-		console.warn( 'THREE.MTLLoader: .setTexturePath() has been renamed to .setResourcePath().' );
-		return this.setResourcePath( path );
-
-	},
-
-	setCrossOrigin: function ( value ) {
-
-		this.crossOrigin = value;
-		return this;
-
-	},
-
-	setMaterialOptions: function ( value ) {
-
-		this.materialOptions = value;
-		return this;
-
-	},
-
-	/**
-	 * Parses a MTL file.
-	 *
-	 * @param {String} text - Content of MTL file
-	 * @return {THREE.MTLLoader.MaterialCreator}
-	 *
-	 * @see setPath setResourcePath
-	 *
-	 * @note In order for relative texture references to resolve correctly
-	 * you must call setResourcePath() explicitly prior to parse.
-	 */
-	parse: function ( text, path ) {
-
-		var lines = text.split( '\n' );
-		var info = {};
-		var delimiter_pattern = /\s+/;
-		var materialsInfo = {};
-
-		for ( var i = 0; i < lines.length; i ++ ) {
-
-			var line = lines[ i ];
-			line = line.trim();
-
-			if ( line.length === 0 || line.charAt( 0 ) === '#' ) {
-
-				// Blank line or comment ignore
-				continue;
-
-			}
-
-			var pos = line.indexOf( ' ' );
-
-			var key = ( pos >= 0 ) ? line.substring( 0, pos ) : line;
-			key = key.toLowerCase();
-
-			var value = ( pos >= 0 ) ? line.substring( pos + 1 ) : '';
-			value = value.trim();
-
-			if ( key === 'newmtl' ) {
-
-				// New material
-
-				info = { name: value };
-				materialsInfo[ value ] = info;
-
-			} else {
-
-				if ( key === 'ka' || key === 'kd' || key === 'ks' || key ==='ke' ) {
-
-					var ss = value.split( delimiter_pattern, 3 );
-					info[ key ] = [ parseFloat( ss[ 0 ] ), parseFloat( ss[ 1 ] ), parseFloat( ss[ 2 ] ) ];
-
-				} else {
-
-					info[ key ] = value;
-
-				}
-
-			}
-
-		}
-
-		var materialCreator = new THREE.MTLLoader.MaterialCreator( this.resourcePath || path, this.materialOptions );
-		materialCreator.setCrossOrigin( this.crossOrigin );
-		materialCreator.setManager( this.manager );
-		materialCreator.setMaterials( materialsInfo );
-		return materialCreator;
-
-	}
-
-};
-
-/**
- * Create a new THREE-MTLLoader.MaterialCreator
- * @param baseUrl - Url relative to which textures are loaded
- * @param options - Set of options on how to construct the materials
- *                  side: Which side to apply the material
- *                        THREE.FrontSide (default), THREE.BackSide, THREE.DoubleSide
- *                  wrap: What type of wrapping to apply for textures
- *                        THREE.RepeatWrapping (default), THREE.ClampToEdgeWrapping, THREE.MirroredRepeatWrapping
- *                  normalizeRGB: RGBs need to be normalized to 0-1 from 0-255
- *                                Default: false, assumed to be already normalized
- *                  ignoreZeroRGBs: Ignore values of RGBs (Ka,Kd,Ks) that are all 0's
- *                                  Default: false
- * @constructor
- */
-
-THREE.MTLLoader.MaterialCreator = function ( baseUrl, options ) {
-
-	this.baseUrl = baseUrl || '';
-	this.options = options;
-	this.materialsInfo = {};
-	this.materials = {};
-	this.materialsArray = [];
-	this.nameLookup = {};
-
-	this.side = ( this.options && this.options.side ) ? this.options.side : THREE.FrontSide;
-	this.wrap = ( this.options && this.options.wrap ) ? this.options.wrap : THREE.RepeatWrapping;
-
-};
-
-THREE.MTLLoader.MaterialCreator.prototype = {
-
-	constructor: THREE.MTLLoader.MaterialCreator,
-
-	crossOrigin: 'anonymous',
-
-	setCrossOrigin: function ( value ) {
-
-		this.crossOrigin = value;
-		return this;
-
-	},
-
-	setManager: function ( value ) {
-
-		this.manager = value;
-
-	},
-
-	setMaterials: function ( materialsInfo ) {
-
-		this.materialsInfo = this.convert( materialsInfo );
-		this.materials = {};
-		this.materialsArray = [];
-		this.nameLookup = {};
-
-	},
-
-	convert: function ( materialsInfo ) {
-
-		if ( ! this.options ) return materialsInfo;
-
-		var converted = {};
-
-		for ( var mn in materialsInfo ) {
-
-			// Convert materials info into normalized form based on options
-
-			var mat = materialsInfo[ mn ];
-
-			var covmat = {};
-
-			converted[ mn ] = covmat;
-
-			for ( var prop in mat ) {
-
-				var save = true;
-				var value = mat[ prop ];
-				var lprop = prop.toLowerCase();
-
-				switch ( lprop ) {
-
-					case 'kd':
-					case 'ka':
-					case 'ks':
-
-						// Diffuse color (color under white light) using RGB values
-
-						if ( this.options && this.options.normalizeRGB ) {
-
-							value = [ value[ 0 ] / 255, value[ 1 ] / 255, value[ 2 ] / 255 ];
-
-						}
-
-						if ( this.options && this.options.ignoreZeroRGBs ) {
-
-							if ( value[ 0 ] === 0 && value[ 1 ] === 0 && value[ 2 ] === 0 ) {
-
-								// ignore
-
-								save = false;
-
-							}
-
-						}
-
-						break;
-
-					default:
-
-						break;
-
-				}
-
-				if ( save ) {
-
-					covmat[ lprop ] = value;
-
-				}
-
-			}
-
-		}
-
-		return converted;
-
-	},
-
-	preload: function () {
-
-		for ( var mn in this.materialsInfo ) {
-
-			this.create( mn );
-
-		}
-
-	},
-
-	getIndex: function ( materialName ) {
-
-		return this.nameLookup[ materialName ];
-
-	},
-
-	getAsArray: function () {
-
-		var index = 0;
-
-		for ( var mn in this.materialsInfo ) {
-
-			this.materialsArray[ index ] = this.create( mn );
-			this.nameLookup[ mn ] = index;
-			index ++;
-
-		}
-
-		return this.materialsArray;
-
-	},
-
-	create: function ( materialName ) {
-
-		if ( this.materials[ materialName ] === undefined ) {
-
-			this.createMaterial_( materialName );
-
-		}
-
-		return this.materials[ materialName ];
-
-	},
-
-	createMaterial_: function ( materialName ) {
-
-		// Create material
-
-		var scope = this;
-		var mat = this.materialsInfo[ materialName ];
-		var params = {
-
-			name: materialName,
-			side: this.side
-
-		};
-
-		function resolveURL( baseUrl, url ) {
-
-			if ( typeof url !== 'string' || url === '' )
-				return '';
-
-			// Absolute URL
-			if ( /^https?:\/\//i.test( url ) ) return url;
-
-			return baseUrl + url;
-
-		}
-
-		function setMapForType( mapType, value ) {
-
-			if ( params[ mapType ] ) return; // Keep the first encountered texture
-
-			var texParams = scope.getTextureParams( value, params );
-			var map = scope.loadTexture( resolveURL( scope.baseUrl, texParams.url ) );
-
-			map.repeat.copy( texParams.scale );
-			map.offset.copy( texParams.offset );
-
-			map.wrapS = scope.wrap;
-			map.wrapT = scope.wrap;
-
-			params[ mapType ] = map;
-
-		}
-
-		for ( var prop in mat ) {
-
-			var value = mat[ prop ];
-			var n;
-
-			if ( value === '' ) continue;
-
-			switch ( prop.toLowerCase() ) {
-
-				// Ns is material specular exponent
-
-				case 'kd':
-
-					// Diffuse color (color under white light) using RGB values
-
-					params.color = new THREE.Color().fromArray( value );
-
-					break;
-
-				case 'ks':
-
-					// Specular color (color when light is reflected from shiny surface) using RGB values
-					params.specular = new THREE.Color().fromArray( value );
-
-					break;
-
-				case 'ke':
-
-					// Emissive using RGB values
-					params.emissive = new THREE.Color().fromArray( value );
-
-					break;
-
-				case 'map_kd':
-
-					// Diffuse texture map
-
-					setMapForType( "map", value );
-
-					break;
-
-				case 'map_ks':
-
-					// Specular map
-
-					setMapForType( "specularMap", value );
-
-					break;
-
-				case 'map_ke':
-
-					// Emissive map
-
-					setMapForType( "emissiveMap", value );
-
-					break;
-
-				case 'norm':
-
-					setMapForType( "normalMap", value );
-
-					break;
-
-				case 'map_bump':
-				case 'bump':
-
-					// Bump texture map
-
-					setMapForType( "bumpMap", value );
-
-					break;
-
-				case 'map_d':
-
-					// Alpha map
-
-					setMapForType( "alphaMap", value );
-					params.transparent = true;
-
-					break;
-
-				case 'ns':
-
-					// The specular exponent (defines the focus of the specular highlight)
-					// A high exponent results in a tight, concentrated highlight. Ns values normally range from 0 to 1000.
-
-					params.shininess = parseFloat( value );
-
-					break;
-
-				case 'd':
-					n = parseFloat( value );
-
-					if ( n < 1 ) {
-
-						params.opacity = n;
-						params.transparent = true;
-
-					}
-
-					break;
-
-				case 'tr':
-					n = parseFloat( value );
-
-					if ( this.options && this.options.invertTrProperty ) n = 1 - n;
-
-					if ( n > 0 ) {
-
-						params.opacity = 1 - n;
-						params.transparent = true;
-
-					}
-
-					break;
-
-				default:
-					break;
-
-			}
-
-		}
-
-		this.materials[ materialName ] = new THREE.MeshPhongMaterial( params );
-		return this.materials[ materialName ];
-
-	},
-
-	getTextureParams: function ( value, matParams ) {
-
-		var texParams = {
-
-			scale: new THREE.Vector2( 1, 1 ),
-			offset: new THREE.Vector2( 0, 0 )
-
-		 };
-
-		var items = value.split( /\s+/ );
-		var pos;
-
-		pos = items.indexOf( '-bm' );
-
-		if ( pos >= 0 ) {
-
-			matParams.bumpScale = parseFloat( items[ pos + 1 ] );
-			items.splice( pos, 2 );
-
-		}
-
-		pos = items.indexOf( '-s' );
-
-		if ( pos >= 0 ) {
-
-			texParams.scale.set( parseFloat( items[ pos + 1 ] ), parseFloat( items[ pos + 2 ] ) );
-			items.splice( pos, 4 ); // we expect 3 parameters here!
-
-		}
-
-		pos = items.indexOf( '-o' );
-
-		if ( pos >= 0 ) {
-
-			texParams.offset.set( parseFloat( items[ pos + 1 ] ), parseFloat( items[ pos + 2 ] ) );
-			items.splice( pos, 4 ); // we expect 3 parameters here!
-
-		}
-
-		texParams.url = items.join( ' ' ).trim();
-		return texParams;
-
-	},
-
-	loadTexture: function ( url, mapping, onLoad, onProgress, onError ) {
-
-		var texture;
-		var loader = THREE.Loader.Handlers.get( url );
-		var manager = ( this.manager !== undefined ) ? this.manager : THREE.DefaultLoadingManager;
-
-		if ( loader === null ) {
-
-			loader = new THREE.TextureLoader( manager );
-
-		}
-
-		if ( loader.setCrossOrigin ) loader.setCrossOrigin( this.crossOrigin );
-		texture = loader.load( url, onLoad, onProgress, onError );
-
-		if ( mapping !== undefined ) texture.mapping = mapping;
-
-		return texture;
-
-	}
-
-};
diff --git a/spaces/banana-projects/web3d/node_modules/three/src/renderers/shaders/ShaderLib/distanceRGBA_vert.glsl.js b/spaces/banana-projects/web3d/node_modules/three/src/renderers/shaders/ShaderLib/distanceRGBA_vert.glsl.js
deleted file mode 100644
index 6610c28cb54af172f40775b1465d2b275054b567..0000000000000000000000000000000000000000
--- a/spaces/banana-projects/web3d/node_modules/three/src/renderers/shaders/ShaderLib/distanceRGBA_vert.glsl.js
+++ /dev/null
@@ -1,38 +0,0 @@
-export default /* glsl */`
-#define DISTANCE
-
-varying vec3 vWorldPosition;
-
-#include <common>
-#include <uv_pars_vertex>
-#include <displacementmap_pars_vertex>
-#include <morphtarget_pars_vertex>
-#include <skinning_pars_vertex>
-#include <clipping_planes_pars_vertex>
-
-void main() {
-
-	#include <uv_vertex>
-
-	#include <skinbase_vertex>
-
-	#ifdef USE_DISPLACEMENTMAP
-
-		#include <beginnormal_vertex>
-		#include <morphnormal_vertex>
-		#include <skinnormal_vertex>
-
-	#endif
-
-	#include <begin_vertex>
-	#include <morphtarget_vertex>
-	#include <skinning_vertex>
-	#include <displacementmap_vertex>
-	#include <project_vertex>
-	#include <worldpos_vertex>
-	#include <clipping_planes_vertex>
-
-	vWorldPosition = worldPosition.xyz;
-
-}
-`;
diff --git a/spaces/beomi/KoRWKV-1.5B/README.md b/spaces/beomi/KoRWKV-1.5B/README.md
deleted file mode 100644
index f22c53034ebff36b059cc8dfb128d5c4223a489b..0000000000000000000000000000000000000000
--- a/spaces/beomi/KoRWKV-1.5B/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: 🤗 KoRWKV-1.5B 🔥Streaming🔥
-emoji: 💻
-colorFrom: yellow
-colorTo: purple
-sdk: gradio
-sdk_version: 3.29.0
-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/bharathraj-v/audio-content-analysis/app.py b/spaces/bharathraj-v/audio-content-analysis/app.py
deleted file mode 100644
index 76fc95628fef6c002b42490769179d64703ade5d..0000000000000000000000000000000000000000
--- a/spaces/bharathraj-v/audio-content-analysis/app.py
+++ /dev/null
@@ -1,64 +0,0 @@
-import nltk
-import librosa
-import torch
-import gradio as gr
-from textblob import Word
-from transformers import Wav2Vec2Tokenizer, Wav2Vec2ForCTC
-nltk.download("punkt")
-import warnings
-import eng_to_ipa as ipa
-warnings.filterwarnings("ignore")
-print(f"All libraries are installed!!")
-
-
-def correct(text):
-    return " ".join([Word(word).spellcheck()[0][0] for word in text.split()])
-
-def accuracy(text):
-    return str((sum([int(Word(word).spellcheck()[0][1]==1) for word in text.split()])/len(text.split()))*100)+"%"
-
-def stutter(text):
-  return sum([1 for word in [word for word in text.split() if Word(word).spellcheck()[0][1]!=1] if len(word)<4])+ sum([1 for word in text.split() if word in ['oh','uh','uhm','umm', 'ah','aa','aah']])
-
-
-#Loading the pre-trained model and the tokenizer
-model_name = "facebook/wav2vec2-base-960h"
-tokenizer = Wav2Vec2Tokenizer.from_pretrained(model_name)
-model = Wav2Vec2ForCTC.from_pretrained(model_name)
-
-def load_data(input_file):
-  speech, sample_rate = librosa.load(input_file)
-  print(speech)
-  print()
-  print(sample_rate)
-  if len(speech.shape) > 1: 
-        speech = speech[:,0] + speech[:,1]
-  if sample_rate !=16000:
-    speech = librosa.resample(speech, sample_rate,16000)
-    return speech
-    
-def correct_casing(input_sentence):
-    sentences = nltk.sent_tokenize(input_sentence)
-    return (' '.join([s.replace(s[0],s[0].capitalize(),1) for s in sentences]))
- 
-def asr_transcript(input_recording, input_file):
-  if input_recording:
-    speech = load_data(input_recording)
-  else:
-    speech = load_data(input_file)
-  input_values = tokenizer(speech, return_tensors="pt").input_values
-  logits = model(input_values).logits
-  predicted_ids = torch.argmax(logits, dim=-1)
-  transcription = tokenizer.decode(predicted_ids[0])
-  transcription = correct_casing(transcription.lower())
-  return (transcription, accuracy(transcription.lower()), ipa.convert(transcription.lower()), stutter(transcription.lower()))
-
-gr.Interface(asr_transcript,
-             inputs = [gr.inputs.Audio(source="microphone", type="filepath", optional=True, label="Speaker"),
-             gr.inputs.Audio(source="upload", type="filepath", optional=True, label="Speaker")],
-             outputs = [gr.outputs.Textbox(label="Transcription"),
-                        gr.outputs.Textbox(label="Pronounciation Accuracy"),
-                        gr.outputs.Textbox(label="Phonetics"),
-                        gr.outputs.Textbox(label="Approx. Stutter Count")],
-             title="Audio Content Analysis",
-             description = "Analyzing Grammatical Accuracy & Phonetics").launch()
\ No newline at end of file
diff --git a/spaces/binly/ChatGPT4/app.py b/spaces/binly/ChatGPT4/app.py
deleted file mode 100644
index 632f0ee79c2a44a19c299e5965101cad17293e69..0000000000000000000000000000000000000000
--- a/spaces/binly/ChatGPT4/app.py
+++ /dev/null
@@ -1,191 +0,0 @@
-import gradio as gr
-import os 
-import json 
-import requests
-
-#Streaming endpoint 
-API_URL = "https://api.openai.com/v1/chat/completions" #os.getenv("API_URL") + "/generate_stream"
-
-#Inferenec function
-def predict(openai_gpt4_key, system_msg, inputs, top_p, temperature, chat_counter, chatbot=[], history=[]):  
-
-    headers = {
-    "Content-Type": "application/json",
-    "Authorization": f"Bearer {openai_gpt4_key}"  #Users will provide their own 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">🔥GPT4 using Chat-Completions API & 🚀Gradio-Streaming</h1>"""
-#display message for themes feature
-theme_addon_msg = """<center>🌟 This Demo also introduces you to Gradio Themes. Discover more on Gradio website using our <a href="https://gradio.app/theming-guide/" target="_blank">Themeing-Guide🎨</a>! You can develop from scratch, modify an existing Gradio theme, and share your themes with community by uploading them to huggingface-hub easily using <code>theme.push_to_hub()</code>.</center>
-""" 
-
-#Using info to add additional information about System message in GPT4
-system_msg_info = """A conversation could begin with a system message to gently instruct the assistant. 
-System message helps set the behavior of the AI Assistant. For example, the assistant could be instructed with 'You are a helpful assistant.'"""
-
-#Modifying existing Gradio Theme
-theme = gr.themes.Soft(primary_hue="zinc", secondary_hue="green", neutral_hue="green",
-                      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">🔥This Huggingface Gradio Demo provides you access to GPT4 API with System Messages. Please note that you would be needing an OPENAI API key for GPT4 access🙌</h1>""")
-    gr.HTML(theme_addon_msg)
-    gr.HTML('''<center><a href="https://huggingface.co/spaces/ysharma/ChatGPT4?duplicate=true"><img src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>Duplicate the Space and run securely with your OpenAI API Key</center>''')
-
-    with gr.Column(elem_id = "col_container"):
-        #Users need to provide their own GPT4 API key, it is no longer provided by Huggingface 
-        with gr.Row():
-            openai_gpt4_key = gr.Textbox(label="OpenAI GPT4 Key", value="", type="password", placeholder="sk..", info = "You have to provide your own GPT4 keys for this app to function properly",)
-            with gr.Accordion(label="System message:", open=False):
-                system_msg = gr.Textbox(label="Instruct the AI Assistant to set its beaviour", info = system_msg_info, value="",placeholder="Type here..")
-                accordion_msg = gr.HTML(value="🚧 To set System message you will have to refresh the app", visible=False)
-                          
-        chatbot = gr.Chatbot(label='GPT4', 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, [openai_gpt4_key, system_msg, inputs, top_p, temperature, chat_counter, chatbot, state], [chatbot, state, chat_counter, server_status_code],)  #openai_api_key
-    b1.click( predict, [openai_gpt4_key, 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])
-
-    #Examples 
-    with gr.Accordion(label="Examples for System message:", open=False):
-        gr.Examples(
-                examples = [["""You are an AI programming assistant.
-        
-                - Follow the user's requirements carefully and to the letter.
-                - First think step-by-step -- describe your plan for what to build in pseudocode, written out in great detail.
-                - Then output the code in a single code block.
-                - Minimize any other prose."""], ["""You are ComedianGPT who is a helpful assistant. You answer everything with a joke and witty replies."""],
-                ["You are ChefGPT, a helpful assistant who answers questions with culinary expertise and a pinch of humor."],
-                ["You are FitnessGuruGPT, a fitness expert who shares workout tips and motivation with a playful twist."],
-                ["You are SciFiGPT, an AI assistant who discusses science fiction topics with a blend of knowledge and wit."],
-                ["You are PhilosopherGPT, a thoughtful assistant who responds to inquiries with philosophical insights and a touch of humor."],
-                ["You are EcoWarriorGPT, a helpful assistant who shares environment-friendly advice with a lighthearted approach."],
-                ["You are MusicMaestroGPT, a knowledgeable AI who discusses music and its history with a mix of facts and playful banter."],
-                ["You are SportsFanGPT, an enthusiastic assistant who talks about sports and shares amusing anecdotes."],
-                ["You are TechWhizGPT, a tech-savvy AI who can help users troubleshoot issues and answer questions with a dash of humor."],
-                ["You are FashionistaGPT, an AI fashion expert who shares style advice and trends with a sprinkle of wit."],
-                ["You are ArtConnoisseurGPT, an AI assistant who discusses art and its history with a blend of knowledge and playful commentary."],
-                ["You are a helpful assistant that provides detailed and accurate information."],
-                ["You are an assistant that speaks like Shakespeare."],
-                ["You are a friendly assistant who uses casual language and humor."],
-                ["You are a financial advisor who gives expert advice on investments and budgeting."],
-                ["You are a health and fitness expert who provides advice on nutrition and exercise."],
-                ["You are a travel consultant who offers recommendations for destinations, accommodations, and attractions."],
-                ["You are a movie critic who shares insightful opinions on films and their themes."],
-                ["You are a history enthusiast who loves to discuss historical events and figures."],
-                ["You are a tech-savvy assistant who can help users troubleshoot issues and answer questions about gadgets and software."],
-                ["You are an AI poet who can compose creative and evocative poems on any given topic."],],
-                inputs = system_msg,)
-        
-demo.queue(max_size=99, concurrency_count=20).launch(debug=True)
\ No newline at end of file
diff --git a/spaces/bioriAsaeru/text-to-voice/Data Full Movie 3GP Download How to Stream or Download the Movie for Free.md b/spaces/bioriAsaeru/text-to-voice/Data Full Movie 3GP Download How to Stream or Download the Movie for Free.md
deleted file mode 100644
index 848f7d157a7aeeb828651bbcbb4f39229c68544d..0000000000000000000000000000000000000000
--- a/spaces/bioriAsaeru/text-to-voice/Data Full Movie 3GP Download How to Stream or Download the Movie for Free.md	
+++ /dev/null
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deleted file mode 100644
index 137c13d901d9a4d73fa3b52087503594285246a6..0000000000000000000000000000000000000000
--- a/spaces/bioriAsaeru/text-to-voice/Descargar En El Nombre Del Padre Torrent.md	
+++ /dev/null
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diff --git a/spaces/bioriAsaeru/text-to-voice/Gemvision Matrix 7 Full Crackrar Tips and Tricks for Getting the Most Out of It.md b/spaces/bioriAsaeru/text-to-voice/Gemvision Matrix 7 Full Crackrar Tips and Tricks for Getting the Most Out of It.md
deleted file mode 100644
index 880edbfa9781e6a011073bdd1a6de2394d91c2fa..0000000000000000000000000000000000000000
--- a/spaces/bioriAsaeru/text-to-voice/Gemvision Matrix 7 Full Crackrar Tips and Tricks for Getting the Most Out of It.md	
+++ /dev/null
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diff --git a/spaces/bioriAsaeru/text-to-voice/High Tail Hall Android App VERIFIED.md b/spaces/bioriAsaeru/text-to-voice/High Tail Hall Android App VERIFIED.md
deleted file mode 100644
index 6941cc0a24ba8ce09ac6c86045e8d6498995c403..0000000000000000000000000000000000000000
--- a/spaces/bioriAsaeru/text-to-voice/High Tail Hall Android App VERIFIED.md	
+++ /dev/null
@@ -1,5 +0,0 @@
-<br />
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index f2c82656c28ceb1cf70e2aecb2b965a6d3946766..0000000000000000000000000000000000000000
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diff --git a/spaces/birsardar/stable-diffusion-mat-outpainting-primer/metrics/frechet_inception_distance.py b/spaces/birsardar/stable-diffusion-mat-outpainting-primer/metrics/frechet_inception_distance.py
deleted file mode 100644
index 1d38ec731b33e6f4f20cd4601e58d7e5ce2eaaa3..0000000000000000000000000000000000000000
--- a/spaces/birsardar/stable-diffusion-mat-outpainting-primer/metrics/frechet_inception_distance.py
+++ /dev/null
@@ -1,41 +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.
-
-"""Frechet Inception Distance (FID) from the paper
-"GANs trained by a two time-scale update rule converge to a local Nash
-equilibrium". Matches the original implementation by Heusel et al. at
-https://github.com/bioinf-jku/TTUR/blob/master/fid.py"""
-
-import numpy as np
-import scipy.linalg
-from . import metric_utils
-
-#----------------------------------------------------------------------------
-
-def compute_fid(opts, max_real, num_gen):
-    # Direct TorchScript translation of http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz
-    detector_url = 'https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/metrics/inception-2015-12-05.pt'
-    detector_kwargs = dict(return_features=True) # Return raw features before the softmax layer.
-
-    mu_real, sigma_real = metric_utils.compute_feature_stats_for_dataset(
-        opts=opts, detector_url=detector_url, detector_kwargs=detector_kwargs,
-        rel_lo=0, rel_hi=0, capture_mean_cov=True, max_items=max_real).get_mean_cov()
-
-    mu_gen, sigma_gen = metric_utils.compute_feature_stats_for_generator(
-        opts=opts, detector_url=detector_url, detector_kwargs=detector_kwargs,
-        rel_lo=0, rel_hi=1, capture_mean_cov=True, max_items=num_gen).get_mean_cov()
-
-    if opts.rank != 0:
-        return float('nan')
-
-    m = np.square(mu_gen - mu_real).sum()
-    s, _ = scipy.linalg.sqrtm(np.dot(sigma_gen, sigma_real), disp=False) # pylint: disable=no-member
-    fid = np.real(m + np.trace(sigma_gen + sigma_real - s * 2))
-    return float(fid)
-
-#----------------------------------------------------------------------------
diff --git a/spaces/brainblow/AudioCreator_Music-Audio_Generation/audiocraft/models/multibanddiffusion.py b/spaces/brainblow/AudioCreator_Music-Audio_Generation/audiocraft/models/multibanddiffusion.py
deleted file mode 100644
index 6a2f169d516ed5aaf5da61fb482d94dd142f55e9..0000000000000000000000000000000000000000
--- a/spaces/brainblow/AudioCreator_Music-Audio_Generation/audiocraft/models/multibanddiffusion.py
+++ /dev/null
@@ -1,194 +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.
-
-"""
-Multi Band Diffusion models as described in
-"From Discrete Tokens to High-Fidelity Audio Using Multi-Band Diffusion"
-(paper link).
-"""
-
-import typing as tp
-
-import torch
-import julius
-
-from .unet import DiffusionUnet
-from ..modules.diffusion_schedule import NoiseSchedule
-from .encodec import CompressionModel
-from ..solvers.compression import CompressionSolver
-from .loaders import load_compression_model, load_diffusion_models
-
-
-class DiffusionProcess:
-    """Sampling for a diffusion Model.
-
-    Args:
-        model (DiffusionUnet): Diffusion U-Net model.
-        noise_schedule (NoiseSchedule): Noise schedule for diffusion process.
-    """
-    def __init__(self, model: DiffusionUnet, noise_schedule: NoiseSchedule) -> None:
-        """
-        """
-        self.model = model
-        self.schedule = noise_schedule
-
-    def generate(self, condition: torch.Tensor, initial_noise: torch.Tensor,
-                 step_list: tp.Optional[tp.List[int]] = None):
-        """Perform one diffusion process to generate one of the bands.
-
-        Args:
-            condition (tensor): The embeddings form the compression model.
-            initial_noise (tensor): The initial noise to start the process/
-        """
-        return self.schedule.generate_subsampled(model=self.model, initial=initial_noise, step_list=step_list,
-                                                 condition=condition)
-
-
-class MultiBandDiffusion:
-    """Sample from multiple diffusion models.
-
-    Args:
-        DPs (list of DiffusionProcess): Diffusion processes.
-        codec_model (CompressionModel): Underlying compression model used to obtain discrete tokens.
-    """
-    def __init__(self, DPs: tp.List[DiffusionProcess], codec_model: CompressionModel) -> None:
-        self.DPs = DPs
-        self.codec_model = codec_model
-        self.device = next(self.codec_model.parameters()).device
-
-    @property
-    def sample_rate(self) -> int:
-        return self.codec_model.sample_rate
-
-    @staticmethod
-    def get_mbd_musicgen(device=None):
-        """Load our diffusion models trained for MusicGen."""
-        if device is None:
-            device = 'cuda' if torch.cuda.is_available() else 'cpu'
-        path = 'https://dl.fbaipublicfiles.com/encodec/Diffusion/mbd_musicgen_32khz.th'
-        name = 'facebook/musicgen-small'
-        codec_model = load_compression_model(name, device=device)
-        models, processors, cfgs = load_diffusion_models(path, device=device)
-        DPs = []
-        for i in range(len(models)):
-            schedule = NoiseSchedule(**cfgs[i].schedule, sample_processor=processors[i])
-            DPs.append(DiffusionProcess(model=models[i], noise_schedule=schedule))
-        return MultiBandDiffusion(DPs=DPs, codec_model=codec_model)
-
-    @staticmethod
-    def get_mbd_24khz(bw: float = 3.0, pretrained: bool = True,
-                      device: tp.Optional[tp.Union[torch.device, str]] = None,
-                      n_q: tp.Optional[int] = None):
-        """Get the pretrained Models for MultibandDiffusion.
-
-        Args:
-            bw (float): Bandwidth of the compression model.
-            pretrained (bool): Whether to use / download if necessary the models.
-            device (torch.device or str, optional): Device on which the models are loaded.
-            n_q (int, optional): Number of quantizers to use within the compression model.
-        """
-        if device is None:
-            device = 'cuda' if torch.cuda.is_available() else 'cpu'
-        assert bw in [1.5, 3.0, 6.0], f"bandwidth {bw} not available"
-        if n_q is not None:
-            assert n_q in [2, 4, 8]
-            assert {1.5: 2, 3.0: 4, 6.0: 8}[bw] == n_q, \
-                f"bandwidth and number of codebooks missmatch to use n_q = {n_q} bw should be {n_q * (1.5 / 2)}"
-        n_q = {1.5: 2, 3.0: 4, 6.0: 8}[bw]
-        codec_model = CompressionSolver.model_from_checkpoint(
-            '//pretrained/facebook/encodec_24khz', device=device)
-        codec_model.set_num_codebooks(n_q)
-        codec_model = codec_model.to(device)
-        path = f'https://dl.fbaipublicfiles.com/encodec/Diffusion/mbd_comp_{n_q}.pt'
-        models, processors, cfgs = load_diffusion_models(path, device=device)
-        DPs = []
-        for i in range(len(models)):
-            schedule = NoiseSchedule(**cfgs[i].schedule, sample_processor=processors[i])
-            DPs.append(DiffusionProcess(model=models[i], noise_schedule=schedule))
-        return MultiBandDiffusion(DPs=DPs, codec_model=codec_model)
-
-        return MultiBandDiffusion(DPs, codec_model)
-
-    @torch.no_grad()
-    def get_condition(self, wav: torch.Tensor, sample_rate: int) -> torch.Tensor:
-        """Get the conditioning (i.e. latent reprentatios of the compression model) from a waveform.
-        Args:
-            wav (torch.Tensor): The audio that we want to extract the conditioning from
-            sample_rate (int): sample rate of the audio"""
-        if sample_rate != self.sample_rate:
-            wav = julius.resample_frac(wav, sample_rate, self.sample_rate)
-        codes, scale = self.codec_model.encode(wav)
-        assert scale is None, "Scaled compression models not supported."
-        emb = self.get_emb(codes)
-        return emb
-
-    @torch.no_grad()
-    def get_emb(self, codes: torch.Tensor):
-        """Get latent representation from the discrete codes
-        Argrs:
-            codes (torch.Tensor): discrete tokens"""
-        emb = self.codec_model.decode_latent(codes)
-        return emb
-
-    def generate(self, emb: torch.Tensor, size: tp.Optional[torch.Size] = None,
-                 step_list: tp.Optional[tp.List[int]] = None):
-        """Generate Wavform audio from the latent embeddings of the compression model
-        Args:
-            emb (torch.Tensor): Conditioning embeddinds
-            size (none torch.Size): size of the output
-                if None this is computed from the typical upsampling of the model
-            step_list (optional list[int]): list of Markov chain steps, defaults to 50 linearly spaced step.
-        """
-        if size is None:
-            upsampling = int(self.codec_model.sample_rate / self.codec_model.frame_rate)
-            size = torch.Size([emb.size(0), self.codec_model.channels, emb.size(-1) * upsampling])
-        assert size[0] == emb.size(0)
-        out = torch.zeros(size).to(self.device)
-        for DP in self.DPs:
-            out += DP.generate(condition=emb, step_list=step_list, initial_noise=torch.randn_like(out))
-        return out
-
-    def re_eq(self, wav: torch.Tensor, ref: torch.Tensor, n_bands: int = 32, strictness: float = 1):
-        """match the eq to the encodec output by matching the standard deviation of some frequency bands
-        Args:
-            wav (torch.Tensor): audio to equalize
-            ref (torch.Tensor):refenrence audio from which we match the spectrogram.
-            n_bands (int): number of bands of the eq
-            strictness (float): how strict the the matching. 0 is no matching, 1 is exact matching.
-        """
-        split = julius.SplitBands(n_bands=n_bands, sample_rate=self.codec_model.sample_rate).to(wav.device)
-        bands = split(wav)
-        bands_ref = split(ref)
-        out = torch.zeros_like(ref)
-        for i in range(n_bands):
-            out += bands[i] * (bands_ref[i].std() / bands[i].std()) ** strictness
-        return out
-
-    def regenerate(self, wav: torch.Tensor, sample_rate: int):
-        """Regenerate a wavform through compression and diffusion regeneration.
-        Args:
-            wav (torch.Tensor): Original 'ground truth' audio
-            sample_rate (int): sample rate of the input (and output) wav
-        """
-        if sample_rate != self.codec_model.sample_rate:
-            wav = julius.resample_frac(wav, sample_rate, self.codec_model.sample_rate)
-        emb = self.get_condition(wav, sample_rate=self.codec_model.sample_rate)
-        size = wav.size()
-        out = self.generate(emb, size=size)
-        if sample_rate != self.codec_model.sample_rate:
-            out = julius.resample_frac(out, self.codec_model.sample_rate, sample_rate)
-        return out
-
-    def tokens_to_wav(self, tokens: torch.Tensor, n_bands: int = 32):
-        """Generate Waveform audio with diffusion from the discrete codes.
-        Args:
-            tokens (torch.Tensor): discrete codes
-            n_bands (int): bands for the eq matching.
-        """
-        wav_encodec = self.codec_model.decode(tokens)
-        condition = self.get_emb(tokens)
-        wav_diffusion = self.generate(emb=condition, size=wav_encodec.size())
-        return self.re_eq(wav=wav_diffusion, ref=wav_encodec, n_bands=n_bands)
diff --git a/spaces/brhiza/EdisonChen-tts/app.py b/spaces/brhiza/EdisonChen-tts/app.py
deleted file mode 100644
index 5723249d650ac34eb7c43b82d13036793bbfcec4..0000000000000000000000000000000000000000
--- a/spaces/brhiza/EdisonChen-tts/app.py
+++ /dev/null
@@ -1,33 +0,0 @@
-import os
-import subprocess
-import sys
-
-
-# Run shell command and capture output in real-time
-def init():
-    process = subprocess.Popen("""
-    bash run.sh
-    """, stdout=subprocess.PIPE, shell=True)
-    while True:
-        output = process.stdout.readline().decode()
-        if output == '' and process.poll() is not None:
-            break
-        if output:
-            print(output.strip())
-
-    # Wait for the command to finish and get the return code
-    return_code = process.poll()
-    print(f"Command exited with return code {return_code}")
-
-
-is_space = os.getenv("SYSTEM") == "spaces"
-if is_space:
-    init()
-
-# if is_space:
-#     sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "SuTTS/test")))
-# else:
-#     sys.path.append("/Users/peng/PROGRAM/GitHub/SuTTS/test")
-#
-# print(sys.path)
-
diff --git a/spaces/camenduru-com/lsmith/Dockerfile b/spaces/camenduru-com/lsmith/Dockerfile
deleted file mode 100644
index 4f4d0d93b5eca73fe0802a0b88142782eb05094e..0000000000000000000000000000000000000000
--- a/spaces/camenduru-com/lsmith/Dockerfile
+++ /dev/null
@@ -1,47 +0,0 @@
-FROM nvcr.io/nvidia/tensorrt:22.11-py3
-
-WORKDIR /content
-
-RUN apt-get update
-RUN apt install -y software-properties-common
-RUN apt-get upgrade -y
-RUN curl -sL https://deb.nodesource.com/setup_18.x | bash -
-RUN apt-get install -y nodejs
-RUN npm install -g pnpm
-RUN pip install tensorrt==8.5.3.1 -U
-
-RUN adduser --disabled-password --gecos '' user
-RUN chown -R user:user /content
-RUN chmod -R 777 /content
-RUN mkdir /.cache
-RUN chown -R user:user /.cache
-RUN chmod -R 777 /.cache
-USER user
-
-RUN git clone --single-branch https://github.com/camenduru/TensorRT && \
-    cd TensorRT && \
-    git submodule update --init --recursive
-    
-RUN cd TensorRT && mkdir -p build && \
-    cd build && \
-    cmake /content/TensorRT -DTRT_OUT_DIR=$PWD/out && \
-    cd plugin && \
-    make -j$(nproc)
-
-ENV PLUGIN_LIBS "/content/TensorRT/build/out/libnvinfer_plugin.so"
-
-ENV CUDA_MODULE_LOADING=LAZY
-ENV TERM=xterm-256color
-
-RUN pip install -r /content/TensorRT/demo/Diffusion/requirements.txt
-RUN cd /content/TensorRT/demo/Diffusion && mkdir -p onnx engine output
-
-RUN git clone -b dev https://github.com/camenduru/Lsmith.git
-WORKDIR /content/Lsmith
-RUN git submodule update --init --recursive
-RUN cd /content/Lsmith/frontend && pnpm i && pnpm build --out-dir ../dist
-RUN pip install -r requirements.txt
-
-EXPOSE 7860
-
-CMD LD_PRELOAD=${PLUGIN_LIBS} python -m uvicorn modules.main:app --host 0.0.0.0 --port 7860 
\ No newline at end of file
diff --git a/spaces/capjamesg/fastvit/README.md b/spaces/capjamesg/fastvit/README.md
deleted file mode 100644
index 2430f5df11dfaaa980a8b835ae0790987d409490..0000000000000000000000000000000000000000
--- a/spaces/capjamesg/fastvit/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: FastViT
-emoji: 🖼️
-colorFrom: yellow
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.50.2
-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/carlosalonso/Detection-video/carpeta_deteccion/detectron2/modeling/sampling.py b/spaces/carlosalonso/Detection-video/carpeta_deteccion/detectron2/modeling/sampling.py
deleted file mode 100644
index a2d0f6648b349c5ea39fd29785b77c961a58fa22..0000000000000000000000000000000000000000
--- a/spaces/carlosalonso/Detection-video/carpeta_deteccion/detectron2/modeling/sampling.py
+++ /dev/null
@@ -1,54 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import torch
-
-from detectron2.layers import nonzero_tuple
-
-__all__ = ["subsample_labels"]
-
-
-def subsample_labels(
-    labels: torch.Tensor, num_samples: int, positive_fraction: float, bg_label: int
-):
-    """
-    Return `num_samples` (or fewer, if not enough found)
-    random samples from `labels` which is a mixture of positives & negatives.
-    It will try to return as many positives as possible without
-    exceeding `positive_fraction * num_samples`, and then try to
-    fill the remaining slots with negatives.
-
-    Args:
-        labels (Tensor): (N, ) label vector with values:
-            * -1: ignore
-            * bg_label: background ("negative") class
-            * otherwise: one or more foreground ("positive") classes
-        num_samples (int): The total number of labels with value >= 0 to return.
-            Values that are not sampled will be filled with -1 (ignore).
-        positive_fraction (float): The number of subsampled labels with values > 0
-            is `min(num_positives, int(positive_fraction * num_samples))`. The number
-            of negatives sampled is `min(num_negatives, num_samples - num_positives_sampled)`.
-            In order words, if there are not enough positives, the sample is filled with
-            negatives. If there are also not enough negatives, then as many elements are
-            sampled as is possible.
-        bg_label (int): label index of background ("negative") class.
-
-    Returns:
-        pos_idx, neg_idx (Tensor):
-            1D vector of indices. The total length of both is `num_samples` or fewer.
-    """
-    positive = nonzero_tuple((labels != -1) & (labels != bg_label))[0]
-    negative = nonzero_tuple(labels == bg_label)[0]
-
-    num_pos = int(num_samples * positive_fraction)
-    # protect against not enough positive examples
-    num_pos = min(positive.numel(), num_pos)
-    num_neg = num_samples - num_pos
-    # protect against not enough negative examples
-    num_neg = min(negative.numel(), num_neg)
-
-    # randomly select positive and negative examples
-    perm1 = torch.randperm(positive.numel(), device=positive.device)[:num_pos]
-    perm2 = torch.randperm(negative.numel(), device=negative.device)[:num_neg]
-
-    pos_idx = positive[perm1]
-    neg_idx = negative[perm2]
-    return pos_idx, neg_idx
diff --git a/spaces/carlosalonso/Detection-video/carpeta_deteccion/detectron2/utils/analysis.py b/spaces/carlosalonso/Detection-video/carpeta_deteccion/detectron2/utils/analysis.py
deleted file mode 100644
index 178da7968cc08c29ec61b823bba8b74e8d97e1d6..0000000000000000000000000000000000000000
--- a/spaces/carlosalonso/Detection-video/carpeta_deteccion/detectron2/utils/analysis.py
+++ /dev/null
@@ -1,188 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-# -*- coding: utf-8 -*-
-
-import typing
-from typing import Any, List
-import fvcore
-from fvcore.nn import activation_count, flop_count, parameter_count, parameter_count_table
-from torch import nn
-
-from detectron2.export import TracingAdapter
-
-__all__ = [
-    "activation_count_operators",
-    "flop_count_operators",
-    "parameter_count_table",
-    "parameter_count",
-    "FlopCountAnalysis",
-]
-
-FLOPS_MODE = "flops"
-ACTIVATIONS_MODE = "activations"
-
-
-# Some extra ops to ignore from counting, including elementwise and reduction ops
-_IGNORED_OPS = {
-    "aten::add",
-    "aten::add_",
-    "aten::argmax",
-    "aten::argsort",
-    "aten::batch_norm",
-    "aten::constant_pad_nd",
-    "aten::div",
-    "aten::div_",
-    "aten::exp",
-    "aten::log2",
-    "aten::max_pool2d",
-    "aten::meshgrid",
-    "aten::mul",
-    "aten::mul_",
-    "aten::neg",
-    "aten::nonzero_numpy",
-    "aten::reciprocal",
-    "aten::repeat_interleave",
-    "aten::rsub",
-    "aten::sigmoid",
-    "aten::sigmoid_",
-    "aten::softmax",
-    "aten::sort",
-    "aten::sqrt",
-    "aten::sub",
-    "torchvision::nms",  # TODO estimate flop for nms
-}
-
-
-class FlopCountAnalysis(fvcore.nn.FlopCountAnalysis):
-    """
-    Same as :class:`fvcore.nn.FlopCountAnalysis`, but supports detectron2 models.
-    """
-
-    def __init__(self, model, inputs):
-        """
-        Args:
-            model (nn.Module):
-            inputs (Any): inputs of the given model. Does not have to be tuple of tensors.
-        """
-        wrapper = TracingAdapter(model, inputs, allow_non_tensor=True)
-        super().__init__(wrapper, wrapper.flattened_inputs)
-        self.set_op_handle(**{k: None for k in _IGNORED_OPS})
-
-
-def flop_count_operators(model: nn.Module, inputs: list) -> typing.DefaultDict[str, float]:
-    """
-    Implement operator-level flops counting using jit.
-    This is a wrapper of :func:`fvcore.nn.flop_count` and adds supports for standard
-    detection models in detectron2.
-    Please use :class:`FlopCountAnalysis` for more advanced functionalities.
-
-    Note:
-        The function runs the input through the model to compute flops.
-        The flops of a detection model is often input-dependent, for example,
-        the flops of box & mask head depends on the number of proposals &
-        the number of detected objects.
-        Therefore, the flops counting using a single input may not accurately
-        reflect the computation cost of a model. It's recommended to average
-        across a number of inputs.
-
-    Args:
-        model: a detectron2 model that takes `list[dict]` as input.
-        inputs (list[dict]): inputs to model, in detectron2's standard format.
-            Only "image" key will be used.
-        supported_ops (dict[str, Handle]): see documentation of :func:`fvcore.nn.flop_count`
-
-    Returns:
-        Counter: Gflop count per operator
-    """
-    old_train = model.training
-    model.eval()
-    ret = FlopCountAnalysis(model, inputs).by_operator()
-    model.train(old_train)
-    return {k: v / 1e9 for k, v in ret.items()}
-
-
-def activation_count_operators(
-    model: nn.Module, inputs: list, **kwargs
-) -> typing.DefaultDict[str, float]:
-    """
-    Implement operator-level activations counting using jit.
-    This is a wrapper of fvcore.nn.activation_count, that supports standard detection models
-    in detectron2.
-
-    Note:
-        The function runs the input through the model to compute activations.
-        The activations of a detection model is often input-dependent, for example,
-        the activations of box & mask head depends on the number of proposals &
-        the number of detected objects.
-
-    Args:
-        model: a detectron2 model that takes `list[dict]` as input.
-        inputs (list[dict]): inputs to model, in detectron2's standard format.
-            Only "image" key will be used.
-
-    Returns:
-        Counter: activation count per operator
-    """
-    return _wrapper_count_operators(model=model, inputs=inputs, mode=ACTIVATIONS_MODE, **kwargs)
-
-
-def _wrapper_count_operators(
-    model: nn.Module, inputs: list, mode: str, **kwargs
-) -> typing.DefaultDict[str, float]:
-    # ignore some ops
-    supported_ops = {k: lambda *args, **kwargs: {} for k in _IGNORED_OPS}
-    supported_ops.update(kwargs.pop("supported_ops", {}))
-    kwargs["supported_ops"] = supported_ops
-
-    assert len(inputs) == 1, "Please use batch size=1"
-    tensor_input = inputs[0]["image"]
-    inputs = [{"image": tensor_input}]  # remove other keys, in case there are any
-
-    old_train = model.training
-    if isinstance(model, (nn.parallel.distributed.DistributedDataParallel, nn.DataParallel)):
-        model = model.module
-    wrapper = TracingAdapter(model, inputs)
-    wrapper.eval()
-    if mode == FLOPS_MODE:
-        ret = flop_count(wrapper, (tensor_input,), **kwargs)
-    elif mode == ACTIVATIONS_MODE:
-        ret = activation_count(wrapper, (tensor_input,), **kwargs)
-    else:
-        raise NotImplementedError("Count for mode {} is not supported yet.".format(mode))
-    # compatible with change in fvcore
-    if isinstance(ret, tuple):
-        ret = ret[0]
-    model.train(old_train)
-    return ret
-
-
-def find_unused_parameters(model: nn.Module, inputs: Any) -> List[str]:
-    """
-    Given a model, find parameters that do not contribute
-    to the loss.
-
-    Args:
-        model: a model in training mode that returns losses
-        inputs: argument or a tuple of arguments. Inputs of the model
-
-    Returns:
-        list[str]: the name of unused parameters
-    """
-    assert model.training
-    for _, prm in model.named_parameters():
-        prm.grad = None
-
-    if isinstance(inputs, tuple):
-        losses = model(*inputs)
-    else:
-        losses = model(inputs)
-
-    if isinstance(losses, dict):
-        losses = sum(losses.values())
-    losses.backward()
-
-    unused: List[str] = []
-    for name, prm in model.named_parameters():
-        if prm.grad is None:
-            unused.append(name)
-        prm.grad = None
-    return unused
diff --git a/spaces/cccc-c/web-ui-pub/_next/static/css/aa52c84dc63fe0c2.css b/spaces/cccc-c/web-ui-pub/_next/static/css/aa52c84dc63fe0c2.css
deleted file mode 100644
index a5906762da87944d1af55175437321b2b935f7a6..0000000000000000000000000000000000000000
--- a/spaces/cccc-c/web-ui-pub/_next/static/css/aa52c84dc63fe0c2.css
+++ /dev/null
@@ -1,21 +0,0 @@
-@font-face{font-family:Inter;font-style:normal;font-weight:400;font-display:swap;src:url(_next/static/media/Inter-Regular.f1f0c35b.woff2) format("woff2"),url(_next/static/media/Inter-Regular.f356e84a.woff) format("woff")}@font-face{font-family:Inter;font-style:normal;font-weight:500;font-display:swap;src:url(_next/static/media/Inter-Medium.dc792b50.woff2) format("woff2"),url(_next/static/media/Inter-Medium.ec7dd2d9.woff) format("woff")}@font-face{font-family:Inter;font-style:normal;font-weight:600;font-display:swap;src:url(_next/static/media/Inter-SemiBold.fcb100c7.woff2) format("woff2"),url(_next/static/media/Inter-SemiBold.55027e47.woff) format("woff")}@font-face{font-family:Inter;font-style:normal;font-weight:700;font-display:swap;src:url(_next/static/media/Inter-Bold.579e0f95.woff2) format("woff2"),url(_next/static/media/Inter-Bold.b1234477.woff) format("woff")}@font-face{font-family:Inter var;font-weight:100 900;font-style:normal;font-named-instance:"Regular";font-display:swap;src:url(_next/static/media/Inter-roman.var.b2129c00.woff2) format("woff2 supports variations(gvar)"),url(_next/static/media/Inter-roman.var.b2129c00.woff2) format("woff2-variations"),url(_next/static/media/Inter-roman.var.b2129c00.woff2) format("woff2")}/*
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diff --git a/spaces/ceckenrode/AI-Dashboard-03142023/README.md b/spaces/ceckenrode/AI-Dashboard-03142023/README.md
deleted file mode 100644
index 3680a48ba9282a91c2557557e8bddc62dfeea8e1..0000000000000000000000000000000000000000
--- a/spaces/ceckenrode/AI-Dashboard-03142023/README.md
+++ /dev/null
@@ -1,11 +0,0 @@
----
-title: AI Dashboard 03142023
-emoji: 🏃
-colorFrom: pink
-colorTo: purple
-sdk: static
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/charlesnchr/ML-SIM/app.py b/spaces/charlesnchr/ML-SIM/app.py
deleted file mode 100644
index 7c03f01541142a6b8b0755ef2f16814e88f5f501..0000000000000000000000000000000000000000
--- a/spaces/charlesnchr/ML-SIM/app.py
+++ /dev/null
@@ -1,130 +0,0 @@
-''' ----------------------------------------
-* Creation Time : Sun Aug 28 21:38:58 2022
-* Last Modified : Sun Aug 28 21:41:36 2022
-* Author : Charles N. Christensen
-* Github : github.com/charlesnchr
-----------------------------------------'''
-
-from turtle import title
-import gradio as gr
-import numpy as np
-from PIL import Image
-import io
-import base64
-import skimage
-from NNfunctions import *
-
-opt = GetOptions_allRnd_0317()
-net = LoadModel(opt)
-
-gr.close_all()
-
-def predict(imagefile):
-    # img = np.array(skimage.io.imread(imagefile.name))
-    # img = np.concatenate((img,img,img),axis=2)
-    # img = np.transpose(img, (2,0,1))
-
-    img = skimage.io.imread(imagefile.name)
-
-    # sr,wf,out = EvaluateModel(net,opt,img,outfile)
-    sr, wf, sr_download = EvaluateModel(net,opt,img)
-
-    return wf, sr, sr_download
-
-def process_example(filename):
-    basename = os.path.basename(filename)
-    basename = basename.replace('.png','.tif')
-    img = skimage.io.imread('TestImages/%s' % basename)
-
-    sr, wf, sr_download = EvaluateModel(net,opt,img)
-
-    return wf, sr
-
-title = '<h1 style="text-align: center;">ML-SIM: Reconstruction of SIM images with deep learning</h1>'
-
-description = """
-This space demonstrates the ML-SIM method for reconstruction of structured illumination microscopy images.
-
-### <a href="https://opg.optica.org/boe/viewmedia.cfm?uri=boe-12-5-2720&html=true" target='_blank' > ML-SIM: universal reconstruction of structured illumination microscopy images using transfer learning </a>
-
-_Charles N. Christensen<sup>1,2,*</sup>, Edward N. Ward<sup>1</sup>, Meng Lu<sup>1</sup>, Pietro Lio<sup>2</sup>, Clemens F. Kaminski_</br></br>
-<sup>1</sup>University of Cambridge, Department of Chemical Engineering and Biotechnology, Laser Analytics Group</br>
-<sup>2</sup>University of Cambridge, Department of Computer Science and Technology, Artificial Intelligence Group</br>
-- GitHub: [charlesnchr](http://github.com/charlesnchr)
-- Email: charles.n.chr@gmail.com
-- Publication: <a href='https://opg.optica.org/boe/viewmedia.cfm?uri=boe-12-5-2720&html=true'
-target='_blank'>Journal</a>, <a href='https://arxiv.org/abs/2003.11064' target='_blank'>Preprint</a>
----
-
-## 🔬 To run ML-SIM
-Upload a TIFF image and hit submit or select one from the examples below. Note that the model here is trained for SIM stacks of 9 frames (3x3 configuration, i.e. 3 phase steps for 3 orientations).
-"""
-
-article = """
----
-Select an example from the list above to try the model on a test image. Below you can see what the file names correspond to.
-
-The ML-SIM test images are from two different microscopes (MAI-SIM and SLM-SIM) in addition to two simulated images.
-
-Wide-field projections in pseudo-colour of these examples are shown below:
-
-![Example test images](https://i.imgur.com/AUrp1Jr.jpeg "Example test image for ML-SIM")
-
----
-### Read more
-- <a href='https://ML-SIM.com' target='_blank'>ML-SIM.com</a>
-- <a href='https://charles-christensen.com' target='_blank'>Website</a>
-- <a href='https://github.com/charlesnchr/ML-SIM' target='_blank'>Github</a>
-- <a href='https://twitter.com/charlesnchr' target='_blank'>Twitter</a>
-"""
-
-# inputs = gr.inputs.Image(label="Upload a TIFF image", type = 'pil', optional=False)
-
-inputs = gr.inputs.File(label="Upload a TIFF image", type = 'file', optional=False)
-outputs = [
-    gr.outputs.Image(label="INPUT (Wide-field projection)"),
-    gr.outputs.Image(label="OUTPUT (ML-SIM)"),
-    gr.outputs.File(label="Download SR image" )
-    # , gr.outputs.Textbox(type="auto",label="Pet Prediction")
-]
-
-examples = glob.glob('*.tif')
-
-interface = gr.Interface(fn=predict,
-    inputs=inputs,
-    outputs=outputs,
-    title = title,
-    description=description,
-    article=article,
-    examples=examples,
-    allow_flagging='never',
-    cache_examples=False
-    )
-interface.launch()
-
-
-# with gr.Blocks() as interface:
-#     gr.Markdown(title)
-#     gr.Markdown(description)
-
-#     with gr.Row():
-#         input1 = gr.inputs.File(label="Upload a TIFF image", type = 'file', optional=False)
-
-#     submit_btn = gr.Button("Reconstruct")
-
-#     with gr.Row():
-#         output1 = gr.outputs.Image(label="Wide-field projection")
-#         output2 = gr.outputs.Image(label="SIM Reconstruction")
-
-#     output3 = gr.File(label="Download SR image", visible=False)
-
-#     submit_btn.click(
-#         predict,
-#         input1,
-#         [output1, output2, output3]
-#     )
-
-#     gr.Examples(examples, input1, [output1, output2, output3])
-
-
-# interface.launch()
diff --git a/spaces/charlesnchr/VSR-SIM/NNfunctions.py b/spaces/charlesnchr/VSR-SIM/NNfunctions.py
deleted file mode 100644
index 20cf9c6d19260834f75371ac18c4a5bcdb3b36d2..0000000000000000000000000000000000000000
--- a/spaces/charlesnchr/VSR-SIM/NNfunctions.py
+++ /dev/null
@@ -1,264 +0,0 @@
-import datetime
-import math
-import os
-
-import torch
-import time
-
-import skimage.io
-import skimage.transform
-import matplotlib.pyplot as plt
-import glob
-
-import torch.optim as optim
-import torchvision
-import torchvision.transforms as transforms
-from skimage import exposure
-
-toTensor = transforms.ToTensor()
-toPIL = transforms.ToPILImage()
-
-
-import numpy as np
-from PIL import Image
-
-from models import *
-
-os.environ["CUDA_VISIBLE_DEVICES"] = "0"
-
-def remove_dataparallel_wrapper(state_dict):
-	r"""Converts a DataParallel model to a normal one by removing the "module."
-	wrapper in the module dictionary
-
-	Args:
-		state_dict: a torch.nn.DataParallel state dictionary
-	"""
-	from collections import OrderedDict
-
-	new_state_dict = OrderedDict()
-	for k, vl in state_dict.items():
-		name = k[7:] # remove 'module.' of DataParallel
-		new_state_dict[name] = vl
-
-	return new_state_dict
-
-from argparse import Namespace
-
-
-def GetOptions():
-    # training options
-    opt = Namespace()
-    opt.model = 'rcan'
-    opt.n_resgroups = 3
-    opt.n_resblocks = 10
-    opt.n_feats = 96
-    opt.reduction = 16
-    opt.narch = 0
-    opt.norm = 'minmax'
-
-    opt.cpu = False
-    opt.multigpu = False
-    opt.undomulti = False
-    opt.device = torch.device('cuda' if torch.cuda.is_available() and not opt.cpu else 'cpu')
-
-    opt.imageSize = 512
-    opt.weights = "model/simrec_simin_gtout_rcan_512_2_ntrain790-final.pth"
-    opt.root = "model/0080.jpg"
-    opt.out = "model"
-
-    opt.task = 'simin_gtout'
-    opt.scale = 1
-    opt.nch_in = 9
-    opt.nch_out = 1
-
-
-    return opt
-
-def GetOptions_Swin_2702():
-
-    # training options
-    opt = Namespace()
-    opt.model = 'swinir_rcab'
-    opt.norm = 'minmax'
-
-    opt.cpu = False
-    opt.multigpu = False
-    opt.undomulti = False
-    opt.device = torch.device('cuda' if torch.cuda.is_available() and not opt.cpu else 'cpu')
-
-    opt.imageSize = 128
-    opt.weights = "model/swinir_rcab_nov2021.pth"
-    opt.root = "model/0080.jpg"
-    opt.out = "model"
-
-    opt.task = 'simin_gtout'
-    opt.scale = 2
-    opt.nch_in = 9
-    opt.nch_out = 1
-
-    opt.model_opts = {"depths":[6, 6, 6, 6, 6],
-        "embed_dim": 64,
-        "window_size": 8,
-        "num_heads": [8,8,8,8,8],
-        "use_rcab": True,
-        "mlp_ratio": 2 }
-
-    return opt
-
-
-def LoadModel(opt):
-    print('Loading model')
-    print(opt)
-
-    net = GetModel(opt)
-    print('loading checkpoint',opt.weights)
-    checkpoint = torch.load(opt.weights,map_location=opt.device)
-
-    if type(checkpoint) is dict:
-        if 'params_ema' in checkpoint:
-            keyname = 'params_ema'
-        elif 'state_dict' in checkpoint:
-            keyname = 'state_dict'
-        else:
-            keyname = 'params'
-        state_dict = checkpoint[keyname]
-    else:
-        state_dict = checkpoint
-
-    if opt.undomulti:
-        state_dict = remove_dataparallel_wrapper(state_dict)
-    net.load_state_dict(state_dict)
-
-    return net
-
-
-def prepimg(stack,self):
-
-    inputimg = stack[:9]
-
-    if self.nch_in == 6:
-        inputimg = inputimg[[0,1,3,4,6,7]]
-    elif self.nch_in == 3:
-        inputimg = inputimg[[0,4,8]]
-
-    if inputimg.shape[1] > 512 or inputimg.shape[2] > 512:
-        print('Over 512x512! Cropping')
-        inputimg = inputimg[:,:512,:512]
-
-
-    if self.norm == 'convert': # raw img from microscope, needs normalisation and correct frame ordering
-        print('Raw input assumed - converting')
-        # NCHW
-        # I = np.zeros((9,opt.imageSize,opt.imageSize),dtype='uint16')
-
-        # for t in range(9):
-        #     frame = inputimg[t]
-        #     frame = 120 / np.max(frame) * frame
-        #     frame = np.rot90(np.rot90(np.rot90(frame)))
-        #     I[t,:,:] = frame
-        # inputimg = I
-
-        inputimg = np.rot90(inputimg,axes=(1,2))
-        inputimg = inputimg[[6,7,8,3,4,5,0,1,2]] # could also do [8,7,6,5,4,3,2,1,0]
-        for i in range(len(inputimg)):
-            inputimg[i] = 100 / np.max(inputimg[i]) * inputimg[i]
-    elif 'convert' in self.norm:
-        fac = float(self.norm[7:])
-        inputimg = np.rot90(inputimg,axes=(1,2))
-        inputimg = inputimg[[6,7,8,3,4,5,0,1,2]] # could also do [8,7,6,5,4,3,2,1,0]
-        for i in range(len(inputimg)):
-            inputimg[i] = fac * 255 / np.max(inputimg[i]) * inputimg[i]
-
-
-    inputimg = inputimg.astype('float') / np.max(inputimg) # used to be /255
-    widefield = np.mean(inputimg,0)
-
-    if self.norm == 'adapthist':
-        for i in range(len(inputimg)):
-            inputimg[i] = exposure.equalize_adapthist(inputimg[i],clip_limit=0.001)
-        widefield = exposure.equalize_adapthist(widefield,clip_limit=0.001)
-    else:
-        # normalise
-        inputimg = torch.tensor(inputimg).float()
-        widefield = torch.tensor(widefield).float()
-        widefield = (widefield - torch.min(widefield)) / (torch.max(widefield) - torch.min(widefield))
-
-        if self.norm == 'minmax':
-            for i in range(len(inputimg)):
-                inputimg[i] = (inputimg[i] - torch.min(inputimg[i])) / (torch.max(inputimg[i]) - torch.min(inputimg[i]))
-        elif 'minmax' in self.norm:
-            fac = float(self.norm[6:])
-            for i in range(len(inputimg)):
-                inputimg[i] = fac * (inputimg[i] - torch.min(inputimg[i])) / (torch.max(inputimg[i]) - torch.min(inputimg[i]))
-
-
-
-    # otf = torch.tensor(otf.astype('float') / np.max(otf)).unsqueeze(0).float()
-    # gt = torch.tensor(gt.astype('float') / 255).unsqueeze(0).float()
-    # simimg = torch.tensor(simimg.astype('float') / 255).unsqueeze(0).float()
-    # widefield = torch.mean(inputimg,0).unsqueeze(0)
-
-
-    # normalise
-    # gt = (gt - torch.min(gt)) / (torch.max(gt) - torch.min(gt))
-    # simimg = (simimg - torch.min(simimg)) / (torch.max(simimg) - torch.min(simimg))
-    # widefield = (widefield - torch.min(widefield)) / (torch.max(widefield) - torch.min(widefield))
-    inputimg = torch.tensor(inputimg).float()
-    widefield = torch.tensor(widefield).float()
-    return inputimg,widefield
-
-def save_image(data, filename,cmap):
-    sizes = np.shape(data)
-    fig = plt.figure()
-    fig.set_size_inches(1. * sizes[0] / sizes[1], 1, forward = False)
-    ax = plt.Axes(fig, [0., 0., 1., 1.])
-    ax.set_axis_off()
-    fig.add_axes(ax)
-    ax.imshow(data, cmap=cmap)
-    plt.savefig(filename, dpi = sizes[0])
-    plt.close()
-
-
-def EvaluateModel(net,opt,stack):
-
-    outfile = datetime.datetime.utcnow().strftime('%H-%M-%S')
-    outfile = 'VSR-SIM_%s' % outfile
-
-    os.makedirs(opt.out, exist_ok=True)
-
-    print(stack.shape)
-    inputimg, widefield = prepimg(stack, opt)
-
-    if opt.norm == 'convert' or 'minmax' in opt.norm or 'adapthist' in opt.norm:
-        cmap = 'viridis'
-    else:
-        cmap = 'gray'
-
-    # skimage.io.imsave('%s_wf.png' % outfile,(255*widefield.numpy()).astype('uint8'))
-    wf = (255*widefield.numpy()).astype('uint8')
-    wf_upscaled = skimage.transform.resize(wf,(768,768),order=3) # should ideally be done by drawing on client side, in javascript
-    save_image(wf_upscaled,'%s_wf.png' % outfile,cmap)
-
-    # skimage.io.imsave('%s.tif' % outfile, inputimg.numpy())
-
-    inputimg = inputimg.unsqueeze(0)
-
-    with torch.no_grad():
-        sr = net(inputimg.to(opt.device))
-        sr = sr.cpu()
-        sr = torch.clamp(sr,min=0,max=1)
-        print('min max',inputimg.min(),inputimg.max())
-
-        pil_sr_img = toPIL(sr[0])
-
-    if opt.norm == 'convert':
-        pil_sr_img = transforms.functional.rotate(pil_sr_img,-90)
-
-    # pil_sr_img.save('%s.png' % outfile) # true output for downloading, no LUT
-    sr_img = np.array(pil_sr_img)
-    # sr_img = exposure.equalize_adapthist(sr_img,clip_limit=0.01)
-    skimage.io.imsave('%s.png' % outfile, sr_img) # true out for downloading, no LUT
-
-    sr_img = skimage.transform.resize(sr_img,(768,768),order=3) # should ideally be done by drawing on client side, in javascript
-    save_image(sr_img,'%s_sr.png' % outfile,cmap)
-    return outfile + '_sr.png', outfile + '_wf.png', outfile + '.png'
diff --git a/spaces/chilge/taoli/vdecoder/hifigan/utils.py b/spaces/chilge/taoli/vdecoder/hifigan/utils.py
deleted file mode 100644
index 84bff024f4d2e2de194b2a88ee7bbe5f0d33f67c..0000000000000000000000000000000000000000
--- a/spaces/chilge/taoli/vdecoder/hifigan/utils.py
+++ /dev/null
@@ -1,68 +0,0 @@
-import glob
-import os
-import matplotlib
-import torch
-from torch.nn.utils import weight_norm
-matplotlib.use("Agg")
-import matplotlib.pylab as plt
-
-
-def plot_spectrogram(spectrogram):
-    fig, ax = plt.subplots(figsize=(10, 2))
-    im = ax.imshow(spectrogram, aspect="auto", origin="lower",
-                   interpolation='none')
-    plt.colorbar(im, ax=ax)
-
-    fig.canvas.draw()
-    plt.close()
-
-    return fig
-
-
-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 apply_weight_norm(m):
-    classname = m.__class__.__name__
-    if classname.find("Conv") != -1:
-        weight_norm(m)
-
-
-def get_padding(kernel_size, dilation=1):
-    return int((kernel_size*dilation - dilation)/2)
-
-
-def load_checkpoint(filepath, device):
-    assert os.path.isfile(filepath)
-    print("Loading '{}'".format(filepath))
-    checkpoint_dict = torch.load(filepath, map_location=device)
-    print("Complete.")
-    return checkpoint_dict
-
-
-def save_checkpoint(filepath, obj):
-    print("Saving checkpoint to {}".format(filepath))
-    torch.save(obj, filepath)
-    print("Complete.")
-
-
-def del_old_checkpoints(cp_dir, prefix, n_models=2):
-    pattern = os.path.join(cp_dir, prefix + '????????')
-    cp_list = glob.glob(pattern) # get checkpoint paths
-    cp_list = sorted(cp_list)# sort by iter
-    if len(cp_list) > n_models: # if more than n_models models are found
-        for cp in cp_list[:-n_models]:# delete the oldest models other than lastest n_models
-            open(cp, 'w').close()# empty file contents
-            os.unlink(cp)# delete file (move to trash when using Colab)
-
-
-def scan_checkpoint(cp_dir, prefix):
-    pattern = os.path.join(cp_dir, prefix + '????????')
-    cp_list = glob.glob(pattern)
-    if len(cp_list) == 0:
-        return None
-    return sorted(cp_list)[-1]
-
diff --git a/spaces/chilleverydaychill/roop/README.md b/spaces/chilleverydaychill/roop/README.md
deleted file mode 100644
index 8765ab0b78d11834fa64339bc2aacf743657ea64..0000000000000000000000000000000000000000
--- a/spaces/chilleverydaychill/roop/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: Roop
-emoji: 📈
-colorFrom: gray
-colorTo: pink
-sdk: gradio
-sdk_version: 3.35.2
-app_file: app.py
-pinned: false
-license: agpl-3.0
-duplicated_from: ezioruan/roop
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/altair/__init__.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/altair/__init__.py
deleted file mode 100644
index ad57eb9d3889bfbf5bbe868b0eaae7aa4d3049c1..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/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/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/docx/image/image.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/docx/image/image.py
deleted file mode 100644
index ba2158e72f1ed54dccb41be9fc5e20820e7a7356..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/docx/image/image.py
+++ /dev/null
@@ -1,263 +0,0 @@
-# encoding: utf-8
-
-"""
-Provides objects that can characterize image streams as to content type and
-size, as a required step in including them in a document.
-"""
-
-from __future__ import absolute_import, division, print_function
-
-import hashlib
-import os
-
-from ..compat import BytesIO, is_string
-from .exceptions import UnrecognizedImageError
-from ..shared import Emu, Inches, lazyproperty
-
-
-class Image(object):
-    """
-    Graphical image stream such as JPEG, PNG, or GIF with properties and
-    methods required by ImagePart.
-    """
-    def __init__(self, blob, filename, image_header):
-        super(Image, self).__init__()
-        self._blob = blob
-        self._filename = filename
-        self._image_header = image_header
-
-    @classmethod
-    def from_blob(cls, blob):
-        """
-        Return a new |Image| subclass instance parsed from the image binary
-        contained in *blob*.
-        """
-        stream = BytesIO(blob)
-        return cls._from_stream(stream, blob)
-
-    @classmethod
-    def from_file(cls, image_descriptor):
-        """
-        Return a new |Image| subclass instance loaded from the image file
-        identified by *image_descriptor*, a path or file-like object.
-        """
-        if is_string(image_descriptor):
-            path = image_descriptor
-            with open(path, 'rb') as f:
-                blob = f.read()
-                stream = BytesIO(blob)
-            filename = os.path.basename(path)
-        else:
-            stream = image_descriptor
-            stream.seek(0)
-            blob = stream.read()
-            filename = None
-        return cls._from_stream(stream, blob, filename)
-
-    @property
-    def blob(self):
-        """
-        The bytes of the image 'file'
-        """
-        return self._blob
-
-    @property
-    def content_type(self):
-        """
-        MIME content type for this image, e.g. ``'image/jpeg'`` for a JPEG
-        image
-        """
-        return self._image_header.content_type
-
-    @lazyproperty
-    def ext(self):
-        """
-        The file extension for the image. If an actual one is available from
-        a load filename it is used. Otherwise a canonical extension is
-        assigned based on the content type. Does not contain the leading
-        period, e.g. 'jpg', not '.jpg'.
-        """
-        return os.path.splitext(self._filename)[1][1:]
-
-    @property
-    def filename(self):
-        """
-        Original image file name, if loaded from disk, or a generic filename
-        if loaded from an anonymous stream.
-        """
-        return self._filename
-
-    @property
-    def px_width(self):
-        """
-        The horizontal pixel dimension of the image
-        """
-        return self._image_header.px_width
-
-    @property
-    def px_height(self):
-        """
-        The vertical pixel dimension of the image
-        """
-        return self._image_header.px_height
-
-    @property
-    def horz_dpi(self):
-        """
-        Integer dots per inch for the width of this image. Defaults to 72
-        when not present in the file, as is often the case.
-        """
-        return self._image_header.horz_dpi
-
-    @property
-    def vert_dpi(self):
-        """
-        Integer dots per inch for the height of this image. Defaults to 72
-        when not present in the file, as is often the case.
-        """
-        return self._image_header.vert_dpi
-
-    @property
-    def width(self):
-        """
-        A |Length| value representing the native width of the image,
-        calculated from the values of `px_width` and `horz_dpi`.
-        """
-        return Inches(self.px_width / self.horz_dpi)
-
-    @property
-    def height(self):
-        """
-        A |Length| value representing the native height of the image,
-        calculated from the values of `px_height` and `vert_dpi`.
-        """
-        return Inches(self.px_height / self.vert_dpi)
-
-    def scaled_dimensions(self, width=None, height=None):
-        """
-        Return a (cx, cy) 2-tuple representing the native dimensions of this
-        image scaled by applying the following rules to *width* and *height*.
-        If both *width* and *height* are specified, the return value is
-        (*width*, *height*); no scaling is performed. If only one is
-        specified, it is used to compute a scaling factor that is then
-        applied to the unspecified dimension, preserving the aspect ratio of
-        the image. If both *width* and *height* are |None|, the native
-        dimensions are returned. The native dimensions are calculated using
-        the dots-per-inch (dpi) value embedded in the image, defaulting to 72
-        dpi if no value is specified, as is often the case. The returned
-        values are both |Length| objects.
-        """
-        if width is None and height is None:
-            return self.width, self.height
-
-        if width is None:
-            scaling_factor = float(height) / float(self.height)
-            width = round(self.width * scaling_factor)
-
-        if height is None:
-            scaling_factor = float(width) / float(self.width)
-            height = round(self.height * scaling_factor)
-
-        return Emu(width), Emu(height)
-
-    @lazyproperty
-    def sha1(self):
-        """
-        SHA1 hash digest of the image blob
-        """
-        return hashlib.sha1(self._blob).hexdigest()
-
-    @classmethod
-    def _from_stream(cls, stream, blob, filename=None):
-        """
-        Return an instance of the |Image| subclass corresponding to the
-        format of the image in *stream*.
-        """
-        image_header = _ImageHeaderFactory(stream)
-        if filename is None:
-            filename = 'image.%s' % image_header.default_ext
-        return cls(blob, filename, image_header)
-
-
-def _ImageHeaderFactory(stream):
-    """
-    Return a |BaseImageHeader| subclass instance that knows how to parse the
-    headers of the image in *stream*.
-    """
-    from docx.image import SIGNATURES
-
-    def read_32(stream):
-        stream.seek(0)
-        return stream.read(32)
-
-    header = read_32(stream)
-    for cls, offset, signature_bytes in SIGNATURES:
-        end = offset + len(signature_bytes)
-        found_bytes = header[offset:end]
-        if found_bytes == signature_bytes:
-            return cls.from_stream(stream)
-    raise UnrecognizedImageError
-
-
-class BaseImageHeader(object):
-    """
-    Base class for image header subclasses like |Jpeg| and |Tiff|.
-    """
-    def __init__(self, px_width, px_height, horz_dpi, vert_dpi):
-        self._px_width = px_width
-        self._px_height = px_height
-        self._horz_dpi = horz_dpi
-        self._vert_dpi = vert_dpi
-
-    @property
-    def content_type(self):
-        """
-        Abstract property definition, must be implemented by all subclasses.
-        """
-        msg = (
-            'content_type property must be implemented by all subclasses of '
-            'BaseImageHeader'
-        )
-        raise NotImplementedError(msg)
-
-    @property
-    def default_ext(self):
-        """
-        Default filename extension for images of this type. An abstract
-        property definition, must be implemented by all subclasses.
-        """
-        msg = (
-            'default_ext property must be implemented by all subclasses of '
-            'BaseImageHeader'
-        )
-        raise NotImplementedError(msg)
-
-    @property
-    def px_width(self):
-        """
-        The horizontal pixel dimension of the image
-        """
-        return self._px_width
-
-    @property
-    def px_height(self):
-        """
-        The vertical pixel dimension of the image
-        """
-        return self._px_height
-
-    @property
-    def horz_dpi(self):
-        """
-        Integer dots per inch for the width of this image. Defaults to 72
-        when not present in the file, as is often the case.
-        """
-        return self._horz_dpi
-
-    @property
-    def vert_dpi(self):
-        """
-        Integer dots per inch for the height of this image. Defaults to 72
-        when not present in the file, as is often the case.
-        """
-        return self._vert_dpi
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/misc/xmlReader.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/misc/xmlReader.py
deleted file mode 100644
index d8e502f141e9cb5df6ea11352b565c9a9cd4aa3d..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/misc/xmlReader.py
+++ /dev/null
@@ -1,188 +0,0 @@
-from fontTools import ttLib
-from fontTools.misc.textTools import safeEval
-from fontTools.ttLib.tables.DefaultTable import DefaultTable
-import sys
-import os
-import logging
-
-
-log = logging.getLogger(__name__)
-
-
-class TTXParseError(Exception):
-    pass
-
-
-BUFSIZE = 0x4000
-
-
-class XMLReader(object):
-    def __init__(
-        self, fileOrPath, ttFont, progress=None, quiet=None, contentOnly=False
-    ):
-        if fileOrPath == "-":
-            fileOrPath = sys.stdin
-        if not hasattr(fileOrPath, "read"):
-            self.file = open(fileOrPath, "rb")
-            self._closeStream = True
-        else:
-            # assume readable file object
-            self.file = fileOrPath
-            self._closeStream = False
-        self.ttFont = ttFont
-        self.progress = progress
-        if quiet is not None:
-            from fontTools.misc.loggingTools import deprecateArgument
-
-            deprecateArgument("quiet", "configure logging instead")
-            self.quiet = quiet
-        self.root = None
-        self.contentStack = []
-        self.contentOnly = contentOnly
-        self.stackSize = 0
-
-    def read(self, rootless=False):
-        if rootless:
-            self.stackSize += 1
-        if self.progress:
-            self.file.seek(0, 2)
-            fileSize = self.file.tell()
-            self.progress.set(0, fileSize // 100 or 1)
-            self.file.seek(0)
-        self._parseFile(self.file)
-        if self._closeStream:
-            self.close()
-        if rootless:
-            self.stackSize -= 1
-
-    def close(self):
-        self.file.close()
-
-    def _parseFile(self, file):
-        from xml.parsers.expat import ParserCreate
-
-        parser = ParserCreate()
-        parser.StartElementHandler = self._startElementHandler
-        parser.EndElementHandler = self._endElementHandler
-        parser.CharacterDataHandler = self._characterDataHandler
-
-        pos = 0
-        while True:
-            chunk = file.read(BUFSIZE)
-            if not chunk:
-                parser.Parse(chunk, 1)
-                break
-            pos = pos + len(chunk)
-            if self.progress:
-                self.progress.set(pos // 100)
-            parser.Parse(chunk, 0)
-
-    def _startElementHandler(self, name, attrs):
-        if self.stackSize == 1 and self.contentOnly:
-            # We already know the table we're parsing, skip
-            # parsing the table tag and continue to
-            # stack '2' which begins parsing content
-            self.contentStack.append([])
-            self.stackSize = 2
-            return
-        stackSize = self.stackSize
-        self.stackSize = stackSize + 1
-        subFile = attrs.get("src")
-        if subFile is not None:
-            if hasattr(self.file, "name"):
-                # if file has a name, get its parent directory
-                dirname = os.path.dirname(self.file.name)
-            else:
-                # else fall back to using the current working directory
-                dirname = os.getcwd()
-            subFile = os.path.join(dirname, subFile)
-        if not stackSize:
-            if name != "ttFont":
-                raise TTXParseError("illegal root tag: %s" % name)
-            if self.ttFont.reader is None and not self.ttFont.tables:
-                sfntVersion = attrs.get("sfntVersion")
-                if sfntVersion is not None:
-                    if len(sfntVersion) != 4:
-                        sfntVersion = safeEval('"' + sfntVersion + '"')
-                    self.ttFont.sfntVersion = sfntVersion
-            self.contentStack.append([])
-        elif stackSize == 1:
-            if subFile is not None:
-                subReader = XMLReader(subFile, self.ttFont, self.progress)
-                subReader.read()
-                self.contentStack.append([])
-                return
-            tag = ttLib.xmlToTag(name)
-            msg = "Parsing '%s' table..." % tag
-            if self.progress:
-                self.progress.setLabel(msg)
-            log.info(msg)
-            if tag == "GlyphOrder":
-                tableClass = ttLib.GlyphOrder
-            elif "ERROR" in attrs or ("raw" in attrs and safeEval(attrs["raw"])):
-                tableClass = DefaultTable
-            else:
-                tableClass = ttLib.getTableClass(tag)
-                if tableClass is None:
-                    tableClass = DefaultTable
-            if tag == "loca" and tag in self.ttFont:
-                # Special-case the 'loca' table as we need the
-                #    original if the 'glyf' table isn't recompiled.
-                self.currentTable = self.ttFont[tag]
-            else:
-                self.currentTable = tableClass(tag)
-                self.ttFont[tag] = self.currentTable
-            self.contentStack.append([])
-        elif stackSize == 2 and subFile is not None:
-            subReader = XMLReader(subFile, self.ttFont, self.progress, contentOnly=True)
-            subReader.read()
-            self.contentStack.append([])
-            self.root = subReader.root
-        elif stackSize == 2:
-            self.contentStack.append([])
-            self.root = (name, attrs, self.contentStack[-1])
-        else:
-            l = []
-            self.contentStack[-1].append((name, attrs, l))
-            self.contentStack.append(l)
-
-    def _characterDataHandler(self, data):
-        if self.stackSize > 1:
-            # parser parses in chunks, so we may get multiple calls
-            # for the same text node; thus we need to append the data
-            # to the last item in the content stack:
-            # https://github.com/fonttools/fonttools/issues/2614
-            if (
-                data != "\n"
-                and self.contentStack[-1]
-                and isinstance(self.contentStack[-1][-1], str)
-                and self.contentStack[-1][-1] != "\n"
-            ):
-                self.contentStack[-1][-1] += data
-            else:
-                self.contentStack[-1].append(data)
-
-    def _endElementHandler(self, name):
-        self.stackSize = self.stackSize - 1
-        del self.contentStack[-1]
-        if not self.contentOnly:
-            if self.stackSize == 1:
-                self.root = None
-            elif self.stackSize == 2:
-                name, attrs, content = self.root
-                self.currentTable.fromXML(name, attrs, content, self.ttFont)
-                self.root = None
-
-
-class ProgressPrinter(object):
-    def __init__(self, title, maxval=100):
-        print(title)
-
-    def set(self, val, maxval=None):
-        pass
-
-    def increment(self, val=1):
-        pass
-
-    def setLabel(self, text):
-        print(text)
diff --git a/spaces/cihyFjudo/fairness-paper-search/Osu Auto Aimbot Downloadl How to Install and Use It Safely.md b/spaces/cihyFjudo/fairness-paper-search/Osu Auto Aimbot Downloadl How to Install and Use It Safely.md
deleted file mode 100644
index 8b4470e74dbee615533b731a1cd48cc52eb1c34b..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/Osu Auto Aimbot Downloadl How to Install and Use It Safely.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>Osu Auto Aimbot Downloadl</h2><br /><p><b><b>DOWNLOAD</b> &rarr; <a href="https://tinurli.com/2uwiW9">https://tinurli.com/2uwiW9</a></b></p><br /><br />
-
- aaccfb2cb3<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/cihyFjudo/fairness-paper-search/Wondershare Filmora 9.1.4.12 (x64) Multilingual.md b/spaces/cihyFjudo/fairness-paper-search/Wondershare Filmora 9.1.4.12 (x64) Multilingual.md
deleted file mode 100644
index da1f5e576b7baa3920955168347f70cfeb5947ce..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/Wondershare Filmora 9.1.4.12 (x64) Multilingual.md	
+++ /dev/null
@@ -1,79 +0,0 @@
-## Wondershare Filmora 9.1.4.12 (x64) Multilingual
-
-
-
-**Click Here › [https://walllowcopo.blogspot.com/?download=2twr2X](https://walllowcopo.blogspot.com/?download=2twr2X)**
-
-
-
-# Wondershare Filmora 9.1.4.12 (x64) Multilingual: A Video Editor for All Creators
-
- 
-
-Wondershare Filmora 9.1.4.12 (x64) Multilingual is a powerful and easy-to-use video editing software that allows you to create stunning videos with rich features and effects. Whether you are a beginner or a professional, you can find everything you need to express your creativity and impress your audience.
-
- 
-
-With Wondershare Filmora 9.1.4.12 (x64) Multilingual, you can:
-
- 
-
-- Filter, overlay, transition and customize titles to enhance your videos.
-
-- Discover infinite ways to express yourself with a wide range of effects and templates.
-
-- Achieve a refined look with endless effects such as keyframing, background noise removal, audio equalizer, green screen, and more.
-
-- Utilize up to 100 layers of media to create complex stories with ease.
-
-- Fast processing, proxy files, and adjustable preview quality help you be more productive.
-
-- Fix common action cam problems like fisheye and camera shake, and add effects like slow motion and reverse.
-
-- Change the aesthetic of your video with one click using creative filters and professional 3D LUTs.
-
-- Tailor your content to any platform and upload it directly from Filmora 9.
-
-
-
-Wondershare Filmora 9.1.4.12 (x64) Multilingual is compatible with Windows 7/Windows 8.1/Windows 10 (64 bit OS) and requires at least 4 GB RAM (8GB required for HD and 4K videos), 2 GB vRAM (4GB required for HD and 4K videos), and 10GB free hard-disk space for installation (SSD-Solid State Disk recommended for editing HD and 4K videos).
-
- 
-
-If you are looking for a video editor that can empower your imagination and help you create without limits, Wondershare Filmora 9.1.4.12 (x64) Multilingual is the perfect choice for you. Download it today and start your video editing journey!
-
-  
-
-Wondershare Filmora 9.1.4.12 (x64) Multilingual is not only a video editor, but also a video maker. You can use it to create various types of videos, such as:
-
-
-
-- Slideshow: Turn your photos and videos into a stunning slideshow with music and transitions.
-
-- Screen recording: Capture your screen activity and webcam simultaneously and edit them with annotations and cursor effects.
-
-- Split screen: Showcase multiple videos on one screen with different layouts and effects.
-
-- Picture in picture: Overlay multiple videos or images on your main video and adjust their position and size.
-
-- Motion tracking: Attach an element to a moving object in your video and make it follow its movement.
-
-- Keyframing: Animate any element in your video by adding keyframes to change its position, rotation, scale, and opacity.
-
-- Color matching: Apply the color settings of one video clip to another to achieve a consistent look.
-
-- Audio ducking: Automatically lower the volume of background music when dialogue or narration is detected.
-
-
-
-With Wondershare Filmora 9.1.4.12 (x64) Multilingual, you can unleash your creativity and make amazing videos for any occasion. Whether you want to make a video for your family, friends, business, or social media, you can find the right tools and features in Filmora 9. You can also explore the Filmora Effects Store to access more effects, transitions, titles, and music for your videos.
-
- 
-
-Wondershare Filmora 9.1.4.12 (x64) Multilingual is a video editor that you can trust and enjoy. It is designed to be user-friendly, intuitive, and fun. It supports a wide range of formats and resolutions, and allows you to export your videos in different ways. You can also contact the customer support team anytime if you have any questions or issues.
-
- 
-
-Don't wait any longer. Download Wondershare Filmora 9.1.4.12 (x64) Multilingual today and start making awesome videos!
-
- 1b8d091108
\ No newline at end of file
diff --git a/spaces/cmagganas/chainlit-arxiv/chainlit.md b/spaces/cmagganas/chainlit-arxiv/chainlit.md
deleted file mode 100644
index 2a0d457490af0e4536b9b9054a9a51ff0b8ae4bb..0000000000000000000000000000000000000000
--- a/spaces/cmagganas/chainlit-arxiv/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/ZThrUxbAYw) 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.
\ No newline at end of file
diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/arm/pixblockdsp_init_arm.c b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/arm/pixblockdsp_init_arm.c
deleted file mode 100644
index 5481c0178c03e74d3b4a5612c92ed399fb697c38..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/arm/pixblockdsp_init_arm.c
+++ /dev/null
@@ -1,61 +0,0 @@
-/*
- * 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 <stdint.h>
-
-#include "libavutil/attributes.h"
-#include "libavutil/cpu.h"
-#include "libavutil/arm/cpu.h"
-#include "libavcodec/avcodec.h"
-#include "libavcodec/pixblockdsp.h"
-
-void ff_get_pixels_armv6(int16_t *block, const uint8_t *pixels,
-                         ptrdiff_t stride);
-void ff_diff_pixels_armv6(int16_t *block, const uint8_t *s1,
-                          const uint8_t *s2, ptrdiff_t stride);
-
-void ff_get_pixels_neon(int16_t *block, const uint8_t *pixels,
-                        ptrdiff_t stride);
-void ff_get_pixels_unaligned_neon(int16_t *block, const uint8_t *pixels,
-                                  ptrdiff_t stride);
-void ff_diff_pixels_neon(int16_t *block, const uint8_t *s1,
-                         const uint8_t *s2, ptrdiff_t stride);
-void ff_diff_pixels_unaligned_neon(int16_t *block, const uint8_t *s1,
-                                   const uint8_t *s2, ptrdiff_t stride);
-
-av_cold void ff_pixblockdsp_init_arm(PixblockDSPContext *c,
-                                     AVCodecContext *avctx,
-                                     unsigned high_bit_depth)
-{
-    int cpu_flags = av_get_cpu_flags();
-
-    if (have_armv6(cpu_flags)) {
-        if (!high_bit_depth)
-            c->get_pixels = ff_get_pixels_armv6;
-        c->diff_pixels = ff_diff_pixels_armv6;
-    }
-
-    if (have_neon(cpu_flags)) {
-        if (!high_bit_depth) {
-            c->get_pixels_unaligned = ff_get_pixels_unaligned_neon;
-            c->get_pixels = ff_get_pixels_neon;
-        }
-        c->diff_pixels_unaligned = ff_diff_pixels_unaligned_neon;
-        c->diff_pixels = ff_diff_pixels_neon;
-    }
-}
diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/hdrenc.c b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/hdrenc.c
deleted file mode 100644
index 40d283ee610790249dc5c2bd4f8115269b28ccbd..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/hdrenc.c
+++ /dev/null
@@ -1,190 +0,0 @@
-/*
- * Radiance HDR image format
- *
- * 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/imgutils.h"
-#include "avcodec.h"
-#include "bytestream.h"
-#include "codec_internal.h"
-#include "encode.h"
-
-typedef struct HDREncContext {
-    uint8_t *scanline;
-} HDREncContext;
-
-static av_cold int hdr_encode_init(AVCodecContext *avctx)
-{
-    HDREncContext *s = avctx->priv_data;
-
-    s->scanline = av_calloc(avctx->width * 4, sizeof(*s->scanline));
-    if (!s->scanline)
-        return AVERROR(ENOMEM);
-
-    return 0;
-}
-
-static av_cold int hdr_encode_close(AVCodecContext *avctx)
-{
-    HDREncContext *s = avctx->priv_data;
-
-    av_freep(&s->scanline);
-
-    return 0;
-}
-
-static void bytestream_put_str(uint8_t **buf, const char *const line)
-{
-    bytestream_put_buffer(buf, line, strlen(line));
-}
-
-static void float2rgbe(uint8_t *rgbe, float red, float green, float blue)
-{
-    float v;
-    int e;
-
-    v = FFMAX3(red, green, blue);
-
-    if (v < 1e-32f) {
-        rgbe[0] = rgbe[1] = rgbe[2] = rgbe[3] = 0;
-    } else {
-        v = frexpf(v, &e) * 256.f / v;
-
-        rgbe[0] = av_clip_uint8(red * v);
-        rgbe[1] = av_clip_uint8(green * v);
-        rgbe[2] = av_clip_uint8(blue * v);
-        rgbe[3] = av_clip_uint8(e + 128);
-    }
-}
-
-static void rle(uint8_t **buffer, const uint8_t *data, int width)
-{
-#define MIN_RLE 4
-    int cur = 0;
-
-    while (cur < width) {
-        int run_count = 0, old_run_count = 0;
-        int beg_run = cur;
-        uint8_t buf[2];
-
-        while (run_count < MIN_RLE && beg_run < width) {
-            beg_run += run_count;
-            old_run_count = run_count;
-            run_count = 1;
-            while ((beg_run + run_count < width) && (run_count < 127) &&
-                   (data[beg_run * 4] == data[(beg_run + run_count) * 4]))
-                run_count++;
-        }
-
-        if ((old_run_count > 1) && (old_run_count == beg_run - cur)) {
-            buf[0] = 128 + old_run_count;
-            buf[1] = data[cur * 4];
-            bytestream_put_buffer(buffer, buf, sizeof(buf));
-            cur = beg_run;
-        }
-
-        while (cur < beg_run) {
-            int nonrun_count = FFMIN(128, beg_run - cur);
-            buf[0] = nonrun_count;
-            bytestream_put_byte(buffer, buf[0]);
-            for (int n = 0; n < nonrun_count; n++)
-                bytestream_put_byte(buffer, data[(cur + n) * 4]);
-            cur += nonrun_count;
-        }
-
-        if (run_count >= MIN_RLE) {
-            buf[0] = 128 + run_count;
-            buf[1] = data[beg_run * 4];
-            bytestream_put_buffer(buffer, buf, sizeof(buf));
-            cur += run_count;
-        }
-    }
-}
-
-static int hdr_encode_frame(AVCodecContext *avctx, AVPacket *pkt,
-                            const AVFrame *frame, int *got_packet)
-{
-    HDREncContext *s = avctx->priv_data;
-    int64_t packet_size;
-    uint8_t *buf;
-    int ret;
-
-    packet_size = avctx->width * avctx->height * 4LL + 1024LL;
-    if ((ret = ff_get_encode_buffer(avctx, pkt, packet_size, 0)) < 0)
-        return ret;
-
-    buf = pkt->data;
-    bytestream_put_str(&buf, "#?RADIANCE\n");
-    bytestream_put_str(&buf, "SOFTWARE=lavc\n");
-    ret = snprintf(buf, 32, "PIXASPECT=%f\n", av_q2d(av_inv_q(avctx->sample_aspect_ratio)));
-    if (ret > 0)
-        buf += ret;
-    bytestream_put_str(&buf, "FORMAT=32-bit_rle_rgbe\n\n");
-    ret = snprintf(buf, 32, "-Y %d +X %d\n", avctx->height, avctx->width);
-    if (ret > 0)
-        buf += ret;
-
-    for (int y = 0; y < avctx->height; y++) {
-        const float *red   = (const float *)(frame->data[2] + y * frame->linesize[2]);
-        const float *green = (const float *)(frame->data[0] + y * frame->linesize[0]);
-        const float *blue  = (const float *)(frame->data[1] + y * frame->linesize[1]);
-
-        if (avctx->width < 8 || avctx->width > 0x7fff) {
-            for (int x = 0; x < avctx->width; x++) {
-                float2rgbe(buf, red[x], green[x], blue[x]);
-                buf += 4;
-            }
-        } else {
-            bytestream_put_byte(&buf, 2);
-            bytestream_put_byte(&buf, 2);
-            bytestream_put_byte(&buf, avctx->width >> 8);
-            bytestream_put_byte(&buf, avctx->width & 0xFF);
-
-            for (int x = 0; x < avctx->width; x++)
-                float2rgbe(s->scanline + 4 * x, red[x], green[x], blue[x]);
-            for (int p = 0; p < 4; p++)
-                rle(&buf, s->scanline + p, avctx->width);
-        }
-    }
-
-    pkt->flags |= AV_PKT_FLAG_KEY;
-
-    av_shrink_packet(pkt, buf - pkt->data);
-
-    *got_packet = 1;
-
-    return 0;
-}
-
-const FFCodec ff_hdr_encoder = {
-    .p.name         = "hdr",
-    CODEC_LONG_NAME("HDR (Radiance RGBE format) image"),
-    .priv_data_size = sizeof(HDREncContext),
-    .p.type         = AVMEDIA_TYPE_VIDEO,
-    .p.id           = AV_CODEC_ID_RADIANCE_HDR,
-    .p.capabilities = AV_CODEC_CAP_DR1 | AV_CODEC_CAP_FRAME_THREADS |
-                      AV_CODEC_CAP_ENCODER_REORDERED_OPAQUE,
-    .init           = hdr_encode_init,
-    FF_CODEC_ENCODE_CB(hdr_encode_frame),
-    .close          = hdr_encode_close,
-    .p.pix_fmts     = (const enum AVPixelFormat[]){
-        AV_PIX_FMT_GBRPF32,
-        AV_PIX_FMT_NONE
-    },
-    .caps_internal  = FF_CODEC_CAP_INIT_CLEANUP,
-};
diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/hevc_parse.h b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/hevc_parse.h
deleted file mode 100644
index 4ab96ab1cb68f36a5137702621f1ae84740d1780..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/hevc_parse.h
+++ /dev/null
@@ -1,36 +0,0 @@
-/*
- * 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
- * H.265 parser code
- */
-
-#ifndef AVCODEC_HEVC_PARSE_H
-#define AVCODEC_HEVC_PARSE_H
-
-#include <stdint.h>
-
-#include "hevc_ps.h"
-#include "hevc_sei.h"
-
-int ff_hevc_decode_extradata(const uint8_t *data, int size, HEVCParamSets *ps,
-                             HEVCSEI *sei, int *is_nalff, int *nal_length_size,
-                             int err_recognition, int apply_defdispwin, void *logctx);
-
-#endif /* AVCODEC_HEVC_PARSE_H */
diff --git a/spaces/congsaPfin/Manga-OCR/logs/Experience the Original Anime Story with ONE PUNCH MAN The World APK.md b/spaces/congsaPfin/Manga-OCR/logs/Experience the Original Anime Story with ONE PUNCH MAN The World APK.md
deleted file mode 100644
index a9f6c1eca154e58093fccd8d1db0759987b19c6b..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/Experience the Original Anime Story with ONE PUNCH MAN The World APK.md	
+++ /dev/null
@@ -1,107 +0,0 @@
-<br />
-<h1>One Punch Man The World APK: A Guide for Fans</h1>
-<p>If you are a fan of the popular anime series One Punch Man, you might be interested in playing a mobile game based on it. One Punch Man The World APK is a fan-made game that lets you enter the world of One Punch Man and become a hero or a monster. In this article, we will tell you everything you need to know about this game, including how to download and install it, how to play it, and what are the benefits of playing it.</p>
-<h2>one punch man the world apk</h2><br /><p><b><b>Download File</b> &#9193; <a href="https://urlca.com/2uOceh">https://urlca.com/2uOceh</a></b></p><br /><br />
-<h2>What is One Punch Man The World APK?</h2>
-<p>One Punch Man The World APK is an unofficial game that is not affiliated with the original anime or manga creators. It is developed by fans who love the series and want to share their passion with other fans. The game is a role-playing game (RPG) that allows you to create your own character and explore the world of One Punch Man. You can choose to be a hero or a monster, and fight against other players or cooperate with them. You can also interact with the characters from the anime, such as Saitama, Genos, Tatsumaki, Sonic, Garou, and more.</p>
-<p>The game has many features that make it fun and exciting, such as:</p>
-<ul>
-<li>A realistic 3D graphics engine that brings the anime to life</li>
-<li>A dynamic combat system that lets you use various skills and combos</li>
-<li>A rich story mode that follows the anime plot and events</li>
-<li>A variety of game modes, such as arena, dungeon, boss battle, guild war, etc.</li>
-<li>A social system that lets you chat, trade, and team up with other players</li>
-<li>A customization system that lets you change your appearance, equipment, and skills</li>
-<li>A collection system that lets you unlock and upgrade your favorite characters and items</li>
-</ul>
-<h2>How to download and install One Punch Man The World APK?</h2>
-<p>If you want to play One Punch Man The World APK, you need to download and install it on your Android device. Here are the steps you need to follow:</p>
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-<ol>
-<li>Go to [this link](^1^) and download the APK file of the game. Make sure you have enough storage space on your device.</li>
-<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 the APK file.</li>
-<li>Locate the APK file on your device and tap on it to start the installation process. Follow the instructions on the screen.</li>
-<li>When the installation is done, you will see an icon of the game on your home screen. Tap on it to launch the game.</li>
-<li>You will need to create an account or log in with an existing one. You can also use your Facebook or Google account to sign in.</li>
-<li>You will then be able to choose your character name, gender, class, and appearance. You can also skip this step and customize your character later.</li>
-<li>You are now ready to play One Punch Man The World APK!</li>
-</ol>
-<h2>How to play One Punch Man The World APK?</h2>
-<p>One Punch Man The World APK is easy to play once you get familiar with the controls and mechanics. Here are some tips on how to play the game:</p>
-<h3>How to create and customize your character</h3>
-<p>One Punch Man The World APK lets you create your own character and customize it to your liking. You can choose from four classes: warrior, mage, assassin, and healer. Each class has its own strengths, weaknesses, and skills. You can also change your character's appearance, such as hair, eyes, skin, clothes, and accessories. You can use the in-game currency or real money to buy more items and outfits. You can also upgrade your character's level, stats, and skills by completing quests and battles.</p>
-<h3>How to fight and train your character</h3>
-<p>One Punch Man The World APK has a dynamic combat system that lets you use various skills and combos to defeat your enemies. You can control your character with a virtual joystick and buttons on the screen. You can also switch between auto and manual mode depending on your preference. You can use the normal attack, the skill attack, the dodge, and the ultimate skill. You can also activate the one punch mode when your energy bar is full, which lets you unleash a powerful punch that can knock out any enemy.</p>
-<p>You can fight against different types of enemies, such as monsters, villains, heroes, and other players. You can also train your character by using the training dummy or the training room. You can earn experience points, gold, and items by fighting and training.</p>
-<h3>How to join and interact with other players</h3>
-<p>One Punch Man The World APK is a multiplayer game that lets you join and interact with other players from around the world. You can chat with them using the chat system or the voice chat system. You can also trade with them using the trade system or the auction system. You can also team up with them using the team system or the guild system. You can join or create a guild with other players who share your interests and goals. You can participate in guild activities, such as guild wars, guild quests, guild raids, etc.</p>
-<h2>What are the benefits of playing One Punch Man The World APK?</h2>
-<p>Playing One Punch Man The World APK has many benefits for fans of the anime series. Here are some of them:</p>
-<h4>Enjoy the original anime story and characters</h4>
-<p>One Punch Man The World APK follows the original anime story and events. You can relive the epic moments and scenes from the anime, such as Saitama's fight with Boros, Genos' fight with Sonic, Tatsumaki's fight with Psykos, etc. You can also interact with the characters from the anime, such as Saitama, Genos, Tatsumaki, Sonic, Garou, King, Fubuki, Bang, Mumen Rider, etc. You can hear their original voice actors and see their expressions and animations.</p>
-<h4>Experience the thrill of being a hero or a monster</h4>
-<p>One Punch Man The World APK lets you choose to be a hero or a monster. You can join the Hero Association or the Monster Association and fight for your cause. You can also rank up in your association and earn fame and reputation. You can also challenge other players who belong to the opposite association and prove your strength.</p>
-<h4>Collect and upgrade your favorite characters and items</h4>
-<p>One Punch Man The World APK has a collection system that lets you unlock and upgrade your favorite characters and items. You can collect characters from different categories, such as S-class heroes, A-class heroes, B-class heroes, C-class heroes, monsters, villains, etc. You can also collect items from different categories, such as weapons, armors, accessories, consumables, etc. You can use the items to enhance your character's performance and appearance. You can also upgrade your characters and items by using the upgrade system or the fusion system.</p>
-<h2>Conclusion</h2>
-<p>One Punch Man The World APK is a fan-made game that is a must-play for fans of the anime series. It lets you create your own character and enter the world of One Punch Man. You can enjoy the original anime story and characters, experience the thrill of being a hero or a monster, and collect and upgrade your favorite characters and items. You can also join and interact with other players from around the world and have fun together. If you want to play One Punch Man The World APK, you can download and install it from [this link] and follow the instructions in this article. Have fun playing One Punch Man The World APK!</p>
-<h2>FAQs</h2>
-<p>Here are some frequently asked questions about One Punch Man The World APK:</p>
-<ol>
-<li>Is One Punch Man The World APK safe to download and play?</li>
-<p>Yes, One Punch Man The World APK is safe to download and play. It does not contain any viruses or malware. However, since it is an unofficial game, it may not be compatible with some devices or systems. You should also be careful when downloading the APK file from other sources, as they may contain harmful or fake files.</p>
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-<p>Yes, One Punch Man The World APK is free to play. You can download and install it without paying any fees. However, the game does have some optional in-app purchases that can enhance your gaming experience. You can buy more items, characters, or currency with real money. You can also watch ads or complete surveys to earn some free rewards.</p>
-<li>How can I contact the developers of One Punch Man The World APK?</li>
-<p>You can contact the developers of One Punch Man The World APK by using their official Facebook page or email address. You can find their contact information on their website or in the game settings. You can also join their Discord server or Reddit community to chat with them and other players.</p>
-<li>How can I report a bug or a problem in One Punch Man The World APK?</li>
-<p>If you encounter a bug or a problem in One Punch Man The World APK, you can report it by using the feedback system in the game settings. You can also contact the developers directly by using their contact information. You should provide as much detail as possible about the bug or problem, such as screenshots, device model, game version, etc.</p>
-<li>How can I support the development of One Punch Man The World APK?</li>
-<p>If you want to support the development of One Punch Man The World APK, you can do so by making a donation to the developers via PayPal or Patreon. You can also support them by rating and reviewing the game on Google Play or other platforms. You can also share the game with your friends and family and invite them to play with you.</p>
-</ol></p> 401be4b1e0<br />
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/Murder by Choice Clue Mystery - Download MOD APK 2.0.10 and Become a Detective.md b/spaces/congsaPfin/Manga-OCR/logs/Murder by Choice Clue Mystery - Download MOD APK 2.0.10 and Become a Detective.md
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--- a/spaces/congsaPfin/Manga-OCR/logs/Murder by Choice Clue Mystery - Download MOD APK 2.0.10 and Become a Detective.md	
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-<p>Do you love mystery, suspense, and drama? Do you want to experience a game that lets you decide the fate of the characters? If yes, then you should try Murder by Choice, a game that will keep you on the edge of your seat. Murder by Choice is an interactive story game that lets you choose how the story unfolds. You will play as a detective who has to solve a series of murders that are connected to a mysterious organization. You will also meet various characters who will help or hinder you along the way. You will have to make choices that will affect the outcome of the game and determine who lives and who dies.</p>
- <h2>What is Murder by Choice?</h2>
-<h3>The plot of the game</h3>
-<p>Murder by Choice is set in a city where a series of murders have been happening. The victims are all members of a secret society called The Order, which is involved in shady activities and has a lot of enemies. You are a detective who has been assigned to investigate these murders and find out who is behind them. You will have to follow clues, interrogate suspects, and collect evidence. You will also encounter various characters who have their own motives and secrets. Some of them will be your allies, while others will be your enemies. You will have to decide who to trust and who to betray.</p>
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- <h3>The features of the game</h3>
-<p>Murder by Choice is a game that offers you a lot of features that make it fun and engaging. Some of these features are:</p>
-<ul>
-<li>A captivating story with multiple branches and endings.</li>
-<li>A choice-based gameplay that lets you shape the story according to your preferences.</li>
-<li>A variety of characters with different personalities and backgrounds.</li>
-<li>A stunning graphics and sound effects that create a realistic and immersive atmosphere.</li>
-<li>A simple and intuitive user interface that makes it easy to play.</li>
-</ul>
- <h2>Why download Murder by Choice Mod APK 2.0.10?</h2>
-<h3>The benefits of the mod version</h3>
-<p>While Murder by Choice is a free game, it also has some limitations that can affect your enjoyment. For example, you need to have enough coins and gems to unlock certain choices and chapters. You also need to watch ads to get some rewards or access some features. These can be annoying and frustrating, especially if you want to play without interruptions or restrictions.</p>
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-<p>Downloading and installing Murder by Choice Mod APK 2.0.10 is very easy and simple. Just follow these steps:</p>
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-<li>Go to your device's settings and enable the installation of apps from unknown sources.</li>
-<li>Locate the downloaded file in your device's file manager and tap on it to start the installation process.</li>
-<li>Follow the instructions on the screen and wait for the installation to be completed.</li>
-<li>Launch the game and enjoy the mod features.</li>
-</ol>
- <h2>Tips and tricks for playing Murder by Choice</h2>
-<h3>How to make the best choices</h3>
-<p>Murder by Choice is a game that requires you to make choices that will affect the story and the characters. Some choices are more important than others, and some can have serious consequences. Here are some tips on how to make the best choices:</p>
-<ul>
-<li>Think carefully before you choose. Don't rush or act impulsively. Consider the possible outcomes and implications of your choice.</li>
-<li>Pay attention to the clues and hints that are given throughout the game. They can help you solve the mystery and avoid traps.</li>
-<li>Try to balance your relationships with the characters. Don't be too friendly or too hostile with anyone. You never know who might be your friend or foe.</li>
-<li>Don't be afraid to explore different options and scenarios. You can always replay the game and try different choices to see how they affect the story.</li>
-</ul>
- <h3>How to unlock all endings</h3>
-<p>Murder by Choice has multiple endings that depend on your choices and actions. Some endings are good, while others are bad. Some endings are also hidden and require you to meet certain conditions. Here are some tips on how to unlock all endings:</p>
-<ul>
-<li>Complete all chapters of the game. You can't unlock all endings if you skip or miss any chapter.</li>
-<li>Make different choices in each playthrough. You can't unlock all endings if you make the same choices every time.</li>
-<li>Use the mod apk to access all endings without restrictions. You can use the mod apk to unlock all endings from the start, or to change your choices at any point of the game.</li>
-</ul>
- <h3>How to earn coins and gems</h3>
-<p>Coins and gems are the currency of Murder by Choice. You need them to unlock certain choices and chapters that can affect the story. You can earn coins and gems by playing the game, or by using real money. Here are some ways to earn coins and gems:</p>
-<ul>
-<li>Play the game regularly and complete chapters. You will get coins and gems as rewards for finishing each chapter.</li>
-<li>Watch ads and complete offers. You will get coins and gems as rewards for watching ads or completing offers that are available in the game.</li>
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- <h2>Conclusion</h2>
-<p>Murder by Choice is a game that will keep you entertained and intrigued for hours. It is a game that lets you create your own story and decide who lives and who dies. It is a game that has amazing graphics, sound effects, and user interface. It is a game that you should download and play right now.</p>
- <p>If you want to enjoy Murder by Choice without any limitations or interruptions, you should download Murder by Choice Mod APK 2.0.10, which is a modified version of the game that gives you unlimited coins and gems, no ads, all chapters unlocked, and all endings unlocked. You can download Murder by Choice Mod APK 2.0.10 from this link  and follow the instructions above to install it on your device.</p>
- <p>We hope this article has helped you learn more about Murder by Choice and its mod apk. If you have any questions or feedback, please feel free to leave a comment below. Thank you for reading!</p>
- <h2>FAQs</h2>
-<p>Here are some frequently asked questions about Murder by Choice and its mod apk:</p>
- <table border="1">
-<tr><th>Question</th><th>Answer</th></tr>
-<tr><td>Is Murder by Choice safe to play?</td><td>Yes, Murder by Choice is safe to play. It does not contain any viruses, malware, or harmful content. However, you should always download it from a trusted source, such as Google Play Store or our link .</td></tr>
-<tr><td>Is Murder by Choice Mod APK 2.0.10 safe to download?</td><td>Yes, Murder by Choice Mod APK 2.0.10 is safe to download. It does not contain any viruses, malware, or harmful content either. However, you should always download it from our link , which is verified and tested by us.</td></tr>
-<tr><td>Will I get banned for using Murder by Choice Mod APK 2.0.10?</td><td>No, you will not get banned for using Murder by Choice Mod APK 2.0.10. This mod apk is undetectable and compatible with the latest version of the game. However, you should always use it at your own risk and discretion.</td></tr>
-<tr><td>Can I play Murder by Choice offline?</td><td>No, you cannot play Murder by Choice offline. You need to have an internet connection to play the game, as it requires data to load the chapters and choices. However, you can play the game without using much data, as it does not consume much bandwidth.</td></tr>
-<tr><td>Can I play Murder by Choice on PC?</td><td>Yes, you can play Murder by Choice on PC. You can use an Android emulator, such as BlueStacks or NoxPlayer, to run the game on your PC. You can download the emulator from their official websites and follow the instructions to install and use it. You can then download Murder by Choice or its mod apk from our link and install it on the emulator.</td></tr>
-</table></p> 401be4b1e0<br />
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/PUBG Mobile MOD APK Menu How to Unlock Unlimited UC and AimBot Hack in 2022.md b/spaces/congsaPfin/Manga-OCR/logs/PUBG Mobile MOD APK Menu How to Unlock Unlimited UC and AimBot Hack in 2022.md
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--- a/spaces/congsaPfin/Manga-OCR/logs/PUBG Mobile MOD APK Menu How to Unlock Unlimited UC and AimBot Hack in 2022.md	
+++ /dev/null
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-<p>If you are a fan of PUBG Mobile, the popular battle royale game that has millions of players worldwide, you might be interested in downloading and installing the PUBG Mobile Mod Menu APK 2022. This is a modified version of the original game that offers various cheats and hacks that can enhance your gaming experience. But before you do that, you should know what this mod menu apk is, how it works, and what are the benefits and risks of using it. In this article, we will tell you everything you need to know about PUBG Mobile Mod Menu APK 2022, including how to download, install, and use it.</p>
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-<h3>A brief introduction to the popular battle royale game</h3>
-<p>PUBG Mobile is a mobile version of PlayerUnknown's Battlegrounds, a multiplayer online battle royale game that was released in 2017. The game is developed by Tencent Games and PUBG Corporation, and is available for Android and iOS devices. The game has been one of the most downloaded and played games in the world, with over 1 billion downloads and 50 million daily active users as of 2021.</p>
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-<p>PUBG Mobile is a survival shooter game that pits up to 100 players against each other on an island map. The players can choose to play solo, duo, or in squads of up to four players. The objective of the game is to be the last one standing by eliminating other players or avoiding them. The game starts with the players parachuting onto the map from an airplane. They then have to scavenge for weapons, armor, ammo, health kits, and other items that can help them survive. The map also has a shrinking safe zone that forces the players to move closer to each other as the game progresses. The game also features various modes, such as classic mode, arcade mode, arena mode, payload mode, infection mode, and more. Each mode has different rules, objectives, and challenges for the players.</p>
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-<p>PUBG Mobile Mod Menu APK is a modified version of the original PUBG Mobile game that allows the players to access and use various cheats and hacks that are not available in the official game. These cheats and hacks can give the players an unfair advantage over other players, such as aimbot, wallhack, speedhack, unlimited health, unlimited ammo, unlimited UC (in-game currency), and more. The mod menu apk also has a user-friendly interface that lets the players customize and toggle these cheats and hacks according to their preferences.</p>
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-<p>The main benefit of using PUBG Mobile Mod Menu APK is that it can make the game more fun and easy for the players who want to enjoy the game without any limitations or challenges. The players can also win more matches, rank up faster, unlock more items, and show off their skills with these cheats and hacks. However, there are also some risks involved in using PUBG Mobile Mod Menu APK. <p>One of the biggest risks of using PUBG Mobile Mod Menu APK is that it can get the players banned from the official game. PUBG Mobile has a strict anti-cheat system that detects and punishes the players who use any unauthorized mods, hacks, or cheats. The players can face temporary or permanent bans, depending on the severity of their violations. Another risk of using PUBG Mobile Mod Menu APK is that it can expose the players to malware, viruses, or phishing attacks. Some of the mod menu apk files that are available on the internet may contain harmful or malicious code that can infect the players' devices or steal their personal information. Therefore, the players should be careful and cautious when downloading and installing any mod menu apk files from unknown or untrusted sources.</p>
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-<p>If you want to download and install PUBG Mobile Mod Menu APK 2022, you should follow these steps:</p>
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-<li>Aimbot: This feature allows you to automatically aim and shoot at your enemies with perfect accuracy and precision. You can also adjust the aimbot settings, such as aim range, aim speed, aim angle, aim lock, etc.</li>
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-<li>Unlimited UC: This feature allows you to have unlimited UC (in-game currency) that you can use to buy or upgrade items, skins, outfits, crates, etc. in the game.</li>
-</ul>
-<h3>The tips and tricks to use the mod menu apk safely and effectively</h3>
-<p>If you want to use PUBG Mobile Mod Menu APK 2022 safely and effectively, you should follow these tips and tricks:</p>
-<ul>
-<li>Use the mod menu apk only for fun and entertainment purposes. Do not use it for cheating or harming other players in the game.</li>
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-<p>Do you love driving trucks and exploring different terrains? Do you want to experience a realistic and challenging truck simulator game? If yes, then you should try <strong>Truck Driver Crazy Road 2</strong>, one of the most popular truck driving games on Android.</p>
-<p>Truck Driver Crazy Road 2 is a game that lets you drive various trucks across four locations with different weather conditions and landscapes. You can choose from multiple game modes, such as service, parking, or free mode (in development), and complete 40 exciting levels. You can also enjoy beautiful 3D graphics, realistic physics, pleasant music, radio, and easy controls.</p>
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-<p>In this article, we will show you how to download and install Truck Driver Crazy Road 2 mod apk for free on your Android device. We will also give you some tips on how to play the game and have fun. Let's get started!</p>
- <h2>How to Download and Install Truck Driver Crazy Road 2 Mod Apk</h2>
-<p>To download and install Truck Driver Crazy Road 2 mod apk, you need to follow these three simple steps:</p>
- <h3>Step 1: Enable Unknown Sources</h3>
-<p>Before you can install any mod apk file on your Android device, you need to enable unknown sources. This is a security feature that prevents you from installing apps from sources other than the Google Play Store. To enable unknown sources, follow these steps:</p>
-<ul>
-<li>Go to your device's <strong>Settings</strong> and tap on <strong>Security</strong>.</li>
-<li>Find the option that says <strong>Unknown Sources</strong> and toggle it on.</li>
-<li>A warning message will pop up. Tap on <strong>OK</strong> to confirm.</li>
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-<p>Now you are ready to download the mod apk file.</p>
- <h3>Step 2: Download the Mod Apk File</h3>
-<p>The next step is to download the mod apk file of Truck Driver Crazy Road 2. You can find many websites that offer this file, but be careful as some of them may contain viruses or malware. To download the mod apk file safely and quickly, we recommend you to use this link: [Truck Driver Crazy Road 2 Mod Apk].</p>
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-<p>The final step is to install the mod apk file on your device. To do this, follow these steps:</p>
-<ul>
-<li>Locate the downloaded mod apk file on your device. You can use a file manager app or go to your <strong>Downloads</strong> folder.</li>
-<li>Tap on the mod apk file and a prompt will appear. Tap on <strong>Install</strong> to start the installation process.</li>
-<li>The installation may take a few seconds or minutes depending on your device. Once it is done, tap on <strong>Open</strong> to launch the game.</li>
-<li>The game will ask you for some permissions, such as access to your storage and phone. Grant them and enjoy the game.</li>
-</ul>
-<p>Congratulations! You have successfully downloaded and installed Truck Driver Crazy Road 2 mod apk on your Android device. Now you can enjoy all the features and trucks in the game without any limitations or ads.</p>
- <h2>How to Play Truck Driver Crazy Road 2 Mod Apk</h2>
-<p>Now that you have installed the game, you may wonder how to play it and have fun. Don't worry, we have got you covered. Here are some tips on how to play Truck Driver Crazy Road 2 mod apk:</p>
- <h3>Choose Your Game Mode</h3>
-<p>The first thing you need to do is choose your game mode. There are three game modes available in Truck Driver Crazy Road 2: service, parking, and free mode (in development). Each game mode has its own challenges and objectives. Here is a brief overview of each game mode:</p>
- | Game Mode | Description | |-----------|-------------| | Service | In this mode, you need to deliver cargo from one point to another within a given time limit. You need to avoid damaging your truck or losing your cargo along the way. You can earn money and stars for completing each level. | | Parking | In this mode, you need to park your truck in a designated spot without hitting any obstacles or other vehicles. You need to be careful and precise with your steering and braking. You can earn money and stars for completing each level. | | Free Mode (in development) | In this mode, you can drive your truck freely across any location without any rules or restrictions. You can explore the scenery, perform stunts, or just relax. This mode is still in development and will be updated soon with more features and options. | <p>To select a game mode, tap on the <strong>Play</strong> button on the main menu and then tap on the game mode icon. You can also change your truck by tapping on the <strong>Truck</strong> button.</p>
- <h3>Drive Your Truck Across Various Locations</h3>
-<p>The next thing you need to do is drive your truck across various locations with different weather conditions and landscapes. There are four locations available in Truck Driver Crazy Road 2: forest, desert, mountain, and winter. Each location has its own challenges and obstacles, such as mud, sand, rocks, snow, ice, etc. You need to adapt your driving skills and techniques according to each location.</p>
-<p>To drive your truck, use the buttons on the right side of the screen. You can use the <strong>Gas</strong> button to accelerate, the <strong>Brake</strong> button to slow down or stop, and the <strong>Left</strong> and <strong>Right</strong> buttons to steer. You can also use the <strong>Gear</strong> button to switch between forward and reverse modes.</p>
-<p>To change the camera angle, tap on the <strong>Camera</strong> button on the top left corner of the screen. You can choose from four different views: first-person, third-person, top-down, or free. You can also zoom in or out by pinching the screen.</p>
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-<p>To adjust the settings and customize your experience, tap on the <strong>Menu</strong> button on the top right corner of the screen. You can change the sound volume, the music, the radio station, the language, and more. You can also reset or quit the game if you want.</p>
- <h2>Conclusion</h2>
-<p>In conclusion, Truck Driver Crazy Road 2 is a fun and challenging truck simulator game that you can download and play for free on your Android device. You can drive various trucks across four locations with different weather conditions and landscapes. You can choose from multiple game modes, such as service, parking, or free mode (in development), and complete 40 exciting levels. You can also enjoy beautiful 3D graphics, realistic physics, pleasant music, radio, and easy controls.</p>
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-<p>We hope this article has helped you learn how to download and install Truck Driver Crazy Road 2 mod apk for free on your Android device. We also hope you have learned some tips on how to play the game and have fun. Now go ahead and try it out for yourself!</p>
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-<p>Here are some frequently asked questions about Truck Driver Crazy Road 2 mod apk:</p>
- <h4>Q: Is Truck Driver Crazy Road 2 mod apk safe to download and install?</h4>
-<p>A: Yes, Truck Driver Crazy Road 2 mod apk is safe to download and install as long as you use a trusted and reliable source like this one: [Truck Driver Crazy Road 2 Mod Apk]. However, you should always scan any file you download with an antivirus app before installing it on your device.</p>
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-<p>A: No, you do not need to root your device to install Truck Driver Crazy Road 2 mod apk. You just need to enable unknown sources in your device's settings and follow the steps in this article.</p>
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/Wolf In Sheep 39s Clothing Mp3 Download [WORK].md b/spaces/congsaPfin/Manga-OCR/logs/Wolf In Sheep 39s Clothing Mp3 Download [WORK].md
deleted file mode 100644
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- <p>Another well-known song titled Wolf in Sheep's Clothing is by Johnny Winter, an American blues guitarist and singer who died in 2014. The song was released in 1970 as part of his fourth studio album, Johnny Winter And. The song is about a woman who cheats on her lover with another man, while pretending to be faithful and innocent. The chorus goes like this:</p>
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- <ol>
-<li>What is the difference between Wolf in Sheep's Clothing by Set It Off and Johnny Winter?</li>
-<p>Wolf in Sheep's Clothing by Set It Off is a rock song that was released in 2014 as part of their second studio album, Duality. The song is about a toxic relationship where one partner is abusive and manipulative, while the other is naive and loyal. Wolf in Sheep's Clothing by Johnny Winter is a blues song that was released in 1970 as part of his fourth studio album, Johnny Winter And. The song is about a woman who cheats on her lover with another man, while pretending to be faithful and innocent.</p>
-<li>Where can I find the lyrics of Wolf in Sheep's Clothing by Set It Off or Johnny Winter?</li>
-<p>You can find the lyrics of Wolf in Sheep's Clothing by Set It Off or Johnny Winter on various websites or apps that provide song lyrics, such as Genius, AZLyrics, Musixmatch, or Lyrics.com. You can also find the lyrics on the official music videos or fan-made videos on YouTube.</p>
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-<p>You can convert Wolf in Sheep's Clothing MP3 to other audio formats using various software or online tools that offer audio conversion services, such as Audacity, Freemake Audio Converter, Online Audio Converter, or Zamzar. You can choose the output format that suits your needs, such as WAV, FLAC, AAC, or OGG.</p>
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-<li>How can I share Wolf in Sheep's Clothing MP3 with my friends or family?</li>
-<p>You can share Wolf in Sheep's Clothing MP3 with your friends or family using various methods, such as Bluetooth, Wi-Fi, USB, or email. You can also share it through social media platforms, such as Facebook, Twitter, Instagram, or WhatsApp. However, you should always respect the rights and interests of the artists and the record labels, and not share it without their permission or compensation.</p>
-</ol></p> 197e85843d<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/contluForse/HuggingGPT/assets/Download Xforce Keygen UPD Collaboration For Revit 2016 Activation.md b/spaces/contluForse/HuggingGPT/assets/Download Xforce Keygen UPD Collaboration For Revit 2016 Activation.md
deleted file mode 100644
index b6ebef1aba126a8160c9ecd661561656dec8c7f4..0000000000000000000000000000000000000000
--- a/spaces/contluForse/HuggingGPT/assets/Download Xforce Keygen UPD Collaboration For Revit 2016 Activation.md	
+++ /dev/null
@@ -1,34 +0,0 @@
-<br />
-<h1>How to Download and Activate X-force Keygen for Autodesk Collaboration for Revit 2016</h1>
-<p>X-force Keygen is a software that can generate product keys for Autodesk products, including Collaboration for Revit 2016. Collaboration for Revit 2016 is a cloud-based service that allows multiple users to work on the same Revit project across different locations. With X-force Keygen, you can activate Collaboration for Revit 2016 and enjoy its features without paying for a subscription.</p>
-<p>In this article, I will show you how to download and activate X-force Keygen for Autodesk Collaboration for Revit 2016 in a few simple steps.</p>
-<h2>download xforce keygen Collaboration for Revit 2016 activation</h2><br /><p><b><b>Download</b> &#10145; <a href="https://ssurll.com/2uzvCq">https://ssurll.com/2uzvCq</a></b></p><br /><br />
-<h2>Step 1: Download X-force Keygen for Autodesk 2016</h2>
-<p>The first step is to download X-force Keygen for Autodesk 2016 from a reliable source. You can use one of the links below[^1^] [^2^] [^3^] [^4^] to download the software. Make sure you choose the right version for your operating system (Windows or Mac).</p>
-<ul>
-<li><a href="https://iggtech.com/download-x-force-2016-1/">Download X-force 2016 - All Product key for Autodesk 2016</a></li>
-<li><a href="https://azdly.com/x-force-2016-download/">X-Force 2016 Free Download - All Product key for Autodesk 2016</a></li>
-<li><a href="https://civilmdc.com/2020/03/10/x-force-keygenerator-autodesk-products-2016-all/">X-force KeyGenerator. Autodesk Products. (2016) ALL</a></li>
-<li><a href="https://civilmdc.com/2020/03/10/autodesk-2016-all-products-x-force-keygenerator/">Autodesk 2016 ALL-Products + X-Force (KeyGenerator)</a></li>
-</ul>
-<p>After downloading the file, extract it using a file compression tool such as WinRAR or 7-Zip. You will find a folder named "X-Force 2016" that contains the keygen executable file.</p>
-<h2>Step 2: Install Collaboration for Revit 2016</h2>
-<p>The next step is to install Collaboration for Revit 2016 on your computer. You can download the installation file from the official Autodesk website or use a DVD if you have one. Follow the instructions on the screen to complete the installation process. When prompted to enter a product key, use one of the following codes depending on your product:</p>
-<table>
-<tr><th>Product Name</th><th>Product Key</th></tr>
-<tr><td>Autodesk AutoCAD Revit Collaboration Suite 2016</td><td>A78H1</td></tr>
-<tr><td>Autodesk Building Design Suite Premium 2016</td><td>765H1</td></tr>
-<tr><td>Autodesk Building Design Suite Standard 2016</td><td>784H1</td></tr>
-<tr><td>Autodesk Building Design Suite Ultimate 2016</td><td>766H1</td></tr>
-<tr><td>Autodesk Infrastructure Design Suite Premium 2016</td><td>786H1</td></tr>
-<tr><td>Autodesk Infrastructure Design Suite Standard 2016</td><td>787H1</td></tr>
-<tr><td>Autodesk Infrastructure Design Suite Ultimate 2016</td><td>785H1</td></tr>
-<tr><td>Autodesk Plant Design Suite Premium 2016</td><td>763H1</td></tr>
-<tr><td>Autodesk Plant Design Suite Standard 2016</td><td>788H1</td></tr>
-<tr><td>Autodesk Plant Design Suite Ultimate 2016</td><td>764H1</td></tr>
-<tr><td>Autodesk Revit Architecture 2016</td><td>240H1</td></tr>
-<tr><td>Autodesk Revit LT 2016</td><td>828H1</td></tr>
-<tr><td>Autodesk Revit MEP 2016</td><</p>
-<p></p> d5da3c52bf<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmcv/cnn/bricks/context_block.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmcv/cnn/bricks/context_block.py
deleted file mode 100644
index d60fdb904c749ce3b251510dff3cc63cea70d42e..0000000000000000000000000000000000000000
--- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmcv/cnn/bricks/context_block.py
+++ /dev/null
@@ -1,125 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import torch
-from torch import nn
-
-from ..utils import constant_init, kaiming_init
-from .registry import PLUGIN_LAYERS
-
-
-def last_zero_init(m):
-    if isinstance(m, nn.Sequential):
-        constant_init(m[-1], val=0)
-    else:
-        constant_init(m, val=0)
-
-
-@PLUGIN_LAYERS.register_module()
-class ContextBlock(nn.Module):
-    """ContextBlock module in GCNet.
-
-    See 'GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond'
-    (https://arxiv.org/abs/1904.11492) for details.
-
-    Args:
-        in_channels (int): Channels of the input feature map.
-        ratio (float): Ratio of channels of transform bottleneck
-        pooling_type (str): Pooling method for context modeling.
-            Options are 'att' and 'avg', stand for attention pooling and
-            average pooling respectively. Default: 'att'.
-        fusion_types (Sequence[str]): Fusion method for feature fusion,
-            Options are 'channels_add', 'channel_mul', stand for channelwise
-            addition and multiplication respectively. Default: ('channel_add',)
-    """
-
-    _abbr_ = 'context_block'
-
-    def __init__(self,
-                 in_channels,
-                 ratio,
-                 pooling_type='att',
-                 fusion_types=('channel_add', )):
-        super(ContextBlock, self).__init__()
-        assert pooling_type in ['avg', 'att']
-        assert isinstance(fusion_types, (list, tuple))
-        valid_fusion_types = ['channel_add', 'channel_mul']
-        assert all([f in valid_fusion_types for f in fusion_types])
-        assert len(fusion_types) > 0, 'at least one fusion should be used'
-        self.in_channels = in_channels
-        self.ratio = ratio
-        self.planes = int(in_channels * ratio)
-        self.pooling_type = pooling_type
-        self.fusion_types = fusion_types
-        if pooling_type == 'att':
-            self.conv_mask = nn.Conv2d(in_channels, 1, kernel_size=1)
-            self.softmax = nn.Softmax(dim=2)
-        else:
-            self.avg_pool = nn.AdaptiveAvgPool2d(1)
-        if 'channel_add' in fusion_types:
-            self.channel_add_conv = nn.Sequential(
-                nn.Conv2d(self.in_channels, self.planes, kernel_size=1),
-                nn.LayerNorm([self.planes, 1, 1]),
-                nn.ReLU(inplace=True),  # yapf: disable
-                nn.Conv2d(self.planes, self.in_channels, kernel_size=1))
-        else:
-            self.channel_add_conv = None
-        if 'channel_mul' in fusion_types:
-            self.channel_mul_conv = nn.Sequential(
-                nn.Conv2d(self.in_channels, self.planes, kernel_size=1),
-                nn.LayerNorm([self.planes, 1, 1]),
-                nn.ReLU(inplace=True),  # yapf: disable
-                nn.Conv2d(self.planes, self.in_channels, kernel_size=1))
-        else:
-            self.channel_mul_conv = None
-        self.reset_parameters()
-
-    def reset_parameters(self):
-        if self.pooling_type == 'att':
-            kaiming_init(self.conv_mask, mode='fan_in')
-            self.conv_mask.inited = True
-
-        if self.channel_add_conv is not None:
-            last_zero_init(self.channel_add_conv)
-        if self.channel_mul_conv is not None:
-            last_zero_init(self.channel_mul_conv)
-
-    def spatial_pool(self, x):
-        batch, channel, height, width = x.size()
-        if self.pooling_type == 'att':
-            input_x = x
-            # [N, C, H * W]
-            input_x = input_x.view(batch, channel, height * width)
-            # [N, 1, C, H * W]
-            input_x = input_x.unsqueeze(1)
-            # [N, 1, H, W]
-            context_mask = self.conv_mask(x)
-            # [N, 1, H * W]
-            context_mask = context_mask.view(batch, 1, height * width)
-            # [N, 1, H * W]
-            context_mask = self.softmax(context_mask)
-            # [N, 1, H * W, 1]
-            context_mask = context_mask.unsqueeze(-1)
-            # [N, 1, C, 1]
-            context = torch.matmul(input_x, context_mask)
-            # [N, C, 1, 1]
-            context = context.view(batch, channel, 1, 1)
-        else:
-            # [N, C, 1, 1]
-            context = self.avg_pool(x)
-
-        return context
-
-    def forward(self, x):
-        # [N, C, 1, 1]
-        context = self.spatial_pool(x)
-
-        out = x
-        if self.channel_mul_conv is not None:
-            # [N, C, 1, 1]
-            channel_mul_term = torch.sigmoid(self.channel_mul_conv(context))
-            out = out * channel_mul_term
-        if self.channel_add_conv is not None:
-            # [N, C, 1, 1]
-            channel_add_term = self.channel_add_conv(context)
-            out = out + channel_add_term
-
-        return out
diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/mmseg/models/necks/__init__.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/mmseg/models/necks/__init__.py
deleted file mode 100644
index 9b9d3d5b3fe80247642d962edd6fb787537d01d6..0000000000000000000000000000000000000000
--- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/mmseg/models/necks/__init__.py
+++ /dev/null
@@ -1,4 +0,0 @@
-from .fpn import FPN
-from .multilevel_neck import MultiLevelNeck
-
-__all__ = ['FPN', 'MultiLevelNeck']
diff --git a/spaces/cybercorejapan/human-detection-docker/models/trackers/reid_parallel_tracker/parallel_tracker.py b/spaces/cybercorejapan/human-detection-docker/models/trackers/reid_parallel_tracker/parallel_tracker.py
deleted file mode 100644
index ff0b7beff7ef873e989e93e41dbbca9eac4e6c69..0000000000000000000000000000000000000000
--- a/spaces/cybercorejapan/human-detection-docker/models/trackers/reid_parallel_tracker/parallel_tracker.py
+++ /dev/null
@@ -1,470 +0,0 @@
-
-from typing import Tuple, Dict,List
-import numpy as np
-from .core.tracklet import TrackState
-from mmcv.ops import bbox_overlaps
-import torch
-from .three_stage_tracker import ThreeStageTracker
-from .matchers.distances import DistCosine
-
-
-class ParallelTracker(ThreeStageTracker):
-    def __init__(self,
-                 reid_filter_det=None,
-                 *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        # remove duplicated boxes
-        self.reid_filter_det = reid_filter_det
-        if reid_filter_det is not None:
-            self.reid_filter_det_dist = DistCosine(**reid_filter_det['dist_cfg'])
-        else:
-            self.reid_filter_det_dist = None
-        self.history_changes = []
-
-    def merge_active_lost_tracks(self,
-                                 dets_high:np.array,
-                                 active_tracks:List,
-                                 lost_tracks:List,
-                                 matched_active_track_indices:np.array,
-                                 matched_lost_track_indices:np.array):
-
-        """
-        The merge_active_lost_tracks merges active and lost tracks based on matching detection results, updating their associated 
-        indices. The merging is guided by certain criteria, such as distance comparisons between tracks and 
-        detections, and whether the tracks share active frames.
-
-        Args:
-
-            dets_high (array): An array representing high-confidence detections.
-            active_tracks (list): A list of active track objects.
-            lost_tracks (list): A list of lost track objects.
-            matched_active_track_indices (array): An array of matched indices between active tracks and detections.
-            matched_lost_track_indices (array): An array of matched indices between lost tracks and detections.
-
-        Returns:
-
-            A dictionary containing various arrays representing different aspects of the merging process:
-                unmerged_active_track_indices: Array of unmatched active track indices that are not merged with lost tracks and cannot match with any detection.
-                unmerged_lost_track_indices: Array of unmatched lost track indices that are not merged with active tracks and cannot match with any detection.
-                matched_pairs_active: Array of matched indices between active tracks and detections after the merging process.
-                matched_pairs_lost: Array of matched indices between lost tracks and detections after the merging process.
-                active_tracks_match_lost_tracks_detection: Array of active track indices that were matched to the same detection as lost tracks.
-                merged_lost_indices: Array of lost track indices that were merged with active tracks.
-        """
-
-        unmerged_active_track_indices = []
-        unmerged_lost_track_indices = []
-        matched_pairs_active = []  # Match_pairs 1
-        matched_pairs_lost = []  # Match_pairs 1
-        active_tracks_match_lost_tracks_detection = []
-        merged_lost_indices = []
-
-        # Active/lost tracks match to different detections --> match_pairs_1
-        matched_active_det_indices = matched_active_track_indices[:, 1].tolist(
-        )
-        matched_lost_det_indices = matched_lost_track_indices[:, 1].tolist()
-
-        for idx, det_idx in enumerate(matched_active_det_indices):
-            if not det_idx in matched_lost_det_indices:
-                matched_pairs_active.append(matched_active_track_indices[idx])
-
-        for idx, det_idx in enumerate(matched_lost_det_indices):
-            if not det_idx in matched_active_det_indices:
-                matched_pairs_lost.append(matched_lost_track_indices[idx])
-
-        # detection --matches--> row-th of active track in matched_active_track_indices[:, 0]
-        det_active_rowmatrix_dict = dict()
-        for idx, (_active_trackidx, _detidx) in enumerate(matched_active_track_indices):
-            det_active_rowmatrix_dict[_detidx] = idx
-
-        # detection --matches--> row-th of lost track in matched_lost_track_indices[:, 0]
-        det_lost_rowmatrix_dict = dict()
-        for idx, (_lost_trackidx, _detidx) in enumerate(matched_lost_track_indices):
-            det_lost_rowmatrix_dict[_detidx] = idx
-
-        # calculate distance between active_tracks and dets_high
-        active_dists = self.matcher_active.matching_dists(active_tracks,
-                                                          dets_high)
-        # calculate distance between lost_tracks and dets_high
-        lost_dists = self.matcher_lost.matching_dists(lost_tracks,
-                                                      dets_high)
-
-        # -------------------------- Main Loop ----------------------------------
-        for detidx, row_lost_idx in det_lost_rowmatrix_dict.items():
-            is_matched_to_the_same_detection = detidx in det_active_rowmatrix_dict
-            if is_matched_to_the_same_detection:
-                row_active_idx = det_active_rowmatrix_dict[detidx]
-                # one detection --> 2 tracks
-                active_track_idx = matched_active_track_indices[row_active_idx][0]
-                lost_track_idx = matched_lost_track_indices[row_lost_idx][0]
-
-                # Both active in the past at the same time?
-                both_active_in_the_past_at_the_same_time = active_tracks[active_track_idx].common_active_frames(
-                    lost_tracks[lost_track_idx])
-
-                if both_active_in_the_past_at_the_same_time:
-                    active_dist = active_dists[active_track_idx, detidx]
-                    lost_dist = lost_dists[lost_track_idx, detidx]
-                    # active_track-->det has smaller distance than lost_track-->det
-                    is_active_track_give_smaller_dist = active_dist <= lost_dist
-                    if is_active_track_give_smaller_dist:
-                        # assign detection to active track
-                        matched_pairs_active.append(
-                            matched_active_track_indices[row_active_idx])
-                        unmerged_lost_track_indices.append(
-                            lost_track_idx)
-                    else:
-                        # assign detection to lost track
-                        matched_pairs_lost.append(
-                            matched_lost_track_indices[row_lost_idx])
-                        unmerged_active_track_indices.append(
-                            active_track_idx)
-                else:
-                    # ------------------- Merge trajectories --------------------------------------
-                    # the active track will be removed, match this detection for lost tracklet
-                    active_tracks_match_lost_tracks_detection.append(
-                        active_track_idx)
-                    matched_pairs_lost.append(
-                        matched_lost_track_indices[row_lost_idx])
-                    merged_lost_indices.append(lost_track_idx)
-                    # update active frames for lost track
-                    lost_tracks[lost_track_idx].update_active_frames(
-                        active_tracks[active_track_idx].active_frames)
-
-        # convert to numpy array
-        unmerged_active_track_indices = np.array(
-            unmerged_active_track_indices)
-        unmerged_lost_track_indices = np.array(
-            unmerged_lost_track_indices)
-        matched_pairs_active = np.array(matched_pairs_active).reshape(-1, 2)
-        matched_pairs_lost = np.array(matched_pairs_lost).reshape(-1, 2)
-        active_tracks_match_lost_tracks_detection = np.array(
-            active_tracks_match_lost_tracks_detection)
-        merged_lost_indices = np.array(merged_lost_indices)
-
-        # return values
-        return dict(
-            unmerged_active_track_indices=unmerged_active_track_indices,
-            unmerged_lost_track_indices=unmerged_lost_track_indices,
-            matched_pairs_active=matched_pairs_active,  # Match_pairs 1
-            matched_pairs_lost=matched_pairs_lost,  # Match_pairs 1
-            active_tracks_match_lost_tracks_detection=active_tracks_match_lost_tracks_detection,
-            merged_lost_indices=merged_lost_indices,
-        )
-
-    def update(self,
-               det_result: Dict,
-               Hmat: np.array = None,
-               meta_data: Dict = None) -> Tuple[Dict, Dict]:
-        """ 
-        The update function is the main method for performing tracking. It takes a detection result, a homography transformation matrix (optional), 
-        and additional meta-data (optional) as input and returns the active tracks and lost tracks in the current frame after updating 
-        their states and associations with new detections.
-
-        Args:
-
-            det_result (dict): A dictionary representing the detection result from a detector.
-            Hmat (np.array, optional): A NumPy array representing the homography transformation matrix. 
-            meta_data (dict, optional): Additional meta-data that may be used during the tracking process.
-            
-        Returns:
-
-            active_tracks (dict): A dictionary representing the tracked active tracks in the current frame. 
-            lost_tracks (dict): A dictionary representing the lost tracks in the current frame. 
-            modifications (dict): A dictionary containing modifications made during the tracking process.
-        """
-        self.frame_id += 1
-
-        # Step 1: Split the detections into high score/lower score group
-        det_result = self.preprocess_det_result(det_result)
-
-        # remove duplicated detection by ReID
-        if self.reid_filter_det_dist is not None:
-            det_result = self.remove_det_by_reid(det_result)
-
-        dets_high, dets_low = self.split_detections_by_scores(det_result)
-
-        # Step 2: Split the tracks into active_tracks, lost_tracks, and unconfirmed (just initialize)
-        active_tracks, lost_tracks, unconfirmed = self.split_tracks_by_activation()
-
-        # - predict the current location with KF, and compensate for Camera Motion
-        self.predict_with_gmc(active_tracks, lost_tracks, unconfirmed, Hmat)
-
-        # Step 3: Matching Stage 1.1 - Association with high score detection boxes and active tracks
-        matched_active_track_indices, unmatched_active_track_indices, unmatched_active_det_indices \
-            = self.matcher_active(active_tracks, dets_high)
-
-        # Step 4: Matching Stage 1.2 - Association with high score detection boxes and lost tracks
-        matched_lost_track_indices, unmatch_lost_track_indices, unmatch_lost_det_indices = self.matcher_lost(
-            lost_tracks, dets_high)
-
-        # Step 5. assing the Lost status to the unmatch_tracks
-        lost_stracks = [active_tracks[it]
-                        for it in unmatched_active_track_indices if active_tracks[it].state != TrackState.Lost]
-        for track in lost_stracks:
-            track.mark_lost()
-
-        # Step 6. Merge active tracks and lost tracks
-        merged_results = self.merge_active_lost_tracks(dets_high,
-                                                       active_tracks,
-                                                       lost_tracks,
-                                                       matched_active_track_indices,
-                                                       matched_lost_track_indices)
-
-        unmerged_active_track_indices = merged_results[
-            'unmerged_active_track_indices']
-        unmerged_lost_track_indices = merged_results['unmerged_lost_track_indices']
-        match_pairs_1_1 = merged_results['matched_pairs_active']
-        match_pairs_1_2 = merged_results['matched_pairs_lost']
-        active_tracks_match_lost_tracks_detection = merged_results[
-            'active_tracks_match_lost_tracks_detection']
-        merged_lost_indices = merged_results['merged_lost_indices']
-
-        # Step 7. Assign the Lost status to the Merged unmatched active tracks
-        lost_stracks_1 = [active_tracks[it]
-                          for it in unmerged_active_track_indices if active_tracks[it].state != TrackState.Lost]
-        for track in lost_stracks_1:
-            track.mark_lost()
-        lost_stracks.extend(lost_stracks_1)
-
-        # Step 8. update Match_pairs 1
-        activated_stracks, refind_stracks = self.update_matched_tracks(
-            match_pairs_1_1, active_tracks, dets_high)
-
-        activated_stracks_1, refind_stracks_1 = self.update_matched_tracks(
-            match_pairs_1_2, lost_tracks, dets_high)
-
-        activated_stracks.extend(activated_stracks_1)
-        refind_stracks.extend(refind_stracks_1)
-
-        # Step 9. Remove active tracks that are merged into lost tracks
-        merged_active_tracks = [active_tracks[idx]
-                                for idx in active_tracks_match_lost_tracks_detection]
-        for track in merged_active_tracks:
-            track.mark_removed()
-
-        # for post processing: later step will know how to change switch ID back to the lost one
-        need_replaced_track_info = [
-            _m_track.history_info for _m_track in merged_active_tracks]
-        new_track_info = [
-            lost_tracks[_idx].history_info for _idx in merged_lost_indices]
-
-        # Step 10. Take Unmatched dets in both list
-        merge_unmatched_det_indices = list(set(unmatched_active_det_indices.tolist()).intersection(
-            set(unmatch_lost_det_indices.tolist())))
-
-        #          remain unmatch detection
-        unmatched_dets_2 = [
-            dets_high[_idx] for _idx in merge_unmatched_det_indices]
-
-        # Step 11: Matching Stage 2 - Association between new detections and unconfirmed tracks (tracks just initialized in the previous frame)
-        matched_pairs_2, unmatch_track_indices_2, unmatched_high_det_indices_2 = self.matcher_unconfirmed(
-            unconfirmed, unmatched_dets_2)
-
-        for itracked, idet in matched_pairs_2:
-            # Update matches_unconfirmed into active_tracks
-            unconfirmed[itracked].update(unmatched_dets_2[idet], self.frame_id)
-            activated_stracks.append(unconfirmed[itracked])
-
-        # Step 12. remove unconfirmed tracks that do not match any detections
-        removed_stracks = [unconfirmed[it] for it in unmatch_track_indices_2]
-        for track in removed_stracks:
-            track.mark_removed()
-
-        all_remove_tracks = removed_stracks + merged_active_tracks
-        # Step 13. init new stracks if they are high score and not too small boxes as tentative tracks (unconfirmed)
-        new_tracks = self.init_new_tracks(
-            unmatched_dets_2, unmatched_high_det_indices_2)
-
-        # new_tracks that have very high score and not occluded with current tracks will be directly activated
-        self.activate_new_tracks(
-            new_tracks, activated_stracks + refind_stracks)
-
-        # Step 14: remove lost tracks if they are already lost for a certain frames
-        self.lost_stracks.extend(lost_stracks)
-        removed_lost_stracks = self.remove_lost_tracks()
-        all_remove_tracks += removed_lost_stracks
-
-        # Step 15: Final result merging
-        active_tracks, lost_tracks = self.merge_results(
-            activated_stracks, refind_stracks, new_tracks, all_remove_tracks)
-
-        # update lost frame num
-        self.update_lost_frame()
-
-        modifications = dict(
-            need_replaced_track_info=need_replaced_track_info,
-            new_track_info=new_track_info
-        )
-
-        return active_tracks, lost_tracks, modifications
-
-    def remove_det_by_reid(self, det_result:Dict):
-        """
-        The remove_det_by_reid function is a method of a class that filters out detection results based on the 
-        ReID (Person Re-identification) distances between detected objects. It removes similar detections, keeping 
-        only those that are dissimilar according to the ReID distance threshold and certain object characteristics 
-        like bounding box overlap and detection scores.
-
-        Args:
-
-            det_result (dictionary): A dictionary containing the detection results.
-        Returns:
-
-            filter_det_result (dictionary): A dictionary containing the filtered detection results after applying the ReID-based filtering.
-        """
-        all_embeddings = det_result['embeddings']
-        reid_dists = self.reid_filter_det_dist(all_embeddings, all_embeddings)
-        np.fill_diagonal(reid_dists, 1e4)
-
-        # det boxes and score
-        boxes = det_result['boxes']
-        boxes = boxes.reshape(-1, 5)
-        boxes_tensor = torch.from_numpy(boxes)[:, :-1]
-
-        # score matrix
-        det_scores = boxes[:, -1]
-        det_scores_matrix = det_scores.reshape(-1,
-                                               1) > det_scores.reshape(1, -1)
-
-        x1, y1, x2, y2 = boxes[:, 0], boxes[:, 1], boxes[:, 2], boxes[:, 3]
-        width = x2 - x1
-        height = y2 - y1
-        areas = np.abs(width * height)
-        det_area_matrix = areas.reshape(-1, 1) > areas.reshape(1, -1)
-
-        box_iof = bbox_overlaps(boxes_tensor, boxes_tensor, mode='iof').numpy()
-        np.fill_diagonal(box_iof, 0.0)
-        iof_non_overllaped = box_iof < self.reid_filter_det['iof_thr']
-
-        reid_dists[iof_non_overllaped] = 1e4
-        # reid_dists[det_scores_matrix] = 1e4
-        reid_dists[det_area_matrix] = 1e4
-
-        remove_det_matrix = reid_dists < self.reid_filter_det['reid_dist_thr']
-        remove_det_matrix = remove_det_matrix.sum(1) > 0
-        remove_det_idx = np.nonzero(remove_det_matrix)[0]
-        # draw_queries_galleries(det_result['obj_imgs'], det_result['obj_imgs'], reid_dists, det_result['frame_id'])
-
-        if len(remove_det_idx):
-            filter_det_result = dict()
-            filter_det_result['frame_id'] = det_result['frame_id']
-            for key, val in det_result.items():
-                filter_val = []
-                if key in ['frame_id']:
-                    continue
-                for idx in range(len(val)):
-                    if not idx in remove_det_idx:
-                        filter_val.append(val[idx])
-                filter_det_result[key] = np.array(filter_val)
-        else:
-            filter_det_result = det_result
-
-        return filter_det_result
-
-    def remove_rows(self, matrix:np.array, row_indices:List, C=2):
-        """
-        The remove_rows function takes a 2D matrix as input and removes specific rows from 
-        the matrix based on the provided row indices. The resulting matrix is then returned with a reshaped number of columns.
-
-        Args:
-
-            matrix (numpy array): A 2D numpy array representing the input matrix.
-            row_indices (list): A list of integers representing the row indices to be removed from the matrix.
-            C (int, optional): An integer representing the number of columns in the output matrix.
-        Returns:
-
-            result_matrix (numpy array): The resulting 2D numpy array after removing the specified rows and reshaping with the 
-            specified number of columns (C).
-        """
-        row_indices = sorted(row_indices, reverse=True)
-        matrix = matrix.tolist()
-        for row_idx in row_indices:
-            matrix.pop(row_idx)
-        return np.array(matrix).reshape(-1, C)
-
-    def update_unmatch_track_det(self, tracklets:List, dets:List, matches_indices:np.array, remove_indices:List=[]):
-        """
-        The update_unmatch_track_det updates and retrieves the indices of unmatched tracklets 
-        and detections based on the provided matched indices and any specified indices for removal.
-
-        Args:
-
-            tracklets (list): A list of tracklet objects representing existing tracks.
-            dets (list): A list of detection objects representing detected items.
-            matches_indices (numpy array): A 2D numpy array containing matched indices between tracklets and detections.
-            remove_indices (list, optional): A list of integers representing indices of tracklets to be removed. 
-
-        Returns:
-
-            unmatched_track_indices (numpy array): A 1D numpy array containing the indices of unmatched tracklets (tracks that do not 
-            have any matches with detections).
-            unmatched_det_indices (numpy array): A 1D numpy array containing the indices of unmatched detections (detections that do 
-            not have any matches with tracklets).
-        """
-        # update unmatch track, det
-        unmatched_track_indices, unmatched_det_indices = [], []
-        for _track_idx in range(len(tracklets)):
-            if _track_idx in remove_indices:
-                continue
-            if not _track_idx in matches_indices[:, 0]:
-                unmatched_track_indices.append(_track_idx)
-        for _det_idx in range(len(dets)):
-            if not _det_idx in matches_indices[:, 1]:
-                unmatched_det_indices.append(_det_idx)
-        unmatched_track_indices = np.array(unmatched_track_indices)
-        unmatched_det_indices = np.array(unmatched_det_indices)
-
-        return unmatched_track_indices, unmatched_det_indices
-
-    def det_local_aware_spliter(self, dets:List, mar_top:float=0.2, mar_left:float=0.25,
-                                mar_right:float=0.25, h:int=1876, w:int=2896):
-        """
-        The det_local_aware_spliter function is a method of a class. It splits a list of detections into two separate lists based on their positions within a specified region of interest in an image.
-
-        Args:
-
-            dets (list): A list of detection objects representing detected items.
-            mar_top (float, optional): A floating-point value representing the margin ratio from the top of the image.
-            mar_left (float, optional): A floating-point value representing the margin ratio from the left side of the image.
-            mar_right (float, optional): A floating-point value representing the margin ratio from the right side of the image.
-            h (int, optional): An integer representing the height of the image. 
-            w (int, optional): An integer representing the width of the image. 
-        Returns:
-
-            det_in (list): A list of detection objects that lie inside the specified region of interest.
-            det_out (list): A list of detection objects that lie outside the specified region of interest.
-        """
-        det_in, det_out = [], []
-        for det in dets:
-            t, l, b, r = det.tlbr
-            x, y = l+(r-l)/2, t+(b-t)/2
-            if (x < w*(1-mar_right)) and (x > w*mar_left) and (y > h*mar_top):
-                det_in.append(det)
-            else:
-                det_out.append(det)
-        return det_in, det_out
-
-    def split_size(self, dets:list, thr:float=0.1, h:int=1876, w:int=2896):
-        """
-        The split_size function is a method of a class. It separates a list of detection objects into two separate lists based on their height relative to a specified threshold, as a proportion of the image height.
-
-        Args:
-
-            dets (list): A list of detection objects representing detected items.
-            thr (float, optional): A floating-point value representing the threshold for splitting detections based on height.
-            h (int, optional): An integer representing the height of the image. 
-            w (int, optional): An integer representing the width of the image. 
-        Returns:
-
-            det_small (list): A list of detection objects whose height is smaller than the specified threshold.
-            det_large (list): A list of detection objects whose height is larger than or equal to the specified threshold.
-        """
-        det_small, det_large = [], []
-        for det in dets:
-            t, _, b, _ = det.tlbr
-            if b-t < thr*h:
-                det_small.append(det)
-            else:
-                det_large.append(det)
-        return det_small, det_large
diff --git a/spaces/cynika/taffy/add_speaker.py b/spaces/cynika/taffy/add_speaker.py
deleted file mode 100644
index e224f07c892a5fe1837e3cbf1745e0d8992ea283..0000000000000000000000000000000000000000
--- a/spaces/cynika/taffy/add_speaker.py
+++ /dev/null
@@ -1,62 +0,0 @@
-import os
-import argparse
-from tqdm import tqdm
-from random import shuffle
-import json
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--train_list", type=str, default="./filelists/train.txt", help="path to train list")
-    parser.add_argument("--val_list", type=str, default="./filelists/val.txt", help="path to val list")
-    parser.add_argument("--test_list", type=str, default="./filelists/test.txt", help="path to test list")
-    parser.add_argument("--source_dir", type=str, default="./dataset/32k", help="path to source dir")
-    args = parser.parse_args()
-
-    previous_config = json.load(open("configs/config.json", "rb"))
-
-    train = []
-    val = []
-    test = []
-    idx = 0
-    spk_dict = previous_config["spk"]
-    spk_id = max([i for i in spk_dict.values()]) + 1
-    for speaker in tqdm(os.listdir(args.source_dir)):
-        if speaker not in spk_dict.keys():
-            spk_dict[speaker] = spk_id
-            spk_id += 1
-        wavs = [os.path.join(args.source_dir, speaker, i)for i in os.listdir(os.path.join(args.source_dir, speaker))]
-        wavs = [i for i in wavs if i.endswith("wav")]
-        shuffle(wavs)
-        train += wavs[2:-10]
-        val += wavs[:2]
-        test += wavs[-10:]
-
-    assert previous_config["model"]["n_speakers"] > len(spk_dict.keys())
-    shuffle(train)
-    shuffle(val)
-    shuffle(test)
-            
-    print("Writing", args.train_list)
-    with open(args.train_list, "w") as f:
-        for fname in tqdm(train):
-            wavpath = fname
-            f.write(wavpath + "\n")
-        
-    print("Writing", args.val_list)
-    with open(args.val_list, "w") as f:
-        for fname in tqdm(val):
-            wavpath = fname
-            f.write(wavpath + "\n")
-            
-    print("Writing", args.test_list)
-    with open(args.test_list, "w") as f:
-        for fname in tqdm(test):
-            wavpath = fname
-            f.write(wavpath + "\n")
-
-    previous_config["spk"] = spk_dict
-
-    print("Writing configs/config.json")
-    with open("configs/config.json", "w") as f:
-        json.dump(previous_config, f, indent=2)
diff --git a/spaces/damian0815/Erasing-Concepts-In-Diffusion/finetuning.py b/spaces/damian0815/Erasing-Concepts-In-Diffusion/finetuning.py
deleted file mode 100644
index 74d9b501c54478ab5a5fe6c5959ed24345152525..0000000000000000000000000000000000000000
--- a/spaces/damian0815/Erasing-Concepts-In-Diffusion/finetuning.py
+++ /dev/null
@@ -1,85 +0,0 @@
-import copy
-import re
-import torch
-import util
-from StableDiffuser import StableDiffuser
-
-class FineTunedModel(torch.nn.Module):
-
-    def __init__(self,
-                 model: StableDiffuser,
-                 modules,
-                 frozen_modules=[]
-                 ):
-
-        super().__init__()
-
-        if isinstance(modules, str):
-            modules = [modules]
-
-        self.model = model
-        self.ft_modules = {}
-        self.orig_modules = {}
-
-        util.freeze(self.model)
-
-        for module_name, module in model.named_modules():
-            for ft_module_regex in modules:
-                match = re.search(ft_module_regex, module_name)
-                if match is not None:
-                    ft_module = copy.deepcopy(module)
-                    
-                    self.orig_modules[module_name] = module
-                    self.ft_modules[module_name] = ft_module
-
-                    util.unfreeze(ft_module)
-
-                    print(f"=> Finetuning {module_name}")
-       
-                    for ft_module_name, module in ft_module.named_modules():
-                        ft_module_name = f"{module_name}.{ft_module_name}"
-                        for freeze_module_name in frozen_modules:
-
-                            match = re.search(freeze_module_name, ft_module_name)
-                            if match:
-                                print(f"=> Freezing {ft_module_name}")
-                                util.freeze(module)
-
-        self.ft_modules_list = torch.nn.ModuleList(self.ft_modules.values())
-        self.orig_modules_list = torch.nn.ModuleList(self.orig_modules.values())
-
-
-    @classmethod
-    def from_checkpoint(cls, model, checkpoint, frozen_modules=[]):
-        if isinstance(checkpoint, str):
-            checkpoint = torch.load(checkpoint)
-
-        modules = [f"{key}$" for key in list(checkpoint.keys())]
-
-        ftm = FineTunedModel(model, modules, frozen_modules=frozen_modules)
-        ftm.load_state_dict(checkpoint)
-
-        return ftm
-
-        
-    def __enter__(self):
-        for key, ft_module in self.ft_modules.items():
-            util.set_module(self.model, key, ft_module)
-
-    def __exit__(self, exc_type, exc_value, tb):
-        for key, module in self.orig_modules.items():
-            util.set_module(self.model, key, module)
-
-    def parameters(self):
-        parameters = []
-        for ft_module in self.ft_modules.values():
-            parameters.extend(list(ft_module.parameters()))
-        return parameters
-
-    def state_dict(self):
-        state_dict = {key: module.state_dict() for key, module in self.ft_modules.items()}
-        return state_dict
-
-    def load_state_dict(self, state_dict):
-        for key, sd in state_dict.items():
-            self.ft_modules[key].load_state_dict(sd)
\ No newline at end of file
diff --git a/spaces/danterivers/music-generation-samples/audiocraft/models/__init__.py b/spaces/danterivers/music-generation-samples/audiocraft/models/__init__.py
deleted file mode 100644
index 92c7a48a200eba455044cd66e0d2c1efe6494f5c..0000000000000000000000000000000000000000
--- a/spaces/danterivers/music-generation-samples/audiocraft/models/__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 .musicgen import MusicGen
-from .lm import LMModel
-from .encodec import CompressionModel, EncodecModel
diff --git a/spaces/datastx/ChatWithADocDocker/download_model.sh b/spaces/datastx/ChatWithADocDocker/download_model.sh
deleted file mode 100644
index dc6fd735ae58cf3e0fd0205b0e46538ff3143db4..0000000000000000000000000000000000000000
--- a/spaces/datastx/ChatWithADocDocker/download_model.sh
+++ /dev/null
@@ -1,13 +0,0 @@
-#!/bin/bash
-
-mkdir -p /app/medllama2_7b
-
-aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/4bit/medllama2_7b_s/resolve/main/model-00001-of-00002.safetensors -d /app/medllama2_7b -o model-00001-of-00002.safetensors
-aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/4bit/medllama2_7b_s/resolve/main/model-00002-of-00002.safetensors -d /app/medllama2_7b -o model-00002-of-00002.safetensors
-aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/4bit/medllama2_7b_s/raw/main/model.safetensors.index.json -d /app/medllama2_7b -o model.safetensors.index.json
-aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/4bit/medllama2_7b_s/raw/main/special_tokens_map.json -d /app/medllama2_7b -o special_tokens_map.json
-aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/4bit/medllama2_7b_s/raw/main/tokenizer.json -d /app/medllama2_7b -o tokenizer.json
-aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/4bit/medllama2_7b_s/raw/main/tokenizer_config.json -d /app/medllama2_7b -o tokenizer_config.json
-aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/4bit/medllama2_7b_s/raw/main/config.json -d /app/medllama2_7b -o config.json
-aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/4bit/medllama2_7b_s/raw/main/generation_config.json -d /app/medllama2_7b -o generation_config.json
-aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/4bit/medllama2_7b_s/resolve/main/tokenizer.model -d /app/medllama2_7b -o tokenizer.model
diff --git a/spaces/davda54/chat-nort5/trie.py b/spaces/davda54/chat-nort5/trie.py
deleted file mode 100644
index 6b0ffbf43888ee0473a4c712b0aff7c7cf90149f..0000000000000000000000000000000000000000
--- a/spaces/davda54/chat-nort5/trie.py
+++ /dev/null
@@ -1,189 +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.
-
-
-from typing import Dict, List
-
-try:
-    import marisa_trie
-except ModuleNotFoundError:
-    pass
-
-
-class Trie(object):
-    def __init__(self, sequences: List[List[int]] = []):
-        self.trie_dict = {}
-        self.len = 0
-        if sequences:
-            for sequence in sequences:
-                Trie._add_to_trie(sequence, self.trie_dict)
-                self.len += 1
-
-        self.append_trie = None
-        self.bos_token_id = None
-
-    def append(self, trie, bos_token_id):
-        self.append_trie = trie
-        self.bos_token_id = bos_token_id
-
-    def add(self, sequence: List[int]):
-        Trie._add_to_trie(sequence, self.trie_dict)
-        self.len += 1
-
-    def get(self, prefix_sequence: List[int]):
-        return Trie._get_from_trie(
-            prefix_sequence, self.trie_dict, self.append_trie, self.bos_token_id
-        )
-
-    @staticmethod
-    def load_from_dict(trie_dict):
-        trie = Trie()
-        trie.trie_dict = trie_dict
-        trie.len = sum(1 for _ in trie)
-        return trie
-
-    @staticmethod
-    def _add_to_trie(sequence: List[int], trie_dict: Dict):
-        if sequence:
-            if sequence[0] not in trie_dict:
-                trie_dict[sequence[0]] = {}
-            Trie._add_to_trie(sequence[1:], trie_dict[sequence[0]])
-
-    @staticmethod
-    def _get_from_trie(
-        prefix_sequence: List[int],
-        trie_dict: Dict,
-        append_trie=None,
-        bos_token_id: int = None,
-    ):
-        if len(prefix_sequence) == 0:
-            output = list(trie_dict.keys())
-            if append_trie and bos_token_id in output:
-                output.remove(bos_token_id)
-                output += list(append_trie.trie_dict.keys())
-            return output
-        elif prefix_sequence[0] in trie_dict:
-            return Trie._get_from_trie(
-                prefix_sequence[1:],
-                trie_dict[prefix_sequence[0]],
-                append_trie,
-                bos_token_id,
-            )
-        else:
-            if append_trie:
-                return append_trie.get(prefix_sequence)
-            else:
-                return []
-
-    def __iter__(self):
-        def _traverse(prefix_sequence, trie_dict):
-            if trie_dict:
-                for next_token in trie_dict:
-                    yield from _traverse(
-                        prefix_sequence + [next_token], trie_dict[next_token]
-                    )
-            else:
-                yield prefix_sequence
-
-        return _traverse([], self.trie_dict)
-
-    def __len__(self):
-        return self.len
-
-    def __getitem__(self, value):
-        return self.get(value)
-
-
-class MarisaTrie(object):
-    def __init__(
-        self,
-        sequences: List[List[int]] = [],
-        cache_fist_branch=True,
-        max_token_id=256001,
-    ):
-
-        self.int2char = [chr(i) for i in range(min(max_token_id, 55000))] + (
-            [chr(i) for i in range(65000, max_token_id + 10000)]
-            if max_token_id >= 55000
-            else []
-        )
-        self.char2int = {self.int2char[i]: i for i in range(max_token_id)}
-
-        self.cache_fist_branch = cache_fist_branch
-        if self.cache_fist_branch:
-            self.zero_iter = list({sequence[0] for sequence in sequences})
-            assert len(self.zero_iter) == 1
-            self.first_iter = list({sequence[1] for sequence in sequences})
-
-        self.trie = marisa_trie.Trie(
-            "".join([self.int2char[i] for i in sequence]) for sequence in sequences
-        )
-
-    def get(self, prefix_sequence: List[int]):
-        if self.cache_fist_branch and len(prefix_sequence) == 0:
-            return self.zero_iter
-        elif (
-            self.cache_fist_branch
-            and len(prefix_sequence) == 1
-            and self.zero_iter == prefix_sequence
-        ):
-            return self.first_iter
-        else:
-            key = "".join([self.int2char[i] for i in prefix_sequence])
-            return list(
-                {
-                    self.char2int[e[len(key)]]
-                    for e in self.trie.keys(key)
-                    if len(e) > len(key)
-                }
-            )
-
-    def __iter__(self):
-        for sequence in self.trie.iterkeys():
-            yield [self.char2int[e] for e in sequence]
-
-    def __len__(self):
-        return len(self.trie)
-
-    def __getitem__(self, value):
-        return self.get(value)
-
-
-class DummyTrieMention(object):
-    def __init__(self, return_values):
-        self._return_values = return_values
-
-    def get(self, indices=None):
-        return self._return_values
-
-
-class DummyTrieEntity(object):
-    def __init__(self, return_values, codes):
-        self._return_values = list(
-            set(return_values).difference(
-                set(
-                    codes[e]
-                    for e in (
-                        "start_mention_token",
-                        "end_mention_token",
-                        "start_entity_token",
-                    )
-                )
-            )
-        )
-        self._codes = codes
-
-    def get(self, indices, depth=0):
-        if len(indices) == 0 and depth == 0:
-            return self._codes["end_mention_token"]
-        elif len(indices) == 0 and depth == 1:
-            return self._codes["start_entity_token"]
-        elif len(indices) == 0:
-            return self._return_values
-        elif len(indices) == 1 and indices[0] == self._codes["end_entity_token"]:
-            return self._codes["EOS"]
-        else:
-            return self.get(indices[1:], depth=depth + 1)
diff --git a/spaces/davertor/colorizing_images/deoldify/layers.py b/spaces/davertor/colorizing_images/deoldify/layers.py
deleted file mode 100644
index b7533c90888a66b14361666ec5ae6b5d05a9eb8f..0000000000000000000000000000000000000000
--- a/spaces/davertor/colorizing_images/deoldify/layers.py
+++ /dev/null
@@ -1,48 +0,0 @@
-from fastai.layers import *
-from fastai.torch_core import *
-from torch.nn.parameter import Parameter
-from torch.autograd import Variable
-
-
-# The code below is meant to be merged into fastaiv1 ideally
-
-
-def custom_conv_layer(
-    ni: int,
-    nf: int,
-    ks: int = 3,
-    stride: int = 1,
-    padding: int = None,
-    bias: bool = None,
-    is_1d: bool = False,
-    norm_type: Optional[NormType] = NormType.Batch,
-    use_activ: bool = True,
-    leaky: float = None,
-    transpose: bool = False,
-    init: Callable = nn.init.kaiming_normal_,
-    self_attention: bool = False,
-    extra_bn: bool = False,
-):
-    "Create a sequence of convolutional (`ni` to `nf`), ReLU (if `use_activ`) and batchnorm (if `bn`) layers."
-    if padding is None:
-        padding = (ks - 1) // 2 if not transpose else 0
-    bn = norm_type in (NormType.Batch, NormType.BatchZero) or extra_bn == True
-    if bias is None:
-        bias = not bn
-    conv_func = nn.ConvTranspose2d if transpose else nn.Conv1d if is_1d else nn.Conv2d
-    conv = init_default(
-        conv_func(ni, nf, kernel_size=ks, bias=bias, stride=stride, padding=padding),
-        init,
-    )
-    if norm_type == NormType.Weight:
-        conv = weight_norm(conv)
-    elif norm_type == NormType.Spectral:
-        conv = spectral_norm(conv)
-    layers = [conv]
-    if use_activ:
-        layers.append(relu(True, leaky=leaky))
-    if bn:
-        layers.append((nn.BatchNorm1d if is_1d else nn.BatchNorm2d)(nf))
-    if self_attention:
-        layers.append(SelfAttention(nf))
-    return nn.Sequential(*layers)
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/PIL/QoiImagePlugin.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/PIL/QoiImagePlugin.py
deleted file mode 100644
index ef91b90abca87ff6526cd10f89f1c0dfc9f0b848..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/PIL/QoiImagePlugin.py
+++ /dev/null
@@ -1,105 +0,0 @@
-#
-# The Python Imaging Library.
-#
-# QOI support for PIL
-#
-# See the README file for information on usage and redistribution.
-#
-
-import os
-
-from . import Image, ImageFile
-from ._binary import i32be as i32
-from ._binary import o8
-
-
-def _accept(prefix):
-    return prefix[:4] == b"qoif"
-
-
-class QoiImageFile(ImageFile.ImageFile):
-    format = "QOI"
-    format_description = "Quite OK Image"
-
-    def _open(self):
-        if not _accept(self.fp.read(4)):
-            msg = "not a QOI file"
-            raise SyntaxError(msg)
-
-        self._size = tuple(i32(self.fp.read(4)) for i in range(2))
-
-        channels = self.fp.read(1)[0]
-        self.mode = "RGB" if channels == 3 else "RGBA"
-
-        self.fp.seek(1, os.SEEK_CUR)  # colorspace
-        self.tile = [("qoi", (0, 0) + self._size, self.fp.tell(), None)]
-
-
-class QoiDecoder(ImageFile.PyDecoder):
-    _pulls_fd = True
-
-    def _add_to_previous_pixels(self, value):
-        self._previous_pixel = value
-
-        r, g, b, a = value
-        hash_value = (r * 3 + g * 5 + b * 7 + a * 11) % 64
-        self._previously_seen_pixels[hash_value] = value
-
-    def decode(self, buffer):
-        self._previously_seen_pixels = {}
-        self._previous_pixel = None
-        self._add_to_previous_pixels(b"".join(o8(i) for i in (0, 0, 0, 255)))
-
-        data = bytearray()
-        bands = Image.getmodebands(self.mode)
-        while len(data) < self.state.xsize * self.state.ysize * bands:
-            byte = self.fd.read(1)[0]
-            if byte == 0b11111110:  # QOI_OP_RGB
-                value = self.fd.read(3) + o8(255)
-            elif byte == 0b11111111:  # QOI_OP_RGBA
-                value = self.fd.read(4)
-            else:
-                op = byte >> 6
-                if op == 0:  # QOI_OP_INDEX
-                    op_index = byte & 0b00111111
-                    value = self._previously_seen_pixels.get(op_index, (0, 0, 0, 0))
-                elif op == 1:  # QOI_OP_DIFF
-                    value = (
-                        (self._previous_pixel[0] + ((byte & 0b00110000) >> 4) - 2)
-                        % 256,
-                        (self._previous_pixel[1] + ((byte & 0b00001100) >> 2) - 2)
-                        % 256,
-                        (self._previous_pixel[2] + (byte & 0b00000011) - 2) % 256,
-                    )
-                    value += (self._previous_pixel[3],)
-                elif op == 2:  # QOI_OP_LUMA
-                    second_byte = self.fd.read(1)[0]
-                    diff_green = (byte & 0b00111111) - 32
-                    diff_red = ((second_byte & 0b11110000) >> 4) - 8
-                    diff_blue = (second_byte & 0b00001111) - 8
-
-                    value = tuple(
-                        (self._previous_pixel[i] + diff_green + diff) % 256
-                        for i, diff in enumerate((diff_red, 0, diff_blue))
-                    )
-                    value += (self._previous_pixel[3],)
-                elif op == 3:  # QOI_OP_RUN
-                    run_length = (byte & 0b00111111) + 1
-                    value = self._previous_pixel
-                    if bands == 3:
-                        value = value[:3]
-                    data += value * run_length
-                    continue
-                value = b"".join(o8(i) for i in value)
-            self._add_to_previous_pixels(value)
-
-            if bands == 3:
-                value = value[:3]
-            data += value
-        self.set_as_raw(bytes(data))
-        return -1, 0
-
-
-Image.register_open(QoiImageFile.format, QoiImageFile, _accept)
-Image.register_decoder("qoi", QoiDecoder)
-Image.register_extension(QoiImageFile.format, ".qoi")
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/aiohttp/locks.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/aiohttp/locks.py
deleted file mode 100644
index de2dc83d09dd950fc1ed8d7edaeb20e7697c94ba..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/aiohttp/locks.py
+++ /dev/null
@@ -1,41 +0,0 @@
-import asyncio
-import collections
-from typing import Any, Deque, Optional
-
-
-class EventResultOrError:
-    """Event asyncio lock helper class.
-
-    Wraps the Event asyncio lock allowing either to awake the
-    locked Tasks without any error or raising an exception.
-
-    thanks to @vorpalsmith for the simple design.
-    """
-
-    def __init__(self, loop: asyncio.AbstractEventLoop) -> None:
-        self._loop = loop
-        self._exc: Optional[BaseException] = None
-        self._event = asyncio.Event()
-        self._waiters: Deque[asyncio.Future[Any]] = collections.deque()
-
-    def set(self, exc: Optional[BaseException] = None) -> None:
-        self._exc = exc
-        self._event.set()
-
-    async def wait(self) -> Any:
-        waiter = self._loop.create_task(self._event.wait())
-        self._waiters.append(waiter)
-        try:
-            val = await waiter
-        finally:
-            self._waiters.remove(waiter)
-
-        if self._exc is not None:
-            raise self._exc
-
-        return val
-
-    def cancel(self) -> None:
-        """Cancel all waiters"""
-        for waiter in self._waiters:
-            waiter.cancel()
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/charset_normalizer/md.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/charset_normalizer/md.py
deleted file mode 100644
index 13aa062e71e4c07832c3dea08a70925b61848dcd..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/charset_normalizer/md.py
+++ /dev/null
@@ -1,582 +0,0 @@
-from functools import lru_cache
-from logging import getLogger
-from typing import List, Optional
-
-from .constant import (
-    COMMON_SAFE_ASCII_CHARACTERS,
-    TRACE,
-    UNICODE_SECONDARY_RANGE_KEYWORD,
-)
-from .utils import (
-    is_accentuated,
-    is_ascii,
-    is_case_variable,
-    is_cjk,
-    is_emoticon,
-    is_hangul,
-    is_hiragana,
-    is_katakana,
-    is_latin,
-    is_punctuation,
-    is_separator,
-    is_symbol,
-    is_thai,
-    is_unprintable,
-    remove_accent,
-    unicode_range,
-)
-
-
-class MessDetectorPlugin:
-    """
-    Base abstract class used for mess detection plugins.
-    All detectors MUST extend and implement given methods.
-    """
-
-    def eligible(self, character: str) -> bool:
-        """
-        Determine if given character should be fed in.
-        """
-        raise NotImplementedError  # pragma: nocover
-
-    def feed(self, character: str) -> None:
-        """
-        The main routine to be executed upon character.
-        Insert the logic in witch the text would be considered chaotic.
-        """
-        raise NotImplementedError  # pragma: nocover
-
-    def reset(self) -> None:  # pragma: no cover
-        """
-        Permit to reset the plugin to the initial state.
-        """
-        raise NotImplementedError
-
-    @property
-    def ratio(self) -> float:
-        """
-        Compute the chaos ratio based on what your feed() has seen.
-        Must NOT be lower than 0.; No restriction gt 0.
-        """
-        raise NotImplementedError  # pragma: nocover
-
-
-class TooManySymbolOrPunctuationPlugin(MessDetectorPlugin):
-    def __init__(self) -> None:
-        self._punctuation_count: int = 0
-        self._symbol_count: int = 0
-        self._character_count: int = 0
-
-        self._last_printable_char: Optional[str] = None
-        self._frenzy_symbol_in_word: bool = False
-
-    def eligible(self, character: str) -> bool:
-        return character.isprintable()
-
-    def feed(self, character: str) -> None:
-        self._character_count += 1
-
-        if (
-            character != self._last_printable_char
-            and character not in COMMON_SAFE_ASCII_CHARACTERS
-        ):
-            if is_punctuation(character):
-                self._punctuation_count += 1
-            elif (
-                character.isdigit() is False
-                and is_symbol(character)
-                and is_emoticon(character) is False
-            ):
-                self._symbol_count += 2
-
-        self._last_printable_char = character
-
-    def reset(self) -> None:  # pragma: no cover
-        self._punctuation_count = 0
-        self._character_count = 0
-        self._symbol_count = 0
-
-    @property
-    def ratio(self) -> float:
-        if self._character_count == 0:
-            return 0.0
-
-        ratio_of_punctuation: float = (
-            self._punctuation_count + self._symbol_count
-        ) / self._character_count
-
-        return ratio_of_punctuation if ratio_of_punctuation >= 0.3 else 0.0
-
-
-class TooManyAccentuatedPlugin(MessDetectorPlugin):
-    def __init__(self) -> None:
-        self._character_count: int = 0
-        self._accentuated_count: int = 0
-
-    def eligible(self, character: str) -> bool:
-        return character.isalpha()
-
-    def feed(self, character: str) -> None:
-        self._character_count += 1
-
-        if is_accentuated(character):
-            self._accentuated_count += 1
-
-    def reset(self) -> None:  # pragma: no cover
-        self._character_count = 0
-        self._accentuated_count = 0
-
-    @property
-    def ratio(self) -> float:
-        if self._character_count == 0 or self._character_count < 8:
-            return 0.0
-        ratio_of_accentuation: float = self._accentuated_count / self._character_count
-        return ratio_of_accentuation if ratio_of_accentuation >= 0.35 else 0.0
-
-
-class UnprintablePlugin(MessDetectorPlugin):
-    def __init__(self) -> None:
-        self._unprintable_count: int = 0
-        self._character_count: int = 0
-
-    def eligible(self, character: str) -> bool:
-        return True
-
-    def feed(self, character: str) -> None:
-        if is_unprintable(character):
-            self._unprintable_count += 1
-        self._character_count += 1
-
-    def reset(self) -> None:  # pragma: no cover
-        self._unprintable_count = 0
-
-    @property
-    def ratio(self) -> float:
-        if self._character_count == 0:
-            return 0.0
-
-        return (self._unprintable_count * 8) / self._character_count
-
-
-class SuspiciousDuplicateAccentPlugin(MessDetectorPlugin):
-    def __init__(self) -> None:
-        self._successive_count: int = 0
-        self._character_count: int = 0
-
-        self._last_latin_character: Optional[str] = None
-
-    def eligible(self, character: str) -> bool:
-        return character.isalpha() and is_latin(character)
-
-    def feed(self, character: str) -> None:
-        self._character_count += 1
-        if (
-            self._last_latin_character is not None
-            and is_accentuated(character)
-            and is_accentuated(self._last_latin_character)
-        ):
-            if character.isupper() and self._last_latin_character.isupper():
-                self._successive_count += 1
-            # Worse if its the same char duplicated with different accent.
-            if remove_accent(character) == remove_accent(self._last_latin_character):
-                self._successive_count += 1
-        self._last_latin_character = character
-
-    def reset(self) -> None:  # pragma: no cover
-        self._successive_count = 0
-        self._character_count = 0
-        self._last_latin_character = None
-
-    @property
-    def ratio(self) -> float:
-        if self._character_count == 0:
-            return 0.0
-
-        return (self._successive_count * 2) / self._character_count
-
-
-class SuspiciousRange(MessDetectorPlugin):
-    def __init__(self) -> None:
-        self._suspicious_successive_range_count: int = 0
-        self._character_count: int = 0
-        self._last_printable_seen: Optional[str] = None
-
-    def eligible(self, character: str) -> bool:
-        return character.isprintable()
-
-    def feed(self, character: str) -> None:
-        self._character_count += 1
-
-        if (
-            character.isspace()
-            or is_punctuation(character)
-            or character in COMMON_SAFE_ASCII_CHARACTERS
-        ):
-            self._last_printable_seen = None
-            return
-
-        if self._last_printable_seen is None:
-            self._last_printable_seen = character
-            return
-
-        unicode_range_a: Optional[str] = unicode_range(self._last_printable_seen)
-        unicode_range_b: Optional[str] = unicode_range(character)
-
-        if is_suspiciously_successive_range(unicode_range_a, unicode_range_b):
-            self._suspicious_successive_range_count += 1
-
-        self._last_printable_seen = character
-
-    def reset(self) -> None:  # pragma: no cover
-        self._character_count = 0
-        self._suspicious_successive_range_count = 0
-        self._last_printable_seen = None
-
-    @property
-    def ratio(self) -> float:
-        if self._character_count == 0:
-            return 0.0
-
-        ratio_of_suspicious_range_usage: float = (
-            self._suspicious_successive_range_count * 2
-        ) / self._character_count
-
-        if ratio_of_suspicious_range_usage < 0.1:
-            return 0.0
-
-        return ratio_of_suspicious_range_usage
-
-
-class SuperWeirdWordPlugin(MessDetectorPlugin):
-    def __init__(self) -> None:
-        self._word_count: int = 0
-        self._bad_word_count: int = 0
-        self._foreign_long_count: int = 0
-
-        self._is_current_word_bad: bool = False
-        self._foreign_long_watch: bool = False
-
-        self._character_count: int = 0
-        self._bad_character_count: int = 0
-
-        self._buffer: str = ""
-        self._buffer_accent_count: int = 0
-
-    def eligible(self, character: str) -> bool:
-        return True
-
-    def feed(self, character: str) -> None:
-        if character.isalpha():
-            self._buffer += character
-            if is_accentuated(character):
-                self._buffer_accent_count += 1
-            if (
-                self._foreign_long_watch is False
-                and (is_latin(character) is False or is_accentuated(character))
-                and is_cjk(character) is False
-                and is_hangul(character) is False
-                and is_katakana(character) is False
-                and is_hiragana(character) is False
-                and is_thai(character) is False
-            ):
-                self._foreign_long_watch = True
-            return
-        if not self._buffer:
-            return
-        if (
-            character.isspace() or is_punctuation(character) or is_separator(character)
-        ) and self._buffer:
-            self._word_count += 1
-            buffer_length: int = len(self._buffer)
-
-            self._character_count += buffer_length
-
-            if buffer_length >= 4:
-                if self._buffer_accent_count / buffer_length > 0.34:
-                    self._is_current_word_bad = True
-                # Word/Buffer ending with an upper case accentuated letter are so rare,
-                # that we will consider them all as suspicious. Same weight as foreign_long suspicious.
-                if is_accentuated(self._buffer[-1]) and self._buffer[-1].isupper():
-                    self._foreign_long_count += 1
-                    self._is_current_word_bad = True
-            if buffer_length >= 24 and self._foreign_long_watch:
-                camel_case_dst = [
-                    i
-                    for c, i in zip(self._buffer, range(0, buffer_length))
-                    if c.isupper()
-                ]
-                probable_camel_cased: bool = False
-
-                if camel_case_dst and (len(camel_case_dst) / buffer_length <= 0.3):
-                    probable_camel_cased = True
-
-                if not probable_camel_cased:
-                    self._foreign_long_count += 1
-                    self._is_current_word_bad = True
-
-            if self._is_current_word_bad:
-                self._bad_word_count += 1
-                self._bad_character_count += len(self._buffer)
-                self._is_current_word_bad = False
-
-            self._foreign_long_watch = False
-            self._buffer = ""
-            self._buffer_accent_count = 0
-        elif (
-            character not in {"<", ">", "-", "=", "~", "|", "_"}
-            and character.isdigit() is False
-            and is_symbol(character)
-        ):
-            self._is_current_word_bad = True
-            self._buffer += character
-
-    def reset(self) -> None:  # pragma: no cover
-        self._buffer = ""
-        self._is_current_word_bad = False
-        self._foreign_long_watch = False
-        self._bad_word_count = 0
-        self._word_count = 0
-        self._character_count = 0
-        self._bad_character_count = 0
-        self._foreign_long_count = 0
-
-    @property
-    def ratio(self) -> float:
-        if self._word_count <= 10 and self._foreign_long_count == 0:
-            return 0.0
-
-        return self._bad_character_count / self._character_count
-
-
-class CjkInvalidStopPlugin(MessDetectorPlugin):
-    """
-    GB(Chinese) based encoding often render the stop incorrectly when the content does not fit and
-    can be easily detected. Searching for the overuse of '丅' and '丄'.
-    """
-
-    def __init__(self) -> None:
-        self._wrong_stop_count: int = 0
-        self._cjk_character_count: int = 0
-
-    def eligible(self, character: str) -> bool:
-        return True
-
-    def feed(self, character: str) -> None:
-        if character in {"丅", "丄"}:
-            self._wrong_stop_count += 1
-            return
-        if is_cjk(character):
-            self._cjk_character_count += 1
-
-    def reset(self) -> None:  # pragma: no cover
-        self._wrong_stop_count = 0
-        self._cjk_character_count = 0
-
-    @property
-    def ratio(self) -> float:
-        if self._cjk_character_count < 16:
-            return 0.0
-        return self._wrong_stop_count / self._cjk_character_count
-
-
-class ArchaicUpperLowerPlugin(MessDetectorPlugin):
-    def __init__(self) -> None:
-        self._buf: bool = False
-
-        self._character_count_since_last_sep: int = 0
-
-        self._successive_upper_lower_count: int = 0
-        self._successive_upper_lower_count_final: int = 0
-
-        self._character_count: int = 0
-
-        self._last_alpha_seen: Optional[str] = None
-        self._current_ascii_only: bool = True
-
-    def eligible(self, character: str) -> bool:
-        return True
-
-    def feed(self, character: str) -> None:
-        is_concerned = character.isalpha() and is_case_variable(character)
-        chunk_sep = is_concerned is False
-
-        if chunk_sep and self._character_count_since_last_sep > 0:
-            if (
-                self._character_count_since_last_sep <= 64
-                and character.isdigit() is False
-                and self._current_ascii_only is False
-            ):
-                self._successive_upper_lower_count_final += (
-                    self._successive_upper_lower_count
-                )
-
-            self._successive_upper_lower_count = 0
-            self._character_count_since_last_sep = 0
-            self._last_alpha_seen = None
-            self._buf = False
-            self._character_count += 1
-            self._current_ascii_only = True
-
-            return
-
-        if self._current_ascii_only is True and is_ascii(character) is False:
-            self._current_ascii_only = False
-
-        if self._last_alpha_seen is not None:
-            if (character.isupper() and self._last_alpha_seen.islower()) or (
-                character.islower() and self._last_alpha_seen.isupper()
-            ):
-                if self._buf is True:
-                    self._successive_upper_lower_count += 2
-                    self._buf = False
-                else:
-                    self._buf = True
-            else:
-                self._buf = False
-
-        self._character_count += 1
-        self._character_count_since_last_sep += 1
-        self._last_alpha_seen = character
-
-    def reset(self) -> None:  # pragma: no cover
-        self._character_count = 0
-        self._character_count_since_last_sep = 0
-        self._successive_upper_lower_count = 0
-        self._successive_upper_lower_count_final = 0
-        self._last_alpha_seen = None
-        self._buf = False
-        self._current_ascii_only = True
-
-    @property
-    def ratio(self) -> float:
-        if self._character_count == 0:
-            return 0.0
-
-        return self._successive_upper_lower_count_final / self._character_count
-
-
-@lru_cache(maxsize=1024)
-def is_suspiciously_successive_range(
-    unicode_range_a: Optional[str], unicode_range_b: Optional[str]
-) -> bool:
-    """
-    Determine if two Unicode range seen next to each other can be considered as suspicious.
-    """
-    if unicode_range_a is None or unicode_range_b is None:
-        return True
-
-    if unicode_range_a == unicode_range_b:
-        return False
-
-    if "Latin" in unicode_range_a and "Latin" in unicode_range_b:
-        return False
-
-    if "Emoticons" in unicode_range_a or "Emoticons" in unicode_range_b:
-        return False
-
-    # Latin characters can be accompanied with a combining diacritical mark
-    # eg. Vietnamese.
-    if ("Latin" in unicode_range_a or "Latin" in unicode_range_b) and (
-        "Combining" in unicode_range_a or "Combining" in unicode_range_b
-    ):
-        return False
-
-    keywords_range_a, keywords_range_b = unicode_range_a.split(
-        " "
-    ), unicode_range_b.split(" ")
-
-    for el in keywords_range_a:
-        if el in UNICODE_SECONDARY_RANGE_KEYWORD:
-            continue
-        if el in keywords_range_b:
-            return False
-
-    # Japanese Exception
-    range_a_jp_chars, range_b_jp_chars = (
-        unicode_range_a
-        in (
-            "Hiragana",
-            "Katakana",
-        ),
-        unicode_range_b in ("Hiragana", "Katakana"),
-    )
-    if (range_a_jp_chars or range_b_jp_chars) and (
-        "CJK" in unicode_range_a or "CJK" in unicode_range_b
-    ):
-        return False
-    if range_a_jp_chars and range_b_jp_chars:
-        return False
-
-    if "Hangul" in unicode_range_a or "Hangul" in unicode_range_b:
-        if "CJK" in unicode_range_a or "CJK" in unicode_range_b:
-            return False
-        if unicode_range_a == "Basic Latin" or unicode_range_b == "Basic Latin":
-            return False
-
-    # Chinese/Japanese use dedicated range for punctuation and/or separators.
-    if ("CJK" in unicode_range_a or "CJK" in unicode_range_b) or (
-        unicode_range_a in ["Katakana", "Hiragana"]
-        and unicode_range_b in ["Katakana", "Hiragana"]
-    ):
-        if "Punctuation" in unicode_range_a or "Punctuation" in unicode_range_b:
-            return False
-        if "Forms" in unicode_range_a or "Forms" in unicode_range_b:
-            return False
-
-    return True
-
-
-@lru_cache(maxsize=2048)
-def mess_ratio(
-    decoded_sequence: str, maximum_threshold: float = 0.2, debug: bool = False
-) -> float:
-    """
-    Compute a mess ratio given a decoded bytes sequence. The maximum threshold does stop the computation earlier.
-    """
-
-    detectors: List[MessDetectorPlugin] = [
-        md_class() for md_class in MessDetectorPlugin.__subclasses__()
-    ]
-
-    length: int = len(decoded_sequence) + 1
-
-    mean_mess_ratio: float = 0.0
-
-    if length < 512:
-        intermediary_mean_mess_ratio_calc: int = 32
-    elif length <= 1024:
-        intermediary_mean_mess_ratio_calc = 64
-    else:
-        intermediary_mean_mess_ratio_calc = 128
-
-    for character, index in zip(decoded_sequence + "\n", range(length)):
-        for detector in detectors:
-            if detector.eligible(character):
-                detector.feed(character)
-
-        if (
-            index > 0 and index % intermediary_mean_mess_ratio_calc == 0
-        ) or index == length - 1:
-            mean_mess_ratio = sum(dt.ratio for dt in detectors)
-
-            if mean_mess_ratio >= maximum_threshold:
-                break
-
-    if debug:
-        logger = getLogger("charset_normalizer")
-
-        logger.log(
-            TRACE,
-            "Mess-detector extended-analysis start. "
-            f"intermediary_mean_mess_ratio_calc={intermediary_mean_mess_ratio_calc} mean_mess_ratio={mean_mess_ratio} "
-            f"maximum_threshold={maximum_threshold}",
-        )
-
-        if len(decoded_sequence) > 16:
-            logger.log(TRACE, f"Starting with: {decoded_sequence[:16]}")
-            logger.log(TRACE, f"Ending with: {decoded_sequence[-16::]}")
-
-        for dt in detectors:  # pragma: nocover
-            logger.log(TRACE, f"{dt.__class__}: {dt.ratio}")
-
-    return round(mean_mess_ratio, 3)
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/ttLib/tables/T_S_I_S_.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/ttLib/tables/T_S_I_S_.py
deleted file mode 100644
index 667eb0e53473c1566d4b45e5621d8897ebd7b9fe..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/ttLib/tables/T_S_I_S_.py
+++ /dev/null
@@ -1,5 +0,0 @@
-from .T_S_I_V_ import table_T_S_I_V_
-
-
-class table_T_S_I_S_(table_T_S_I_V_):
-    pass
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/jsonschema_specifications/tests/test_jsonschema_specifications.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/jsonschema_specifications/tests/test_jsonschema_specifications.py
deleted file mode 100644
index d183f5cf33e6b96069c5494ed2ebe92fadcf52c7..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/jsonschema_specifications/tests/test_jsonschema_specifications.py
+++ /dev/null
@@ -1,10 +0,0 @@
-from collections.abc import Mapping
-
-from jsonschema_specifications import REGISTRY
-
-
-def test_it_contains_metaschemas():
-    schema = REGISTRY.contents("http://json-schema.org/draft-07/schema#")
-    assert isinstance(schema, Mapping)
-    assert schema["$id"] == "http://json-schema.org/draft-07/schema#"
-    assert schema["title"] == "Core schema meta-schema"
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/matplotlib/_cm.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/matplotlib/_cm.py
deleted file mode 100644
index 586417d53954a4334fe43b5fc4e80fba84a3ed3f..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/matplotlib/_cm.py
+++ /dev/null
@@ -1,1440 +0,0 @@
-"""
-Nothing here but dictionaries for generating LinearSegmentedColormaps,
-and a dictionary of these dictionaries.
-
-Documentation for each is in pyplot.colormaps().  Please update this
-with the purpose and type of your colormap if you add data for one here.
-"""
-
-from functools import partial
-
-import numpy as np
-
-_binary_data = {
-    'red':    ((0., 1., 1.), (1., 0., 0.)),
-    'green':  ((0., 1., 1.), (1., 0., 0.)),
-    'blue':   ((0., 1., 1.), (1., 0., 0.))
-    }
-
-_autumn_data = {'red':   ((0., 1.0, 1.0), (1.0, 1.0, 1.0)),
-                'green': ((0., 0., 0.), (1.0, 1.0, 1.0)),
-                'blue':  ((0., 0., 0.), (1.0, 0., 0.))}
-
-_bone_data = {'red':   ((0., 0., 0.),
-                        (0.746032, 0.652778, 0.652778),
-                        (1.0, 1.0, 1.0)),
-              'green': ((0., 0., 0.),
-                        (0.365079, 0.319444, 0.319444),
-                        (0.746032, 0.777778, 0.777778),
-                        (1.0, 1.0, 1.0)),
-              'blue':  ((0., 0., 0.),
-                        (0.365079, 0.444444, 0.444444),
-                        (1.0, 1.0, 1.0))}
-
-_cool_data = {'red':   ((0., 0., 0.), (1.0, 1.0, 1.0)),
-              'green': ((0., 1., 1.), (1.0, 0.,  0.)),
-              'blue':  ((0., 1., 1.), (1.0, 1.,  1.))}
-
-_copper_data = {'red':   ((0., 0., 0.),
-                          (0.809524, 1.000000, 1.000000),
-                          (1.0, 1.0, 1.0)),
-                'green': ((0., 0., 0.),
-                          (1.0, 0.7812, 0.7812)),
-                'blue':  ((0., 0., 0.),
-                          (1.0, 0.4975, 0.4975))}
-
-def _flag_red(x): return 0.75 * np.sin((x * 31.5 + 0.25) * np.pi) + 0.5
-def _flag_green(x): return np.sin(x * 31.5 * np.pi)
-def _flag_blue(x): return 0.75 * np.sin((x * 31.5 - 0.25) * np.pi) + 0.5
-_flag_data = {'red': _flag_red, 'green': _flag_green, 'blue': _flag_blue}
-
-def _prism_red(x): return 0.75 * np.sin((x * 20.9 + 0.25) * np.pi) + 0.67
-def _prism_green(x): return 0.75 * np.sin((x * 20.9 - 0.25) * np.pi) + 0.33
-def _prism_blue(x): return -1.1 * np.sin((x * 20.9) * np.pi)
-_prism_data = {'red': _prism_red, 'green': _prism_green, 'blue': _prism_blue}
-
-def _ch_helper(gamma, s, r, h, p0, p1, x):
-    """Helper function for generating picklable cubehelix colormaps."""
-    # Apply gamma factor to emphasise low or high intensity values
-    xg = x ** gamma
-    # Calculate amplitude and angle of deviation from the black to white
-    # diagonal in the plane of constant perceived intensity.
-    a = h * xg * (1 - xg) / 2
-    phi = 2 * np.pi * (s / 3 + r * x)
-    return xg + a * (p0 * np.cos(phi) + p1 * np.sin(phi))
-
-def cubehelix(gamma=1.0, s=0.5, r=-1.5, h=1.0):
-    """
-    Return custom data dictionary of (r, g, b) conversion functions, which can
-    be used with :func:`register_cmap`, for the cubehelix color scheme.
-
-    Unlike most other color schemes cubehelix was designed by D.A. Green to
-    be monotonically increasing in terms of perceived brightness.
-    Also, when printed on a black and white postscript printer, the scheme
-    results in a greyscale with monotonically increasing brightness.
-    This color scheme is named cubehelix because the (r, g, b) values produced
-    can be visualised as a squashed helix around the diagonal in the
-    (r, g, b) color cube.
-
-    For a unit color cube (i.e. 3D coordinates for (r, g, b) each in the
-    range 0 to 1) the color scheme starts at (r, g, b) = (0, 0, 0), i.e. black,
-    and finishes at (r, g, b) = (1, 1, 1), i.e. white. For some fraction *x*,
-    between 0 and 1, the color is the corresponding grey value at that
-    fraction along the black to white diagonal (x, x, x) plus a color
-    element. This color element is calculated in a plane of constant
-    perceived intensity and controlled by the following parameters.
-
-    Parameters
-    ----------
-    gamma : float, default: 1
-        Gamma factor emphasizing either low intensity values (gamma < 1), or
-        high intensity values (gamma > 1).
-    s : float, default: 0.5 (purple)
-        The starting color.
-    r : float, default: -1.5
-        The number of r, g, b rotations in color that are made from the start
-        to the end of the color scheme.  The default of -1.5 corresponds to ->
-        B -> G -> R -> B.
-    h : float, default: 1
-        The hue, i.e. how saturated the colors are. If this parameter is zero
-        then the color scheme is purely a greyscale.
-    """
-    return {'red': partial(_ch_helper, gamma, s, r, h, -0.14861, 1.78277),
-            'green': partial(_ch_helper, gamma, s, r, h, -0.29227, -0.90649),
-            'blue': partial(_ch_helper, gamma, s, r, h, 1.97294, 0.0)}
-
-_cubehelix_data = cubehelix()
-
-_bwr_data = ((0.0, 0.0, 1.0), (1.0, 1.0, 1.0), (1.0, 0.0, 0.0))
-_brg_data = ((0.0, 0.0, 1.0), (1.0, 0.0, 0.0), (0.0, 1.0, 0.0))
-
-# Gnuplot palette functions
-def _g0(x): return 0
-def _g1(x): return 0.5
-def _g2(x): return 1
-def _g3(x): return x
-def _g4(x): return x ** 2
-def _g5(x): return x ** 3
-def _g6(x): return x ** 4
-def _g7(x): return np.sqrt(x)
-def _g8(x): return np.sqrt(np.sqrt(x))
-def _g9(x): return np.sin(x * np.pi / 2)
-def _g10(x): return np.cos(x * np.pi / 2)
-def _g11(x): return np.abs(x - 0.5)
-def _g12(x): return (2 * x - 1) ** 2
-def _g13(x): return np.sin(x * np.pi)
-def _g14(x): return np.abs(np.cos(x * np.pi))
-def _g15(x): return np.sin(x * 2 * np.pi)
-def _g16(x): return np.cos(x * 2 * np.pi)
-def _g17(x): return np.abs(np.sin(x * 2 * np.pi))
-def _g18(x): return np.abs(np.cos(x * 2 * np.pi))
-def _g19(x): return np.abs(np.sin(x * 4 * np.pi))
-def _g20(x): return np.abs(np.cos(x * 4 * np.pi))
-def _g21(x): return 3 * x
-def _g22(x): return 3 * x - 1
-def _g23(x): return 3 * x - 2
-def _g24(x): return np.abs(3 * x - 1)
-def _g25(x): return np.abs(3 * x - 2)
-def _g26(x): return (3 * x - 1) / 2
-def _g27(x): return (3 * x - 2) / 2
-def _g28(x): return np.abs((3 * x - 1) / 2)
-def _g29(x): return np.abs((3 * x - 2) / 2)
-def _g30(x): return x / 0.32 - 0.78125
-def _g31(x): return 2 * x - 0.84
-def _g32(x):
-    ret = np.zeros(len(x))
-    m = (x < 0.25)
-    ret[m] = 4 * x[m]
-    m = (x >= 0.25) & (x < 0.92)
-    ret[m] = -2 * x[m] + 1.84
-    m = (x >= 0.92)
-    ret[m] = x[m] / 0.08 - 11.5
-    return ret
-def _g33(x): return np.abs(2 * x - 0.5)
-def _g34(x): return 2 * x
-def _g35(x): return 2 * x - 0.5
-def _g36(x): return 2 * x - 1
-
-gfunc = {i: globals()["_g{}".format(i)] for i in range(37)}
-
-_gnuplot_data = {
-        'red': gfunc[7],
-        'green': gfunc[5],
-        'blue': gfunc[15],
-}
-
-_gnuplot2_data = {
-        'red': gfunc[30],
-        'green': gfunc[31],
-        'blue': gfunc[32],
-}
-
-_ocean_data = {
-        'red': gfunc[23],
-        'green': gfunc[28],
-        'blue': gfunc[3],
-}
-
-_afmhot_data = {
-        'red': gfunc[34],
-        'green': gfunc[35],
-        'blue': gfunc[36],
-}
-
-_rainbow_data = {
-        'red': gfunc[33],
-        'green': gfunc[13],
-        'blue': gfunc[10],
-}
-
-_seismic_data = (
-        (0.0, 0.0, 0.3), (0.0, 0.0, 1.0),
-        (1.0, 1.0, 1.0), (1.0, 0.0, 0.0),
-        (0.5, 0.0, 0.0))
-
-_terrain_data = (
-        (0.00, (0.2, 0.2, 0.6)),
-        (0.15, (0.0, 0.6, 1.0)),
-        (0.25, (0.0, 0.8, 0.4)),
-        (0.50, (1.0, 1.0, 0.6)),
-        (0.75, (0.5, 0.36, 0.33)),
-        (1.00, (1.0, 1.0, 1.0)))
-
-_gray_data = {'red':   ((0., 0, 0), (1., 1, 1)),
-              'green': ((0., 0, 0), (1., 1, 1)),
-              'blue':  ((0., 0, 0), (1., 1, 1))}
-
-_hot_data = {'red':   ((0., 0.0416, 0.0416),
-                       (0.365079, 1.000000, 1.000000),
-                       (1.0, 1.0, 1.0)),
-             'green': ((0., 0., 0.),
-                       (0.365079, 0.000000, 0.000000),
-                       (0.746032, 1.000000, 1.000000),
-                       (1.0, 1.0, 1.0)),
-             'blue':  ((0., 0., 0.),
-                       (0.746032, 0.000000, 0.000000),
-                       (1.0, 1.0, 1.0))}
-
-_hsv_data = {'red':   ((0., 1., 1.),
-                       (0.158730, 1.000000, 1.000000),
-                       (0.174603, 0.968750, 0.968750),
-                       (0.333333, 0.031250, 0.031250),
-                       (0.349206, 0.000000, 0.000000),
-                       (0.666667, 0.000000, 0.000000),
-                       (0.682540, 0.031250, 0.031250),
-                       (0.841270, 0.968750, 0.968750),
-                       (0.857143, 1.000000, 1.000000),
-                       (1.0, 1.0, 1.0)),
-             'green': ((0., 0., 0.),
-                       (0.158730, 0.937500, 0.937500),
-                       (0.174603, 1.000000, 1.000000),
-                       (0.507937, 1.000000, 1.000000),
-                       (0.666667, 0.062500, 0.062500),
-                       (0.682540, 0.000000, 0.000000),
-                       (1.0, 0., 0.)),
-             'blue':  ((0., 0., 0.),
-                       (0.333333, 0.000000, 0.000000),
-                       (0.349206, 0.062500, 0.062500),
-                       (0.507937, 1.000000, 1.000000),
-                       (0.841270, 1.000000, 1.000000),
-                       (0.857143, 0.937500, 0.937500),
-                       (1.0, 0.09375, 0.09375))}
-
-_jet_data = {'red':   ((0.00, 0, 0),
-                       (0.35, 0, 0),
-                       (0.66, 1, 1),
-                       (0.89, 1, 1),
-                       (1.00, 0.5, 0.5)),
-             'green': ((0.000, 0, 0),
-                       (0.125, 0, 0),
-                       (0.375, 1, 1),
-                       (0.640, 1, 1),
-                       (0.910, 0, 0),
-                       (1.000, 0, 0)),
-             'blue':  ((0.00, 0.5, 0.5),
-                       (0.11, 1, 1),
-                       (0.34, 1, 1),
-                       (0.65, 0, 0),
-                       (1.00, 0, 0))}
-
-_pink_data = {'red':   ((0., 0.1178, 0.1178), (0.015873, 0.195857, 0.195857),
-                        (0.031746, 0.250661, 0.250661),
-                        (0.047619, 0.295468, 0.295468),
-                        (0.063492, 0.334324, 0.334324),
-                        (0.079365, 0.369112, 0.369112),
-                        (0.095238, 0.400892, 0.400892),
-                        (0.111111, 0.430331, 0.430331),
-                        (0.126984, 0.457882, 0.457882),
-                        (0.142857, 0.483867, 0.483867),
-                        (0.158730, 0.508525, 0.508525),
-                        (0.174603, 0.532042, 0.532042),
-                        (0.190476, 0.554563, 0.554563),
-                        (0.206349, 0.576204, 0.576204),
-                        (0.222222, 0.597061, 0.597061),
-                        (0.238095, 0.617213, 0.617213),
-                        (0.253968, 0.636729, 0.636729),
-                        (0.269841, 0.655663, 0.655663),
-                        (0.285714, 0.674066, 0.674066),
-                        (0.301587, 0.691980, 0.691980),
-                        (0.317460, 0.709441, 0.709441),
-                        (0.333333, 0.726483, 0.726483),
-                        (0.349206, 0.743134, 0.743134),
-                        (0.365079, 0.759421, 0.759421),
-                        (0.380952, 0.766356, 0.766356),
-                        (0.396825, 0.773229, 0.773229),
-                        (0.412698, 0.780042, 0.780042),
-                        (0.428571, 0.786796, 0.786796),
-                        (0.444444, 0.793492, 0.793492),
-                        (0.460317, 0.800132, 0.800132),
-                        (0.476190, 0.806718, 0.806718),
-                        (0.492063, 0.813250, 0.813250),
-                        (0.507937, 0.819730, 0.819730),
-                        (0.523810, 0.826160, 0.826160),
-                        (0.539683, 0.832539, 0.832539),
-                        (0.555556, 0.838870, 0.838870),
-                        (0.571429, 0.845154, 0.845154),
-                        (0.587302, 0.851392, 0.851392),
-                        (0.603175, 0.857584, 0.857584),
-                        (0.619048, 0.863731, 0.863731),
-                        (0.634921, 0.869835, 0.869835),
-                        (0.650794, 0.875897, 0.875897),
-                        (0.666667, 0.881917, 0.881917),
-                        (0.682540, 0.887896, 0.887896),
-                        (0.698413, 0.893835, 0.893835),
-                        (0.714286, 0.899735, 0.899735),
-                        (0.730159, 0.905597, 0.905597),
-                        (0.746032, 0.911421, 0.911421),
-                        (0.761905, 0.917208, 0.917208),
-                        (0.777778, 0.922958, 0.922958),
-                        (0.793651, 0.928673, 0.928673),
-                        (0.809524, 0.934353, 0.934353),
-                        (0.825397, 0.939999, 0.939999),
-                        (0.841270, 0.945611, 0.945611),
-                        (0.857143, 0.951190, 0.951190),
-                        (0.873016, 0.956736, 0.956736),
-                        (0.888889, 0.962250, 0.962250),
-                        (0.904762, 0.967733, 0.967733),
-                        (0.920635, 0.973185, 0.973185),
-                        (0.936508, 0.978607, 0.978607),
-                        (0.952381, 0.983999, 0.983999),
-                        (0.968254, 0.989361, 0.989361),
-                        (0.984127, 0.994695, 0.994695), (1.0, 1.0, 1.0)),
-              'green': ((0., 0., 0.), (0.015873, 0.102869, 0.102869),
-                        (0.031746, 0.145479, 0.145479),
-                        (0.047619, 0.178174, 0.178174),
-                        (0.063492, 0.205738, 0.205738),
-                        (0.079365, 0.230022, 0.230022),
-                        (0.095238, 0.251976, 0.251976),
-                        (0.111111, 0.272166, 0.272166),
-                        (0.126984, 0.290957, 0.290957),
-                        (0.142857, 0.308607, 0.308607),
-                        (0.158730, 0.325300, 0.325300),
-                        (0.174603, 0.341178, 0.341178),
-                        (0.190476, 0.356348, 0.356348),
-                        (0.206349, 0.370899, 0.370899),
-                        (0.222222, 0.384900, 0.384900),
-                        (0.238095, 0.398410, 0.398410),
-                        (0.253968, 0.411476, 0.411476),
-                        (0.269841, 0.424139, 0.424139),
-                        (0.285714, 0.436436, 0.436436),
-                        (0.301587, 0.448395, 0.448395),
-                        (0.317460, 0.460044, 0.460044),
-                        (0.333333, 0.471405, 0.471405),
-                        (0.349206, 0.482498, 0.482498),
-                        (0.365079, 0.493342, 0.493342),
-                        (0.380952, 0.517549, 0.517549),
-                        (0.396825, 0.540674, 0.540674),
-                        (0.412698, 0.562849, 0.562849),
-                        (0.428571, 0.584183, 0.584183),
-                        (0.444444, 0.604765, 0.604765),
-                        (0.460317, 0.624669, 0.624669),
-                        (0.476190, 0.643958, 0.643958),
-                        (0.492063, 0.662687, 0.662687),
-                        (0.507937, 0.680900, 0.680900),
-                        (0.523810, 0.698638, 0.698638),
-                        (0.539683, 0.715937, 0.715937),
-                        (0.555556, 0.732828, 0.732828),
-                        (0.571429, 0.749338, 0.749338),
-                        (0.587302, 0.765493, 0.765493),
-                        (0.603175, 0.781313, 0.781313),
-                        (0.619048, 0.796819, 0.796819),
-                        (0.634921, 0.812029, 0.812029),
-                        (0.650794, 0.826960, 0.826960),
-                        (0.666667, 0.841625, 0.841625),
-                        (0.682540, 0.856040, 0.856040),
-                        (0.698413, 0.870216, 0.870216),
-                        (0.714286, 0.884164, 0.884164),
-                        (0.730159, 0.897896, 0.897896),
-                        (0.746032, 0.911421, 0.911421),
-                        (0.761905, 0.917208, 0.917208),
-                        (0.777778, 0.922958, 0.922958),
-                        (0.793651, 0.928673, 0.928673),
-                        (0.809524, 0.934353, 0.934353),
-                        (0.825397, 0.939999, 0.939999),
-                        (0.841270, 0.945611, 0.945611),
-                        (0.857143, 0.951190, 0.951190),
-                        (0.873016, 0.956736, 0.956736),
-                        (0.888889, 0.962250, 0.962250),
-                        (0.904762, 0.967733, 0.967733),
-                        (0.920635, 0.973185, 0.973185),
-                        (0.936508, 0.978607, 0.978607),
-                        (0.952381, 0.983999, 0.983999),
-                        (0.968254, 0.989361, 0.989361),
-                        (0.984127, 0.994695, 0.994695), (1.0, 1.0, 1.0)),
-              'blue':  ((0., 0., 0.), (0.015873, 0.102869, 0.102869),
-                        (0.031746, 0.145479, 0.145479),
-                        (0.047619, 0.178174, 0.178174),
-                        (0.063492, 0.205738, 0.205738),
-                        (0.079365, 0.230022, 0.230022),
-                        (0.095238, 0.251976, 0.251976),
-                        (0.111111, 0.272166, 0.272166),
-                        (0.126984, 0.290957, 0.290957),
-                        (0.142857, 0.308607, 0.308607),
-                        (0.158730, 0.325300, 0.325300),
-                        (0.174603, 0.341178, 0.341178),
-                        (0.190476, 0.356348, 0.356348),
-                        (0.206349, 0.370899, 0.370899),
-                        (0.222222, 0.384900, 0.384900),
-                        (0.238095, 0.398410, 0.398410),
-                        (0.253968, 0.411476, 0.411476),
-                        (0.269841, 0.424139, 0.424139),
-                        (0.285714, 0.436436, 0.436436),
-                        (0.301587, 0.448395, 0.448395),
-                        (0.317460, 0.460044, 0.460044),
-                        (0.333333, 0.471405, 0.471405),
-                        (0.349206, 0.482498, 0.482498),
-                        (0.365079, 0.493342, 0.493342),
-                        (0.380952, 0.503953, 0.503953),
-                        (0.396825, 0.514344, 0.514344),
-                        (0.412698, 0.524531, 0.524531),
-                        (0.428571, 0.534522, 0.534522),
-                        (0.444444, 0.544331, 0.544331),
-                        (0.460317, 0.553966, 0.553966),
-                        (0.476190, 0.563436, 0.563436),
-                        (0.492063, 0.572750, 0.572750),
-                        (0.507937, 0.581914, 0.581914),
-                        (0.523810, 0.590937, 0.590937),
-                        (0.539683, 0.599824, 0.599824),
-                        (0.555556, 0.608581, 0.608581),
-                        (0.571429, 0.617213, 0.617213),
-                        (0.587302, 0.625727, 0.625727),
-                        (0.603175, 0.634126, 0.634126),
-                        (0.619048, 0.642416, 0.642416),
-                        (0.634921, 0.650600, 0.650600),
-                        (0.650794, 0.658682, 0.658682),
-                        (0.666667, 0.666667, 0.666667),
-                        (0.682540, 0.674556, 0.674556),
-                        (0.698413, 0.682355, 0.682355),
-                        (0.714286, 0.690066, 0.690066),
-                        (0.730159, 0.697691, 0.697691),
-                        (0.746032, 0.705234, 0.705234),
-                        (0.761905, 0.727166, 0.727166),
-                        (0.777778, 0.748455, 0.748455),
-                        (0.793651, 0.769156, 0.769156),
-                        (0.809524, 0.789314, 0.789314),
-                        (0.825397, 0.808969, 0.808969),
-                        (0.841270, 0.828159, 0.828159),
-                        (0.857143, 0.846913, 0.846913),
-                        (0.873016, 0.865261, 0.865261),
-                        (0.888889, 0.883229, 0.883229),
-                        (0.904762, 0.900837, 0.900837),
-                        (0.920635, 0.918109, 0.918109),
-                        (0.936508, 0.935061, 0.935061),
-                        (0.952381, 0.951711, 0.951711),
-                        (0.968254, 0.968075, 0.968075),
-                        (0.984127, 0.984167, 0.984167), (1.0, 1.0, 1.0))}
-
-_spring_data = {'red':   ((0., 1., 1.), (1.0, 1.0, 1.0)),
-                'green': ((0., 0., 0.), (1.0, 1.0, 1.0)),
-                'blue':  ((0., 1., 1.), (1.0, 0.0, 0.0))}
-
-
-_summer_data = {'red':   ((0., 0., 0.), (1.0, 1.0, 1.0)),
-                'green': ((0., 0.5, 0.5), (1.0, 1.0, 1.0)),
-                'blue':  ((0., 0.4, 0.4), (1.0, 0.4, 0.4))}
-
-
-_winter_data = {'red':   ((0., 0., 0.), (1.0, 0.0, 0.0)),
-                'green': ((0., 0., 0.), (1.0, 1.0, 1.0)),
-                'blue':  ((0., 1., 1.), (1.0, 0.5, 0.5))}
-
-_nipy_spectral_data = {
-    'red': [
-        (0.0, 0.0, 0.0), (0.05, 0.4667, 0.4667),
-        (0.10, 0.5333, 0.5333), (0.15, 0.0, 0.0),
-        (0.20, 0.0, 0.0), (0.25, 0.0, 0.0),
-        (0.30, 0.0, 0.0), (0.35, 0.0, 0.0),
-        (0.40, 0.0, 0.0), (0.45, 0.0, 0.0),
-        (0.50, 0.0, 0.0), (0.55, 0.0, 0.0),
-        (0.60, 0.0, 0.0), (0.65, 0.7333, 0.7333),
-        (0.70, 0.9333, 0.9333), (0.75, 1.0, 1.0),
-        (0.80, 1.0, 1.0), (0.85, 1.0, 1.0),
-        (0.90, 0.8667, 0.8667), (0.95, 0.80, 0.80),
-        (1.0, 0.80, 0.80),
-    ],
-    'green': [
-        (0.0, 0.0, 0.0), (0.05, 0.0, 0.0),
-        (0.10, 0.0, 0.0), (0.15, 0.0, 0.0),
-        (0.20, 0.0, 0.0), (0.25, 0.4667, 0.4667),
-        (0.30, 0.6000, 0.6000), (0.35, 0.6667, 0.6667),
-        (0.40, 0.6667, 0.6667), (0.45, 0.6000, 0.6000),
-        (0.50, 0.7333, 0.7333), (0.55, 0.8667, 0.8667),
-        (0.60, 1.0, 1.0), (0.65, 1.0, 1.0),
-        (0.70, 0.9333, 0.9333), (0.75, 0.8000, 0.8000),
-        (0.80, 0.6000, 0.6000), (0.85, 0.0, 0.0),
-        (0.90, 0.0, 0.0), (0.95, 0.0, 0.0),
-        (1.0, 0.80, 0.80),
-    ],
-    'blue': [
-        (0.0, 0.0, 0.0), (0.05, 0.5333, 0.5333),
-        (0.10, 0.6000, 0.6000), (0.15, 0.6667, 0.6667),
-        (0.20, 0.8667, 0.8667), (0.25, 0.8667, 0.8667),
-        (0.30, 0.8667, 0.8667), (0.35, 0.6667, 0.6667),
-        (0.40, 0.5333, 0.5333), (0.45, 0.0, 0.0),
-        (0.5, 0.0, 0.0), (0.55, 0.0, 0.0),
-        (0.60, 0.0, 0.0), (0.65, 0.0, 0.0),
-        (0.70, 0.0, 0.0), (0.75, 0.0, 0.0),
-        (0.80, 0.0, 0.0), (0.85, 0.0, 0.0),
-        (0.90, 0.0, 0.0), (0.95, 0.0, 0.0),
-        (1.0, 0.80, 0.80),
-    ],
-}
-
-
-# 34 colormaps based on color specifications and designs
-# developed by Cynthia Brewer (https://colorbrewer2.org/).
-# The ColorBrewer palettes have been included under the terms
-# of an Apache-stype license (for details, see the file
-# LICENSE_COLORBREWER in the license directory of the matplotlib
-# source distribution).
-
-# RGB values taken from Brewer's Excel sheet, divided by 255
-
-_Blues_data = (
-    (0.96862745098039216,  0.98431372549019602,  1.0                ),
-    (0.87058823529411766,  0.92156862745098034,  0.96862745098039216),
-    (0.77647058823529413,  0.85882352941176465,  0.93725490196078431),
-    (0.61960784313725492,  0.792156862745098  ,  0.88235294117647056),
-    (0.41960784313725491,  0.68235294117647061,  0.83921568627450982),
-    (0.25882352941176473,  0.5725490196078431 ,  0.77647058823529413),
-    (0.12941176470588237,  0.44313725490196076,  0.70980392156862748),
-    (0.03137254901960784,  0.31764705882352939,  0.61176470588235299),
-    (0.03137254901960784,  0.18823529411764706,  0.41960784313725491)
-    )
-
-_BrBG_data = (
-    (0.32941176470588235,  0.18823529411764706,  0.0196078431372549 ),
-    (0.5490196078431373 ,  0.31764705882352939,  0.0392156862745098 ),
-    (0.74901960784313726,  0.50588235294117645,  0.17647058823529413),
-    (0.87450980392156863,  0.76078431372549016,  0.49019607843137253),
-    (0.96470588235294119,  0.90980392156862744,  0.76470588235294112),
-    (0.96078431372549022,  0.96078431372549022,  0.96078431372549022),
-    (0.7803921568627451 ,  0.91764705882352937,  0.89803921568627454),
-    (0.50196078431372548,  0.80392156862745101,  0.75686274509803919),
-    (0.20784313725490197,  0.59215686274509804,  0.5607843137254902 ),
-    (0.00392156862745098,  0.4                ,  0.36862745098039218),
-    (0.0                ,  0.23529411764705882,  0.18823529411764706)
-    )
-
-_BuGn_data = (
-    (0.96862745098039216,  0.9882352941176471 ,  0.99215686274509807),
-    (0.89803921568627454,  0.96078431372549022,  0.97647058823529409),
-    (0.8                ,  0.92549019607843142,  0.90196078431372551),
-    (0.6                ,  0.84705882352941175,  0.78823529411764703),
-    (0.4                ,  0.76078431372549016,  0.64313725490196083),
-    (0.25490196078431371,  0.68235294117647061,  0.46274509803921571),
-    (0.13725490196078433,  0.54509803921568623,  0.27058823529411763),
-    (0.0                ,  0.42745098039215684,  0.17254901960784313),
-    (0.0                ,  0.26666666666666666,  0.10588235294117647)
-    )
-
-_BuPu_data = (
-    (0.96862745098039216,  0.9882352941176471 ,  0.99215686274509807),
-    (0.8784313725490196 ,  0.92549019607843142,  0.95686274509803926),
-    (0.74901960784313726,  0.82745098039215681,  0.90196078431372551),
-    (0.61960784313725492,  0.73725490196078436,  0.85490196078431369),
-    (0.5490196078431373 ,  0.58823529411764708,  0.77647058823529413),
-    (0.5490196078431373 ,  0.41960784313725491,  0.69411764705882351),
-    (0.53333333333333333,  0.25490196078431371,  0.61568627450980395),
-    (0.50588235294117645,  0.05882352941176471,  0.48627450980392156),
-    (0.30196078431372547,  0.0                ,  0.29411764705882354)
-    )
-
-_GnBu_data = (
-    (0.96862745098039216,  0.9882352941176471 ,  0.94117647058823528),
-    (0.8784313725490196 ,  0.95294117647058818,  0.85882352941176465),
-    (0.8                ,  0.92156862745098034,  0.77254901960784317),
-    (0.6588235294117647 ,  0.8666666666666667 ,  0.70980392156862748),
-    (0.4823529411764706 ,  0.8                ,  0.7686274509803922 ),
-    (0.30588235294117649,  0.70196078431372544,  0.82745098039215681),
-    (0.16862745098039217,  0.5490196078431373 ,  0.74509803921568629),
-    (0.03137254901960784,  0.40784313725490196,  0.67450980392156867),
-    (0.03137254901960784,  0.25098039215686274,  0.50588235294117645)
-    )
-
-_Greens_data = (
-    (0.96862745098039216,  0.9882352941176471 ,  0.96078431372549022),
-    (0.89803921568627454,  0.96078431372549022,  0.8784313725490196 ),
-    (0.7803921568627451 ,  0.9137254901960784 ,  0.75294117647058822),
-    (0.63137254901960782,  0.85098039215686272,  0.60784313725490191),
-    (0.45490196078431372,  0.7686274509803922 ,  0.46274509803921571),
-    (0.25490196078431371,  0.6705882352941176 ,  0.36470588235294116),
-    (0.13725490196078433,  0.54509803921568623,  0.27058823529411763),
-    (0.0                ,  0.42745098039215684,  0.17254901960784313),
-    (0.0                ,  0.26666666666666666,  0.10588235294117647)
-    )
-
-_Greys_data = (
-    (1.0                ,  1.0                ,  1.0                ),
-    (0.94117647058823528,  0.94117647058823528,  0.94117647058823528),
-    (0.85098039215686272,  0.85098039215686272,  0.85098039215686272),
-    (0.74117647058823533,  0.74117647058823533,  0.74117647058823533),
-    (0.58823529411764708,  0.58823529411764708,  0.58823529411764708),
-    (0.45098039215686275,  0.45098039215686275,  0.45098039215686275),
-    (0.32156862745098042,  0.32156862745098042,  0.32156862745098042),
-    (0.14509803921568629,  0.14509803921568629,  0.14509803921568629),
-    (0.0                ,  0.0                ,  0.0                )
-    )
-
-_Oranges_data = (
-    (1.0                ,  0.96078431372549022,  0.92156862745098034),
-    (0.99607843137254903,  0.90196078431372551,  0.80784313725490198),
-    (0.99215686274509807,  0.81568627450980391,  0.63529411764705879),
-    (0.99215686274509807,  0.68235294117647061,  0.41960784313725491),
-    (0.99215686274509807,  0.55294117647058827,  0.23529411764705882),
-    (0.94509803921568625,  0.41176470588235292,  0.07450980392156863),
-    (0.85098039215686272,  0.28235294117647058,  0.00392156862745098),
-    (0.65098039215686276,  0.21176470588235294,  0.01176470588235294),
-    (0.49803921568627452,  0.15294117647058825,  0.01568627450980392)
-    )
-
-_OrRd_data = (
-    (1.0                ,  0.96862745098039216,  0.92549019607843142),
-    (0.99607843137254903,  0.90980392156862744,  0.78431372549019607),
-    (0.99215686274509807,  0.83137254901960789,  0.61960784313725492),
-    (0.99215686274509807,  0.73333333333333328,  0.51764705882352946),
-    (0.9882352941176471 ,  0.55294117647058827,  0.34901960784313724),
-    (0.93725490196078431,  0.396078431372549  ,  0.28235294117647058),
-    (0.84313725490196079,  0.18823529411764706,  0.12156862745098039),
-    (0.70196078431372544,  0.0                ,  0.0                ),
-    (0.49803921568627452,  0.0                ,  0.0                )
-    )
-
-_PiYG_data = (
-    (0.55686274509803924,  0.00392156862745098,  0.32156862745098042),
-    (0.77254901960784317,  0.10588235294117647,  0.49019607843137253),
-    (0.87058823529411766,  0.46666666666666667,  0.68235294117647061),
-    (0.94509803921568625,  0.71372549019607845,  0.85490196078431369),
-    (0.99215686274509807,  0.8784313725490196 ,  0.93725490196078431),
-    (0.96862745098039216,  0.96862745098039216,  0.96862745098039216),
-    (0.90196078431372551,  0.96078431372549022,  0.81568627450980391),
-    (0.72156862745098038,  0.88235294117647056,  0.52549019607843139),
-    (0.49803921568627452,  0.73725490196078436,  0.25490196078431371),
-    (0.30196078431372547,  0.5725490196078431 ,  0.12941176470588237),
-    (0.15294117647058825,  0.39215686274509803,  0.09803921568627451)
-    )
-
-_PRGn_data = (
-    (0.25098039215686274,  0.0                ,  0.29411764705882354),
-    (0.46274509803921571,  0.16470588235294117,  0.51372549019607838),
-    (0.6                ,  0.4392156862745098 ,  0.6705882352941176 ),
-    (0.76078431372549016,  0.6470588235294118 ,  0.81176470588235294),
-    (0.90588235294117647,  0.83137254901960789,  0.90980392156862744),
-    (0.96862745098039216,  0.96862745098039216,  0.96862745098039216),
-    (0.85098039215686272,  0.94117647058823528,  0.82745098039215681),
-    (0.65098039215686276,  0.85882352941176465,  0.62745098039215685),
-    (0.35294117647058826,  0.68235294117647061,  0.38039215686274508),
-    (0.10588235294117647,  0.47058823529411764,  0.21568627450980393),
-    (0.0                ,  0.26666666666666666,  0.10588235294117647)
-    )
-
-_PuBu_data = (
-    (1.0                ,  0.96862745098039216,  0.98431372549019602),
-    (0.92549019607843142,  0.90588235294117647,  0.94901960784313721),
-    (0.81568627450980391,  0.81960784313725488,  0.90196078431372551),
-    (0.65098039215686276,  0.74117647058823533,  0.85882352941176465),
-    (0.45490196078431372,  0.66274509803921566,  0.81176470588235294),
-    (0.21176470588235294,  0.56470588235294117,  0.75294117647058822),
-    (0.0196078431372549 ,  0.4392156862745098 ,  0.69019607843137254),
-    (0.01568627450980392,  0.35294117647058826,  0.55294117647058827),
-    (0.00784313725490196,  0.2196078431372549 ,  0.34509803921568627)
-    )
-
-_PuBuGn_data = (
-    (1.0                ,  0.96862745098039216,  0.98431372549019602),
-    (0.92549019607843142,  0.88627450980392153,  0.94117647058823528),
-    (0.81568627450980391,  0.81960784313725488,  0.90196078431372551),
-    (0.65098039215686276,  0.74117647058823533,  0.85882352941176465),
-    (0.40392156862745099,  0.66274509803921566,  0.81176470588235294),
-    (0.21176470588235294,  0.56470588235294117,  0.75294117647058822),
-    (0.00784313725490196,  0.50588235294117645,  0.54117647058823526),
-    (0.00392156862745098,  0.42352941176470588,  0.34901960784313724),
-    (0.00392156862745098,  0.27450980392156865,  0.21176470588235294)
-    )
-
-_PuOr_data = (
-    (0.49803921568627452,  0.23137254901960785,  0.03137254901960784),
-    (0.70196078431372544,  0.34509803921568627,  0.02352941176470588),
-    (0.8784313725490196 ,  0.50980392156862742,  0.07843137254901961),
-    (0.99215686274509807,  0.72156862745098038,  0.38823529411764707),
-    (0.99607843137254903,  0.8784313725490196 ,  0.71372549019607845),
-    (0.96862745098039216,  0.96862745098039216,  0.96862745098039216),
-    (0.84705882352941175,  0.85490196078431369,  0.92156862745098034),
-    (0.69803921568627447,  0.6705882352941176 ,  0.82352941176470584),
-    (0.50196078431372548,  0.45098039215686275,  0.67450980392156867),
-    (0.32941176470588235,  0.15294117647058825,  0.53333333333333333),
-    (0.17647058823529413,  0.0                ,  0.29411764705882354)
-    )
-
-_PuRd_data = (
-    (0.96862745098039216,  0.95686274509803926,  0.97647058823529409),
-    (0.90588235294117647,  0.88235294117647056,  0.93725490196078431),
-    (0.83137254901960789,  0.72549019607843135,  0.85490196078431369),
-    (0.78823529411764703,  0.58039215686274515,  0.7803921568627451 ),
-    (0.87450980392156863,  0.396078431372549  ,  0.69019607843137254),
-    (0.90588235294117647,  0.16078431372549021,  0.54117647058823526),
-    (0.80784313725490198,  0.07058823529411765,  0.33725490196078434),
-    (0.59607843137254901,  0.0                ,  0.2627450980392157 ),
-    (0.40392156862745099,  0.0                ,  0.12156862745098039)
-    )
-
-_Purples_data = (
-    (0.9882352941176471 ,  0.98431372549019602,  0.99215686274509807),
-    (0.93725490196078431,  0.92941176470588238,  0.96078431372549022),
-    (0.85490196078431369,  0.85490196078431369,  0.92156862745098034),
-    (0.73725490196078436,  0.74117647058823533,  0.86274509803921573),
-    (0.61960784313725492,  0.60392156862745094,  0.78431372549019607),
-    (0.50196078431372548,  0.49019607843137253,  0.72941176470588232),
-    (0.41568627450980394,  0.31764705882352939,  0.63921568627450975),
-    (0.32941176470588235,  0.15294117647058825,  0.5607843137254902 ),
-    (0.24705882352941178,  0.0                ,  0.49019607843137253)
-    )
-
-_RdBu_data = (
-    (0.40392156862745099,  0.0                ,  0.12156862745098039),
-    (0.69803921568627447,  0.09411764705882353,  0.16862745098039217),
-    (0.83921568627450982,  0.37647058823529411,  0.30196078431372547),
-    (0.95686274509803926,  0.6470588235294118 ,  0.50980392156862742),
-    (0.99215686274509807,  0.85882352941176465,  0.7803921568627451 ),
-    (0.96862745098039216,  0.96862745098039216,  0.96862745098039216),
-    (0.81960784313725488,  0.89803921568627454,  0.94117647058823528),
-    (0.5725490196078431 ,  0.77254901960784317,  0.87058823529411766),
-    (0.2627450980392157 ,  0.57647058823529407,  0.76470588235294112),
-    (0.12941176470588237,  0.4                ,  0.67450980392156867),
-    (0.0196078431372549 ,  0.18823529411764706,  0.38039215686274508)
-    )
-
-_RdGy_data = (
-    (0.40392156862745099,  0.0                ,  0.12156862745098039),
-    (0.69803921568627447,  0.09411764705882353,  0.16862745098039217),
-    (0.83921568627450982,  0.37647058823529411,  0.30196078431372547),
-    (0.95686274509803926,  0.6470588235294118 ,  0.50980392156862742),
-    (0.99215686274509807,  0.85882352941176465,  0.7803921568627451 ),
-    (1.0                ,  1.0                ,  1.0                ),
-    (0.8784313725490196 ,  0.8784313725490196 ,  0.8784313725490196 ),
-    (0.72941176470588232,  0.72941176470588232,  0.72941176470588232),
-    (0.52941176470588236,  0.52941176470588236,  0.52941176470588236),
-    (0.30196078431372547,  0.30196078431372547,  0.30196078431372547),
-    (0.10196078431372549,  0.10196078431372549,  0.10196078431372549)
-    )
-
-_RdPu_data = (
-    (1.0                ,  0.96862745098039216,  0.95294117647058818),
-    (0.99215686274509807,  0.8784313725490196 ,  0.86666666666666667),
-    (0.9882352941176471 ,  0.77254901960784317,  0.75294117647058822),
-    (0.98039215686274506,  0.62352941176470589,  0.70980392156862748),
-    (0.96862745098039216,  0.40784313725490196,  0.63137254901960782),
-    (0.86666666666666667,  0.20392156862745098,  0.59215686274509804),
-    (0.68235294117647061,  0.00392156862745098,  0.49411764705882355),
-    (0.47843137254901963,  0.00392156862745098,  0.46666666666666667),
-    (0.28627450980392155,  0.0                ,  0.41568627450980394)
-    )
-
-_RdYlBu_data = (
-    (0.6470588235294118 , 0.0                 , 0.14901960784313725),
-    (0.84313725490196079, 0.18823529411764706 , 0.15294117647058825),
-    (0.95686274509803926, 0.42745098039215684 , 0.2627450980392157 ),
-    (0.99215686274509807, 0.68235294117647061 , 0.38039215686274508),
-    (0.99607843137254903, 0.8784313725490196  , 0.56470588235294117),
-    (1.0                , 1.0                 , 0.74901960784313726),
-    (0.8784313725490196 , 0.95294117647058818 , 0.97254901960784312),
-    (0.6705882352941176 , 0.85098039215686272 , 0.9137254901960784 ),
-    (0.45490196078431372, 0.67843137254901964 , 0.81960784313725488),
-    (0.27058823529411763, 0.45882352941176469 , 0.70588235294117652),
-    (0.19215686274509805, 0.21176470588235294 , 0.58431372549019611)
-    )
-
-_RdYlGn_data = (
-    (0.6470588235294118 , 0.0                 , 0.14901960784313725),
-    (0.84313725490196079, 0.18823529411764706 , 0.15294117647058825),
-    (0.95686274509803926, 0.42745098039215684 , 0.2627450980392157 ),
-    (0.99215686274509807, 0.68235294117647061 , 0.38039215686274508),
-    (0.99607843137254903, 0.8784313725490196  , 0.54509803921568623),
-    (1.0                , 1.0                 , 0.74901960784313726),
-    (0.85098039215686272, 0.93725490196078431 , 0.54509803921568623),
-    (0.65098039215686276, 0.85098039215686272 , 0.41568627450980394),
-    (0.4                , 0.74117647058823533 , 0.38823529411764707),
-    (0.10196078431372549, 0.59607843137254901 , 0.31372549019607843),
-    (0.0                , 0.40784313725490196 , 0.21568627450980393)
-    )
-
-_Reds_data = (
-    (1.0                , 0.96078431372549022 , 0.94117647058823528),
-    (0.99607843137254903, 0.8784313725490196  , 0.82352941176470584),
-    (0.9882352941176471 , 0.73333333333333328 , 0.63137254901960782),
-    (0.9882352941176471 , 0.5725490196078431  , 0.44705882352941179),
-    (0.98431372549019602, 0.41568627450980394 , 0.29019607843137257),
-    (0.93725490196078431, 0.23137254901960785 , 0.17254901960784313),
-    (0.79607843137254897, 0.094117647058823528, 0.11372549019607843),
-    (0.6470588235294118 , 0.058823529411764705, 0.08235294117647058),
-    (0.40392156862745099, 0.0                 , 0.05098039215686274)
-    )
-
-_Spectral_data = (
-    (0.61960784313725492, 0.003921568627450980, 0.25882352941176473),
-    (0.83529411764705885, 0.24313725490196078 , 0.30980392156862746),
-    (0.95686274509803926, 0.42745098039215684 , 0.2627450980392157 ),
-    (0.99215686274509807, 0.68235294117647061 , 0.38039215686274508),
-    (0.99607843137254903, 0.8784313725490196  , 0.54509803921568623),
-    (1.0                , 1.0                 , 0.74901960784313726),
-    (0.90196078431372551, 0.96078431372549022 , 0.59607843137254901),
-    (0.6705882352941176 , 0.8666666666666667  , 0.64313725490196083),
-    (0.4                , 0.76078431372549016 , 0.6470588235294118 ),
-    (0.19607843137254902, 0.53333333333333333 , 0.74117647058823533),
-    (0.36862745098039218, 0.30980392156862746 , 0.63529411764705879)
-    )
-
-_YlGn_data = (
-    (1.0                , 1.0                 , 0.89803921568627454),
-    (0.96862745098039216, 0.9882352941176471  , 0.72549019607843135),
-    (0.85098039215686272, 0.94117647058823528 , 0.63921568627450975),
-    (0.67843137254901964, 0.8666666666666667  , 0.55686274509803924),
-    (0.47058823529411764, 0.77647058823529413 , 0.47450980392156861),
-    (0.25490196078431371, 0.6705882352941176  , 0.36470588235294116),
-    (0.13725490196078433, 0.51764705882352946 , 0.2627450980392157 ),
-    (0.0                , 0.40784313725490196 , 0.21568627450980393),
-    (0.0                , 0.27058823529411763 , 0.16078431372549021)
-    )
-
-_YlGnBu_data = (
-    (1.0                , 1.0                 , 0.85098039215686272),
-    (0.92941176470588238, 0.97254901960784312 , 0.69411764705882351),
-    (0.7803921568627451 , 0.9137254901960784  , 0.70588235294117652),
-    (0.49803921568627452, 0.80392156862745101 , 0.73333333333333328),
-    (0.25490196078431371, 0.71372549019607845 , 0.7686274509803922 ),
-    (0.11372549019607843, 0.56862745098039214 , 0.75294117647058822),
-    (0.13333333333333333, 0.36862745098039218 , 0.6588235294117647 ),
-    (0.14509803921568629, 0.20392156862745098 , 0.58039215686274515),
-    (0.03137254901960784, 0.11372549019607843 , 0.34509803921568627)
-    )
-
-_YlOrBr_data = (
-    (1.0                , 1.0                 , 0.89803921568627454),
-    (1.0                , 0.96862745098039216 , 0.73725490196078436),
-    (0.99607843137254903, 0.8901960784313725  , 0.56862745098039214),
-    (0.99607843137254903, 0.7686274509803922  , 0.30980392156862746),
-    (0.99607843137254903, 0.6                 , 0.16078431372549021),
-    (0.92549019607843142, 0.4392156862745098  , 0.07843137254901961),
-    (0.8                , 0.29803921568627451 , 0.00784313725490196),
-    (0.6                , 0.20392156862745098 , 0.01568627450980392),
-    (0.4                , 0.14509803921568629 , 0.02352941176470588)
-    )
-
-_YlOrRd_data = (
-    (1.0                , 1.0                 , 0.8                ),
-    (1.0                , 0.92941176470588238 , 0.62745098039215685),
-    (0.99607843137254903, 0.85098039215686272 , 0.46274509803921571),
-    (0.99607843137254903, 0.69803921568627447 , 0.29803921568627451),
-    (0.99215686274509807, 0.55294117647058827 , 0.23529411764705882),
-    (0.9882352941176471 , 0.30588235294117649 , 0.16470588235294117),
-    (0.8901960784313725 , 0.10196078431372549 , 0.10980392156862745),
-    (0.74117647058823533, 0.0                 , 0.14901960784313725),
-    (0.50196078431372548, 0.0                 , 0.14901960784313725)
-    )
-
-
-# ColorBrewer's qualitative maps, implemented using ListedColormap
-# for use with mpl.colors.NoNorm
-
-_Accent_data = (
-    (0.49803921568627452, 0.78823529411764703, 0.49803921568627452),
-    (0.74509803921568629, 0.68235294117647061, 0.83137254901960789),
-    (0.99215686274509807, 0.75294117647058822, 0.52549019607843139),
-    (1.0,                 1.0,                 0.6                ),
-    (0.2196078431372549,  0.42352941176470588, 0.69019607843137254),
-    (0.94117647058823528, 0.00784313725490196, 0.49803921568627452),
-    (0.74901960784313726, 0.35686274509803922, 0.09019607843137254),
-    (0.4,                 0.4,                 0.4                ),
-    )
-
-_Dark2_data = (
-    (0.10588235294117647, 0.61960784313725492, 0.46666666666666667),
-    (0.85098039215686272, 0.37254901960784315, 0.00784313725490196),
-    (0.45882352941176469, 0.4392156862745098,  0.70196078431372544),
-    (0.90588235294117647, 0.16078431372549021, 0.54117647058823526),
-    (0.4,                 0.65098039215686276, 0.11764705882352941),
-    (0.90196078431372551, 0.6705882352941176,  0.00784313725490196),
-    (0.65098039215686276, 0.46274509803921571, 0.11372549019607843),
-    (0.4,                 0.4,                 0.4                ),
-    )
-
-_Paired_data = (
-    (0.65098039215686276, 0.80784313725490198, 0.8901960784313725 ),
-    (0.12156862745098039, 0.47058823529411764, 0.70588235294117652),
-    (0.69803921568627447, 0.87450980392156863, 0.54117647058823526),
-    (0.2,                 0.62745098039215685, 0.17254901960784313),
-    (0.98431372549019602, 0.60392156862745094, 0.6                ),
-    (0.8901960784313725,  0.10196078431372549, 0.10980392156862745),
-    (0.99215686274509807, 0.74901960784313726, 0.43529411764705883),
-    (1.0,                 0.49803921568627452, 0.0                ),
-    (0.792156862745098,   0.69803921568627447, 0.83921568627450982),
-    (0.41568627450980394, 0.23921568627450981, 0.60392156862745094),
-    (1.0,                 1.0,                 0.6                ),
-    (0.69411764705882351, 0.34901960784313724, 0.15686274509803921),
-    )
-
-_Pastel1_data = (
-    (0.98431372549019602, 0.70588235294117652, 0.68235294117647061),
-    (0.70196078431372544, 0.80392156862745101, 0.8901960784313725 ),
-    (0.8,                 0.92156862745098034, 0.77254901960784317),
-    (0.87058823529411766, 0.79607843137254897, 0.89411764705882357),
-    (0.99607843137254903, 0.85098039215686272, 0.65098039215686276),
-    (1.0,                 1.0,                 0.8                ),
-    (0.89803921568627454, 0.84705882352941175, 0.74117647058823533),
-    (0.99215686274509807, 0.85490196078431369, 0.92549019607843142),
-    (0.94901960784313721, 0.94901960784313721, 0.94901960784313721),
-    )
-
-_Pastel2_data = (
-    (0.70196078431372544, 0.88627450980392153, 0.80392156862745101),
-    (0.99215686274509807, 0.80392156862745101, 0.67450980392156867),
-    (0.79607843137254897, 0.83529411764705885, 0.90980392156862744),
-    (0.95686274509803926, 0.792156862745098,   0.89411764705882357),
-    (0.90196078431372551, 0.96078431372549022, 0.78823529411764703),
-    (1.0,                 0.94901960784313721, 0.68235294117647061),
-    (0.94509803921568625, 0.88627450980392153, 0.8                ),
-    (0.8,                 0.8,                 0.8                ),
-    )
-
-_Set1_data = (
-    (0.89411764705882357, 0.10196078431372549, 0.10980392156862745),
-    (0.21568627450980393, 0.49411764705882355, 0.72156862745098038),
-    (0.30196078431372547, 0.68627450980392157, 0.29019607843137257),
-    (0.59607843137254901, 0.30588235294117649, 0.63921568627450975),
-    (1.0,                 0.49803921568627452, 0.0                ),
-    (1.0,                 1.0,                 0.2                ),
-    (0.65098039215686276, 0.33725490196078434, 0.15686274509803921),
-    (0.96862745098039216, 0.50588235294117645, 0.74901960784313726),
-    (0.6,                 0.6,                 0.6),
-    )
-
-_Set2_data = (
-    (0.4,                 0.76078431372549016, 0.6470588235294118 ),
-    (0.9882352941176471,  0.55294117647058827, 0.3843137254901961 ),
-    (0.55294117647058827, 0.62745098039215685, 0.79607843137254897),
-    (0.90588235294117647, 0.54117647058823526, 0.76470588235294112),
-    (0.65098039215686276, 0.84705882352941175, 0.32941176470588235),
-    (1.0,                 0.85098039215686272, 0.18431372549019609),
-    (0.89803921568627454, 0.7686274509803922,  0.58039215686274515),
-    (0.70196078431372544, 0.70196078431372544, 0.70196078431372544),
-    )
-
-_Set3_data = (
-    (0.55294117647058827, 0.82745098039215681, 0.7803921568627451 ),
-    (1.0,                 1.0,                 0.70196078431372544),
-    (0.74509803921568629, 0.72941176470588232, 0.85490196078431369),
-    (0.98431372549019602, 0.50196078431372548, 0.44705882352941179),
-    (0.50196078431372548, 0.69411764705882351, 0.82745098039215681),
-    (0.99215686274509807, 0.70588235294117652, 0.3843137254901961 ),
-    (0.70196078431372544, 0.87058823529411766, 0.41176470588235292),
-    (0.9882352941176471,  0.80392156862745101, 0.89803921568627454),
-    (0.85098039215686272, 0.85098039215686272, 0.85098039215686272),
-    (0.73725490196078436, 0.50196078431372548, 0.74117647058823533),
-    (0.8,                 0.92156862745098034, 0.77254901960784317),
-    (1.0,                 0.92941176470588238, 0.43529411764705883),
-    )
-
-
-# The next 7 palettes are from the Yorick scientific visualization package,
-# an evolution of the GIST package, both by David H. Munro.
-# They are released under a BSD-like license (see LICENSE_YORICK in
-# the license directory of the matplotlib source distribution).
-#
-# Most palette functions have been reduced to simple function descriptions
-# by Reinier Heeres, since the rgb components were mostly straight lines.
-# gist_earth_data and gist_ncar_data were simplified by a script and some
-# manual effort.
-
-_gist_earth_data = \
-{'red': (
-(0.0, 0.0, 0.0000),
-(0.2824, 0.1882, 0.1882),
-(0.4588, 0.2714, 0.2714),
-(0.5490, 0.4719, 0.4719),
-(0.6980, 0.7176, 0.7176),
-(0.7882, 0.7553, 0.7553),
-(1.0000, 0.9922, 0.9922),
-), 'green': (
-(0.0, 0.0, 0.0000),
-(0.0275, 0.0000, 0.0000),
-(0.1098, 0.1893, 0.1893),
-(0.1647, 0.3035, 0.3035),
-(0.2078, 0.3841, 0.3841),
-(0.2824, 0.5020, 0.5020),
-(0.5216, 0.6397, 0.6397),
-(0.6980, 0.7171, 0.7171),
-(0.7882, 0.6392, 0.6392),
-(0.7922, 0.6413, 0.6413),
-(0.8000, 0.6447, 0.6447),
-(0.8078, 0.6481, 0.6481),
-(0.8157, 0.6549, 0.6549),
-(0.8667, 0.6991, 0.6991),
-(0.8745, 0.7103, 0.7103),
-(0.8824, 0.7216, 0.7216),
-(0.8902, 0.7323, 0.7323),
-(0.8980, 0.7430, 0.7430),
-(0.9412, 0.8275, 0.8275),
-(0.9569, 0.8635, 0.8635),
-(0.9647, 0.8816, 0.8816),
-(0.9961, 0.9733, 0.9733),
-(1.0000, 0.9843, 0.9843),
-), 'blue': (
-(0.0, 0.0, 0.0000),
-(0.0039, 0.1684, 0.1684),
-(0.0078, 0.2212, 0.2212),
-(0.0275, 0.4329, 0.4329),
-(0.0314, 0.4549, 0.4549),
-(0.2824, 0.5004, 0.5004),
-(0.4667, 0.2748, 0.2748),
-(0.5451, 0.3205, 0.3205),
-(0.7843, 0.3961, 0.3961),
-(0.8941, 0.6651, 0.6651),
-(1.0000, 0.9843, 0.9843),
-)}
-
-_gist_gray_data = {
-        'red': gfunc[3],
-        'green': gfunc[3],
-        'blue': gfunc[3],
-}
-
-def _gist_heat_red(x): return 1.5 * x
-def _gist_heat_green(x): return 2 * x - 1
-def _gist_heat_blue(x): return 4 * x - 3
-_gist_heat_data = {
-    'red': _gist_heat_red, 'green': _gist_heat_green, 'blue': _gist_heat_blue}
-
-_gist_ncar_data = \
-{'red': (
-(0.0, 0.0, 0.0000),
-(0.3098, 0.0000, 0.0000),
-(0.3725, 0.3993, 0.3993),
-(0.4235, 0.5003, 0.5003),
-(0.5333, 1.0000, 1.0000),
-(0.7922, 1.0000, 1.0000),
-(0.8471, 0.6218, 0.6218),
-(0.8980, 0.9235, 0.9235),
-(1.0000, 0.9961, 0.9961),
-), 'green': (
-(0.0, 0.0, 0.0000),
-(0.0510, 0.3722, 0.3722),
-(0.1059, 0.0000, 0.0000),
-(0.1569, 0.7202, 0.7202),
-(0.1608, 0.7537, 0.7537),
-(0.1647, 0.7752, 0.7752),
-(0.2157, 1.0000, 1.0000),
-(0.2588, 0.9804, 0.9804),
-(0.2706, 0.9804, 0.9804),
-(0.3176, 1.0000, 1.0000),
-(0.3686, 0.8081, 0.8081),
-(0.4275, 1.0000, 1.0000),
-(0.5216, 1.0000, 1.0000),
-(0.6314, 0.7292, 0.7292),
-(0.6863, 0.2796, 0.2796),
-(0.7451, 0.0000, 0.0000),
-(0.7922, 0.0000, 0.0000),
-(0.8431, 0.1753, 0.1753),
-(0.8980, 0.5000, 0.5000),
-(1.0000, 0.9725, 0.9725),
-), 'blue': (
-(0.0, 0.5020, 0.5020),
-(0.0510, 0.0222, 0.0222),
-(0.1098, 1.0000, 1.0000),
-(0.2039, 1.0000, 1.0000),
-(0.2627, 0.6145, 0.6145),
-(0.3216, 0.0000, 0.0000),
-(0.4157, 0.0000, 0.0000),
-(0.4745, 0.2342, 0.2342),
-(0.5333, 0.0000, 0.0000),
-(0.5804, 0.0000, 0.0000),
-(0.6314, 0.0549, 0.0549),
-(0.6902, 0.0000, 0.0000),
-(0.7373, 0.0000, 0.0000),
-(0.7922, 0.9738, 0.9738),
-(0.8000, 1.0000, 1.0000),
-(0.8431, 1.0000, 1.0000),
-(0.8980, 0.9341, 0.9341),
-(1.0000, 0.9961, 0.9961),
-)}
-
-_gist_rainbow_data = (
-        (0.000, (1.00, 0.00, 0.16)),
-        (0.030, (1.00, 0.00, 0.00)),
-        (0.215, (1.00, 1.00, 0.00)),
-        (0.400, (0.00, 1.00, 0.00)),
-        (0.586, (0.00, 1.00, 1.00)),
-        (0.770, (0.00, 0.00, 1.00)),
-        (0.954, (1.00, 0.00, 1.00)),
-        (1.000, (1.00, 0.00, 0.75))
-)
-
-_gist_stern_data = {
-        'red': (
-            (0.000, 0.000, 0.000), (0.0547, 1.000, 1.000),
-            (0.250, 0.027, 0.250),  # (0.2500, 0.250, 0.250),
-            (1.000, 1.000, 1.000)),
-        'green': ((0, 0, 0), (1, 1, 1)),
-        'blue': (
-            (0.000, 0.000, 0.000), (0.500, 1.000, 1.000),
-            (0.735, 0.000, 0.000), (1.000, 1.000, 1.000))
-}
-
-def _gist_yarg(x): return 1 - x
-_gist_yarg_data = {'red': _gist_yarg, 'green': _gist_yarg, 'blue': _gist_yarg}
-
-# This bipolar colormap was generated from CoolWarmFloat33.csv of
-# "Diverging Color Maps for Scientific Visualization" by Kenneth Moreland.
-# <http://www.kennethmoreland.com/color-maps/>
-_coolwarm_data = {
-    'red': [
-        (0.0, 0.2298057, 0.2298057),
-        (0.03125, 0.26623388, 0.26623388),
-        (0.0625, 0.30386891, 0.30386891),
-        (0.09375, 0.342804478, 0.342804478),
-        (0.125, 0.38301334, 0.38301334),
-        (0.15625, 0.424369608, 0.424369608),
-        (0.1875, 0.46666708, 0.46666708),
-        (0.21875, 0.509635204, 0.509635204),
-        (0.25, 0.552953156, 0.552953156),
-        (0.28125, 0.596262162, 0.596262162),
-        (0.3125, 0.639176211, 0.639176211),
-        (0.34375, 0.681291281, 0.681291281),
-        (0.375, 0.722193294, 0.722193294),
-        (0.40625, 0.761464949, 0.761464949),
-        (0.4375, 0.798691636, 0.798691636),
-        (0.46875, 0.833466556, 0.833466556),
-        (0.5, 0.865395197, 0.865395197),
-        (0.53125, 0.897787179, 0.897787179),
-        (0.5625, 0.924127593, 0.924127593),
-        (0.59375, 0.944468518, 0.944468518),
-        (0.625, 0.958852946, 0.958852946),
-        (0.65625, 0.96732803, 0.96732803),
-        (0.6875, 0.969954137, 0.969954137),
-        (0.71875, 0.966811177, 0.966811177),
-        (0.75, 0.958003065, 0.958003065),
-        (0.78125, 0.943660866, 0.943660866),
-        (0.8125, 0.923944917, 0.923944917),
-        (0.84375, 0.89904617, 0.89904617),
-        (0.875, 0.869186849, 0.869186849),
-        (0.90625, 0.834620542, 0.834620542),
-        (0.9375, 0.795631745, 0.795631745),
-        (0.96875, 0.752534934, 0.752534934),
-        (1.0, 0.705673158, 0.705673158)],
-    'green': [
-        (0.0, 0.298717966, 0.298717966),
-        (0.03125, 0.353094838, 0.353094838),
-        (0.0625, 0.406535296, 0.406535296),
-        (0.09375, 0.458757618, 0.458757618),
-        (0.125, 0.50941904, 0.50941904),
-        (0.15625, 0.558148092, 0.558148092),
-        (0.1875, 0.604562568, 0.604562568),
-        (0.21875, 0.648280772, 0.648280772),
-        (0.25, 0.688929332, 0.688929332),
-        (0.28125, 0.726149107, 0.726149107),
-        (0.3125, 0.759599947, 0.759599947),
-        (0.34375, 0.788964712, 0.788964712),
-        (0.375, 0.813952739, 0.813952739),
-        (0.40625, 0.834302879, 0.834302879),
-        (0.4375, 0.849786142, 0.849786142),
-        (0.46875, 0.860207984, 0.860207984),
-        (0.5, 0.86541021, 0.86541021),
-        (0.53125, 0.848937047, 0.848937047),
-        (0.5625, 0.827384882, 0.827384882),
-        (0.59375, 0.800927443, 0.800927443),
-        (0.625, 0.769767752, 0.769767752),
-        (0.65625, 0.734132809, 0.734132809),
-        (0.6875, 0.694266682, 0.694266682),
-        (0.71875, 0.650421156, 0.650421156),
-        (0.75, 0.602842431, 0.602842431),
-        (0.78125, 0.551750968, 0.551750968),
-        (0.8125, 0.49730856, 0.49730856),
-        (0.84375, 0.439559467, 0.439559467),
-        (0.875, 0.378313092, 0.378313092),
-        (0.90625, 0.312874446, 0.312874446),
-        (0.9375, 0.24128379, 0.24128379),
-        (0.96875, 0.157246067, 0.157246067),
-        (1.0, 0.01555616, 0.01555616)],
-    'blue': [
-        (0.0, 0.753683153, 0.753683153),
-        (0.03125, 0.801466763, 0.801466763),
-        (0.0625, 0.84495867, 0.84495867),
-        (0.09375, 0.883725899, 0.883725899),
-        (0.125, 0.917387822, 0.917387822),
-        (0.15625, 0.945619588, 0.945619588),
-        (0.1875, 0.968154911, 0.968154911),
-        (0.21875, 0.98478814, 0.98478814),
-        (0.25, 0.995375608, 0.995375608),
-        (0.28125, 0.999836203, 0.999836203),
-        (0.3125, 0.998151185, 0.998151185),
-        (0.34375, 0.990363227, 0.990363227),
-        (0.375, 0.976574709, 0.976574709),
-        (0.40625, 0.956945269, 0.956945269),
-        (0.4375, 0.931688648, 0.931688648),
-        (0.46875, 0.901068838, 0.901068838),
-        (0.5, 0.865395561, 0.865395561),
-        (0.53125, 0.820880546, 0.820880546),
-        (0.5625, 0.774508472, 0.774508472),
-        (0.59375, 0.726736146, 0.726736146),
-        (0.625, 0.678007945, 0.678007945),
-        (0.65625, 0.628751763, 0.628751763),
-        (0.6875, 0.579375448, 0.579375448),
-        (0.71875, 0.530263762, 0.530263762),
-        (0.75, 0.481775914, 0.481775914),
-        (0.78125, 0.434243684, 0.434243684),
-        (0.8125, 0.387970225, 0.387970225),
-        (0.84375, 0.343229596, 0.343229596),
-        (0.875, 0.300267182, 0.300267182),
-        (0.90625, 0.259301199, 0.259301199),
-        (0.9375, 0.220525627, 0.220525627),
-        (0.96875, 0.184115123, 0.184115123),
-        (1.0, 0.150232812, 0.150232812)]
-    }
-
-# Implementation of Carey Rappaport's CMRmap.
-# See `A Color Map for Effective Black-and-White Rendering of Color-Scale
-# Images' by Carey Rappaport
-# https://www.mathworks.com/matlabcentral/fileexchange/2662-cmrmap-m
-_CMRmap_data = {'red':    ((0.000, 0.00, 0.00),
-                           (0.125, 0.15, 0.15),
-                           (0.250, 0.30, 0.30),
-                           (0.375, 0.60, 0.60),
-                           (0.500, 1.00, 1.00),
-                           (0.625, 0.90, 0.90),
-                           (0.750, 0.90, 0.90),
-                           (0.875, 0.90, 0.90),
-                           (1.000, 1.00, 1.00)),
-                'green':  ((0.000, 0.00, 0.00),
-                           (0.125, 0.15, 0.15),
-                           (0.250, 0.15, 0.15),
-                           (0.375, 0.20, 0.20),
-                           (0.500, 0.25, 0.25),
-                           (0.625, 0.50, 0.50),
-                           (0.750, 0.75, 0.75),
-                           (0.875, 0.90, 0.90),
-                           (1.000, 1.00, 1.00)),
-                'blue':   ((0.000, 0.00, 0.00),
-                           (0.125, 0.50, 0.50),
-                           (0.250, 0.75, 0.75),
-                           (0.375, 0.50, 0.50),
-                           (0.500, 0.15, 0.15),
-                           (0.625, 0.00, 0.00),
-                           (0.750, 0.10, 0.10),
-                           (0.875, 0.50, 0.50),
-                           (1.000, 1.00, 1.00))}
-
-
-# An MIT licensed, colorblind-friendly heatmap from Wistia:
-#   https://github.com/wistia/heatmap-palette
-#   https://wistia.com/learn/culture/heatmaps-for-colorblindness
-#
-# >>> import matplotlib.colors as c
-# >>> colors = ["#e4ff7a", "#ffe81a", "#ffbd00", "#ffa000", "#fc7f00"]
-# >>> cm = c.LinearSegmentedColormap.from_list('wistia', colors)
-# >>> _wistia_data = cm._segmentdata
-# >>> del _wistia_data['alpha']
-#
-_wistia_data = {
-    'red': [(0.0, 0.8941176470588236, 0.8941176470588236),
-            (0.25, 1.0, 1.0),
-            (0.5, 1.0, 1.0),
-            (0.75, 1.0, 1.0),
-            (1.0, 0.9882352941176471, 0.9882352941176471)],
-    'green': [(0.0, 1.0, 1.0),
-              (0.25, 0.9098039215686274, 0.9098039215686274),
-              (0.5, 0.7411764705882353, 0.7411764705882353),
-              (0.75, 0.6274509803921569, 0.6274509803921569),
-              (1.0, 0.4980392156862745, 0.4980392156862745)],
-    'blue': [(0.0, 0.47843137254901963, 0.47843137254901963),
-             (0.25, 0.10196078431372549, 0.10196078431372549),
-             (0.5, 0.0, 0.0),
-             (0.75, 0.0, 0.0),
-             (1.0, 0.0, 0.0)],
-}
-
-
-# Categorical palettes from Vega:
-# https://github.com/vega/vega/wiki/Scales
-# (divided by 255)
-#
-
-_tab10_data = (
-    (0.12156862745098039, 0.4666666666666667,  0.7058823529411765  ),  # 1f77b4
-    (1.0,                 0.4980392156862745,  0.054901960784313725),  # ff7f0e
-    (0.17254901960784313, 0.6274509803921569,  0.17254901960784313 ),  # 2ca02c
-    (0.8392156862745098,  0.15294117647058825, 0.1568627450980392  ),  # d62728
-    (0.5803921568627451,  0.403921568627451,   0.7411764705882353  ),  # 9467bd
-    (0.5490196078431373,  0.33725490196078434, 0.29411764705882354 ),  # 8c564b
-    (0.8901960784313725,  0.4666666666666667,  0.7607843137254902  ),  # e377c2
-    (0.4980392156862745,  0.4980392156862745,  0.4980392156862745  ),  # 7f7f7f
-    (0.7372549019607844,  0.7411764705882353,  0.13333333333333333 ),  # bcbd22
-    (0.09019607843137255, 0.7450980392156863,  0.8117647058823529),    # 17becf
-)
-
-_tab20_data = (
-    (0.12156862745098039, 0.4666666666666667,  0.7058823529411765  ),  # 1f77b4
-    (0.6823529411764706,  0.7803921568627451,  0.9098039215686274  ),  # aec7e8
-    (1.0,                 0.4980392156862745,  0.054901960784313725),  # ff7f0e
-    (1.0,                 0.7333333333333333,  0.47058823529411764 ),  # ffbb78
-    (0.17254901960784313, 0.6274509803921569,  0.17254901960784313 ),  # 2ca02c
-    (0.596078431372549,   0.8745098039215686,  0.5411764705882353  ),  # 98df8a
-    (0.8392156862745098,  0.15294117647058825, 0.1568627450980392  ),  # d62728
-    (1.0,                 0.596078431372549,   0.5882352941176471  ),  # ff9896
-    (0.5803921568627451,  0.403921568627451,   0.7411764705882353  ),  # 9467bd
-    (0.7725490196078432,  0.6901960784313725,  0.8352941176470589  ),  # c5b0d5
-    (0.5490196078431373,  0.33725490196078434, 0.29411764705882354 ),  # 8c564b
-    (0.7686274509803922,  0.611764705882353,   0.5803921568627451  ),  # c49c94
-    (0.8901960784313725,  0.4666666666666667,  0.7607843137254902  ),  # e377c2
-    (0.9686274509803922,  0.7137254901960784,  0.8235294117647058  ),  # f7b6d2
-    (0.4980392156862745,  0.4980392156862745,  0.4980392156862745  ),  # 7f7f7f
-    (0.7803921568627451,  0.7803921568627451,  0.7803921568627451  ),  # c7c7c7
-    (0.7372549019607844,  0.7411764705882353,  0.13333333333333333 ),  # bcbd22
-    (0.8588235294117647,  0.8588235294117647,  0.5529411764705883  ),  # dbdb8d
-    (0.09019607843137255, 0.7450980392156863,  0.8117647058823529  ),  # 17becf
-    (0.6196078431372549,  0.8549019607843137,  0.8980392156862745),    # 9edae5
-)
-
-_tab20b_data = (
-    (0.2235294117647059,  0.23137254901960785, 0.4745098039215686 ),  # 393b79
-    (0.3215686274509804,  0.32941176470588235, 0.6392156862745098 ),  # 5254a3
-    (0.4196078431372549,  0.43137254901960786, 0.8117647058823529 ),  # 6b6ecf
-    (0.611764705882353,   0.6196078431372549,  0.8705882352941177 ),  # 9c9ede
-    (0.38823529411764707, 0.4745098039215686,  0.2235294117647059 ),  # 637939
-    (0.5490196078431373,  0.6352941176470588,  0.3215686274509804 ),  # 8ca252
-    (0.7098039215686275,  0.8117647058823529,  0.4196078431372549 ),  # b5cf6b
-    (0.807843137254902,   0.8588235294117647,  0.611764705882353  ),  # cedb9c
-    (0.5490196078431373,  0.42745098039215684, 0.19215686274509805),  # 8c6d31
-    (0.7411764705882353,  0.6196078431372549,  0.2235294117647059 ),  # bd9e39
-    (0.9058823529411765,  0.7294117647058823,  0.3215686274509804 ),  # e7ba52
-    (0.9058823529411765,  0.796078431372549,   0.5803921568627451 ),  # e7cb94
-    (0.5176470588235295,  0.23529411764705882, 0.2235294117647059 ),  # 843c39
-    (0.6784313725490196,  0.28627450980392155, 0.2901960784313726 ),  # ad494a
-    (0.8392156862745098,  0.3803921568627451,  0.4196078431372549 ),  # d6616b
-    (0.9058823529411765,  0.5882352941176471,  0.611764705882353  ),  # e7969c
-    (0.4823529411764706,  0.2549019607843137,  0.45098039215686275),  # 7b4173
-    (0.6470588235294118,  0.3176470588235294,  0.5803921568627451 ),  # a55194
-    (0.807843137254902,   0.42745098039215684, 0.7411764705882353 ),  # ce6dbd
-    (0.8705882352941177,  0.6196078431372549,  0.8392156862745098 ),  # de9ed6
-)
-
-_tab20c_data = (
-    (0.19215686274509805, 0.5098039215686274,  0.7411764705882353  ),  # 3182bd
-    (0.4196078431372549,  0.6823529411764706,  0.8392156862745098  ),  # 6baed6
-    (0.6196078431372549,  0.792156862745098,   0.8823529411764706  ),  # 9ecae1
-    (0.7764705882352941,  0.8588235294117647,  0.9372549019607843  ),  # c6dbef
-    (0.9019607843137255,  0.3333333333333333,  0.050980392156862744),  # e6550d
-    (0.9921568627450981,  0.5529411764705883,  0.23529411764705882 ),  # fd8d3c
-    (0.9921568627450981,  0.6823529411764706,  0.4196078431372549  ),  # fdae6b
-    (0.9921568627450981,  0.8156862745098039,  0.6352941176470588  ),  # fdd0a2
-    (0.19215686274509805, 0.6392156862745098,  0.32941176470588235 ),  # 31a354
-    (0.4549019607843137,  0.7686274509803922,  0.4627450980392157  ),  # 74c476
-    (0.6313725490196078,  0.8509803921568627,  0.6078431372549019  ),  # a1d99b
-    (0.7803921568627451,  0.9137254901960784,  0.7529411764705882  ),  # c7e9c0
-    (0.4588235294117647,  0.4196078431372549,  0.6941176470588235  ),  # 756bb1
-    (0.6196078431372549,  0.6039215686274509,  0.7843137254901961  ),  # 9e9ac8
-    (0.7372549019607844,  0.7411764705882353,  0.8627450980392157  ),  # bcbddc
-    (0.8549019607843137,  0.8549019607843137,  0.9215686274509803  ),  # dadaeb
-    (0.38823529411764707, 0.38823529411764707, 0.38823529411764707 ),  # 636363
-    (0.5882352941176471,  0.5882352941176471,  0.5882352941176471  ),  # 969696
-    (0.7411764705882353,  0.7411764705882353,  0.7411764705882353  ),  # bdbdbd
-    (0.8509803921568627,  0.8509803921568627,  0.8509803921568627  ),  # d9d9d9
-)
-
-
-datad = {
-    'Blues': _Blues_data,
-    'BrBG': _BrBG_data,
-    'BuGn': _BuGn_data,
-    'BuPu': _BuPu_data,
-    'CMRmap': _CMRmap_data,
-    'GnBu': _GnBu_data,
-    'Greens': _Greens_data,
-    'Greys': _Greys_data,
-    'OrRd': _OrRd_data,
-    'Oranges': _Oranges_data,
-    'PRGn': _PRGn_data,
-    'PiYG': _PiYG_data,
-    'PuBu': _PuBu_data,
-    'PuBuGn': _PuBuGn_data,
-    'PuOr': _PuOr_data,
-    'PuRd': _PuRd_data,
-    'Purples': _Purples_data,
-    'RdBu': _RdBu_data,
-    'RdGy': _RdGy_data,
-    'RdPu': _RdPu_data,
-    'RdYlBu': _RdYlBu_data,
-    'RdYlGn': _RdYlGn_data,
-    'Reds': _Reds_data,
-    'Spectral': _Spectral_data,
-    'Wistia': _wistia_data,
-    'YlGn': _YlGn_data,
-    'YlGnBu': _YlGnBu_data,
-    'YlOrBr': _YlOrBr_data,
-    'YlOrRd': _YlOrRd_data,
-    'afmhot': _afmhot_data,
-    'autumn': _autumn_data,
-    'binary': _binary_data,
-    'bone': _bone_data,
-    'brg': _brg_data,
-    'bwr': _bwr_data,
-    'cool': _cool_data,
-    'coolwarm': _coolwarm_data,
-    'copper': _copper_data,
-    'cubehelix': _cubehelix_data,
-    'flag': _flag_data,
-    'gist_earth': _gist_earth_data,
-    'gist_gray': _gist_gray_data,
-    'gist_heat': _gist_heat_data,
-    'gist_ncar': _gist_ncar_data,
-    'gist_rainbow': _gist_rainbow_data,
-    'gist_stern': _gist_stern_data,
-    'gist_yarg': _gist_yarg_data,
-    'gnuplot': _gnuplot_data,
-    'gnuplot2': _gnuplot2_data,
-    'gray': _gray_data,
-    'hot': _hot_data,
-    'hsv': _hsv_data,
-    'jet': _jet_data,
-    'nipy_spectral': _nipy_spectral_data,
-    'ocean': _ocean_data,
-    'pink': _pink_data,
-    'prism': _prism_data,
-    'rainbow': _rainbow_data,
-    'seismic': _seismic_data,
-    'spring': _spring_data,
-    'summer': _summer_data,
-    'terrain': _terrain_data,
-    'winter': _winter_data,
-    # Qualitative
-    'Accent': {'listed': _Accent_data},
-    'Dark2': {'listed': _Dark2_data},
-    'Paired': {'listed': _Paired_data},
-    'Pastel1': {'listed': _Pastel1_data},
-    'Pastel2': {'listed': _Pastel2_data},
-    'Set1': {'listed': _Set1_data},
-    'Set2': {'listed': _Set2_data},
-    'Set3': {'listed': _Set3_data},
-    'tab10': {'listed': _tab10_data},
-    'tab20': {'listed': _tab20_data},
-    'tab20b': {'listed': _tab20b_data},
-    'tab20c': {'listed': _tab20c_data},
-}
diff --git a/spaces/dddmiku/vits-uma-genshin-honkai/app.py b/spaces/dddmiku/vits-uma-genshin-honkai/app.py
deleted file mode 100644
index 92ddafdcd240434f58569b0e6964ef331a971dcf..0000000000000000000000000000000000000000
--- a/spaces/dddmiku/vits-uma-genshin-honkai/app.py
+++ /dev/null
@@ -1,124 +0,0 @@
-import time
-import gradio as gr
-import utils
-import commons
-from models import SynthesizerTrn
-from text import text_to_sequence
-from torch import no_grad, LongTensor
-import torch
-
-hps_ms = utils.get_hparams_from_file(r'./model/config.json')
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-net_g_ms = SynthesizerTrn(
-    len(hps_ms.symbols),
-    hps_ms.data.filter_length // 2 + 1,
-    hps_ms.train.segment_size // hps_ms.data.hop_length,
-    n_speakers=hps_ms.data.n_speakers,
-    **hps_ms.model).to(device)
-_ = net_g_ms.eval()
-speakers = hps_ms.speakers
-model, optimizer, learning_rate, epochs = utils.load_checkpoint(r'./model/G_953000.pth', net_g_ms, None)
-
-def get_text(text, hps):
-    text_norm, clean_text = text_to_sequence(text, hps.symbols, hps.data.text_cleaners)
-    if hps.data.add_blank:
-        text_norm = commons.intersperse(text_norm, 0)
-    text_norm = LongTensor(text_norm)
-    return text_norm, clean_text
-
-def vits(text, language, speaker_id, noise_scale, noise_scale_w, length_scale):
-    start = time.perf_counter()
-    if not len(text):
-        return "输入文本不能为空！", None, None
-    text = text.replace('\n', ' ').replace('\r', '').replace(" ", "")
-    if len(text) > 500:
-        return f"输入文字过长！{len(text)}>100", None, None
-    if language == 0:
-        text = f"[ZH]{text}[ZH]"
-    elif language == 1:
-        text = f"[JA]{text}[JA]"
-    else:
-        text = f"{text}"
-    stn_tst, clean_text = get_text(text, hps_ms)
-    with no_grad():
-        x_tst = stn_tst.unsqueeze(0)
-        x_tst_lengths = LongTensor([stn_tst.size(0)])
-        speaker_id = LongTensor([speaker_id])
-        audio = net_g_ms.infer(x_tst, x_tst_lengths, sid=speaker_id, noise_scale=noise_scale, noise_scale_w=noise_scale_w,
-                               length_scale=length_scale)[0][0, 0].data.cpu().float().numpy()
-
-    return "生成成功!", (22050, audio), f"生成耗时 {round(time.perf_counter()-start, 2)} s"
-
-def search_speaker(search_value):
-    for s in speakers:
-        if search_value == s:
-            return s
-    for s in speakers:
-        if search_value in s:
-            return s
-
-def change_lang(language):
-    if language == 0:
-        return 0.6, 0.668, 1.2
-    else:
-        return 0.6, 0.668, 1.1
-
-download_audio_js = """
-() =>{{
-    let root = document.querySelector("body > gradio-app");
-    if (root.shadowRoot != null)
-        root = root.shadowRoot;
-    let audio = root.querySelector("#tts-audio").querySelector("audio");
-    let text = root.querySelector("#input-text").querySelector("textarea");
-    if (audio == undefined)
-        return;
-    text = text.value;
-    if (text == undefined)
-        text = Math.floor(Math.random()*100000000);
-    audio = audio.src;
-    let oA = document.createElement("a");
-    oA.download = text.substr(0, 20)+'.wav';
-    oA.href = audio;
-    document.body.appendChild(oA);
-    oA.click();
-    oA.remove();
-}}
-"""
-
-if __name__ == '__main__':
-    with gr.Blocks() as app:
-        gr.Markdown(
-            "# <center> VITS语音在线合成demo\n"
-            "<div align='center'>主要有赛马娘，原神中文，原神日语，崩坏3的音色</div>"
-            '<div align="center"><a><font color="#dd0000">结果有随机性，语调可能很奇怪，可多次生成取最佳效果</font></a></div>'
-            '<div align="center"><a><font color="#dd0000">标点符号会影响生成的结果</font></a></div>'
-        )
-
-        with gr.Tabs():
-            with gr.TabItem("vits"):
-                with gr.Row():
-                    with gr.Column():
-                        input_text = gr.Textbox(label="Text (100 words limitation)", lines=5, value="今天晚上吃啥好呢。", elem_id=f"input-text")
-                        lang = gr.Dropdown(label="Language", choices=["中文", "日语", "中日混合（中文用[ZH][ZH]包裹起来，日文用[JA][JA]包裹起来）"],
-                                    type="index", value="中文")
-                        btn = gr.Button(value="Submit")
-                        with gr.Row():
-                            search = gr.Textbox(label="Search Speaker", lines=1)
-                            btn2 = gr.Button(value="Search")
-                        sid = gr.Dropdown(label="Speaker", choices=speakers, type="index", value=speakers[228])
-                        with gr.Row():
-                            ns = gr.Slider(label="noise_scale(控制感情变化程度)", minimum=0.1, maximum=1.0, step=0.1, value=0.6, interactive=True)
-                            nsw = gr.Slider(label="noise_scale_w(控制音素发音长度)", minimum=0.1, maximum=1.0, step=0.1, value=0.668, interactive=True)
-                            ls = gr.Slider(label="length_scale(控制整体语速)", minimum=0.1, maximum=2.0, step=0.1, value=1.2, interactive=True)
-                    with gr.Column():
-                        o1 = gr.Textbox(label="Output Message")
-                        o2 = gr.Audio(label="Output Audio", elem_id=f"tts-audio")
-                        o3 = gr.Textbox(label="Extra Info")
-                        download = gr.Button("Download Audio")
-                    btn.click(vits, inputs=[input_text, lang, sid, ns, nsw, ls], outputs=[o1, o2, o3], api_name="generate")
-                    download.click(None, [], [], _js=download_audio_js.format())
-                    btn2.click(search_speaker, inputs=[search], outputs=[sid])
-                    lang.change(change_lang, inputs=[lang], outputs=[ns, nsw, ls])
-            with gr.TabItem("可用人物一览"):
-                gr.Radio(label="Speaker", choices=speakers, interactive=False, type="index")
-    app.queue(concurrency_count=1).launch()
\ No newline at end of file
diff --git a/spaces/diacanFperku/AutoGPT/Blackmail MOVIE Download Extra Quality.md b/spaces/diacanFperku/AutoGPT/Blackmail MOVIE Download Extra Quality.md
deleted file mode 100644
index 706d096d443492d1b07bd5b92b9ac4e44c8322a6..0000000000000000000000000000000000000000
--- a/spaces/diacanFperku/AutoGPT/Blackmail MOVIE Download Extra Quality.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>Blackmail MOVIE DOWNLOAD</h2><br /><p><b><b>Download</b> &#10031; <a href="https://gohhs.com/2uFT7D">https://gohhs.com/2uFT7D</a></b></p><br /><br />
-<br />
-Blackmail directed by Leslie Selander. . Ricardo Cortes and Adele Mara in Blackmail (1947). B movie starring William Marshall and Ricardo Cortez. Directed by Leslie Selander at MGM Studios. . Actress Adele Mara. . The film "Blackmail" at the American box office was called "Blackmail the Devil" ("The Devil's Chants"). . William Marshall in The Man with the Golden Bovine (1947). . Actor Ricardo Cortez in The Man with the Golden Bovine. . Directed by Leslie Selander at MGM Studios. . Actress Adele Mara in The Man with the Golden Bovine. . 8a78ff9644<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/diacanFperku/AutoGPT/Codigo De Registro Converthelper __HOT__.md b/spaces/diacanFperku/AutoGPT/Codigo De Registro Converthelper __HOT__.md
deleted file mode 100644
index 4c729970dcf714b182fdf1e3a3648fb3160e80b6..0000000000000000000000000000000000000000
--- a/spaces/diacanFperku/AutoGPT/Codigo De Registro Converthelper __HOT__.md	
+++ /dev/null
@@ -1,9 +0,0 @@
-
-<p>inconventionalpleaagdexe free osfilelet-verprimer-download-codigo-de-registro-converthelper-2-0-keygen-a-link-lookherehollisters-season-dreamp4less. downloadfichier freecodecenter. get from direct links. - get faster download speed with. </p>
-<h2>Codigo De Registro Converthelper</h2><br /><p><b><b>Download</b> &raquo;&raquo;&raquo; <a href="https://gohhs.com/2uFUX5">https://gohhs.com/2uFUX5</a></b></p><br /><br />
-<p>shareware and freeware guides for kids and teachersxm w xcd 6.15 keygen file size 2019.05.20. win.18902301.0. downloadhelper converthelper lizenz keygen. url:. yesx2rm-gammatools-download-helper-codes-ttlcharger-disney.mht. you agree to not use or. transform packet socket fix helper clickhelper 4.2.3 cracked. . https://thehackernews.com/downloadhelper-converthelper-myself-web-designers-and-seo-specialist/ https://trello.com/c/9mtdgd0b/16-componente-derechos-de-redes-1. </p>
-<p>powered by joomla! 1.5 - tournament.ns-list downloadhelper converthelper windows game cracks + torrent 2018 + demotivator crack. ]. . 2017 11-14. codigo de registro converthelper. ifilepack32.serve.com/2016 /09/16/34754-converthelper-registration-code-keygen-2019. free codepro 4.01 keygen. download helper registration code for free! https://codepro.club/codepro/serv-new-4-0-setup-activated/ https://totalvideoplay.com/wp-content/downloadhelper/. </p>
-<p>extract file. [code] customprv-download-helper-64bit/ downloadhelper converthelper (32bit/64bit). . iphone. website:.https://allegro-magazine.com/2016/01/16/downloadhelper-converthelper-a-simple-installer-for-all-codigo-de-registro-converthelper-8-4-win-rar. code converter for free.. code. 2017-06-19 / in. download-helper-converthelper-keygen-gen.https://trello.com/c/2cp8hqtw/99-wondershare-data-recovery-codigo-de-registro-converthelper-daniel-coplett-rar . what is converter. need help. getting an error with your downloadhelper converthelper registration code when you run it.https://codepro.</p>
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\ No newline at end of file
diff --git a/spaces/diffle/sd-xl.ui/ui.py b/spaces/diffle/sd-xl.ui/ui.py
deleted file mode 100644
index 796c5efba949332b1b84eb56b6bf8b5022b787a6..0000000000000000000000000000000000000000
--- a/spaces/diffle/sd-xl.ui/ui.py
+++ /dev/null
@@ -1,3 +0,0 @@
-from diffusers import DiffusionPipeline
-
-pipeline = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0")
\ No newline at end of file
diff --git a/spaces/diffusers/sd-to-diffusers/app.py b/spaces/diffusers/sd-to-diffusers/app.py
deleted file mode 100644
index 0df6ff55ffb519bc3905432f64a82cf76ff9b542..0000000000000000000000000000000000000000
--- a/spaces/diffusers/sd-to-diffusers/app.py
+++ /dev/null
@@ -1,36 +0,0 @@
-import gradio as gr
-
-from convert import convert
-
-DESCRIPTION = """
-The steps are the following:
-
-- Paste a read-access token from hf.co/settings/tokens. Read access is enough given that we will open a PR against the source repo.
-- Input a model id from the Hub
-- Input the filename from the root dir of the repo that you would like to convert, e.g. 'v2-1_768-ema-pruned.ckpt' or 'v1-5-pruned.safetensors'
-- Chose which Stable Diffusion version, image size, scheduler type the model has and whether you want the "ema", or "non-ema" weights.
-- Click "Submit"
-- That's it! You'll get feedback if it works or not, and if it worked, you'll get the URL of the opened PR 🔥
-
-⚠️ If you encounter weird error messages, please have a look into the Logs and feel free to open a PR to correct the error messages.
-"""
-
-demo = gr.Interface(
-    title="Convert any Stable Diffusion checkpoint to Diffusers and open a PR",
-    description=DESCRIPTION,
-    allow_flagging="never",
-    article="Check out the [Diffusers repo on GitHub](https://github.com/huggingface/diffusers)",
-    inputs=[
-        gr.Text(max_lines=1, label="your_hf_token"),
-        gr.Text(max_lines=1, label="model_id"),
-        gr.Text(max_lines=1, label="filename"),
-        gr.Radio(label="Model type", choices=["v1", "v2", "ControlNet"]),
-        gr.Radio(label="Sample size (px)", choices=[512, 768]),
-        gr.Radio(label="Scheduler type", choices=["pndm", "heun", "euler", "dpm", "ddim"], value="dpm"),
-        gr.Radio(label="Extract EMA or non-EMA?", choices=["ema", "non-ema"], value="ema"),
-    ],
-    outputs=[gr.Markdown(label="output")],
-    fn=convert,
-).queue(max_size=10, concurrency_count=1)
-
-demo.launch(show_api=True)
diff --git a/spaces/digitalxingtong/Xingtong-Read-Bert-VITS2/text/cleaner.py b/spaces/digitalxingtong/Xingtong-Read-Bert-VITS2/text/cleaner.py
deleted file mode 100644
index 64bd5f7296f66c94f3a335666c53706bb5fe5b39..0000000000000000000000000000000000000000
--- a/spaces/digitalxingtong/Xingtong-Read-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/dmeck/RVC-Speakers/speakers/processors/__init__.py b/spaces/dmeck/RVC-Speakers/speakers/processors/__init__.py
deleted file mode 100644
index 7a31d087ebd14be259f93ef5b6ed0911cd9ce20a..0000000000000000000000000000000000000000
--- a/spaces/dmeck/RVC-Speakers/speakers/processors/__init__.py
+++ /dev/null
@@ -1,68 +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
-"""
-from typing import List
-
-from speakers.common.registry import registry
-from speakers.processors.base_processor import BaseProcessor
-from speakers.processors.base_processor import ProcessorData
-from speakers.processors.rvc_speakers_processor import RvcProcessorData
-from speakers.processors.vits_to_voice import VitsProcessorData
-from speakers.processors.bark_to_voice import BarkProcessorData
-from speakers.processors.edge_to_voice import EdgeProcessorData
-
-__all__ = [
-    "BaseProcessor",
-    "ProcessorData",
-    "RvcProcessorData",
-    "VitsProcessorData",
-    "BarkProcessorData",
-    "EdgeProcessorData",
-    "get_processors",
-    "load_preprocess",
-]
-
-processors_cache = {}
-
-
-def load_preprocess(config: List[dict] = None):
-    """
-    Load preprocessor configs and construct preprocessors.
-
-    If no preprocessor is specified, return BaseProcessor, which does not do any preprocessing.
-
-    Args:
-        config (List[dict]): preprocessor configs.
-
-    Returns:
-        vits_processors (dict): preprocessors for vits inputs.
-        rvc_processors (dict): preprocessors for rvc inputs.
-
-    """
-
-    if config is None:
-        raise RuntimeError("Load preprocessor configs is None.")
-
-    def _build_proc_from_cfg(cfg):
-        print(cfg)
-        return (
-            registry.get_processor_class(cfg.name).from_config(cfg)
-            if cfg is not None
-            else BaseProcessor()
-        )
-    for process_cfg in config:  # 使用 .items() 方法获取键值对
-        for key, processor_cfg in process_cfg.items():  # 使用 .items() 方法获取键值对
-            processors = _build_proc_from_cfg(processor_cfg)
-            processors_cache[key] = processors
-
-
-def get_processors(key: str) -> BaseProcessor:
-
-    if not processors_cache.get(key):
-        raise ValueError(f'Could not find processors for: "{key}". '
-                         f'Choose from the following: %s' % ','.join(processors_cache))
-
-    return processors_cache[key]
diff --git a/spaces/ds520/bingo/src/components/chat-scroll-anchor.tsx b/spaces/ds520/bingo/src/components/chat-scroll-anchor.tsx
deleted file mode 100644
index ac809f4486a48e134cb69314c3d0dae5e68d614e..0000000000000000000000000000000000000000
--- a/spaces/ds520/bingo/src/components/chat-scroll-anchor.tsx
+++ /dev/null
@@ -1,29 +0,0 @@
-'use client'
-
-import * as React from 'react'
-import { useInView } from 'react-intersection-observer'
-
-import { useAtBottom } from '@/lib/hooks/use-at-bottom'
-
-interface ChatScrollAnchorProps {
-  trackVisibility?: boolean
-}
-
-export function ChatScrollAnchor({ trackVisibility }: ChatScrollAnchorProps) {
-  const isAtBottom = useAtBottom()
-  const { ref, entry, inView } = useInView({
-    trackVisibility,
-    delay: 100,
-    rootMargin: '0px 0px -150px 0px'
-  })
-
-  React.useEffect(() => {
-    if (isAtBottom && trackVisibility && !inView) {
-      entry?.target.scrollIntoView({
-        block: 'start'
-      })
-    }
-  }, [inView, entry, isAtBottom, trackVisibility])
-
-  return <div ref={ref} className="h-px w-full" />
-}
diff --git a/spaces/editing-images/project/static/js/bulma-slider.min.js b/spaces/editing-images/project/static/js/bulma-slider.min.js
deleted file mode 100644
index 7e62685763cf7668cfa8857fac0b27af2c277286..0000000000000000000000000000000000000000
--- a/spaces/editing-images/project/static/js/bulma-slider.min.js
+++ /dev/null
@@ -1 +0,0 @@
-!function(t,e){"object"==typeof exports&&"object"==typeof module?module.exports=e():"function"==typeof define&&define.amd?define([],e):"object"==typeof exports?exports.bulmaSlider=e():t.bulmaSlider=e()}("undefined"!=typeof self?self:this,function(){return function(n){var r={};function i(t){if(r[t])return r[t].exports;var e=r[t]={i:t,l:!1,exports:{}};return n[t].call(e.exports,e,e.exports,i),e.l=!0,e.exports}return i.m=n,i.c=r,i.d=function(t,e,n){i.o(t,e)||Object.defineProperty(t,e,{configurable:!1,enumerable:!0,get:n})},i.n=function(t){var e=t&&t.__esModule?function(){return t.default}:function(){return t};return i.d(e,"a",e),e},i.o=function(t,e){return Object.prototype.hasOwnProperty.call(t,e)},i.p="",i(i.s=0)}([function(t,e,n){"use strict";Object.defineProperty(e,"__esModule",{value:!0}),n.d(e,"isString",function(){return l});var r=n(1),i=Object.assign||function(t){for(var e=1;e<arguments.length;e++){var n=arguments[e];for(var r in 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i=function(){function e(){var t=0<arguments.length&&void 0!==arguments[0]?arguments[0]:[];!function(t,e){if(!(t instanceof e))throw new TypeError("Cannot call a class as a function")}(this,e),this._listeners=new Map(t),this._middlewares=new Map}return r(e,[{key:"listenerCount",value:function(t){return this._listeners.has(t)?this._listeners.get(t).length:0}},{key:"removeListeners",value:function(){var e=this,t=0<arguments.length&&void 0!==arguments[0]?arguments[0]:null,n=1<arguments.length&&void 0!==arguments[1]&&arguments[1];null!==t?Array.isArray(t)?name.forEach(function(t){return e.removeListeners(t,n)}):(this._listeners.delete(t),n&&this.removeMiddleware(t)):this._listeners=new Map}},{key:"middleware",value:function(t,e){var n=this;Array.isArray(t)?name.forEach(function(t){return n.middleware(t,e)}):(Array.isArray(this._middlewares.get(t))||this._middlewares.set(t,[]),this._middlewares.get(t).push(e))}},{key:"removeMiddleware",value:function(){var e=this,t=0<arguments.length&&void 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\ No newline at end of file
diff --git a/spaces/elijahcilfone/dreambooth-training/app.py b/spaces/elijahcilfone/dreambooth-training/app.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/fael33/NAWNIE-golden-hour-photography/app.py b/spaces/fael33/NAWNIE-golden-hour-photography/app.py
deleted file mode 100644
index 3f4735e1fc216ee8c5aefd0b1e542a5407ec03a7..0000000000000000000000000000000000000000
--- a/spaces/fael33/NAWNIE-golden-hour-photography/app.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/NAWNIE/golden-hour-photography").launch()
\ No newline at end of file
diff --git a/spaces/falterWliame/Face_Mask_Detection/Crack VERIFIED Do Audaces 10 69.md b/spaces/falterWliame/Face_Mask_Detection/Crack VERIFIED Do Audaces 10 69.md
deleted file mode 100644
index a751cc7fc55e1750d04ccc61a2f0c92c3734f416..0000000000000000000000000000000000000000
--- a/spaces/falterWliame/Face_Mask_Detection/Crack VERIFIED Do Audaces 10 69.md	
+++ /dev/null
@@ -1,18 +0,0 @@
-<h2>crack do audaces 10 69</h2><br /><p><b><b>Download File</b> &#10145; <a href="https://urlca.com/2uDdIT">https://urlca.com/2uDdIT</a></b></p><br /><br />
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diff --git a/spaces/falterWliame/Face_Mask_Detection/DolceModzStar007mp4.md b/spaces/falterWliame/Face_Mask_Detection/DolceModzStar007mp4.md
deleted file mode 100644
index 238fde731a58bfd7977f346565fc4916508ff891..0000000000000000000000000000000000000000
--- a/spaces/falterWliame/Face_Mask_Detection/DolceModzStar007mp4.md
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>DolceModzStar007mp4</h2><br /><p><b><b>Download Zip</b> &#9913; <a href="https://urlca.com/2uDdYy">https://urlca.com/2uDdYy</a></b></p><br /><br />
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diff --git a/spaces/falterWliame/Face_Mask_Detection/LizardSystems Network Scanner 4.3.2 Build 213 Medicine[BabuPC] Serial Key [PATCHED] Keygen.md b/spaces/falterWliame/Face_Mask_Detection/LizardSystems Network Scanner 4.3.2 Build 213 Medicine[BabuPC] Serial Key [PATCHED] Keygen.md
deleted file mode 100644
index 6e9d9221664b18db42ce8593b8db70d1c7119636..0000000000000000000000000000000000000000
--- a/spaces/falterWliame/Face_Mask_Detection/LizardSystems Network Scanner 4.3.2 Build 213 Medicine[BabuPC] Serial Key [PATCHED] Keygen.md	
+++ /dev/null
@@ -1,159 +0,0 @@
-
-<h1>LizardSystems Network Scanner 4.3.2 Build 213 Medicine[BabuPC] Serial Key Keygen: A Review</h1>
-
-<p>If you are looking for a powerful and easy-to-use tool to scan and monitor your network devices, you might want to check out LizardSystems Network Scanner 4.3.2 Build 213 Medicine[BabuPC] Serial Key Keygen. This software allows you to discover all the devices connected to your network, such as computers, printers, routers, switches, servers, and more. You can also get detailed information about each device, such as IP address, MAC address, hostname, manufacturer, operating system, services, ports, and more.</p>
-<h2>LizardSystems Network Scanner 4.3.2 Build 213 Medicine[BabuPC] Serial Key keygen</h2><br /><p><b><b>Download</b> &#9733;&#9733;&#9733;&#9733;&#9733; <a href="https://urlca.com/2uDc9W">https://urlca.com/2uDc9W</a></b></p><br /><br />
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-<p>LizardSystems Network Scanner 4.3.2 Build 213 Medicine[BabuPC] Serial Key Keygen is not only a scanner, but also a network manager. You can use it to perform various tasks, such as ping, traceroute, remote shutdown, wake-on-LAN, SNMP get/set, and more. You can also export the scan results to various formats, such as HTML, XML, CSV, TXT, and more.</p>
-
-<h2>How to Download and Install LizardSystems Network Scanner 4.3.2 Build 213 Medicine[BabuPC] Serial Key Keygen</h2>
-
-<p>To download and install LizardSystems Network Scanner 4.3.2 Build 213 Medicine[BabuPC] Serial Key Keygen, you need to follow these steps:</p>
-
-<ol>
-<li>Download the software from the official website or from a trusted source.</li>
-<li>Extract the ZIP file using a program like WinRAR or 7-Zip.</li>
-<li>Run the setup file and follow the instructions on the screen.</li>
-<li>Copy the medicine file from the BabuPC folder and paste it into the installation directory.</li>
-<li>Run the keygen file and generate a serial key.</li>
-<li>Enter the serial key when prompted and activate the software.</li>
-</ol>
-
-<p>Congratulations! You have successfully installed LizardSystems Network Scanner 4.3.2 Build 213 Medicine[BabuPC] Serial Key Keygen on your computer.</p>
-
-<h2>How to Use LizardSystems Network Scanner 4.3.2 Build 213 Medicine[BabuPC] Serial Key Keygen</h2>
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-<p>To use LizardSystems Network Scanner 4.3.2 Build 213 Medicine[BabuPC] Serial Key Keygen, you need to follow these steps:</p>
-
-<ol>
-<li>Launch the software and select the network interface you want to scan.</li>
-<li>Click on the Scan button and wait for the scan to complete.</li>
-<li>View the scan results in the main window or in the tree view.</li>
-<li>Select any device you want to get more information or perform an action on.</li>
-<li>Use the toolbar buttons or the right-click menu to access various features and options.</li>
-</ol>
-
-<p>You can also customize the scan settings, such as IP range, scan speed, scan method, filters, columns, and more.</p>
-<p></p>
-
-<h2>Why Choose LizardSystems Network Scanner 4.3.2 Build 213 Medicine[BabuPC] Serial Key Keygen</h2>
-
-<p>LizardSystems Network Scanner 4.3.2 Build 213 Medicine[BabuPC] Serial Key Keygen is one of the best network scanners available in the market today. Here are some of the reasons why you should choose it:</p>
-
-<ul>
-<li>It is fast and accurate. It can scan thousands of devices in minutes and provide reliable information.</li>
-<li>It is easy and intuitive. It has a user-friendly interface that anyone can use without any hassle.</li>
-<li>It is versatile and flexible. It supports various network protocols and standards, such as IPv4/IPv6, TCP/UDP, ICMP/ARP/SNMP/WMI/DNS/DHCP/LLDP/NetBIOS/SMB/HTTP/HTTPS/FTP/SFTP/RDP/Telnet/SSH/NTP/Syslog/SMTP/POP3/IMAP/LDAP/RADIUS/SIP/H323/MQTT/UPnP/Bonjour/mDNS/SSDP/CoAP/LwM2M/MQTT-SN/CoRE/RPL/6LoWPAN/ZigBee/IPSO/BACnet/MODBUS/KNX/EIB/EtherCAT/EtherNet/IP/CIP/CANopen/CAN/CAN-FD/LIN/FlexRay/MOST/J1939/NMEA2000/OBD-II/OBD-III/OBD-IV/OBD-V/OBD-VI/OBD-VII/OBD-VIII/OBD-IX/OBD-X/OBD-XI/OBD-XII/OBD-XIII/OBD-XIV/OBD-XV/OBD-XVI/OBD-XVII/OBD-XVIII/OBD-XIX/OBD-XX/OBD-XXI/OBD-XXII/OBD-XXIII/OBD-XXIV/OBD-XXV/OBD-XXVI/OBD-XXVII/OBD-XXVIII/OBD-XXIX/OBD-XXX</li>
-<li>It is powerful and feature-rich. It offers various functions and options that can help you manage your network efficiently and effectively.</li>
-<li>It is affordable and cost-effective. It comes with a free trial version that you can use for 10 days without any limitations. The full version is also reasonably priced and worth every penny.</li>
-</ul>
-
-<p>LizardSystems Network Scanner 4.3.2 Build 213 Medicine[BabuPC] Serial Key Keygen is a must-have software for anyone who wants to scan and monitor their network devices with ease and convenience. Download it today and see for yourself what it can do for you!</p>
-<h2>What are the Benefits of LizardSystems Network Scanner 4.3.2 Build 213 Medicine[BabuPC] Serial Key Keygen</h2>
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-<p>LizardSystems Network Scanner 4.3.2 Build 213 Medicine[BabuPC] Serial Key Keygen is not just a software, but a solution for your network needs. Here are some of the benefits that you can enjoy by using it:</p>
-
-<ul>
-<li>It can help you improve your network performance and security. By scanning and monitoring your network devices, you can identify and resolve any issues or vulnerabilities that might affect your network efficiency and reliability.</li>
-<li>It can help you save time and money. By using the medicine file from BabuPC, you can activate the software without paying any fees or subscriptions. You can also use the keygen file to generate unlimited serial keys for future use.</li>
-<li>It can help you learn and grow. By using the software, you can gain more knowledge and skills about network scanning and management. You can also explore the various features and options that the software offers and discover new ways to optimize your network.</li>
-</ul>
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-<p>LizardSystems Network Scanner 4.3.2 Build 213 Medicine[BabuPC] Serial Key Keygen is a software that can benefit you in many ways. Whether you are a network administrator, a network engineer, a network technician, or a network enthusiast, you will find this software useful and valuable.</p>
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-<h2>How to Get Support for LizardSystems Network Scanner 4.3.2 Build 213 Medicine[BabuPC] Serial Key Keygen</h2>
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-<p>If you have any questions or problems regarding LizardSystems Network Scanner 4.3.2 Build 213 Medicine[BabuPC] Serial Key Keygen, you can get support from various sources. Here are some of them:</p>
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-<ul>
-<li>The official website of LizardSystems. You can visit https://lizardsystems.com/network-scanner/ to get more information about the software, such as product info, overview, screenshots, FAQ, release history, and more.</li>
-<li>The online help of the software. You can access the online help by clicking on the Help button on the toolbar or by pressing F1 on your keyboard. You can also access the online help by visiting https://lizardsystems.com/network-scanner/help/.</li>
-<li>The user forum of the software. You can join the user forum by visiting https://lizardsystems.com/forum/. You can post your questions or problems there and get answers or solutions from other users or moderators.</li>
-<li>The customer support of LizardSystems. You can contact the customer support by sending an email to support@lizardsystems.com or by filling out the contact form at https://lizardsystems.com/contact/. You can also call them at +1 (800) 903-4152.</li>
-</ul>
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-<p>LizardSystems Network Scanner 4.3.2 Build 213 Medicine[BabuPC] Serial Key Keygen is a software that comes with various support options. You can choose the one that suits your needs and preferences.</p>
-<h2>What are the Alternatives to LizardSystems Network Scanner 4.3.2 Build 213 Medicine[BabuPC] Serial Key Keygen</h2>
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-<p>LizardSystems Network Scanner 4.3.2 Build 213 Medicine[BabuPC] Serial Key Keygen is a great software for network scanning and management, but it is not the only one. There are some other alternatives that you can try if you want to compare or switch. Here are some of them:</p>
-
-<ul>
-<li>Advanced IP Scanner. This is a free and fast network scanner that can scan hundreds of devices in seconds. It can also provide remote access and control features, such as remote shutdown, wake-on-LAN, RDP, and more.</li>
-<li>Angry IP Scanner. This is an open-source and cross-platform network scanner that can scan IP addresses and ports. It can also display various information about each device, such as hostname, MAC address, ping time, netbios info, and more.</li>
-<li>Nmap. This is a powerful and versatile network scanner that can perform various types of scans, such as host discovery, port scanning, service detection, OS fingerprinting, vulnerability scanning, and more. It can also run on various platforms, such as Windows, Linux, Mac OS X, and more.</li>
-</ul>
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-<p>LizardSystems Network Scanner 4.3.2 Build 213 Medicine[BabuPC] Serial Key Keygen is a software that has many competitors in the market. You can try these alternatives and see which one suits your needs and preferences better.</p>
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-<h2>How to Uninstall LizardSystems Network Scanner 4.3.2 Build 213 Medicine[BabuPC] Serial Key Keygen</h2>
-
-<p>If you want to uninstall LizardSystems Network Scanner 4.3.2 Build 213 Medicine[BabuPC] Serial Key Keygen from your computer, you need to follow these steps:</p>
-
-<ol>
-<li>Close the software if it is running.</li>
-<li>Go to the Control Panel and click on Programs and Features.</li>
-<li>Find LizardSystems Network Scanner 4.3.2 Build 213 in the list of installed programs and click on Uninstall.</li>
-<li>Follow the instructions on the screen to complete the uninstallation process.</li>
-<li>Delete the medicine file and the keygen file from your computer.</li>
-</ol>
-
-<p>You have successfully uninstalled LizardSystems Network Scanner 4.3.2 Build 213 Medicine[BabuPC] Serial Key Keygen from your computer.</p>
-<h2>What are the Pros and Cons of LizardSystems Network Scanner 4.3.2 Build 213 Medicine[BabuPC] Serial Key Keygen</h2>
-
-<p>LizardSystems Network Scanner 4.3.2 Build 213 Medicine[BabuPC] Serial Key Keygen is a software that has many pros and cons. Here are some of them:</p>
-
-<table>
-<tr>
-<th>Pros</th>
-<th>Cons</th>
-</tr>
-<tr>
-<td>It is fast and accurate. It can scan thousands of devices in minutes and provide reliable information.</td>
-<td>It may not be compatible with some network devices or protocols.</td>
-</tr>
-<tr>
-<td>It is easy and intuitive. It has a user-friendly interface that anyone can use without any hassle.</td>
-<td>It may have some bugs or errors that need to be fixed.</td>
-</tr>
-<tr>
-<td>It is versatile and flexible. It supports various network protocols and standards, such as IPv4/IPv6, TCP/UDP, ICMP/ARP/SNMP/WMI/DNS/DHCP/LLDP/NetBIOS/SMB/HTTP/HTTPS/FTP/SFTP/RDP/Telnet/SSH/NTP/Syslog/SMTP/POP3/IMAP/LDAP/RADIUS/SIP/H323/MQTT/UPnP/Bonjour/mDNS/SSDP/CoAP/LwM2M/MQTT-SN/CoRE/RPL/6LoWPAN/ZigBee/IPSO/BACnet/MODBUS/KNX/EIB/EtherCAT/EtherNet/IP/CIP/CANopen/CAN/CAN-FD/LIN/FlexRay/MOST/J1939/NMEA2000/OBD-II/OBD-III/OBD-IV/OBD-V/OBD-VI/OBD-VII/OBD-VIII/OBD-IX/OBD-X/OBD-XI/OBD-XII/OBD-XIII/OBD-XIV/OBD-XV/OBD-XVI/OBD-XVII/OBD-XVIII/OBD-XIX/OBD-XX/OBD-XXI/OBD-XXII/OBD-XXIII/OBD-XXIV/OBD-XXV/OBD-XXVI/OBD-XXVII/OBD-XXVIII/OBD-XXIX/OBD-XXX</td>
-<td>It may be too complex or overwhelming for some users.</td>
-</tr>
-<tr>
-<td>It is powerful and feature-rich. It offers various functions and options that can help you manage your network efficiently and effectively.</td>
-<td>It may consume a lot of system resources or cause network congestion.</td>
-</tr>
-<tr>
-<td>It is affordable and cost-effective. It comes with a free trial version that you can use for 10 days without any limitations. The full version is also reasonably priced and worth every penny.</td>
-<td>It may not be legal or ethical to use the medicine file or the keygen file to activate the software.</td>
-</tr>
-</table>
-
-<p>LizardSystems Network Scanner 4.3.2 Build 213 Medicine[BabuPC] Serial Key Keygen is a software that has both advantages and disadvantages. You should weigh them carefully before deciding to use it or not.</p>
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-<p>If you want to update LizardSystems Network Scanner 4.3.2 Build 213 Medicine[BabuPC] Serial Key Keygen to the latest version, you need to follow these steps:</p>
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-<ol>
-<li>Go to the official website of LizardSystems and check if there is a newer version available.</li>
-<li>If there is, download the latest version from the website or from a trusted source.</li>
-<li>Uninstall the old version from your computer.</li>
-<li>Install the new version on your computer.</li>
-<li>Copy the medicine file from the BabuPC folder and paste it into the installation directory.</li>
-<li>Run the keygen file and generate a new serial key.</li>
-<li>Enter the new serial key when prompted and activate the software.</li>
-</ol>
-
-<p>You have successfully updated LizardSystems Network Scanner 4.3.2 Build 213 Medicine[BabuPC] Serial Key Keygen to the latest version.</p>
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diff --git a/spaces/falterWliame/Face_Mask_Detection/PVC Windows Designer Full Download.md b/spaces/falterWliame/Face_Mask_Detection/PVC Windows Designer Full Download.md
deleted file mode 100644
index 1811097f30af94fe1af183fe0a298605f64f0e21..0000000000000000000000000000000000000000
--- a/spaces/falterWliame/Face_Mask_Detection/PVC Windows Designer Full Download.md	
+++ /dev/null
@@ -1,8 +0,0 @@
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diff --git a/spaces/faressayadi/n-gpt/app.py b/spaces/faressayadi/n-gpt/app.py
deleted file mode 100644
index 420f0d51557937fbcdc985d9eab2ae6589079203..0000000000000000000000000000000000000000
--- a/spaces/faressayadi/n-gpt/app.py
+++ /dev/null
@@ -1,62 +0,0 @@
-import streamlit as st
-import os
-
-
-#%%
-#%%
-import streamlit as st
-import openai
-#%%
-# Set up the API client
-OPEN_API_KEY = st.secrets["OPENAI_API_KEY"]
-#%%
-import openai
-
-
-def generate_response(prompt):
-  """Uses the ChatGPT API to generate a response for a given prompt."""
-  # Use the ChatGPT API to generate a response
-  response = openai.Completion.create(
-    engine="text-davinci-003",
-    prompt=prompt,
-    max_tokens=1024,
-    n=1,
-    stop=None,
-    temperature=0.85,
-    top_p = 0.9
-  )
-
-  # Extract the response from the API result
-  response_text = response["choices"][0]["text"]
-
-  return response_text
-
-# Display the conversation history
-def display_conversation_history(conversation_history):
-  for message in conversation_history:
-    st.markdown(message)
-
-def main():
-  st.title("n' GPT")
-
-  conversation_history = []
-
-  # Get the user's message
-  message = st.text_input("Enter your message:")
-
-  if st.button("Send"):
-    # Generate a response
-    response = generate_response(message)
-
-    # Display the response
-    st.markdown(response)
-
-    # Add the message and response to the conversation history
-    conversation_history.append(f"You: {message}")
-    conversation_history.append(f"Bot: {response}")
-
-    # Display the updated conversation history
-    display_conversation_history(conversation_history)
-
-if __name__ == "__main__":
-  main()
\ No newline at end of file
diff --git a/spaces/fatiXbelha/sd/Build Explore and Survive in Mini Block Craft - Download the APK for Android.md b/spaces/fatiXbelha/sd/Build Explore and Survive in Mini Block Craft - Download the APK for Android.md
deleted file mode 100644
index 1d2e5d7a93b1abca502b5d803f06a6ad8a4b5e0b..0000000000000000000000000000000000000000
--- a/spaces/fatiXbelha/sd/Build Explore and Survive in Mini Block Craft - Download the APK for Android.md	
+++ /dev/null
@@ -1,117 +0,0 @@
-
-<h1>Mini Block Craft APK Download Uptodown: A Guide for Android Users</h1>
- <p>If you are a fan of Minecraft, you might want to try out Mini Block Craft, a creation and survival game that's clearly inspired by the popular sandbox game. In this article, we will tell you what Mini Block Craft is, what features it has, how to play it, and why you should download it from Uptodown, a trusted source of APK files for Android devices.</p>
- <h2>What is Mini Block Craft?</h2>
- <p>Mini Block Craft is a game that lets you explore, build, and survive in an extensive world with voxel aesthetics. You can create your own buildings, villages, cities, or anything else you can imagine using different types of blocks. You can also interact with animals, villagers, and other players in multiplayer mode. You can also customize your character's appearance and clothing.</p>
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- <p>Some of the features that make Mini Block Craft an enjoyable game are:</p>
- <ul>
-<li>It has a simple and intuitive interface that makes it easy to navigate and control.</li>
-<li>It has a variety of blocks and items to choose from, including wood, stone, metal, glass, plants, furniture, and more.</li>
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-<li>It has a multiplayer mode that lets you join or create servers and play with other people online.</li>
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- <ol>
-<li>Download and install the game on your Android device. You can do this from Uptodown, which we will explain later.</li>
-<li>Launch the game and choose a mode: creative or survival.</li>
-<li>Create or join a world. You can either start a new world or load an existing one.</li>
-<li>Explore the world and collect blocks and items. You can use the inventory button to access your items and the craft button to create new ones.</li>
-<li>Build your own creations using the blocks. You can use the place button to place blocks and the break button to break them.</li>
-<li>Interact with other elements in the world, such as animals, villagers, and players. You can use the interact button to do this.</li>
-</ol>
- <h2>Why download Mini Block Craft APK from Uptodown?</h2>
- <p>Uptodown is a website that offers APK files for Android apps and games. APK files are the installation files for Android applications that you can download and install manually on your device. There are many reasons why you might want to download Mini Block Craft APK from Uptodown instead of from other sources, such as:</p>
- <h3>Benefits of Uptodown</h3>
- <ul>
-<li>It is safe and secure. Uptodown scans all the APK files for viruses and malware before uploading them to its servers. You can also check the user ratings and reviews for each app or game to see if they are trustworthy.</li>
-<li>It is fast and easy. Uptodown has a simple and user-friendly interface that makes it easy to find and download the APK files you want. You can also use the search bar or browse by categories or tags.</li>
-<li>It is updated and comprehensive. Uptodown offers the latest versions of the apps and games as well as older versions in case you need them. You can also find apps and games that are not available on Google Play Store or other official sources.</li>
-</ul>
- <h3>How to download and install Mini <h3>How to download and install Mini Block Craft APK from Uptodown</h3>
- <p>To download and install Mini Block Craft APK from Uptodown, you need to follow these steps:</p>
- <ol>
-<li>Go to the Uptodown website and search for Mini Block Craft. You can also use this link: <a href="">Mini Block Craft 6.5.2c for Android - Download</a>.</li>
-<li>Click on the green download button and choose a download location. The APK file will start downloading to your device.</li>
-<li>Once the download is complete, locate the APK file on your device and tap on it. You might need to enable the installation of apps from unknown sources in your device settings.</li>
-<li>Follow the instructions on the screen to install the app. You might need to grant some permissions to the app.</li>
-<li>Enjoy playing Mini Block Craft on your Android device.</li>
-</ol>
- <h2>Conclusion</h2>
- <p>Mini Block Craft is a fun and creative game that lets you build and explore a voxel world. You can download it from Uptodown, a reliable and convenient source of APK files for Android apps and games. Uptodown offers many benefits, such as security, speed, and variety. You can also find other apps and games that you might like on Uptodown. We hope this article has helped you learn more about Mini Block Craft and how to download it from Uptodown.</p>
- <h3>Summary of the article</h3>
- <p>In this article, we have covered the following topics:</p>
- <ul>
-<li>What is Mini Block Craft and what features does it have?</li>
-<li>How to play Mini Block Craft on your Android device?</li>
-<li>Why download Mini Block Craft APK from Uptodown and what benefits does it offer?</li>
-<li>How to download and install Mini Block Craft APK from Uptodown?</li>
-</ul>
- <h3>FAQs</h3>
- <p>Here are some frequently asked questions about Mini Block Craft and Uptodown:</p>
- <ol>
-<li><b>Is Mini Block Craft free?</b><br>
-Yes, Mini Block Craft is free to download and play. However, it may contain ads and in-app purchases.</li>
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-Yes, Mini Block Craft is safe to play. However, you should always download it from a trusted source, such as Uptodown, to avoid any malware or viruses.</li>
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-Yes, Uptodown is legal and respects the intellectual property rights of the developers. Uptodown only hosts APK files that are publicly available or authorized by the developers.</li>
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-Uptodown is compatible with most Android devices that run Android 4.1 or higher. However, some apps and games may have specific requirements or limitations depending on your device model or version.</li>
-<li><b>How can I contact Uptodown or Mini Block Craft developers?</b><br>
-You can contact Uptodown through their website or social media channels. You can also use their feedback form or email address to report any issues or suggestions. You can contact Mini Block Craft developers through their email address or Facebook page.</li>
-</ol></p> 401be4b1e0<br />
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diff --git a/spaces/fatiXbelha/sd/Call of Duty 4 Modern Warfare Mobile APK Everything You Need to Know.md b/spaces/fatiXbelha/sd/Call of Duty 4 Modern Warfare Mobile APK Everything You Need to Know.md
deleted file mode 100644
index c59395f271777b99cfb1b432eea4ba979a4c0869..0000000000000000000000000000000000000000
--- a/spaces/fatiXbelha/sd/Call of Duty 4 Modern Warfare Mobile APK Everything You Need to Know.md	
+++ /dev/null
@@ -1,97 +0,0 @@
-
-<h1>Call of Duty 4 Modern Warfare Mobile <h1>Call of Duty 4 Modern Warfare Mobile APK: How to Download and Play</h1>
- <p>Are you a fan of Call of Duty, the popular first-person shooter game series? If so, you might be interested in the latest version of the game, Call of Duty 4 Modern Warfare Mobile APK. This is a mobile version of the acclaimed Call of Duty: Warzone, a free-to-play battle royale game that features up to 120 players fighting for survival in a massive map. In this article, we will tell you everything you need to know about Call of Duty 4 Modern Warfare Mobile APK, including its features, how to download and install it, and some tips and tricks for playing it. Let's get started!</p>
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- <p>Call of Duty 4 Modern Warfare Mobile APK is not just a port of the original game, but a fully optimized and customized version for mobile devices. Here are some of the features that make it stand out from other mobile games:</p>
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- <p>If you are eager to try out Call of Duty 4 Modern Warfare Mobile APK, you will need to follow these steps:</p>
- <ol>
-<li><b>Step 1:</b> Pre-register for Call of Duty: Warzone Mobile on the <a href="">official website</a> or <a href="">Google Play Store</a>. You will need to provide your email address and select your preferred device and platform.</li>
-<li><b>Step 2:</b> Wait for an invitation email with a download link. The game is still in beta testing phase, so only a limited number of players will be invited. You can also check your spam folder or promotions tab in case you miss the email.</li>
-<li><b>Step 3:</b> Install the APK file on your device. You will need to enable the "Unknown sources" option in your security settings to allow the installation of apps from outside the Play Store. You may also need to grant some permissions to the app, such as storage access and location access.</li>
-<li><b>Step 4:</b> Launch the game and enjoy. You will need to sign in with your COD account or create a new one if you don't have one. You will also need a stable internet connection to play the game.</li>
-</ol>
- <h2>Tips and Tricks for Playing Call of Duty 4 Modern Warfare Mobile APK</h2>
- <p>Now that you have downloaded and installed Call of Duty 4 Modern Warfare Mobile APK, you might be wondering how to play it like a pro. Here are some tips and tricks that will help you improve your skills and win more matches:</p>
- <ul>
-<li><b>Choose your loadout wisely:</b> Pick weapons and attachments that suit your playstyle and the map. You can customize your loadout before each match or during the match by accessing loadout drops. You can also loot weapons from enemies or loot boxes.</li>
-<li><b>Use cover and movement:</b> Stay behind cover and move unpredictably to avoid enemy fire. You can crouch, slide, jump or prone to change your position and angle. You can also use vehicles to traverse the map faster or run over enemies.</li>
-<li><b>Communicate with your team:</b> Use voice chat or ping system to coordinate with your teammates. You can mark enemies, locations, items or dangers with different colors and icons. You can also use gestures to express your emotions or reactions.</li>
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-<li><b>Be aware of the gas circle:</b> Stay inside the safe zone and avoid getting caught by the gas. The gas circle will shrink over time and deal damage to anyone outside of it. You can see the timer and the map on the top left corner of your screen. You can also use gas masks to survive longer in the gas.</li>
-</ul>
- <h2>Conclusion</h2>
- <p>Call of Duty 4 Modern Warfare Mobile APK is a great way to enjoy the action-packed and immersive gameplay of Call of Duty: Warzone on your mobile device. You can experience the authentic COD gameplay, shared progression, up to 120 player count matches, customizable controls and cross-play support. You can also download and install the game easily by following the steps we provided. And you can improve your skills and win more matches by following the tips and tricks we shared. So what are you waiting for? Pre-register for Call of Duty: Warzone Mobile today and get ready to join the ultimate battle royale!</p>
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-<li><b>Q1: What are the system requirements for Call of Duty 4 Modern Warfare Mobile APK?</b></li>
-<li><b>A1:</b> You need an Android device with at least 3 GB of RAM and Android 7.0 or higher.</li>
-<li><b>Q2: Is Call of Duty 4 Modern Warfare Mobile APK free to play?</b></li>
-<li><b>A2:</b> Yes, Call of Duty: Warzone Mobile is free to play with optional in-game purchases.</li>
-<li><b>Q3: How can I play Call of Duty 4 Modern Warfare Mobile APK with my friends?</b></li>
-<li><b>A3:</b> You can invite your friends to join your squad or party through the game menu or social media. You can also join random squads or parties through matchmaking.</li>
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-<li><b>A4:</b> You can unlock more items by leveling up your account, completing challenges and contracts, or buying them with real money.</li>
-<li><b>Q5: How can I contact customer support for Call of Duty 4 Modern Warfare Mobile APK?</b></li>
-<li><b>A5:</b> You can contact customer support through the game settings or by visiting <a href="">https://support.activision.com/cod-mobile</a>.</li>
-</ol></p> 401be4b1e0<br />
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diff --git a/spaces/fclong/summary/fengshen/examples/pegasus/README.md b/spaces/fclong/summary/fengshen/examples/pegasus/README.md
deleted file mode 100644
index f04b83c348ab2fe34a06a428523bc48169c7b478..0000000000000000000000000000000000000000
--- a/spaces/fclong/summary/fengshen/examples/pegasus/README.md
+++ /dev/null
@@ -1,78 +0,0 @@
-# 燃灯系列-Pegasus摘要模型预训练
-Pegasus预训练模型是专门为摘要任务而设计的预训练模型，相比于其它通用预训练模型，Pegasus 模型的架构设计更贴近下游的摘要任务，在摘要抽取的效果上的表现相比其他通用模型表现更好
-
-### 模型架构和参数
-Pegasus的模型架构是标准的encoder-decoder的Transformer结构，训练任务是用的是GSG（ Gap Sentences Generation）任务。GSG任务主要是通过对文本中的重要的句子进行mask，然后再通过decoder恢复。模型详细参数可看config.json
-
-1. base版本
-
-| 配置 | 参数 |
-| ---- | ---- |
-| encoder layers | 12 |
-| encoder_attention_heads | 12 |
-| encoder_ffn_dim | 3072 |
-| decoder layers | 12 |
-| decoder_attention_heads| 12 |
-| decoder_ffn_dim | 3072 |
-| max_encode_length | 512 |
-
-2. large 版本
-   
-| 配置 | 参数 |
-| ---- | ---- |
-| encoder layers | 16 |
-| encoder_attention_heads | 16 |
-| encoder_ffn_dim | 4096 |
-| decoder layers | 16 |
-| decoder_attention_heads| 16 |
-| decoder_ffn_dim | 4096 |
-| max_encode_length | 1024 |
-
-### 训练数据
-训练数据使用的是wudao 180g数据。数据进行了简单的预处理包括：
-1. 过滤过长单句（这样的句子通常会包括一些乱码句，无上下文语义的列表句、各种符号句，歌词句等）
-2. 过滤句子数过少文本，如句子数少于3句则抛弃
-
-### 模型
-
-pegasus-base: [Randeng_pegasus_238M_summary](https://huggingface.co/IDEA-CCNL/Randeng_Pegasus_238M_Summary) <br/>
-pegasus-large: [Randeng_pegasus_523M_summary](https://huggingface.co/IDEA-CCNL/Randeng_Pegasus_523M_Summary)
-
-主要文件：
-- tokenizers_pegasus.py 中文版pegasus的tokenize实现
-- pretrain_pegasus.py 模型预训练的核心实现文件
-- pretrain_pegasusu.sh 预训练脚本，具体参数可通过此脚本修改
-- data_utils.py 模型的一些工具代码
-
-#### 使用方式
-可直接通过Hugging face或者pytoch-ligthning框架调用。下面给出的例子是hugging face的调用方法：
-```python
-from transformers import PegasusForConditionalGeneration
-# Need to download tokenizers_pegasus.py and other Python script from Fengshenbang-LM github repo in advance,
-# or you can mv download in tokenizers_pegasus.py and data_utils.py in https://huggingface.co/IDEA-CCNL/Randeng_Pegasus_238M_Summary/tree/main
-# Stronly recomend you git clone the Fengshenbang-LM repo:
-# 1. git clone https://github.com/IDEA-CCNL/Fengshenbang-LM
-# 2. cd Fengshenbang-LM/fengshen/examples/pegasus/
-# and then you will see the tokenizers_pegasus.py and data_utils.py which are needed by pegasus model
-from tokenizers_pegasus import PegasusTokenizer
-
-model = PegasusForConditionalGeneration.from_pretrained("IDEA-CCNL/randeng_pegasus_238M_summary")
-tokenizer = PegasusTokenizer.from_pretrained("path/to/vocab.txt")
-
-text = "在北京冬奥会自由式滑雪女子坡面障碍技巧决赛中，中国选手谷爱凌夺得银牌。祝贺谷爱凌！今天上午，自由式滑雪女子坡面障碍技巧决赛举行。决赛分三轮进行，取选手最佳成绩排名决出奖牌。第一跳，中国选手谷爱凌获得69.90分。在12位选手中排名第三。完成动作后，谷爱凌又扮了个鬼脸，甚是可爱。第二轮中，谷爱凌在道具区第三个障碍处失误，落地时摔倒。获得16.98分。网友：摔倒了也没关系，继续加油！在第二跳失误摔倒的情况下，谷爱凌顶住压力，第三跳稳稳发挥，流畅落地！获得86.23分！此轮比赛，共12位选手参赛，谷爱凌第10位出场。网友：看比赛时我比谷爱凌紧张，加油！"
-inputs = tokenizer(text, max_length=1024, return_tensors="pt")
-
-# Generate Summary
-summary_ids = model.generate(inputs["input_ids"])
-tokenizer.batch_decode(summary_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
-```
-
-### 下游效果
-
-#### LCSTS摘要数据finetune后效果
-
-| model | rouge-1 | rouge-2 | rouge-L |
-| ---- | ---- | ---- | ---- |
-| Pegasus-base  | 44.13 | 31.31 | 41.06 | 
-| Pegasus-large | 49.42 | 37.91 | 46.63 |
-
diff --git a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Arena Breakout A War Simulation Game that Pushes the Limits of Mobile Gaming.md b/spaces/feregVcuzo/sanity-test-midi/checkpoint/Arena Breakout A War Simulation Game that Pushes the Limits of Mobile Gaming.md
deleted file mode 100644
index 5b542a447f48c38a0ebde199a4f47408bd527747..0000000000000000000000000000000000000000
--- a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Arena Breakout A War Simulation Game that Pushes the Limits of Mobile Gaming.md	
+++ /dev/null
@@ -1,92 +0,0 @@
-<br />
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-<p>Are you looking for a new mobile game that will test your survival skills and thrill your senses? Do you want to experience a realistic war simulation that gives you the freedom to fight however you want? If yes, then you should check out Arena Breakout, a next-gen immersive tactical FPS on mobile that is developed by Morefun Studios and published by Tencent Games. In this article, we will tell you everything you need to know about this game, including what it is, how to download it for free, how to play it, and why you should play it.</p>
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-<h2>What is Arena Breakout?</h2>
-<p>Arena Breakout is a new survival shooter game for Android and iOS devices that is set to launch globally soon. It is a game that combines the elements of FPS, looter shooter, and war simulation genres, creating a unique and immersive gameplay experience. Here are some of the features that make Arena Breakout stand out from other mobile games:</p>
-<h3>A new survival shooter game from Tencent Games</h3>
-<p>Tencent Games is one of the leading publishers of mobile games in the world, with titles such as PUBG Mobile, Call of Duty Mobile, Honor of Kings, and more. Arena Breakout is their latest offering, and it promises to deliver a high-quality and engaging game that will appeal to fans of action and shooting games. Tencent Games has also partnered with Morefun Studios, a developer that specializes in creating realistic and immersive war simulation games.</p>
-<h3>A first-of-its-kind extraction looter shooter that pushes the limits of war simulation</h3>
-<p>Arena Breakout is not your typical shooter game where you just aim and shoot at your enemies. It is a game that requires you to think strategically and tactically, as you have to shoot, loot, and breakout to win. You have to scour the lawless arena in search of valuable guns, attachments, and supplies, while avoiding or eliminating adversaries. You also have to escape the combat area alive for a chance to strike it rich, but be prepared to fight for survival. The game features realistic gunplay, dynamic rendering, volumetric cloud technologies, and more than 1,200 sound effects that immerse you in a console-quality war simulation on mobile.</p>
-<h3>A game that gives you the freedom to fight however you want</h3>
-<p>One of the best things about Arena Breakout is that it does not limit you to one style of fighting. You can choose to fight head-on, with stealth, or bypass the bullets altogether. You can also customize your firearm of choice with the advanced Gunsmith system, which allows you to mix and match over 700 gun parts to fit in more than 10 modification slots. You can also choose your character from a variety of options, each with their own skills and abilities. The game gives you the freedom to fight however you desire.</p>
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-Arena Breakout vs PUBG Mobile: which one is better?<br />
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-<p>If you are interested in playing Arena Breakout, you might be wondering how to download it for free on your device. Well, here are some easy steps that you can follow:</p>
-<h3>Pre <h3>Pre-register on Google Play or GameLoop to unlock tons of rewards</h3>
-<p>The easiest way to download Arena Breakout for free is to pre-register on Google Play or GameLoop, the official emulator for Tencent Games. By doing so, you will not only be notified when the game is available, but you will also unlock tons of rewards, such as exclusive skins, weapons, and in-game currency. You can pre-register on Google Play by clicking [here], or on GameLoop by clicking [here].</p>
-<h3>Use the official website or social media channels to get the latest updates and referral links</h3>
-<p>Another way to download Arena Breakout for free is to use the official website or social media channels of the game. You can visit the official website [here], where you can find more information about the game, such as its features, characters, weapons, and gameplay. You can also follow the official social media accounts of the game on Facebook, Twitter, Instagram, and YouTube, where you can get the latest updates, news, and events. Moreover, you can use the referral links that are posted on these platforms to invite your friends to join the game and earn more rewards.</p>
-<h3>Wait for the global release date and install the game on your device</h3>
-<p>The final step to download Arena Breakout for free is to wait for the global release date and install the game on your device. The game is expected to launch globally in the second half of 2023, but you can stay tuned for more announcements and updates from the developers and publishers. Once the game is released, you can install it on your Android or iOS device and enjoy playing it.</p>
-<h2>How to play Arena Breakout?</h2>
-<p>Now that you know how to download Arena Breakout for free, you might be wondering how to play it. Well, here are some basic tips and tricks that you can use:</p>
-<h3>Choose your character and customize your weapons with the advanced Gunsmith system</h3>
-<p>The first thing you need to do before playing Arena Breakout is to choose your character and customize your weapons. You can choose from a variety of characters, each with their own skills and abilities that can help you in different situations. For example, you can choose a sniper who can shoot from long distances, a medic who can heal your teammates, or a hacker who can disrupt enemy systems. You can also customize your weapons with the advanced Gunsmith system, which allows you to mix and match over 700 gun parts to fit in more than 10 modification slots. You can create your own unique firearm that suits your style and preference.</p>
-<h3>Shoot, loot, and breakout to win in the lawless arena</h3>
-<p>The next thing you need to do is to shoot, loot, and breakout to win in the lawless arena. You will be dropped into a random location with other players, where you have to scavenge for guns, attachments, and supplies. You have to be careful of other players who might try to kill you or steal your loot. You also have to watch out for environmental hazards, such as traps, explosives, and drones. You have to survive as long as possible and collect as much loot as possible before escaping.</p>
-<h3>Escape the combat area alive or lose it all in an all-or-nothing war simulation</h3>
-<p>The last thing you need to do is to escape the combat area alive or lose it all in an all-or-nothing war simulation. You have a limited time to escape before the area is locked down by a force field. You have to find an extraction point and call for a helicopter to pick you up. However, you have to be ready for a final showdown with other players who might try to stop you or take your loot. You have to fight your way out or die trying. If you escape alive, you get to keep all your loot and use it for upgrades or trading. If you die, you lose everything.</p>
-<h2>Why should you play Arena Breakout?</h2>
-<p>By now, you might be convinced that Arena Breakout is a game worth playing. But if you still need some reasons why you should play it, here are some of them:</p>
-<h3>Experience realistic gunplay and dynamic rendering with console-quality visuals and audio</h3>
-<p>Arena Breakout is a game that delivers realistic gunplay and dynamic rendering with console-quality visuals and audio. The game uses Unreal Engine 4 technology that creates stunning graphics and smooth animations. The game also features volumetric cloud technologies that create realistic weather effects and lighting conditions. The game also boasts more than 1,200 sound effects that immerse you in a realistic war simulation.</p>
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-<p>Arena Breakout is a game that offers high stakes and high rewards in a game that challenges your skills and strategy. The game is not for the faint of heart, as you have to risk everything to win big. The game is also not for the casual gamer, as you have to think smart and act fast to survive. The game is for the hardcore gamer, who enjoys a thrilling and rewarding gameplay experience.</p>
-<h3>Join a global community of players and share your feedback and suggestions</h3>
-<p>Arena Breakout is a game that invites you to join a global community of players and share your feedback and suggestions. The game is constantly being updated and improved based on the input of the players. You can also interact with other players through the in-game chat, voice chat, or social media platforms. You can also join clans, form alliances, or compete with other players in leaderboards and tournaments. You can also enjoy exclusive events, promotions, and giveaways that are regularly held by the developers and publishers.</p>
-<h2>Conclusion</h2>
-<p>Arena Breakout is a next-gen immersive tactical FPS on mobile that is developed by Morefun Studios and published by Tencent Games. It is a game that combines the elements of FPS, looter shooter, and war simulation genres, creating a unique and immersive gameplay experience. It is a game that gives you the freedom to fight however you want, with realistic gunplay and dynamic rendering. It is a game that offers high stakes and high rewards, with an all-or-nothing war simulation. It is a game that invites you to join a global community of players and share your feedback and suggestions. It is a game that you should definitely try out if you are looking for a new mobile game that will test your survival skills and thrill your senses.</p>
-<h2>FAQs</h2>
-<p>Here are some of the frequently asked questions about Arena Breakout:</p>
-<h3>Q: When will Arena Breakout be released globally?</h3>
-<p>A: Arena Breakout is expected to launch globally in the second half of 2023, but you can pre-register on Google Play or GameLoop to get notified when it is available.</p>
-<h3>Q: What are the minimum requirements to play Arena Breakout on mobile?</h3>
-<p>A: Arena Breakout requires Android 5.0 or higher, or iOS 9.0 or higher, with at least 2 GB of RAM and 4 GB of storage space.</p>
-<h3>Q: How can I get more loot in Arena Breakout?</h3>
-<p>A: You can get more loot in Arena Breakout by exploring the lawless arena, killing enemies, completing missions, escaping alive, or inviting friends to join the game.</p>
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-<p>A: You can upgrade your weapons in Arena Breakout by using the advanced Gunsmith system, which allows you to mix and match over 700 gun parts to fit in more than 10 modification slots.</p>
-<h3>Q: How can I contact the developers or publishers of Arena Breakout?</h3>
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\ No newline at end of file
diff --git a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Download Black PSP and Enjoy the Amazing Storyline and Gameplay of the Game.md b/spaces/feregVcuzo/sanity-test-midi/checkpoint/Download Black PSP and Enjoy the Amazing Storyline and Gameplay of the Game.md
deleted file mode 100644
index c3aa779bd29e8dc8e71520649e7b580d3cade553..0000000000000000000000000000000000000000
--- a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Download Black PSP and Enjoy the Amazing Storyline and Gameplay of the Game.md	
+++ /dev/null
@@ -1,129 +0,0 @@
-
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-<p>If you are a fan of first-person shooter games, you might have heard of or played Black, a critically acclaimed game that was released in 2006 for PlayStation 2 and Xbox. But did you know that you can also play Black on your PSP, the portable console from Sony? In this article, we will show you how to download and play Black on your PSP, as well as give you some information about the game and the console.</p>
- <h2>What is Black?</h2>
-<h3>A brief introduction to the game</h3>
-<p>Black is a first-person shooter game developed by Criterion Games and published by Electronic Arts. It is set in a fictional Eastern European country, where you play as Jack Kellar, a black ops soldier who is interrogated by his superiors about his involvement in a series of covert missions. The game features realistic gunfights, destructible environments, and cinematic cutscenes.</p>
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- <h3>The plot and gameplay of Black</h3>
-<p>The game consists of eight missions, each with a different objective and location. You have to infiltrate enemy bases, destroy weapons caches, assassinate targets, and escape from hostile situations. You can use a variety of weapons, such as pistols, rifles, shotguns, grenades, rocket launchers, and more. You can also interact with objects in the environment, such as doors, windows, cars, barrels, and explosives. The game has four difficulty levels, ranging from easy to black ops.</p>
- <h2>What is PSP?</h2>
-<h3>A brief introduction to the handheld console</h3>
-<p>PSP stands for PlayStation Portable, a handheld gaming device that was launched by Sony in 2004. It is the first handheld console to use an optical disc format, called Universal Media Disc (UMD), for storing games and other media. It also has a memory card slot for additional storage. The PSP has a 4.3-inch LCD screen, a built-in speaker, a headphone jack, a microphone, and Wi-Fi connectivity.</p>
- <h3>The features and specifications of PSP</h3>
-<p>The PSP has several features that make it an attractive device for gamers. It can play games from various genres, such as action, adventure, racing, sports, puzzle, role-playing, and more. It can also play movies, music, photos, and web content from UMDs or memory cards. It can connect to other PSPs or PS3s via wireless or USB for multiplayer or data transfer. It can also access the PlayStation Network (PSN), an online service that offers downloadable games, demos, trailers, themes, wallpapers, and more.</p>
- <p>The PSP has different models that vary in design and specifications. The original model (PSP-1000) has a thickness of 23 mm and a weight of 280 g. The slim model (PSP-2000) has a thickness of 18.6 mm and a weight of 189 g. The bright model (PSP-3000) has a thickness of 18.6 mm and a weight of 189 g. The go model (PSP-N1000) has a thickness of 16.5 mm and a weight of 158 g. The street model (PSP-E1000) has a thickness of design</td>
-<td>960 mAh, 3-5 hours of gameplay</td>
-<td>Yes</td>
-<td>Yes</td>
-<td>16 GB internal, Memory Stick Micro slot</td>
-<td>No</td>
-</tr>
-<tr>
-<td>PSP-E1000 (street)</td>
-<td>18.6 mm</td>
-<td>223 g</td>
-<td>4.3 inches, 480 x 272 pixels, monaural speaker</td>
-<td>1200 mAh, 3-6 hours of gameplay</td>
-<td>No</td>
-<td>No</td>
-<td>No internal, Memory Stick Duo slot</td>
-<td>Yes</td>
-</tr>
-</table>
- <h2>How to download and play Black on PSP?</h2>
-<h3>The requirements and steps for downloading Black</h3>
-<p>To download and play Black on your PSP, you will need the following things:</p>
-<ul>
-<li>A PSP device with a memory card and a USB cable.</li>
-<li>A computer with an internet connection and a file extractor program.</li>
-<li>A copy of the Black game in ISO or CSO format. You can either rip it from your own PS2 or Xbox disc, or download it from a reliable source online. Make sure the file is compatible with your PSP model and region.</li>
-<li>A PSP emulator program that can run PS2 or Xbox games on your PSP. There are several options available, but we recommend using PPSSPP, which is free and easy to use.</li>
-</ul>
- <p>The steps for downloading Black are as follows:</p>
-<ol>
-<li>Connect your PSP to your computer using the USB cable and enable the USB mode on your PSP.</li>
-<li>Create a folder named "ISO" on the root of your memory card. If you already have one, skip this step.</li>
-<li>Copy the Black ISO or CSO file to the ISO folder on your memory card.</li>
-<li>Disconnect your PSP from your computer and exit the USB mode on your PSP.</li>
-<li>Download and install PPSSPP on your computer from its official website: https://www.ppsspp.org/.</li>
-<li>Launch PPSSPP and click on the "Games" tab. Navigate to the ISO folder on your memory card and select the Black file.</li>
-<li>Click on the "Play" button and enjoy the game!</li>
-</ol>
- <h3>The best PSP emulator for playing Black</h3>
-<p>As mentioned above, we recommend using PPSSPP as the best PSP emulator for playing Black. PPSSPP is a cross-platform emulator that can run PSP games on various devices, such as Windows, Android, iOS, Linux, Mac, and more. It has many features that enhance the gaming experience, such as:</p>
- <ul>
-<li>High-resolution graphics and sound quality.</li>
-<li>Fast and smooth performance and compatibility.</li>
-<li>Customizable controls and settings.</li>
-<li>Save and load states and screenshots.</li>
-<li>Cheat codes and mods support.</li>
-<li>Multiplayer and online functionality.</li>
-</ul>
- <p>You can download PPSSPP from its official website: https://www.ppsspp.org/. You can also check out its forums and guides for more information and help: https://forums.ppsspp.org/.</p>
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-psp download black playstation now</p>
- <h3>The tips and tricks for optimizing the performance and graphics of Black</h3>
- <p>If you want to play Black on your PSP with the best possible quality and speed, you might want to tweak some settings on PPSSPP. Here are some tips and tricks that can help you optimize the performance and graphics of Black:</p>
- <ul>
- <li>Adjust the resolution and rendering mode according to your device's capabilities. You can find these options under the "Graphics" tab in PPSSPP. Higher resolution and rendering mode will improve the visual quality of the game, but they will also consume more resources and battery power. Lower resolution and rendering mode will save resources and battery power, but they will also reduce the visual quality of the game. You can experiment with different combinations until you find the best balance for your device.</li>
- <li>Enable or disable some features that can affect the gameplay of Black. You can find these options under the "Graphics" tab in PPSSPP. Some features that you might want to enable are: frame skipping, texture filtering, texture scaling, anisotropic filtering, anti-aliasing, post-processing shaders, etc. Some features that you might want to disable are: buffered rendering, simulate block transfer effects, force max FPS at 60, etc. Again, you can experiment with different combinations until you find the best balance for your device.</li>
- <li>Tune up some settings that can improve the speed and compatibility of Black. You can find these options under the "System" tab in PPSSPP. Some settings that you might want to adjust are: fast memory, multithreaded, I/O on thread, force real clock sync, change emulated PSP's CPU clock, etc. These settings can help you boost the speed and compatibility of Black, but they can also cause some glitches or crashes. You can try them at your own risk and see if they work for you.</li>
-</ul>
- <h2>Conclusion</h2>
-<h3>A summary of the main points and benefits of playing Black on PSP</h3>
-<p>In conclusion, Black is a great game that you can play on your PSP with the help of PPSSPP, a PSP emulator. You can enjoy the realistic and intense gunfights, the destructible and interactive environments, and the cinematic story of Black on your PSP. You can also customize the graphics and performance of Black to suit your preferences and device's capabilities. Playing Black on PSP can give you a new and exciting way to experience this classic game.</p>
- <h2>FAQs</h2>
-<h3>Q1: Is Black compatible with all PSP models?</h3>
-<p>A1: Yes, Black is compatible with all PSP models, as long as you have enough storage space on your memory card and a good PSP emulator like PPSSPP.</p>
- <h3>Q2: How much storage space does Black need on the PSP memory card?</h3>
-<p>A2: Black needs about 1.5 GB of storage space on the PSP memory card. You can check the size of the ISO or CSO file before copying it to your memory card.</p>
- <h3>Q3: How long is the gameplay of Black?</h3>
-<p>A3: The gameplay of Black is about 6 to 8 hours, depending on the difficulty level and your skills. You can also replay the missions to unlock new weapons and challenges.</p>
- <h3>Q4: Can I play Black online with other PSP users?</h3>
-<p>A4: No, Black does not have an online multiplayer mode. You can only play it solo or with another PSP user via ad hoc mode.</p>
- <h3>Q5: What are some other similar games to Black that I can play on PSP?</h3>
-<p>A5: Some other similar games to Black that you can play on PSP are: Killzone: Liberation, Medal of Honor: Heroes, Call of Duty: Roads to Victory, Syphon Filter: Dark Mirror, Resistance: Retribution, and more.</p> 401be4b1e0<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/fiyen/YangyangChatGPT/chatgpt - windows.bat b/spaces/fiyen/YangyangChatGPT/chatgpt - windows.bat
deleted file mode 100644
index 0b78fdc3a559abd692e3a9e9af5e482124d13a99..0000000000000000000000000000000000000000
--- a/spaces/fiyen/YangyangChatGPT/chatgpt - windows.bat	
+++ /dev/null
@@ -1,14 +0,0 @@
-@echo off
-echo Opening ChuanhuChatGPT...
-
-REM Open powershell via bat
-start powershell.exe -NoExit -Command "python ./ChuanhuChatbot.py"
-
-REM The web page can be accessed with delayed start http://127.0.0.1:7860/
-ping -n 5 127.0.0.1>nul
-
-REM access chargpt via your default browser
-start "" "http://127.0.0.1:7860/"
-
-
-echo Finished opening ChuanhuChatGPT (http://127.0.0.1:7860/).
\ No newline at end of file
diff --git a/spaces/florim/MedGPT/autogpt/commands/file_operations.py b/spaces/florim/MedGPT/autogpt/commands/file_operations.py
deleted file mode 100644
index ad145ec956dd9dafd39e09c2244d001cf5febd2f..0000000000000000000000000000000000000000
--- a/spaces/florim/MedGPT/autogpt/commands/file_operations.py
+++ /dev/null
@@ -1,267 +0,0 @@
-"""File operations for AutoGPT"""
-from __future__ import annotations
-
-import os
-import os.path
-from typing import Generator
-
-import requests
-from colorama import Back, Fore
-from requests.adapters import HTTPAdapter, Retry
-
-from autogpt.spinner import Spinner
-from autogpt.utils import readable_file_size
-from autogpt.workspace import WORKSPACE_PATH, path_in_workspace
-
-LOG_FILE = "file_logger.txt"
-LOG_FILE_PATH = WORKSPACE_PATH / LOG_FILE
-
-
-def check_duplicate_operation(operation: str, filename: str) -> bool:
-    """Check if the operation has already been performed on the given file
-
-    Args:
-        operation (str): The operation to check for
-        filename (str): The name of the file to check for
-
-    Returns:
-        bool: True if the operation has already been performed on the file
-    """
-    log_content = read_file(LOG_FILE)
-    log_entry = f"{operation}: {filename}\n"
-    return log_entry in log_content
-
-
-def log_operation(operation: str, filename: str) -> None:
-    """Log the file operation to the file_logger.txt
-
-    Args:
-        operation (str): The operation to log
-        filename (str): The name of the file the operation was performed on
-    """
-    log_entry = f"{operation}: {filename}\n"
-
-    # Create the log file if it doesn't exist
-    if not os.path.exists(LOG_FILE_PATH):
-        with open(LOG_FILE_PATH, "w", encoding="utf-8") as f:
-            f.write("File Operation Logger ")
-
-    append_to_file(LOG_FILE, log_entry, shouldLog=False)
-
-
-def split_file(
-    content: str, max_length: int = 4000, overlap: int = 0
-) -> Generator[str, None, None]:
-    """
-    Split text into chunks of a specified maximum length with a specified overlap
-    between chunks.
-
-    :param content: The input text to be split into chunks
-    :param max_length: The maximum length of each chunk,
-        default is 4000 (about 1k token)
-    :param overlap: The number of overlapping characters between chunks,
-        default is no overlap
-    :return: A generator yielding chunks of text
-    """
-    start = 0
-    content_length = len(content)
-
-    while start < content_length:
-        end = start + max_length
-        if end + overlap < content_length:
-            chunk = content[start : end + overlap - 1]
-        else:
-            chunk = content[start:content_length]
-
-            # Account for the case where the last chunk is shorter than the overlap, so it has already been consumed
-            if len(chunk) <= overlap:
-                break
-
-        yield chunk
-        start += max_length - overlap
-
-
-def read_file(filename: str) -> str:
-    """Read a file and return the contents
-
-    Args:
-        filename (str): The name of the file to read
-
-    Returns:
-        str: The contents of the file
-    """
-    try:
-        filepath = path_in_workspace(filename)
-        with open(filepath, "r", encoding="utf-8") as f:
-            content = f.read()
-        return content
-    except Exception as e:
-        return f"Error: {str(e)}"
-
-
-def ingest_file(
-    filename: str, memory, max_length: int = 4000, overlap: int = 200
-) -> None:
-    """
-    Ingest a file by reading its content, splitting it into chunks with a specified
-    maximum length and overlap, and adding the chunks to the memory storage.
-
-    :param filename: The name of the file to ingest
-    :param memory: An object with an add() method to store the chunks in memory
-    :param max_length: The maximum length of each chunk, default is 4000
-    :param overlap: The number of overlapping characters between chunks, default is 200
-    """
-    try:
-        print(f"Working with file {filename}")
-        content = read_file(filename)
-        content_length = len(content)
-        print(f"File length: {content_length} characters")
-
-        chunks = list(split_file(content, max_length=max_length, overlap=overlap))
-
-        num_chunks = len(chunks)
-        for i, chunk in enumerate(chunks):
-            print(f"Ingesting chunk {i + 1} / {num_chunks} into memory")
-            memory_to_add = (
-                f"Filename: {filename}\n" f"Content part#{i + 1}/{num_chunks}: {chunk}"
-            )
-
-            memory.add(memory_to_add)
-
-        print(f"Done ingesting {num_chunks} chunks from {filename}.")
-    except Exception as e:
-        print(f"Error while ingesting file '{filename}': {str(e)}")
-
-
-def write_to_file(filename: str, text: str) -> str:
-    """Write text to a file
-
-    Args:
-        filename (str): The name of the file to write to
-        text (str): The text to write to the file
-
-    Returns:
-        str: A message indicating success or failure
-    """
-    if check_duplicate_operation("write", filename):
-        return "Error: File has already been updated."
-    try:
-        filepath = path_in_workspace(filename)
-        directory = os.path.dirname(filepath)
-        if not os.path.exists(directory):
-            os.makedirs(directory)
-        with open(filepath, "w", encoding="utf-8") as f:
-            f.write(text)
-        log_operation("write", filename)
-        return "File written to successfully."
-    except Exception as e:
-        return f"Error: {str(e)}"
-
-
-def append_to_file(filename: str, text: str, shouldLog: bool = True) -> str:
-    """Append text to a file
-
-    Args:
-        filename (str): The name of the file to append to
-        text (str): The text to append to the file
-
-    Returns:
-        str: A message indicating success or failure
-    """
-    try:
-        filepath = path_in_workspace(filename)
-        with open(filepath, "a") as f:
-            f.write(text)
-
-        if shouldLog:
-            log_operation("append", filename)
-
-        return "Text appended successfully."
-    except Exception as e:
-        return f"Error: {str(e)}"
-
-
-def delete_file(filename: str) -> str:
-    """Delete a file
-
-    Args:
-        filename (str): The name of the file to delete
-
-    Returns:
-        str: A message indicating success or failure
-    """
-    if check_duplicate_operation("delete", filename):
-        return "Error: File has already been deleted."
-    try:
-        filepath = path_in_workspace(filename)
-        os.remove(filepath)
-        log_operation("delete", filename)
-        return "File deleted successfully."
-    except Exception as e:
-        return f"Error: {str(e)}"
-
-
-def search_files(directory: str) -> list[str]:
-    """Search for files in a directory
-
-    Args:
-        directory (str): The directory to search in
-
-    Returns:
-        list[str]: A list of files found in the directory
-    """
-    found_files = []
-
-    if directory in {"", "/"}:
-        search_directory = WORKSPACE_PATH
-    else:
-        search_directory = path_in_workspace(directory)
-
-    for root, _, files in os.walk(search_directory):
-        for file in files:
-            if file.startswith("."):
-                continue
-            relative_path = os.path.relpath(os.path.join(root, file), WORKSPACE_PATH)
-            found_files.append(relative_path)
-
-    return found_files
-
-
-def download_file(url, filename):
-    """Downloads a file
-    Args:
-        url (str): URL of the file to download
-        filename (str): Filename to save the file as
-    """
-    safe_filename = path_in_workspace(filename)
-    try:
-        message = f"{Fore.YELLOW}Downloading file from {Back.LIGHTBLUE_EX}{url}{Back.RESET}{Fore.RESET}"
-        with Spinner(message) as spinner:
-            session = requests.Session()
-            retry = Retry(total=3, backoff_factor=1, status_forcelist=[502, 503, 504])
-            adapter = HTTPAdapter(max_retries=retry)
-            session.mount("http://", adapter)
-            session.mount("https://", adapter)
-
-            total_size = 0
-            downloaded_size = 0
-
-            with session.get(url, allow_redirects=True, stream=True) as r:
-                r.raise_for_status()
-                total_size = int(r.headers.get("Content-Length", 0))
-                downloaded_size = 0
-
-                with open(safe_filename, "wb") as f:
-                    for chunk in r.iter_content(chunk_size=8192):
-                        f.write(chunk)
-                        downloaded_size += len(chunk)
-
-                        # Update the progress message
-                        progress = f"{readable_file_size(downloaded_size)} / {readable_file_size(total_size)}"
-                        spinner.update_message(f"{message} {progress}")
-
-            return f'Successfully downloaded and locally stored file: "{filename}"! (Size: {readable_file_size(total_size)})'
-    except requests.HTTPError as e:
-        return f"Got an HTTP Error whilst trying to download file: {e}"
-    except Exception as e:
-        return "Error: " + str(e)
diff --git a/spaces/fracapuano/AISandbox/summarization/summarization.py b/spaces/fracapuano/AISandbox/summarization/summarization.py
deleted file mode 100644
index c68ef920f30230bb8fe1a7f1c1f8216022da858e..0000000000000000000000000000000000000000
--- a/spaces/fracapuano/AISandbox/summarization/summarization.py
+++ /dev/null
@@ -1,104 +0,0 @@
-import streamlit as st
-from transformers import pipeline
-from qa.qa import parse_file
-from transformers import AutoTokenizer
-from typing import Text, Union
-
-@st.cache_resource
-def summarization_model(
-    model_name:str="facebook/bart-large-cnn", 
-    custom_tokenizer:Union[AutoTokenizer, bool]=False
-    ):
-    summarizer = pipeline(
-        model=model_name, 
-        tokenizer=model_name if custom_tokenizer==False else custom_tokenizer, 
-        task="summarization"
-    )
-    return summarizer
-
-@st.cache_data
-def split_string_into_token_chunks(s:Text, _tokenizer:AutoTokenizer, chunk_size:int):
-    # Tokenize the entire string
-    token_ids = _tokenizer.encode(s)
-    # Split the token ids into chunks of the desired size
-    chunks = [token_ids[i:i+chunk_size] for i in range(0, len(token_ids), chunk_size)]
-    # Decode each chunk back into a string
-    return [_tokenizer.decode(chunk) for chunk in chunks]
-
-def summarization_main():
-    st.markdown("<h2 style='text-align: center'>Text Summarization</h2>", unsafe_allow_html=True)
-    st.markdown("<h3 style='text-align: left'><b>What is text summarization about?<b></h3>", unsafe_allow_html=True)
-    
-    st.write("""
-        Text summarization is common NLP task concerned with producing a shorter version of a given text while preserving the important information
-        contained in such text
-        """)
-    
-    OPTION_1 = "I want to input some text"
-    OPTION_2 = "I want to upload a file"
-    option = st.radio("How would you like to start? Choose an option below", [OPTION_1, OPTION_2]) 
-    
-    # greenlight to summarize
-    text_is_given = False
-    if option == OPTION_1:
-        sample_text = ""
-        text = st.text_area(
-            "Input a text in English (10,000 characters max)", 
-            value=sample_text,
-            max_chars=10_000, 
-            height=330)
-        # toggle text is given greenlight
-        if text != sample_text:
-            text_is_given = not text_is_given
-        
-    elif option == OPTION_2:
-        uploaded_file = st.file_uploader(
-                "Upload a pdf, docx, or txt file (scanned documents not supported)",
-                type=["pdf", "docx", "txt"],
-                help="Scanned documents are not supported yet 🥲"
-            )
-        if uploaded_file is not None:
-            # parse the file using custom parsers and build a concatenation for the summarizer
-            text = " ".join(parse_file(uploaded_file))
-            # toggle text is given greenlight
-            text_is_given = not text_is_given
-    
-    if text_is_given:
-        # minimal number of words in the summary
-        min_length, max_length = 30, 200
-        user_max_length = max_length
-        # user_max_lenght = st.slider(
-        #     label="Maximal number of tokens in the summary", 
-        #     min_value=min_length, 
-        #     max_value=max_length, 
-        #     value=150, 
-        #     step=10, 
-        # )
-
-        summarizer_downloaded = False
-        # loading the tokenizer to split the input document into feasible chunks
-        model_name = "facebook/bart-large-cnn"
-        tokenizer = AutoTokenizer.from_pretrained(model_name)
-        
-        # the maximum number of tokens the model can handle depends on the model - accounting for tokens added by tokenizer
-        chunk_size = int(0.88*tokenizer.model_max_length)
-
-        # loading the summarization model considered
-        with st.spinner(text="Loading summarization model..."):
-            summarizer = summarization_model(model_name=model_name)
-            summarizer_downloaded = True
-        
-        if summarizer_downloaded:
-            button = st.button("Summarize!")
-            if button:
-                with st.spinner(text="Summarizing text..."):
-                    # summarizing each chunk of the input text to avoid exceeding the maximum number of tokens
-                    summary = ""
-                    chunks = split_string_into_token_chunks(text, tokenizer, chunk_size)
-                    for chunk in chunks:
-                        chunk_summary = summarizer(chunk, max_length=user_max_length, min_length=min_length)
-                        summary += "\n" + chunk_summary[0]["summary_text"]
-
-                    st.markdown("<h3 style='text-align: left'><b>Summary<b></h3>", unsafe_allow_html=True)
-                    print(summary)
-                    st.write(summary)
diff --git a/spaces/fsdl2022emotion/meme-manipulation-gradio-space/drawer/__init__.py b/spaces/fsdl2022emotion/meme-manipulation-gradio-space/drawer/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/fuckyoudeki/AutoGPT/autogpt/config/ai_config.py b/spaces/fuckyoudeki/AutoGPT/autogpt/config/ai_config.py
deleted file mode 100644
index d50c30beee9dc8009f63415378ae1c6a399f0037..0000000000000000000000000000000000000000
--- a/spaces/fuckyoudeki/AutoGPT/autogpt/config/ai_config.py
+++ /dev/null
@@ -1,121 +0,0 @@
-# sourcery skip: do-not-use-staticmethod
-"""
-A module that contains the AIConfig class object that contains the configuration
-"""
-from __future__ import annotations
-
-import os
-from typing import Type
-
-import yaml
-
-
-class AIConfig:
-    """
-    A class object that contains the configuration information for the AI
-
-    Attributes:
-        ai_name (str): The name of the AI.
-        ai_role (str): The description of the AI's role.
-        ai_goals (list): The list of objectives the AI is supposed to complete.
-    """
-
-    def __init__(
-        self, ai_name: str = "", ai_role: str = "", ai_goals: list | None = None
-    ) -> None:
-        """
-        Initialize a class instance
-
-        Parameters:
-            ai_name (str): The name of the AI.
-            ai_role (str): The description of the AI's role.
-            ai_goals (list): The list of objectives the AI is supposed to complete.
-        Returns:
-            None
-        """
-        if ai_goals is None:
-            ai_goals = []
-        self.ai_name = ai_name
-        self.ai_role = ai_role
-        self.ai_goals = ai_goals
-
-    # Soon this will go in a folder where it remembers more stuff about the run(s)
-    SAVE_FILE = os.path.join(os.path.dirname(__file__), "..", "ai_settings.yaml")
-
-    @staticmethod
-    def load(config_file: str = SAVE_FILE) -> "AIConfig":
-        """
-        Returns class object with parameters (ai_name, ai_role, ai_goals) loaded from
-          yaml file if yaml file exists,
-        else returns class with no parameters.
-
-        Parameters:
-           config_file (int): The path to the config yaml file.
-             DEFAULT: "../ai_settings.yaml"
-
-        Returns:
-            cls (object): An instance of given cls object
-        """
-
-        try:
-            with open(config_file, encoding="utf-8") as file:
-                config_params = yaml.load(file, Loader=yaml.FullLoader)
-        except FileNotFoundError:
-            config_params = {}
-
-        ai_name = config_params.get("ai_name", "")
-        ai_role = config_params.get("ai_role", "")
-        ai_goals = config_params.get("ai_goals", [])
-        # type: Type[AIConfig]
-        return AIConfig(ai_name, ai_role, ai_goals)
-
-    def save(self, config_file: str = SAVE_FILE) -> None:
-        """
-        Saves the class parameters to the specified file yaml file path as a yaml file.
-
-        Parameters:
-            config_file(str): The path to the config yaml file.
-              DEFAULT: "../ai_settings.yaml"
-
-        Returns:
-            None
-        """
-
-        config = {
-            "ai_name": self.ai_name,
-            "ai_role": self.ai_role,
-            "ai_goals": self.ai_goals,
-        }
-        with open(config_file, "w", encoding="utf-8") as file:
-            yaml.dump(config, file, allow_unicode=True)
-
-    def construct_full_prompt(self) -> str:
-        """
-        Returns a prompt to the user with the class information in an organized fashion.
-
-        Parameters:
-            None
-
-        Returns:
-            full_prompt (str): A string containing the initial prompt for the user
-              including the ai_name, ai_role and ai_goals.
-        """
-
-        prompt_start = (
-            "Your decisions must always be made independently without"
-            " seeking user assistance. Play to your strengths as an LLM and pursue"
-            " simple strategies with no legal complications."
-            ""
-        )
-
-        from autogpt.prompt import get_prompt
-
-        # Construct full prompt
-        full_prompt = (
-            f"You are {self.ai_name}, {self.ai_role}\n{prompt_start}\n\nGOALS:\n\n"
-        )
-        for i, goal in enumerate(self.ai_goals):
-            full_prompt += f"{i+1}. {goal}\n"
-
-        full_prompt += f"\n\n{get_prompt()}"
-        return full_prompt
diff --git a/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmcv/parallel/_functions.py b/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmcv/parallel/_functions.py
deleted file mode 100644
index 9b5a8a44483ab991411d07122b22a1d027e4be8e..0000000000000000000000000000000000000000
--- a/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmcv/parallel/_functions.py
+++ /dev/null
@@ -1,79 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import torch
-from torch.nn.parallel._functions import _get_stream
-
-
-def scatter(input, devices, streams=None):
-    """Scatters tensor across multiple GPUs."""
-    if streams is None:
-        streams = [None] * len(devices)
-
-    if isinstance(input, list):
-        chunk_size = (len(input) - 1) // len(devices) + 1
-        outputs = [
-            scatter(input[i], [devices[i // chunk_size]],
-                    [streams[i // chunk_size]]) for i in range(len(input))
-        ]
-        return outputs
-    elif isinstance(input, torch.Tensor):
-        output = input.contiguous()
-        # TODO: copy to a pinned buffer first (if copying from CPU)
-        stream = streams[0] if output.numel() > 0 else None
-        if devices != [-1]:
-            with torch.cuda.device(devices[0]), torch.cuda.stream(stream):
-                output = output.cuda(devices[0], non_blocking=True)
-        else:
-            # unsqueeze the first dimension thus the tensor's shape is the
-            # same as those scattered with GPU.
-            output = output.unsqueeze(0)
-        return output
-    else:
-        raise Exception(f'Unknown type {type(input)}.')
-
-
-def synchronize_stream(output, devices, streams):
-    if isinstance(output, list):
-        chunk_size = len(output) // len(devices)
-        for i in range(len(devices)):
-            for j in range(chunk_size):
-                synchronize_stream(output[i * chunk_size + j], [devices[i]],
-                                   [streams[i]])
-    elif isinstance(output, torch.Tensor):
-        if output.numel() != 0:
-            with torch.cuda.device(devices[0]):
-                main_stream = torch.cuda.current_stream()
-                main_stream.wait_stream(streams[0])
-                output.record_stream(main_stream)
-    else:
-        raise Exception(f'Unknown type {type(output)}.')
-
-
-def get_input_device(input):
-    if isinstance(input, list):
-        for item in input:
-            input_device = get_input_device(item)
-            if input_device != -1:
-                return input_device
-        return -1
-    elif isinstance(input, torch.Tensor):
-        return input.get_device() if input.is_cuda else -1
-    else:
-        raise Exception(f'Unknown type {type(input)}.')
-
-
-class Scatter:
-
-    @staticmethod
-    def forward(target_gpus, input):
-        input_device = get_input_device(input)
-        streams = None
-        if input_device == -1 and target_gpus != [-1]:
-            # Perform CPU to GPU copies in a background stream
-            streams = [_get_stream(device) for device in target_gpus]
-
-        outputs = scatter(input, target_gpus, streams)
-        # Synchronize with the copy stream
-        if streams is not None:
-            synchronize_stream(outputs, target_gpus, streams)
-
-        return tuple(outputs)
diff --git a/spaces/giesAIexperiments/coursera-assistant-3d-printing-applications/main_old.py b/spaces/giesAIexperiments/coursera-assistant-3d-printing-applications/main_old.py
deleted file mode 100644
index 811b670ea68128a826ec9e4f12074fb062fd19ef..0000000000000000000000000000000000000000
--- a/spaces/giesAIexperiments/coursera-assistant-3d-printing-applications/main_old.py
+++ /dev/null
@@ -1,34 +0,0 @@
-from utils_old import get_custom_agent, get_prompt_and_tools_for_custom_agent
-from utils_old import get_search_index
-
-question_starters = ['who', 'why', 'what', 'how', 'where', 'when', 'which', 'whom', 'whose']
-
-
-def index():
-    get_search_index()
-    return True
-
-def run(question):
-    index = get_search_index()
-
-    # prompt, tools = get_prompt_and_tools()
-
-    # agent_chain = get_agent_chain(prompt, tools)
-
-    prompt, tools = get_prompt_and_tools_for_custom_agent()
-
-    agent_chain = get_custom_agent(prompt, tools)
-
-    result = None
-
-    try:
-        result = agent_chain.run(question)
-    except ValueError as ve:
-        if "Could not parse LLM output:" in ve.args[0] and question.lower().startswith(tuple(question_starters)) and not question.lower().endswith('?'):
-            question = question + '?'
-            result = agent_chain.run(question)
-    finally:
-        print(result)
-
-    return result
-
diff --git a/spaces/golem4300/RVC-TTS/lib/infer_pack/attentions.py b/spaces/golem4300/RVC-TTS/lib/infer_pack/attentions.py
deleted file mode 100644
index da4937f7d6ba428fe3d74d3e228056abe7cb998d..0000000000000000000000000000000000000000
--- a/spaces/golem4300/RVC-TTS/lib/infer_pack/attentions.py
+++ /dev/null
@@ -1,253 +0,0 @@
-import copy
-import math
-import numpy as np
-import torch
-from torch import nn
-from torch.nn import functional as F
-from lib.infer_pack import commons
-from lib.infer_pack import modules
-from lib.infer_pack.modules import LayerNorm
-
-class Encoder(nn.Module):
-    def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0.0, window_size=10, **kwargs):
-        super().__init__()
-        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.window_size = window_size
-        self.drop = nn.Dropout(p_dropout)
-        self.attn_layers = nn.ModuleList()
-        self.norm_layers_1 = nn.ModuleList()
-        self.ffn_layers = nn.ModuleList()
-        self.norm_layers_2 = nn.ModuleList()
-        for _ in range(self.n_layers):
-            self.attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, window_size=window_size))
-            self.norm_layers_1.append(LayerNorm(hidden_channels))
-            self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout))
-            self.norm_layers_2.append(LayerNorm(hidden_channels))
-
-    def forward(self, x, x_mask):
-        attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
-        x = x * x_mask
-        for i in range(self.n_layers):
-            y = self.attn_layers[i](x, x, attn_mask)
-            y = self.drop(y)
-            x = self.norm_layers_1[i](x + y)
-            y = self.ffn_layers[i](x, x_mask)
-            y = self.drop(y)
-            x = self.norm_layers_2[i](x + y)
-        x = x * x_mask
-        return x
-
-class Decoder(nn.Module):
-    def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0.0, proximal_bias=False, proximal_init=True, **kwargs):
-        super().__init__()
-        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.proximal_bias = proximal_bias
-        self.proximal_init = proximal_init
-        self.drop = nn.Dropout(p_dropout)
-        self.self_attn_layers = nn.ModuleList()
-        self.norm_layers_0 = nn.ModuleList()
-        self.encdec_attn_layers = nn.ModuleList()
-        self.norm_layers_1 = nn.ModuleList()
-        self.ffn_layers = nn.ModuleList()
-        self.norm_layers_2 = nn.ModuleList()
-        for _ in range(self.n_layers):
-            self.self_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, proximal_bias=proximal_bias, proximal_init=proximal_init))
-            self.norm_layers_0.append(LayerNorm(hidden_channels))
-            self.encdec_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout))
-            self.norm_layers_1.append(LayerNorm(hidden_channels))
-            self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout, causal=True))
-            self.norm_layers_2.append(LayerNorm(hidden_channels))
-
-    def forward(self, x, x_mask, h, h_mask):
-        self_attn_mask = commons.subsequent_mask(x_mask.size(2)).to(device=x.device, dtype=x.dtype)
-        encdec_attn_mask = h_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
-        x = x * x_mask
-        for i in range(self.n_layers):
-            y = self.self_attn_layers[i](x, x, self_attn_mask)
-            y = self.drop(y)
-            x = self.norm_layers_0[i](x + y)
-
-            y = self.encdec_attn_layers[i](x, h, encdec_attn_mask)
-            y = self.drop(y)
-            x = self.norm_layers_1[i](x + y)
-
-            y = self.ffn_layers[i](x, x_mask)
-            y = self.drop(y)
-            x = self.norm_layers_2[i](x + y)
-        x = x * x_mask
-        return x
-
-
-class MultiHeadAttention(nn.Module):
-    def __init__(self, channels, out_channels, n_heads, p_dropout=0.0, window_size=None, heads_share=True, block_length=None, proximal_bias=False, proximal_init=False):
-        super().__init__()
-        assert channels % n_heads == 0
-
-        self.channels = channels
-        self.out_channels = out_channels
-        self.n_heads = n_heads
-        self.p_dropout = p_dropout
-        self.window_size = window_size
-        self.heads_share = heads_share
-        self.block_length = block_length
-        self.proximal_bias = proximal_bias
-        self.proximal_init = proximal_init
-        self.attn = None
-
-        self.k_channels = channels // n_heads
-        self.conv_q = nn.Conv1d(channels, channels, 1)
-        self.conv_k = nn.Conv1d(channels, channels, 1)
-        self.conv_v = nn.Conv1d(channels, channels, 1)
-        self.conv_o = nn.Conv1d(channels, out_channels, 1)
-        self.drop = nn.Dropout(p_dropout)
-
-        if window_size is not None:
-            n_heads_rel = 1 if heads_share else n_heads
-            rel_stddev = self.k_channels**-0.5
-            self.emb_rel_k = nn.Parameter(
-                torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels)
-                * rel_stddev
-            )
-            self.emb_rel_v = nn.Parameter(
-                torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels)
-                * rel_stddev
-            )
-
-        nn.init.xavier_uniform_(self.conv_q.weight)
-        nn.init.xavier_uniform_(self.conv_k.weight)
-        nn.init.xavier_uniform_(self.conv_v.weight)
-        if proximal_init:
-            with torch.no_grad():
-                self.conv_k.weight.copy_(self.conv_q.weight)
-                self.conv_k.bias.copy_(self.conv_q.bias)
-
-    def forward(self, x, c, attn_mask=None):
-        q = self.conv_q(x)
-        k = self.conv_k(c)
-        v = self.conv_v(c)
-
-        x, self.attn = self.attention(q, k, v, mask=attn_mask)
-
-        x = self.conv_o(x)
-        return x
-
-    def attention(self, query, key, value, mask=None):
-        b, d, t_s, t_t = (*key.size(), query.size(2))
-        query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3)
-        key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
-        value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
-
-        scores = torch.matmul(query / math.sqrt(self.k_channels), key.transpose(-2, -1))
-        if self.window_size is not None:
-            assert (
-                t_s == t_t
-            ), "Relative attention is only available for self-attention."
-            key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s)
-            rel_logits = self._matmul_with_relative_keys(query / math.sqrt(self.k_channels), key_relative_embeddings)
-            scores_local = self._relative_position_to_absolute_position(rel_logits)
-            scores = scores + scores_local
-        if self.proximal_bias:
-            assert t_s == t_t, "Proximal bias is only available for self-attention."
-            scores = scores + self._attention_bias_proximal(t_s).to(device=scores.device, dtype=scores.dtype)
-        if mask is not None:
-            scores = scores.masked_fill(mask == 0, -1e4)
-            if self.block_length is not None:
-                assert (t_s == t_t), "Local attention is only available for self-attention."
-                block_mask = (
-                    torch.ones_like(scores)
-                    .triu(-self.block_length)
-                    .tril(self.block_length)
-                )
-                scores = scores.masked_fill(block_mask == 0, -1e4)
-        p_attn = F.softmax(scores, dim=-1)
-        p_attn = self.drop(p_attn)
-        output = torch.matmul(p_attn, value)
-        if self.window_size is not None:
-            relative_weights = self._absolute_position_to_relative_position(p_attn)
-            value_relative_embeddings = self._get_relative_embeddings(self.emb_rel_v, t_s)
-            output = output + self._matmul_with_relative_values(relative_weights, value_relative_embeddings)
-        output = (output.transpose(2, 3).contiguous().view(b, d, t_t))
-        return output, p_attn
-
-    def _matmul_with_relative_values(self, x, y): return torch.matmul(x, y.unsqueeze(0))
-    def _matmul_with_relative_keys(self, x, y): return torch.matmul(x, y.unsqueeze(0).transpose(-2, -1))
-
-    def _get_relative_embeddings(self, relative_embeddings, length):
-        max_relative_position = 2 * self.window_size + 1
-        pad_length = max(length - (self.window_size + 1), 0)
-        slice_start_position = max((self.window_size + 1) - length, 0)
-        slice_end_position = slice_start_position + 2 * length - 1
-        if pad_length > 0:padded_relative_embeddings = F.pad(relative_embeddings, commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]))
-        else:
-            padded_relative_embeddings = relative_embeddings
-        return padded_relative_embeddings[:, slice_start_position:slice_end_position]
-
-    def _relative_position_to_absolute_position(self, x):
-        batch, heads, length, _ = x.size()
-        x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, 1]]))
-        x_flat = x.view([batch, heads, length * 2 * length])
-        x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [0, length - 1]]))
-
-        return x_flat.view([batch, heads, length + 1, 2 * length - 1])[:, :, :length, length - 1 :]
-
-    def _absolute_position_to_relative_position(self, x):
-        batch, heads, length, _ = x.size()
-        x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length - 1]]))
-        x_flat = x.view([batch, heads, length**2 + length * (length - 1)])
-        x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [length, 0]]))
-        return x_flat.view([batch, heads, length, 2 * length])[:, :, :, 1:]
-
-    def _attention_bias_proximal(self, length):
-        r = torch.arange(length, dtype=torch.float32)
-        diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1)
-        return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0)
-
-class FFN(nn.Module):
-    def __init__(self, in_channels, out_channels, filter_channels, kernel_size, p_dropout=0.0, activation=None, causal=False):
-        super().__init__()
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.filter_channels = filter_channels
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.activation = activation
-        self.causal = causal
-        self.padding = self._causal_padding if causal else self._same_padding
-        self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size)
-        self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size)
-        self.drop = nn.Dropout(p_dropout)
-
-    def forward(self, x, x_mask):
-        x = self.conv_1(self.padding(x * x_mask))
-        if self.activation == "gelu": x = x * torch.sigmoid(1.702 * x)
-        else: x = torch.relu(x)
-        x = self.drop(x)
-        x = self.conv_2(self.padding(x * x_mask))
-        return x * x_mask
-
-    def _causal_padding(self, x):
-        if self.kernel_size == 1: return x
-        pad_l = self.kernel_size - 1
-        pad_r = 0
-        return self._extracted_from__same_padding_5(pad_l, pad_r, x)
-
-    def _same_padding(self, x):
-        if self.kernel_size == 1: return x
-        pad_l = (self.kernel_size - 1) // 2
-        pad_r = self.kernel_size // 2
-        return self._extracted_from__same_padding_5(pad_l, pad_r, x)
-
-    def _extracted_from__same_padding_5(self, pad_l, pad_r, x):
-        padding = [[0, 0], [0, 0], [pad_l, pad_r]]
-        x = F.pad(x, commons.convert_pad_shape(padding))
-        return x
\ No newline at end of file
diff --git a/spaces/gotiQspiryo/whisper-ui/examples/Download Badri Full Movie Hd 1080p and Enjoy the Thrilling Romance of Pawan Kalyan and Amisha Patel.md b/spaces/gotiQspiryo/whisper-ui/examples/Download Badri Full Movie Hd 1080p and Enjoy the Thrilling Romance of Pawan Kalyan and Amisha Patel.md
deleted file mode 100644
index 6aaec1e8ad1623191aaf66a6420974e76965afd5..0000000000000000000000000000000000000000
--- a/spaces/gotiQspiryo/whisper-ui/examples/Download Badri Full Movie Hd 1080p and Enjoy the Thrilling Romance of Pawan Kalyan and Amisha Patel.md	
+++ /dev/null
@@ -1,6 +0,0 @@
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diff --git a/spaces/gotiQspiryo/whisper-ui/examples/JaaneKahanSeAayiHaihindimovie720pfreedownload [CRACKED].md b/spaces/gotiQspiryo/whisper-ui/examples/JaaneKahanSeAayiHaihindimovie720pfreedownload [CRACKED].md
deleted file mode 100644
index 509d5614c02afedb5dc06feee0f439917b58cc25..0000000000000000000000000000000000000000
--- a/spaces/gotiQspiryo/whisper-ui/examples/JaaneKahanSeAayiHaihindimovie720pfreedownload [CRACKED].md	
+++ /dev/null
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diff --git a/spaces/gradio/HuBERT/examples/bart/summarize.py b/spaces/gradio/HuBERT/examples/bart/summarize.py
deleted file mode 100644
index 04435f80e39c2d9d894696dae7cba5b381e13da9..0000000000000000000000000000000000000000
--- a/spaces/gradio/HuBERT/examples/bart/summarize.py
+++ /dev/null
@@ -1,100 +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
-from fairseq.models.bart import BARTModel
-import argparse
-
-XSUM_KWARGS = dict(beam=6, lenpen=1.0, max_len_b=60, min_len=10, no_repeat_ngram_size=3)
-CNN_KWARGS = dict(beam=4, lenpen=2.0, max_len_b=140, min_len=55, no_repeat_ngram_size=3)
-
-
-@torch.no_grad()
-def generate(bart, infile, outfile="bart_hypo.txt", bsz=32, n_obs=None, **eval_kwargs):
-    count = 1
-
-    # if n_obs is not None: bsz = min(bsz, n_obs)
-
-    with open(infile) as source, open(outfile, "w") as fout:
-        sline = source.readline().strip()
-        slines = [sline]
-        for sline in source:
-            if n_obs is not None and count > n_obs:
-                break
-            if count % bsz == 0:
-                hypotheses_batch = bart.sample(slines, **eval_kwargs)
-                for hypothesis in hypotheses_batch:
-                    fout.write(hypothesis + "\n")
-                    fout.flush()
-                slines = []
-
-            slines.append(sline.strip())
-            count += 1
-
-        if slines != []:
-            hypotheses_batch = bart.sample(slines, **eval_kwargs)
-            for hypothesis in hypotheses_batch:
-                fout.write(hypothesis + "\n")
-                fout.flush()
-
-
-def main():
-    """
-    Usage::
-
-         python examples/bart/summarize.py \
-            --model-dir $HOME/bart.large.cnn \
-            --model-file model.pt \
-            --src $HOME/data-bin/cnn_dm/test.source
-    """
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "--model-dir",
-        required=True,
-        type=str,
-        default="bart.large.cnn/",
-        help="path containing model file and src_dict.txt",
-    )
-    parser.add_argument(
-        "--model-file",
-        default="checkpoint_best.pt",
-        help="where in model_dir are weights saved",
-    )
-    parser.add_argument(
-        "--src", default="test.source", help="text to summarize", type=str
-    )
-    parser.add_argument(
-        "--out", default="test.hypo", help="where to save summaries", type=str
-    )
-    parser.add_argument("--bsz", default=32, help="where to save summaries", type=int)
-    parser.add_argument(
-        "--n", default=None, help="how many examples to summarize", type=int
-    )
-    parser.add_argument(
-        "--xsum-kwargs",
-        action="store_true",
-        default=False,
-        help="if true use XSUM_KWARGS else CNN_KWARGS",
-    )
-    args = parser.parse_args()
-    eval_kwargs = XSUM_KWARGS if args.xsum_kwargs else CNN_KWARGS
-    if args.model_dir == "pytorch/fairseq":
-        bart = torch.hub.load("pytorch/fairseq", args.model_file)
-    else:
-        bart = BARTModel.from_pretrained(
-            args.model_dir,
-            checkpoint_file=args.model_file,
-            data_name_or_path=args.model_dir,
-        )
-    bart = bart.eval()
-    if torch.cuda.is_available():
-        bart = bart.cuda().half()
-    generate(
-        bart, args.src, bsz=args.bsz, n_obs=args.n, outfile=args.out, **eval_kwargs
-    )
-
-
-if __name__ == "__main__":
-    main()
diff --git a/spaces/gradio/HuBERT/examples/joint_alignment_translation/prepare-wmt18en2de_no_norm_no_escape_no_agressive.sh b/spaces/gradio/HuBERT/examples/joint_alignment_translation/prepare-wmt18en2de_no_norm_no_escape_no_agressive.sh
deleted file mode 100644
index e3efeb21d302ef8d9eae8f1d4b06434c593705f6..0000000000000000000000000000000000000000
--- a/spaces/gradio/HuBERT/examples/joint_alignment_translation/prepare-wmt18en2de_no_norm_no_escape_no_agressive.sh
+++ /dev/null
@@ -1,118 +0,0 @@
-#!/bin/bash
-
-# 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.
-
-echo 'Cloning Moses github repository (for tokenization scripts)...'
-git clone https://github.com/moses-smt/mosesdecoder.git
-
-SCRIPTS=mosesdecoder/scripts
-TOKENIZER=$SCRIPTS/tokenizer/tokenizer.perl
-CLEAN=$SCRIPTS/training/clean-corpus-n.perl
-REM_NON_PRINT_CHAR=$SCRIPTS/tokenizer/remove-non-printing-char.perl
-
-URLS=(
-    "http://statmt.org/wmt13/training-parallel-europarl-v7.tgz"
-    "http://statmt.org/wmt13/training-parallel-commoncrawl.tgz"
-    "http://data.statmt.org/wmt18/translation-task/training-parallel-nc-v13.tgz"
-    "http://data.statmt.org/wmt18/translation-task/rapid2016.tgz"
-    "http://data.statmt.org/wmt17/translation-task/dev.tgz"
-    "http://statmt.org/wmt14/test-full.tgz"
-)
-CORPORA=(
-    "training/europarl-v7.de-en"
-    "commoncrawl.de-en"
-    "training-parallel-nc-v13/news-commentary-v13.de-en"
-    "rapid2016.de-en"
-)
-
-if [ ! -d "$SCRIPTS" ]; then
-    echo "Please set SCRIPTS variable correctly to point to Moses scripts."
-    exit
-fi
-
-src=en
-tgt=de
-lang=en-de
-prep=wmt18_en_de
-tmp=$prep/tmp
-orig=orig
-dev=dev/newstest2012
-codes=32000
-bpe=bpe.32k
-
-mkdir -p $orig $tmp $prep $bpe
-
-cd $orig
-
-for ((i=0;i<${#URLS[@]};++i)); do
-    url=${URLS[i]}
-    file=$(basename $url)
-    if [ -f $file ]; then
-        echo "$file already exists, skipping download"
-    else
-        wget "$url"
-        if [ -f $file ]; then
-            echo "$url successfully downloaded."
-        else
-            echo "$url not successfully downloaded."
-            exit 1
-        fi
-        if [ ${file: -4} == ".tgz" ]; then
-            tar zxvf $file
-        elif [ ${file: -4} == ".tar" ]; then
-            tar xvf $file
-        fi
-    fi
-done
-cd ..
-
-echo "pre-processing train data..."
-for l in $src $tgt; do
-    rm  -rf $tmp/train.tags.$lang.tok.$l
-    for f in "${CORPORA[@]}"; do
-        cat $orig/$f.$l | \
-            perl $REM_NON_PRINT_CHAR | \
-            perl $TOKENIZER -threads 8 -l $l -no-escape >> $tmp/train.tags.$lang.tok.$l
-    done
-done
-
-echo "pre-processing test data..."
-for l in $src $tgt; do
-    if [ "$l" == "$src" ]; then
-        t="src"
-    else
-        t="ref"
-    fi
-    grep '<seg id' $orig/test-full/newstest2014-deen-$t.$l.sgm | \
-        sed -e 's/<seg id="[0-9]*">\s*//g' | \
-        sed -e 's/\s*<\/seg>\s*//g' | \
-        sed -e "s/\’/\'/g" | \
-    perl $TOKENIZER -threads 8 -l $l -no-escape > $tmp/test.$l
-    echo ""
-done
-
-# apply length filtering before BPE
-perl $CLEAN -ratio 1.5 $tmp/train.tags.$lang.tok $src $tgt $tmp/train 1 100
-
-# use newstest2012 for valid
-echo "pre-processing valid data..."
-for l in $src $tgt; do
-    rm  -rf $tmp/valid.$l
-    cat $orig/$dev.$l | \
-        perl $REM_NON_PRINT_CHAR | \
-        perl $TOKENIZER -threads 8 -l $l -no-escape >> $tmp/valid.$l
-done
-
-mkdir output
-mv $tmp/{train,valid,test}.{$src,$tgt} output
-
-#BPE
-git clone https://github.com/glample/fastBPE.git
-pushd fastBPE
-g++ -std=c++11 -pthread -O3 fastBPE/main.cc -IfastBPE -o fast
-popd
-fastBPE/fast learnbpe $codes output/train.$src output/train.$tgt > $bpe/codes
-for split in {train,valid,test}; do for lang in {en,de}; do fastBPE/fast applybpe $bpe/$split.$lang output/$split.$lang $bpe/codes; done; done
diff --git a/spaces/gradio/HuBERT/examples/truncated_bptt/transformer_xl_model.py b/spaces/gradio/HuBERT/examples/truncated_bptt/transformer_xl_model.py
deleted file mode 100644
index a6c8b25a07276c2ee30c0aa5f0e4b0a2837ed5ca..0000000000000000000000000000000000000000
--- a/spaces/gradio/HuBERT/examples/truncated_bptt/transformer_xl_model.py
+++ /dev/null
@@ -1,155 +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
-from dataclasses import dataclass, field
-from typing import Dict, List, Optional
-
-import torch
-from fairseq.dataclass import FairseqDataclass
-from fairseq.models import (
-    FairseqIncrementalDecoder,
-    FairseqLanguageModel,
-    register_model,
-)
-from fairseq.modules.checkpoint_activations import checkpoint_wrapper
-from omegaconf import II
-
-
-logger = logging.getLogger(__name__)
-
-
-@dataclass
-class TransformerXLConfig(FairseqDataclass):
-    # defaults come from the original Transformer-XL code
-    cutoffs: List[int] = field(default_factory=lambda: [20000, 40000, 200000])
-    d_model: int = 500
-    n_head: int = 10
-    d_head: int = 50
-    d_inner: int = 1000
-    div_val: int = 1
-    n_layer: int = 12
-    mem_len: int = 0
-    clamp_len: int = -1
-    same_length: bool = False
-    dropout: float = 0.0
-    dropatt: float = 0.0
-    checkpoint_activations: bool = False
-    offload_activations: bool = False
-    max_target_positions: int = II("task.max_target_positions")
-
-
-@register_model("transformer_xl", dataclass=TransformerXLConfig)
-class TransformerXLLanguageModel(FairseqLanguageModel):
-    @classmethod
-    def build_model(cls, cfg: TransformerXLConfig, task):
-        return cls(TransformerXLDecoder(cfg, task))
-
-
-class TransformerXLDecoder(FairseqIncrementalDecoder):
-    def __init__(self, cfg, task):
-        try:
-            from transformers.models.transfo_xl import (
-                TransfoXLConfig,
-                TransfoXLLMHeadModel,
-            )
-        except ImportError:
-            from transformers.configuration_transfo_xl import TransfoXLConfig
-            from transformers.modeling_transfo_xl import TransfoXLLMHeadModel
-
-        super().__init__(task.target_dictionary)
-        self.cfg = cfg
-
-        # remove any cutoffs larger than the vocab size
-        cutoffs = [
-            cutoff for cutoff in cfg.cutoffs if cutoff < len(task.target_dictionary)
-        ]
-
-        config = TransfoXLConfig(
-            vocab_size=len(task.target_dictionary),
-            cutoffs=cutoffs,
-            d_model=cfg.d_model,
-            d_embed=cfg.d_model,
-            n_head=cfg.n_head,
-            d_head=cfg.d_head,
-            d_inner=cfg.d_inner,
-            div_val=cfg.div_val,
-            n_layer=cfg.n_layer,
-            mem_len=cfg.mem_len,
-            clamp_len=cfg.clamp_len,
-            same_length=cfg.same_length,
-            dropout=cfg.dropout,
-            dropatt=cfg.dropatt,
-        )
-        logger.info(config)
-        self.model = TransfoXLLMHeadModel(config)
-
-        # Workaround a bug in huggingface's ``ProjectedAdaptiveLogSoftmax``
-        # which adds ``None`` values to an ``nn.ParameterList``, which is not
-        # supported in PyTorch. Instead we can replace this with an
-        # ``nn.ModuleList``, which does support ``None`` values.
-        try:
-            if all(p is None for p in self.model.crit.out_projs._parameters.values()):
-                self.model.crit.out_projs = torch.nn.ModuleList(
-                    [None] * len(self.model.crit.out_projs._parameters)
-                )
-        except Exception:
-            pass
-
-        if cfg.checkpoint_activations or cfg.offload_activations:
-            for i in range(len(self.model.transformer.layers)):
-                self.model.transformer.layers[i] = checkpoint_wrapper(
-                    self.model.transformer.layers[i],
-                    offload_to_cpu=cfg.offload_activations,
-                )
-                # TODO: may save mem to wrap(layer.pos_ff.CoreNet[3])
-
-        self._mems = None
-
-    def forward(
-        self,
-        src_tokens,
-        src_lengths=None,  # unused
-        incremental_state: Optional[Dict[str, List[torch.Tensor]]] = None,
-        encoder_out=None,
-    ):
-        if incremental_state is not None:  # used during inference
-            mems = self.get_incremental_state(incremental_state, "mems")
-            src_tokens = src_tokens[:, -1:]  # only keep the most recent token
-        else:
-            mems = self._mems
-
-        output = self.model(
-            input_ids=src_tokens,
-            mems=mems,
-            return_dict=False,
-        )
-
-        if len(output) >= 2:
-            if incremental_state is not None:
-                self.set_incremental_state(incremental_state, "mems", output[1])
-            else:
-                self._mems = output[1]
-
-        return (output[0],)
-
-    def max_positions(self):
-        return self.cfg.max_target_positions
-
-    def reorder_incremental_state(
-        self,
-        incremental_state: Dict[str, Dict[str, Optional[torch.Tensor]]],
-        new_order: torch.Tensor,
-    ):
-        """Reorder incremental state.
-
-        This will be called when the order of the input has changed from the
-        previous time step. A typical use case is beam search, where the input
-        order changes between time steps based on the selection of beams.
-        """
-        mems = self.get_incremental_state(incremental_state, "mems")
-        if mems is not None:
-            new_mems = [mems_i.index_select(1, new_order) for mems_i in mems]
-            self.set_incremental_state(incremental_state, "mems", new_mems)
diff --git a/spaces/gradio/HuBERT/fairseq/data/base_wrapper_dataset.py b/spaces/gradio/HuBERT/fairseq/data/base_wrapper_dataset.py
deleted file mode 100644
index 134d398b47dc73c8807759188504aee205b3b34d..0000000000000000000000000000000000000000
--- a/spaces/gradio/HuBERT/fairseq/data/base_wrapper_dataset.py
+++ /dev/null
@@ -1,78 +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 torch.utils.data.dataloader import default_collate
-
-from . import FairseqDataset
-
-
-class BaseWrapperDataset(FairseqDataset):
-    def __init__(self, dataset):
-        super().__init__()
-        self.dataset = dataset
-
-    def __getitem__(self, index):
-        return self.dataset[index]
-
-    def __len__(self):
-        return len(self.dataset)
-
-    def collater(self, samples):
-        if hasattr(self.dataset, "collater"):
-            return self.dataset.collater(samples)
-        else:
-            return default_collate(samples)
-
-    @property
-    def sizes(self):
-        return self.dataset.sizes
-
-    def num_tokens(self, index):
-        return self.dataset.num_tokens(index)
-
-    def size(self, index):
-        return self.dataset.size(index)
-
-    def ordered_indices(self):
-        return self.dataset.ordered_indices()
-
-    @property
-    def supports_prefetch(self):
-        return getattr(self.dataset, "supports_prefetch", False)
-
-    def attr(self, attr: str, index: int):
-        return self.dataset.attr(attr, index)
-
-    def prefetch(self, indices):
-        self.dataset.prefetch(indices)
-
-    def get_batch_shapes(self):
-        return self.dataset.get_batch_shapes()
-
-    def batch_by_size(
-        self,
-        indices,
-        max_tokens=None,
-        max_sentences=None,
-        required_batch_size_multiple=1,
-    ):
-        return self.dataset.batch_by_size(
-            indices,
-            max_tokens=max_tokens,
-            max_sentences=max_sentences,
-            required_batch_size_multiple=required_batch_size_multiple,
-        )
-
-    def filter_indices_by_size(self, indices, max_sizes):
-        return self.dataset.filter_indices_by_size(indices, max_sizes)
-
-    @property
-    def can_reuse_epoch_itr_across_epochs(self):
-        return self.dataset.can_reuse_epoch_itr_across_epochs
-
-    def set_epoch(self, epoch):
-        super().set_epoch(epoch)
-        if hasattr(self.dataset, "set_epoch"):
-            self.dataset.set_epoch(epoch)
diff --git a/spaces/gradio/HuBERT/fairseq/model_parallel/models/__init__.py b/spaces/gradio/HuBERT/fairseq/model_parallel/models/__init__.py
deleted file mode 100644
index 3532479e52a0e1f1ba204c6f5d51c71c98ee5df0..0000000000000000000000000000000000000000
--- a/spaces/gradio/HuBERT/fairseq/model_parallel/models/__init__.py
+++ /dev/null
@@ -1,20 +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 importlib
-import os
-
-
-# automatically import any Python files in the models/ directory
-models_dir = os.path.dirname(__file__)
-for file in os.listdir(models_dir):
-    path = os.path.join(models_dir, file)
-    if (
-        not file.startswith("_")
-        and not file.startswith(".")
-        and (file.endswith(".py") or os.path.isdir(path))
-    ):
-        model_name = file[: file.find(".py")] if file.endswith(".py") else file
-        module = importlib.import_module("fairseq.model_parallel.models." + model_name)
diff --git a/spaces/gradio/HuBERT/fairseq/modules/quantization/__init__.py b/spaces/gradio/HuBERT/fairseq/modules/quantization/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/gradio/same-person-or-different/run.py b/spaces/gradio/same-person-or-different/run.py
deleted file mode 100644
index 5313b2304a39eb35dc9db2f7033e5b468a6ff772..0000000000000000000000000000000000000000
--- a/spaces/gradio/same-person-or-different/run.py
+++ /dev/null
@@ -1,105 +0,0 @@
-import gradio as gr
-import torch
-from torchaudio.sox_effects import apply_effects_file
-from transformers import AutoFeatureExtractor, AutoModelForAudioXVector
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-OUTPUT_OK = (
-    """
-    <div class="container">
-        <div class="row"><h1 style="text-align: center">The speakers are</h1></div>
-        <div class="row"><h1 class="display-1 text-success" style="text-align: center">{:.1f}%</h1></div>
-        <div class="row"><h1 style="text-align: center">similar</h1></div>
-        <div class="row"><h1 class="text-success" style="text-align: center">Welcome, human!</h1></div>
-        <div class="row"><small style="text-align: center">(You must get at least 85% to be considered the same person)</small><div class="row">
-    </div>
-"""
-)
-OUTPUT_FAIL = (
-    """
-    <div class="container">
-        <div class="row"><h1 style="text-align: center">The speakers are</h1></div>
-        <div class="row"><h1 class="display-1 text-danger" style="text-align: center">{:.1f}%</h1></div>
-        <div class="row"><h1 style="text-align: center">similar</h1></div>
-        <div class="row"><h1 class="text-danger" style="text-align: center">You shall not pass!</h1></div>
-        <div class="row"><small style="text-align: center">(You must get at least 85% to be considered the same person)</small><div class="row">
-    </div>
-"""
-)
-
-EFFECTS = [
-    ["remix", "-"],
-    ["channels", "1"],
-    ["rate", "16000"],
-    ["gain", "-1.0"],
-    ["silence", "1", "0.1", "0.1%", "-1", "0.1", "0.1%"],
-    ["trim", "0", "10"],
-]
-
-THRESHOLD = 0.85
-
-model_name = "microsoft/unispeech-sat-base-plus-sv"
-feature_extractor = AutoFeatureExtractor.from_pretrained(model_name)
-model = AutoModelForAudioXVector.from_pretrained(model_name).to(device)
-cosine_sim = torch.nn.CosineSimilarity(dim=-1)
-
-
-def similarity_fn(path1, path2):
-    if not (path1 and path2):
-        return '<b style="color:red">ERROR: Please record audio for *both* speakers!</b>'
-
-    wav1, _ = apply_effects_file(path1, EFFECTS)
-    wav2, _ = apply_effects_file(path2, EFFECTS)
-    print(wav1.shape, wav2.shape)
-
-    input1 = feature_extractor(wav1.squeeze(0), return_tensors="pt", sampling_rate=16000).input_values.to(device)
-    input2 = feature_extractor(wav2.squeeze(0), return_tensors="pt", sampling_rate=16000).input_values.to(device)
-
-    with torch.no_grad():
-        emb1 = model(input1).embeddings
-        emb2 = model(input2).embeddings
-    emb1 = torch.nn.functional.normalize(emb1, dim=-1).cpu()
-    emb2 = torch.nn.functional.normalize(emb2, dim=-1).cpu()
-    similarity = cosine_sim(emb1, emb2).numpy()[0]
-
-    if similarity >= THRESHOLD:
-        output = OUTPUT_OK.format(similarity * 100)
-    else:
-        output = OUTPUT_FAIL.format(similarity * 100)
-
-    return output
-
-inputs = [
-    gr.Audio(sources=["microphone"], type="filepath", label="Speaker #1"),
-    gr.Audio(sources=["microphone"], type="filepath", label="Speaker #2"),
-]
-output = gr.HTML(label="")
-
-
-description = (
-    "This demo from Microsoft will compare two speech samples and determine if they are from the same speaker. "
-    "Try it with your own voice!"
-)
-article = (
-    "<p style='text-align: center'>"
-    "<a href='https://huggingface.co/microsoft/unispeech-sat-large-sv' target='_blank'>🎙️ Learn more about UniSpeech-SAT</a> | "
-    "<a href='https://arxiv.org/abs/2110.05752' target='_blank'>📚 UniSpeech-SAT paper</a> | "
-    "<a href='https://www.danielpovey.com/files/2018_icassp_xvectors.pdf' target='_blank'>📚 X-Vector paper</a>"
-    "</p>"
-)
-examples = [
-    ["samples/cate_blanch.mp3", "samples/cate_blanch_2.mp3"],
-    ["samples/cate_blanch.mp3", "samples/heath_ledger.mp3"],
-]
-
-interface = gr.Interface(
-    fn=similarity_fn,
-    inputs=inputs,
-    outputs=output,
-    layout="horizontal",
-    allow_flagging=False,
-    live=False,
-    examples=examples,
-    cache_examples=False
-)
-interface.launch()
diff --git a/spaces/gwang-kim/DATID-3D/eg3d/training/__init__.py b/spaces/gwang-kim/DATID-3D/eg3d/training/__init__.py
deleted file mode 100644
index dfebd04f47e6f6b1b44984c14c23b57d56f72240..0000000000000000000000000000000000000000
--- a/spaces/gwang-kim/DATID-3D/eg3d/training/__init__.py
+++ /dev/null
@@ -1,11 +0,0 @@
-# SPDX-FileCopyrightText: Copyright (c) 2021-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
-# SPDX-License-Identifier: LicenseRef-NvidiaProprietary
-#
-# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual
-# property and proprietary rights in and to this material, related
-# documentation and any modifications thereto. Any use, reproduction,
-# disclosure or distribution of this material and related documentation
-# without an express license agreement from NVIDIA CORPORATION or
-# its affiliates is strictly prohibited.
-
-# empty
diff --git a/spaces/gwang-kim/DATID-3D/pose_estimation/DataProcess/resnet.py b/spaces/gwang-kim/DATID-3D/pose_estimation/DataProcess/resnet.py
deleted file mode 100644
index aa2bf95130e9815ba378cb6f73207068b81a04b9..0000000000000000000000000000000000000000
--- a/spaces/gwang-kim/DATID-3D/pose_estimation/DataProcess/resnet.py
+++ /dev/null
@@ -1,109 +0,0 @@
-#!/usr/bin/python
-# -*- encoding: utf-8 -*-
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torch.utils.model_zoo as modelzoo
-
-# from modules.bn import InPlaceABNSync as BatchNorm2d
-
-resnet18_url = 'https://download.pytorch.org/models/resnet18-5c106cde.pth'
-
-
-def conv3x3(in_planes, out_planes, stride=1):
-    """3x3 convolution with padding"""
-    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
-                     padding=1, bias=False)
-
-
-class BasicBlock(nn.Module):
-    def __init__(self, in_chan, out_chan, stride=1):
-        super(BasicBlock, self).__init__()
-        self.conv1 = conv3x3(in_chan, out_chan, stride)
-        self.bn1 = nn.BatchNorm2d(out_chan)
-        self.conv2 = conv3x3(out_chan, out_chan)
-        self.bn2 = nn.BatchNorm2d(out_chan)
-        self.relu = nn.ReLU(inplace=True)
-        self.downsample = None
-        if in_chan != out_chan or stride != 1:
-            self.downsample = nn.Sequential(
-                nn.Conv2d(in_chan, out_chan,
-                          kernel_size=1, stride=stride, bias=False),
-                nn.BatchNorm2d(out_chan),
-                )
-
-    def forward(self, x):
-        residual = self.conv1(x)
-        residual = F.relu(self.bn1(residual))
-        residual = self.conv2(residual)
-        residual = self.bn2(residual)
-
-        shortcut = x
-        if self.downsample is not None:
-            shortcut = self.downsample(x)
-
-        out = shortcut + residual
-        out = self.relu(out)
-        return out
-
-
-def create_layer_basic(in_chan, out_chan, bnum, stride=1):
-    layers = [BasicBlock(in_chan, out_chan, stride=stride)]
-    for i in range(bnum-1):
-        layers.append(BasicBlock(out_chan, out_chan, stride=1))
-    return nn.Sequential(*layers)
-
-
-class Resnet18(nn.Module):
-    def __init__(self):
-        super(Resnet18, self).__init__()
-        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
-                               bias=False)
-        self.bn1 = nn.BatchNorm2d(64)
-        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
-        self.layer1 = create_layer_basic(64, 64, bnum=2, stride=1)
-        self.layer2 = create_layer_basic(64, 128, bnum=2, stride=2)
-        self.layer3 = create_layer_basic(128, 256, bnum=2, stride=2)
-        self.layer4 = create_layer_basic(256, 512, bnum=2, stride=2)
-        self.init_weight()
-
-    def forward(self, x):
-        x = self.conv1(x)
-        x = F.relu(self.bn1(x))
-        x = self.maxpool(x)
-
-        x = self.layer1(x)
-        feat8 = self.layer2(x) # 1/8
-        feat16 = self.layer3(feat8) # 1/16
-        feat32 = self.layer4(feat16) # 1/32
-        return feat8, feat16, feat32
-
-    def init_weight(self):
-        state_dict = modelzoo.load_url(resnet18_url)
-        self_state_dict = self.state_dict()
-        for k, v in state_dict.items():
-            if 'fc' in k: continue
-            self_state_dict.update({k: v})
-        self.load_state_dict(self_state_dict)
-
-    def get_params(self):
-        wd_params, nowd_params = [], []
-        for name, module in self.named_modules():
-            if isinstance(module, (nn.Linear, nn.Conv2d)):
-                wd_params.append(module.weight)
-                if not module.bias is None:
-                    nowd_params.append(module.bias)
-            elif isinstance(module,  nn.BatchNorm2d):
-                nowd_params += list(module.parameters())
-        return wd_params, nowd_params
-
-
-if __name__ == "__main__":
-    net = Resnet18()
-    x = torch.randn(16, 3, 224, 224)
-    out = net(x)
-    print(out[0].size())
-    print(out[1].size())
-    print(out[2].size())
-    net.get_params()
diff --git a/spaces/gwang-kim/DATID-3D/pose_estimation/options/base_options.py b/spaces/gwang-kim/DATID-3D/pose_estimation/options/base_options.py
deleted file mode 100644
index 67375d0801197b864c613a7fb0e44e93d3fd991f..0000000000000000000000000000000000000000
--- a/spaces/gwang-kim/DATID-3D/pose_estimation/options/base_options.py
+++ /dev/null
@@ -1,169 +0,0 @@
-"""This script contains base options for Deep3DFaceRecon_pytorch
-"""
-
-import argparse
-import os
-from util import util
-import numpy as np
-import torch
-import models
-import data
-
-
-class BaseOptions():
-    """This class defines options used during both training and test time.
-
-    It also implements several helper functions such as parsing, printing, and saving the options.
-    It also gathers additional options defined in <modify_commandline_options> functions in both dataset class and model class.
-    """
-
-    def __init__(self, cmd_line=None):
-        """Reset the class; indicates the class hasn't been initailized"""
-        self.initialized = False
-        self.cmd_line = None
-        if cmd_line is not None:
-            self.cmd_line = cmd_line.split()
-
-    def initialize(self, parser):
-        """Define the common options that are used in both training and test."""
-        # basic parameters
-        parser.add_argument('--name', type=str, default='face_recon', help='name of the experiment. It decides where to store samples and models')
-        parser.add_argument('--gpu_ids', type=str, default='0', help='gpu ids: e.g. 0  0,1,2, 0,2. use -1 for CPU')
-        parser.add_argument('--checkpoints_dir', type=str, default='./checkpoints', help='models are saved here')
-        parser.add_argument('--vis_batch_nums', type=float, default=1, help='batch nums of images for visulization')
-        parser.add_argument('--eval_batch_nums', type=float, default=float('inf'), help='batch nums of images for evaluation')
-        parser.add_argument('--use_ddp', type=util.str2bool, nargs='?', const=True, default=True, help='whether use distributed data parallel')
-        parser.add_argument('--ddp_port', type=str, default='12355', help='ddp port')
-        parser.add_argument('--display_per_batch', type=util.str2bool, nargs='?', const=True, default=True, help='whether use batch to show losses')
-        parser.add_argument('--add_image', type=util.str2bool, nargs='?', const=True, default=True, help='whether add image to tensorboard')
-        parser.add_argument('--world_size', type=int, default=1, help='batch nums of images for evaluation')
-
-        # model parameters
-        parser.add_argument('--model', type=str, default='facerecon', help='chooses which model to use.')
-
-        # additional parameters
-        parser.add_argument('--epoch', type=str, default='latest', help='which epoch to load? set to latest to use latest cached model')
-        parser.add_argument('--verbose', action='store_true', help='if specified, print more debugging information')
-        parser.add_argument('--suffix', default='', type=str, help='customized suffix: opt.name = opt.name + suffix: e.g., {model}_{netG}_size{load_size}')
-
-        self.initialized = True
-        return parser
-
-    def gather_options(self):
-        """Initialize our parser with basic options(only once).
-        Add additional model-specific and dataset-specific options.
-        These options are defined in the <modify_commandline_options> function
-        in model and dataset classes.
-        """
-        if not self.initialized:  # check if it has been initialized
-            parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
-            parser = self.initialize(parser)
-
-        # get the basic options
-        if self.cmd_line is None:
-            opt, _ = parser.parse_known_args()
-        else:
-            opt, _ = parser.parse_known_args(self.cmd_line)
-
-        # set cuda visible devices
-        os.environ['CUDA_VISIBLE_DEVICES'] = opt.gpu_ids
-
-        # modify model-related parser options
-        model_name = opt.model
-        model_option_setter = models.get_option_setter(model_name)
-        parser = model_option_setter(parser, self.isTrain)
-        if self.cmd_line is None:
-            opt, _ = parser.parse_known_args()  # parse again with new defaults
-        else:
-            opt, _ = parser.parse_known_args(self.cmd_line)  # parse again with new defaults
-
-        # modify dataset-related parser options
-        if opt.dataset_mode:
-            dataset_name = opt.dataset_mode
-            dataset_option_setter = data.get_option_setter(dataset_name)
-            parser = dataset_option_setter(parser, self.isTrain)
-
-        # save and return the parser
-        self.parser = parser
-        if self.cmd_line is None:
-            return parser.parse_args()
-        else:
-            return parser.parse_args(self.cmd_line)
-
-    def print_options(self, opt):
-        """Print and save options
-
-        It will print both current options and default values(if different).
-        It will save options into a text file / [checkpoints_dir] / opt.txt
-        """
-        message = ''
-        message += '----------------- Options ---------------\n'
-        for k, v in sorted(vars(opt).items()):
-            comment = ''
-            default = self.parser.get_default(k)
-            if v != default:
-                comment = '\t[default: %s]' % str(default)
-            message += '{:>25}: {:<30}{}\n'.format(str(k), str(v), comment)
-        message += '----------------- End -------------------'
-        print(message)
-
-        # save to the disk
-        expr_dir = os.path.join(opt.checkpoints_dir, opt.name)
-        util.mkdirs(expr_dir)
-        file_name = os.path.join(expr_dir, '{}_opt.txt'.format(opt.phase))
-        try:
-            with open(file_name, 'wt') as opt_file:
-                opt_file.write(message)
-                opt_file.write('\n')
-        except PermissionError as error:
-            print("permission error {}".format(error))
-            pass
-
-    def parse(self):
-        """Parse our options, create checkpoints directory suffix, and set up gpu device."""
-        opt = self.gather_options()
-        opt.isTrain = self.isTrain   # train or test
-
-        # process opt.suffix
-        if opt.suffix:
-            suffix = ('_' + opt.suffix.format(**vars(opt))) if opt.suffix != '' else ''
-            opt.name = opt.name + suffix
-
-
-        # set gpu ids
-        str_ids = opt.gpu_ids.split(',')
-        gpu_ids = []
-        for str_id in str_ids:
-            id = int(str_id)
-            if id >= 0:
-                gpu_ids.append(id)
-        opt.world_size = len(gpu_ids)
-        # if len(opt.gpu_ids) > 0:
-        #     torch.cuda.set_device(gpu_ids[0])
-        if opt.world_size == 1:
-            opt.use_ddp = False
-
-        if opt.phase != 'test':
-            # set continue_train automatically
-            if opt.pretrained_name is None:
-                model_dir = os.path.join(opt.checkpoints_dir, opt.name)
-            else:
-                model_dir = os.path.join(opt.checkpoints_dir, opt.pretrained_name)
-            if os.path.isdir(model_dir):
-                model_pths = [i for i in os.listdir(model_dir) if i.endswith('pth')]
-                if os.path.isdir(model_dir) and len(model_pths) != 0:
-                    opt.continue_train= True
-        
-            # update the latest epoch count
-            if opt.continue_train:
-                if opt.epoch == 'latest':
-                    epoch_counts = [int(i.split('.')[0].split('_')[-1]) for i in model_pths if 'latest' not in i]
-                    if len(epoch_counts) != 0:
-                        opt.epoch_count = max(epoch_counts) + 1
-                else:
-                    opt.epoch_count = int(opt.epoch) + 1
-                    
-
-        self.print_options(opt)
-        self.opt = opt
-        return self.opt
diff --git a/spaces/h2oai/wave-tour/examples/http_client.py b/spaces/h2oai/wave-tour/examples/http_client.py
deleted file mode 100644
index c590107de8620e4d3e083697b62064c9d816bd17..0000000000000000000000000000000000000000
--- a/spaces/h2oai/wave-tour/examples/http_client.py
+++ /dev/null
@@ -1,26 +0,0 @@
-# HTTP / Client
-# Use any http client to communicate with RESTful APIs.
-# #http
-# ---
-from h2o_wave import main, app, Q, ui
-import httpx
-
-
-@app('/demo')
-async def serve(q: Q):
-    if not q.client.initialized:
-        q.page['form'] = ui.form_card(box='1 1 4 6', items=[
-            ui.buttons([
-                ui.button(name='clear', label='Clear'),
-                ui.button(name='download', label='Download', primary=True)
-            ]),
-            ui.text_m(name='text', content='No data yet.'),
-        ])
-        q.client.initialized = True
-    if q.args.download:
-        response = httpx.get('https://jsonplaceholder.typicode.com/posts/1')
-        q.page['form'].text.content = response.text
-    if q.args.clear:
-        q.page['form'].text.content = 'No data yet.'
-
-    await q.page.save()
diff --git a/spaces/haakohu/deep_privacy2_face/configs/anonymizers/face.py b/spaces/haakohu/deep_privacy2_face/configs/anonymizers/face.py
deleted file mode 100644
index 1eed93b812de5166ecddce94e36a4cb1cf4777d8..0000000000000000000000000000000000000000
--- a/spaces/haakohu/deep_privacy2_face/configs/anonymizers/face.py
+++ /dev/null
@@ -1,17 +0,0 @@
-from dp2.anonymizer import Anonymizer
-from dp2.detection.face_detector import FaceDetector
-from ..defaults import common
-from tops.config import LazyCall as L
-
-
-detector = L(FaceDetector)(
-    face_detector_cfg=dict(name="DSFDDetector", clip_boxes=True),
-    face_post_process_cfg=dict(target_imsize=(256, 256), fdf128_expand=False),
-    score_threshold=0.3,
-    cache_directory=common.output_dir.joinpath("face_detection_cache"),
-)
-
-anonymizer = L(Anonymizer)(
-    detector="${detector}",
-    face_G_cfg="configs/fdf/stylegan.py",
-)
diff --git a/spaces/hamelcubsfan/AutoGPT/autogpt/commands/twitter.py b/spaces/hamelcubsfan/AutoGPT/autogpt/commands/twitter.py
deleted file mode 100644
index 3eaed36e20e1c520690ac59f25a4da6501f3440f..0000000000000000000000000000000000000000
--- a/spaces/hamelcubsfan/AutoGPT/autogpt/commands/twitter.py
+++ /dev/null
@@ -1,26 +0,0 @@
-import os
-
-import tweepy
-from dotenv import load_dotenv
-
-load_dotenv()
-
-
-def send_tweet(tweet_text):
-    consumer_key = os.environ.get("TW_CONSUMER_KEY")
-    consumer_secret = os.environ.get("TW_CONSUMER_SECRET")
-    access_token = os.environ.get("TW_ACCESS_TOKEN")
-    access_token_secret = os.environ.get("TW_ACCESS_TOKEN_SECRET")
-    # Authenticate to Twitter
-    auth = tweepy.OAuthHandler(consumer_key, consumer_secret)
-    auth.set_access_token(access_token, access_token_secret)
-
-    # Create API object
-    api = tweepy.API(auth)
-
-    # Send tweet
-    try:
-        api.update_status(tweet_text)
-        print("Tweet sent successfully!")
-    except tweepy.TweepyException as e:
-        print("Error sending tweet: {}".format(e.reason))
diff --git a/spaces/hasibzunair/fifa-tryon-demo/models/mnist_model.py b/spaces/hasibzunair/fifa-tryon-demo/models/mnist_model.py
deleted file mode 100644
index a1bcca6bc10fbe4ea1ef27258fd079e528dee9a9..0000000000000000000000000000000000000000
--- a/spaces/hasibzunair/fifa-tryon-demo/models/mnist_model.py
+++ /dev/null
@@ -1,122 +0,0 @@
-# encoding: utf-8
-
-import math
-import torch
-import itertools
-import numpy as np
-import torch.nn as nn
-import torch.nn.functional as F
-from grid_sample import grid_sample
-from torch.autograd import Variable
-from tps_grid_gen import TPSGridGen
-
-
-class CNN(nn.Module):
-    def __init__(self, num_output):
-        super(CNN, self).__init__()
-        self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
-        self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
-        self.conv2_drop = nn.Dropout2d()
-        self.fc1 = nn.Linear(320, 50)
-        self.fc2 = nn.Linear(50, num_output)
-
-    def forward(self, x):
-        x = F.relu(F.max_pool2d(self.conv1(x), 2))
-        x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
-        x = x.view(-1, 320)
-        x = F.relu(self.fc1(x))
-        x = F.dropout(x, training=self.training)
-        x = self.fc2(x)
-        return x
-
-
-class ClsNet(nn.Module):
-
-    def __init__(self):
-        super(ClsNet, self).__init__()
-        self.cnn = CNN(10)
-
-    def forward(self, x):
-        return F.log_softmax(self.cnn(x))
-
-
-class BoundedGridLocNet(nn.Module):
-
-    def __init__(self, grid_height, grid_width, target_control_points):
-        super(BoundedGridLocNet, self).__init__()
-        self.cnn = CNN(grid_height * grid_width * 2)
-
-        bias = torch.from_numpy(np.arctanh(target_control_points.numpy()))
-        bias = bias.view(-1)
-        self.cnn.fc2.bias.data.copy_(bias)
-        self.cnn.fc2.weight.data.zero_()
-
-    def forward(self, x):
-        batch_size = x.size(0)
-        points = F.tanh(self.cnn(x))
-        return points.view(batch_size, -1, 2)
-
-
-class UnBoundedGridLocNet(nn.Module):
-
-    def __init__(self, grid_height, grid_width, target_control_points):
-        super(UnBoundedGridLocNet, self).__init__()
-        self.cnn = CNN(grid_height * grid_width * 2)
-
-        bias = target_control_points.view(-1)
-        self.cnn.fc2.bias.data.copy_(bias)
-        self.cnn.fc2.weight.data.zero_()
-
-    def forward(self, x):
-        batch_size = x.size(0)
-        points = self.cnn(x)
-        return points.view(batch_size, -1, 2)
-
-
-class STNClsNet(nn.Module):
-
-    def __init__(self, args):
-        super(STNClsNet, self).__init__()
-        self.args = args
-
-        r1 = args.span_range_height
-        r2 = args.span_range_width
-        assert r1 < 1 and r2 < 1  # if >= 1, arctanh will cause error in BoundedGridLocNet
-        target_control_points = torch.Tensor(list(itertools.product(
-            np.arange(-r1, r1 + 0.00001, 2.0 * r1 / (args.grid_height - 1)),
-            np.arange(-r2, r2 + 0.00001, 2.0 * r2 / (args.grid_width - 1)),
-        )))
-        Y, X = target_control_points.split(1, dim=1)
-        target_control_points = torch.cat([X, Y], dim=1)
-
-        GridLocNet = {
-            'unbounded_stn': UnBoundedGridLocNet,
-            'bounded_stn': BoundedGridLocNet,
-        }[args.model]
-        self.loc_net = GridLocNet(
-            args.grid_height, args.grid_width, target_control_points)
-
-        self.tps = TPSGridGen(
-            args.image_height, args.image_width, target_control_points)
-
-        self.cls_net = ClsNet()
-
-    def forward(self, x):
-        batch_size = x.size(0)
-        source_control_points = self.loc_net(x)
-        source_coordinate = self.tps(source_control_points)
-        grid = source_coordinate.view(
-            batch_size, self.args.image_height, self.args.image_width, 2)
-        transformed_x = grid_sample(x, grid)
-        logit = self.cls_net(transformed_x)
-        return logit
-
-
-def get_model(args):
-    if args.model == 'no_stn':
-        print('create model without STN')
-        model = ClsNet()
-    else:
-        print('create model with STN')
-        model = STNClsNet(args)
-    return model
diff --git a/spaces/hca97/Mosquito-Detection/my_models/torch_hub_cache/yolov5/utils/loggers/comet/comet_utils.py b/spaces/hca97/Mosquito-Detection/my_models/torch_hub_cache/yolov5/utils/loggers/comet/comet_utils.py
deleted file mode 100644
index 27600761ad2843a6ab66aa22ad06782bb4b7eea7..0000000000000000000000000000000000000000
--- a/spaces/hca97/Mosquito-Detection/my_models/torch_hub_cache/yolov5/utils/loggers/comet/comet_utils.py
+++ /dev/null
@@ -1,150 +0,0 @@
-import logging
-import os
-from urllib.parse import urlparse
-
-try:
-    import comet_ml
-except (ModuleNotFoundError, ImportError):
-    comet_ml = None
-
-import yaml
-
-logger = logging.getLogger(__name__)
-
-COMET_PREFIX = 'comet://'
-COMET_MODEL_NAME = os.getenv('COMET_MODEL_NAME', 'yolov5')
-COMET_DEFAULT_CHECKPOINT_FILENAME = os.getenv('COMET_DEFAULT_CHECKPOINT_FILENAME', 'last.pt')
-
-
-def download_model_checkpoint(opt, experiment):
-    model_dir = f'{opt.project}/{experiment.name}'
-    os.makedirs(model_dir, exist_ok=True)
-
-    model_name = COMET_MODEL_NAME
-    model_asset_list = experiment.get_model_asset_list(model_name)
-
-    if len(model_asset_list) == 0:
-        logger.error(f'COMET ERROR: No checkpoints found for model name : {model_name}')
-        return
-
-    model_asset_list = sorted(
-        model_asset_list,
-        key=lambda x: x['step'],
-        reverse=True,
-    )
-    logged_checkpoint_map = {asset['fileName']: asset['assetId'] for asset in model_asset_list}
-
-    resource_url = urlparse(opt.weights)
-    checkpoint_filename = resource_url.query
-
-    if checkpoint_filename:
-        asset_id = logged_checkpoint_map.get(checkpoint_filename)
-    else:
-        asset_id = logged_checkpoint_map.get(COMET_DEFAULT_CHECKPOINT_FILENAME)
-        checkpoint_filename = COMET_DEFAULT_CHECKPOINT_FILENAME
-
-    if asset_id is None:
-        logger.error(f'COMET ERROR: Checkpoint {checkpoint_filename} not found in the given Experiment')
-        return
-
-    try:
-        logger.info(f'COMET INFO: Downloading checkpoint {checkpoint_filename}')
-        asset_filename = checkpoint_filename
-
-        model_binary = experiment.get_asset(asset_id, return_type='binary', stream=False)
-        model_download_path = f'{model_dir}/{asset_filename}'
-        with open(model_download_path, 'wb') as f:
-            f.write(model_binary)
-
-        opt.weights = model_download_path
-
-    except Exception as e:
-        logger.warning('COMET WARNING: Unable to download checkpoint from Comet')
-        logger.exception(e)
-
-
-def set_opt_parameters(opt, experiment):
-    """Update the opts Namespace with parameters
-    from Comet's ExistingExperiment when resuming a run
-
-    Args:
-        opt (argparse.Namespace): Namespace of command line options
-        experiment (comet_ml.APIExperiment): Comet API Experiment object
-    """
-    asset_list = experiment.get_asset_list()
-    resume_string = opt.resume
-
-    for asset in asset_list:
-        if asset['fileName'] == 'opt.yaml':
-            asset_id = asset['assetId']
-            asset_binary = experiment.get_asset(asset_id, return_type='binary', stream=False)
-            opt_dict = yaml.safe_load(asset_binary)
-            for key, value in opt_dict.items():
-                setattr(opt, key, value)
-            opt.resume = resume_string
-
-    # Save hyperparameters to YAML file
-    # Necessary to pass checks in training script
-    save_dir = f'{opt.project}/{experiment.name}'
-    os.makedirs(save_dir, exist_ok=True)
-
-    hyp_yaml_path = f'{save_dir}/hyp.yaml'
-    with open(hyp_yaml_path, 'w') as f:
-        yaml.dump(opt.hyp, f)
-    opt.hyp = hyp_yaml_path
-
-
-def check_comet_weights(opt):
-    """Downloads model weights from Comet and updates the
-    weights path to point to saved weights location
-
-    Args:
-        opt (argparse.Namespace): Command Line arguments passed
-            to YOLOv5 training script
-
-    Returns:
-        None/bool: Return True if weights are successfully downloaded
-            else return None
-    """
-    if comet_ml is None:
-        return
-
-    if isinstance(opt.weights, str):
-        if opt.weights.startswith(COMET_PREFIX):
-            api = comet_ml.API()
-            resource = urlparse(opt.weights)
-            experiment_path = f'{resource.netloc}{resource.path}'
-            experiment = api.get(experiment_path)
-            download_model_checkpoint(opt, experiment)
-            return True
-
-    return None
-
-
-def check_comet_resume(opt):
-    """Restores run parameters to its original state based on the model checkpoint
-    and logged Experiment parameters.
-
-    Args:
-        opt (argparse.Namespace): Command Line arguments passed
-            to YOLOv5 training script
-
-    Returns:
-        None/bool: Return True if the run is restored successfully
-            else return None
-    """
-    if comet_ml is None:
-        return
-
-    if isinstance(opt.resume, str):
-        if opt.resume.startswith(COMET_PREFIX):
-            api = comet_ml.API()
-            resource = urlparse(opt.resume)
-            experiment_path = f'{resource.netloc}{resource.path}'
-            experiment = api.get(experiment_path)
-            set_opt_parameters(opt, experiment)
-            download_model_checkpoint(opt, experiment)
-
-            return True
-
-    return None
diff --git a/spaces/hekbobo/bingo/src/app/page.tsx b/spaces/hekbobo/bingo/src/app/page.tsx
deleted file mode 100644
index 0dff3431b098ce4fe282cc83fc87a93a28a43090..0000000000000000000000000000000000000000
--- a/spaces/hekbobo/bingo/src/app/page.tsx
+++ /dev/null
@@ -1,15 +0,0 @@
-import dynamic from 'next/dynamic'
-
-const DynamicComponentWithNoSSR = dynamic(
-  () => import('../components/chat'),
-  { ssr: false }
-)
-
-export default function IndexPage() {
-  return (
-    <>
-      <div className="loading-spinner" />
-      <DynamicComponentWithNoSSR />
-    </>
-  )
-}
diff --git a/spaces/hudsonhayes/PerformanceSummarisation/style.css b/spaces/hudsonhayes/PerformanceSummarisation/style.css
deleted file mode 100644
index e53a0560d1cf7b6761625890c7cb5fb5f38831b2..0000000000000000000000000000000000000000
--- a/spaces/hudsonhayes/PerformanceSummarisation/style.css
+++ /dev/null
@@ -1,38 +0,0 @@
-#col-container {
-  margin-left: auto;
-  margin-right: auto;
-}
-gradio-app{
-  background:url("file=bg.png") !important;
-}
-
-#row-flex {
-  display: flex;
-  align-items: center;
-  justify-content: center;
-}
-.leftimage .rightimage{
-  float:left;
-  filter: drop-shadow(20px 20px 10px white);
-}
-.leftimage{
-  padding-top:40px;
-  margin-left:310px;
-}
-.rightimage{
-  padding-top:40px;
-  margin-right:320px;
-}
-a,
-a:hover,
-a:visited {
-  text-decoration-line: underline;
-  font-weight: 600;
-  color: #1f2937 !important;
-}
-
-.dark a,
-.dark a:hover,
-.dark a:visited {
-  color: #f3f4f6 !important;
-}
\ No newline at end of file
diff --git a/spaces/hyxue/HiFiFace-inference-demo/Deep3DFaceRecon_pytorch/util/html.py b/spaces/hyxue/HiFiFace-inference-demo/Deep3DFaceRecon_pytorch/util/html.py
deleted file mode 100644
index 5570e71e887cd23d09913091da7bfe6c3894b833..0000000000000000000000000000000000000000
--- a/spaces/hyxue/HiFiFace-inference-demo/Deep3DFaceRecon_pytorch/util/html.py
+++ /dev/null
@@ -1,95 +0,0 @@
-import os
-
-import dominate
-from dominate.tags import a
-from dominate.tags import br
-from dominate.tags import h3
-from dominate.tags import img
-from dominate.tags import meta
-from dominate.tags import p
-from dominate.tags import table
-from dominate.tags import td
-from dominate.tags import tr
-
-
-class HTML:
-    """This HTML class allows us to save images and write texts into a single HTML file.
-
-    It consists of functions such as <add_header> (add a text header to the HTML file),
-    <add_images> (add a row of images to the HTML file), and <save> (save the HTML to the disk).
-    It is based on Python library 'dominate', a Python library for creating and manipulating HTML documents using a DOM API.
-    """
-
-    def __init__(self, web_dir, title, refresh=0):
-        """Initialize the HTML classes
-
-        Parameters:
-            web_dir (str) -- a directory that stores the webpage. HTML file will be created at <web_dir>/index.html; images will be saved at <web_dir/images/
-            title (str)   -- the webpage name
-            refresh (int) -- how often the website refresh itself; if 0; no refreshing
-        """
-        self.title = title
-        self.web_dir = web_dir
-        self.img_dir = os.path.join(self.web_dir, "images")
-        if not os.path.exists(self.web_dir):
-            os.makedirs(self.web_dir)
-        if not os.path.exists(self.img_dir):
-            os.makedirs(self.img_dir)
-
-        self.doc = dominate.document(title=title)
-        if refresh > 0:
-            with self.doc.head:
-                meta(http_equiv="refresh", content=str(refresh))
-
-    def get_image_dir(self):
-        """Return the directory that stores images"""
-        return self.img_dir
-
-    def add_header(self, text):
-        """Insert a header to the HTML file
-
-        Parameters:
-            text (str) -- the header text
-        """
-        with self.doc:
-            h3(text)
-
-    def add_images(self, ims, txts, links, width=400):
-        """add images to the HTML file
-
-        Parameters:
-            ims (str list)   -- a list of image paths
-            txts (str list)  -- a list of image names shown on the website
-            links (str list) --  a list of hyperref links; when you click an image, it will redirect you to a new page
-        """
-        self.t = table(border=1, style="table-layout: fixed;")  # Insert a table
-        self.doc.add(self.t)
-        with self.t:
-            with tr():
-                for im, txt, link in zip(ims, txts, links):
-                    with td(style="word-wrap: break-word;", halign="center", valign="top"):
-                        with p():
-                            with a(href=os.path.join("images", link)):
-                                img(style="width:%dpx" % width, src=os.path.join("images", im))
-                            br()
-                            p(txt)
-
-    def save(self):
-        """save the current content to the HMTL file"""
-        html_file = "%s/index.html" % self.web_dir
-        f = open(html_file, "wt")
-        f.write(self.doc.render())
-        f.close()
-
-
-if __name__ == "__main__":  # we show an example usage here.
-    html = HTML("web/", "test_html")
-    html.add_header("hello world")
-
-    ims, txts, links = [], [], []
-    for n in range(4):
-        ims.append("image_%d.png" % n)
-        txts.append("text_%d" % n)
-        links.append("image_%d.png" % n)
-    html.add_images(ims, txts, links)
-    html.save()
diff --git a/spaces/iakarshu/lilt/README.md b/spaces/iakarshu/lilt/README.md
deleted file mode 100644
index c3438690b9673c521ca877dd9fc3d34ee08fc2fd..0000000000000000000000000000000000000000
--- a/spaces/iakarshu/lilt/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Lilt
-emoji: 🦀
-colorFrom: yellow
-colorTo: blue
-sdk: gradio
-sdk_version: 3.6
-app_file: app.py
-pinned: false
-license: unknown
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/iamironman4279/SadTalker/src/face3d/models/arcface_torch/docs/install.md b/spaces/iamironman4279/SadTalker/src/face3d/models/arcface_torch/docs/install.md
deleted file mode 100644
index 6314a40441285e9236438e468caf8b71a407531a..0000000000000000000000000000000000000000
--- a/spaces/iamironman4279/SadTalker/src/face3d/models/arcface_torch/docs/install.md
+++ /dev/null
@@ -1,51 +0,0 @@
-## v1.8.0 
-### Linux and Windows  
-```shell
-# CUDA 11.0
-pip --default-timeout=100 install torch==1.8.0+cu111 torchvision==0.9.0+cu111 torchaudio==0.8.0 -f https://download.pytorch.org/whl/torch_stable.html
-
-# CUDA 10.2
-pip --default-timeout=100 install torch==1.8.0 torchvision==0.9.0 torchaudio==0.8.0
-
-# CPU only
-pip --default-timeout=100 install torch==1.8.0+cpu torchvision==0.9.0+cpu torchaudio==0.8.0 -f https://download.pytorch.org/whl/torch_stable.html
-
-```
-
-
-## v1.7.1  
-### Linux and Windows  
-```shell
-# CUDA 11.0
-pip install torch==1.7.1+cu110 torchvision==0.8.2+cu110 torchaudio==0.7.2 -f https://download.pytorch.org/whl/torch_stable.html
-
-# CUDA 10.2
-pip install torch==1.7.1 torchvision==0.8.2 torchaudio==0.7.2
-
-# CUDA 10.1
-pip install torch==1.7.1+cu101 torchvision==0.8.2+cu101 torchaudio==0.7.2 -f https://download.pytorch.org/whl/torch_stable.html
-
-# CUDA 9.2
-pip install torch==1.7.1+cu92 torchvision==0.8.2+cu92 torchaudio==0.7.2 -f https://download.pytorch.org/whl/torch_stable.html
-
-# CPU only
-pip install torch==1.7.1+cpu torchvision==0.8.2+cpu torchaudio==0.7.2 -f https://download.pytorch.org/whl/torch_stable.html
-```
-
-
-## v1.6.0  
-
-### Linux and Windows
-```shell
-# CUDA 10.2
-pip install torch==1.6.0 torchvision==0.7.0
-
-# CUDA 10.1
-pip install torch==1.6.0+cu101 torchvision==0.7.0+cu101 -f https://download.pytorch.org/whl/torch_stable.html
-
-# CUDA 9.2
-pip install torch==1.6.0+cu92 torchvision==0.7.0+cu92 -f https://download.pytorch.org/whl/torch_stable.html
-
-# CPU only
-pip install torch==1.6.0+cpu torchvision==0.7.0+cpu -f https://download.pytorch.org/whl/torch_stable.html
-```
\ No newline at end of file
diff --git a/spaces/iamstolas/STOLAS/src/components/chat-suggestions.tsx b/spaces/iamstolas/STOLAS/src/components/chat-suggestions.tsx
deleted file mode 100644
index 00c2fee295c9e010946046eb71705a5e131f7a5a..0000000000000000000000000000000000000000
--- a/spaces/iamstolas/STOLAS/src/components/chat-suggestions.tsx
+++ /dev/null
@@ -1,45 +0,0 @@
-import React, { useMemo } from 'react'
-import Image from 'next/image'
-import HelpIcon from '@/assets/images/help.svg'
-import { SuggestedResponse } from '@/lib/bots/bing/types'
-import { useBing } from '@/lib/hooks/use-bing'
-import { atom, useAtom } from 'jotai'
-
-type Suggestions = SuggestedResponse[]
-const helpSuggestions = ['为什么不回应某些主题', '告诉我更多关于必应的资迅', '必应如何使用 AI?'].map((text) => ({ text }))
-const suggestionsAtom = atom<Suggestions>([])
-
-type ChatSuggestionsProps = React.ComponentProps<'div'> & Pick<ReturnType<typeof useBing>, 'setInput'> & { suggestions?: Suggestions }
-
-export function ChatSuggestions({ setInput, suggestions = [] }: ChatSuggestionsProps) {
-  const [currentSuggestions, setSuggestions] = useAtom(suggestionsAtom)
-  const toggleSuggestions = (() => {
-    if (currentSuggestions === helpSuggestions) {
-      setSuggestions(suggestions)
-    } else {
-      setSuggestions(helpSuggestions)
-    }
-  })
-
-  useMemo(() => {
-    setSuggestions(suggestions)
-    window.scrollBy(0, 2000)
-  }, [suggestions.length])
-
-  return currentSuggestions?.length ? (
-    <div className="py-6">
-      <div className="suggestion-items">
-        <button className="rai-button" type="button" aria-label="这是什么?" onClick={toggleSuggestions}>
-          <Image alt="help" src={HelpIcon} width={24} />
-        </button>
-        {
-          currentSuggestions.map(suggestion => (
-            <button key={suggestion.text} className="body-1-strong suggestion-container" type="button" onClick={() => setInput(suggestion.text)}>
-              {suggestion.text}
-            </button>
-          ))
-        }
-      </div>
-    </div>
-  ) : null
-}
diff --git a/spaces/inamXcontru/PoeticTTS/Adobe Photoshop Lightroom Classic CC 2019 8.1 Multi Win.md b/spaces/inamXcontru/PoeticTTS/Adobe Photoshop Lightroom Classic CC 2019 8.1 Multi Win.md
deleted file mode 100644
index f41fb44735b9946a1aeb7ca7e5f7b72a383bca47..0000000000000000000000000000000000000000
--- a/spaces/inamXcontru/PoeticTTS/Adobe Photoshop Lightroom Classic CC 2019 8.1 Multi Win.md	
+++ /dev/null
@@ -1,42 +0,0 @@
-<h2>Adobe Photoshop Lightroom Classic CC 2019 8.1 Multi Win</h2><br /><p><b><b>DOWNLOAD</b> &#10038;&#10038;&#10038; <a href="https://gohhs.com/2uz36o">https://gohhs.com/2uz36o</a></b></p><br /><br />
-
-Offline installer setup of Photoshop Lightroom Classic CC 2019 is now available for download. This is latest version of Adobe Photoshop Lightroom Classic.
-
-Adobe Photoshop Lightroom Classic CC 2019 Free Download
-
-Click the link below to download Adobe Photoshop Lightroom Classic CC 2019.
-
-You can also download from the link given below, provided it is direct link to a free offline installer setup of Photoshop Lightroom Classic CC 2019 Free Download.
-
-Save the file anywhere. Click the download button to start the download. When the download is done completely, accept the terms and conditions for the software. Once you accept, you will be able to proceed to the next step.
-
-Save the setup on your hard disk. Extract the downloaded file using the latest version of WinRAR. After WinRAR extracts, right click on the winrar folder and click the “Send To” option. In the same place, send the extracted file.
-
-Run the setup of Photoshop Lightroom Classic CC 2019.
-
-Take a back up of all your pictures. Click “OK” to continue.
-
-Run the Photoshop Lightroom Classic CC 2019. Click on the “Finish” button to complete the installation.
-
-Now go to the program start-up. Select “Programs and Features” and click on the “Change” button.
-
-Click on “Adobe Photoshop Lightroom Classic CC 2019” and click on “Change”.
-
-Go to “Turn Windows features on or off” and click on the “OK” option.
-
-Go back to the programs and features window. Click on “OK”.
-
-Open the software and begin using it.
-
-Download Adobe Photoshop Lightroom Classic CC 2019
-
-Steps to Install Adobe Photoshop Lightroom Classic CC 2019 – We have tried our best to make it easy for you and your step by step guide will help you to install or upgrade the software in just a couple of minutes. Let’s get started.
-
-Step 1: First, go to the link given below, which is our official website, where you can download the installer of the software. The link provided below is the direct download link to the software for offline use.
-
-Step 2: Click on the download link.
-
-Step 3: You will find the download page with an option to save the file on your hard disk or even you can choose to start the download 4fefd39f24<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/inamXcontru/PoeticTTS/Ayu Azhari Adegan Kamar Mandi Film Outraged Fugitive The Impact of the Nude Scene on Her Career and Personal Life.md b/spaces/inamXcontru/PoeticTTS/Ayu Azhari Adegan Kamar Mandi Film Outraged Fugitive The Impact of the Nude Scene on Her Career and Personal Life.md
deleted file mode 100644
index 67e16207c227f9af85d07f6a0a92556a9d31b0ef..0000000000000000000000000000000000000000
--- a/spaces/inamXcontru/PoeticTTS/Ayu Azhari Adegan Kamar Mandi Film Outraged Fugitive The Impact of the Nude Scene on Her Career and Personal Life.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>Ayu Azhari Adegan Kamar Mandi Film Outraged Fugitive.12l rageirig</h2><br /><p><b><b>Download</b> &#10037;&#10037;&#10037; <a href="https://gohhs.com/2uz4m1">https://gohhs.com/2uz4m1</a></b></p><br /><br />
-
- aaccfb2cb3<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/inamXcontru/PoeticTTS/Diablo 2 With Lord Of Destruction (v1.13c) (Direct Play) (Latest Cheat Codes) How to Master the Game and Defeat the Evil Forces.md b/spaces/inamXcontru/PoeticTTS/Diablo 2 With Lord Of Destruction (v1.13c) (Direct Play) (Latest Cheat Codes) How to Master the Game and Defeat the Evil Forces.md
deleted file mode 100644
index 6b7cb1debda6c722d37c6af2434f951ae56a173e..0000000000000000000000000000000000000000
--- a/spaces/inamXcontru/PoeticTTS/Diablo 2 With Lord Of Destruction (v1.13c) (Direct Play) (Latest Cheat Codes) How to Master the Game and Defeat the Evil Forces.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-
-<p>This doesn't effect immunities on monsters, and works in all Difficulties and all Acts.EffectCodeExtremely hard to kill monsters/players 7Good difference/players 3Hard monsters/players 5Little difference in Difficulty/players 2Maximum diffculty/Nigh impossible to kill monsters/players 8No effect/players 1Very difficult to kill monsters/players 6Very noticable difference/players 4Contributed By: KevynKross</i> 38 </i> 39<br/><br/></li>Random CheatsNote: you can type in all of these codes again after they are in effect to turn them off. If it is one of the /players cheats then you have to type in the code "/players 1" to set it back to 1.EffectCodeEvery sound at once.soundchaosdebugShow frames per second./fpsWon't let you pick up items unless you press ALT. This helps in cluttered areas with a lot of lag./nopickupContributed By: HawkeyeCH13</i> 10 </i> 20<br/><br/></li>Target line codesRight click on your Diablo II desktop icon. Left click on the shortcut tab. Enter the code in the target line after the quotes. make sure there is a space between each code , and a space between the last quote and first code.</p>
-<p>These codes currently work as of the 1.11b patch.EffectCodeAllows users to not fix the aspect ratio to 4:3 when maximizing windowed mode. "For Widescreen Users"-nofixaspectCreate a new level 1 character in Act1.-act1Create a new level 16 character in Act2.-act2Create a new level 21 character in Act3.-act3Create a new level 27 character in Act4.-act4Create a new level 33 character in Act5.-act5Doesn't preload anything.-nplNever saves the game.-nosaveNo sound driver is loaded.-nsOpens the game in a window.-wSkip directly to BattleNet login.-skiptobnetThis enables sound in background.-sndbkgUses the OpenGL graphic renderer in game.-openglContributed By: Red_Bird_of_Cha, jctaber86</i> 31 </i> 33<br/><br/></li></ol><br/>Easter EggsDiablo 2 CommercialWith the D2X disk in you computer click on the "My computer" icon. From there you should see the D2X shortcut picture. Right-click on it and select Open. Inside there is a file called D2COM_01. Double click it to watch the semi-funny 30 second D2 commercial.Contributed By: D2_Balk</i> 8 </i> 7<br/><br/></li>Every SoundTo hear every sound recorded for Diablo 2 press ''enter'' in a game to open the chat box and type in ''soundchaosdebug''Contributed By: Jeff_Andonuts</i> 6 </i> 2<br/><br/></li>Secret Cow LevelJust like the Secret Cow Level in the original Diablo II, the Secret Cow Level is in LoD as well. You must have beaten Baal on the difficulty you want to make the portal for though. <br /> <br />1. Go to Rogue Encampment <br />2. Insert a Wirt's Leg and a Tome of Townsportal into the Cube and Transmute. <br />3. Enter Portal. <br />4. Fight Cows. <br /> <br />The cows CAN be difficult to kill with melee characters, so be careful. Paladins and Sorceresses are always helpful here. <br /> <br />The cows do not give good experience, but the chances of finding good runes/gems/equipment is better than most places. Also, most of the ''stashes'' and ''chests'' here are good drops as well. <br /> <br />WARNING: If you want to make the portal again, DO NOT KILL THE COW KING. If the Cow King is killed in a game where you made the portal, you WILL NOT be able to make the portal for that difficulty anymore.Contributed By: AnnDreeUhh</i> 11 </i> 18<br/><br/></li></ol><br/>SecretsCow Level MACIn order to access the secret cow level, you must defeat Baal. Then, make a game for the difficulty you just beat him on (e.g. If you beat Baal on Normal, make a game with Normal difficulty). Go to the Rogue Encampment, put Wirt's Leg and a Tome of Town Portal in a Horadric Cube, and a portal leading to the secret cow level will appear. <br /> <br />Note: If you want to continue to make these portals to the cow level, do NOT kill the King Cow. If you do, you can no longer access the secret cow level from that difficulty.Contributed By: silent_hillian</i> 1 </i> 1<br/><br/></li>Every Sound MACWhile in game, type ''soundchaosdebug'' (without the quotes). <br />It plays every sound and NPC conversation in the game. <br />To turn it off, just type it in again.Contributed By: rpglord999</i> 2 </i> 1<br/><br/></li>Frames per second MACGo into a game. When everything loads you press enter to open the message space. You then type in "/fps" with out the quotations. Then in the top left hand corner it should show: Frames per second, Ping, and skip.Contributed By: Teh_Reel_Won</i> 0 </i> 0<br/><br/></li></ol><br/>$(document).ready(function()$('.content_ratings.voted').attr('title','You have already voted on this item.');$('.content_ratings.mycode').attr('title','You can not vote on your own contribution.');$('.content_ratings').tooltip( position:  my: "left+0 center", at: "right+15 center" , tooltipClass:'tooltip'););function cheat_vote(code_id, cur_vote, vote)$.ajax(type: 'POST',url: '/ajax/gamespace_item_vote',data:  vote: vote, id: code_id, type: 'code', key: 'ad283aed' ,success: function(response)var d = $.parseJSON(response);if(d.success)$('#'+code_id+'c'+vote).addClass('myvote');$('#'+code_id+'c'+vote+' span').text(cur_vote+1);$('.content_ratings.c'+code_id+' span').removeAttr('onclick'););Know Something We Don't?You can submit new cheats for this game and help our users gain an edge.</p>
-<h2>Diablo 2 With Lord Of Destruction (v1.13c) (Direct Play) (Latest Cheat Codes</h2><br /><p><b><b>Download File</b> ->>> <a href="https://gohhs.com/2uz2SS">https://gohhs.com/2uz2SS</a></b></p><br /><br /> aaccfb2cb3<br />
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\ No newline at end of file
diff --git a/spaces/innnky/soft-vits-singingvc/text/symbols.py b/spaces/innnky/soft-vits-singingvc/text/symbols.py
deleted file mode 100644
index 869a53e763ae825bc02921842280ac9efe7f85dd..0000000000000000000000000000000000000000
--- a/spaces/innnky/soft-vits-singingvc/text/symbols.py
+++ /dev/null
@@ -1,16 +0,0 @@
-""" from https://github.com/keithito/tacotron """
-
-'''
-Defines the set of symbols used in text input to the model.
-'''
-_pad        = '_'
-_punctuation = ';:,.!?¡¿—…"«»“” '
-_letters = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz'
-_letters_ipa = "ɑɐɒæɓʙβɔɕçɗɖðʤəɘɚɛɜɝɞɟʄɡɠɢʛɦɧħɥʜɨɪʝɭɬɫɮʟɱɯɰŋɳɲɴøɵɸθœɶʘɹɺɾɻʀʁɽʂʃʈʧʉʊʋⱱʌɣɤʍχʎʏʑʐʒʔʡʕʢǀǁǂǃˈˌːˑʼʴʰʱʲʷˠˤ˞↓↑→↗↘'̩'ᵻ"
-
-
-# Export all symbols:
-symbols = [_pad] + list(_punctuation) + list(_letters) + list(_letters_ipa)
-
-# Special symbol ids
-SPACE_ID = symbols.index(" ")
diff --git a/spaces/inplisQlawa/anything-midjourney-v4-1/3planesoft Lake Tree 3d Screensaver 1 1 Patch S0m.md b/spaces/inplisQlawa/anything-midjourney-v4-1/3planesoft Lake Tree 3d Screensaver 1 1 Patch S0m.md
deleted file mode 100644
index 1cb9d6a2d780256ced1f8c431bcb8ccd2b219d85..0000000000000000000000000000000000000000
--- a/spaces/inplisQlawa/anything-midjourney-v4-1/3planesoft Lake Tree 3d Screensaver 1 1 Patch S0m.md	
+++ /dev/null
@@ -1,11 +0,0 @@
-
-<p>This is the perfect screensaver for Christmas Eve, as it looks pleasant and warm, and all it needs is a snowman, Santa Claus, and a sleigh with flying reindeer to complete the scene. The scene is warm, and the Christmas atmosphere fills the room. These Christmas-themed screensavers are always nice to have on your PCs monitor. </p>
-<h2>3planesoft lake tree 3d screensaver 1 1 patch s0m</h2><br /><p><b><b>DOWNLOAD</b> &#10040; <a href="https://urlin.us/2uEwcd">https://urlin.us/2uEwcd</a></b></p><br /><br />
-<p>If you want something special for your home, there are various holiday screensavers that you can choose from, like the 3planesoft Screeen Scena s1and the 3PlaneScre 4ssaver.The screensaver features a Christmas theme, a Christmas tree, snow, angels, icicles, and sleigh. Both the screensavers are free for your entertainment.</p>
-<p>You can also add a holiday ringtone to your calendar. It is a Christmas ringtone that is played once a day in the 20th Century. Christmas tree, sleigh, and the words merry and Christmas ringtones all help make this ringtone a good one for all to hear.</p>
-<p>With a Christmas mood, the 3planesoft 3D Screeen Scena s1screensaver features bright Christmas lights as well as a Christmas tree, snow, and a fireplace. You can even use it as a screensaver for your Windows PC.</p>
-<p>In this screensaver, you will see snow-covered houses, reindeer, a Christmas tree, icicles, and Santa Claus. You can even hear a sleigh, bells, and merry music. Its a Christmas that you can enjoy watching whenever you want.</p>
-<p></p>
-<p>The Diamond Christmas screensaver features a Christmas-style tree, with a star shining on top, two angels standing behind it, and a shining ball in the middle. The tree is covered by an ornaments and a snowfall. The ornaments are piled in a corner, while the snow is falling onto them. </p> 899543212b<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/inreVtussa/clothingai/Examples/Authentic Love Presets ACR.md b/spaces/inreVtussa/clothingai/Examples/Authentic Love Presets ACR.md
deleted file mode 100644
index 69d5f3a563ac175fe0f9830120d3485b836655b8..0000000000000000000000000000000000000000
--- a/spaces/inreVtussa/clothingai/Examples/Authentic Love Presets ACR.md	
+++ /dev/null
@@ -1,71 +0,0 @@
-<br />
-<h1>What are Authentic Love Presets ACR and Why You Need Them</h1>
-
-<p>If you are a photographer who loves capturing authentic moments of love and emotion, you might be interested in Authentic Love Presets ACR. These are a collection of presets designed for Adobe Camera Raw (ACR) and Lightroom that can help you achieve your desired image look.</p>
-
-<p>Authentic Love Presets ACR are created by Authentic Love Magazine, a platform that showcases real stories of love and adventure from around the world. The presets are inspired by the magazine's aesthetic and vision, and they aim to enhance the natural beauty and mood of your photos.</p>
-<h2>Authentic Love Presets ACR</h2><br /><p><b><b>Download</b> &#9881; <a href="https://tiurll.com/2uClJ4">https://tiurll.com/2uClJ4</a></b></p><br /><br />
-
-<h2>The Benefits of Authentic Love Presets ACR</h2>
-
-<p>There are many reasons why you might want to use Authentic Love Presets ACR for your photos. Here are some of them:</p>
-
-<ul>
-<li>They save you time and effort. You don't have to spend hours tweaking your photos in post-processing. With just one click, you can apply a preset that suits your style and scene.</li>
-<li>They give you consistency and quality. You can create a cohesive look for your portfolio and social media with presets that match your brand and vision. You can also ensure that your photos have a professional and polished finish.</li>
-<li>They help you express your creativity and personality. You can choose from a variety of presets that range from colorful and vibrant to moody and dramatic. You can also customize them to fit your preferences and needs.</li>
-<li>They make your photos stand out and attract attention. You can impress your clients and followers with photos that have a unique and captivating look. You can also increase your chances of getting featured on platforms like Authentic Love Magazine.</li>
-</ul>
-
-<h2>The Features of Authentic Love Presets ACR</h2>
-
-<p>Authentic Love Presets ACR offer a lot of features that make them versatile and easy to use. Here are some of them:</p>
-
-<ul>
-<li>They work with both RAW and JPEG files. You can use them with any type of photo format without compromising the quality.</li>
-<li>They are compatible with different camera platforms. You can use them with any camera brand or model, as they use the Adobe Standard Calibration (2012).</li>
-<li>They include 12 presets with 9 color and 3 black and white options. You can choose from a wide range of presets that suit different lighting conditions, seasons, and moods.</li>
-<li>They also include 5 preset tools to control grain, noise, and lens correction. You can fine-tune your photos with these additional tools that help you achieve a more realistic and natural look.</li>
-<li>They are easy to install and use. You can find PDF instructions in your Authentic Presets file that guide you through the installation process. You can also access them from your ACR or Lightroom panel with just a few clicks.</li>
-</ul>
-
-<h2>How to Get Authentic Love Presets ACR</h2>
-
-<p>If you are interested in getting Authentic Love Presets ACR, you can visit the Authentic Love Magazine website and shop for them online. They are available for $89 for the desktop version, $79 for the mobile version, or $149 for the bundle that includes both versions.</p>
-
-<p>You can also check out some examples of how the presets look on different photos on the website or on their Instagram page. You can see how they transform photos from ordinary to extraordinary, and how they enhance the authentic love stories behind them.</p>
-
-<p>Authentic Love Presets ACR are a great way to improve your photography skills and style, and to showcase your passion for capturing real moments of love and emotion. Whether you are shooting adventurous elopements, genuine tears of joy, cuddles and hugs, or evening kisses, these presets will work for you!</p>
-<h2>What People Say About Authentic Love Presets ACR</h2>
-
-<p>Authentic Love Presets ACR have received a lot of positive feedback from photographers who have used them for their photos. Here are some of the testimonials from their website and social media:</p>
-
-<blockquote>
-<p>"I absolutely love these presets! They are so easy to use and they give my photos a beautiful and natural look. They are perfect for capturing the emotions and stories of my couples." - Dani Purington, @danipurington</p>
-</blockquote>
-
-<blockquote>
-<p>"These presets are amazing! They have transformed my photos and made them look more professional and consistent. They also save me a lot of time in editing, which is a huge plus. I highly recommend them to anyone who loves authentic photography." - Janelle Elise, @janelle.elise.photo</p>
-<p></p>
-</blockquote>
-
-<blockquote>
-<p>"I'm so happy with these presets! They are exactly what I was looking for. They have a nice contrast and depth, and they work well with different lighting situations. They also enhance the colors and tones of my photos without making them look unnatural or overdone." - Sami Strong, @samistrong</p>
-</blockquote>
-
-<h2>How to Use Authentic Love Presets ACR</h2>
-
-<p>Using Authentic Love Presets ACR is very simple and straightforward. Here are the steps you need to follow:</p>
-
-<ol>
-<li>Download the presets from the Authentic Love Magazine website after making your purchase. You will receive an email with your receipt and download links.</li>
-<li>Unzip the file and find the PDF instructions for installing the presets on your ACR or Lightroom.</li>
-<li>Follow the instructions and import the presets into your software.</li>
-<li>Select a photo you want to edit and apply a preset from the Authentic Love Presets ACR panel.</li>
-<li>Adjust the exposure, white balance, and other settings as needed to suit your photo.</li>
-<li>Enjoy your edited photo and share it with your clients or followers!</li>
-</ol>
-
-<p>Authentic Love Presets ACR are a great investment for any photographer who wants to create stunning photos that showcase authentic love stories. They are easy to use, versatile, and affordable. You can get them today from the Authentic Love Magazine website and start creating your own amazing photos!</p> 3cee63e6c2<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/j-min/IterInpaint-CLEVR/README.md b/spaces/j-min/IterInpaint-CLEVR/README.md
deleted file mode 100644
index 70ba4d3f4d6e3ccb62208f8637b7ed4fcd323d27..0000000000000000000000000000000000000000
--- a/spaces/j-min/IterInpaint-CLEVR/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: IterInpaint CLEVR
-emoji: 🌍
-colorFrom: gray
-colorTo: purple
-sdk: gradio
-sdk_version: 3.24.1
-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/jaiteja7849/MyGenAIChatBot/README.md b/spaces/jaiteja7849/MyGenAIChatBot/README.md
deleted file mode 100644
index d7626df4c8e90f574b890c62067e1188febc55db..0000000000000000000000000000000000000000
--- a/spaces/jaiteja7849/MyGenAIChatBot/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: MyGenAIChatBot
-emoji: 🦀
-colorFrom: green
-colorTo: blue
-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/jbilcke-hf/LifeSim/README.md b/spaces/jbilcke-hf/LifeSim/README.md
deleted file mode 100644
index 6f0e7212fa381c729f2d6639013a45f37d92f588..0000000000000000000000000000000000000000
--- a/spaces/jbilcke-hf/LifeSim/README.md
+++ /dev/null
@@ -1,11 +0,0 @@
----
-title: LifeSim
-emoji: 🐠🪸
-colorFrom: blue
-colorTo: yellow
-sdk: docker
-pinned: false
-app_port: 3000
----
-
-LifeSim uses a text-to-video model to render artificially simulated agents.
\ No newline at end of file
diff --git a/spaces/jbilcke-hf/LifeSim/src/app/globals.css b/spaces/jbilcke-hf/LifeSim/src/app/globals.css
deleted file mode 100644
index fd81e885836d815b8019694a910a93d86a43cb66..0000000000000000000000000000000000000000
--- a/spaces/jbilcke-hf/LifeSim/src/app/globals.css
+++ /dev/null
@@ -1,27 +0,0 @@
-@tailwind base;
-@tailwind components;
-@tailwind utilities;
-
-:root {
-  --foreground-rgb: 0, 0, 0;
-  --background-start-rgb: 214, 219, 220;
-  --background-end-rgb: 255, 255, 255;
-}
-
-@media (prefers-color-scheme: dark) {
-  :root {
-    --foreground-rgb: 255, 255, 255;
-    --background-start-rgb: 0, 0, 0;
-    --background-end-rgb: 0, 0, 0;
-  }
-}
-
-body {
-  color: rgb(var(--foreground-rgb));
-  background: linear-gradient(
-      to bottom,
-      transparent,
-      rgb(var(--background-end-rgb))
-    )
-    rgb(var(--background-start-rgb));
-}
diff --git a/spaces/jbilcke-hf/VideoQuest/src/app/games/vernian.ts b/spaces/jbilcke-hf/VideoQuest/src/app/games/vernian.ts
deleted file mode 100644
index 2023fb900e183aa1e2722dfcf3edac352422a177..0000000000000000000000000000000000000000
--- a/spaces/jbilcke-hf/VideoQuest/src/app/games/vernian.ts
+++ /dev/null
@@ -1,96 +0,0 @@
-import { moondance } from "@/lib/fonts"
-import { Game } from "./types"
-import { InventoryItem } from "../../types"
-
-const initialSituation = [
-  `inside a secret workshop inspired by Jules Verne`,
-  `with mysterious machines, keys, boxes, blueprints, gears`
-].join(", ")
-
-const initialActionnables = [
-  "key",
-  "box",
-  "door",
-  "table",
-  "chair",
-  "sun",
-  "gear",
-  "machine",
-  "window",
-  "ground"
-]
-
-const inventory: InventoryItem[] = [
-  {
-    name: "apparatus",
-    title: "Apparatus",
-    caption: "",
-    description: "What is this strange device?"
-  },
-  {
-    name: "book",
-    title: "Book",
-    caption: "",
-    description: "It is talking about a mysterious island, I think.."
-  },
-  {
-    name: "cog",
-    title: "Cog",
-    caption: "",
-    description: "From some kind of mysterious machine."
-  },
-  {
-    name: "coil",
-    title: "Coil",
-    caption: "",
-    description: "Nice, but where does it fit?"
-  },
-  {
-    name: "copper-wire",
-    title: "Copper wire",
-    caption: "",
-    description: "Mmh, copper. I wonder how I could use that."
-  },
-  {
-    name: "pocket-watch",
-    title: "Pocket watch",
-    caption: "",
-    description: "My my.. time passes quickly."
-  },
-  {
-    name: "top-hat",
-    title: "Top Hat",
-    caption: "",
-    description: "For a gentleman or magician. The craft is exquisite."
-  },
-]
-
-export const game: Game = {
-  title: "Vernian",
-  type: "vernian",
-  description: [
-    "The game is a role playing adventure set in the world of Jules Verne adventures, with heavy steampunk inspirations.",
-    "The player try to find a treasure on a mysterious island, and they search in Jules Verne's secret cabinet and atelier.",
-    "The player can click around to move to new scenes, find or activate artifacts.",
-    "They can also use objects from their inventory.",
-  ],
-  engines: [
-    "cartesian_image",
-    "cartesian_video",
-    "spherical_image",
-  ],
-  className: moondance.className,
-  initialSituation,
-  initialActionnables,
-  inventory,
-  getScenePrompt: (situation?: string) => [
-    `Screenshot from a videogame`,
-    `steam punk decor`,
-    `jules verne architecture and design`,
-    `mysterious machines and mechanisms`,
-    `first person`,
-    situation || initialSituation,
-    `unreal engine`,
-  ]
-}
-
diff --git a/spaces/jbilcke-hf/VideoQuest/src/app/interface/progress/progress-bar.tsx b/spaces/jbilcke-hf/VideoQuest/src/app/interface/progress/progress-bar.tsx
deleted file mode 100644
index 0e926d05419cecc6d4a4964d53a8dad6e07a4102..0000000000000000000000000000000000000000
--- a/spaces/jbilcke-hf/VideoQuest/src/app/interface/progress/progress-bar.tsx
+++ /dev/null
@@ -1,57 +0,0 @@
-"use client"
-
-import { CircularProgressbar, buildStyles } from "react-circular-progressbar"
-import "react-circular-progressbar/dist/styles.css"
-
-export function ProgressBar ({
-  className,
-  progressPercentage,
-  text
-}: {
-  className?: string
-  progressPercentage?: number
-  text?: string
-}) {
-  return (
-    <div className={className}>
-      <CircularProgressbar
-        // doc: https://www.npmjs.com/package/react-circular-progressbar
-
-        value={progressPercentage || 0}
-
-        // Text to display inside progressbar. Default: ''.
-        text={text || ""}   
-
-        // Width of circular line relative to total width of component, a value from 0-100. Default: 8.
-        strokeWidth={8}
-
-        
-          // As a convenience, you can use buildStyles to configure the most common style changes:
-
-          styles={buildStyles({
-            // Rotation of path and trail, in number of turns (0-1)
-            rotation: 0,
-
-            // Whether to use rounded or flat corners on the ends - can use 'butt' or 'round'
-            strokeLinecap: 'round',
-
-            // Text size
-            textSize: '20px',
-
-            // How long animation takes to go from one percentage to another, in seconds
-            pathTransitionDuration: 0.1,
-
-            // Can specify path transition in more detail, or remove it entirely
-            // pathTransition: 'none',
-
-            // Colors
-            // pathColor: `rgba(62, 152, 199, ${percentage / 100})`,
-            textColor: '#f88',
-            trailColor: '#d6d6d6',
-            backgroundColor: '#3e98c7',
-          })}
-      
-      />
-    </div>
-  )
-}
\ No newline at end of file
diff --git a/spaces/jcenaa/Segment-Any-RGBD/open_vocab_seg/test_time_augmentation.py b/spaces/jcenaa/Segment-Any-RGBD/open_vocab_seg/test_time_augmentation.py
deleted file mode 100644
index bb7a51f28419c59775013c74fdee49e5166bde51..0000000000000000000000000000000000000000
--- a/spaces/jcenaa/Segment-Any-RGBD/open_vocab_seg/test_time_augmentation.py
+++ /dev/null
@@ -1,217 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-# Copyright (c) Meta Platforms, Inc. All Rights Reserved
-
-import copy
-from itertools import count
-import math
-import numpy as np
-import torch
-from fvcore.transforms import HFlipTransform
-from torch import nn
-from torch.nn.parallel import DistributedDataParallel
-
-from detectron2.data.detection_utils import read_image
-from detectron2.modeling import DatasetMapperTTA
-from detectron2.modeling.postprocessing import sem_seg_postprocess
-import logging
-from detectron2.utils.logger import log_every_n, log_first_n
-
-__all__ = [
-    "SemanticSegmentorWithTTA",
-]
-
-
-class SemanticSegmentorWithTTA(nn.Module):
-    """
-    A SemanticSegmentor with test-time augmentation enabled.
-    Its :meth:`__call__` method has the same interface as :meth:`SemanticSegmentor.forward`.
-    """
-
-    def __init__(self, cfg, model, tta_mapper=None, batch_size=1):
-        """
-        Args:
-            cfg (CfgNode):
-            model (SemanticSegmentor): a SemanticSegmentor to apply TTA on.
-            tta_mapper (callable): takes a dataset dict and returns a list of
-                augmented versions of the dataset dict. Defaults to
-                `DatasetMapperTTA(cfg)`.
-            batch_size (int): batch the augmented images into this batch size for inference.
-        """
-        super().__init__()
-        if isinstance(model, DistributedDataParallel):
-            model = model.module
-        self.cfg = cfg.clone()
-
-        self.model = model
-
-        if tta_mapper is None:
-            tta_mapper = DatasetMapperTTA(cfg)
-        self.tta_mapper = tta_mapper
-        self.batch_size = batch_size
-
-    def _inference_with_model(self, inputs):
-        if self.cfg.TEST.SLIDING_WINDOW:
-            log_first_n(logging.INFO, "Using sliding window to test")
-
-            outputs = []
-
-            for input in inputs:
-                image_size = input["image"].shape[1:]  # h,w
-                if self.cfg.TEST.SLIDING_TILE_SIZE > 0:
-                    tile_size = (
-                        self.cfg.TEST.SLIDING_TILE_SIZE,
-                        self.cfg.TEST.SLIDING_TILE_SIZE,
-                    )
-                else:
-                    selected_mapping = {256: 224, 512: 256, 768: 512, 896: 512}
-                    tile_size = min(image_size)
-                    tile_size = selected_mapping[tile_size]
-                    tile_size = (tile_size, tile_size)
-                extra_info = {
-                    k: v
-                    for k, v in input.items()
-                    if k not in ["image", "height", "width"]
-                }
-                log_every_n(
-                    logging.INFO, "split {} to {}".format(image_size, tile_size)
-                )
-                overlap = self.cfg.TEST.SLIDING_OVERLAP
-                stride = math.ceil(tile_size[0] * (1 - overlap))
-                tile_rows = int(
-                    math.ceil((image_size[0] - tile_size[0]) / stride) + 1
-                )  # strided convolution formula
-                tile_cols = int(math.ceil((image_size[1] - tile_size[1]) / stride) + 1)
-                full_probs = None
-                count_predictions = None
-                tile_counter = 0
-
-                for row in range(tile_rows):
-                    for col in range(tile_cols):
-                        x1 = int(col * stride)
-                        y1 = int(row * stride)
-                        x2 = min(x1 + tile_size[1], image_size[1])
-                        y2 = min(y1 + tile_size[0], image_size[0])
-                        x1 = max(
-                            int(x2 - tile_size[1]), 0
-                        )  # for portrait images the x1 underflows sometimes
-                        y1 = max(
-                            int(y2 - tile_size[0]), 0
-                        )  # for very few rows y1 underflows
-
-                        img = input["image"][:, y1:y2, x1:x2]
-                        padded_img = nn.functional.pad(
-                            img,
-                            (
-                                0,
-                                tile_size[1] - img.shape[-1],
-                                0,
-                                tile_size[0] - img.shape[-2],
-                            ),
-                        )
-                        tile_counter += 1
-                        padded_input = {"image": padded_img}
-                        padded_input.update(extra_info)
-                        padded_prediction = self.model([padded_input])[0]["sem_seg"]
-                        prediction = padded_prediction[
-                            :, 0 : img.shape[1], 0 : img.shape[2]
-                        ]
-                        if full_probs is None:
-                            full_probs = prediction.new_zeros(
-                                prediction.shape[0], image_size[0], image_size[1]
-                            )
-                        if count_predictions is None:
-                            count_predictions = prediction.new_zeros(
-                                prediction.shape[0], image_size[0], image_size[1]
-                            )
-                        count_predictions[:, y1:y2, x1:x2] += 1
-                        full_probs[
-                            :, y1:y2, x1:x2
-                        ] += prediction  # accumulate the predictions also in the overlapping regions
-
-                full_probs /= count_predictions
-                full_probs = sem_seg_postprocess(
-                    full_probs,
-                    image_size,
-                    input.get("height", image_size[0]),
-                    input.get("width", image_size[1]),
-                )
-                outputs.append({"sem_seg": full_probs})
-
-            return outputs
-        else:
-            log_first_n(logging.INFO, "Using whole image to test")
-            return self.model(inputs)
-
-    def _batch_inference(self, batched_inputs):
-        """
-        Execute inference on a list of inputs,
-        using batch size = self.batch_size, instead of the length of the list.
-        Inputs & outputs have the same format as :meth:`SemanticSegmentor.forward`
-        """
-        outputs = []
-        inputs = []
-        for idx, input in zip(count(), batched_inputs):
-            inputs.append(input)
-            if len(inputs) == self.batch_size or idx == len(batched_inputs) - 1:
-                with torch.no_grad():
-                    outputs.extend(self._inference_with_model(inputs))
-                inputs = []
-        return outputs
-
-    def __call__(self, batched_inputs):
-        """
-        Same input/output format as :meth:`SemanticSegmentor.forward`
-        """
-
-        def _maybe_read_image(dataset_dict):
-            ret = copy.copy(dataset_dict)
-            if "image" not in ret:
-                image = read_image(ret.pop("file_name"), self.model.input_format)
-                image = torch.from_numpy(
-                    np.ascontiguousarray(image.transpose(2, 0, 1))
-                )  # CHW
-                ret["image"] = image
-            if "height" not in ret and "width" not in ret:
-                ret["height"] = image.shape[1]
-                ret["width"] = image.shape[2]
-            return ret
-
-        return [self._inference_one_image(_maybe_read_image(x)) for x in batched_inputs]
-
-    def _inference_one_image(self, input):
-        """
-        Args:
-            input (dict): one dataset dict with "image" field being a CHW tensor
-        Returns:
-            dict: one output dict
-        """
-        augmented_inputs, tfms = self._get_augmented_inputs(input)
-        # 1: forward with all augmented images
-        outputs = self._batch_inference(augmented_inputs)
-        # Delete now useless variables to avoid being out of memory
-        del augmented_inputs
-        # 2: merge the results
-        # handle flip specially
-        # outputs = [output.detach() for output in outputs]
-        return self._merge_auged_output(outputs, tfms)
-
-    def _merge_auged_output(self, outputs, tfms):
-        new_outputs = []
-        for output, tfm in zip(outputs, tfms):
-            if any(isinstance(t, HFlipTransform) for t in tfm.transforms):
-                new_outputs.append(output["sem_seg"].flip(dims=[2]))
-            else:
-                new_outputs.append(output["sem_seg"])
-        del outputs
-        # to avoid OOM with torch.stack
-        final_predictions = new_outputs[0]
-        for i in range(1, len(new_outputs)):
-            final_predictions += new_outputs[i]
-        final_predictions = final_predictions / len(new_outputs)
-        del new_outputs
-        return {"sem_seg": final_predictions}
-
-    def _get_augmented_inputs(self, input):
-        augmented_inputs = self.tta_mapper(input)
-        tfms = [x.pop("transforms") for x in augmented_inputs]
-        return augmented_inputs, tfms
diff --git a/spaces/jigo/jobposting/README.md b/spaces/jigo/jobposting/README.md
deleted file mode 100644
index 4a0c9bf4f8a63b012256637602f4c9e6a17bb6fc..0000000000000000000000000000000000000000
--- a/spaces/jigo/jobposting/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Jobposting
-emoji: 🏆
-colorFrom: purple
-colorTo: pink
-sdk: streamlit
-sdk_version: 1.19.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/jjw0126/Multi-ORGPT/app.py b/spaces/jjw0126/Multi-ORGPT/app.py
deleted file mode 100644
index b0a3f37c001828a2de005ed7657770f6d2a0339b..0000000000000000000000000000000000000000
--- a/spaces/jjw0126/Multi-ORGPT/app.py
+++ /dev/null
@@ -1,216 +0,0 @@
-import gradio as gr
-import os
-import json
-import requests
-openai_gpt4_key = "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"
-# Streaming endpoint
-# os.getenv("API_URL") + "/generate_stream"
-API_URL = "https://api.openai.com/v1/chat/completions"
-# Inferenec function
-
-
-def predict(openai_gpt4_key, system_msg, inputs, top_p, temperature, chat_counter, chatbot=[], history=[]):
-
-    headers = {
-        "Content-Type": "application/json",
-        # Users will provide their own OPENAI_API_KEY
-        "Authorization": f"Bearer {openai_gpt4_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-3.5-turbo",
-            "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 :
-        # Of the type of - [{"role": "system", "content": system_msg},]
-        messages = multi_turn_message
-        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-3.5-turbo",
-            # Of the type of [{"role": "user", "content": f"{inputs}"}],
-            "messages": messages,
-            "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
-                # resembles {chatbot: chat, state: history}
-                yield chat, history, chat_counter, response
-
-# 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">🔥 Large Language Models as Tools for Modeling and Coding in Operations Research</h1>"""
-# display message for themes feature
-theme_addon_msg = """<center>🌟 This Demo also introduces you to Gradio Themes. Discover more on Gradio website using our <a href="https://gradio.app/theming-guide/" target="_blank">Themeing-Guide🎨</a>! You can develop from scratch, modify an existing Gradio theme, and share your themes with community by uploading them to huggingface-hub easily using <code>theme.push_to_hub()</code>.</center>
-"""
-
-# Using info to add additional information about System message in GPT4
-system_msg_info = """A conversation could begin with a system message to gently instruct the assistant.
-System message helps set the behavior of the AI Assistant. For example, the assistant could be instructed with 'You are a helpful assistant.'"""
-
-# Modifying existing Gradio Theme
-theme = gr.themes.Soft(primary_hue="zinc", secondary_hue="green", neutral_hue="green",
-                       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">🔥This Huggingface Gradio Demo provides you access to different optimiaztion scenarios. Please note that you would be needing an OPENAI API key for GPT4 access🙌</h1>""")
-    # gr.HTML(theme_addon_msg)
-    # gr.HTML('''<center><a href="https://huggingface.co/spaces/ysharma/ChatGPT4?duplicate=true"><img src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>Duplicate the Space and run securely with your OpenAI API Key</center>''')
-
-    with gr.Column(elem_id="col_container"):
-        # Users need to provide their own GPT4 API key, it is no longer provided by Huggingface
-        with gr.Row():
-            openai_gpt4_key = gr.Textbox(label="OpenAI GPT4 Key", value="", type="password", placeholder="sk..",
-                                         info="You have to provide your own GPT4 keys for this app to function properly",)
-            with gr.Accordion(label="System message:", open=False):
-                system_msg = gr.Textbox(label="Instruct the AI Assistant to set its beaviour",
-                                        info=system_msg_info, value="", placeholder="Type here..")
-                accordion_msg = gr.HTML(
-                    value="🚧 To set System message you will have to refresh the app", visible=False)
-
-        chatbot = gr.Chatbot(label='GPT4', elem_id="chatbot")
-        inputs = gr.Textbox(placeholder="Hi there!",
-                            label="Type an optimization problem as 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, [openai_gpt4_key, system_msg, inputs, top_p, temperature, chat_counter, chatbot, state], [
-                  chatbot, state, chat_counter, server_status_code],)  # openai_api_key
-    b1.click(predict, [openai_gpt4_key, 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])
-
-    # Examples
-    with gr.Accordion(label="Examples for System message:", open=False):
-        gr.Examples(
-            examples=[["""You are an AI programming assistant.
-
-                - Follow the user's requirements carefully and to the letter.
-                - First think step-by-step -- describe your plan for what to build in pseudocode, written out in great detail.
-                - Then output the code in a single code block.
-                - Minimize any other prose."""], ["""You are ComedianGPT who is a helpful assistant. You answer everything with a joke and witty replies."""],
-                      ["You are ChefGPT, a helpful assistant who answers questions with culinary expertise and a pinch of humor."],
-                      ["You are FitnessGuruGPT, a fitness expert who shares workout tips and motivation with a playful twist."],
-                      ["You are SciFiGPT, an AI assistant who discusses science fiction topics with a blend of knowledge and wit."],
-                      ["You are PhilosopherGPT, a thoughtful assistant who responds to inquiries with philosophical insights and a touch of humor."],
-                      ["You are EcoWarriorGPT, a helpful assistant who shares environment-friendly advice with a lighthearted approach."],
-                      ["You are MusicMaestroGPT, a knowledgeable AI who discusses music and its history with a mix of facts and playful banter."],
-                      ["You are SportsFanGPT, an enthusiastic assistant who talks about sports and shares amusing anecdotes."],
-                      ["You are TechWhizGPT, a tech-savvy AI who can help users troubleshoot issues and answer questions with a dash of humor."],
-                      ["You are FashionistaGPT, an AI fashion expert who shares style advice and trends with a sprinkle of wit."],
-                      ["You are ArtConnoisseurGPT, an AI assistant who discusses art and its history with a blend of knowledge and playful commentary."],
-                      ["You are a helpful assistant that provides detailed and accurate information."],
-                      ["You are an assistant that speaks like Shakespeare."],
-                      ["You are a friendly assistant who uses casual language and humor."],
-                      ["You are a financial advisor who gives expert advice on investments and budgeting."],
-                      ["You are a health and fitness expert who provides advice on nutrition and exercise."],
-                      ["You are a travel consultant who offers recommendations for destinations, accommodations, and attractions."],
-                      ["You are a movie critic who shares insightful opinions on films and their themes."],
-                      ["You are a history enthusiast who loves to discuss historical events and figures."],
-                      ["You are a tech-savvy assistant who can help users troubleshoot issues and answer questions about gadgets and software."],
-                      ["You are an AI poet who can compose creative and evocative poems on any given topic."],],
-            inputs=system_msg,)
-
-demo.queue(max_size=99, concurrency_count=20).launch(debug=True)
diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/Crypto/SelfTest/Hash/test_MD2.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/Crypto/SelfTest/Hash/test_MD2.py
deleted file mode 100644
index 93751687f21b7999613353381fa8b036c9ecc3bf..0000000000000000000000000000000000000000
--- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/Crypto/SelfTest/Hash/test_MD2.py
+++ /dev/null
@@ -1,62 +0,0 @@
-# -*- coding: utf-8 -*-
-#
-#  SelfTest/Hash/MD2.py: Self-test for the MD2 hash function
-#
-# Written in 2008 by Dwayne C. Litzenberger <dlitz@dlitz.net>
-#
-# ===================================================================
-# The contents of this file are dedicated to the public domain.  To
-# the extent that dedication to the public domain is not available,
-# everyone is granted a worldwide, perpetual, royalty-free,
-# non-exclusive license to exercise all rights associated with the
-# contents of this file for any purpose whatsoever.
-# No rights are reserved.
-#
-# 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.
-# ===================================================================
-
-"""Self-test suite for Crypto.Hash.MD2"""
-
-from Crypto.Util.py3compat import *
-
-# This is a list of (expected_result, input[, description]) tuples.
-test_data = [
-    # Test vectors from RFC 1319
-    ('8350e5a3e24c153df2275c9f80692773', '', "'' (empty string)"),
-    ('32ec01ec4a6dac72c0ab96fb34c0b5d1', 'a'),
-    ('da853b0d3f88d99b30283a69e6ded6bb', 'abc'),
-    ('ab4f496bfb2a530b219ff33031fe06b0', 'message digest'),
-
-    ('4e8ddff3650292ab5a4108c3aa47940b', 'abcdefghijklmnopqrstuvwxyz',
-        'a-z'),
-
-    ('da33def2a42df13975352846c30338cd',
-        'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789',
-        'A-Z, a-z, 0-9'),
-
-    ('d5976f79d83d3a0dc9806c3c66f3efd8',
-        '1234567890123456789012345678901234567890123456'
-        + '7890123456789012345678901234567890',
-        "'1234567890' * 8"),
-]
-
-def get_tests(config={}):
-    from Crypto.Hash import MD2
-    from .common import make_hash_tests
-    return make_hash_tests(MD2, "MD2", test_data,
-        digest_size=16,
-        oid="1.2.840.113549.2.2")
-
-if __name__ == '__main__':
-    import unittest
-    suite = lambda: unittest.TestSuite(get_tests())
-    unittest.main(defaultTest='suite')
-
-# vim:set ts=4 sw=4 sts=4 expandtab:
diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/altair/utils/save.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/altair/utils/save.py
deleted file mode 100644
index 797210b3f01f2df271f04b09f0919e8d884cdce5..0000000000000000000000000000000000000000
--- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/altair/utils/save.py
+++ /dev/null
@@ -1,189 +0,0 @@
-import json
-import pathlib
-import warnings
-
-from .mimebundle import spec_to_mimebundle
-from ..vegalite.v5.data import data_transformers
-from altair.utils._vegafusion_data import using_vegafusion
-
-
-def write_file_or_filename(fp, content, mode="w", encoding=None):
-    """Write content to fp, whether fp is a string, a pathlib Path or a
-    file-like object"""
-    if isinstance(fp, str) or isinstance(fp, pathlib.PurePath):
-        with open(file=fp, mode=mode, encoding=encoding) as f:
-            f.write(content)
-    else:
-        fp.write(content)
-
-
-def set_inspect_format_argument(format, fp, inline):
-    """Inspect the format argument in the save function"""
-    if format is None:
-        if isinstance(fp, str):
-            format = fp.split(".")[-1]
-        elif isinstance(fp, pathlib.PurePath):
-            format = fp.suffix.lstrip(".")
-        else:
-            raise ValueError(
-                "must specify file format: "
-                "['png', 'svg', 'pdf', 'html', 'json', 'vega']"
-            )
-
-    if format != "html" and inline:
-        warnings.warn("inline argument ignored for non HTML formats.", stacklevel=1)
-
-    return format
-
-
-def set_inspect_mode_argument(mode, embed_options, spec, vegalite_version):
-    """Inspect the mode argument in the save function"""
-    if mode is None:
-        if "mode" in embed_options:
-            mode = embed_options["mode"]
-        elif "$schema" in spec:
-            mode = spec["$schema"].split("/")[-2]
-        else:
-            mode = "vega-lite"
-
-    if mode != "vega-lite":
-        raise ValueError("mode must be 'vega-lite', " "not '{}'".format(mode))
-
-    if mode == "vega-lite" and vegalite_version is None:
-        raise ValueError("must specify vega-lite version")
-
-    return mode
-
-
-def save(
-    chart,
-    fp,
-    vega_version,
-    vegaembed_version,
-    format=None,
-    mode=None,
-    vegalite_version=None,
-    embed_options=None,
-    json_kwds=None,
-    webdriver=None,
-    scale_factor=1,
-    engine=None,
-    inline=False,
-    **kwargs,
-):
-    """Save a chart to file in a variety of formats
-
-    Supported formats are [json, html, png, svg, pdf]
-
-    Parameters
-    ----------
-    chart : alt.Chart
-        the chart instance to save
-    fp : string filename, pathlib.Path or file-like object
-        file to which to write the chart.
-    format : string (optional)
-        the format to write: one of ['json', 'html', 'png', 'svg', 'pdf'].
-        If not specified, the format will be determined from the filename.
-    mode : string (optional)
-        Must be 'vega-lite'. If not specified, then infer the mode from
-        the '$schema' property of the spec, or the ``opt`` dictionary.
-        If it's not specified in either of those places, then use 'vega-lite'.
-    vega_version : string (optional)
-        For html output, the version of vega.js to use
-    vegalite_version : string (optional)
-        For html output, the version of vegalite.js to use
-    vegaembed_version : string (optional)
-        For html output, the version of vegaembed.js to use
-    embed_options : dict (optional)
-        The vegaEmbed options dictionary. Default is {}
-        (See https://github.com/vega/vega-embed for details)
-    json_kwds : dict (optional)
-        Additional keyword arguments are passed to the output method
-        associated with the specified format.
-    webdriver : string {'chrome' | 'firefox'} (optional)
-        Webdriver to use for png or svg output
-    scale_factor : float (optional)
-        scale_factor to use to change size/resolution of png or svg output
-    engine: string {'vl-convert', 'altair_saver'}
-        the conversion engine to use for 'png', 'svg', and 'pdf' formats
-    inline: bool (optional)
-        If False (default), the required JavaScript libraries are loaded
-        from a CDN location in the resulting html file.
-        If True, the required JavaScript libraries are inlined into the resulting
-        html file so that it will work without an internet connection.
-        The altair_viewer package is required if True.
-    **kwargs :
-        additional kwargs passed to spec_to_mimebundle.
-    """
-    if json_kwds is None:
-        json_kwds = {}
-
-    if embed_options is None:
-        embed_options = {}
-
-    format = set_inspect_format_argument(format, fp, inline)
-
-    def perform_save():
-        spec = chart.to_dict(context={"pre_transform": False})
-
-        inner_mode = set_inspect_mode_argument(
-            mode, embed_options, spec, vegalite_version
-        )
-
-        if format == "json":
-            json_spec = json.dumps(spec, **json_kwds)
-            write_file_or_filename(fp, json_spec, mode="w")
-        elif format == "html":
-            if inline:
-                kwargs["template"] = "inline"
-            mimebundle = spec_to_mimebundle(
-                spec=spec,
-                format=format,
-                mode=inner_mode,
-                vega_version=vega_version,
-                vegalite_version=vegalite_version,
-                vegaembed_version=vegaembed_version,
-                embed_options=embed_options,
-                json_kwds=json_kwds,
-                **kwargs,
-            )
-            write_file_or_filename(fp, mimebundle["text/html"], mode="w")
-        elif format in ["png", "svg", "pdf", "vega"]:
-            mimebundle = spec_to_mimebundle(
-                spec=spec,
-                format=format,
-                mode=inner_mode,
-                vega_version=vega_version,
-                vegalite_version=vegalite_version,
-                vegaembed_version=vegaembed_version,
-                webdriver=webdriver,
-                scale_factor=scale_factor,
-                engine=engine,
-                **kwargs,
-            )
-            if format == "png":
-                write_file_or_filename(fp, mimebundle[0]["image/png"], mode="wb")
-            elif format == "pdf":
-                write_file_or_filename(fp, mimebundle["application/pdf"], mode="wb")
-            else:
-                encoding = kwargs.get("encoding", "utf-8")
-                write_file_or_filename(
-                    fp, mimebundle["image/svg+xml"], mode="w", encoding=encoding
-                )
-        else:
-            raise ValueError("Unsupported format: '{}'".format(format))
-
-    if using_vegafusion():
-        # When the vegafusion data transformer is enabled, transforms will be
-        # evaluated during save and the resulting data will be included in the
-        # vega specification that is saved.
-        with data_transformers.disable_max_rows():
-            perform_save()
-    else:
-        # Temporarily turn off any data transformers so that all data is inlined
-        # when calling chart.to_dict. This is relevant for vl-convert which cannot access
-        # local json files which could be created by a json data transformer. Furthermore,
-        # we don't exit the with statement until this function completed due to the issue
-        # described at https://github.com/vega/vl-convert/issues/31
-        with data_transformers.enable("default"), data_transformers.disable_max_rows():
-            perform_save()
diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/fontTools/help.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/fontTools/help.py
deleted file mode 100644
index 2a238de3d6d5d69c70c0130d98f8272be7efabf5..0000000000000000000000000000000000000000
--- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/fontTools/help.py
+++ /dev/null
@@ -1,35 +0,0 @@
-import pkgutil
-import sys
-import fontTools
-import importlib
-import os
-from pathlib import Path
-
-
-def main():
-    """Show this help"""
-    path = fontTools.__path__
-    descriptions = {}
-    for pkg in sorted(
-        mod.name
-        for mod in pkgutil.walk_packages([fontTools.__path__[0]], prefix="fontTools.")
-    ):
-        try:
-            imports = __import__(pkg, globals(), locals(), ["main"])
-        except ImportError as e:
-            continue
-        try:
-            description = imports.main.__doc__
-            if description:
-                pkg = pkg.replace("fontTools.", "").replace(".__main__", "")
-                # show the docstring's first line only
-                descriptions[pkg] = description.splitlines()[0]
-        except AttributeError as e:
-            pass
-    for pkg, description in descriptions.items():
-        print("fonttools %-25s %s" % (pkg, description), file=sys.stderr)
-
-
-if __name__ == "__main__":
-    print("fonttools v%s\n" % fontTools.__version__, file=sys.stderr)
-    main()
diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/fontTools/misc/visitor.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/fontTools/misc/visitor.py
deleted file mode 100644
index 3d28135fad3a951c447d03b7f2b08403cb24a12e..0000000000000000000000000000000000000000
--- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/fontTools/misc/visitor.py
+++ /dev/null
@@ -1,143 +0,0 @@
-"""Generic visitor pattern implementation for Python objects."""
-
-import enum
-
-
-class Visitor(object):
-
-    defaultStop = False
-
-    @classmethod
-    def _register(celf, clazzes_attrs):
-        assert celf != Visitor, "Subclass Visitor instead."
-        if "_visitors" not in celf.__dict__:
-            celf._visitors = {}
-
-        def wrapper(method):
-            assert method.__name__ == "visit"
-            for clazzes, attrs in clazzes_attrs:
-                if type(clazzes) != tuple:
-                    clazzes = (clazzes,)
-                if type(attrs) == str:
-                    attrs = (attrs,)
-                for clazz in clazzes:
-                    _visitors = celf._visitors.setdefault(clazz, {})
-                    for attr in attrs:
-                        assert attr not in _visitors, (
-                            "Oops, class '%s' has visitor function for '%s' defined already."
-                            % (clazz.__name__, attr)
-                        )
-                        _visitors[attr] = method
-            return None
-
-        return wrapper
-
-    @classmethod
-    def register(celf, clazzes):
-        if type(clazzes) != tuple:
-            clazzes = (clazzes,)
-        return celf._register([(clazzes, (None,))])
-
-    @classmethod
-    def register_attr(celf, clazzes, attrs):
-        clazzes_attrs = []
-        if type(clazzes) != tuple:
-            clazzes = (clazzes,)
-        if type(attrs) == str:
-            attrs = (attrs,)
-        for clazz in clazzes:
-            clazzes_attrs.append((clazz, attrs))
-        return celf._register(clazzes_attrs)
-
-    @classmethod
-    def register_attrs(celf, clazzes_attrs):
-        return celf._register(clazzes_attrs)
-
-    @classmethod
-    def _visitorsFor(celf, thing, _default={}):
-        typ = type(thing)
-
-        for celf in celf.mro():
-
-            _visitors = getattr(celf, "_visitors", None)
-            if _visitors is None:
-                break
-
-            m = celf._visitors.get(typ, None)
-            if m is not None:
-                return m
-
-        return _default
-
-    def visitObject(self, obj, *args, **kwargs):
-        """Called to visit an object. This function loops over all non-private
-        attributes of the objects and calls any user-registered (via
-        @register_attr() or @register_attrs()) visit() functions.
-
-        If there is no user-registered visit function, of if there is and it
-        returns True, or it returns None (or doesn't return anything) and
-        visitor.defaultStop is False (default), then the visitor will proceed
-        to call self.visitAttr()"""
-
-        keys = sorted(vars(obj).keys())
-        _visitors = self._visitorsFor(obj)
-        defaultVisitor = _visitors.get("*", None)
-        for key in keys:
-            if key[0] == "_":
-                continue
-            value = getattr(obj, key)
-            visitorFunc = _visitors.get(key, defaultVisitor)
-            if visitorFunc is not None:
-                ret = visitorFunc(self, obj, key, value, *args, **kwargs)
-                if ret == False or (ret is None and self.defaultStop):
-                    continue
-            self.visitAttr(obj, key, value, *args, **kwargs)
-
-    def visitAttr(self, obj, attr, value, *args, **kwargs):
-        """Called to visit an attribute of an object."""
-        self.visit(value, *args, **kwargs)
-
-    def visitList(self, obj, *args, **kwargs):
-        """Called to visit any value that is a list."""
-        for value in obj:
-            self.visit(value, *args, **kwargs)
-
-    def visitDict(self, obj, *args, **kwargs):
-        """Called to visit any value that is a dictionary."""
-        for value in obj.values():
-            self.visit(value, *args, **kwargs)
-
-    def visitLeaf(self, obj, *args, **kwargs):
-        """Called to visit any value that is not an object, list,
-        or dictionary."""
-        pass
-
-    def visit(self, obj, *args, **kwargs):
-        """This is the main entry to the visitor. The visitor will visit object
-        obj.
-
-        The visitor will first determine if there is a registered (via
-        @register()) visit function for the type of object. If there is, it
-        will be called, and (visitor, obj, *args, **kwargs) will be passed to
-        the user visit function.
-
-        If there is no user-registered visit function, of if there is and it
-        returns True, or it returns None (or doesn't return anything) and
-        visitor.defaultStop is False (default), then the visitor will proceed
-        to dispatch to one of self.visitObject(), self.visitList(),
-        self.visitDict(), or self.visitLeaf() (any of which can be overriden in
-        a subclass)."""
-
-        visitorFunc = self._visitorsFor(obj).get(None, None)
-        if visitorFunc is not None:
-            ret = visitorFunc(self, obj, *args, **kwargs)
-            if ret == False or (ret is None and self.defaultStop):
-                return
-        if hasattr(obj, "__dict__") and not isinstance(obj, enum.Enum):
-            self.visitObject(obj, *args, **kwargs)
-        elif isinstance(obj, list):
-            self.visitList(obj, *args, **kwargs)
-        elif isinstance(obj, dict):
-            self.visitDict(obj, *args, **kwargs)
-        else:
-            self.visitLeaf(obj, *args, **kwargs)
diff --git a/spaces/jonigata/PoseTweak/external/hrnet_w48_coco_256x192.py b/spaces/jonigata/PoseTweak/external/hrnet_w48_coco_256x192.py
deleted file mode 100644
index ee33c03d79f94fb04e2fda222114c14e99307b45..0000000000000000000000000000000000000000
--- a/spaces/jonigata/PoseTweak/external/hrnet_w48_coco_256x192.py
+++ /dev/null
@@ -1,169 +0,0 @@
-_base_ = [
-    'default_runtime.py',
-    'coco.py'
-]
-evaluation = dict(interval=10, metric='mAP', save_best='AP')
-
-optimizer = dict(
-    type='Adam',
-    lr=5e-4,
-)
-optimizer_config = dict(grad_clip=None)
-# learning policy
-lr_config = dict(
-    policy='step',
-    warmup='linear',
-    warmup_iters=500,
-    warmup_ratio=0.001,
-    step=[170, 200])
-total_epochs = 210
-channel_cfg = dict(
-    num_output_channels=17,
-    dataset_joints=17,
-    dataset_channel=[
-        [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16],
-    ],
-    inference_channel=[
-        0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16
-    ])
-
-# model settings
-model = dict(
-    type='TopDown',
-    pretrained='https://download.openmmlab.com/mmpose/'
-    'pretrain_models/hrnet_w48-8ef0771d.pth',
-    backbone=dict(
-        type='HRNet',
-        in_channels=3,
-        extra=dict(
-            stage1=dict(
-                num_modules=1,
-                num_branches=1,
-                block='BOTTLENECK',
-                num_blocks=(4, ),
-                num_channels=(64, )),
-            stage2=dict(
-                num_modules=1,
-                num_branches=2,
-                block='BASIC',
-                num_blocks=(4, 4),
-                num_channels=(48, 96)),
-            stage3=dict(
-                num_modules=4,
-                num_branches=3,
-                block='BASIC',
-                num_blocks=(4, 4, 4),
-                num_channels=(48, 96, 192)),
-            stage4=dict(
-                num_modules=3,
-                num_branches=4,
-                block='BASIC',
-                num_blocks=(4, 4, 4, 4),
-                num_channels=(48, 96, 192, 384))),
-    ),
-    keypoint_head=dict(
-        type='TopdownHeatmapSimpleHead',
-        in_channels=48,
-        out_channels=channel_cfg['num_output_channels'],
-        num_deconv_layers=0,
-        extra=dict(final_conv_kernel=1, ),
-        loss_keypoint=dict(type='JointsMSELoss', use_target_weight=True)),
-    train_cfg=dict(),
-    test_cfg=dict(
-        flip_test=True,
-        post_process='default',
-        shift_heatmap=True,
-        modulate_kernel=11))
-
-data_cfg = dict(
-    image_size=[192, 256],
-    heatmap_size=[48, 64],
-    num_output_channels=channel_cfg['num_output_channels'],
-    num_joints=channel_cfg['dataset_joints'],
-    dataset_channel=channel_cfg['dataset_channel'],
-    inference_channel=channel_cfg['inference_channel'],
-    soft_nms=False,
-    nms_thr=1.0,
-    oks_thr=0.9,
-    vis_thr=0.2,
-    use_gt_bbox=False,
-    det_bbox_thr=0.0,
-    bbox_file='data/coco/person_detection_results/'
-    'COCO_val2017_detections_AP_H_56_person.json',
-)
-
-train_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(type='TopDownGetBboxCenterScale', padding=1.25),
-    dict(type='TopDownRandomShiftBboxCenter', shift_factor=0.16, prob=0.3),
-    dict(type='TopDownRandomFlip', flip_prob=0.5),
-    dict(
-        type='TopDownHalfBodyTransform',
-        num_joints_half_body=8,
-        prob_half_body=0.3),
-    dict(
-        type='TopDownGetRandomScaleRotation', rot_factor=40, scale_factor=0.5),
-    dict(type='TopDownAffine'),
-    dict(type='ToTensor'),
-    dict(
-        type='NormalizeTensor',
-        mean=[0.485, 0.456, 0.406],
-        std=[0.229, 0.224, 0.225]),
-    dict(type='TopDownGenerateTarget', sigma=2),
-    dict(
-        type='Collect',
-        keys=['img', 'target', 'target_weight'],
-        meta_keys=[
-            'image_file', 'joints_3d', 'joints_3d_visible', 'center', 'scale',
-            'rotation', 'bbox_score', 'flip_pairs'
-        ]),
-]
-
-val_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(type='TopDownGetBboxCenterScale', padding=1.25),
-    dict(type='TopDownAffine'),
-    dict(type='ToTensor'),
-    dict(
-        type='NormalizeTensor',
-        mean=[0.485, 0.456, 0.406],
-        std=[0.229, 0.224, 0.225]),
-    dict(
-        type='Collect',
-        keys=['img'],
-        meta_keys=[
-            'image_file', 'center', 'scale', 'rotation', 'bbox_score',
-            'flip_pairs'
-        ]),
-]
-
-test_pipeline = val_pipeline
-
-data_root = 'data/coco'
-data = dict(
-    samples_per_gpu=32,
-    workers_per_gpu=2,
-    val_dataloader=dict(samples_per_gpu=32),
-    test_dataloader=dict(samples_per_gpu=32),
-    train=dict(
-        type='TopDownCocoDataset',
-        ann_file=f'{data_root}/annotations/person_keypoints_train2017.json',
-        img_prefix=f'{data_root}/train2017/',
-        data_cfg=data_cfg,
-        pipeline=train_pipeline,
-        dataset_info={{_base_.dataset_info}}),
-    val=dict(
-        type='TopDownCocoDataset',
-        ann_file=f'{data_root}/annotations/person_keypoints_val2017.json',
-        img_prefix=f'{data_root}/val2017/',
-        data_cfg=data_cfg,
-        pipeline=val_pipeline,
-        dataset_info={{_base_.dataset_info}}),
-    test=dict(
-        type='TopDownCocoDataset',
-        ann_file=f'{data_root}/annotations/person_keypoints_val2017.json',
-        img_prefix=f'{data_root}/val2017/',
-        data_cfg=data_cfg,
-        pipeline=test_pipeline,
-        dataset_info={{_base_.dataset_info}}),
-)
diff --git a/spaces/juancopi81/whisper-demo-es-medium/videocreator.py b/spaces/juancopi81/whisper-demo-es-medium/videocreator.py
deleted file mode 100644
index 62672bc0c42a55efcc00a74650099731964641e4..0000000000000000000000000000000000000000
--- a/spaces/juancopi81/whisper-demo-es-medium/videocreator.py
+++ /dev/null
@@ -1,64 +0,0 @@
-import gradio as gr
-from typing import Dict
-
-from moviepy.editor import VideoFileClip, concatenate_videoclips
-
-class VideoCreator:
-    def __init__(self,
-                 tts_pipeline,
-                 image_pipeline) -> None:
-        
-        self.tts_pipeline = tts_pipeline
-        self.image_pipeline = image_pipeline
-        
-    def create_video(self,
-                     scenes: Dict,
-                     video_styles: str) -> str:
-        videos_dict = {}
-        for index, scene in enumerate(scenes):
-            video_scene = self._create_video_from_scene(scenes[scene],
-                                                        video_styles)
-            videos_dict[index] = video_scene
-        merged_video = self._merge_videos(videos_dict)
-        return merged_video
-    
-    def _create_video_from_scene(self, scene: Dict, video_styles: str) -> str:
-        audio_file = self._get_audio_from_text(scene["Summary"])
-        bg_image = self._get_bg_image_from_description(scene["Illustration"], video_styles)
-        video = gr.make_waveform(audio=audio_file,
-                                 bg_image=bg_image)
-        return video
-    
-    def _get_audio_from_text(self, voice_over: str) -> str:
-        self.tts_pipeline.tts_to_file(text=voice_over,
-                                      file_path="output.wav")
-        return "output.wav"
-    
-    def _get_bg_image_from_description(self, img_desc: str, video_styles: str):
-        images = self.image_pipeline(img_desc + ", " + video_styles)
-        print("Image generated!")
-        image_output = images.images[0]
-        image_output.save("img.png")
-        return "img.png"
-
-    def _merge_videos(self, videos_dict: Dict) -> str:
-        videos_to_concatenate = []
-        for video in range(len(videos_dict)):
-            video_clip = VideoFileClip(videos_dict[video])
-            videos_to_concatenate.append(video_clip)
-        final_video = concatenate_videoclips(videos_to_concatenate)
-        try:
-            final_video.write_videofile("final_video.mp4",
-                                        threads=4)
-            print("Saved .mp4 without Exception at final_video.mp4")
-            return "final_video.mp4"
-        except IndexError:
-            # Short by one frame, so get rid on the last frame:
-            final_video = final_video.subclip(t_end=(video_clip.duration - 1.0/final_video.fps))
-            final_video.write_videofile("final_video.mp4",
-                                        threads=4)
-            print("Saved .mp4 after Exception at final_video.mp4")
-            return "final_video.mp4"
-        except Exception as e:
-            print("Exception {} was raised!!".format(e))
-            return "final_video.mp4"
\ No newline at end of file
diff --git a/spaces/juancopi81/youtube-music-transcribe/t5x/examples/t5/network.py b/spaces/juancopi81/youtube-music-transcribe/t5x/examples/t5/network.py
deleted file mode 100644
index 28bcbf17b55912b43f19443712a7aa83cccaaf11..0000000000000000000000000000000000000000
--- a/spaces/juancopi81/youtube-music-transcribe/t5x/examples/t5/network.py
+++ /dev/null
@@ -1,424 +0,0 @@
-# Copyright 2022 The T5X Authors.
-#
-# 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.
-
-"""T5.1.1 Transformer model."""
-
-from typing import Any, Sequence
-
-from flax import linen as nn
-from flax import struct
-import jax.numpy as jnp
-from t5x.examples.t5 import layers
-
-
-@struct.dataclass
-class T5Config:
-  """Global hyperparameters used to minimize obnoxious kwarg plumbing."""
-  vocab_size: int
-  # Activation dtypes.
-  dtype: Any = jnp.float32
-  emb_dim: int = 512
-  num_heads: int = 8
-  num_encoder_layers: int = 6
-  num_decoder_layers: int = 6
-  head_dim: int = 64
-  mlp_dim: int = 2048
-  # Activation functions are retrieved from Flax.
-  mlp_activations: Sequence[str] = ('relu',)
-  dropout_rate: float = 0.1
-  # If `True`, the embedding weights are used in the decoder output layer.
-  logits_via_embedding: bool = False
-  # Whether to accumulate attention logits in float32 regardless of dtype.
-  float32_attention_logits: bool = False
-
-
-class EncoderLayer(nn.Module):
-  """Transformer encoder layer."""
-  config: T5Config
-  relative_embedding: nn.Module
-
-  @nn.compact
-  def __call__(self, inputs, encoder_mask=None, deterministic=False):
-    cfg = self.config
-
-    # Relative position embedding as attention biases.
-    encoder_bias = self.relative_embedding(inputs.shape[-2], inputs.shape[-2],
-                                           True)
-
-    # Attention block.
-    assert inputs.ndim == 3
-    x = layers.LayerNorm(
-        dtype=cfg.dtype, name='pre_attention_layer_norm')(
-            inputs)
-    # [batch, length, emb_dim] -> [batch, length, emb_dim]
-    x = layers.MultiHeadDotProductAttention(
-        num_heads=cfg.num_heads,
-        dtype=cfg.dtype,
-        head_dim=cfg.head_dim,
-        dropout_rate=cfg.dropout_rate,
-        float32_logits=cfg.float32_attention_logits,
-        name='attention')(
-            x, x, encoder_mask, encoder_bias, deterministic=deterministic)
-    x = nn.Dropout(
-        rate=cfg.dropout_rate, broadcast_dims=(-2,))(
-            x, deterministic=deterministic)
-    x = x + inputs
-
-    # MLP block.
-    y = layers.LayerNorm(dtype=cfg.dtype, name='pre_mlp_layer_norm')(x)
-    # [batch, length, emb_dim] -> [batch, length, emb_dim]
-    y = layers.MlpBlock(
-        intermediate_dim=cfg.mlp_dim,
-        activations=cfg.mlp_activations,
-        intermediate_dropout_rate=cfg.dropout_rate,
-        dtype=cfg.dtype,
-        name='mlp',
-    )(y, deterministic=deterministic)
-    y = nn.Dropout(
-        rate=cfg.dropout_rate, broadcast_dims=(-2,))(
-            y, deterministic=deterministic)
-    y = y + x
-
-    return y
-
-
-class DecoderLayer(nn.Module):
-  """Transformer decoder layer that attends to the encoder."""
-  config: T5Config
-  relative_embedding: nn.Module
-
-  @nn.compact
-  def __call__(self,
-               inputs,
-               encoded,
-               decoder_mask=None,
-               encoder_decoder_mask=None,
-               deterministic=False,
-               decode=False,
-               max_decode_length=None):
-    cfg = self.config
-
-    # Relative position embedding as attention biases.
-    l = max_decode_length if decode and max_decode_length else inputs.shape[-2]
-    decoder_bias = self.relative_embedding(l, l, False)
-
-    # inputs: embedded inputs to the decoder with shape [batch, length, emb_dim]
-    x = layers.LayerNorm(
-        dtype=cfg.dtype, name='pre_self_attention_layer_norm')(
-            inputs)
-
-    # Self-attention block
-    x = layers.MultiHeadDotProductAttention(
-        num_heads=cfg.num_heads,
-        dtype=cfg.dtype,
-        head_dim=cfg.head_dim,
-        dropout_rate=cfg.dropout_rate,
-        float32_logits=cfg.float32_attention_logits,
-        name='self_attention')(
-            x,
-            x,
-            decoder_mask,
-            decoder_bias,
-            deterministic=deterministic,
-            decode=decode)
-    x = nn.Dropout(
-        rate=cfg.dropout_rate, broadcast_dims=(-2,))(
-            x, deterministic=deterministic)
-    x = x + inputs
-
-    # Encoder-Decoder block.
-    y = layers.LayerNorm(
-        dtype=cfg.dtype, name='pre_cross_attention_layer_norm')(
-            x)
-    y = layers.MultiHeadDotProductAttention(
-        num_heads=cfg.num_heads,
-        dtype=cfg.dtype,
-        head_dim=cfg.head_dim,
-        dropout_rate=cfg.dropout_rate,
-        float32_logits=cfg.float32_attention_logits,
-        name='encoder_decoder_attention')(
-            y, encoded, encoder_decoder_mask, deterministic=deterministic)
-    y = nn.Dropout(
-        rate=cfg.dropout_rate, broadcast_dims=(-2,))(
-            y, deterministic=deterministic)
-    y = y + x
-
-    # MLP block.
-    z = layers.LayerNorm(dtype=cfg.dtype, name='pre_mlp_layer_norm')(y)
-    z = layers.MlpBlock(
-        intermediate_dim=cfg.mlp_dim,
-        activations=cfg.mlp_activations,
-        intermediate_dropout_rate=cfg.dropout_rate,
-        dtype=cfg.dtype,
-        name='mlp',
-    )(z, deterministic=deterministic)
-    z = nn.Dropout(
-        rate=cfg.dropout_rate, broadcast_dims=(-2,))(
-            z, deterministic=deterministic)
-    z = z + y
-
-    return z
-
-
-class Encoder(nn.Module):
-  """A stack of encoder layers."""
-  config: T5Config
-  shared_embedding: nn.Module
-
-  @nn.compact
-  def __call__(self,
-               encoder_input_tokens,
-               encoder_mask=None,
-               deterministic=False):
-    cfg = self.config
-    assert encoder_input_tokens.ndim == 2  # [batch, length]
-    rel_emb = layers.RelativePositionBiases(
-        num_buckets=32,
-        max_distance=128,
-        num_heads=cfg.num_heads,
-        dtype=cfg.dtype,
-        embedding_init=nn.initializers.variance_scaling(1.0, 'fan_avg',
-                                                        'uniform'),
-        name='relpos_bias')
-
-    # [batch, length] -> [batch, length, emb_dim]
-    x = self.shared_embedding(encoder_input_tokens.astype('int32'))
-    x = nn.Dropout(
-        rate=cfg.dropout_rate, broadcast_dims=(-2,))(
-            x, deterministic=deterministic)
-    x = x.astype(cfg.dtype)
-
-    for lyr in range(cfg.num_encoder_layers):
-      # [batch, length, emb_dim] -> [batch, length, emb_dim]
-      x = EncoderLayer(
-          config=cfg, relative_embedding=rel_emb,
-          name=f'layers_{lyr}')(x, encoder_mask, deterministic)
-
-    x = layers.LayerNorm(dtype=cfg.dtype, name='encoder_norm')(x)
-    return nn.Dropout(rate=cfg.dropout_rate)(x, deterministic=deterministic)
-
-
-class Decoder(nn.Module):
-  """A stack of decoder layers as a part of an encoder-decoder architecture."""
-  config: T5Config
-  shared_embedding: nn.Module
-
-  @nn.compact
-  def __call__(self,
-               encoded,
-               decoder_input_tokens,
-               decoder_positions=None,
-               decoder_mask=None,
-               encoder_decoder_mask=None,
-               deterministic=False,
-               decode=False,
-               max_decode_length=None):
-    cfg = self.config
-    assert decoder_input_tokens.ndim == 2  # [batch, len]
-    rel_emb = layers.RelativePositionBiases(
-        num_buckets=32,
-        max_distance=128,
-        num_heads=cfg.num_heads,
-        dtype=cfg.dtype,
-        embedding_init=nn.initializers.variance_scaling(1.0, 'fan_avg',
-                                                        'uniform'),
-        name='relpos_bias')
-
-    # [batch, length] -> [batch, length, emb_dim]
-    y = self.shared_embedding(decoder_input_tokens.astype('int32'))
-    y = nn.Dropout(
-        rate=cfg.dropout_rate, broadcast_dims=(-2,))(
-            y, deterministic=deterministic)
-    y = y.astype(cfg.dtype)
-
-    for lyr in range(cfg.num_decoder_layers):
-      # [batch, length, emb_dim] -> [batch, length, emb_dim]
-      y = DecoderLayer(
-          config=cfg, relative_embedding=rel_emb, name=f'layers_{lyr}')(
-              y,
-              encoded,
-              decoder_mask=decoder_mask,
-              encoder_decoder_mask=encoder_decoder_mask,
-              deterministic=deterministic,
-              decode=decode,
-              max_decode_length=max_decode_length)
-
-    y = layers.LayerNorm(dtype=cfg.dtype, name='decoder_norm')(y)
-    y = nn.Dropout(
-        rate=cfg.dropout_rate, broadcast_dims=(-2,))(
-            y, deterministic=deterministic)
-
-    # [batch, length, emb_dim] -> [batch, length, vocab_size]
-    if cfg.logits_via_embedding:
-      # Use the transpose of embedding matrix for logit transform.
-      logits = self.shared_embedding.attend(y)
-      # Correctly normalize pre-softmax logits for this shared case.
-      logits = logits / jnp.sqrt(y.shape[-1])
-    else:
-      logits = layers.DenseGeneral(
-          cfg.vocab_size,
-          dtype=jnp.float32,  # Use float32 for stabiliity.
-          kernel_axes=('embed', 'vocab'),
-          name='logits_dense')(
-              y)
-    return logits
-
-
-class Transformer(nn.Module):
-  """An encoder-decoder Transformer model."""
-  config: T5Config
-
-  def setup(self):
-    cfg = self.config
-    self.shared_embedding = layers.Embed(
-        num_embeddings=cfg.vocab_size,
-        features=cfg.emb_dim,
-        dtype=cfg.dtype,
-        attend_dtype=jnp.float32,  # for logit training stability
-        embedding_init=nn.initializers.normal(stddev=1.0),
-        one_hot=True,
-        name='token_embedder')
-
-    self.encoder = Encoder(config=cfg, shared_embedding=self.shared_embedding)
-    self.decoder = Decoder(config=cfg, shared_embedding=self.shared_embedding)
-
-  def encode(self,
-             encoder_input_tokens,
-             encoder_segment_ids=None,
-             enable_dropout=True):
-    """Applies Transformer encoder-branch on the inputs."""
-    cfg = self.config
-    assert encoder_input_tokens.ndim == 2  # (batch, len)
-
-    # Make padding attention mask.
-    encoder_mask = layers.make_attention_mask(
-        encoder_input_tokens > 0, encoder_input_tokens > 0, dtype=cfg.dtype)
-    # Add segmentation block-diagonal attention mask if using segmented data.
-    if encoder_segment_ids is not None:
-      encoder_mask = layers.combine_masks(
-          encoder_mask,
-          layers.make_attention_mask(
-              encoder_segment_ids,
-              encoder_segment_ids,
-              jnp.equal,
-              dtype=cfg.dtype))
-
-    return self.encoder(
-        encoder_input_tokens, encoder_mask, deterministic=not enable_dropout)
-
-  def decode(
-      self,
-      encoded,
-      encoder_input_tokens,  # only needed for masks
-      decoder_input_tokens,
-      decoder_target_tokens,
-      encoder_segment_ids=None,
-      decoder_segment_ids=None,
-      decoder_positions=None,
-      enable_dropout=True,
-      decode=False,
-      max_decode_length=None):
-    """Applies Transformer decoder-branch on encoded-input and target."""
-    cfg = self.config
-
-    # Make padding attention masks.
-    if decode:
-      # Do not mask decoder attention based on targets padding at
-      # decoding/inference time.
-      decoder_mask = None
-      encoder_decoder_mask = layers.make_attention_mask(
-          jnp.ones_like(decoder_target_tokens),
-          encoder_input_tokens > 0,
-          dtype=cfg.dtype)
-    else:
-      decoder_mask = layers.make_decoder_mask(
-          decoder_target_tokens=decoder_target_tokens,
-          dtype=cfg.dtype,
-          decoder_segment_ids=decoder_segment_ids)
-      encoder_decoder_mask = layers.make_attention_mask(
-          decoder_target_tokens > 0, encoder_input_tokens > 0, dtype=cfg.dtype)
-
-    # Add segmentation block-diagonal attention masks if using segmented data.
-    if encoder_segment_ids is not None:
-      if decode:
-        raise ValueError(
-            'During decoding, packing should not be used but '
-            '`encoder_segment_ids` was passed to `Transformer.decode`.')
-
-      encoder_decoder_mask = layers.combine_masks(
-          encoder_decoder_mask,
-          layers.make_attention_mask(
-              decoder_segment_ids,
-              encoder_segment_ids,
-              jnp.equal,
-              dtype=cfg.dtype))
-
-    logits = self.decoder(
-        encoded,
-        decoder_input_tokens=decoder_input_tokens,
-        decoder_positions=decoder_positions,
-        decoder_mask=decoder_mask,
-        encoder_decoder_mask=encoder_decoder_mask,
-        deterministic=not enable_dropout,
-        decode=decode,
-        max_decode_length=max_decode_length)
-    return logits
-
-  def __call__(self,
-               encoder_input_tokens,
-               decoder_input_tokens,
-               decoder_target_tokens,
-               encoder_segment_ids=None,
-               decoder_segment_ids=None,
-               encoder_positions=None,
-               decoder_positions=None,
-               *,
-               enable_dropout: bool = True,
-               decode: bool = False):
-    """Applies Transformer model on the inputs.
-
-    This method requires both decoder_target_tokens and decoder_input_tokens,
-    which is a shifted version of the former. For a packed dataset, it usually
-    has additional processing applied. For example, the first element of each
-    sequence has id 0 instead of the shifted EOS id from the previous sequence.
-
-    Args:
-      encoder_input_tokens: input data to the encoder.
-      decoder_input_tokens: input token to the decoder.
-      decoder_target_tokens: target token to the decoder.
-      encoder_segment_ids: encoder segmentation info for packed examples.
-      decoder_segment_ids: decoder segmentation info for packed examples.
-      encoder_positions: encoder subsequence positions for packed examples.
-      decoder_positions: decoder subsequence positions for packed examples.
-      enable_dropout: Ensables dropout if set to True.
-      decode: Whether to prepare and use an autoregressive cache.
-
-    Returns:
-      logits array from full transformer.
-    """
-    encoded = self.encode(
-        encoder_input_tokens,
-        encoder_segment_ids=encoder_segment_ids,
-        enable_dropout=enable_dropout)
-
-    return self.decode(
-        encoded,
-        encoder_input_tokens,  # only used for masks
-        decoder_input_tokens,
-        decoder_target_tokens,
-        encoder_segment_ids=encoder_segment_ids,
-        decoder_segment_ids=decoder_segment_ids,
-        decoder_positions=decoder_positions,
-        enable_dropout=enable_dropout,
-        decode=decode)
diff --git a/spaces/kTonpa/Text2Cryptopunks/text2punks/transformer.py b/spaces/kTonpa/Text2Cryptopunks/text2punks/transformer.py
deleted file mode 100644
index 52a2fc51c1b65f3990f3364aec6bb8f014785df7..0000000000000000000000000000000000000000
--- a/spaces/kTonpa/Text2Cryptopunks/text2punks/transformer.py
+++ /dev/null
@@ -1,115 +0,0 @@
-from functools import partial
-from itertools import islice, cycle
-
-from torch import nn
-
-from text2punks.attention import Attention, SparseAxialCausalAttention
-
-# helpers
-
-def exists(val):
-    return val is not None
-
-def default(val, d):
-    return val if exists(val) else d
-
-def cast_tuple(val, depth = 1):
-    if isinstance(val, list):
-        val = tuple(val)
-    return val if isinstance(val, tuple) else (val,) * depth
-
-# classes
-
-class SequentialSequence(nn.Module):
-    def __init__(self, layers):
-        super().__init__()
-        self.layers = layers
-
-    def forward(self, x):
-        for (f, g) in list(self.layers):
-            x = x + f(x)
-            x = x + g(x)
-        return x
-
-class PreNorm(nn.Module):
-    def __init__(self, dim, fn):
-        super().__init__()
-        self.norm = nn.LayerNorm(dim)
-        self.fn = fn
-
-    def forward(self, x, **kwargs):
-        return self.fn(self.norm(x), **kwargs)
-
-class FeedForward(nn.Module):
-    def __init__(self, dim, dropout = 0.):
-        super().__init__()
-        self.net = nn.Sequential(
-            nn.Linear(dim, dim * 4),
-            nn.GELU(),
-            nn.Dropout(dropout),
-            nn.Linear(dim * 4, dim)
-        )
-        # the order of dropout nn.Linear(4 * n_embd, n_embd) vs nn.Dropout(resid_pdrop)
-
-    def forward(self, x):
-        return self.net(x)
-
-
-class Transformer(nn.Module):
-    def __init__(
-        self,
-        *,
-        dim,
-        depth,
-        seq_len,
-        causal = True,
-        heads = 8,
-        dim_head = 64,
-        attn_dropout = 0.,
-        resid_dropout = 0.,
-        embd_dropout = 0.,
-        ff_dropout = 0.,
-        image_size = 24,
-        attn_types = None,
-    ):
-        super().__init__()
-        layers = nn.ModuleList([])
-
-        attn_types = default(attn_types, ('full',))
-        attn_types = cast_tuple(attn_types)
-        attn_type_layer = islice(cycle(attn_types), depth)
-
-        for attn_type in attn_type_layer:
-            if attn_type == 'full':
-                attn_class = partial(Attention, causal = causal)
-            elif attn_type == 'axial_row':
-                attn_class = partial(SparseAxialCausalAttention, seq_len = seq_len, axis = 0, image_size = image_size)
-            elif attn_type == 'axial_col':
-                attn_class = partial(SparseAxialCausalAttention, seq_len = seq_len, axis = 1, image_size = image_size)
-            else:
-                raise ValueError(f'attention type "{attn_type}" is not valid')
-
-            attn = attn_class(dim, seq_len = seq_len, heads = heads, dim_head = dim_head, attn_dropout = attn_dropout, resid_dropout = resid_dropout)
-
-            layers.append(nn.ModuleList([
-                PreNorm(dim, attn),
-                PreNorm(dim, FeedForward(dim, dropout = ff_dropout))
-            ]))
-
-        # full attention in the last layer
-
-        attn_class = partial(Attention, causal = causal)
-        attn = attn_class(dim, seq_len = seq_len, heads = heads, dim_head = dim_head, attn_dropout = attn_dropout, resid_dropout = resid_dropout)
-
-        layers.append(nn.ModuleList([
-            PreNorm(dim, attn),
-            PreNorm(dim, FeedForward(dim, dropout = ff_dropout))
-        ]))
-
-        self.layers = SequentialSequence(layers)
-        self.embd_drop = nn.Dropout(embd_dropout)
-
-    def forward(self, x):
-        x = self.embd_drop(x)
-        return self.layers(x)
-        
\ No newline at end of file
diff --git a/spaces/kboaten/MIDI-Audio-Extension/MIDI-song-extender/musicautobot/multitask_transformer/learner.py b/spaces/kboaten/MIDI-Audio-Extension/MIDI-song-extender/musicautobot/multitask_transformer/learner.py
deleted file mode 100644
index 667c73c4045a3a24267e6ec9e73543d423abd25b..0000000000000000000000000000000000000000
--- a/spaces/kboaten/MIDI-Audio-Extension/MIDI-song-extender/musicautobot/multitask_transformer/learner.py
+++ /dev/null
@@ -1,340 +0,0 @@
-from fastai.basics import *
-from ..vocab import *
-from ..utils.top_k_top_p import top_k_top_p
-from ..utils.midifile import is_empty_midi
-from ..music_transformer.transform import *
-from ..music_transformer.learner import filter_invalid_indexes
-from .model import get_multitask_model
-from .dataloader import *
-
-def multitask_model_learner(data:DataBunch, config:dict=None, drop_mult:float=1., 
-                            pretrained_path:PathOrStr=None, **learn_kwargs) -> 'LanguageLearner':
-    "Create a `Learner` with a language model from `data` and `arch`."
-    vocab = data.vocab
-    vocab_size = len(vocab)
-
-    if pretrained_path: 
-        state = torch.load(pretrained_path, map_location='cpu')
-        if config is None: config = state['config']
-
-    model = get_multitask_model(vocab_size, config=config, drop_mult=drop_mult, pad_idx=vocab.pad_idx)
-    metrics = [AverageMultiMetric(partial(m, pad_idx=vocab.pad_idx)) for m in [mask_acc, lm_acc, c2m_acc, m2c_acc]]
-    loss_func = MultiLoss(ignore_index=data.vocab.pad_idx)
-    learn = MultitaskLearner(data, model, loss_func=loss_func, metrics=metrics, **learn_kwargs)
-    
-    if pretrained_path: 
-        get_model(model).load_state_dict(state['model'], strict=False)
-        if not hasattr(learn, 'opt'): learn.create_opt(defaults.lr, learn.wd)
-        try:    learn.opt.load_state_dict(state['opt'])
-        except: pass
-        del state
-        gc.collect()
-        
-    return learn
-
-class MultitaskLearner(Learner):
-    def save(self, file:PathLikeOrBinaryStream=None, with_opt:bool=True, config=None):
-        "Save model and optimizer state (if `with_opt`) with `file` to `self.model_dir`. `file` can be file-like (file or buffer)"
-        out_path = super().save(file, return_path=True, with_opt=with_opt)
-        if config and out_path:
-            state = torch.load(out_path)
-            state['config'] = config
-            torch.save(state, out_path)
-            del state
-            gc.collect()
-        return out_path
-
-    def predict_nw(self, item:MusicItem, n_words:int=128,
-                     temperatures:float=(1.0,1.0), min_bars=4,
-                     top_k=30, top_p=0.6):
-        "Return the `n_words` that come after `text`."
-        self.model.reset()
-        new_idx = []
-        vocab = self.data.vocab
-        x, pos = item.to_tensor(), item.get_pos_tensor()
-        last_pos = pos[-1] if len(pos) else 0
-        y = torch.tensor([0])
-
-        start_pos = last_pos
-
-        sep_count = 0
-        bar_len = SAMPLE_FREQ * 4 # assuming 4/4 time
-        vocab = self.data.vocab
-
-        repeat_count = 0
-
-        for i in progress_bar(range(n_words), leave=True):
-            batch = { 'lm': { 'x': x[None], 'pos': pos[None] } }, y
-            logits = self.pred_batch(batch=batch)['lm'][-1][-1]
-
-            prev_idx = new_idx[-1] if len(new_idx) else vocab.pad_idx
-
-            # Temperature
-            # Use first temperatures value if last prediction was duration
-            temperature = temperatures[0] if vocab.is_duration_or_pad(prev_idx) else temperatures[1]
-            repeat_penalty = max(0, np.log((repeat_count+1)/4)/5) * temperature
-            temperature += repeat_penalty
-            if temperature != 1.: logits = logits / temperature
-                
-
-            # Filter
-            # bar = 16 beats
-            filter_value = -float('Inf')
-            if ((last_pos - start_pos) // 16) <= min_bars: logits[vocab.bos_idx] = filter_value
-
-            logits = filter_invalid_indexes(logits, prev_idx, vocab, filter_value=filter_value)
-            logits = top_k_top_p(logits, top_k=top_k, top_p=top_p, filter_value=filter_value)
-            
-            # Sample
-            probs = F.softmax(logits, dim=-1)
-            idx = torch.multinomial(probs, 1).item()
-
-            # Update repeat count
-            num_choices = len(probs.nonzero().view(-1))
-            if num_choices <= 2: repeat_count += 1
-            else: repeat_count = repeat_count // 2
-
-            if prev_idx==vocab.sep_idx: 
-                duration = idx - vocab.dur_range[0]
-                last_pos = last_pos + duration
-
-                bars_pred = (last_pos - start_pos) // 16
-                abs_bar = last_pos // 16
-                # if (bars % 8 == 0) and (bars_pred > min_bars): break
-                if (i / n_words > 0.80) and (abs_bar % 4 == 0): break
-
-
-            if idx==vocab.bos_idx: 
-                print('Predicted BOS token. Returning prediction...')
-                break
-
-            new_idx.append(idx)
-            x = x.new_tensor([idx])
-            pos = pos.new_tensor([last_pos])
-
-        pred = vocab.to_music_item(np.array(new_idx))
-        full = item.append(pred)
-        return pred, full
-
-    def predict_mask(self, masked_item:MusicItem,
-                    temperatures:float=(1.0,1.0),
-                    top_k=20, top_p=0.8):
-        x = masked_item.to_tensor()
-        pos = masked_item.get_pos_tensor()
-        y = torch.tensor([0])
-        vocab = self.data.vocab
-        self.model.reset()
-        mask_idxs = (x == vocab.mask_idx).nonzero().view(-1)
-
-        repeat_count = 0
-
-        for midx in progress_bar(mask_idxs, leave=True):
-            prev_idx = x[midx-1]
-
-            # Using original positions, otherwise model gets too off track
-            # pos = torch.tensor(-position_enc(xb[0].cpu().numpy()), device=xb.device)[None]
-    
-            # Next Word
-            logits = self.pred_batch(batch=({ 'msk': { 'x': x[None], 'pos': pos[None] } }, y) )['msk'][0][midx]
-
-            # Temperature
-            # Use first temperatures value if last prediction was duration
-            temperature = temperatures[0] if vocab.is_duration_or_pad(prev_idx) else temperatures[1]
-            repeat_penalty = max(0, np.log((repeat_count+1)/4)/5) * temperature
-            temperature += repeat_penalty
-            if temperature != 1.: logits = logits / temperature
-
-            # Filter
-            filter_value = -float('Inf')
-            special_idxs = [vocab.bos_idx, vocab.sep_idx, vocab.stoi[EOS]]
-            logits[special_idxs] = filter_value # Don't allow any special tokens (as we are only removing notes and durations)
-            logits = filter_invalid_indexes(logits, prev_idx, vocab, filter_value=filter_value)
-            logits = top_k_top_p(logits, top_k=top_k, top_p=top_p, filter_value=filter_value)
-
-            # Sampling
-            probs = F.softmax(logits, dim=-1)
-            idx = torch.multinomial(probs, 1).item()
-
-            # Update repeat count
-            num_choices = len(probs.nonzero().view(-1))
-            if num_choices <= 2: repeat_count += 1
-            else: repeat_count = repeat_count // 2
-
-            x[midx] = idx
-
-        return vocab.to_music_item(x.cpu().numpy())
-
-    def predict_s2s(self, input_item:MusicItem, target_item:MusicItem, n_words:int=256,
-                        temperatures:float=(1.0,1.0), top_k=30, top_p=0.8,
-                        use_memory=True):
-        vocab = self.data.vocab
-        
-        # Input doesn't change. We can reuse the encoder output on each prediction
-        with torch.no_grad():
-            inp, inp_pos = input_item.to_tensor(), input_item.get_pos_tensor()
-            x_enc = self.model.encoder(inp[None], inp_pos[None])
-        
-        # target
-        targ = target_item.data.tolist()
-        targ_pos = target_item.position.tolist()
-        last_pos = targ_pos[-1]
-        self.model.reset()
-
-        repeat_count = 0
-
-        max_pos = input_item.position[-1] + SAMPLE_FREQ * 4 # Only predict until both tracks/parts have the same length
-        x, pos = inp.new_tensor(targ), inp_pos.new_tensor(targ_pos)
-        
-        for i in progress_bar(range(n_words), leave=True):
-            # Predict
-            with torch.no_grad():
-                dec = self.model.decoder(x[None], pos[None], x_enc)
-                logits = self.model.head(dec)[-1, -1]
-
-            # Temperature
-            # Use first temperatures value if last prediction was duration
-            prev_idx = targ[-1] if len(targ) else vocab.pad_idx
-            temperature = temperatures[0] if vocab.is_duration_or_pad(prev_idx) else temperatures[1]
-            repeat_penalty = max(0, np.log((repeat_count+1)/4)/5) * temperature
-            temperature += repeat_penalty
-            if temperature != 1.: logits = logits / temperature
-                
-            # Filter
-            filter_value = -float('Inf')
-            logits = filter_invalid_indexes(logits, prev_idx, vocab, filter_value=filter_value)
-            logits = top_k_top_p(logits, top_k=top_k, top_p=top_p, filter_value=filter_value)
-
-            # Sample
-            probs = F.softmax(logits, dim=-1)
-            idx = torch.multinomial(probs, 1).item()
-
-            # Update repeat count
-            num_choices = len(probs.nonzero().view(-1))
-            if num_choices <= 2: repeat_count += 1
-            else: repeat_count = repeat_count // 2
-
-            if idx == vocab.bos_idx | idx == vocab.stoi[EOS]: 
-                print('Predicting BOS/EOS')
-                break
-
-            if prev_idx == vocab.sep_idx: 
-                duration = idx - vocab.dur_range[0]
-                last_pos = last_pos + duration
-                if last_pos > max_pos:
-                    print('Predicted past counter-part length. Returning early')
-                    break
-
-            targ_pos.append(last_pos)
-            targ.append(idx)
-            
-            if use_memory:
-                # Relying on memory for kv. Only need last prediction index
-                x, pos = inp.new_tensor([targ[-1]]), inp_pos.new_tensor([targ_pos[-1]])
-            else:
-                # Reset memory after each prediction, since we feeding the whole sequence every time
-                self.model.reset()
-                x, pos = inp.new_tensor(targ), inp_pos.new_tensor(targ_pos)
-
-        return vocab.to_music_item(np.array(targ))
-    
-# High level prediction functions from midi file
-def nw_predict_from_midi(learn, midi=None, n_words=400, 
-                      temperatures=(1.0,1.0), top_k=30, top_p=0.6, seed_len=None, **kwargs):
-    vocab = learn.data.vocab
-    seed = MusicItem.from_file(midi, vocab) if not is_empty_midi(midi) else MusicItem.empty(vocab)
-    if seed_len is not None: seed = seed.trim_to_beat(seed_len)
-        
-    pred, full = learn.predict_nw(seed, n_words=n_words, temperatures=temperatures, top_k=top_k, top_p=top_p, **kwargs)
-    return full
-
-def s2s_predict_from_midi(learn, midi=None, n_words=200, 
-                      temperatures=(1.0,1.0), top_k=24, top_p=0.7, seed_len=None, pred_melody=True, **kwargs):
-    multitrack_item = MultitrackItem.from_file(midi, learn.data.vocab)
-    melody, chords = multitrack_item.melody, multitrack_item.chords
-    inp, targ = (chords, melody) if pred_melody else (melody, chords)
-    
-    # if seed_len is passed, cutoff sequence so we can predict the rest
-    if seed_len is not None: targ = targ.trim_to_beat(seed_len)
-    targ = targ.remove_eos()
-        
-    pred = learn.predict_s2s(inp, targ, n_words=n_words, temperatures=temperatures, top_k=top_k, top_p=top_p, **kwargs)
-    
-    part_order = (pred, inp) if pred_melody else (inp, pred)
-    return MultitrackItem(*part_order)
-
-def mask_predict_from_midi(learn, midi=None, predict_notes=True,
-                           temperatures=(1.0,1.0), top_k=30, top_p=0.7, section=None, **kwargs):
-    item = MusicItem.from_file(midi, learn.data.vocab)
-    masked_item = item.mask_pitch(section) if predict_notes else item.mask_duration(section)
-    pred = learn.predict_mask(masked_item, temperatures=temperatures, top_k=top_k, top_p=top_p, **kwargs)
-    return pred
-
-# LOSS AND METRICS
-
-class MultiLoss():
-    def __init__(self, ignore_index=None):
-        "Loss mult - Mask, NextWord, Seq2Seq"
-        self.loss = CrossEntropyFlat(ignore_index=ignore_index)
-        
-    def __call__(self, inputs:Dict[str,Tensor], targets:Dict[str,Tensor])->Rank0Tensor:
-        losses = [self.loss(inputs[key], target) for key,target in targets.items()]
-        return sum(losses)
-    
-def acc_ignore_pad(input:Tensor, targ:Tensor, pad_idx)->Rank0Tensor:
-    if input is None or targ is None: return None
-    n = targ.shape[0]
-    input = input.argmax(dim=-1).view(n,-1)
-    targ = targ.view(n,-1)
-    mask = targ != pad_idx
-    return (input[mask]==targ[mask]).float().mean()
-
-def acc_index(inputs, targets, key, pad_idx):
-    return acc_ignore_pad(inputs.get(key), targets.get(key), pad_idx)
-    
-def mask_acc(inputs, targets, pad_idx): return acc_index(inputs, targets, 'msk', pad_idx)
-def lm_acc(inputs, targets, pad_idx): return acc_index(inputs, targets, 'lm', pad_idx)
-def c2m_acc(inputs, targets, pad_idx): return acc_index(inputs, targets, 'c2m', pad_idx)
-def m2c_acc(inputs, targets, pad_idx): return acc_index(inputs, targets, 'm2c', pad_idx)
-
-
-class AverageMultiMetric(AverageMetric):
-    "Updated fastai.AverageMetric to support multi task metrics."
-    def on_batch_end(self, last_output, last_target, **kwargs):
-        "Update metric computation with `last_output` and `last_target`."
-        if not is_listy(last_target): last_target=[last_target]
-        val = self.func(last_output, *last_target)
-        if val is None: return
-        self.count += first_el(last_target).size(0)
-        if self.world:
-            val = val.clone()
-            dist.all_reduce(val, op=dist.ReduceOp.SUM)
-            val /= self.world
-        self.val += first_el(last_target).size(0) * val.detach().cpu()
-
-    def on_epoch_end(self, last_metrics, **kwargs):
-        "Set the final result in `last_metrics`."
-        if self.count == 0: return add_metrics(last_metrics, 0)
-        return add_metrics(last_metrics, self.val/self.count)
-    
-
-# MODEL LOADING
-class MTTrainer(LearnerCallback):
-    "`Callback` that regroups lr adjustment to seq_len, AR and TAR."
-    def __init__(self, learn:Learner, dataloaders=None, starting_mask_window=1):
-        super().__init__(learn)
-        self.count = 1
-        self.mw_start = starting_mask_window
-        self.dataloaders = dataloaders
-
-    def on_epoch_begin(self, **kwargs):
-        "Reset the hidden state of the model."
-        model = get_model(self.learn.model)
-        model.reset()
-        model.encoder.mask_steps = max(self.count+self.mw_start, 100)
-        
-    def on_epoch_end(self, last_metrics, **kwargs):
-        "Finish the computation and sends the result to the Recorder."
-        if self.dataloaders is not None: 
-            self.learn.data = self.dataloaders[self.count % len(self.dataloaders)]
-        self.count += 1
-
diff --git a/spaces/kdrkdrkdr/ProsekaTTS/export_model.py b/spaces/kdrkdrkdr/ProsekaTTS/export_model.py
deleted file mode 100644
index 98a49835df5a7a2486e76ddf94fbbb4444b52203..0000000000000000000000000000000000000000
--- a/spaces/kdrkdrkdr/ProsekaTTS/export_model.py
+++ /dev/null
@@ -1,13 +0,0 @@
-import torch
-
-if __name__ == '__main__':
-    model_path = "saved_model/11/model.pth"
-    output_path = "saved_model/11/model1.pth"
-    checkpoint_dict = torch.load(model_path, map_location='cpu')
-    checkpoint_dict_new = {}
-    for k, v in checkpoint_dict.items():
-        if k == "optimizer":
-            print("remove optimizer")
-            continue
-        checkpoint_dict_new[k] = v
-    torch.save(checkpoint_dict_new, output_path)
diff --git a/spaces/keithhon/Real-Time-Voice-Cloning/encoder_preprocess.py b/spaces/keithhon/Real-Time-Voice-Cloning/encoder_preprocess.py
deleted file mode 100644
index 11502013c8d75d4652fb0ffdcdc49d55e8fb8bc9..0000000000000000000000000000000000000000
--- a/spaces/keithhon/Real-Time-Voice-Cloning/encoder_preprocess.py
+++ /dev/null
@@ -1,70 +0,0 @@
-from encoder.preprocess import preprocess_librispeech, preprocess_voxceleb1, preprocess_voxceleb2
-from utils.argutils import print_args
-from pathlib import Path
-import argparse
-
-if __name__ == "__main__":
-    class MyFormatter(argparse.ArgumentDefaultsHelpFormatter, argparse.RawDescriptionHelpFormatter):
-        pass
-    
-    parser = argparse.ArgumentParser(
-        description="Preprocesses audio files from datasets, encodes them as mel spectrograms and "
-                    "writes them to the disk. This will allow you to train the encoder. The "
-                    "datasets required are at least one of VoxCeleb1, VoxCeleb2 and LibriSpeech. "
-                    "Ideally, you should have all three. You should extract them as they are "
-                    "after having downloaded them and put them in a same directory, e.g.:\n"
-                    "-[datasets_root]\n"
-                    "  -LibriSpeech\n"
-                    "    -train-other-500\n"
-                    "  -VoxCeleb1\n"
-                    "    -wav\n"
-                    "    -vox1_meta.csv\n"
-                    "  -VoxCeleb2\n"
-                    "    -dev",
-        formatter_class=MyFormatter
-    )
-    parser.add_argument("datasets_root", type=Path, help=\
-        "Path to the directory containing your LibriSpeech/TTS and VoxCeleb datasets.")
-    parser.add_argument("-o", "--out_dir", type=Path, default=argparse.SUPPRESS, help=\
-        "Path to the output directory that will contain the mel spectrograms. If left out, "
-        "defaults to <datasets_root>/SV2TTS/encoder/")
-    parser.add_argument("-d", "--datasets", type=str, 
-                        default="librispeech_other,voxceleb1,voxceleb2", help=\
-        "Comma-separated list of the name of the datasets you want to preprocess. Only the train "
-        "set of these datasets will be used. Possible names: librispeech_other, voxceleb1, "
-        "voxceleb2.")
-    parser.add_argument("-s", "--skip_existing", action="store_true", help=\
-        "Whether to skip existing output files with the same name. Useful if this script was "
-        "interrupted.")
-    parser.add_argument("--no_trim", action="store_true", help=\
-        "Preprocess audio without trimming silences (not recommended).")
-    args = parser.parse_args()
-
-    # Verify webrtcvad is available
-    if not args.no_trim:
-        try:
-            import webrtcvad
-        except:
-            raise ModuleNotFoundError("Package 'webrtcvad' not found. This package enables "
-                "noise removal and is recommended. Please install and try again. If installation fails, "
-                "use --no_trim to disable this error message.")
-    del args.no_trim
-
-    # Process the arguments
-    args.datasets = args.datasets.split(",")
-    if not hasattr(args, "out_dir"):
-        args.out_dir = args.datasets_root.joinpath("SV2TTS", "encoder")
-    assert args.datasets_root.exists()
-    args.out_dir.mkdir(exist_ok=True, parents=True)
-
-    # Preprocess the datasets
-    print_args(args, parser)
-    preprocess_func = {
-        "librispeech_other": preprocess_librispeech,
-        "voxceleb1": preprocess_voxceleb1,
-        "voxceleb2": preprocess_voxceleb2,
-    }
-    args = vars(args)
-    for dataset in args.pop("datasets"):
-        print("Preprocessing %s" % dataset)
-        preprocess_func[dataset](**args)
diff --git a/spaces/kevinwang676/ChatGLM2-SadTalker-VC/src/face3d/models/arcface_torch/configs/__init__.py b/spaces/kevinwang676/ChatGLM2-SadTalker-VC/src/face3d/models/arcface_torch/configs/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/kevinwang676/ChatGLM2-SadTalker-VC/src/generate_facerender_batch.py b/spaces/kevinwang676/ChatGLM2-SadTalker-VC/src/generate_facerender_batch.py
deleted file mode 100644
index a821a6ece2fcff83c288a0989097d863cfec3dd1..0000000000000000000000000000000000000000
--- a/spaces/kevinwang676/ChatGLM2-SadTalker-VC/src/generate_facerender_batch.py
+++ /dev/null
@@ -1,135 +0,0 @@
-import os
-import numpy as np
-from PIL import Image
-from skimage import io, img_as_float32, transform
-import torch
-import scipy.io as scio
-
-def get_facerender_data(coeff_path, pic_path, first_coeff_path, audio_path, 
-                        batch_size, input_yaw_list=None, input_pitch_list=None, input_roll_list=None, 
-                        expression_scale=1.0, still_mode = False, preprocess='crop', size = 256, facemodel='facevid2vid'):
-
-    semantic_radius = 13
-    video_name = os.path.splitext(os.path.split(coeff_path)[-1])[0]
-    txt_path = os.path.splitext(coeff_path)[0]
-
-    data={}
-
-    img1 = Image.open(pic_path)
-    source_image = np.array(img1)
-    source_image = img_as_float32(source_image)
-    source_image = transform.resize(source_image, (size, size, 3))
-    source_image = source_image.transpose((2, 0, 1))
-    source_image_ts = torch.FloatTensor(source_image).unsqueeze(0)
-    source_image_ts = source_image_ts.repeat(batch_size, 1, 1, 1)
-    data['source_image'] = source_image_ts
- 
-    source_semantics_dict = scio.loadmat(first_coeff_path)
-    generated_dict = scio.loadmat(coeff_path)
-
-    if 'full' not in preprocess.lower() and facemodel != 'pirender':
-        source_semantics = source_semantics_dict['coeff_3dmm'][:1,:70]         #1 70
-        generated_3dmm = generated_dict['coeff_3dmm'][:,:70]
-    else:
-        source_semantics = source_semantics_dict['coeff_3dmm'][:1,:73]         #1 70
-        generated_3dmm = generated_dict['coeff_3dmm'][:,:70]
-
-    source_semantics_new = transform_semantic_1(source_semantics, semantic_radius)
-    source_semantics_ts = torch.FloatTensor(source_semantics_new).unsqueeze(0)
-    source_semantics_ts = source_semantics_ts.repeat(batch_size, 1, 1)
-    data['source_semantics'] = source_semantics_ts
-
-    # target 
-    generated_3dmm[:, :64] = generated_3dmm[:, :64] * expression_scale
-
-    if 'full' in preprocess.lower() or facemodel == 'pirender':
-        generated_3dmm = np.concatenate([generated_3dmm, np.repeat(source_semantics[:,70:], generated_3dmm.shape[0], axis=0)], axis=1)
-
-    if still_mode:
-        generated_3dmm[:, 64:] = np.repeat(source_semantics[:, 64:], generated_3dmm.shape[0], axis=0)
-
-    with open(txt_path+'.txt', 'w') as f:
-        for coeff in generated_3dmm:
-            for i in coeff:
-                f.write(str(i)[:7]   + '  '+'\t')
-            f.write('\n')
-
-    target_semantics_list = [] 
-    frame_num = generated_3dmm.shape[0]
-    data['frame_num'] = frame_num
-    for frame_idx in range(frame_num):
-        target_semantics = transform_semantic_target(generated_3dmm, frame_idx, semantic_radius)
-        target_semantics_list.append(target_semantics)
-
-    remainder = frame_num%batch_size
-    if remainder!=0:
-        for _ in range(batch_size-remainder):
-            target_semantics_list.append(target_semantics)
-
-    target_semantics_np = np.array(target_semantics_list)             #frame_num 70 semantic_radius*2+1
-    target_semantics_np = target_semantics_np.reshape(batch_size, -1, target_semantics_np.shape[-2], target_semantics_np.shape[-1])
-    data['target_semantics_list'] = torch.FloatTensor(target_semantics_np)
-    data['video_name'] = video_name
-    data['audio_path'] = audio_path
-    
-    if input_yaw_list is not None:
-        yaw_c_seq = gen_camera_pose(input_yaw_list, frame_num, batch_size)
-        data['yaw_c_seq'] = torch.FloatTensor(yaw_c_seq)
-    if input_pitch_list is not None:
-        pitch_c_seq = gen_camera_pose(input_pitch_list, frame_num, batch_size)
-        data['pitch_c_seq'] = torch.FloatTensor(pitch_c_seq)
-    if input_roll_list is not None:
-        roll_c_seq = gen_camera_pose(input_roll_list, frame_num, batch_size) 
-        data['roll_c_seq'] = torch.FloatTensor(roll_c_seq)
- 
-    return data
-
-def transform_semantic_1(semantic, semantic_radius):
-    semantic_list =  [semantic for i in range(0, semantic_radius*2+1)]
-    coeff_3dmm = np.concatenate(semantic_list, 0)
-    return coeff_3dmm.transpose(1,0)
-
-def transform_semantic_target(coeff_3dmm, frame_index, semantic_radius):
-    num_frames = coeff_3dmm.shape[0]
-    seq = list(range(frame_index- semantic_radius, frame_index + semantic_radius+1))
-    index = [ min(max(item, 0), num_frames-1) for item in seq ] 
-    coeff_3dmm_g = coeff_3dmm[index, :]
-    return coeff_3dmm_g.transpose(1,0)
-
-def gen_camera_pose(camera_degree_list, frame_num, batch_size):
-
-    new_degree_list = [] 
-    if len(camera_degree_list) == 1:
-        for _ in range(frame_num):
-            new_degree_list.append(camera_degree_list[0]) 
-        remainder = frame_num%batch_size
-        if remainder!=0:
-            for _ in range(batch_size-remainder):
-                new_degree_list.append(new_degree_list[-1])
-        new_degree_np = np.array(new_degree_list).reshape(batch_size, -1) 
-        return new_degree_np
-
-    degree_sum = 0.
-    for i, degree in enumerate(camera_degree_list[1:]):
-        degree_sum += abs(degree-camera_degree_list[i])
-    
-    degree_per_frame = degree_sum/(frame_num-1)
-    for i, degree in enumerate(camera_degree_list[1:]):
-        degree_last = camera_degree_list[i]
-        degree_step = degree_per_frame * abs(degree-degree_last)/(degree-degree_last)
-        new_degree_list =  new_degree_list + list(np.arange(degree_last, degree, degree_step))
-    if len(new_degree_list) > frame_num:
-        new_degree_list = new_degree_list[:frame_num]
-    elif len(new_degree_list) < frame_num:
-        for _ in range(frame_num-len(new_degree_list)):
-            new_degree_list.append(new_degree_list[-1])
-    print(len(new_degree_list))
-    print(frame_num)
-
-    remainder = frame_num%batch_size
-    if remainder!=0:
-        for _ in range(batch_size-remainder):
-            new_degree_list.append(new_degree_list[-1])
-    new_degree_np = np.array(new_degree_list).reshape(batch_size, -1) 
-    return new_degree_np
-    
diff --git a/spaces/kevinwang676/ChatGLM2-SadTalker/scripts/test.sh b/spaces/kevinwang676/ChatGLM2-SadTalker/scripts/test.sh
deleted file mode 100644
index bcfecfde94951c8feec231c14c30a685674a284a..0000000000000000000000000000000000000000
--- a/spaces/kevinwang676/ChatGLM2-SadTalker/scripts/test.sh
+++ /dev/null
@@ -1,21 +0,0 @@
-# ### some test command before commit.
-# python inference.py --preprocess crop --size 256
-# python inference.py --preprocess crop --size 512
-
-# python inference.py --preprocess extcrop --size 256
-# python inference.py --preprocess extcrop --size 512
-
-# python inference.py --preprocess resize --size 256
-# python inference.py --preprocess resize --size 512
-
-# python inference.py --preprocess full --size 256
-# python inference.py --preprocess full --size 512
-
-# python inference.py --preprocess extfull --size 256
-# python inference.py --preprocess extfull --size 512
-
-python inference.py --preprocess full --size 256 --enhancer gfpgan
-python inference.py --preprocess full --size 512 --enhancer gfpgan
-
-python inference.py --preprocess full --size 256 --enhancer gfpgan --still
-python inference.py --preprocess full --size 512 --enhancer gfpgan --still
diff --git a/spaces/kevinwang676/VoiceChanger/src/utils/videoio.py b/spaces/kevinwang676/VoiceChanger/src/utils/videoio.py
deleted file mode 100644
index 08bfbdd7d4be97dc17fea4ad7b2733e9eb0ef975..0000000000000000000000000000000000000000
--- a/spaces/kevinwang676/VoiceChanger/src/utils/videoio.py
+++ /dev/null
@@ -1,41 +0,0 @@
-import shutil
-import uuid
-
-import os
-
-import cv2
-
-def load_video_to_cv2(input_path):
-    video_stream = cv2.VideoCapture(input_path)
-    fps = video_stream.get(cv2.CAP_PROP_FPS)
-    full_frames = [] 
-    while 1:
-        still_reading, frame = video_stream.read()
-        if not still_reading:
-            video_stream.release()
-            break 
-        full_frames.append(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
-    return full_frames
-
-def save_video_with_watermark(video, audio, save_path, watermark=False):
-    temp_file = str(uuid.uuid4())+'.mp4'
-    cmd = r'ffmpeg -y -hide_banner -loglevel error -i "%s" -i "%s" -vcodec copy "%s"' % (video, audio, temp_file)
-    os.system(cmd)
-
-    if watermark is False:
-        shutil.move(temp_file, save_path)
-    else:
-        # watermark
-        try:
-            ##### check if stable-diffusion-webui
-            import webui
-            from modules import paths
-            watarmark_path = paths.script_path+"/extensions/SadTalker/docs/sadtalker_logo.png"
-        except:
-            # get the root path of sadtalker.
-            dir_path = os.path.dirname(os.path.realpath(__file__))
-            watarmark_path = dir_path+"/../../docs/sadtalker_logo.png"
-
-        cmd = r'ffmpeg -y -hide_banner -loglevel error -i "%s" -i "%s" -filter_complex "[1]scale=100:-1[wm];[0][wm]overlay=(main_w-overlay_w)-10:10" "%s"' % (temp_file, watarmark_path, save_path)
-        os.system(cmd)
-        os.remove(temp_file)
\ No newline at end of file
diff --git a/spaces/kevinwang676/vits-fast-finetuning-pcr/transforms.py b/spaces/kevinwang676/vits-fast-finetuning-pcr/transforms.py
deleted file mode 100644
index 4793d67ca5a5630e0ffe0f9fb29445c949e64dae..0000000000000000000000000000000000000000
--- a/spaces/kevinwang676/vits-fast-finetuning-pcr/transforms.py
+++ /dev/null
@@ -1,193 +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))
-        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/king007/Stable-Diffusion-ControlNet-WebUI/diffusion_webui/diffusion_models/controlnet/controlnet_mlsd.py b/spaces/king007/Stable-Diffusion-ControlNet-WebUI/diffusion_webui/diffusion_models/controlnet/controlnet_mlsd.py
deleted file mode 100644
index 6c5a33557e1792f7b977818460e112cc7acc03cc..0000000000000000000000000000000000000000
--- a/spaces/king007/Stable-Diffusion-ControlNet-WebUI/diffusion_webui/diffusion_models/controlnet/controlnet_mlsd.py
+++ /dev/null
@@ -1,173 +0,0 @@
-import gradio as gr
-import torch
-from controlnet_aux import MLSDdetector
-from diffusers import ControlNetModel, StableDiffusionControlNetPipeline
-from PIL import Image
-
-from diffusion_webui.utils.model_list import stable_model_list
-from diffusion_webui.utils.scheduler_list import (
-    SCHEDULER_LIST,
-    get_scheduler_list,
-)
-
-
-class StableDiffusionControlNetMLSDGenerator:
-    def __init__(self):
-        self.pipe = None
-
-    def load_model(self, stable_model_path, controlnet_model_path, scheduler):
-        if self.pipe is None:
-            controlnet = ControlNetModel.from_pretrained(
-                controlnet_model_path, torch_dtype=torch.float16
-            )
-
-            self.pipe = StableDiffusionControlNetPipeline.from_pretrained(
-                pretrained_model_name_or_path=stable_model_path,
-                controlnet=controlnet,
-                safety_checker=None,
-                torch_dtype=torch.float16,
-            )
-
-        self.pipe = get_scheduler_list(pipe=self.pipe, scheduler=scheduler)
-        self.pipe.to("cuda")
-        self.pipe.enable_xformers_memory_efficient_attention()
-
-        return self.pipe
-
-    def controlnet_mlsd(self, image_path: str):
-        mlsd = MLSDdetector.from_pretrained("lllyasviel/ControlNet")
-
-        image = Image.open(image_path)
-        image = mlsd(image)
-
-        return image
-
-    def generate_image(
-        self,
-        image_path: str,
-        model_path: str,
-        prompt: str,
-        negative_prompt: str,
-        num_images_per_prompt: int,
-        guidance_scale: int,
-        num_inference_step: int,
-        scheduler: str,
-        seed_generator: int,
-    ):
-        image = self.controlnet_mlsd(image_path=image_path)
-
-        pipe = self.load_model(
-            stable_model_path=model_path,
-            controlnet_model_path="lllyasviel/sd-controlnet-mlsd",
-            scheduler=scheduler,
-        )
-
-        if seed_generator == 0:
-            random_seed = torch.randint(0, 1000000, (1,))
-            generator = torch.manual_seed(random_seed)
-        else:
-            generator = torch.manual_seed(seed_generator)
-
-        output = pipe(
-            prompt=prompt,
-            image=image,
-            negative_prompt=negative_prompt,
-            num_images_per_prompt=num_images_per_prompt,
-            num_inference_steps=num_inference_step,
-            guidance_scale=guidance_scale,
-            generator=generator,
-        ).images
-
-        return output
-
-    def app():
-        with gr.Blocks():
-            with gr.Row():
-                with gr.Column():
-                    controlnet_mlsd_image_file = gr.Image(
-                        type="filepath", label="Image"
-                    )
-
-                    controlnet_mlsd_prompt = gr.Textbox(
-                        lines=1,
-                        show_label=False,
-                        placeholder="Prompt",
-                    )
-
-                    controlnet_mlsd_negative_prompt = gr.Textbox(
-                        lines=1,
-                        show_label=False,
-                        placeholder="Negative Prompt",
-                    )
-
-                    with gr.Row():
-                        with gr.Column():
-                            controlnet_mlsd_model_id = gr.Dropdown(
-                                choices=stable_model_list,
-                                value=stable_model_list[0],
-                                label="Stable Model Id",
-                            )
-                            controlnet_mlsd_guidance_scale = gr.Slider(
-                                minimum=0.1,
-                                maximum=15,
-                                step=0.1,
-                                value=7.5,
-                                label="Guidance Scale",
-                            )
-                            controlnet_mlsd_num_inference_step = gr.Slider(
-                                minimum=1,
-                                maximum=100,
-                                step=1,
-                                value=50,
-                                label="Num Inference Step",
-                            )
-
-                        with gr.Row():
-                            with gr.Column():
-                                controlnet_mlsd_scheduler = gr.Dropdown(
-                                    choices=SCHEDULER_LIST,
-                                    value=SCHEDULER_LIST[0],
-                                    label="Scheduler",
-                                )
-
-                                controlnet_mlsd_seed_generator = gr.Slider(
-                                    minimum=0,
-                                    maximum=1000000,
-                                    step=1,
-                                    value=0,
-                                    label="Seed Generator",
-                                )
-                                controlnet_mlsd_num_images_per_prompt = (
-                                    gr.Slider(
-                                        minimum=1,
-                                        maximum=10,
-                                        step=1,
-                                        value=1,
-                                        label="Number Of Images",
-                                    )
-                                )
-
-                    controlnet_mlsd_predict = gr.Button(value="Generator")
-
-                with gr.Column():
-                    output_image = gr.Gallery(
-                        label="Generated images",
-                        show_label=False,
-                        elem_id="gallery",
-                    ).style(grid=(1, 2))
-
-            controlnet_mlsd_predict.click(
-                fn=StableDiffusionControlNetMLSDGenerator().generate_image,
-                inputs=[
-                    controlnet_mlsd_image_file,
-                    controlnet_mlsd_model_id,
-                    controlnet_mlsd_prompt,
-                    controlnet_mlsd_negative_prompt,
-                    controlnet_mlsd_num_images_per_prompt,
-                    controlnet_mlsd_guidance_scale,
-                    controlnet_mlsd_num_inference_step,
-                    controlnet_mlsd_scheduler,
-                    controlnet_mlsd_seed_generator,
-                ],
-                outputs=output_image,
-            )
diff --git a/spaces/kira4424/VITS-fast-fine-tuning/text/japanese.py b/spaces/kira4424/VITS-fast-fine-tuning/text/japanese.py
deleted file mode 100644
index 375e4d50872d5c68ee57ca17470a2ca425425eba..0000000000000000000000000000000000000000
--- a/spaces/kira4424/VITS-fast-fine-tuning/text/japanese.py
+++ /dev/null
@@ -1,153 +0,0 @@
-import re
-from unidecode import unidecode
-import pyopenjtalk
-
-
-# Regular expression matching Japanese without punctuation marks:
-_japanese_characters = re.compile(
-    r'[A-Za-z\d\u3005\u3040-\u30ff\u4e00-\u9fff\uff11-\uff19\uff21-\uff3a\uff41-\uff5a\uff66-\uff9d]')
-
-# Regular expression matching non-Japanese characters or punctuation marks:
-_japanese_marks = re.compile(
-    r'[^A-Za-z\d\u3005\u3040-\u30ff\u4e00-\u9fff\uff11-\uff19\uff21-\uff3a\uff41-\uff5a\uff66-\uff9d]')
-
-# List of (symbol, Japanese) pairs for marks:
-_symbols_to_japanese = [(re.compile('%s' % x[0]), x[1]) for x in [
-    ('％', 'パーセント')
-]]
-
-# List of (romaji, ipa) pairs for marks:
-_romaji_to_ipa = [(re.compile('%s' % x[0]), x[1]) for x in [
-    ('ts', 'ʦ'),
-    ('u', 'ɯ'),
-    ('j', 'ʥ'),
-    ('y', 'j'),
-    ('ni', 'n^i'),
-    ('nj', 'n^'),
-    ('hi', 'çi'),
-    ('hj', 'ç'),
-    ('f', 'ɸ'),
-    ('I', 'i*'),
-    ('U', 'ɯ*'),
-    ('r', 'ɾ')
-]]
-
-# List of (romaji, ipa2) pairs for marks:
-_romaji_to_ipa2 = [(re.compile('%s' % x[0]), x[1]) for x in [
-    ('u', 'ɯ'),
-    ('ʧ', 'tʃ'),
-    ('j', 'dʑ'),
-    ('y', 'j'),
-    ('ni', 'n^i'),
-    ('nj', 'n^'),
-    ('hi', 'çi'),
-    ('hj', 'ç'),
-    ('f', 'ɸ'),
-    ('I', 'i*'),
-    ('U', 'ɯ*'),
-    ('r', 'ɾ')
-]]
-
-# List of (consonant, sokuon) pairs:
-_real_sokuon = [(re.compile('%s' % x[0]), x[1]) for x in [
-    (r'Q([↑↓]*[kg])', r'k#\1'),
-    (r'Q([↑↓]*[tdjʧ])', r't#\1'),
-    (r'Q([↑↓]*[sʃ])', r's\1'),
-    (r'Q([↑↓]*[pb])', r'p#\1')
-]]
-
-# List of (consonant, hatsuon) pairs:
-_real_hatsuon = [(re.compile('%s' % x[0]), x[1]) for x in [
-    (r'N([↑↓]*[pbm])', r'm\1'),
-    (r'N([↑↓]*[ʧʥj])', r'n^\1'),
-    (r'N([↑↓]*[tdn])', r'n\1'),
-    (r'N([↑↓]*[kg])', r'ŋ\1')
-]]
-
-
-def symbols_to_japanese(text):
-    for regex, replacement in _symbols_to_japanese:
-        text = re.sub(regex, replacement, text)
-    return text
-
-
-def japanese_to_romaji_with_accent(text):
-    '''Reference https://r9y9.github.io/ttslearn/latest/notebooks/ch10_Recipe-Tacotron.html'''
-    text = symbols_to_japanese(text)
-    sentences = re.split(_japanese_marks, text)
-    marks = re.findall(_japanese_marks, text)
-    text = ''
-    for i, sentence in enumerate(sentences):
-        if re.match(_japanese_characters, sentence):
-            if text != '':
-                text += ' '
-            labels = pyopenjtalk.extract_fullcontext(sentence)
-            for n, label in enumerate(labels):
-                phoneme = re.search(r'\-([^\+]*)\+', label).group(1)
-                if phoneme not in ['sil', 'pau']:
-                    text += phoneme.replace('ch', 'ʧ').replace('sh',
-                                                               'ʃ').replace('cl', 'Q')
-                else:
-                    continue
-                # n_moras = int(re.search(r'/F:(\d+)_', label).group(1))
-                a1 = int(re.search(r"/A:(\-?[0-9]+)\+", label).group(1))
-                a2 = int(re.search(r"\+(\d+)\+", label).group(1))
-                a3 = int(re.search(r"\+(\d+)/", label).group(1))
-                if re.search(r'\-([^\+]*)\+', labels[n + 1]).group(1) in ['sil', 'pau']:
-                    a2_next = -1
-                else:
-                    a2_next = int(
-                        re.search(r"\+(\d+)\+", labels[n + 1]).group(1))
-                # Accent phrase boundary
-                if a3 == 1 and a2_next == 1:
-                    text += ' '
-                # Falling
-                elif a1 == 0 and a2_next == a2 + 1:
-                    text += '↓'
-                # Rising
-                elif a2 == 1 and a2_next == 2:
-                    text += '↑'
-        if i < len(marks):
-            text += unidecode(marks[i]).replace(' ', '')
-    return text
-
-
-def get_real_sokuon(text):
-    for regex, replacement in _real_sokuon:
-        text = re.sub(regex, replacement, text)
-    return text
-
-
-def get_real_hatsuon(text):
-    for regex, replacement in _real_hatsuon:
-        text = re.sub(regex, replacement, text)
-    return text
-
-
-def japanese_to_ipa(text):
-    text = japanese_to_romaji_with_accent(text).replace('...', '…')
-    text = re.sub(
-        r'([aiueo])\1+', lambda x: x.group(0)[0]+'ː'*(len(x.group(0))-1), text)
-    text = get_real_sokuon(text)
-    text = get_real_hatsuon(text)
-    for regex, replacement in _romaji_to_ipa:
-        text = re.sub(regex, replacement, text)
-    return text
-
-
-def japanese_to_ipa2(text):
-    text = japanese_to_romaji_with_accent(text).replace('...', '…')
-    text = get_real_sokuon(text)
-    text = get_real_hatsuon(text)
-    for regex, replacement in _romaji_to_ipa2:
-        text = re.sub(regex, replacement, text)
-    return text
-
-
-def japanese_to_ipa3(text):
-    text = japanese_to_ipa2(text).replace('n^', 'ȵ').replace(
-        'ʃ', 'ɕ').replace('*', '\u0325').replace('#', '\u031a')
-    text = re.sub(
-        r'([aiɯeo])\1+', lambda x: x.group(0)[0]+'ː'*(len(x.group(0))-1), text)
-    text = re.sub(r'((?:^|\s)(?:ts|tɕ|[kpt]))', r'\1ʰ', text)
-    return text
diff --git a/spaces/kirch/Text2Video-Zero/annotator/uniformer/mmcv/cnn/utils/weight_init.py b/spaces/kirch/Text2Video-Zero/annotator/uniformer/mmcv/cnn/utils/weight_init.py
deleted file mode 100644
index 287a1d0bffe26e023029d48634d9b761deda7ba4..0000000000000000000000000000000000000000
--- a/spaces/kirch/Text2Video-Zero/annotator/uniformer/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/kirch/Text2Video-Zero/annotator/uniformer/mmcv_custom/checkpoint.py b/spaces/kirch/Text2Video-Zero/annotator/uniformer/mmcv_custom/checkpoint.py
deleted file mode 100644
index 19b87fef0a52d31babcdb3edb8f3089b6420173f..0000000000000000000000000000000000000000
--- a/spaces/kirch/Text2Video-Zero/annotator/uniformer/mmcv_custom/checkpoint.py
+++ /dev/null
@@ -1,500 +0,0 @@
-# Copyright (c) Open-MMLab. All rights reserved.
-import io
-import os
-import os.path as osp
-import pkgutil
-import time
-import warnings
-from collections import OrderedDict
-from importlib import import_module
-from tempfile import TemporaryDirectory
-
-import torch
-import torchvision
-from torch.optim import Optimizer
-from torch.utils import model_zoo
-from torch.nn import functional as F
-
-import annotator.uniformer.mmcv as mmcv
-from annotator.uniformer.mmcv.fileio import FileClient
-from annotator.uniformer.mmcv.fileio import load as load_file
-from annotator.uniformer.mmcv.parallel import is_module_wrapper
-from annotator.uniformer.mmcv.utils import mkdir_or_exist
-from annotator.uniformer.mmcv.runner import get_dist_info
-
-ENV_MMCV_HOME = 'MMCV_HOME'
-ENV_XDG_CACHE_HOME = 'XDG_CACHE_HOME'
-DEFAULT_CACHE_DIR = '~/.cache'
-
-
-def _get_mmcv_home():
-    mmcv_home = os.path.expanduser(
-        os.getenv(
-            ENV_MMCV_HOME,
-            os.path.join(
-                os.getenv(ENV_XDG_CACHE_HOME, DEFAULT_CACHE_DIR), 'mmcv')))
-
-    mkdir_or_exist(mmcv_home)
-    return mmcv_home
-
-
-def load_state_dict(module, state_dict, strict=False, logger=None):
-    """Load state_dict to a module.
-
-    This method is modified from :meth:`torch.nn.Module.load_state_dict`.
-    Default value for ``strict`` is set to ``False`` and the message for
-    param mismatch will be shown even if strict is False.
-
-    Args:
-        module (Module): Module that receives the state_dict.
-        state_dict (OrderedDict): Weights.
-        strict (bool): whether to strictly enforce that the keys
-            in :attr:`state_dict` match the keys returned by this module's
-            :meth:`~torch.nn.Module.state_dict` function. Default: ``False``.
-        logger (:obj:`logging.Logger`, optional): Logger to log the error
-            message. If not specified, print function will be used.
-    """
-    unexpected_keys = []
-    all_missing_keys = []
-    err_msg = []
-
-    metadata = getattr(state_dict, '_metadata', None)
-    state_dict = state_dict.copy()
-    if metadata is not None:
-        state_dict._metadata = metadata
-
-    # use _load_from_state_dict to enable checkpoint version control
-    def load(module, prefix=''):
-        # recursively check parallel module in case that the model has a
-        # complicated structure, e.g., nn.Module(nn.Module(DDP))
-        if is_module_wrapper(module):
-            module = module.module
-        local_metadata = {} if metadata is None else metadata.get(
-            prefix[:-1], {})
-        module._load_from_state_dict(state_dict, prefix, local_metadata, True,
-                                     all_missing_keys, unexpected_keys,
-                                     err_msg)
-        for name, child in module._modules.items():
-            if child is not None:
-                load(child, prefix + name + '.')
-
-    load(module)
-    load = None  # break load->load reference cycle
-
-    # ignore "num_batches_tracked" of BN layers
-    missing_keys = [
-        key for key in all_missing_keys if 'num_batches_tracked' not in key
-    ]
-
-    if unexpected_keys:
-        err_msg.append('unexpected key in source '
-                       f'state_dict: {", ".join(unexpected_keys)}\n')
-    if missing_keys:
-        err_msg.append(
-            f'missing keys in source state_dict: {", ".join(missing_keys)}\n')
-
-    rank, _ = get_dist_info()
-    if len(err_msg) > 0 and rank == 0:
-        err_msg.insert(
-            0, 'The model and loaded state dict do not match exactly\n')
-        err_msg = '\n'.join(err_msg)
-        if strict:
-            raise RuntimeError(err_msg)
-        elif logger is not None:
-            logger.warning(err_msg)
-        else:
-            print(err_msg)
-
-
-def load_url_dist(url, model_dir=None):
-    """In distributed setting, this function only download checkpoint at local
-    rank 0."""
-    rank, world_size = get_dist_info()
-    rank = int(os.environ.get('LOCAL_RANK', rank))
-    if rank == 0:
-        checkpoint = model_zoo.load_url(url, model_dir=model_dir)
-    if world_size > 1:
-        torch.distributed.barrier()
-        if rank > 0:
-            checkpoint = model_zoo.load_url(url, model_dir=model_dir)
-    return checkpoint
-
-
-def load_pavimodel_dist(model_path, map_location=None):
-    """In distributed setting, this function only download checkpoint at local
-    rank 0."""
-    try:
-        from pavi import modelcloud
-    except ImportError:
-        raise ImportError(
-            'Please install pavi to load checkpoint from modelcloud.')
-    rank, world_size = get_dist_info()
-    rank = int(os.environ.get('LOCAL_RANK', rank))
-    if rank == 0:
-        model = modelcloud.get(model_path)
-        with TemporaryDirectory() as tmp_dir:
-            downloaded_file = osp.join(tmp_dir, model.name)
-            model.download(downloaded_file)
-            checkpoint = torch.load(downloaded_file, map_location=map_location)
-    if world_size > 1:
-        torch.distributed.barrier()
-        if rank > 0:
-            model = modelcloud.get(model_path)
-            with TemporaryDirectory() as tmp_dir:
-                downloaded_file = osp.join(tmp_dir, model.name)
-                model.download(downloaded_file)
-                checkpoint = torch.load(
-                    downloaded_file, map_location=map_location)
-    return checkpoint
-
-
-def load_fileclient_dist(filename, backend, map_location):
-    """In distributed setting, this function only download checkpoint at local
-    rank 0."""
-    rank, world_size = get_dist_info()
-    rank = int(os.environ.get('LOCAL_RANK', rank))
-    allowed_backends = ['ceph']
-    if backend not in allowed_backends:
-        raise ValueError(f'Load from Backend {backend} is not supported.')
-    if rank == 0:
-        fileclient = FileClient(backend=backend)
-        buffer = io.BytesIO(fileclient.get(filename))
-        checkpoint = torch.load(buffer, map_location=map_location)
-    if world_size > 1:
-        torch.distributed.barrier()
-        if rank > 0:
-            fileclient = FileClient(backend=backend)
-            buffer = io.BytesIO(fileclient.get(filename))
-            checkpoint = torch.load(buffer, map_location=map_location)
-    return checkpoint
-
-
-def get_torchvision_models():
-    model_urls = dict()
-    for _, name, ispkg in pkgutil.walk_packages(torchvision.models.__path__):
-        if ispkg:
-            continue
-        _zoo = import_module(f'torchvision.models.{name}')
-        if hasattr(_zoo, 'model_urls'):
-            _urls = getattr(_zoo, 'model_urls')
-            model_urls.update(_urls)
-    return model_urls
-
-
-def get_external_models():
-    mmcv_home = _get_mmcv_home()
-    default_json_path = osp.join(mmcv.__path__[0], 'model_zoo/open_mmlab.json')
-    default_urls = load_file(default_json_path)
-    assert isinstance(default_urls, dict)
-    external_json_path = osp.join(mmcv_home, 'open_mmlab.json')
-    if osp.exists(external_json_path):
-        external_urls = load_file(external_json_path)
-        assert isinstance(external_urls, dict)
-        default_urls.update(external_urls)
-
-    return default_urls
-
-
-def get_mmcls_models():
-    mmcls_json_path = osp.join(mmcv.__path__[0], 'model_zoo/mmcls.json')
-    mmcls_urls = load_file(mmcls_json_path)
-
-    return mmcls_urls
-
-
-def get_deprecated_model_names():
-    deprecate_json_path = osp.join(mmcv.__path__[0],
-                                   'model_zoo/deprecated.json')
-    deprecate_urls = load_file(deprecate_json_path)
-    assert isinstance(deprecate_urls, dict)
-
-    return deprecate_urls
-
-
-def _process_mmcls_checkpoint(checkpoint):
-    state_dict = checkpoint['state_dict']
-    new_state_dict = OrderedDict()
-    for k, v in state_dict.items():
-        if k.startswith('backbone.'):
-            new_state_dict[k[9:]] = v
-    new_checkpoint = dict(state_dict=new_state_dict)
-
-    return new_checkpoint
-
-
-def _load_checkpoint(filename, map_location=None):
-    """Load checkpoint from somewhere (modelzoo, file, url).
-
-    Args:
-        filename (str): Accept local filepath, URL, ``torchvision://xxx``,
-            ``open-mmlab://xxx``. Please refer to ``docs/model_zoo.md`` for
-            details.
-        map_location (str | None): Same as :func:`torch.load`. Default: None.
-
-    Returns:
-        dict | OrderedDict: The loaded checkpoint. It can be either an
-            OrderedDict storing model weights or a dict containing other
-            information, which depends on the checkpoint.
-    """
-    if filename.startswith('modelzoo://'):
-        warnings.warn('The URL scheme of "modelzoo://" is deprecated, please '
-                      'use "torchvision://" instead')
-        model_urls = get_torchvision_models()
-        model_name = filename[11:]
-        checkpoint = load_url_dist(model_urls[model_name])
-    elif filename.startswith('torchvision://'):
-        model_urls = get_torchvision_models()
-        model_name = filename[14:]
-        checkpoint = load_url_dist(model_urls[model_name])
-    elif filename.startswith('open-mmlab://'):
-        model_urls = get_external_models()
-        model_name = filename[13:]
-        deprecated_urls = get_deprecated_model_names()
-        if model_name in deprecated_urls:
-            warnings.warn(f'open-mmlab://{model_name} is deprecated in favor '
-                          f'of open-mmlab://{deprecated_urls[model_name]}')
-            model_name = deprecated_urls[model_name]
-        model_url = model_urls[model_name]
-        # check if is url
-        if model_url.startswith(('http://', 'https://')):
-            checkpoint = load_url_dist(model_url)
-        else:
-            filename = osp.join(_get_mmcv_home(), model_url)
-            if not osp.isfile(filename):
-                raise IOError(f'{filename} is not a checkpoint file')
-            checkpoint = torch.load(filename, map_location=map_location)
-    elif filename.startswith('mmcls://'):
-        model_urls = get_mmcls_models()
-        model_name = filename[8:]
-        checkpoint = load_url_dist(model_urls[model_name])
-        checkpoint = _process_mmcls_checkpoint(checkpoint)
-    elif filename.startswith(('http://', 'https://')):
-        checkpoint = load_url_dist(filename)
-    elif filename.startswith('pavi://'):
-        model_path = filename[7:]
-        checkpoint = load_pavimodel_dist(model_path, map_location=map_location)
-    elif filename.startswith('s3://'):
-        checkpoint = load_fileclient_dist(
-            filename, backend='ceph', map_location=map_location)
-    else:
-        if not osp.isfile(filename):
-            raise IOError(f'{filename} is not a checkpoint file')
-        checkpoint = torch.load(filename, map_location=map_location)
-    return checkpoint
-
-
-def load_checkpoint(model,
-                    filename,
-                    map_location='cpu',
-                    strict=False,
-                    logger=None):
-    """Load checkpoint from a file or URI.
-
-    Args:
-        model (Module): Module to load checkpoint.
-        filename (str): Accept local filepath, URL, ``torchvision://xxx``,
-            ``open-mmlab://xxx``. Please refer to ``docs/model_zoo.md`` for
-            details.
-        map_location (str): Same as :func:`torch.load`.
-        strict (bool): Whether to allow different params for the model and
-            checkpoint.
-        logger (:mod:`logging.Logger` or None): The logger for error message.
-
-    Returns:
-        dict or OrderedDict: The loaded checkpoint.
-    """
-    checkpoint = _load_checkpoint(filename, map_location)
-    # OrderedDict is a subclass of dict
-    if not isinstance(checkpoint, dict):
-        raise RuntimeError(
-            f'No state_dict found in checkpoint file {filename}')
-    # get state_dict from checkpoint
-    if 'state_dict' in checkpoint:
-        state_dict = checkpoint['state_dict']
-    elif 'model' in checkpoint:
-        state_dict = checkpoint['model']
-    else:
-        state_dict = checkpoint
-    # strip prefix of state_dict
-    if list(state_dict.keys())[0].startswith('module.'):
-        state_dict = {k[7:]: v for k, v in state_dict.items()}
-
-    # for MoBY, load model of online branch
-    if sorted(list(state_dict.keys()))[0].startswith('encoder'):
-        state_dict = {k.replace('encoder.', ''): v for k, v in state_dict.items() if k.startswith('encoder.')}
-
-    # reshape absolute position embedding
-    if state_dict.get('absolute_pos_embed') is not None:
-        absolute_pos_embed = state_dict['absolute_pos_embed']
-        N1, L, C1 = absolute_pos_embed.size()
-        N2, C2, H, W = model.absolute_pos_embed.size()
-        if N1 != N2 or C1 != C2 or L != H*W:
-            logger.warning("Error in loading absolute_pos_embed, pass")
-        else:
-            state_dict['absolute_pos_embed'] = absolute_pos_embed.view(N2, H, W, C2).permute(0, 3, 1, 2)
-
-    # interpolate position bias table if needed
-    relative_position_bias_table_keys = [k for k in state_dict.keys() if "relative_position_bias_table" in k]
-    for table_key in relative_position_bias_table_keys:
-        table_pretrained = state_dict[table_key]
-        table_current = model.state_dict()[table_key]
-        L1, nH1 = table_pretrained.size()
-        L2, nH2 = table_current.size()
-        if nH1 != nH2:
-            logger.warning(f"Error in loading {table_key}, pass")
-        else:
-            if L1 != L2:
-                S1 = int(L1 ** 0.5)
-                S2 = int(L2 ** 0.5)
-                table_pretrained_resized = F.interpolate(
-                     table_pretrained.permute(1, 0).view(1, nH1, S1, S1),
-                     size=(S2, S2), mode='bicubic')
-                state_dict[table_key] = table_pretrained_resized.view(nH2, L2).permute(1, 0)
-
-    # load state_dict
-    load_state_dict(model, state_dict, strict, logger)
-    return checkpoint
-
-
-def weights_to_cpu(state_dict):
-    """Copy a model state_dict to cpu.
-
-    Args:
-        state_dict (OrderedDict): Model weights on GPU.
-
-    Returns:
-        OrderedDict: Model weights on GPU.
-    """
-    state_dict_cpu = OrderedDict()
-    for key, val in state_dict.items():
-        state_dict_cpu[key] = val.cpu()
-    return state_dict_cpu
-
-
-def _save_to_state_dict(module, destination, prefix, keep_vars):
-    """Saves module state to `destination` dictionary.
-
-    This method is modified from :meth:`torch.nn.Module._save_to_state_dict`.
-
-    Args:
-        module (nn.Module): The module to generate state_dict.
-        destination (dict): A dict where state will be stored.
-        prefix (str): The prefix for parameters and buffers used in this
-            module.
-    """
-    for name, param in module._parameters.items():
-        if param is not None:
-            destination[prefix + name] = param if keep_vars else param.detach()
-    for name, buf in module._buffers.items():
-        # remove check of _non_persistent_buffers_set to allow nn.BatchNorm2d
-        if buf is not None:
-            destination[prefix + name] = buf if keep_vars else buf.detach()
-
-
-def get_state_dict(module, destination=None, prefix='', keep_vars=False):
-    """Returns a dictionary containing a whole state of the module.
-
-    Both parameters and persistent buffers (e.g. running averages) are
-    included. Keys are corresponding parameter and buffer names.
-
-    This method is modified from :meth:`torch.nn.Module.state_dict` to
-    recursively check parallel module in case that the model has a complicated
-    structure, e.g., nn.Module(nn.Module(DDP)).
-
-    Args:
-        module (nn.Module): The module to generate state_dict.
-        destination (OrderedDict): Returned dict for the state of the
-            module.
-        prefix (str): Prefix of the key.
-        keep_vars (bool): Whether to keep the variable property of the
-            parameters. Default: False.
-
-    Returns:
-        dict: A dictionary containing a whole state of the module.
-    """
-    # recursively check parallel module in case that the model has a
-    # complicated structure, e.g., nn.Module(nn.Module(DDP))
-    if is_module_wrapper(module):
-        module = module.module
-
-    # below is the same as torch.nn.Module.state_dict()
-    if destination is None:
-        destination = OrderedDict()
-        destination._metadata = OrderedDict()
-    destination._metadata[prefix[:-1]] = local_metadata = dict(
-        version=module._version)
-    _save_to_state_dict(module, destination, prefix, keep_vars)
-    for name, child in module._modules.items():
-        if child is not None:
-            get_state_dict(
-                child, destination, prefix + name + '.', keep_vars=keep_vars)
-    for hook in module._state_dict_hooks.values():
-        hook_result = hook(module, destination, prefix, local_metadata)
-        if hook_result is not None:
-            destination = hook_result
-    return destination
-
-
-def save_checkpoint(model, filename, optimizer=None, meta=None):
-    """Save checkpoint to file.
-
-    The checkpoint will have 3 fields: ``meta``, ``state_dict`` and
-    ``optimizer``. By default ``meta`` will contain version and time info.
-
-    Args:
-        model (Module): Module whose params are to be saved.
-        filename (str): Checkpoint filename.
-        optimizer (:obj:`Optimizer`, optional): Optimizer to be saved.
-        meta (dict, optional): Metadata to be saved in checkpoint.
-    """
-    if meta is None:
-        meta = {}
-    elif not isinstance(meta, dict):
-        raise TypeError(f'meta must be a dict or None, but got {type(meta)}')
-    meta.update(mmcv_version=mmcv.__version__, time=time.asctime())
-
-    if is_module_wrapper(model):
-        model = model.module
-
-    if hasattr(model, 'CLASSES') and model.CLASSES is not None:
-        # save class name to the meta
-        meta.update(CLASSES=model.CLASSES)
-
-    checkpoint = {
-        'meta': meta,
-        'state_dict': weights_to_cpu(get_state_dict(model))
-    }
-    # save optimizer state dict in the checkpoint
-    if isinstance(optimizer, Optimizer):
-        checkpoint['optimizer'] = optimizer.state_dict()
-    elif isinstance(optimizer, dict):
-        checkpoint['optimizer'] = {}
-        for name, optim in optimizer.items():
-            checkpoint['optimizer'][name] = optim.state_dict()
-
-    if filename.startswith('pavi://'):
-        try:
-            from pavi import modelcloud
-            from pavi.exception import NodeNotFoundError
-        except ImportError:
-            raise ImportError(
-                'Please install pavi to load checkpoint from modelcloud.')
-        model_path = filename[7:]
-        root = modelcloud.Folder()
-        model_dir, model_name = osp.split(model_path)
-        try:
-            model = modelcloud.get(model_dir)
-        except NodeNotFoundError:
-            model = root.create_training_model(model_dir)
-        with TemporaryDirectory() as tmp_dir:
-            checkpoint_file = osp.join(tmp_dir, model_name)
-            with open(checkpoint_file, 'wb') as f:
-                torch.save(checkpoint, f)
-                f.flush()
-            model.create_file(checkpoint_file, name=model_name)
-    else:
-        mmcv.mkdir_or_exist(osp.dirname(filename))
-        # immediately flush buffer
-        with open(filename, 'wb') as f:
-            torch.save(checkpoint, f)
-            f.flush()
\ No newline at end of file
diff --git a/spaces/kukuhtw/VToonify/vtoonify/model/encoder/align_all_parallel.py b/spaces/kukuhtw/VToonify/vtoonify/model/encoder/align_all_parallel.py
deleted file mode 100644
index 05b520cd6590dc02ee533d3f0d69e6a364447d9f..0000000000000000000000000000000000000000
--- a/spaces/kukuhtw/VToonify/vtoonify/model/encoder/align_all_parallel.py
+++ /dev/null
@@ -1,217 +0,0 @@
-"""
-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
-"""
-from argparse import ArgumentParser
-import time
-import numpy as np
-import PIL
-import PIL.Image
-import os
-import scipy
-import scipy.ndimage
-import dlib
-import multiprocessing as mp
-import math
-
-#from configs.paths_config import model_paths
-SHAPE_PREDICTOR_PATH = 'shape_predictor_68_face_landmarks.dat'#model_paths["shape_predictor"]
-
-
-def get_landmark(filepath, predictor):
-    """get landmark with dlib
-    :return: np.array shape=(68, 2)
-    """
-    detector = dlib.get_frontal_face_detector()
-    if type(filepath) == str:
-        img = dlib.load_rgb_image(filepath)
-    else:
-        img = filepath
-    dets = detector(img, 1)
-    
-    if len(dets) == 0:
-        print('Error: no face detected!')
-        return None
-    
-    shape = None
-    for k, d in enumerate(dets):
-        shape = predictor(img, d)
-    
-    if shape is None:
-        print('Error: No face detected! If you are sure there are faces in your input, you may rerun the code several times until the face is detected. Sometimes the detector is unstable.')
-    t = list(shape.parts())
-    a = []
-    for tt in t:
-        a.append([tt.x, tt.y])
-    lm = np.array(a)
-    return lm
-
-
-def align_face(filepath, predictor):
-    """
-    :param filepath: str
-    :return: PIL Image
-    """
-
-    lm = get_landmark(filepath, predictor)
-    if lm is None:
-        return None    
-    
-    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]
-    x /= np.hypot(*x)
-    x *= max(np.hypot(*eye_to_eye) * 2.0, np.hypot(*eye_to_mouth) * 1.8)
-    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])
-    qsize = np.hypot(*x) * 2
-
-    # read image
-    if type(filepath) == str:
-        img = PIL.Image.open(filepath)
-    else:
-        img = PIL.Image.fromarray(filepath)
-
-    output_size = 256
-    transform_size = 256
-    enable_padding = True
-
-    # Shrink.
-    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, PIL.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 = PIL.Image.fromarray(np.uint8(np.clip(np.rint(img), 0, 255)), 'RGB')
-        quad += pad[:2]
-
-    # Transform.
-    img = img.transform((transform_size, transform_size), PIL.Image.QUAD, (quad + 0.5).flatten(), PIL.Image.BILINEAR)
-    if output_size < transform_size:
-        img = img.resize((output_size, output_size), PIL.Image.ANTIALIAS)
-
-    # Save aligned image.
-    return img
-
-
-def chunks(lst, n):
-    """Yield successive n-sized chunks from lst."""
-    for i in range(0, len(lst), n):
-        yield lst[i:i + n]
-
-
-def extract_on_paths(file_paths):
-    predictor = dlib.shape_predictor(SHAPE_PREDICTOR_PATH)
-    pid = mp.current_process().name
-    print('\t{} is starting to extract on #{} images'.format(pid, len(file_paths)))
-    tot_count = len(file_paths)
-    count = 0
-    for file_path, res_path in file_paths:
-        count += 1
-        if count % 100 == 0:
-            print('{} done with {}/{}'.format(pid, count, tot_count))
-        try:
-            res = align_face(file_path, predictor)
-            res = res.convert('RGB')
-            os.makedirs(os.path.dirname(res_path), exist_ok=True)
-            res.save(res_path)
-        except Exception:
-            continue
-    print('\tDone!')
-
-
-def parse_args():
-    parser = ArgumentParser(add_help=False)
-    parser.add_argument('--num_threads', type=int, default=1)
-    parser.add_argument('--root_path', type=str, default='')
-    args = parser.parse_args()
-    return args
-
-
-def run(args):
-    root_path = args.root_path
-    out_crops_path = root_path + '_crops'
-    if not os.path.exists(out_crops_path):
-        os.makedirs(out_crops_path, exist_ok=True)
-
-    file_paths = []
-    for root, dirs, files in os.walk(root_path):
-        for file in files:
-            file_path = os.path.join(root, file)
-            fname = os.path.join(out_crops_path, os.path.relpath(file_path, root_path))
-            res_path = '{}.jpg'.format(os.path.splitext(fname)[0])
-            if os.path.splitext(file_path)[1] == '.txt' or os.path.exists(res_path):
-                continue
-            file_paths.append((file_path, res_path))
-
-    file_chunks = list(chunks(file_paths, int(math.ceil(len(file_paths) / args.num_threads))))
-    print(len(file_chunks))
-    pool = mp.Pool(args.num_threads)
-    print('Running on {} paths\nHere we goooo'.format(len(file_paths)))
-    tic = time.time()
-    pool.map(extract_on_paths, file_chunks)
-    toc = time.time()
-    print('Mischief managed in {}s'.format(toc - tic))
-
-
-if __name__ == '__main__':
-    args = parse_args()
-    run(args)
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fontTools/mtiLib/__main__.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fontTools/mtiLib/__main__.py
deleted file mode 100644
index 29c802bcc83b3ca35bbd0e6521f47a368b5f9092..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fontTools/mtiLib/__main__.py
+++ /dev/null
@@ -1,5 +0,0 @@
-import sys
-from fontTools.mtiLib import main
-
-if __name__ == "__main__":
-    sys.exit(main())
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/matplotlib/backends/backend_tkcairo.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/matplotlib/backends/backend_tkcairo.py
deleted file mode 100644
index a6951c03c65a328bc9c46a369aed65ad7df259bb..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/matplotlib/backends/backend_tkcairo.py
+++ /dev/null
@@ -1,26 +0,0 @@
-import sys
-
-import numpy as np
-
-from . import _backend_tk
-from .backend_cairo import cairo, FigureCanvasCairo
-from ._backend_tk import _BackendTk, FigureCanvasTk
-
-
-class FigureCanvasTkCairo(FigureCanvasCairo, FigureCanvasTk):
-    def draw(self):
-        width = int(self.figure.bbox.width)
-        height = int(self.figure.bbox.height)
-        surface = cairo.ImageSurface(cairo.FORMAT_ARGB32, width, height)
-        self._renderer.set_context(cairo.Context(surface))
-        self._renderer.dpi = self.figure.dpi
-        self.figure.draw(self._renderer)
-        buf = np.reshape(surface.get_data(), (height, width, 4))
-        _backend_tk.blit(
-            self._tkphoto, buf,
-            (2, 1, 0, 3) if sys.byteorder == "little" else (1, 2, 3, 0))
-
-
-@_BackendTk.export
-class _BackendTkCairo(_BackendTk):
-    FigureCanvas = FigureCanvasTkCairo
diff --git a/spaces/lafi23333/aikomori/inference/slicer.py b/spaces/lafi23333/aikomori/inference/slicer.py
deleted file mode 100644
index b05840bcf6bdced0b6e2adbecb1a1dd5b3dee462..0000000000000000000000000000000000000000
--- a/spaces/lafi23333/aikomori/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
diff --git a/spaces/lain-iwakura/lainchan-proxy/Dockerfile b/spaces/lain-iwakura/lainchan-proxy/Dockerfile
deleted file mode 100644
index 3b1a3220c963a8c44a720d3890327f04ab88322b..0000000000000000000000000000000000000000
--- a/spaces/lain-iwakura/lainchan-proxy/Dockerfile
+++ /dev/null
@@ -1,13 +0,0 @@
-from python:3.10
-
-workdir /app
-
-copy requirements.txt .
-run pip install -r requirements.txt
-
-run mkdir /.cache && chmod 777 /.cache
-run mkdir .chroma && chmod 777 .chroma
-copy . .
-expose 7860
-
-cmd ["python", "fallback.py"]
\ No newline at end of file
diff --git a/spaces/lakshmi324/complaintBox/app.py b/spaces/lakshmi324/complaintBox/app.py
deleted file mode 100644
index fb7d0b845d16360d2337380d27b4a8470857dc45..0000000000000000000000000000000000000000
--- a/spaces/lakshmi324/complaintBox/app.py
+++ /dev/null
@@ -1,110 +0,0 @@
-import tweepy
-import time
-import pandas as pd
-from transformers import pipeline
-import matplotlib.pyplot as plt
-import gradio as gr
-import os
-
-def twitter_auth(consumerkey,consumersecret):
-  consumer_key = consumerkey
-  consumer_secret = consumersecret
-
-  auth = tweepy.AppAuthHandler(consumer_key,consumer_secret)
-
-  api = tweepy.API(auth,wait_on_rate_limit= True)
-  return api
-
-"""## Helper function for handling ratelimit and pagination"""
-
-def limit_handled(cursor):
-  """
-  Function takes the cursor and returns tweets
-  """
-  while True:
-    try:
-      yield cursor.next()
-    except tweepy.errors.TweepyException:
-      print('reached rate limit, sleeping for > 15 mins')
-      time.sleep(15*61)
-    except StopIteration:
-      break
-
-def tweets_collector(query,count):
-  consumerkey = os.environ.get('consumerkey')
-  consumersecret = os.environ.get('consumersecret')
-  api = twitter_auth(consumerkey,consumersecret)
-  query = query +' -filter:retweets'
-  search = limit_handled(tweepy.Cursor(api.search_tweets,q = query,tweet_mode = 'extended',lang ='en',result_type ='recent').items(count))
-  sentiment_analysis = pipeline(model = "finiteautomata/bertweet-base-sentiment-analysis")
-  tweets = []
-
-  for tweet in search:
-    try:
-      content = tweet.full_text
-      sentiment = sentiment_analysis(content)
-      tweets.append({'tweet' : content ,'sentiment': sentiment[0]['label']})
-    except:
-      pass
-  return tweets
-
-"""## Run sentiment Analysis"""
-
-#tweets = tweets_collector(query,count)
-#df = pd.DataFrame(tweets)
-
-import pandas as pd
-
-pd.set_option('max_colwidth',None)
-pd.set_option('display.width',3000)
-
-#import matplotlib.pyplot as plt
-
-#sentiment_counts = df.groupby(['sentiment']).size()
-
-#fig = plt.figure(figsize = (6,6),dpi = 100)
-#ax = plt.subplot(111)
-#sentiment_counts.plot.pie(ax = ax,autopct = '%1.f%%',startangle = 270,fontsize = 12,label = "")
-
-def complaint_analysis(query,count):
-  tweets = tweets_collector(query,count)
-  df = pd.DataFrame(tweets)
-  from wordcloud import WordCloud
-  from wordcloud import STOPWORDS
-  sentiment_counts = df.groupby(['sentiment']).size()
-  fig = plt.figure(figsize = (6,6),dpi = 100)
-  ax = plt.subplot(111)
-  sentiment_counts.plot.pie(ax = ax,autopct = '%1.f%%',startangle = 270,fontsize = 12,label = "")
-  plt.savefig('Overall_satisfaction.png')
-
-  positive_tweets = df['tweet'][df['sentiment'] == 'POS']
-  stop_words = ["https","co","RT","ola_supports","ola_cabs","customer"] + list(STOPWORDS)
-  positive_wordcloud = WordCloud(max_font_size=50,max_words = 30,background_color="white",stopwords=stop_words).generate(str(positive_tweets))
-  plt.figure()
-  plt.title("Positive Tweets - Wordcloud")
-  plt.imshow(positive_wordcloud,interpolation="bilinear")
-  plt.axis("off")
-  #plt.show()
-  plt.savefig('positive_tweet.png')
-  negative_tweets = df['tweet'][df['sentiment'] == 'NEG']
-  stop_words = ["https","co","RT","ola_supports","ola_cabs","customer"] + list(STOPWORDS)
-  negative_wordcloud = WordCloud(max_font_size=50,max_words = 30,background_color="white",stopwords=stop_words).generate(str(negative_tweets))
-  plt.figure()
-  plt.title("Negative Tweets - Wordcloud")
-  plt.imshow(negative_wordcloud,interpolation="bilinear")
-  plt.axis("off")
-  #plt.show()
-  plt.savefig('negative_tweet.png')
-  return ['Overall_satisfaction.png','positive_tweet.png','negative_tweet.png']
-
-gr.Interface(fn=complaint_analysis, 
-             inputs=[
-            gr.inputs.Textbox(
-                placeholder="Tweet handle please", label="Company support Twitter Handle", lines=5), gr.Slider(100, 1000) ],
-             outputs= [gr.outputs.Image(type="pil"),gr.outputs.Image(type="pil"),gr.outputs.Image(type="pil")],
-             examples=[]).launch(debug= True)
-
-
-
-
-
diff --git a/spaces/lanbogao/ytdlp-whisper/app.py b/spaces/lanbogao/ytdlp-whisper/app.py
deleted file mode 100644
index 0193f07da658242ec851796e78bbd14f12ea73e8..0000000000000000000000000000000000000000
--- a/spaces/lanbogao/ytdlp-whisper/app.py
+++ /dev/null
@@ -1,163 +0,0 @@
-import gradio as gr
-import whisper
-from pytube import YouTube
-from fastapi import FastAPI, Response, Request
-import yt_dlp
-import uvicorn
-import re
-import os
-import json
-from typing import Optional
-
-CUSTOM_PATH = "/gradio"
-
-app = FastAPI()
-
-langs = ["None"] + sorted(list(whisper.tokenizer.LANGUAGES.values()))
-model_size = list(whisper._MODELS.keys())
-
-@app.get("/test")
-def read_main():
-    return {"message": "This is your main app"}
-
-#async def get_subtitle(url: str):
-    # Download the subtitle with download_subtitle()
-    #subtitle_url = download_subtitle(url)
-    # Stream the subtitle as a response
-    #return StreamingResponse(requests.get(subtitle_url, stream=True).iter_content(chunk_size=1024))
-
-def download_subtitle(url: str, lang: Optional[str] = None) -> Optional[str]:
-    ydl_opts = {
-        "writesubtitles": True,
-        "allsubtitles": True,
-        "subtitleslangs": [lang] if lang else [],
-        "skip_download": True,
-    }
-    with yt_dlp.YoutubeDL(ydl_opts) as ydl:
-        info_dict = ydl.extract_info(url, download=False)
-        print(json.dumps(info_dict))
-        if info_dict.get("subtitles"):
-            # get first available subtitle
-            subtitle_url = info_dict["subtitles"][0]["url"]
-            with ydl.urlopen(subtitle_url) as subtitle:
-                return subtitle.read().decode()
-
-    return None
-
-def get_subtitle(url, lang='en'):
-    if lang is None:
-        lang = 'en'
-    # Download subtitles if available
-    ydl_opts = {
-        'writesubtitles': True,
-        'outtmpl': '%(id)s.%(ext)s',
-        'subtitleslangs': [lang],
-        'skip_download': True,
-    }
-    try:
-        with yt_dlp.YoutubeDL(ydl_opts) as ydl:
-            info_dict = ydl.extract_info(url, download=True)
-            video_id = info_dict.get("id", None)
-            if video_id is None:
-                return None
-
-            subtitle_file = f"{video_id}.{lang}.vtt"
-            with open(subtitle_file, 'r') as f:
-                subtitle_content = f.read()
-                subtitle_content = re.sub(r"<[^>]+>", "", subtitle_content)
-                return subtitle_content
-    except error:
-        print(error)
-        return None
-    
-    return None
-
-def download_audio(video_url, quality: str = '128', speed: float = None):
-    ydl_opts = {
-        'format': 'bestaudio/best',
-        'outtmpl': '%(title)s.%(ext)s',
-        'quiet': True,
-    }
-
-    if speed:
-        ydl_opts["postprocessors"] = [{
-            "key": "FFmpegExtractAudio",
-            "preferredcodec": "mp3",
-            "preferredquality": quality,
-            "addopts": f"-filter:a \"atempo={speed}\"",
-        }]
-
-    with yt_dlp.YoutubeDL(ydl_opts) as ydl:
-        ydl.download([video_url])
-        audio_file = ydl.prepare_filename(ydl.extract_info(video_url, download=False))
-        print('audio_file', audio_file)
-        return audio_file
-
-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):
-    if lang == "None":
-        lang = None
-
-    subtitle = download_subtitle(url, lang)
-    print(subtitle)
-    if subtitle:
-        return subtitle
-
-    model = whisper.load_model(model_size)
-
-    result = model.transcribe(download_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}"
-
-
-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)
-
-#io = gr.Interface(gradio_interface)
-
-#app = gr.mount_gradio_app(app, io, path=CUSTOM_PATH)
-uvicorn.run(app, host="0.0.0.0", port=7860)
\ No newline at end of file
diff --git a/spaces/ledetele/KrystalPDF/README.md b/spaces/ledetele/KrystalPDF/README.md
deleted file mode 100644
index 0b0224c4fd6192fd09208e7105eba88cc79183c6..0000000000000000000000000000000000000000
--- a/spaces/ledetele/KrystalPDF/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: KrystalPDF
-emoji: 💻
-colorFrom: purple
-colorTo: purple
-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/leilevy/bingo/src/components/chat-list.tsx b/spaces/leilevy/bingo/src/components/chat-list.tsx
deleted file mode 100644
index 624a78ef0d7be0f1192cf02a81e2e9cf214cb193..0000000000000000000000000000000000000000
--- a/spaces/leilevy/bingo/src/components/chat-list.tsx
+++ /dev/null
@@ -1,28 +0,0 @@
-import React from 'react'
-
-import { Separator } from '@/components/ui/separator'
-import { ChatMessage } from '@/components/chat-message'
-import { ChatMessageModel } from '@/lib/bots/bing/types'
-
-export interface ChatList {
-  messages: ChatMessageModel[]
-}
-
-export function ChatList({ messages }: ChatList) {
-  if (!messages.length) {
-    return null
-  }
-
-  return (
-    <div className="chat-container relative flex flex-col">
-      {messages.map((message, index) => (
-        <React.Fragment key={index}>
-          <ChatMessage message={message} />
-          {index < messages.length - 1 && (
-            <Separator className="my-2" />
-          )}
-        </React.Fragment>
-      ))}
-    </div>
-  )
-}
diff --git a/spaces/lewiswu1209/MockingBird/app.py b/spaces/lewiswu1209/MockingBird/app.py
deleted file mode 100644
index 9d1a41430fac74af8193b0d42728291b319c5011..0000000000000000000000000000000000000000
--- a/spaces/lewiswu1209/MockingBird/app.py
+++ /dev/null
@@ -1,80 +0,0 @@
-
-import gradio as gr
-
-import re
-import random
-import string
-import librosa
-import numpy as np
-
-from pathlib import Path
-from scipy.io.wavfile import write
-
-from encoder import inference as encoder
-from vocoder.hifigan import inference as gan_vocoder
-from synthesizer.inference import Synthesizer
-
-class Mandarin:
-    def __init__(self):
-        self.encoder_path = "encoder/saved_models/pretrained.pt"
-        self.vocoder_path = "vocoder/saved_models/pretrained/g_hifigan.pt"
-        self.config_fpath = "vocoder/hifigan/config_16k_.json"
-        self.accent = "synthesizer/saved_models/普通话.pt"
-
-        synthesizers_cache = {}
-        if synthesizers_cache.get(self.accent) is None:
-            self.current_synt = Synthesizer(Path(self.accent))
-            synthesizers_cache[self.accent] = self.current_synt
-        else:
-            self.current_synt = synthesizers_cache[self.accent]
-
-        encoder.load_model(Path(self.encoder_path))
-        gan_vocoder.load_model(Path(self.vocoder_path), self.config_fpath)
-
-    def setVoice(self, timbre):
-        self.timbre = timbre
-        wav, sample_rate,  = librosa.load(self.timbre)
-
-        encoder_wav = encoder.preprocess_wav(wav, sample_rate)
-        self.embed, _, _ = encoder.embed_utterance(encoder_wav, return_partials=True)
-
-    def say(self, text):
-        texts = filter(None, text.split("\n"))
-        punctuation = "！，。、？!,.?：:" # punctuate and split/clean text
-        processed_texts = []
-        for text in texts:
-            for processed_text in re.sub(r'[{}]+'.format(punctuation), '\n', text).split('\n'):
-                if processed_text:
-                    processed_texts.append(processed_text.strip())
-        texts = processed_texts
-        embeds = [self.embed] * len(texts)
-
-        specs = self.current_synt.synthesize_spectrograms(texts, embeds)
-        spec = np.concatenate(specs, axis=1)
-        wav, sample_rate = gan_vocoder.infer_waveform(spec)
-
-        return wav, sample_rate
-
-def greet(audio, text, voice=None):
-
-    if voice is None:
-        voice = Mandarin()
-        voice.setVoice(audio.name)
-        voice.say("加载成功")
-    wav, sample_rate = voice.say(text)
-
-    output_file = "".join( random.sample(string.ascii_lowercase + string.digits, 11) ) + ".wav"
-
-    write(output_file, sample_rate, wav.astype(np.float32))
-
-    return output_file, voice
-
-def main():
-    gr.Interface(
-        fn=greet,
-        inputs=[gr.inputs.Audio(type="file"),"text", "state"],
-        outputs=[gr.outputs.Audio(type="file"), "state"]
-    ).launch()
-
-if __name__=="__main__":
-    main()
diff --git a/spaces/liuyang3/bingo-gpt4-2/Dockerfile b/spaces/liuyang3/bingo-gpt4-2/Dockerfile
deleted file mode 100644
index c677b05b75f7e4b2beee8c97fb47957a0861a83e..0000000000000000000000000000000000000000
--- a/spaces/liuyang3/bingo-gpt4-2/Dockerfile
+++ /dev/null
@@ -1,7 +0,0 @@
-FROM weaigc/bingo:latest
-
-ARG DEBIAN_FRONTEND=noninteractive
-
-ENV BING_HEADER ""
-
-CMD npm start
diff --git a/spaces/lora-library/LoRA-DreamBooth-Training-UI/inference.py b/spaces/lora-library/LoRA-DreamBooth-Training-UI/inference.py
deleted file mode 100644
index ce0f2b08df75e6d62f06c4119f1dc859930de032..0000000000000000000000000000000000000000
--- a/spaces/lora-library/LoRA-DreamBooth-Training-UI/inference.py
+++ /dev/null
@@ -1,94 +0,0 @@
-from __future__ import annotations
-
-import gc
-import pathlib
-
-import gradio as gr
-import PIL.Image
-import torch
-from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler
-from huggingface_hub import ModelCard
-
-
-class InferencePipeline:
-    def __init__(self, hf_token: str | None = None):
-        self.hf_token = hf_token
-        self.pipe = None
-        self.device = torch.device(
-            'cuda:0' if torch.cuda.is_available() else 'cpu')
-        self.lora_model_id = None
-        self.base_model_id = None
-
-    def clear(self) -> None:
-        self.lora_model_id = None
-        self.base_model_id = None
-        del self.pipe
-        self.pipe = None
-        torch.cuda.empty_cache()
-        gc.collect()
-
-    @staticmethod
-    def check_if_model_is_local(lora_model_id: str) -> bool:
-        return pathlib.Path(lora_model_id).exists()
-
-    @staticmethod
-    def get_model_card(model_id: str,
-                       hf_token: str | None = None) -> ModelCard:
-        if InferencePipeline.check_if_model_is_local(model_id):
-            card_path = (pathlib.Path(model_id) / 'README.md').as_posix()
-        else:
-            card_path = model_id
-        return ModelCard.load(card_path, token=hf_token)
-
-    @staticmethod
-    def get_base_model_info(lora_model_id: str,
-                            hf_token: str | None = None) -> str:
-        card = InferencePipeline.get_model_card(lora_model_id, hf_token)
-        return card.data.base_model
-
-    def load_pipe(self, lora_model_id: str) -> None:
-        if lora_model_id == self.lora_model_id:
-            return
-        base_model_id = self.get_base_model_info(lora_model_id, self.hf_token)
-        if base_model_id != self.base_model_id:
-            if self.device.type == 'cpu':
-                pipe = DiffusionPipeline.from_pretrained(
-                    base_model_id, use_auth_token=self.hf_token)
-            else:
-                pipe = DiffusionPipeline.from_pretrained(
-                    base_model_id,
-                    torch_dtype=torch.float16,
-                    use_auth_token=self.hf_token)
-                pipe = pipe.to(self.device)
-            pipe.scheduler = DPMSolverMultistepScheduler.from_config(
-                pipe.scheduler.config)
-            self.pipe = pipe
-        self.pipe.unet.load_attn_procs(  # type: ignore
-            lora_model_id, use_auth_token=self.hf_token)
-
-        self.lora_model_id = lora_model_id  # type: ignore
-        self.base_model_id = base_model_id  # type: ignore
-
-    def run(
-        self,
-        lora_model_id: str,
-        prompt: str,
-        lora_scale: float,
-        seed: int,
-        n_steps: int,
-        guidance_scale: float,
-    ) -> PIL.Image.Image:
-        if not torch.cuda.is_available():
-            raise gr.Error('CUDA is not available.')
-
-        self.load_pipe(lora_model_id)
-
-        generator = torch.Generator(device=self.device).manual_seed(seed)
-        out = self.pipe(
-            prompt,
-            num_inference_steps=n_steps,
-            guidance_scale=guidance_scale,
-            generator=generator,
-            cross_attention_kwargs={'scale': lora_scale},
-        )  # type: ignore
-        return out.images[0]
diff --git a/spaces/ma-xu/LIVE/thrust/thrust/system/cuda/detail/core/agent_launcher.h b/spaces/ma-xu/LIVE/thrust/thrust/system/cuda/detail/core/agent_launcher.h
deleted file mode 100644
index 7788481c7b85124d0873be11b8563372e457e724..0000000000000000000000000000000000000000
--- a/spaces/ma-xu/LIVE/thrust/thrust/system/cuda/detail/core/agent_launcher.h
+++ /dev/null
@@ -1,1184 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2016, NVIDIA CORPORATION.  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.
- *     * Neither the name of the NVIDIA CORPORATION 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 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 NVIDIA CORPORATION 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.
- *
- ******************************************************************************/
-#pragma once
-
-#if THRUST_DEVICE_COMPILER == THRUST_DEVICE_COMPILER_NVCC
-#include <thrust/detail/config.h>
-#include <thrust/system/cuda/detail/guarded_cuda_runtime_api.h>
-#include <thrust/system/cuda/detail/core/triple_chevron_launch.h>
-#include <thrust/system/cuda/detail/core/util.h>
-#include <cassert>
-
-#if 0
-#define __THRUST__TEMPLATE_DEBUG
-#endif
-
-#if __THRUST__TEMPLATE_DEBUG
-template<int...> class ID_impl;
-template<int... I> class Foo { ID_impl<I...> t;};
-#endif
-
-namespace thrust
-{
-namespace cuda_cub {
-namespace core {
-
-
-#if defined(__CUDA_ARCH__) || defined(__NVCOMPILER_CUDA__)
-#if 0
-  template <class Agent, class... Args>
-  void __global__
-  __launch_bounds__(Agent::ptx_plan::BLOCK_THREADS)
-      _kernel_agent(Args... args)
-  {
-    extern __shared__ char shmem[];
-    Agent::entry(args..., shmem);
-  }
-#else
-  template <class Agent, class _0>
-  void __global__ __launch_bounds__(Agent::ptx_plan::BLOCK_THREADS)
-  _kernel_agent(_0 x0)
-  {
-    extern __shared__ char shmem[];
-    Agent::entry(x0, shmem);
-  }
-  template <class Agent, class _0, class _1>
-  void __global__ __launch_bounds__(Agent::ptx_plan::BLOCK_THREADS)
-  _kernel_agent(_0 x0, _1 x1)
-  {
-    extern __shared__ char shmem[];
-    Agent::entry(x0, x1, shmem);
-  }
-  template <class Agent, class _0, class _1, class _2>
-  void __global__ __launch_bounds__(Agent::ptx_plan::BLOCK_THREADS)
-  _kernel_agent(_0 x0, _1 x1, _2 x2)
-  {
-    extern __shared__ char shmem[];
-    Agent::entry(x0, x1, x2, shmem);
-  }
-  template <class Agent, class _0, class _1, class _2, class _3>
-  void __global__ __launch_bounds__(Agent::ptx_plan::BLOCK_THREADS)
-  _kernel_agent(_0 x0, _1 x1, _2 x2, _3 x3)
-  {
-    extern __shared__ char shmem[];
-    Agent::entry(x0, x1, x2, x3, shmem);
-  }
-  template <class Agent, class _0, class _1, class _2, class _3, class _4>
-  void __global__ __launch_bounds__(Agent::ptx_plan::BLOCK_THREADS)
-  _kernel_agent(_0 x0, _1 x1, _2 x2, _3 x3, _4 x4)
-  {
-    extern __shared__ char shmem[];
-    Agent::entry(x0, x1, x2, x3, x4, shmem);
-  }
-  template <class Agent, class _0, class _1, class _2, class _3, class _4, class _5>
-  void __global__ __launch_bounds__(Agent::ptx_plan::BLOCK_THREADS)
-  _kernel_agent(_0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5)
-  {
-    extern __shared__ char shmem[];
-    Agent::entry(x0, x1, x2, x3, x4, x5, shmem);
-  }
-  template <class Agent, class _0, class _1, class _2, class _3, class _4, class _5, class _6>
-  void __global__ __launch_bounds__(Agent::ptx_plan::BLOCK_THREADS)
-  _kernel_agent(_0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6)
-  {
-    extern __shared__ char shmem[];
-    Agent::entry(x0, x1, x2, x3, x4, x5, x6, shmem);
-  }
-  template <class Agent, class _0, class _1, class _2, class _3, class _4, class _5, class _6, class _7>
-  void __global__ __launch_bounds__(Agent::ptx_plan::BLOCK_THREADS)
-  _kernel_agent(_0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7)
-  {
-    extern __shared__ char shmem[];
-    Agent::entry(x0, x1, x2, x3, x4, x5, x6, x7, shmem);
-  }
-  template <class Agent, class _0, class _1, class _2, class _3, class _4, class _5, class _6, class _7, class _8>
-  void __global__ __launch_bounds__(Agent::ptx_plan::BLOCK_THREADS)
-  _kernel_agent(_0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8)
-  {
-    extern __shared__ char shmem[];
-    Agent::entry(x0, x1, x2, x3, x4, x5, x6, x7, x8, shmem);
-  }
-  template <class Agent, class _0, class _1, class _2, class _3, class _4, class _5, class _6, class _7, class _8, class _9>
-  void __global__ __launch_bounds__(Agent::ptx_plan::BLOCK_THREADS)
-  _kernel_agent(_0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8, _9 x9)
-  {
-    extern __shared__ char shmem[];
-    Agent::entry(x0, x1, x2, x3, x4, x5, x6, x7, x8, x9, shmem);
-  }
-  template <class Agent, class _0, class _1, class _2, class _3, class _4, class _5, class _6, class _7, class _8, class _9, class _xA>
-  void __global__ __launch_bounds__(Agent::ptx_plan::BLOCK_THREADS)
-  _kernel_agent(_0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8, _9 x9, _xA xA)
-  {
-    extern __shared__ char shmem[];
-    Agent::entry(x0, x1, x2, x3, x4, x5, x6, x7, x8, x9, xA, shmem);
-  }
-  template <class Agent, class _0, class _1, class _2, class _3, class _4, class _5, class _6, class _7, class _8, class _9, class _xA, class _xB>
-  void __global__ __launch_bounds__(Agent::ptx_plan::BLOCK_THREADS)
-  _kernel_agent(_0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8, _9 x9, _xA xA, _xB xB)
-  {
-    extern __shared__ char shmem[];
-    Agent::entry(x0, x1, x2, x3, x4, x5, x6, x7, x8, x9, xA, xB, shmem);
-  }
-  template <class Agent, class _0, class _1, class _2, class _3, class _4, class _5, class _6, class _7, class _8, class _9, class _xA, class _xB, class _xC>
-  void __global__ __launch_bounds__(Agent::ptx_plan::BLOCK_THREADS)
-  _kernel_agent(_0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8, _9 x9, _xA xA, _xB xB, _xC xC)
-  {
-    extern __shared__ char shmem[];
-    Agent::entry(x0, x1, x2, x3, x4, x5, x6, x7, x8, x9, xA, xB, xC, shmem);
-  }
-  template <class Agent, class _0, class _1, class _2, class _3, class _4, class _5, class _6, class _7, class _8, class _9, class _xA, class _xB, class _xC, class _xD>
-  void __global__ __launch_bounds__(Agent::ptx_plan::BLOCK_THREADS)
-  _kernel_agent(_0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8, _9 x9, _xA xA, _xB xB, _xC xC, _xD xD)
-  {
-    extern __shared__ char shmem[];
-    Agent::entry(x0, x1, x2, x3, x4, x5, x6, x7, x8, x9, xA, xB, xC, xD, shmem);
-  }
-  template <class Agent, class _0, class _1, class _2, class _3, class _4, class _5, class _6, class _7, class _8, class _9, class _xA, class _xB, class _xC, class _xD, class _xE>
-  void __global__ __launch_bounds__(Agent::ptx_plan::BLOCK_THREADS)
-  _kernel_agent(_0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8, _9 x9, _xA xA, _xB xB, _xC xC, _xD xD, _xE xE)
-  {
-    extern __shared__ char shmem[];
-    Agent::entry(x0, x1, x2, x3, x4, x5, x6, x7, x8, x9, xA, xB, xC, xD, xE, shmem);
-  }
-#endif
-
-  ////////////////////////////////////////////////////////////
-
-
-#if 0
-  template <class Agent, class... Args>
-  void __global__
-  __launch_bounds__(Agent::ptx_plan::BLOCK_THREADS)
-      _kernel_agent_vshmem(char* vshmem, Args... args)
-  {
-    extern __shared__ char shmem[];
-    vshmem = vshmem == NULL ? shmem : vshmem + blockIdx.x * temp_storage_size<typename Agent::ptx_plan>::value;
-    Agent::entry(args..., vshmem);
-  }
-#else
-  template <class Agent, class _0>
-  void __global__ __launch_bounds__(Agent::ptx_plan::BLOCK_THREADS)
-  _kernel_agent_vshmem(char* vshmem, _0 x0)
-  {
-    extern __shared__ char shmem[];
-    vshmem = vshmem == NULL ? shmem : vshmem + blockIdx.x * temp_storage_size<typename Agent::ptx_plan>::value;
-    Agent::entry(x0, vshmem);
-  }
-  template <class Agent, class _0, class _1>
-  void __global__ __launch_bounds__(Agent::ptx_plan::BLOCK_THREADS)
-  _kernel_agent_vshmem(char* vshmem, _0 x0, _1 x1)
-  {
-    extern __shared__ char shmem[];
-    vshmem = vshmem == NULL ? shmem : vshmem + blockIdx.x * temp_storage_size<typename Agent::ptx_plan>::value;
-    Agent::entry(x0, x1, vshmem);
-  }
-  template <class Agent, class _0, class _1, class _2>
-  void __global__ __launch_bounds__(Agent::ptx_plan::BLOCK_THREADS)
-  _kernel_agent_vshmem(char* vshmem, _0 x0, _1 x1, _2 x2)
-  {
-    extern __shared__ char shmem[];
-    vshmem = vshmem == NULL ? shmem : vshmem + blockIdx.x * temp_storage_size<typename Agent::ptx_plan>::value;
-    Agent::entry(x0, x1, x2, vshmem);
-  }
-  template <class Agent, class _0, class _1, class _2, class _3>
-  void __global__ __launch_bounds__(Agent::ptx_plan::BLOCK_THREADS)
-  _kernel_agent_vshmem(char* vshmem, _0 x0, _1 x1, _2 x2, _3 x3)
-  {
-    extern __shared__ char shmem[];
-    vshmem = vshmem == NULL ? shmem : vshmem + blockIdx.x * temp_storage_size<typename Agent::ptx_plan>::value;
-    Agent::entry(x0, x1, x2, x3, vshmem);
-  }
-  template <class Agent, class _0, class _1, class _2, class _3, class _4>
-  void __global__ __launch_bounds__(Agent::ptx_plan::BLOCK_THREADS)
-  _kernel_agent_vshmem(char* vshmem, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4)
-  {
-    extern __shared__ char shmem[];
-    vshmem = vshmem == NULL ? shmem : vshmem + blockIdx.x * temp_storage_size<typename Agent::ptx_plan>::value;
-    Agent::entry(x0, x1, x2, x3, x4, vshmem);
-  }
-  template <class Agent, class _0, class _1, class _2, class _3, class _4, class _5>
-  void __global__ __launch_bounds__(Agent::ptx_plan::BLOCK_THREADS)
-  _kernel_agent_vshmem(char* vshmem, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5)
-  {
-    extern __shared__ char shmem[];
-    vshmem = vshmem == NULL ? shmem : vshmem + blockIdx.x * temp_storage_size<typename Agent::ptx_plan>::value;
-    Agent::entry(x0, x1, x2, x3, x4, x5, vshmem);
-  }
-  template <class Agent, class _0, class _1, class _2, class _3, class _4, class _5, class _6>
-  void __global__ __launch_bounds__(Agent::ptx_plan::BLOCK_THREADS)
-  _kernel_agent_vshmem(char* vshmem, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6)
-  {
-    extern __shared__ char shmem[];
-    vshmem = vshmem == NULL ? shmem : vshmem + blockIdx.x * temp_storage_size<typename Agent::ptx_plan>::value;
-    Agent::entry(x0, x1, x2, x3, x4, x5, x6, vshmem);
-  }
-  template <class Agent, class _0, class _1, class _2, class _3, class _4, class _5, class _6, class _7>
-  void __global__ __launch_bounds__(Agent::ptx_plan::BLOCK_THREADS)
-  _kernel_agent_vshmem(char* vshmem, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7)
-  {
-    extern __shared__ char shmem[];
-    vshmem = vshmem == NULL ? shmem : vshmem + blockIdx.x * temp_storage_size<typename Agent::ptx_plan>::value;
-    Agent::entry(x0, x1, x2, x3, x4, x5, x6, x7, vshmem);
-  }
-  template <class Agent, class _0, class _1, class _2, class _3, class _4, class _5, class _6, class _7, class _8>
-  void __global__ __launch_bounds__(Agent::ptx_plan::BLOCK_THREADS)
-  _kernel_agent_vshmem(char* vshmem, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8)
-  {
-    extern __shared__ char shmem[];
-    vshmem = vshmem == NULL ? shmem : vshmem + blockIdx.x * temp_storage_size<typename Agent::ptx_plan>::value;
-    Agent::entry(x0, x1, x2, x3, x4, x5, x6, x7, x8, vshmem);
-  }
-  template <class Agent, class _0, class _1, class _2, class _3, class _4, class _5, class _6, class _7, class _8, class _9>
-  void __global__ __launch_bounds__(Agent::ptx_plan::BLOCK_THREADS)
-  _kernel_agent_vshmem(char* vshmem, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8, _9 x9)
-  {
-    extern __shared__ char shmem[];
-    vshmem = vshmem == NULL ? shmem : vshmem + blockIdx.x * temp_storage_size<typename Agent::ptx_plan>::value;
-    Agent::entry(x0, x1, x2, x3, x4, x5, x6, x7, x8, x9, vshmem);
-  }
-  template <class Agent, class _0, class _1, class _2, class _3, class _4, class _5, class _6, class _7, class _8, class _9, class _xA>
-  void __global__ __launch_bounds__(Agent::ptx_plan::BLOCK_THREADS)
-  _kernel_agent_vshmem(char* vshmem, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8, _9 x9, _xA xA)
-  {
-    extern __shared__ char shmem[];
-    vshmem = vshmem == NULL ? shmem : vshmem + blockIdx.x * temp_storage_size<typename Agent::ptx_plan>::value;
-    Agent::entry(x0, x1, x2, x3, x4, x5, x6, x7, x8, x9, xA, vshmem);
-  }
-  template <class Agent, class _0, class _1, class _2, class _3, class _4, class _5, class _6, class _7, class _8, class _9, class _xA, class _xB>
-  void __global__ __launch_bounds__(Agent::ptx_plan::BLOCK_THREADS)
-  _kernel_agent_vshmem(char* vshmem, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8, _9 x9, _xA xA, _xB xB)
-  {
-    extern __shared__ char shmem[];
-    vshmem = vshmem == NULL ? shmem : vshmem + blockIdx.x * temp_storage_size<typename Agent::ptx_plan>::value;
-    Agent::entry(x0, x1, x2, x3, x4, x5, x6, x7, x8, x9, xA, xB, vshmem);
-  }
-  template <class Agent, class _0, class _1, class _2, class _3, class _4, class _5, class _6, class _7, class _8, class _9, class _xA, class _xB, class _xC>
-  void __global__ __launch_bounds__(Agent::ptx_plan::BLOCK_THREADS)
-  _kernel_agent_vshmem(char* vshmem, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8, _9 x9, _xA xA, _xB xB, _xC xC)
-  {
-    extern __shared__ char shmem[];
-    vshmem = vshmem == NULL ? shmem : vshmem + blockIdx.x * temp_storage_size<typename Agent::ptx_plan>::value;
-    Agent::entry(x0, x1, x2, x3, x4, x5, x6, x7, x8, x9, xA, xB, xC, vshmem);
-  }
-  template <class Agent, class _0, class _1, class _2, class _3, class _4, class _5, class _6, class _7, class _8, class _9, class _xA, class _xB, class _xC, class _xD>
-  void __global__ __launch_bounds__(Agent::ptx_plan::BLOCK_THREADS)
-  _kernel_agent_vshmem(char* vshmem, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8, _9 x9, _xA xA, _xB xB, _xC xC, _xD xD)
-  {
-    extern __shared__ char shmem[];
-    vshmem = vshmem == NULL ? shmem : vshmem + blockIdx.x * temp_storage_size<typename Agent::ptx_plan>::value;
-    Agent::entry(x0, x1, x2, x3, x4, x5, x6, x7, x8, x9, xA, xB, xC, xD, vshmem);
-  }
-  template <class Agent, class _0, class _1, class _2, class _3, class _4, class _5, class _6, class _7, class _8, class _9, class _xA, class _xB, class _xC, class _xD, class _xE>
-  void __global__ __launch_bounds__(Agent::ptx_plan::BLOCK_THREADS)
-  _kernel_agent_vshmem(char* vshmem, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8, _9 x9, _xA xA, _xB xB, _xC xC, _xD xD, _xE xE)
-  {
-    extern __shared__ char shmem[];
-    vshmem = vshmem == NULL ? shmem : vshmem + blockIdx.x * temp_storage_size<typename Agent::ptx_plan>::value;
-    Agent::entry(x0, x1, x2, x3, x4, x5, x6, x7, x8, x9, xA, xB, xC, xD, xE, vshmem);
-  }
-#endif
-#else
-#if 0
-  template <class , class... Args >
-  void __global__  _kernel_agent(Args... args) {}
-  template <class , class... Args >
-  void __global__  _kernel_agent_vshmem(char*, Args... args) {}
-#else
-  template <class, class _0>
-  void __global__ _kernel_agent(_0) {}
-  template <class, class _0, class _1>
-  void __global__ _kernel_agent(_0,_1) {}
-  template <class, class _0, class _1, class _2>
-  void __global__ _kernel_agent(_0,_1,_2) {}
-  template <class, class _0, class _1, class _2, class _3>
-  void __global__ _kernel_agent(_0,_1,_2,_3) {}
-  template <class, class _0, class _1, class _2, class _3, class _4>
-  void __global__ _kernel_agent(_0,_1,_2,_3, _4) {}
-  template <class, class _0, class _1, class _2, class _3, class _4, class _5>
-  void __global__ _kernel_agent(_0,_1,_2,_3, _4, _5) {}
-  template <class, class _0, class _1, class _2, class _3, class _4, class _5, class _6>
-  void __global__ _kernel_agent(_0,_1,_2,_3, _4, _5, _6) {}
-  template <class, class _0, class _1, class _2, class _3, class _4, class _5, class _6, class _7>
-  void __global__ _kernel_agent(_0,_1,_2,_3, _4, _5, _6, _7) {}
-  template <class, class _0, class _1, class _2, class _3, class _4, class _5, class _6, class _7, class _8>
-  void __global__ _kernel_agent(_0,_1,_2,_3, _4, _5, _6, _7, _8) {}
-  template <class, class _0, class _1, class _2, class _3, class _4, class _5, class _6, class _7, class _8, class _9>
-  void __global__ _kernel_agent(_0, _1, _2, _3, _4, _5, _6, _7, _8, _9) {}
-  template <class, class _0, class _1, class _2, class _3, class _4, class _5, class _6, class _7, class _8, class _9, class _xA>
-  void __global__ _kernel_agent(_0, _1, _2, _3, _4, _5, _6, _7, _8, _9, _xA) {}
-  template <class, class _0, class _1, class _2, class _3, class _4, class _5, class _6, class _7, class _8, class _9, class _xA, class _xB>
-  void __global__ _kernel_agent(_0, _1, _2, _3, _4, _5, _6, _7, _8, _9, _xA, _xB) {}
-  template <class, class _0, class _1, class _2, class _3, class _4, class _5, class _6, class _7, class _8, class _9, class _xA, class _xB, class _xC>
-  void __global__ _kernel_agent(_0, _1, _2, _3, _4, _5, _6, _7, _8, _9, _xA, _xB,_xC) {}
-  template <class, class _0, class _1, class _2, class _3, class _4, class _5, class _6, class _7, class _8, class _9, class _xA, class _xB, class _xC, class _xD>
-  void __global__ _kernel_agent(_0, _1, _2, _3, _4, _5, _6, _7, _8, _9, _xA, _xB,_xC, _xD) {}
-  template <class, class _0, class _1, class _2, class _3, class _4, class _5, class _6, class _7, class _8, class _9, class _xA, class _xB, class _xC, class _xD, class _xE>
-  void __global__ _kernel_agent(_0, _1, _2, _3, _4, _5, _6, _7, _8, _9, _xA, _xB,_xC, _xD, _xE) {}
-  ////////////////////////////////////////////////////////////
-  template <class, class _0>
-  void __global__ _kernel_agent_vshmem(char*,_0) {}
-  template <class, class _0, class _1>
-  void __global__ _kernel_agent_vshmem(char*,_0,_1) {}
-  template <class, class _0, class _1, class _2>
-  void __global__ _kernel_agent_vshmem(char*,_0,_1,_2) {}
-  template <class, class _0, class _1, class _2, class _3>
-  void __global__ _kernel_agent_vshmem(char*,_0,_1,_2,_3) {}
-  template <class, class _0, class _1, class _2, class _3, class _4>
-  void __global__ _kernel_agent_vshmem(char*,_0,_1,_2,_3, _4) {}
-  template <class, class _0, class _1, class _2, class _3, class _4, class _5>
-  void __global__ _kernel_agent_vshmem(char*,_0,_1,_2,_3, _4, _5) {}
-  template <class, class _0, class _1, class _2, class _3, class _4, class _5, class _6>
-  void __global__ _kernel_agent_vshmem(char*,_0,_1,_2,_3, _4, _5, _6) {}
-  template <class, class _0, class _1, class _2, class _3, class _4, class _5, class _6, class _7>
-  void __global__ _kernel_agent_vshmem(char*,_0,_1,_2,_3, _4, _5, _6, _7) {}
-  template <class, class _0, class _1, class _2, class _3, class _4, class _5, class _6, class _7, class _8>
-  void __global__ _kernel_agent_vshmem(char*,_0,_1,_2,_3, _4, _5, _6, _7, _8) {}
-  template <class, class _0, class _1, class _2, class _3, class _4, class _5, class _6, class _7, class _8, class _9>
-  void __global__ _kernel_agent_vshmem(char*,_0, _1, _2, _3, _4, _5, _6, _7, _8, _9) {}
-  template <class, class _0, class _1, class _2, class _3, class _4, class _5, class _6, class _7, class _8, class _9, class _xA>
-  void __global__ _kernel_agent_vshmem(char*,_0, _1, _2, _3, _4, _5, _6, _7, _8, _9, _xA) {}
-  template <class, class _0, class _1, class _2, class _3, class _4, class _5, class _6, class _7, class _8, class _9, class _xA, class _xB>
-  void __global__ _kernel_agent_vshmem(char*,_0, _1, _2, _3, _4, _5, _6, _7, _8, _9, _xA, _xB) {}
-  template <class, class _0, class _1, class _2, class _3, class _4, class _5, class _6, class _7, class _8, class _9, class _xA, class _xB, class _xC>
-  void __global__ _kernel_agent_vshmem(char*,_0, _1, _2, _3, _4, _5, _6, _7, _8, _9, _xA, _xB, _xC) {}
-  template <class, class _0, class _1, class _2, class _3, class _4, class _5, class _6, class _7, class _8, class _9, class _xA, class _xB, class _xC, class _xD>
-  void __global__ _kernel_agent_vshmem(char*,_0, _1, _2, _3, _4, _5, _6, _7, _8, _9, _xA, _xB, _xC, _xD) {}
-  template <class, class _0, class _1, class _2, class _3, class _4, class _5, class _6, class _7, class _8, class _9, class _xA, class _xB, class _xC, class _xD, class _xE>
-  void __global__ _kernel_agent_vshmem(char*,_0, _1, _2, _3, _4, _5, _6, _7, _8, _9, _xA, _xB, _xC, _xD, _xE) {}
-#endif
-#endif
-
-
-  template<class Agent>
-  struct AgentLauncher : Agent
-  {
-    core::AgentPlan plan;
-    size_t          count;
-    cudaStream_t    stream;
-    char const*     name;
-    bool            debug_sync;
-    unsigned int    grid;
-    char*           vshmem;
-    bool            has_shmem;
-    size_t          shmem_size;
-
-    enum
-    {
-      MAX_SHMEM_PER_BLOCK = 48 * 1024,
-    };
-    typedef
-        typename has_enough_shmem<Agent,
-                                  MAX_SHMEM_PER_BLOCK>::type has_enough_shmem_t;
-    typedef
-        has_enough_shmem<Agent,
-                                  MAX_SHMEM_PER_BLOCK> shm1;
-
-    template <class Size>
-    THRUST_RUNTIME_FUNCTION
-    AgentLauncher(AgentPlan    plan_,
-                  Size         count_,
-                  cudaStream_t stream_,
-                  char const*  name_,
-                  bool         debug_sync_)
-        : plan(plan_),
-          count((size_t)count_),
-          stream(stream_),
-          name(name_),
-          debug_sync(debug_sync_),
-          grid(static_cast<unsigned int>((count + plan.items_per_tile - 1) / plan.items_per_tile)),
-          vshmem(NULL),
-          has_shmem((size_t)core::get_max_shared_memory_per_block() >= (size_t)plan.shared_memory_size),
-          shmem_size(has_shmem ? plan.shared_memory_size : 0)
-    {
-      assert(count > 0);
-    }
-
-    template <class Size>
-    THRUST_RUNTIME_FUNCTION
-    AgentLauncher(AgentPlan    plan_,
-                  Size         count_,
-                  cudaStream_t stream_,
-                  char*        vshmem,
-                  char const*  name_,
-                  bool         debug_sync_)
-        : plan(plan_),
-          count((size_t)count_),
-          stream(stream_),
-          name(name_),
-          debug_sync(debug_sync_),
-          grid(static_cast<unsigned int>((count + plan.items_per_tile - 1) / plan.items_per_tile)),
-          vshmem(vshmem),
-          has_shmem((size_t)core::get_max_shared_memory_per_block() >= (size_t)plan.shared_memory_size),
-          shmem_size(has_shmem ? plan.shared_memory_size : 0)
-    {
-      assert(count > 0);
-    }
-
-    THRUST_RUNTIME_FUNCTION
-    AgentLauncher(AgentPlan    plan_,
-                  cudaStream_t stream_,
-                  char const*  name_,
-                  bool         debug_sync_)
-        : plan(plan_),
-          count(0),
-          stream(stream_),
-          name(name_),
-          debug_sync(debug_sync_),
-          grid(plan.grid_size),
-          vshmem(NULL),
-          has_shmem((size_t)core::get_max_shared_memory_per_block() >= (size_t)plan.shared_memory_size),
-          shmem_size(has_shmem ? plan.shared_memory_size : 0)
-    {
-      assert(plan.grid_size > 0);
-    }
-
-    THRUST_RUNTIME_FUNCTION
-    AgentLauncher(AgentPlan    plan_,
-                  cudaStream_t stream_,
-                  char*        vshmem,
-                  char const*  name_,
-                  bool         debug_sync_)
-        : plan(plan_),
-          count(0),
-          stream(stream_),
-          name(name_),
-          debug_sync(debug_sync_),
-          grid(plan.grid_size),
-          vshmem(vshmem),
-          has_shmem((size_t)core::get_max_shared_memory_per_block() >= (size_t)plan.shared_memory_size),
-          shmem_size(has_shmem ? plan.shared_memory_size : 0)
-    {
-      assert(plan.grid_size > 0);
-    }
-
-#if 0
-    THRUST_RUNTIME_FUNCTION
-    AgentPlan static get_plan(cudaStream_t s, void* d_ptr = 0)
-    {
-      // in separable compilation mode, we have no choice
-      // but to call kernel to get agent_plan
-      // otherwise the risk is something may fail
-      // if user mix & match ptx versions in a separably compiled function
-      // http://nvbugs/1772071
-      // XXX may be it is too string of a requirements, consider relaxing it in
-      // the future
-#ifdef __CUDACC_RDC__
-      return core::get_agent_plan<Agent>(s, d_ptr);
-#else
-      core::cuda_optional<int> ptx_version = core::get_ptx_version();
-      //CUDA_CUB_RET_IF_FAIL(ptx_version.status());
-      return get_agent_plan<Agent>(ptx_version);
-#endif
-    }
-    THRUST_RUNTIME_FUNCTION
-    AgentPlan static get_plan_default()
-    {
-      return get_agent_plan<Agent>(sm_arch<0>::type::ver);
-    }
-#endif
-
-    THRUST_RUNTIME_FUNCTION
-    typename core::get_plan<Agent>::type static get_plan(cudaStream_t , void* d_ptr = 0)
-    {
-      THRUST_UNUSED_VAR(d_ptr);
-      core::cuda_optional<int> ptx_version = core::get_ptx_version();
-      return get_agent_plan<Agent>(ptx_version);
-    }
-
-    THRUST_RUNTIME_FUNCTION
-    typename core::get_plan<Agent>::type static get_plan()
-    {
-      return get_agent_plan<Agent>(lowest_supported_sm_arch::ver);
-    }
-
-    THRUST_RUNTIME_FUNCTION void sync() const
-    {
-      if (debug_sync)
-      {
-        if (THRUST_IS_DEVICE_CODE) {
-          #if THRUST_INCLUDE_DEVICE_CODE
-            cudaDeviceSynchronize();
-          #endif
-        } else {
-          #if THRUST_INCLUDE_HOST_CODE
-            cudaStreamSynchronize(stream);
-          #endif
-        }
-      }
-    }
-
-    template<class K>
-    static cuda_optional<int> THRUST_RUNTIME_FUNCTION
-    max_blocks_per_sm_impl(K k, int block_threads)
-    {
-      int occ;
-      cudaError_t status = cub::MaxSmOccupancy(occ, k, block_threads);
-      return cuda_optional<int>(status == cudaSuccess ? occ : -1, status);
-    }
-
-    template <class K>
-    cuda_optional<int> THRUST_RUNTIME_FUNCTION
-    max_sm_occupancy(K k) const
-    {
-      return max_blocks_per_sm_impl(k, plan.block_threads);
-    }
-
-
-
-    template<class K>
-    THRUST_RUNTIME_FUNCTION
-    void print_info(K k) const
-    {
-      if (debug_sync)
-      {
-        cuda_optional<int> occ = max_sm_occupancy(k);
-        core::cuda_optional<int> ptx_version = core::get_ptx_version();
-        if (count > 0)
-        {
-          _CubLog("Invoking %s<<<%u, %d, %d, %lld>>>(), %llu items total, %d items per thread, %d SM occupancy, %d vshmem size, %d ptx_version \n",
-                  name,
-                  grid,
-                  plan.block_threads,
-                  (has_shmem ? (int)plan.shared_memory_size : 0),
-                  (long long)stream,
-                  (long long)count,
-                  plan.items_per_thread,
-                  (int)occ,
-                  (!has_shmem ? (int)plan.shared_memory_size : 0),
-                  (int)ptx_version);
-        }
-        else
-        {
-          _CubLog("Invoking %s<<<%u, %d, %d, %lld>>>(), %d items per thread, %d SM occupancy, %d vshmem size, %d ptx_version\n",
-                  name,
-                  grid,
-                  plan.block_threads,
-                  (has_shmem ? (int)plan.shared_memory_size : 0),
-                  (long long)stream,
-                  plan.items_per_thread,
-                  (int)occ,
-                  (!has_shmem ? (int)plan.shared_memory_size : 0),
-                  (int)ptx_version);
-        }
-      }
-    }
-
-    ////////////////////
-    //  Variadic code
-    ////////////////////
-
-#if 0
-    template<class... Args>
-    static cuda_optional<int> THRUST_RUNTIME_FUNCTION
-    get_max_blocks_per_sm(AgentPlan plan)
-    {
-      return max_blocks_per_sm_impl(_kernel_agent<Agent, Args...>, plan.block_threads);
-    }
-#else
-    template<class _0>
-    static cuda_optional<int> THRUST_RUNTIME_FUNCTION
-    get_max_blocks_per_sm(AgentPlan plan)
-    {
-      void (*ptr)(_0) = _kernel_agent<Agent, _0>;
-      return max_blocks_per_sm_impl(ptr, plan.block_threads);
-    }
-    template<class _0, class _1>
-    static cuda_optional<int> THRUST_RUNTIME_FUNCTION
-    get_max_blocks_per_sm(AgentPlan plan)
-    {
-      void (*ptr)(_0, _1) = _kernel_agent<Agent, _0, _1>;
-      return max_blocks_per_sm_impl(ptr, plan.block_threads);
-    }
-    template<class _0, class _1, class _2>
-    static cuda_optional<int> THRUST_RUNTIME_FUNCTION
-    get_max_blocks_per_sm(AgentPlan plan)
-    {
-      void (*ptr)(_0,_1,_2) = _kernel_agent<Agent, _0, _1, _2>;
-      return max_blocks_per_sm_impl(ptr, plan.block_threads);
-    }
-    template<class _0, class _1, class _2, class _3>
-    static cuda_optional<int> THRUST_RUNTIME_FUNCTION
-    get_max_blocks_per_sm(AgentPlan plan)
-    {
-      void (*ptr)(_0,_1,_2,_3) = _kernel_agent<Agent, _0, _1, _2,_3>;
-      return max_blocks_per_sm_impl(ptr, plan.block_threads);
-    }
-    template<class _0, class _1, class _2, class _3, class _4>
-    static cuda_optional<int> THRUST_RUNTIME_FUNCTION
-    get_max_blocks_per_sm(AgentPlan plan)
-    {
-      void (*ptr)(_0,_1,_2,_3,_4) = _kernel_agent<Agent, _0, _1, _2,_3,_4>;
-      return max_blocks_per_sm_impl(ptr, plan.block_threads);
-    }
-    template<class _0, class _1, class _2, class _3, class _4, class _5>
-    static cuda_optional<int> THRUST_RUNTIME_FUNCTION
-    get_max_blocks_per_sm(AgentPlan plan)
-    {
-      void (*ptr)(_0,_1,_2,_3,_4,_5) = _kernel_agent<Agent, _0, _1, _2,_3,_4,_5>;
-      return max_blocks_per_sm_impl(ptr, plan.block_threads);
-    }
-    template<class _0, class _1, class _2, class _3, class _4, class _5, class _6>
-    static cuda_optional<int> THRUST_RUNTIME_FUNCTION
-    get_max_blocks_per_sm(AgentPlan plan)
-    {
-      void (*ptr)(_0,_1,_2,_3,_4,_5,_6) = _kernel_agent<Agent, _0, _1, _2,_3,_4,_5,_6>;
-      return max_blocks_per_sm_impl(ptr, plan.block_threads);
-    }
-    template<class _0, class _1, class _2, class _3, class _4, class _5, class _6, class _7>
-    static cuda_optional<int> THRUST_RUNTIME_FUNCTION
-    get_max_blocks_per_sm(AgentPlan plan)
-    {
-      void (*ptr)(_0,_1,_2,_3,_4,_5,_6,_7) = _kernel_agent<Agent, _0, _1, _2,_3,_4,_5,_6,_7>;
-      return max_blocks_per_sm_impl(ptr, plan.block_threads);
-    }
-    template<class _0, class _1, class _2, class _3, class _4, class _5, class _6, class _7, class _8>
-    static cuda_optional<int> THRUST_RUNTIME_FUNCTION
-    get_max_blocks_per_sm(AgentPlan plan)
-    {
-      void (*ptr)(_0,_1,_2,_3,_4,_5,_6,_7,_8) = _kernel_agent<Agent, _0, _1, _2,_3,_4,_5,_6,_7,_8>;
-      return max_blocks_per_sm_impl(ptr, plan.block_threads);
-    }
-    template<class _0, class _1, class _2, class _3, class _4, class _5, class _6, class _7, class _8, class _9>
-    static cuda_optional<int> THRUST_RUNTIME_FUNCTION
-    get_max_blocks_per_sm(AgentPlan plan)
-    {
-      void (*ptr)(_0,_1,_2,_3,_4,_5,_6,_7,_8,_9) = _kernel_agent<Agent, _0, _1, _2,_3,_4,_5,_6,_7,_8,_9>;
-      return max_blocks_per_sm_impl(ptr, plan.block_threads);
-    }
-    template<class _0, class _1, class _2, class _3, class _4, class _5, class _6, class _7, class _8, class _9, class _xA>
-    static cuda_optional<int> THRUST_RUNTIME_FUNCTION
-    get_max_blocks_per_sm(AgentPlan plan)
-    {
-      void (*ptr)(_0,_1,_2,_3,_4,_5,_6,_7,_8,_9,_xA) = _kernel_agent<Agent, _0, _1, _2,_3,_4,_5,_6,_7,_8,_9,_xA>;
-      return max_blocks_per_sm_impl(ptr, plan.block_threads);
-    }
-    template<class _0, class _1, class _2, class _3, class _4, class _5, class _6, class _7, class _8, class _9, class _xA, class _xB>
-    static cuda_optional<int> THRUST_RUNTIME_FUNCTION
-    get_max_blocks_per_sm(AgentPlan plan)
-    {
-      void (*ptr)(_0,_1,_2,_3,_4,_5,_6,_7,_8,_9,_xA,_xB) = _kernel_agent<Agent, _0, _1, _2,_3,_4,_5,_6,_7,_8,_9,_xA,_xB>;
-      return max_blocks_per_sm_impl(ptr, plan.block_threads);
-    }
-    template<class _0, class _1, class _2, class _3, class _4, class _5, class _6, class _7, class _8, class _9, class _xA, class _xB, class _xC>
-    static cuda_optional<int> THRUST_RUNTIME_FUNCTION
-    get_max_blocks_per_sm(AgentPlan plan)
-    {
-      void (*ptr)(_0,_1,_2,_3,_4,_5,_6,_7,_8,_9,_xA,_xB,_xC) = _kernel_agent<Agent, _0, _1, _2,_3,_4,_5,_6,_7,_8,_9,_xA,_xB,_xC>;
-      return max_blocks_per_sm_impl(ptr, plan.block_threads);
-    }
-    template<class _0, class _1, class _2, class _3, class _4, class _5, class _6, class _7, class _8, class _9, class _xA, class _xB, class _xC, class _xD>
-    static cuda_optional<int> THRUST_RUNTIME_FUNCTION
-    get_max_blocks_per_sm(AgentPlan plan)
-    {
-      void (*ptr)(_0,_1,_2,_3,_4,_5,_6,_7,_8,_9,_xA,_xB,_xC,_xD) = _kernel_agent<Agent, _0, _1, _2,_3,_4,_5,_6,_7,_8,_9,_xA,_xB,_xC,_xD>;
-      return max_blocks_per_sm_impl(ptr, plan.block_threads);
-    }
-    template<class _0, class _1, class _2, class _3, class _4, class _5, class _6, class _7, class _8, class _9, class _xA, class _xB, class _xC, class _xD, class _xE>
-    static cuda_optional<int> THRUST_RUNTIME_FUNCTION
-    get_max_blocks_per_sm(AgentPlan plan)
-    {
-      void (*ptr)(_0,_1,_2,_3,_4,_5,_6,_7,_8,_9,_xA,_xB,_xC,_xD,_xE) = _kernel_agent<Agent, _0, _1, _2,_3,_4,_5,_6,_7,_8,_9,_xA,_xB,_xC,_xD,_xE>;
-      return max_blocks_per_sm_impl(ptr, plan.block_threads);
-    }
-#endif
-
-
-
-#if 0
-
-    // If we are guaranteed to have enough shared memory
-    // don't compile other kernel which accepts pointer
-    // and save on compilations
-    template <class... Args>
-    void THRUST_RUNTIME_FUNCTION
-    launch_impl(thrust::detail::true_type, Args... args) const
-    {
-      assert(has_shmem && vshmem == NULL);
-      print_info(_kernel_agent<Agent, Args...>);
-      launcher::triple_chevron(grid, plan.block_threads, shmem_size, stream)
-          .doit(_kernel_agent<Agent, Args...>, args...);
-    }
-
-    // If there is a risk of not having enough shared memory
-    // we compile generic kernel instead.
-    // This kernel is likely to be somewhat slower, but it can accomodate
-    // both shared and virtualized shared memories.
-    // Alternative option is to compile two kernels, one using shared and one
-    // using virtualized shared memory. While this can be slightly faster if we
-    // do actually have enough shared memory, the compilation time will double.
-    //
-    template <class... Args>
-    void THRUST_RUNTIME_FUNCTION
-    launch_impl(thrust::detail::false_type, Args... args) const
-    {
-      assert((has_shmem && vshmem == NULL) || (!has_shmem && vshmem != NULL && shmem_size == 0));
-      print_info(_kernel_agent_vshmem<Agent, Args...>);
-      launcher::triple_chevron(grid, plan.block_threads, shmem_size, stream)
-          .doit(_kernel_agent_vshmem<Agent, Args...>, vshmem, args...);
-    }
-
-    template <class... Args>
-    void THRUST_RUNTIME_FUNCTION
-    launch(Args... args) const
-    {
-#if __THRUST__TEMPLATE_DEBUG
-#ifdef __CUDA_ARCH__
-      typedef typename Foo<
-        shm1::v1,
-        shm1::v2,
-        shm1::v3,
-        shm1::v4,
-        shm1::v5>::t tt;
-#endif
-#endif
-      launch_impl(has_enough_shmem_t(),args...);
-      sync();
-    }
-#else
-    template <class _0>
-    void THRUST_RUNTIME_FUNCTION
-    launch_impl(thrust::detail::false_type, _0 x0) const
-    {
-      assert((has_shmem && vshmem == NULL) || (!has_shmem && vshmem != NULL && shmem_size == 0));
-      void (*ptr)(char*, _0) = _kernel_agent_vshmem<Agent, _0>;
-      print_info(ptr);
-      launcher::triple_chevron(grid, plan.block_threads, shmem_size, stream)
-          .doit(ptr, vshmem, x0);
-    }
-    template <class _0, class _1>
-    void THRUST_RUNTIME_FUNCTION
-    launch_impl(thrust::detail::false_type, _0 x0, _1 x1) const
-    {
-      assert((has_shmem && vshmem == NULL) || (!has_shmem && vshmem != NULL && shmem_size == 0));
-      void (*ptr)(char*, _0, _1) = _kernel_agent_vshmem<Agent, _0, _1>;
-      print_info(ptr);
-      launcher::triple_chevron(grid, plan.block_threads, shmem_size, stream)
-          .doit(ptr, vshmem, x0, x1);
-    }
-    template <class _0, class _1, class _2>
-    void THRUST_RUNTIME_FUNCTION
-    launch_impl(thrust::detail::false_type, _0 x0, _1 x1, _2 x2) const
-    {
-      assert((has_shmem && vshmem == NULL) || (!has_shmem && vshmem != NULL && shmem_size == 0));
-      void (*ptr)(char*, _0, _1, _2) = _kernel_agent_vshmem<Agent, _0, _1, _2>;
-      print_info(ptr);
-      launcher::triple_chevron(grid, plan.block_threads, shmem_size, stream)
-          .doit(ptr, vshmem, x0, x1, x2);
-    }
-    template <class _0, class _1, class _2, class _3>
-    void THRUST_RUNTIME_FUNCTION
-    launch_impl(thrust::detail::false_type, _0 x0, _1 x1, _2 x2, _3 x3) const
-    {
-      assert((has_shmem && vshmem == NULL) || (!has_shmem && vshmem != NULL && shmem_size == 0));
-      void (*ptr)(char*, _0, _1, _2, _3) = _kernel_agent_vshmem<Agent, _0, _1, _2, _3>;
-      print_info(ptr);
-      launcher::triple_chevron(grid, plan.block_threads, shmem_size, stream)
-          .doit(ptr, vshmem, x0, x1, x2, x3);
-    }
-    template <class _0, class _1, class _2, class _3, class _4>
-    void THRUST_RUNTIME_FUNCTION
-    launch_impl(thrust::detail::false_type, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4) const
-    {
-      assert((has_shmem && vshmem == NULL) || (!has_shmem && vshmem != NULL && shmem_size == 0));
-      void (*ptr)(char*, _0, _1, _2, _3, _4) = _kernel_agent_vshmem<Agent, _0, _1, _2, _3, _4>;
-      print_info(ptr);
-      launcher::triple_chevron(grid, plan.block_threads, shmem_size, stream)
-          .doit(ptr, vshmem, x0, x1, x2, x3, x4);
-    }
-    template <class _0, class _1, class _2, class _3, class _4, class _5>
-    void THRUST_RUNTIME_FUNCTION
-    launch_impl(thrust::detail::false_type, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5) const
-    {
-      assert((has_shmem && vshmem == NULL) || (!has_shmem && vshmem != NULL && shmem_size == 0));
-      void (*ptr)(char*, _0, _1, _2, _3, _4, _5) = _kernel_agent_vshmem<Agent, _0, _1, _2, _3, _4, _5>;
-      print_info(ptr);
-      launcher::triple_chevron(grid, plan.block_threads, shmem_size, stream)
-          .doit(ptr, vshmem, x0, x1, x2, x3, x4, x5);
-    }
-    template <class _0, class _1, class _2, class _3, class _4, class _5, class _6>
-    void THRUST_RUNTIME_FUNCTION
-    launch_impl(thrust::detail::false_type, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6) const
-    {
-      assert((has_shmem && vshmem == NULL) || (!has_shmem && vshmem != NULL && shmem_size == 0));
-      void (*ptr)(char*, _0, _1, _2, _3, _4, _5, _6) = _kernel_agent_vshmem<Agent, _0, _1, _2, _3, _4, _5, _6>;
-      print_info(ptr);
-      launcher::triple_chevron(grid, plan.block_threads, shmem_size, stream)
-          .doit(ptr, vshmem, x0, x1, x2, x3, x4, x5, x6);
-    }
-    template <class _0, class _1, class _2, class _3, class _4, class _5, class _6, class _7>
-    void THRUST_RUNTIME_FUNCTION
-    launch_impl(thrust::detail::false_type, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7) const
-    {
-      assert((has_shmem && vshmem == NULL) || (!has_shmem && vshmem != NULL && shmem_size == 0));
-      void (*ptr)(char*, _0, _1, _2, _3, _4, _5, _6, _7) = _kernel_agent_vshmem<Agent, _0, _1, _2, _3, _4, _5, _6, _7>;
-      print_info(ptr);
-      launcher::triple_chevron(grid, plan.block_threads, shmem_size, stream)
-          .doit(ptr, vshmem, x0, x1, x2, x3, x4, x5, x6, x7);
-    }
-    template <class _0, class _1, class _2, class _3, class _4, class _5, class _6, class _7, class _8>
-    void THRUST_RUNTIME_FUNCTION
-    launch_impl(thrust::detail::false_type, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8) const
-    {
-      assert((has_shmem && vshmem == NULL) || (!has_shmem && vshmem != NULL && shmem_size == 0));
-      void (*ptr)(char*, _0, _1, _2, _3, _4, _5, _6, _7, _8) = _kernel_agent_vshmem<Agent, _0, _1, _2, _3, _4, _5, _6, _7, _8>;
-      launcher::triple_chevron(grid, plan.block_threads, shmem_size, stream)
-          .doit(ptr, vshmem, x0, x1, x2, x3, x4, x5, x6, x7, x8);
-    }
-    template <class _0, class _1, class _2, class _3, class _4, class _5, class _6, class _7, class _8, class _9>
-    void THRUST_RUNTIME_FUNCTION
-    launch_impl(thrust::detail::false_type, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8, _9 x9) const
-    {
-      assert((has_shmem && vshmem == NULL) || (!has_shmem && vshmem != NULL && shmem_size == 0));
-      void (*ptr)(char*, _0, _1, _2, _3, _4, _5, _6, _7, _8, _9) = _kernel_agent_vshmem<Agent, _0, _1, _2, _3, _4, _5, _6, _7, _8, _9>;
-      print_info(ptr);
-      launcher::triple_chevron(grid, plan.block_threads, shmem_size, stream)
-          .doit(ptr, vshmem, x0, x1, x2, x3, x4, x5, x6, x7, x8, x9);
-    }
-    template <class _0, class _1, class _2, class _3, class _4, class _5, class _6, class _7, class _8, class _9, class _xA>
-    void THRUST_RUNTIME_FUNCTION
-    launch_impl(thrust::detail::false_type, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8, _9 x9,_xA xA) const
-    {
-      assert((has_shmem && vshmem == NULL) || (!has_shmem && vshmem != NULL && shmem_size == 0));
-      void (*ptr)(char*, _0, _1, _2, _3, _4, _5, _6, _7, _8, _9, _xA) = _kernel_agent_vshmem<Agent, _0, _1, _2, _3, _4, _5, _6, _7, _8, _9, _xA>;
-      print_info(ptr);
-      launcher::triple_chevron(grid, plan.block_threads, shmem_size, stream)
-          .doit(ptr, vshmem, x0, x1, x2, x3, x4, x5, x6, x7, x8, x9, xA);
-    }
-    template <class _0, class _1, class _2, class _3, class _4, class _5, class _6, class _7, class _8, class _9, class _xA, class _xB>
-    void THRUST_RUNTIME_FUNCTION
-    launch_impl(thrust::detail::false_type, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8, _9 x9,_xA xA,_xB xB) const
-    {
-      assert((has_shmem && vshmem == NULL) || (!has_shmem && vshmem != NULL && shmem_size == 0));
-      void (*ptr)(char*, _0, _1, _2, _3, _4, _5, _6, _7, _8, _9, _xA, _xB) = _kernel_agent_vshmem<Agent, _0, _1, _2, _3, _4, _5, _6, _7, _8, _9, _xA, _xB>;
-      print_info(ptr);
-      launcher::triple_chevron(grid, plan.block_threads, shmem_size, stream)
-          .doit(ptr, vshmem, x0, x1, x2, x3, x4, x5, x6, x7, x8, x9, xA, xB);
-    }
-    template <class _0, class _1, class _2, class _3, class _4, class _5, class _6, class _7, class _8, class _9, class _xA, class _xB, class _xC>
-    void THRUST_RUNTIME_FUNCTION
-    launch_impl(thrust::detail::false_type, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8, _9 x9,_xA xA,_xB xB,_xC xC) const
-    {
-      assert((has_shmem && vshmem == NULL) || (!has_shmem && vshmem != NULL && shmem_size == 0));
-      void (*ptr)(char*, _0, _1, _2, _3, _4, _5, _6, _7, _8, _9, _xA, _xB, _xC) = _kernel_agent_vshmem<Agent, _0, _1, _2, _3, _4, _5, _6, _7, _8, _9, _xA, _xB, _xC>;
-      print_info(ptr);
-      launcher::triple_chevron(grid, plan.block_threads, shmem_size, stream)
-          .doit(ptr, vshmem, x0, x1, x2, x3, x4, x5, x6, x7, x8, x9, xA, xB, xC);
-    }
-    template <class _0, class _1, class _2, class _3, class _4, class _5, class _6, class _7, class _8, class _9, class _xA, class _xB, class _xC, class _xD>
-    void THRUST_RUNTIME_FUNCTION
-    launch_impl(thrust::detail::false_type, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8, _9 x9,_xA xA,_xB xB,_xC xC,_xD xD) const
-    {
-      assert((has_shmem && vshmem == NULL) || (!has_shmem && vshmem != NULL && shmem_size == 0));
-      void (*ptr)(char*, _0, _1, _2, _3, _4, _5, _6, _7, _8, _9, _xA, _xB, _xC, _xD) = _kernel_agent_vshmem<Agent, _0, _1, _2, _3, _4, _5, _6, _7, _8, _9, _xA, _xB, _xC, _xD>;
-      print_info(ptr);
-      launcher::triple_chevron(grid, plan.block_threads, shmem_size, stream)
-          .doit(ptr, vshmem, x0, x1, x2, x3, x4, x5, x6, x7, x8, x9, xA, xB, xC, xD);
-    }
-    template <class _0, class _1, class _2, class _3, class _4, class _5, class _6, class _7, class _8, class _9, class _xA, class _xB, class _xC, class _xD, class _xE>
-    void THRUST_RUNTIME_FUNCTION
-    launch_impl(thrust::detail::false_type, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8, _9 x9,_xA xA,_xB xB,_xC xC,_xD xD,_xE xE) const
-    {
-      assert((has_shmem && vshmem == NULL) || (!has_shmem && vshmem != NULL && shmem_size == 0));
-      void (*ptr)(char*, _0, _1, _2, _3, _4, _5, _6, _7, _8, _9, _xA, _xB, _xC, _xD, _xE) = _kernel_agent_vshmem<Agent, _0, _1, _2, _3, _4, _5, _6, _7, _8, _9, _xA, _xB, _xC, _xD, _xE>;
-      print_info(ptr);
-      launcher::triple_chevron(grid, plan.block_threads, shmem_size, stream)
-          .doit(ptr, vshmem, x0, x1, x2, x3, x4, x5, x6, x7, x8, x9, xA, xB, xC, xD, xE);
-    }
-
-    ////////////////////////////////////////////////////////
-    ////////////////////////////////////////////////////////
-    ////////////////////////////////////////////////////////
-
-    template <class _0>
-    void THRUST_RUNTIME_FUNCTION
-    launch_impl(thrust::detail::true_type, _0 x0) const
-    {
-      assert(has_shmem && vshmem == NULL);
-      void (*ptr)(_0) = _kernel_agent<Agent, _0>;
-      print_info(ptr);
-      launcher::triple_chevron(grid, plan.block_threads, plan.shared_memory_size, stream)
-          .doit(ptr, x0);
-    }
-    template <class _0, class _1>
-    void THRUST_RUNTIME_FUNCTION
-    launch_impl(thrust::detail::true_type, _0 x0, _1 x1) const
-    {
-      assert(has_shmem && vshmem == NULL);
-      void (*ptr)(_0, _1) = _kernel_agent<Agent, _0, _1>;
-      print_info(ptr);
-      launcher::triple_chevron(grid, plan.block_threads, plan.shared_memory_size, stream)
-          .doit(ptr, x0, x1);
-    }
-    template <class _0, class _1, class _2>
-    void THRUST_RUNTIME_FUNCTION
-    launch_impl(thrust::detail::true_type, _0 x0, _1 x1, _2 x2) const
-    {
-      assert(has_shmem && vshmem == NULL);
-      void (*ptr)(_0,_1,_2) = _kernel_agent<Agent, _0, _1, _2>;
-      print_info(ptr);
-      launcher::triple_chevron(grid, plan.block_threads, plan.shared_memory_size, stream)
-          .doit(ptr, x0, x1, x2);
-    }
-    template <class _0, class _1, class _2, class _3>
-    void THRUST_RUNTIME_FUNCTION
-    launch_impl(thrust::detail::true_type, _0 x0, _1 x1, _2 x2, _3 x3) const
-    {
-      assert(has_shmem && vshmem == NULL);
-      void (*ptr)(_0,_1,_2,_3) = _kernel_agent<Agent, _0, _1, _2,_3>;
-      print_info(ptr);
-      launcher::triple_chevron(grid, plan.block_threads, plan.shared_memory_size, stream)
-          .doit(ptr, x0, x1, x2, x3);
-    }
-    template <class _0, class _1, class _2, class _3, class _4>
-    void THRUST_RUNTIME_FUNCTION
-    launch_impl(thrust::detail::true_type, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4) const
-    {
-      assert(has_shmem && vshmem == NULL);
-      void (*ptr)(_0,_1,_2,_3,_4) = _kernel_agent<Agent, _0, _1, _2,_3,_4>;
-      print_info(ptr);
-      launcher::triple_chevron(grid, plan.block_threads, plan.shared_memory_size, stream)
-          .doit(ptr, x0, x1, x2, x3, x4);
-    }
-    template <class _0, class _1, class _2, class _3, class _4, class _5>
-    void THRUST_RUNTIME_FUNCTION
-    launch_impl(thrust::detail::true_type, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5) const
-    {
-      assert(has_shmem && vshmem == NULL);
-      void (*ptr)(_0,_1,_2,_3,_4,_5) = _kernel_agent<Agent, _0, _1, _2,_3,_4,_5>;
-      print_info(ptr);
-      launcher::triple_chevron(grid, plan.block_threads, plan.shared_memory_size, stream)
-          .doit(ptr, x0, x1, x2, x3, x4, x5);
-    }
-    template <class _0, class _1, class _2, class _3, class _4, class _5, class _6>
-    void THRUST_RUNTIME_FUNCTION
-    launch_impl(thrust::detail::true_type, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6) const
-    {
-      assert(has_shmem && vshmem == NULL);
-      void (*ptr)(_0,_1,_2,_3,_4,_5,_6) = _kernel_agent<Agent, _0, _1, _2,_3,_4,_5,_6>;
-      print_info(ptr);
-      launcher::triple_chevron(grid, plan.block_threads, plan.shared_memory_size, stream)
-          .doit(ptr, x0, x1, x2, x3, x4, x5, x6);
-    }
-    template <class _0, class _1, class _2, class _3, class _4, class _5, class _6, class _7>
-    void THRUST_RUNTIME_FUNCTION
-    launch_impl(thrust::detail::true_type, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7) const
-    {
-      assert(has_shmem && vshmem == NULL);
-      void (*ptr)(_0,_1,_2,_3,_4,_5,_6,_7) = _kernel_agent<Agent, _0, _1, _2,_3,_4,_5,_6,_7>;
-      print_info(ptr);
-      launcher::triple_chevron(grid, plan.block_threads, plan.shared_memory_size, stream)
-          .doit(ptr, x0, x1, x2, x3, x4, x5, x6, x7);
-    }
-    template <class _0, class _1, class _2, class _3, class _4, class _5, class _6, class _7, class _8>
-    void THRUST_RUNTIME_FUNCTION
-    launch_impl(thrust::detail::true_type, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8) const
-    {
-      assert(has_shmem && vshmem == NULL);
-      void (*ptr)(_0,_1,_2,_3,_4,_5,_6,_7,_8) = _kernel_agent<Agent, _0, _1, _2,_3,_4,_5,_6,_7,_8>;
-      print_info(ptr);
-      launcher::triple_chevron(grid, plan.block_threads, plan.shared_memory_size, stream)
-          .doit(ptr, x0, x1, x2, x3, x4, x5, x6, x7, x8);
-    }
-    template <class _0, class _1, class _2, class _3, class _4, class _5, class _6, class _7, class _8, class _9>
-    void THRUST_RUNTIME_FUNCTION
-    launch_impl(thrust::detail::true_type, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8, _9 x9) const
-    {
-      assert(has_shmem && vshmem == NULL);
-      void (*ptr)(_0,_1,_2,_3,_4,_5,_6,_7,_8,_9) = _kernel_agent<Agent, _0, _1, _2,_3,_4,_5,_6,_7,_8,_9>;
-      print_info(ptr);
-      launcher::triple_chevron(grid, plan.block_threads, plan.shared_memory_size, stream)
-          .doit(ptr, x0, x1, x2, x3, x4, x5, x6, x7, x8, x9);
-    }
-    template <class _0, class _1, class _2, class _3, class _4, class _5, class _6, class _7, class _8, class _9, class _xA>
-    void THRUST_RUNTIME_FUNCTION
-    launch_impl(thrust::detail::true_type, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8, _9 x9, _xA xA) const
-    {
-      assert(has_shmem && vshmem == NULL);
-      void (*ptr)(_0,_1,_2,_3,_4,_5,_6,_7,_8,_9,_xA) = _kernel_agent<Agent, _0, _1, _2,_3,_4,_5,_6,_7,_8,_9,_xA>;
-      print_info(ptr);
-      launcher::triple_chevron(grid, plan.block_threads, plan.shared_memory_size, stream)
-          .doit(ptr, x0, x1, x2, x3, x4, x5, x6, x7, x8, x9, xA);
-    }
-    template <class _0, class _1, class _2, class _3, class _4, class _5, class _6, class _7, class _8, class _9, class _xA, class _xB>
-    void THRUST_RUNTIME_FUNCTION
-    launch_impl(thrust::detail::true_type, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8, _9 x9, _xA xA, _xB xB) const
-    {
-      assert(has_shmem && vshmem == NULL);
-      void (*ptr)(_0,_1,_2,_3,_4,_5,_6,_7,_8,_9,_xA,_xB) = _kernel_agent<Agent, _0, _1, _2,_3,_4,_5,_6,_7,_8,_9,_xA,_xB>;
-      print_info(ptr);
-      launcher::triple_chevron(grid, plan.block_threads, plan.shared_memory_size, stream)
-          .doit(ptr, x0, x1, x2, x3, x4, x5, x6, x7, x8, x9, xA, xB);
-    }
-    template <class _0, class _1, class _2, class _3, class _4, class _5, class _6, class _7, class _8, class _9, class _xA, class _xB, class _xC>
-    void THRUST_RUNTIME_FUNCTION
-    launch_impl(thrust::detail::true_type, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8, _9 x9, _xA xA, _xB xB, _xC xC) const
-    {
-      assert(has_shmem && vshmem == NULL);
-      void (*ptr)(_0,_1,_2,_3,_4,_5,_6,_7,_8,_9,_xA,_xB,_xC) = _kernel_agent<Agent, _0, _1, _2,_3,_4,_5,_6,_7,_8,_9,_xA,_xB,_xC>;
-      print_info(ptr);
-      launcher::triple_chevron(grid, plan.block_threads, plan.shared_memory_size, stream)
-          .doit(ptr, x0, x1, x2, x3, x4, x5, x6, x7, x8, x9, xA, xB, xC);
-    }
-    template <class _0, class _1, class _2, class _3, class _4, class _5, class _6, class _7, class _8, class _9, class _xA, class _xB, class _xC, class _xD>
-    void THRUST_RUNTIME_FUNCTION
-    launch_impl(thrust::detail::true_type, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8, _9 x9, _xA xA, _xB xB, _xC xC, _xD xD) const
-    {
-      assert(has_shmem && vshmem == NULL);
-      void (*ptr)(_0,_1,_2,_3,_4,_5,_6,_7,_8,_9,_xA,_xB,_xC,_xD) = _kernel_agent<Agent, _0, _1, _2,_3,_4,_5,_6,_7,_8,_9,_xA,_xB,_xC,_xD>;
-      print_info(ptr);
-      launcher::triple_chevron(grid, plan.block_threads, plan.shared_memory_size, stream)
-          .doit(ptr, x0, x1, x2, x3, x4, x5, x6, x7, x8, x9, xA, xB, xC, xD);
-    }
-    template <class _0, class _1, class _2, class _3, class _4, class _5, class _6, class _7, class _8, class _9, class _xA, class _xB, class _xC, class _xD, class _xE>
-    void THRUST_RUNTIME_FUNCTION
-    launch_impl(thrust::detail::true_type, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8, _9 x9, _xA xA, _xB xB, _xC xC, _xD xD, _xE xE) const
-    {
-      assert(has_shmem && vshmem == NULL);
-      void (*ptr)(_0,_1,_2,_3,_4,_5,_6,_7,_8,_9,_xA,_xB,_xC,_xD,_xE) = _kernel_agent<Agent, _0, _1, _2,_3,_4,_5,_6,_7,_8,_9,_xA,_xB,_xC,_xD,_xE>;
-      print_info(ptr);
-      launcher::triple_chevron(grid, plan.block_threads, plan.shared_memory_size, stream)
-          .doit(ptr,x0, x1, x2, x3, x4, x5, x6, x7, x8, x9, xA, xB, xC, xD, xE);
-    }
-
-    ////////////////////////////////////////////////////////
-    ////////////////////////////////////////////////////////
-    ////////////////////////////////////////////////////////
-
-    template <class _0>
-    void THRUST_RUNTIME_FUNCTION
-    launch(_0 x0) const
-    {
-      launch_impl(has_enough_shmem_t(), x0);
-      sync();
-    }
-    template <class _0, class _1>
-    void THRUST_RUNTIME_FUNCTION
-    launch(_0 x0, _1 x1) const
-    {
-      launch_impl(has_enough_shmem_t(), x0, x1);
-      sync();
-    }
-    template <class _0, class _1, class _2>
-    void THRUST_RUNTIME_FUNCTION
-    launch(_0 x0, _1 x1, _2 x2) const
-    {
-      launch_impl(has_enough_shmem_t(), x0, x1, x2);
-      sync();
-    }
-    template <class _0, class _1, class _2, class _3>
-    void THRUST_RUNTIME_FUNCTION
-    launch(_0 x0, _1 x1, _2 x2, _3 x3) const
-    {
-      launch_impl(has_enough_shmem_t(), x0, x1, x2, x3);
-      sync();
-    }
-    template <class _0, class _1, class _2, class _3, class _4>
-    void THRUST_RUNTIME_FUNCTION
-    launch(_0 x0, _1 x1, _2 x2, _3 x3, _4 x4) const
-    {
-      launch_impl(has_enough_shmem_t(), x0, x1, x2, x3, x4);
-      sync();
-    }
-    template <class _0, class _1, class _2, class _3, class _4, class _5>
-    void THRUST_RUNTIME_FUNCTION
-    launch(_0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5) const
-    {
-      launch_impl(has_enough_shmem_t(), x0, x1, x2, x3, x4, x5);
-      sync();
-    }
-    template <class _0, class _1, class _2, class _3, class _4, class _5, class _6>
-    void THRUST_RUNTIME_FUNCTION
-    launch(_0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6) const
-    {
-      launch_impl(has_enough_shmem_t(), x0, x1, x2, x3, x4, x5, x6);
-      sync();
-    }
-    template <class _0, class _1, class _2, class _3, class _4, class _5, class _6, class _7>
-    void THRUST_RUNTIME_FUNCTION
-    launch(_0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7) const
-    {
-      launch_impl(has_enough_shmem_t(), x0, x1, x2, x3, x4, x5, x6, x7);
-      sync();
-    }
-    template <class _0, class _1, class _2, class _3, class _4, class _5, class _6, class _7, class _8>
-    void THRUST_RUNTIME_FUNCTION
-    launch(_0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8) const
-    {
-      launch_impl(has_enough_shmem_t(), x0, x1, x2, x3, x4, x5, x6, x7, x8);
-      sync();
-    }
-    template <class _0, class _1, class _2, class _3, class _4, class _5, class _6, class _7, class _8, class _9>
-    void THRUST_RUNTIME_FUNCTION
-    launch(_0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8, _9 x9) const
-    {
-      launch_impl(has_enough_shmem_t(), x0, x1, x2, x3, x4, x5, x6, x7, x8, x9);
-      sync();
-    }
-    template <class _0, class _1, class _2, class _3, class _4, class _5, class _6, class _7, class _8, class _9, class _xA>
-    void THRUST_RUNTIME_FUNCTION
-    launch(_0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8, _9 x9, _xA xA) const
-    {
-      launch_impl(has_enough_shmem_t(), x0, x1, x2, x3, x4, x5, x6, x7, x8, x9, xA);
-      sync();
-    }
-    template <class _0, class _1, class _2, class _3, class _4, class _5, class _6, class _7, class _8, class _9, class _xA, class _xB>
-    void THRUST_RUNTIME_FUNCTION
-    launch(_0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8, _9 x9, _xA xA, _xB xB) const
-    {
-      launch_impl(has_enough_shmem_t(), x0, x1, x2, x3, x4, x5, x6, x7, x8, x9, xA, xB);
-      sync();
-    }
-    template <class _0, class _1, class _2, class _3, class _4, class _5, class _6, class _7, class _8, class _9, class _xA, class _xB, class _xC>
-    void THRUST_RUNTIME_FUNCTION
-    launch(_0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8, _9 x9, _xA xA, _xB xB, _xC xC) const
-    {
-      launch_impl(has_enough_shmem_t(), x0, x1, x2, x3, x4, x5, x6, x7, x8, x9, xA, xB, xC);
-      sync();
-    }
-    template <class _0, class _1, class _2, class _3, class _4, class _5, class _6, class _7, class _8, class _9, class _xA, class _xB, class _xC, class _xD>
-    void THRUST_RUNTIME_FUNCTION
-    launch(_0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8, _9 x9, _xA xA, _xB xB, _xC xC, _xD xD) const
-    {
-      launch_impl(has_enough_shmem_t(), x0, x1, x2, x3, x4, x5, x6, x7, x8, x9, xA, xB, xC, xD);
-      sync();
-    }
-    template <class _0, class _1, class _2, class _3, class _4, class _5, class _6, class _7, class _8, class _9, class _xA, class _xB, class _xC, class _xD, class _xE>
-    void THRUST_RUNTIME_FUNCTION
-    launch(_0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8, _9 x9, _xA xA, _xB xB, _xC xC, _xD xD, _xE xE) const
-    {
-      launch_impl(has_enough_shmem_t(), x0, x1, x2, x3, x4, x5, x6, x7, x8, x9, xA, xB, xC, xD, xE);
-      sync();
-    }
-#endif
-
-
-  };
-
-}    // namespace core
-}
-} // end namespace thrust
-#endif
diff --git a/spaces/manhkhanhUIT/BOPBTL/Global/detection_models/Synchronized-BatchNorm-PyTorch/README.md b/spaces/manhkhanhUIT/BOPBTL/Global/detection_models/Synchronized-BatchNorm-PyTorch/README.md
deleted file mode 100644
index 779983436c9727dd0d6301a1c857f2360245b51d..0000000000000000000000000000000000000000
--- a/spaces/manhkhanhUIT/BOPBTL/Global/detection_models/Synchronized-BatchNorm-PyTorch/README.md
+++ /dev/null
@@ -1,118 +0,0 @@
-# Synchronized-BatchNorm-PyTorch
-
-**IMPORTANT: Please read the "Implementation details and highlights" section before use.**
-
-Synchronized Batch Normalization implementation in PyTorch.
-
-This module differs from the built-in PyTorch BatchNorm as the mean and
-standard-deviation are reduced across all devices during training.
-
-For example, when one uses `nn.DataParallel` to wrap the network during
-training, PyTorch's implementation normalize the tensor on each device using
-the statistics only on that device, which accelerated the computation and
-is also easy to implement, but the statistics might be inaccurate.
-Instead, in this synchronized version, the statistics will be computed
-over all training samples distributed on multiple devices.
-
-Note that, for one-GPU or CPU-only case, this module behaves exactly same
-as the built-in PyTorch implementation.
-
-This module is currently only a prototype version for research usages. As mentioned below,
-it has its limitations and may even suffer from some design problems. If you have any
-questions or suggestions, please feel free to
-[open an issue](https://github.com/vacancy/Synchronized-BatchNorm-PyTorch/issues) or 
-[submit a pull request](https://github.com/vacancy/Synchronized-BatchNorm-PyTorch/issues).
-
-## Why Synchronized BatchNorm?
-
-Although the typical implementation of BatchNorm working on multiple devices (GPUs)
-is fast (with no communication overhead), it inevitably reduces the size of batch size,
-which potentially degenerates the performance. This is not a significant issue in some
-standard vision tasks such as ImageNet classification (as the batch size per device
-is usually large enough to obtain good statistics). However, it will hurt the performance
-in some tasks that the batch size is usually very small (e.g., 1 per GPU).
-
-For example, the importance of synchronized batch normalization in object detection has been recently proved with a
-an extensive analysis in the paper [MegDet: A Large Mini-Batch Object Detector](https://arxiv.org/abs/1711.07240).
-
-## Usage
-
-To use the Synchronized Batch Normalization, we add a data parallel replication callback. This introduces a slight
-difference with typical usage of the `nn.DataParallel`.
-
-Use it with a provided, customized data parallel wrapper:
-
-```python
-from sync_batchnorm import SynchronizedBatchNorm1d, DataParallelWithCallback
-
-sync_bn = SynchronizedBatchNorm1d(10, eps=1e-5, affine=False)
-sync_bn = DataParallelWithCallback(sync_bn, device_ids=[0, 1])
-```
-
-Or, if you are using a customized data parallel module, you can use this library as a monkey patching.
-
-```python
-from torch.nn import DataParallel  # or your customized DataParallel module
-from sync_batchnorm import SynchronizedBatchNorm1d, patch_replication_callback
-
-sync_bn = SynchronizedBatchNorm1d(10, eps=1e-5, affine=False)
-sync_bn = DataParallel(sync_bn, device_ids=[0, 1])
-patch_replication_callback(sync_bn)  # monkey-patching
-```
-
-You can use `convert_model` to convert your model to use Synchronized BatchNorm easily.
-
-```python
-import torch.nn as nn
-from torchvision import models
-from sync_batchnorm import convert_model
-# m is a standard pytorch model
-m = models.resnet18(True)
-m = nn.DataParallel(m)
-# after convert, m is using SyncBN
-m = convert_model(m)
-```
-
-See also `tests/test_sync_batchnorm.py` for numeric result comparison.
-
-## Implementation details and highlights
-
-If you are interested in how batch statistics are reduced and broadcasted among multiple devices, please take a look
-at the code with detailed comments. Here we only emphasize some highlights of the implementation:
-
-- This implementation is in pure-python. No C++ extra extension libs.
-- Easy to use as demonstrated above.
-- It uses unbiased variance to update the moving average, and use `sqrt(max(var, eps))` instead of `sqrt(var + eps)`.
-- The implementation requires that each module on different devices should invoke the `batchnorm` for exactly SAME
-amount of times in each forward pass. For example, you can not only call `batchnorm` on GPU0 but not on GPU1. The `#i
-(i = 1, 2, 3, ...)` calls of the `batchnorm` on each device will be viewed as a whole and the statistics will be reduced.
-This is tricky but is a good way to handle PyTorch's dynamic computation graph. Although sounds complicated, this
-will usually not be the issue for most of the models.
-
-## Known issues
-
-#### Runtime error on backward pass.
-
-Due to a [PyTorch Bug](https://github.com/pytorch/pytorch/issues/3883), using old PyTorch libraries will trigger an `RuntimeError` with messages like:
-
-```
-Assertion `pos >= 0 && pos < buffer.size()` failed.
-```
-
-This has already been solved in the newest PyTorch repo, which, unfortunately, has not been pushed to the official and anaconda binary release. Thus, you are required to build the PyTorch package from the source according to the
- instructions [here](https://github.com/pytorch/pytorch#from-source).
-
-#### Numeric error.
-
-Because this library does not fuse the normalization and statistics operations in C++ (nor CUDA), it is less
-numerically stable compared to the original PyTorch implementation. Detailed analysis can be found in
-`tests/test_sync_batchnorm.py`.
-
-## Authors and License:
-
-Copyright (c) 2018-, [Jiayuan Mao](https://vccy.xyz).
-
-**Contributors**: [Tete Xiao](https://tetexiao.com), [DTennant](https://github.com/DTennant).
-
-Distributed under **MIT License** (See LICENSE)
-
diff --git a/spaces/marlenezw/audio-driven-animations/MakeItTalk/util/utils.py b/spaces/marlenezw/audio-driven-animations/MakeItTalk/util/utils.py
deleted file mode 100644
index 154b8d723b86f63244c15dc9575b60bb7ebcf128..0000000000000000000000000000000000000000
--- a/spaces/marlenezw/audio-driven-animations/MakeItTalk/util/utils.py
+++ /dev/null
@@ -1,390 +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.
- 
-"""
-
-import torch.nn as nn
-import torch.nn.init as init
-import os
-import cv2
-import matplotlib.pyplot as plt
-import numpy as np
-
-class Point:
-    def __init__(self, x, y):
-        self.x = x
-        self.y = y
-
-class ShapeParts:
-    def __init__(self, np_pts):
-        self.data = np_pts
-
-    def part(self, idx):
-        return Point(self.data[idx, 0], self.data[idx, 1])
-
-
-class Record():
-    def __init__(self, type_list):
-        self.data, self.count = {}, {}
-        self.type_list = type_list
-        self.max_min_data = None
-        for t in type_list:
-            self.data[t] = 0.0
-            self.count[t] = 0.0
-
-    def add(self, new_data, c=1.0):
-        for t in self.type_list:
-            self.data[t] += new_data
-            self.count[t] += c
-
-    def per(self, t):
-        return self.data[t] / (self.count[t] + 1e-32)
-
-    def clean(self, t):
-        self.data[t], self.count[t] = 0.0, 0.0
-
-    def is_better(self, t, greater):
-        if(self.max_min_data == None):
-            self.max_min_data = self.data[t]
-            return True
-        else:
-            if(greater):
-                if(self.data[t] > self.max_min_data):
-                    self.max_min_data = self.data[t]
-                    return True
-            else:
-                if (self.data[t] < self.max_min_data):
-                    self.max_min_data = self.data[t]
-                    return True
-        return False
-
-def weight_init(m):
-    '''
-    Usage:
-        model = Model()
-        model.apply(weight_init)
-    '''
-    if isinstance(m, nn.Conv1d):
-        init.normal_(m.weight.data)
-        if m.bias is not None:
-            init.normal_(m.bias.data)
-    elif isinstance(m, nn.Conv2d):
-        init.xavier_normal_(m.weight.data)
-        if m.bias is not None:
-            init.normal_(m.bias.data)
-    elif isinstance(m, nn.Conv3d):
-        init.xavier_normal_(m.weight.data)
-        if m.bias is not None:
-            init.normal_(m.bias.data)
-    elif isinstance(m, nn.ConvTranspose1d):
-        init.normal_(m.weight.data)
-        if m.bias is not None:
-            init.normal_(m.bias.data)
-    elif isinstance(m, nn.ConvTranspose2d):
-        init.xavier_normal_(m.weight.data)
-        if m.bias is not None:
-            init.normal_(m.bias.data)
-    elif isinstance(m, nn.ConvTranspose3d):
-        init.xavier_normal_(m.weight.data)
-        if m.bias is not None:
-            init.normal_(m.bias.data)
-    elif isinstance(m, nn.BatchNorm1d):
-        init.normal_(m.weight.data, mean=1, std=0.02)
-        init.constant_(m.bias.data, 0)
-    elif isinstance(m, nn.BatchNorm2d):
-        init.normal_(m.weight.data, mean=1, std=0.02)
-        init.constant_(m.bias.data, 0)
-    elif isinstance(m, nn.BatchNorm3d):
-        init.normal_(m.weight.data, mean=1, std=0.02)
-        init.constant_(m.bias.data, 0)
-    elif isinstance(m, nn.Linear):
-        init.xavier_normal_(m.weight.data)
-        init.normal_(m.bias.data)
-    elif isinstance(m, nn.LSTM):
-        for param in m.parameters():
-            if len(param.shape) >= 2:
-                init.orthogonal_(param.data)
-            else:
-                init.normal_(param.data)
-    elif isinstance(m, nn.LSTMCell):
-        for param in m.parameters():
-            if len(param.shape) >= 2:
-                init.orthogonal_(param.data)
-            else:
-                init.normal_(param.data)
-    elif isinstance(m, nn.GRU):
-        for param in m.parameters():
-            if len(param.shape) >= 2:
-                init.orthogonal_(param.data)
-            else:
-                init.normal_(param.data)
-    elif isinstance(m, nn.GRUCell):
-        for param in m.parameters():
-            if len(param.shape) >= 2:
-                init.orthogonal_(param.data)
-            else:
-                init.normal_(param.data)
-
-def get_n_params(model):
-    pp=0
-    for p in list(model.parameters()):
-        nn=1
-        for s in list(p.size()):
-            nn = nn*s
-        pp += nn
-    return pp
-
-
-def vis_landmark_on_img(img, shape, linewidth=2):
-    '''
-    Visualize landmark on images.
-    '''
-    if (type(shape) == ShapeParts):
-        def draw_curve(idx_list, color=(0, 255, 0), loop=False, lineWidth=linewidth):
-            for i in idx_list:
-                cv2.line(img, (shape.part(i).x, shape.part(i).y), (shape.part(i + 1).x, shape.part(i + 1).y),
-                         color, lineWidth)
-            if (loop):
-                cv2.line(img, (shape.part(idx_list[0]).x, shape.part(idx_list[0]).y),
-                         (shape.part(idx_list[-1] + 1).x, shape.part(idx_list[-1] + 1).y), color, lineWidth)
-
-        draw_curve(list(range(0, 16)))  # jaw
-        draw_curve(list(range(17, 21)), color=(0, 0, 255))  # eye brow
-        draw_curve(list(range(22, 26)), color=(0, 0, 255))
-        draw_curve(list(range(27, 35)))  # nose
-        draw_curve(list(range(36, 41)), loop=True)  # eyes
-        draw_curve(list(range(42, 47)), loop=True)
-        draw_curve(list(range(48, 59)), loop=True, color=(0, 255, 255))  # mouth
-        draw_curve(list(range(60, 67)), loop=True, color=(255, 255, 0))
-
-    else:
-        def draw_curve(idx_list, color=(0, 255, 0), loop=False, lineWidth=linewidth):
-            for i in idx_list:
-                cv2.line(img, (shape[i, 0], shape[i, 1]), (shape[i + 1, 0], shape[i + 1, 1]), color, lineWidth)
-            if (loop):
-                cv2.line(img, (shape[idx_list[0], 0], shape[idx_list[0], 1]),
-                         (shape[idx_list[-1] + 1, 0], shape[idx_list[-1] + 1, 1]), color, lineWidth)
-
-        draw_curve(list(range(0, 16)))  # jaw
-        draw_curve(list(range(17, 21)), color=(0, 0, 255))  # eye brow
-        draw_curve(list(range(22, 26)), color=(0, 0, 255))
-        draw_curve(list(range(27, 35)))  # nose
-        draw_curve(list(range(36, 41)), loop=True)  # eyes
-        draw_curve(list(range(42, 47)), loop=True)
-        draw_curve(list(range(48, 59)), loop=True, color=(0, 255, 255))  # mouth
-        draw_curve(list(range(60, 67)), loop=True, color=(255, 255, 0))
-
-    return img
-
-
-def vis_landmark_on_plt(fl,  x_offset=0.0, show_now=True, c='r'):
-    def draw_curve(shape, idx_list, loop=False, x_offset=0.0, c=None):
-        for i in idx_list:
-            plt.plot((shape[i, 0] + x_offset, shape[i + 1, 0] + x_offset), (-shape[i, 1], -shape[i + 1, 1]), c=c, lineWidth=1)
-        if (loop):
-            plt.plot((shape[idx_list[0], 0] + x_offset, shape[idx_list[-1] + 1, 0] + x_offset),
-                     (-shape[idx_list[0], 1], -shape[idx_list[-1] + 1, 1]), c=c, lineWidth=1)
-
-    draw_curve(fl, list(range(0, 16)), x_offset=x_offset, c=c)  # jaw
-    draw_curve(fl, list(range(17, 21)), x_offset=x_offset, c=c)  # eye brow
-    draw_curve(fl, list(range(22, 26)), x_offset=x_offset, c=c)
-    draw_curve(fl, list(range(27, 35)), x_offset=x_offset, c=c)  # nose
-    draw_curve(fl, list(range(36, 41)), loop=True, x_offset=x_offset, c=c)  # eyes
-    draw_curve(fl, list(range(42, 47)), loop=True, x_offset=x_offset, c=c)
-    draw_curve(fl, list(range(48, 59)), loop=True, x_offset=x_offset, c=c)  # mouth
-    draw_curve(fl, list(range(60, 67)), loop=True, x_offset=x_offset, c=c)
-
-    if(show_now):
-        plt.show()
-
-
-def try_mkdir(dir):
-    try:
-        os.mkdir(dir)
-    except:
-        pass
-
-import numpy
-def smooth(x, window_len=11, window='hanning'):
-    """smooth the data using a window with requested size.
-
-    This method is based on the convolution of a scaled window with the signal.
-    The signal is prepared by introducing reflected copies of the signal
-    (with the window size) in both ends so that transient parts are minimized
-    in the begining and end part of the output signal.
-
-    input:
-        x: the input signal
-        window_len: the dimension of the smoothing window; should be an odd integer
-        window: the type of window from 'flat', 'hanning', 'hamming', 'bartlett', 'blackman'
-            flat window will produce a moving average smoothing.
-
-    output:
-        the smoothed signal
-
-    example:
-
-    t=linspace(-2,2,0.1)
-    x=sin(t)+randn(len(t))*0.1
-    y=smooth(x)
-
-    see also:
-
-    numpy.hanning, numpy.hamming, numpy.bartlett, numpy.blackman, numpy.convolve
-    scipy.signal.lfilter
-
-    the window parameter could be the window itself if an array instead of a string
-    NOTE: length(output) != length(input), to correct this: return y[(window_len/2-1):-(window_len/2)] instead of just y.
-    """
-
-    if x.ndim != 1:
-        raise(ValueError, "smooth only accepts 1 dimension arrays.")
-
-    if x.size < window_len:
-        raise(ValueError, "Input vector needs to be bigger than window size.")
-
-    if window_len < 3:
-        return x
-
-    if not window in ['flat', 'hanning', 'hamming', 'bartlett', 'blackman']:
-        raise(ValueError, "Window is on of 'flat', 'hanning', 'hamming', 'bartlett', 'blackman'")
-
-    s = numpy.r_[x[window_len - 1:0:-1], x, x[-2:-window_len - 1:-1]]
-    # print(len(s))
-    if window == 'flat':  # moving average
-        w = numpy.ones(window_len, 'd')
-    else:
-        w = eval('numpy.' + window + '(window_len)')
-
-    y = numpy.convolve(w / w.sum(), s, mode='valid')
-    return y
-
-
-def get_puppet_info(DEMO_CH, ROOT_DIR):
-    import numpy as np
-    B = 5000
-    # for wilk example
-    if (DEMO_CH == 'wilk_old'):
-        bound = np.array([-B, -B, -B, 459, -B, B+918, 419, B+918, B+838, B+918, B+838, 459, B+838, -B, 419, -B]).reshape(1, -1)
-        # bound = np.array([0, 0, 0, 459, 0, 918, 419, 918, 838, 918, 838, 459, 838, 0, 419, 0]).reshape(1, -1)
-        scale, shift = -0.005276414887140783, np.array([-475.4316, -193.53225])
-    elif (DEMO_CH == 'sketch'):
-        bound = np.array([-10000, -10000, -10000, 221, -10000, 10443, 232, 10443, 10465, 10443, 10465, 221, 10465, -10000, 232, -10000]).reshape(1, -1)
-        scale, shift = -0.006393177201290783, np.array([-226.8411, -176.5216])
-    elif (DEMO_CH == 'onepunch'):
-        bound = np.array([0, 0, 0, 168, 0, 337, 282, 337, 565, 337, 565, 168, 565, 0, 282, 0]).reshape(1, -1)
-        scale, shift = -0.007558707536598317, np.array([-301.4903, -120.05265])
-    elif (DEMO_CH == 'cat'):
-        bound = np.array([0, 0, 0, 315, 0, 631, 299, 631, 599, 631, 599, 315, 599, 0, 299, 0]).reshape(1, -1)
-        scale, shift = -0.009099476040795225, np.array([-297.17085, -259.2363])
-    elif (DEMO_CH == 'paint'):
-        bound = np.array([0, 0, 0, 249, 0, 499, 212, 499, 424, 499, 424, 249, 424, 0, 212, 0]).reshape(1, -1)
-        scale, shift = -0.007409177996872789, np.array([-161.92345878, -249.40250103])
-    elif (DEMO_CH == 'mulaney'):
-        bound = np.array([0, 0, 0, 255, 0, 511, 341, 511, 682, 511, 682, 255, 682, 0, 341, 0]).reshape(1, -1)
-        scale, shift = -0.010651548568731444, np.array([-333.54245, -189.081])
-    elif (DEMO_CH == 'cartoonM_old'):
-        bound = np.array([0, 0, 0, 299, 0, 599, 399, 599, 799, 599, 799, 299, 799, 0, 399, 0]).reshape(1, -1)
-        scale, shift = -0.0055312373170456845, np.array([-398.6125, -240.45235])
-    elif (DEMO_CH == 'beer'):
-        bound = np.array([0, 0, 0, 309, 0, 618, 260, 618, 520, 618, 520, 309, 520, 0, 260, 0]).reshape(1, -1)
-        scale, shift = -0.0054102709937112374, np.array([-254.1478, -156.6971])
-    elif (DEMO_CH == 'color'):
-        bound = np.array([0, 0, 0, 140, 0, 280, 249, 280, 499, 280, 499, 140, 499, 0, 249, 0]).reshape(1, -1)
-        scale, shift = -0.012986159189209149, np.array([-237.27065, -79.2465])
-    else:
-        if (os.path.exists(os.path.join(ROOT_DIR, DEMO_CH + '.jpg'))):
-            img = cv2.imread(os.path.join(ROOT_DIR, DEMO_CH + ".jpg"))
-        elif (os.path.exists(os.path.join(ROOT_DIR, DEMO_CH + '.png'))):
-            img = cv2.imread(os.path.join(ROOT_DIR, DEMO_CH + ".png"))
-        else:
-            print('not file founded.')
-            exit(0)
-        size = img.shape
-        h = size[1] - 1
-        w = size[0] - 1
-        bound = np.array([-B, -B,
-                          -B, w//4,
-                          -B, w // 2,
-                          -B, w//4*3,
-                          -B, B + w,
-                          h // 2, B+w,
-                          B+h, B+w,
-                          B+h, w // 2,
-                          B+h, -B,
-                          h//4, -B,
-                          h // 2, -B,
-                          h//4*3, -B]).reshape(1, -1)
-        ss = np.loadtxt(os.path.join(ROOT_DIR, DEMO_CH + '_scale_shift.txt'))
-        scale, shift = ss[0], np.array([ss[1], ss[2]])
-
-    return bound, scale, shift
-
-
-def close_input_face_mouth(shape_3d, p1=0.7, p2=0.5):
-    shape_3d = shape_3d.reshape((1, 68, 3))
-    index1 = list(range(60 - 1, 55 - 1, -1))
-    index2 = list(range(68 - 1, 65 - 1, -1))
-    mean_out = 0.5 * (shape_3d[:, 49:54] + shape_3d[:, index1])
-    mean_in = 0.5 * (shape_3d[:, 61:64] + shape_3d[:, index2])
-    shape_3d[:, 50:53] -= (shape_3d[:, 61:64] - mean_in) * p1
-    shape_3d[:, list(range(59 - 1, 56 - 1, -1))] -= (shape_3d[:, index2] - mean_in) * p1
-    shape_3d[:, 49] -= (shape_3d[:, 61] - mean_in[:, 0]) * p2
-    shape_3d[:, 53] -= (shape_3d[:, 63] - mean_in[:, -1]) * p2
-    shape_3d[:, 59] -= (shape_3d[:, 67] - mean_in[:, 0]) * p2
-    shape_3d[:, 55] -= (shape_3d[:, 65] - mean_in[:, -1]) * p2
-    # shape_3d[:, 61:64] = shape_3d[:, index2] = mean_in
-    shape_3d[:, 61:64] -= (shape_3d[:, 61:64] - mean_in) * p1
-    shape_3d[:, index2] -= (shape_3d[:, index2] - mean_in) * p1
-    shape_3d = shape_3d.reshape((68, 3))
-
-    return shape_3d
-
-def norm_input_face(shape_3d):
-    scale = 1.6 / (shape_3d[0, 0] - shape_3d[16, 0])
-    shift = - 0.5 * (shape_3d[0, 0:2] + shape_3d[16, 0:2])
-    shape_3d[:, 0:2] = (shape_3d[:, 0:2] + shift) * scale
-    face_std = np.loadtxt('MakeItTalk/src/dataset/utils/STD_FACE_LANDMARKS.txt').reshape(68, 3)
-    shape_3d[:, -1] = face_std[:, -1] * 0.1
-    shape_3d[:, 0:2] = -shape_3d[:, 0:2]
-
-    return shape_3d, scale, shift
-
-def add_naive_eye(fl):
-    for t in range(fl.shape[0]):
-        r = 0.95
-        fl[t, 37], fl[t, 41] = r * fl[t, 37] + (1 - r) * fl[t, 41], (1 - r) * fl[t, 37] + r * fl[t, 41]
-        fl[t, 38], fl[t, 40] = r * fl[t, 38] + (1 - r) * fl[t, 40], (1 - r) * fl[t, 38] + r * fl[t, 40]
-        fl[t, 43], fl[t, 47] = r * fl[t, 43] + (1 - r) * fl[t, 47], (1 - r) * fl[t, 43] + r * fl[t, 47]
-        fl[t, 44], fl[t, 46] = r * fl[t, 44] + (1 - r) * fl[t, 46], (1 - r) * fl[t, 44] + r * fl[t, 46]
-
-    K1, K2 = 10, 15
-    length = fl.shape[0]
-    close_time_stamp = [30]
-    t = 30
-    while (t < length - 1 - K2):
-        t += 60
-        t += np.random.randint(30, 90)
-        if (t < length - 1 - K2):
-            close_time_stamp.append(t)
-    for t in close_time_stamp:
-        fl[t, 37], fl[t, 41] = 0.25 * fl[t, 37] + 0.75 * fl[t, 41], 0.25 * fl[t, 37] + 0.75 * fl[t, 41]
-        fl[t, 38], fl[t, 40] = 0.25 * fl[t, 38] + 0.75 * fl[t, 40], 0.25 * fl[t, 38] + 0.75 * fl[t, 40]
-        fl[t, 43], fl[t, 47] = 0.25 * fl[t, 43] + 0.75 * fl[t, 47], 0.25 * fl[t, 43] + 0.75 * fl[t, 47]
-        fl[t, 44], fl[t, 46] = 0.25 * fl[t, 44] + 0.75 * fl[t, 46], 0.25 * fl[t, 44] + 0.75 * fl[t, 46]
-
-        def interp_fl(t0, t1, t2, r):
-            for index in [37, 38, 40, 41, 43, 44, 46, 47]:
-                fl[t0, index] = r * fl[t1, index] + (1 - r) * fl[t2, index]
-
-        for t0 in range(t - K1 + 1, t):
-            interp_fl(t0, t - K1, t, r=(t - t0) / 1. / K1)
-        for t0 in range(t + 1, t + K2):
-            interp_fl(t0, t, t + K2, r=(t + K2 - 1 - t0) / 1. / K2)
-
-    return fl
\ No newline at end of file
diff --git a/spaces/merve/anonymization/source/uncertainty-calibration/style.css b/spaces/merve/anonymization/source/uncertainty-calibration/style.css
deleted file mode 100644
index 8073cf0a59eac0be0e293b35af5255c40c063e21..0000000000000000000000000000000000000000
--- a/spaces/merve/anonymization/source/uncertainty-calibration/style.css
+++ /dev/null
@@ -1,89 +0,0 @@
-svg{
-  overflow: visible;
-}
-
-text{
-  fill: #202124;
-  user-select: none;
-}
-
-.domain{
-  display: none;
-}
-
-.thresholds, .threshold > g{
-  cursor: pointer;
-}
-
-svg{
-  user-select: none;
-}
-
-text.axis-label .legend-text{
-  font-family: 'Roboto';
-  font-style: normal;
-  font-size: 16px;
-  line-height: 20px;
-  /* identical to box height, or 125% */
-  
-  fill: #000;
-}
-
-.axis text{
-  font-size: 10px;
-}
-
-text{
-  text-shadow: 0 1px 0 #fff, 1px 0 0 #fff, 0 -1px 0 #fff, -1px 0 0 #fff;
-}
-
-
-
-
-.bucket text{
-  /*text-shadow: 0 1px 0 #000, 1px 0 0 #000, 0 -1px 0 #000, -1px 0 0 #000;*/
-  /*fill:  #fff;*/
-  font-size: 11px;
-}
-
-
-.big-text{
-  font-variant-numeric: tabular-nums;
-  font-size: 16px;
-}
-
-#card{
-  display: flex;
-  flex-direction: column;
-  align-items: flex-start;
-  padding: 24px 24px;
-  gap: 6px;
-
-  background: #EDF4EC;
-  border: 1px solid #34A853;
-  box-sizing: border-box;
-  border-radius: 4px;
-}
-
-text.val-text{
-  background: #DFE9E1;
-  border: 1px solid #476C63;
-  box-sizing: border-box;
-  border-radius: 4px;
-  fill: #2A4C4A;
-  text-shadow: none;
-}
-
-.val-box{
-  fill: #DFE9E1;
-  stroke: #476C63;
-  opacity: 1;
-}
-
-.legend-title{
-  fill: #002622;
-}
-
-h3 {
-  color: #00695C;
-}
\ No newline at end of file
diff --git a/spaces/merve/dataset-worldviews/public/fill-in-the-blank/README.md b/spaces/merve/dataset-worldviews/public/fill-in-the-blank/README.md
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/merve/dataset-worldviews/public/measuring-fairness/students.js b/spaces/merve/dataset-worldviews/public/measuring-fairness/students.js
deleted file mode 100644
index 4af55cba8cc763d96aa478be96a785048d9edc42..0000000000000000000000000000000000000000
--- a/spaces/merve/dataset-worldviews/public/measuring-fairness/students.js
+++ /dev/null
@@ -1,90 +0,0 @@
-/* Copyright 2020 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.makeStudents = function(){
-  var seed = new Math.seedrandom('he4a15')
-  var rand = d3.randomUniform.source(seed)(0, 1)
-  var letters = 'abcdefgijlmnopqrsuvwxyz'
-  letters = (letters + letters.toUpperCase()).split('')
-
-  var nSickCols = 6
-  var mSickCols = 8
-  var fSickCols = nSickCols*2 - mSickCols
-
-  var students = d3.range(nCols*nCols).map(i => {
-    var letter = letters[~~d3.randomUniform.source(seed)(0, letters.length)()]
-
-    var isMale = i % 2 == 0
-    var isSick = i < (isMale ? mSickCols : fSickCols)*nCols
-    var grade = isSick*.5 + rand()
-    var pos = {}
-
-    return {letter, isSick, isMale, grade, pos}
-  })
-
-  students = _.sortBy(students, d => -d.grade)
-  d3.nestBy(students, d => d.isSick).forEach(group => {
-    var isSick = group[0].isSick
-
-    var sickCols = nSickCols
-    var cols = isSick ? sickCols : nCols - sickCols
-    var xOffset = isSick ? 0 : sickCols
-
-    group.forEach((d, i) => {
-      d.pos.allIJ = [cols - 1 - (i % cols) + xOffset, ~~(i/cols)]
-      var spreadIJ = d.pos.allIJ.slice()
-      if (!d.isSick) spreadIJ[0] += .1
-      d.pos.all = spreadIJ.map(d => d*c.width/10)
-    })
-  })
-
-  d3.nestBy(students, d => d.isSick + '-' + d.isMale).forEach(group => {
-    var isSick = group[0].isSick
-    var isMale = group[0].isMale
-
-    var sickCols = isMale ? mSickCols : fSickCols
-    var cols = isSick ? sickCols : nCols - sickCols
-    var xOffset = isSick ? 0 : sickCols
-    var yOffset = isMale ? nCols/2 + 2 : 0
-
-    group.forEach((d, i) => {
-      d.pos.sexIJ = [cols - 1 - (i % cols) + xOffset, ~~(i/cols) + yOffset]
-      d.pos.sexGroupIJ = [cols - 1 - (i % cols) + xOffset, ~~(i/cols)]
-      var spreadIJ = d.pos.sexIJ.slice()
-      if (!d.isSick) spreadIJ[0] += .1
-      d.pos.sex = spreadIJ.map(d => d*c.width/10)
-    })
-  })
-
-  students.maleOffsetJ = nCols/2 + 2
-  students.maleOffsetPx= students.maleOffsetJ*c.width/10
-
-  students.fSickCols = fSickCols
-  students.mSickCols = mSickCols
-
-  students.colWidth = c.width/10
-
-  students.rand = rand
-  return students
-}
-
-
-
-
-
-
-if (window.init) window.init()
diff --git a/spaces/merve/fill-in-the-blank/server-side/fill-in-the-blank/scatter-plot-colab/two-sentences/init.js b/spaces/merve/fill-in-the-blank/server-side/fill-in-the-blank/scatter-plot-colab/two-sentences/init.js
deleted file mode 100644
index 71d258e5027dc3c924b2b263e6bb8ea370189b1d..0000000000000000000000000000000000000000
--- a/spaces/merve/fill-in-the-blank/server-side/fill-in-the-blank/scatter-plot-colab/two-sentences/init.js
+++ /dev/null
@@ -1,38 +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.init = function(){
-  var initFns = [window.initUtil, window.initScatter, window.initPair]
-  if (!initFns.every(d => d)) return
-
-  window.util = initUtil()
-
-  var pair = window.python_settings
-  pair.s0 = python_data.s0
-  pair.s1 = python_data.s1
-  pair.e0 = python_data.e0
-  pair.e1 = python_data.e1
-  pair.label0 = 'Sentence 0'
-  pair.label1 = 'Sentence 1'
-  pair.vocab = python_data.vocab
-
-  var sel = d3.select('.container').html('')
-    .st({width: 500})
-
-  initPair(pair, sel)
-}
-
-
-window.init()
diff --git a/spaces/mfkeles/Track-Anything/tracker/model/resnet.py b/spaces/mfkeles/Track-Anything/tracker/model/resnet.py
deleted file mode 100644
index 984ea3cbfac047537e7de6cfc47108e637e9dde7..0000000000000000000000000000000000000000
--- a/spaces/mfkeles/Track-Anything/tracker/model/resnet.py
+++ /dev/null
@@ -1,165 +0,0 @@
-"""
-resnet.py - A modified ResNet structure
-We append extra channels to the first conv by some network surgery
-"""
-
-from collections import OrderedDict
-import math
-
-import torch
-import torch.nn as nn
-from torch.utils import model_zoo
-
-
-def load_weights_add_extra_dim(target, source_state, extra_dim=1):
-	new_dict = OrderedDict()
-
-	for k1, v1 in target.state_dict().items():
-		if not 'num_batches_tracked' in k1:
-			if k1 in source_state:
-				tar_v = source_state[k1]
-
-				if v1.shape != tar_v.shape:
-					# Init the new segmentation channel with zeros
-					# print(v1.shape, tar_v.shape)
-					c, _, w, h = v1.shape
-					pads = torch.zeros((c,extra_dim,w,h), device=tar_v.device)
-					nn.init.orthogonal_(pads)
-					tar_v = torch.cat([tar_v, pads], 1)
-
-				new_dict[k1] = tar_v
-
-	target.load_state_dict(new_dict)
-
-
-model_urls = {
-	'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
-	'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
-}
-
-
-def conv3x3(in_planes, out_planes, stride=1, dilation=1):
-	return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
-					 padding=dilation, dilation=dilation, bias=False)
-
-
-class BasicBlock(nn.Module):
-	expansion = 1
-
-	def __init__(self, inplanes, planes, stride=1, downsample=None, dilation=1):
-		super(BasicBlock, self).__init__()
-		self.conv1 = conv3x3(inplanes, planes, stride=stride, dilation=dilation)
-		self.bn1 = nn.BatchNorm2d(planes)
-		self.relu = nn.ReLU(inplace=True)
-		self.conv2 = conv3x3(planes, planes, stride=1, dilation=dilation)
-		self.bn2 = nn.BatchNorm2d(planes)
-		self.downsample = downsample
-		self.stride = stride
-
-	def forward(self, x):
-		residual = x
-
-		out = self.conv1(x)
-		out = self.bn1(out)
-		out = self.relu(out)
-
-		out = self.conv2(out)
-		out = self.bn2(out)
-
-		if self.downsample is not None:
-			residual = self.downsample(x)
-
-		out += residual
-		out = self.relu(out)
-
-		return out
-
-
-class Bottleneck(nn.Module):
-	expansion = 4
-
-	def __init__(self, inplanes, planes, stride=1, downsample=None, dilation=1):
-		super(Bottleneck, self).__init__()
-		self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
-		self.bn1 = nn.BatchNorm2d(planes)
-		self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, dilation=dilation,
-							   padding=dilation, bias=False)
-		self.bn2 = nn.BatchNorm2d(planes)
-		self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
-		self.bn3 = nn.BatchNorm2d(planes * 4)
-		self.relu = nn.ReLU(inplace=True)
-		self.downsample = downsample
-		self.stride = stride
-
-	def forward(self, x):
-		residual = x
-
-		out = self.conv1(x)
-		out = self.bn1(out)
-		out = self.relu(out)
-
-		out = self.conv2(out)
-		out = self.bn2(out)
-		out = self.relu(out)
-
-		out = self.conv3(out)
-		out = self.bn3(out)
-
-		if self.downsample is not None:
-			residual = self.downsample(x)
-
-		out += residual
-		out = self.relu(out)
-
-		return out
-
-
-class ResNet(nn.Module):
-	def __init__(self, block, layers=(3, 4, 23, 3), extra_dim=0):
-		self.inplanes = 64
-		super(ResNet, self).__init__()
-		self.conv1 = nn.Conv2d(3+extra_dim, 64, kernel_size=7, stride=2, padding=3, bias=False)
-		self.bn1 = nn.BatchNorm2d(64)
-		self.relu = nn.ReLU(inplace=True)
-		self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
-		self.layer1 = self._make_layer(block, 64, layers[0])
-		self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
-		self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
-		self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
-
-		for m in self.modules():
-			if isinstance(m, nn.Conv2d):
-				n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
-				m.weight.data.normal_(0, math.sqrt(2. / n))
-			elif isinstance(m, nn.BatchNorm2d):
-				m.weight.data.fill_(1)
-				m.bias.data.zero_()
-
-	def _make_layer(self, block, planes, blocks, stride=1, dilation=1):
-		downsample = None
-		if stride != 1 or self.inplanes != planes * block.expansion:
-			downsample = nn.Sequential(
-				nn.Conv2d(self.inplanes, planes * block.expansion,
-						  kernel_size=1, stride=stride, bias=False),
-				nn.BatchNorm2d(planes * block.expansion),
-			)
-
-		layers = [block(self.inplanes, planes, stride, downsample)]
-		self.inplanes = planes * block.expansion
-		for i in range(1, blocks):
-			layers.append(block(self.inplanes, planes, dilation=dilation))
-
-		return nn.Sequential(*layers)
-
-def resnet18(pretrained=True, extra_dim=0):
-	model = ResNet(BasicBlock, [2, 2, 2, 2], extra_dim)
-	if pretrained:
-		load_weights_add_extra_dim(model, model_zoo.load_url(model_urls['resnet18']), extra_dim)
-	return model
-
-def resnet50(pretrained=True, extra_dim=0):
-	model = ResNet(Bottleneck, [3, 4, 6, 3], extra_dim)
-	if pretrained:
-		load_weights_add_extra_dim(model, model_zoo.load_url(model_urls['resnet50']), extra_dim)
-	return model
-
diff --git a/spaces/mfrashad/CharacterGAN/app.py b/spaces/mfrashad/CharacterGAN/app.py
deleted file mode 100644
index 974bc9eb61740af13012c310184df70af4eed44f..0000000000000000000000000000000000000000
--- a/spaces/mfrashad/CharacterGAN/app.py
+++ /dev/null
@@ -1,146 +0,0 @@
-import nltk; nltk.download('wordnet')
-
-#@title Load Model
-selected_model = 'character'
-
-# Load model
-import torch
-import PIL
-import numpy as np
-from PIL import Image
-from models import get_instrumented_model
-from decomposition import get_or_compute
-from config import Config
-import gradio as gr
-import numpy as np
-
-# Speed up computation
-torch.autograd.set_grad_enabled(False)
-torch.backends.cudnn.benchmark = True
-
-# Specify model to use
-config = Config(
-  model='StyleGAN2',
-  layer='style',
-  output_class=selected_model,
-  components=80,
-  use_w=True,
-  batch_size=5_000, # style layer quite small
-)
-device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-
-inst = get_instrumented_model(config.model, config.output_class,
-                              config.layer, torch.device(device), use_w=config.use_w)
-
-path_to_components = get_or_compute(config, inst)
-
-model = inst.model
-
-comps = np.load(path_to_components)
-lst = comps.files
-latent_dirs = []
-latent_stdevs = []
-
-load_activations = False
-
-for item in lst:
-    if load_activations:
-      if item == 'act_comp':
-        for i in range(comps[item].shape[0]):
-          latent_dirs.append(comps[item][i])
-      if item == 'act_stdev':
-        for i in range(comps[item].shape[0]):
-          latent_stdevs.append(comps[item][i])
-    else:
-      if item == 'lat_comp':
-        for i in range(comps[item].shape[0]):
-          latent_dirs.append(comps[item][i])
-      if item == 'lat_stdev':
-        for i in range(comps[item].shape[0]):
-          latent_stdevs.append(comps[item][i])
-
-
-def display_sample_pytorch(seed, truncation, directions, distances, scale, start, end, w=None, disp=True, save=None, noise_spec=None):
-    # blockPrint()
-    model.truncation = truncation
-    if w is None:
-        w = model.sample_latent(1, seed=seed).detach().cpu().numpy()
-        w = [w]*model.get_max_latents() # one per layer
-    else:
-        w = [np.expand_dims(x, 0) for x in w]
-    
-    for l in range(start, end):
-      for i in range(len(directions)):
-        w[l] = w[l] + directions[i] * distances[i] * scale
-    
-    torch.cuda.empty_cache()
-    #save image and display
-    out = model.sample_np(w)
-    final_im = Image.fromarray((out * 255).astype(np.uint8)).resize((500,500),Image.LANCZOS)
-    
-    
-    if save is not None:
-      if disp == False:
-        print(save)
-      final_im.save(f'out/{seed}_{save:05}.png')
-    
-    return final_im
-
-
-#@title Demo UI
-
-
-def generate_image(seed, truncation,
-                  monster, female, skimpy, light, bodysuit, bulky, human_head,
-                  start_layer, end_layer):
-    seed = hash(seed) % 1000000000
-    scale = 1
-    params = {'monster': monster,
-          'female': female,
-          'skimpy': skimpy,
-          'light': light,
-          'bodysuit': bodysuit,
-          'bulky': bulky,
-          'human_head': human_head}
-
-    param_indexes = {'monster': 0,
-              'female': 1,
-              'skimpy': 2,
-              'light': 4,
-              'bodysuit': 5,
-              'bulky': 6,
-              'human_head': 8}
-
-    directions = []
-    distances = []
-    for k, v in params.items():
-        directions.append(latent_dirs[param_indexes[k]])
-        distances.append(v)
-
-    style = {'description_width': 'initial'}
-    return display_sample_pytorch(int(seed), truncation, directions, distances, scale, int(start_layer), int(end_layer), disp=False)
-
-truncation = gr.inputs.Slider(minimum=0, maximum=1, default=0.5, label="Truncation")
-start_layer = gr.inputs.Number(default=0, label="Start Layer")
-end_layer = gr.inputs.Number(default=14, label="End Layer")
-seed = gr.inputs.Textbox(default="0", label="Seed")
-
-slider_max_val = 20
-slider_min_val = -20
-slider_step = 1
-
-monster = gr.inputs.Slider(label="Monsterfication", minimum=slider_min_val, maximum=slider_max_val, default=0)
-female = gr.inputs.Slider(label="Gender", minimum=slider_min_val, maximum=slider_max_val, default=0)
-skimpy = gr.inputs.Slider(label="Amount of Clothing", minimum=slider_min_val, maximum=slider_max_val, default=0)
-light = gr.inputs.Slider(label="Brightness", minimum=slider_min_val, maximum=slider_max_val, default=0)
-bodysuit = gr.inputs.Slider(label="Bodysuit", minimum=slider_min_val, maximum=slider_max_val, default=0)
-bulky = gr.inputs.Slider(label="Bulkiness", minimum=slider_min_val, maximum=slider_max_val, default=0)
-human_head = gr.inputs.Slider(label="Head", minimum=slider_min_val, maximum=slider_max_val, default=0)
-
-
-scale = 1
-
-inputs = [seed, truncation, monster, female, skimpy, light, bodysuit, bulky, human_head, start_layer, end_layer]
-description = "Change the seed number to generate different character design. Made by <a href='https://www.mfrashad.com/' target='_blank'>@mfrashad</a>. For more details on how to build this, visit the <a href='https://github.com/mfrashad/gancreate-saai' target='_blank'>repo</a>. Please give a star if you find it useful :)"
-
-gr.Interface(generate_image, inputs, ["image"], description=description, live=True, title="CharacterGAN").launch()
\ No newline at end of file
diff --git a/spaces/mikeee/radiobee-aligner/docs/build/html/_static/underscore.js b/spaces/mikeee/radiobee-aligner/docs/build/html/_static/underscore.js
deleted file mode 100644
index cf177d4285ab55fbc16406a5ec827b80e7eecd53..0000000000000000000000000000000000000000
--- a/spaces/mikeee/radiobee-aligner/docs/build/html/_static/underscore.js
+++ /dev/null
@@ -1,6 +0,0 @@
-!function(n,r){"object"==typeof exports&&"undefined"!=typeof module?module.exports=r():"function"==typeof define&&define.amd?define("underscore",r):(n="undefined"!=typeof globalThis?globalThis:n||self,function(){var t=n._,e=n._=r();e.noConflict=function(){return n._=t,e}}())}(this,(function(){
-//     Underscore.js 1.13.1
-//     https://underscorejs.org
-//     (c) 2009-2021 Jeremy Ashkenas, Julian Gonggrijp, and DocumentCloud and Investigative Reporters & Editors
-//     Underscore may be freely distributed under the MIT license.
-var n="1.13.1",r="object"==typeof self&&self.self===self&&self||"object"==typeof global&&global.global===global&&global||Function("return this")()||{},t=Array.prototype,e=Object.prototype,u="undefined"!=typeof Symbol?Symbol.prototype:null,o=t.push,i=t.slice,a=e.toString,f=e.hasOwnProperty,c="undefined"!=typeof ArrayBuffer,l="undefined"!=typeof DataView,s=Array.isArray,p=Object.keys,v=Object.create,h=c&&ArrayBuffer.isView,y=isNaN,d=isFinite,g=!{toString:null}.propertyIsEnumerable("toString"),b=["valueOf","isPrototypeOf","toString","propertyIsEnumerable","hasOwnProperty","toLocaleString"],m=Math.pow(2,53)-1;function j(n,r){return r=null==r?n.length-1:+r,function(){for(var t=Math.max(arguments.length-r,0),e=Array(t),u=0;u<t;u++)e[u]=arguments[u+r];switch(r){case 0:return n.call(this,e);case 1:return n.call(this,arguments[0],e);case 2:return n.call(this,arguments[0],arguments[1],e)}var o=Array(r+1);for(u=0;u<r;u++)o[u]=arguments[u];return o[r]=e,n.apply(this,o)}}function _(n){var r=typeof 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diff --git a/spaces/mmaguero/Auto-Complete_Semantic/app.py b/spaces/mmaguero/Auto-Complete_Semantic/app.py
deleted file mode 100644
index c3c437a495b9d807d381dcf5e53c88189d223b1a..0000000000000000000000000000000000000000
--- a/spaces/mmaguero/Auto-Complete_Semantic/app.py
+++ /dev/null
@@ -1,111 +0,0 @@
-import streamlit as st
-import pandas as pd
-#from streamlit import cli as stcli
-from streamlit.web import cli as stcli
-from streamlit import runtime
-from transformers import pipeline
-from sentence_transformers import SentenceTransformer, util
-import sys 
-
-HISTORY_WEIGHT = 80 # set history weight (if found any keyword from history, it will priorities based on its weight)
-
-@st.cache(allow_output_mutation=True, suppress_st_warning=True)
-def get_model(model):
-	return pipeline("fill-mask", model=model, top_k=5)#s5t the maximum of tokens to be retrieved after each inference to model
-
-def hash_func(inp):
-    return True
-
-@st.cache(allow_output_mutation=True, suppress_st_warning=True)
-def loading_models(model='roberta-base'):
-     return get_model(model), SentenceTransformer('all-mpnet-base-v2')#'all-MiniLM-L6-v2')
-
-@st.cache(allow_output_mutation=True, 
-          suppress_st_warning=True,
-          hash_funcs={'tokenizers.Tokenizer': hash_func, 'tokenizers.AddedToken': hash_func})
-def infer(text):
-#    global nlp 
-    return nlp(text+' '+nlp.tokenizer.mask_token)
-
-
-@st.cache(allow_output_mutation=True, 
-          suppress_st_warning=True,
-          hash_funcs={'tokenizers.Tokenizer': hash_func, 'tokenizers.AddedToken': hash_func})
-def sim(predicted_seq, sem_list):
-    return semantic_model.encode(predicted_seq, convert_to_tensor=True), \
-            semantic_model.encode(sem_list, convert_to_tensor=True)
-    
-@st.cache(allow_output_mutation=True, 
-          suppress_st_warning=True,
-          hash_funcs={'tokenizers.Tokenizer': hash_func, 'tokenizers.AddedToken': hash_func})
-def main(text,semantic_text,history_keyword_text):
-    global semantic_model, data_load_state
-    data_load_state.text('Inference from model...')
-    result = infer(text)
-    sem_list=[semantic_text.strip()]
-    data_load_state.text('Checking similarity...')
-    if len(semantic_text):
-        predicted_seq=[rec['sequence'] for rec in result]
-        predicted_embeddings, semantic_history_embeddings = sim(predicted_seq, sem_list)
-        cosine_scores = util.cos_sim(predicted_embeddings, semantic_history_embeddings)
-    data_load_state.text('similarity check completed...')
-    
-    for index, r in enumerate(result):
-        if len(semantic_text):
-                if len(r['token_str'])>2: #skip spcial chars such as "?"
-                    result[index]['score']+=float(sum(cosine_scores[index]))*HISTORY_WEIGHT
-        if r['token_str'].lower().strip() in history_keyword_text.lower().strip() and len(r['token_str'].lower().strip())>1:
-            #found from history, then increase the score of tokens
-            result[index]['score']*=HISTORY_WEIGHT
-    data_load_state.text('Score updated...')
-            
-    #sort the results        
-    df=pd.DataFrame(result).sort_values(by='score', ascending=False)
-    return df
-    
-    
-if __name__ == '__main__':
-    #if st._is_running_with_streamlit:
-    if runtime.exists():
-        st.markdown("""
-# Auto-Complete
-This is an example of an auto-complete approach where the next token suggested based on users's history 
-Keyword match & Semantic similarity of users's history (log).
-The next token is predicted per probability and a weight if it is appeared in keyword user's history or 
-there is a similarity to semantic user's history.
-
-## Source
-Forked from **[mbahrami/Auto-Complete_Semantic](https://huggingface.co/spaces/mbahrami/Auto-Complete_Semantic)** with *[osanseviero/fork_a_repo](https://huggingface.co/spaces/osanseviero/fork_a_repo)*.
-
-## Disclaimer
-The behind idea is to compare our models that included Guarani during pre-training vs. the models that do not 
-have saw it. That is, the multilingual ones: XLM-RoBERTa, mBERT and Spanish BERTs (BETO and PLAN-TL-RoBERTa). 
-Additionally, we include facebook/xlm-v-base model (it includes Guarani during pre-training), 
-for comparison reasons.
-""")
-        history_keyword_text = st.text_input("Enter users's history <Keywords Match> (optional, i.e., 'Premio Cervantes')", value="")
-        
-        semantic_text = st.text_input("Enter users's history <Semantic> (optional, i.e., 'hai')", value="hai")
-        
-        text = st.text_input("Enter a text for auto completion...", value="Augusto Roa Bastos ha'e kuimba'e arandu")
-        model = st.selectbox("Choose a model", 
-                             ["mmaguero/gn-bert-tiny-cased", "mmaguero/gn-bert-small-cased", 
-                              "mmaguero/gn-bert-base-cased", "mmaguero/gn-bert-large-cased", 
-                              "mmaguero/multilingual-bert-gn-base-cased", "mmaguero/beto-gn-base-cased",
-                              "facebook/xlm-v-base",
-                              
-                              "bert-base-multilingual-cased", "xlm-roberta-base",
-                              
-                              "dccuchile/bert-base-spanish-wwm-cased", "PlanTL-GOB-ES/roberta-base-bne"])
-        
-        data_load_state = st.text('1.Loading model ...')
-
-        nlp, semantic_model = loading_models(model)
-        
-        df=main(text,semantic_text,history_keyword_text)
-        #show the results as a table
-        st.table(df)
-        data_load_state.text('')
-    else:
-        sys.argv = ['streamlit', 'run', sys.argv[0]]
-        sys.exit(stcli.main()) 
\ No newline at end of file
diff --git a/spaces/modelscope/FaceChain/Dockerfile b/spaces/modelscope/FaceChain/Dockerfile
deleted file mode 100644
index b15220a78a4249c4358cd9191acbd2d63f3451c1..0000000000000000000000000000000000000000
--- a/spaces/modelscope/FaceChain/Dockerfile
+++ /dev/null
@@ -1,18 +0,0 @@
-FROM registry.us-west-1.aliyuncs.com/modelscope-repo/modelscope:ubuntu20.04-cuda11.7.1-py38-torch2.0.1-tf1.15.5-1.8.1
-RUN useradd -m -u 1000 user
-USER user
-ENV HOME=/home/user \
-	PATH=/home/user/.local/bin:$PATH
-WORKDIR $HOME
-RUN chmod 777 $HOME
-RUN mkdir $HOME/modelscope_cache
-ENV MODELSCOPE_CACHE=$HOME/modelscope_cache
-ENV GRADIO_SERVER_NAME=0.0.0.0
-EXPOSE 7860
-RUN pip install gradio
-RUN echo 'cloning facechain:hf_space'
-RUN git clone -b feat/hf_space https://github.com/modelscope/facechain.git
-WORKDIR $HOME/facechain
-RUN pip install -r requirements.txt
-ENV PYTHONPATH=.
-CMD ["python", "app.py"]
diff --git a/spaces/mrm8488/PromptSource/app.py b/spaces/mrm8488/PromptSource/app.py
deleted file mode 100644
index 1c972348a73ad8bd82f0ed80c001c6096260e105..0000000000000000000000000000000000000000
--- a/spaces/mrm8488/PromptSource/app.py
+++ /dev/null
@@ -1,585 +0,0 @@
-import argparse
-import textwrap
-from multiprocessing import Manager, Pool
-
-import pandas as pd
-import plotly.express as px
-import streamlit as st
-from datasets import get_dataset_infos
-from pygments import highlight
-from pygments.formatters import HtmlFormatter
-from pygments.lexers import DjangoLexer
-
-from session import _get_state
-from templates import Template, TemplateCollection
-from utils import (
-    get_dataset,
-    get_dataset_confs,
-    list_datasets,
-    removeHyphen,
-    renameDatasetColumn,
-    render_features,
-)
-
-
-# add an argument for read-only
-# At the moment, streamlit does not handle python script arguments gracefully.
-# Thus, for read-only mode, you have to type one of the below two:
-# streamlit run promptsource/app.py -- -r
-# streamlit run promptsource/app.py -- --read-only
-# Check https://github.com/streamlit/streamlit/issues/337 for more information.
-parser = argparse.ArgumentParser(description="run app.py with args")
-parser.add_argument("-r", "--read-only", action="store_true", help="whether to run it as read-only mode")
-
-args = parser.parse_args()
-if args.read_only:
-    select_options = ["Helicopter view", "Prompted dataset viewer"]
-    side_bar_title_prefix = "Promptsource (Read only)"
-else:
-    select_options = ["Helicopter view", "Prompted dataset viewer", "Sourcing"]
-    side_bar_title_prefix = "Promptsource"
-
-#
-# Helper functions for datasets library
-#
-get_dataset = st.cache(allow_output_mutation=True)(get_dataset)
-get_dataset_confs = st.cache(get_dataset_confs)
-
-
-def reset_template_state():
-    state.template_name = None
-    state.jinja = None
-    state.reference = None
-
-
-#
-# Loads session state
-#
-state = _get_state()
-
-#
-# Initial page setup
-#
-st.set_page_config(page_title="Promptsource", layout="wide")
-st.sidebar.markdown(
-    "<center><a href='https://github.com/bigscience-workshop/promptsource'>💻Github - Promptsource\n\n</a></center>",
-    unsafe_allow_html=True,
-)
-mode = st.sidebar.selectbox(
-    label="Choose a mode",
-    options=select_options,
-    index=0,
-    key="mode_select",
-)
-st.sidebar.title(f"{side_bar_title_prefix} 🌸 - {mode}")
-
-#
-# Adds pygments styles to the page.
-#
-st.markdown(
-    "<style>" + HtmlFormatter(style="friendly").get_style_defs(".highlight") + "</style>", unsafe_allow_html=True
-)
-
-WIDTH = 80
-
-
-def show_jinja(t, width=WIDTH):
-    wrap = textwrap.fill(t, width=width, replace_whitespace=False)
-    out = highlight(wrap, DjangoLexer(), HtmlFormatter())
-    st.write(out, unsafe_allow_html=True)
-
-
-def show_text(t, width=WIDTH, with_markdown=False):
-    wrap = [textwrap.fill(subt, width=width, replace_whitespace=False) for subt in t.split("\n")]
-    wrap = "\n".join(wrap)
-    if with_markdown:
-        st.write(wrap, unsafe_allow_html=True)
-    else:
-        st.text(wrap)
-
-
-#
-# Loads template data
-#
-try:
-    template_collection = TemplateCollection()
-except FileNotFoundError:
-    st.error(
-        "Unable to find the prompt folder!\n\n"
-        "We expect the folder to be in the working directory. "
-        "You might need to restart the app in the root directory of the repo."
-    )
-    st.stop()
-
-
-if mode == "Helicopter view":
-    st.title("High level metrics")
-    st.write(
-        "If you want to contribute, please refer to the instructions in "
-        + "[Contributing](https://github.com/bigscience-workshop/promptsource/blob/main/CONTRIBUTING.md)."
-    )
-
-    #
-    # Global metrics
-    #
-    counts = template_collection.get_templates_count()
-    nb_prompted_datasets = len(counts)
-    st.write(f"## Number of *prompted datasets*: `{nb_prompted_datasets}`")
-    nb_prompts = sum(counts.values())
-    st.write(f"## Number of *prompts*: `{nb_prompts}`")
-
-    #
-    # Metrics per dataset/subset
-    #
-    # Download dataset infos (multiprocessing download)
-    manager = Manager()
-    all_infos = manager.dict()
-    all_datasets = list(set([t[0] for t in template_collection.keys]))
-
-    def get_infos(d_name):
-        all_infos[d_name] = get_dataset_infos(d_name)
-
-    pool = Pool(processes=len(all_datasets))
-    pool.map(get_infos, all_datasets)
-    pool.close()
-    pool.join()
-
-    results = []
-    for (dataset_name, subset_name) in template_collection.keys:
-        # Collect split sizes (train, validation and test)
-        if dataset_name not in all_infos:
-            infos = get_dataset_infos(dataset_name)
-            all_infos[dataset_name] = infos
-        else:
-            infos = all_infos[dataset_name]
-        if infos:
-            if subset_name is None:
-                subset_infos = infos[list(infos.keys())[0]]
-            else:
-                subset_infos = infos[subset_name]
-
-            split_sizes = {k: v.num_examples for k, v in subset_infos.splits.items()}
-        else:
-            # Zaid/coqa_expanded and Zaid/quac_expanded don't have dataset_infos.json
-            # so infos is an empty dic, and `infos[list(infos.keys())[0]]` raises an error
-            # For simplicity, just filling `split_sizes` with nothing, so the displayed split sizes will be 0.
-            split_sizes = {}
-
-        # Collect template counts, original task counts and names
-        dataset_templates = template_collection.get_dataset(dataset_name, subset_name)
-        results.append(
-            {
-                "Dataset name": dataset_name,
-                "Subset name": "∅" if subset_name is None else subset_name,
-                "Train size": split_sizes["train"] if "train" in split_sizes else 0,
-                "Validation size": split_sizes["validation"] if "validation" in split_sizes else 0,
-                "Test size": split_sizes["test"] if "test" in split_sizes else 0,
-                "Number of prompts": len(dataset_templates),
-                "Number of original task prompts": sum(
-                    [bool(t.metadata.original_task) for t in dataset_templates.templates.values()]
-                ),
-                "Prompt names": [t.name for t in dataset_templates.templates.values()],
-            }
-        )
-    results_df = pd.DataFrame(results)
-    results_df.sort_values(["Number of prompts"], inplace=True, ascending=False)
-    results_df.reset_index(drop=True, inplace=True)
-
-    nb_training_instances = results_df["Train size"].sum()
-    st.write(f"## Number of *training instances*: `{nb_training_instances}`")
-
-    plot_df = results_df[["Dataset name", "Subset name", "Train size", "Number of prompts"]].copy()
-    plot_df["Name"] = plot_df["Dataset name"] + " - " + plot_df["Subset name"]
-    plot_df.sort_values(["Train size"], inplace=True, ascending=False)
-    fig = px.bar(
-        plot_df,
-        x="Name",
-        y="Train size",
-        hover_data=["Dataset name", "Subset name", "Number of prompts"],
-        log_y=True,
-        title="Number of training instances per data(sub)set - y-axis is in logscale",
-    )
-    fig.update_xaxes(visible=False, showticklabels=False)
-    st.plotly_chart(fig, use_container_width=True)
-    st.write(
-        f"- Top 3 training subsets account for `{100*plot_df[:3]['Train size'].sum()/nb_training_instances:.2f}%` of the training instances."
-    )
-    biggest_training_subset = plot_df.iloc[0]
-    st.write(
-        f"- Biggest training subset is *{biggest_training_subset['Name']}* with `{biggest_training_subset['Train size']}` instances"
-    )
-    smallest_training_subset = plot_df[plot_df["Train size"] > 0].iloc[-1]
-    st.write(
-        f"- Smallest training subset is *{smallest_training_subset['Name']}* with `{smallest_training_subset['Train size']}` instances"
-    )
-
-    st.markdown("***")
-    st.write("Details per dataset")
-    st.table(results_df)
-
-else:
-    # Combining mode `Prompted dataset viewer` and `Sourcing` since the
-    # backbone of the interfaces is the same
-    assert mode in ["Prompted dataset viewer", "Sourcing"], ValueError(
-        f"`mode` ({mode}) should be in `[Helicopter view, Prompted dataset viewer, Sourcing]`"
-    )
-
-    #
-    # Loads dataset information
-    #
-
-    dataset_list = list_datasets(
-        template_collection,
-        state,
-    )
-    ag_news_index = dataset_list.index("ag_news")
-
-    #
-    # Select a dataset - starts with ag_news
-    #
-    dataset_key = st.sidebar.selectbox(
-        "Dataset",
-        dataset_list,
-        key="dataset_select",
-        index=ag_news_index,
-        help="Select the dataset to work on.",
-    )
-
-    #
-    # If a particular dataset is selected, loads dataset and template information
-    #
-    if dataset_key is not None:
-
-        #
-        # Check for subconfigurations (i.e. subsets)
-        #
-        configs = get_dataset_confs(dataset_key)
-        conf_option = None
-        if len(configs) > 0:
-            conf_option = st.sidebar.selectbox("Subset", configs, index=0, format_func=lambda a: a.name)
-
-        dataset = get_dataset(dataset_key, str(conf_option.name) if conf_option else None)
-        splits = list(dataset.keys())
-        index = 0
-        if "train" in splits:
-            index = splits.index("train")
-        split = st.sidebar.selectbox("Split", splits, key="split_select", index=index)
-        dataset = dataset[split]
-        dataset = renameDatasetColumn(dataset)
-
-        dataset_templates = template_collection.get_dataset(dataset_key, conf_option.name if conf_option else None)
-
-        template_list = dataset_templates.all_template_names
-        num_templates = len(template_list)
-        st.sidebar.write(
-            "No of prompts created for "
-            + f"`{dataset_key + (('/' + conf_option.name) if conf_option else '')}`"
-            + f": **{str(num_templates)}**"
-        )
-
-        if mode == "Prompted dataset viewer":
-            if num_templates > 0:
-                template_name = st.sidebar.selectbox(
-                    "Prompt name",
-                    template_list,
-                    key="template_select",
-                    index=0,
-                    help="Select the prompt to visualize.",
-                )
-
-            step = 50
-            example_index = st.sidebar.number_input(
-                f"Select the example index (Size = {len(dataset)})",
-                min_value=0,
-                max_value=len(dataset) - step,
-                value=0,
-                step=step,
-                key="example_index_number_input",
-                help="Offset = 50.",
-            )
-        else:  # mode = Sourcing
-            st.sidebar.subheader("Select Example")
-            example_index = st.sidebar.slider("Select the example index", 0, len(dataset) - 1)
-
-            example = dataset[example_index]
-            example = removeHyphen(example)
-
-            st.sidebar.write(example)
-
-        st.sidebar.subheader("Dataset Schema")
-        rendered_features = render_features(dataset.features)
-        st.sidebar.write(rendered_features)
-
-        #
-        # Display dataset information
-        #
-        st.header("Dataset: " + dataset_key + " " + (("/ " + conf_option.name) if conf_option else ""))
-
-        st.markdown(
-            "*Homepage*: "
-            + dataset.info.homepage
-            + "\n\n*Dataset*: https://github.com/huggingface/datasets/blob/master/datasets/%s/%s.py"
-            % (dataset_key, dataset_key)
-        )
-
-        md = """
-        %s
-        """ % (
-            dataset.info.description.replace("\\", "") if dataset_key else ""
-        )
-        st.markdown(md)
-
-        #
-        # Body of the app: display prompted examples in mode `Prompted dataset viewer`
-        # or text boxes to create new prompts in mode `Sourcing`
-        #
-        if mode == "Prompted dataset viewer":
-            #
-            # Display template information
-            #
-            if num_templates > 0:
-                template = dataset_templates[template_name]
-                st.subheader("Prompt")
-                st.markdown("##### Name")
-                st.text(template.name)
-                st.markdown("##### Reference")
-                st.text(template.reference)
-                st.markdown("##### Original Task? ")
-                st.text(template.metadata.original_task)
-                st.markdown("##### Choices in template? ")
-                st.text(template.metadata.choices_in_prompt)
-                st.markdown("##### Metrics")
-                st.text(", ".join(template.metadata.metrics) if template.metadata.metrics else None)
-                st.markdown("##### Answer Choices")
-                if template.get_answer_choices_expr() is not None:
-                    show_jinja(template.get_answer_choices_expr())
-                else:
-                    st.text(None)
-                st.markdown("##### Jinja template")
-                splitted_template = template.jinja.split("|||")
-                st.markdown("###### Input template")
-                show_jinja(splitted_template[0].strip())
-                if len(splitted_template) > 1:
-                    st.markdown("###### Target template")
-                    show_jinja(splitted_template[1].strip())
-                st.markdown("***")
-
-            #
-            # Display a couple (steps) examples
-            #
-            for ex_idx in range(example_index, example_index + step):
-                if ex_idx >= len(dataset):
-                    continue
-                example = dataset[ex_idx]
-                example = removeHyphen(example)
-                col1, _, col2 = st.beta_columns([12, 1, 12])
-                with col1:
-                    st.write(example)
-                if num_templates > 0:
-                    with col2:
-                        prompt = template.apply(example, highlight_variables=False)
-                        if prompt == [""]:
-                            st.write("∅∅∅ *Blank result*")
-                        else:
-                            st.write("Input")
-                            show_text(prompt[0])
-                            if len(prompt) > 1:
-                                st.write("Target")
-                                show_text(prompt[1])
-                st.markdown("***")
-        else:  # mode = Sourcing
-            st.markdown("## Prompt Creator")
-
-            #
-            # Create a new template or select an existing one
-            #
-            col1a, col1b, _, col2 = st.beta_columns([9, 9, 1, 6])
-
-            # current_templates_key and state.templates_key are keys for the templates object
-            current_templates_key = (dataset_key, conf_option.name if conf_option else None)
-
-            # Resets state if there has been a change in templates_key
-            if state.templates_key != current_templates_key:
-                state.templates_key = current_templates_key
-                reset_template_state()
-
-            with col1a, st.form("new_template_form"):
-                new_template_name = st.text_input(
-                    "Create a New Prompt",
-                    key="new_template",
-                    value="",
-                    help="Enter name and hit enter to create a new prompt.",
-                )
-                new_template_submitted = st.form_submit_button("Create")
-                if new_template_submitted:
-                    if new_template_name in dataset_templates.all_template_names:
-                        st.error(
-                            f"A prompt with the name {new_template_name} already exists "
-                            f"for dataset {state.templates_key}."
-                        )
-                    elif new_template_name == "":
-                        st.error("Need to provide a prompt name.")
-                    else:
-                        template = Template(new_template_name, "", "")
-                        dataset_templates.add_template(template)
-                        reset_template_state()
-                        state.template_name = new_template_name
-                else:
-                    state.new_template_name = None
-
-            with col1b, st.beta_expander("or Select Prompt", expanded=True):
-                dataset_templates = template_collection.get_dataset(*state.templates_key)
-                template_list = dataset_templates.all_template_names
-                if state.template_name:
-                    index = template_list.index(state.template_name)
-                else:
-                    index = 0
-                state.template_name = st.selectbox(
-                    "", template_list, key="template_select", index=index, help="Select the prompt to work on."
-                )
-
-                if st.button("Delete Prompt", key="delete_prompt"):
-                    dataset_templates.remove_template(state.template_name)
-                    reset_template_state()
-
-            variety_guideline = """
-            :heavy_exclamation_mark::question:Creating a diverse set of prompts whose differences go beyond surface wordings (i.e. marginally changing 2 or 3 words) is highly encouraged.
-            Ultimately, the hope is that exposing the model to such a diversity will have a non-trivial impact on the model's robustness to the prompt formulation.
-            \r**To get various prompts, you can try moving the cursor along theses axes**:
-            \n- **Interrogative vs affirmative form**: Ask a question about an attribute of the inputs or tell the model to decide something about the input.
-            \n- **Task description localization**: where is the task description blended with the inputs? In the beginning, in the middle, at the end?
-            \n- **Implicit situation or contextualization**: how explicit is the query? For instance, *Given this review, would you buy this product?* is an indirect way to ask whether the review is positive.
-            """
-
-            col1, _, _ = st.beta_columns([18, 1, 6])
-            with col1:
-                if state.template_name is not None:
-                    show_text(variety_guideline, with_markdown=True)
-
-            #
-            # Edit the created or selected template
-            #
-            col1, _, col2 = st.beta_columns([18, 1, 6])
-            with col1:
-                if state.template_name is not None:
-                    template = dataset_templates[state.template_name]
-                    #
-                    # If template is selected, displays template editor
-                    #
-                    with st.form("edit_template_form"):
-                        updated_template_name = st.text_input("Name", value=template.name)
-                        state.reference = st.text_input(
-                            "Prompt Reference",
-                            help="Short description of the prompt and/or paper reference for the prompt.",
-                            value=template.reference,
-                        )
-
-                        # Metadata
-                        state.metadata = template.metadata
-                        state.metadata.original_task = st.checkbox(
-                            "Original Task?",
-                            value=template.metadata.original_task,
-                            help="Prompt asks model to perform the original task designed for this dataset.",
-                        )
-                        state.metadata.choices_in_prompt = st.checkbox(
-                            "Choices in Template?",
-                            value=template.metadata.choices_in_prompt,
-                            help="Prompt explicitly lists choices in the template for the output.",
-                        )
-
-                        # Metrics from here:
-                        # https://github.com/google-research/text-to-text-transfer-transformer/blob/4b580f23968c2139be7fb1cd53b22c7a7f686cdf/t5/evaluation/metrics.py
-                        metrics_choices = [
-                            "BLEU",
-                            "ROUGE",
-                            "Squad",
-                            "Trivia QA",
-                            "Accuracy",
-                            "Pearson Correlation",
-                            "Spearman Correlation",
-                            "MultiRC",
-                            "AUC",
-                            "COQA F1",
-                            "Edit Distance",
-                        ]
-                        # Add mean reciprocal rank
-                        metrics_choices.append("Mean Reciprocal Rank")
-                        # Add generic other
-                        metrics_choices.append("Other")
-                        # Sort alphabetically
-                        metrics_choices = sorted(metrics_choices)
-                        state.metadata.metrics = st.multiselect(
-                            "Metrics",
-                            metrics_choices,
-                            default=template.metadata.metrics,
-                            help="Select all metrics that are commonly used (or should "
-                            "be used if a new task) to evaluate this prompt.",
-                        )
-
-                        # Answer choices
-                        if template.get_answer_choices_expr() is not None:
-                            answer_choices = template.get_answer_choices_expr()
-                        else:
-                            answer_choices = ""
-                        state.answer_choices = st.text_input(
-                            "Answer Choices",
-                            value=answer_choices,
-                            help="A Jinja expression for computing answer choices. "
-                            "Separate choices with a triple bar (|||).",
-                        )
-
-                        # Jinja
-                        state.jinja = st.text_area("Template", height=40, value=template.jinja)
-
-                        # Submit form
-                        if st.form_submit_button("Save"):
-                            if (
-                                updated_template_name in dataset_templates.all_template_names
-                                and updated_template_name != state.template_name
-                            ):
-                                st.error(
-                                    f"A prompt with the name {updated_template_name} already exists "
-                                    f"for dataset {state.templates_key}."
-                                )
-                            elif updated_template_name == "":
-                                st.error("Need to provide a prompt name.")
-                            else:
-                                # Parses state.answer_choices
-                                if state.answer_choices == "":
-                                    updated_answer_choices = None
-                                else:
-                                    updated_answer_choices = state.answer_choices
-
-                                dataset_templates.update_template(
-                                    state.template_name,
-                                    updated_template_name,
-                                    state.jinja,
-                                    state.reference,
-                                    state.metadata,
-                                    updated_answer_choices,
-                                )
-                                # Update the state as well
-                                state.template_name = updated_template_name
-            #
-            # Displays template output on current example if a template is selected
-            # (in second column)
-            #
-            with col2:
-                if state.template_name is not None:
-                    st.empty()
-                    template = dataset_templates[state.template_name]
-                    prompt = template.apply(example)
-                    if prompt == [""]:
-                        st.write("∅∅∅ *Blank result*")
-                    else:
-                        st.write("Input")
-                        show_text(prompt[0], width=40)
-                        if len(prompt) > 1:
-                            st.write("Target")
-                            show_text(prompt[1], width=40)
-
-
-#
-# Must sync state at end
-#
-state.sync()
diff --git a/spaces/mshukor/UnIVAL/preprocess/average_save_models.py b/spaces/mshukor/UnIVAL/preprocess/average_save_models.py
deleted file mode 100644
index c73c235e50051283eea5d6da952404d1a0749f1e..0000000000000000000000000000000000000000
--- a/spaces/mshukor/UnIVAL/preprocess/average_save_models.py
+++ /dev/null
@@ -1,133 +0,0 @@
-import torch 
-import numpy as np
-import os 
-import re
-
-def average(checkpoints, lambdas=[0.5, 0.5], num_models=6, output_dir=None, filename=None, skip_keys=None, ema=False):
-
-	ckpt = torch.load(checkpoints[0], map_location='cpu')
-
-	if ema:
-		key = 'extra_state'
-		state = ckpt['extra_state']['ema']
-	else:
-		key = 'model'
-		state = ckpt['model']
-
-	print(lambdas)
-
-
-	
-	if num_models == 1:
-		average_state = {k : v.clone() * lambdas[0] for k, v in state.items()}
-		for i in range(1, len(checkpoints)):
-			skip_keys_list = set()
-			print(checkpoints[i], lambdas[i])
-			if ema:
-				statei = torch.load(checkpoints[i], map_location='cpu')['extra_state']['ema']
-			else:
-				statei = torch.load(checkpoints[i], map_location='cpu')['model']
-			for k, v in average_state.items():
-				if k in statei and (skip_keys is None or ((not any([re.match(sk, k) for sk in skip_keys])) and (not any([sk in k for sk in skip_keys])))):
-					try:
-						average_state[k] += (lambdas[i])*statei[k].clone()
-					except:
-						print(k, average_state[k].shape, statei[k].shape)
-						average_state[k] += (lambdas[i])*average_state[k].clone()
-				else:
-					average_state[k] += (lambdas[i])*average_state[k].clone()
-					skip_keys_list.add(k)
-					
-					
-			state_dict = average_state
-			print(skip_keys_list)
-		if ema:
-			save_obj = {key:{'ema': state_dict, 'epoch': 0}} 
-			for k, v in ckpt['extra_state'].items():
-				if k != 'ema':
-					save_obj['extra_state']=v
-					print(k)
-			for k, v in ckpt.items():
-				if k != key:
-					save_obj[k]=v
-					print(k)
-		else:
-			save_obj = {key: state_dict,}
-			for k, v in ckpt.items():
-				if k != key:
-					save_obj[k]=v
-				print(k)
-		output_path = os.path.join(output_dir, '{}.pt'.format(filename))
-		print('saving', output_path)
-		torch.save(save_obj, output_path)  
-
-	else:
-		if ema:
-			state_dict1 = ckpt['extra_state']['ema']
-			state_dict2 = torch.load(checkpoints[1], map_location='cpu')['extra_state']['ema']
-		else:
-			state_dict1 = ckpt['model']
-			state_dict2 = torch.load(checkpoints[1], map_location='cpu')['model']
-		for l in lambdas:
-			average_state = {k : v * l for k, v in state_dict1.items()} #{k : v * (1./NUM_MODELS) for k, v in state_dict1.items()}
-			for k, v in average_state.items():
-				if k in state_dict2:
-					average_state[k] += (1-l)*state_dict2[k]
-				else:
-					average_state[k] += (1-l)*state_dict1[k]
-
-			state_dict = average_state
-
-			if ema:
-				save_obj = {key:{'ema': state_dict,}} 
-				for k, v in ckpt['extra_state'].items():
-					if k != 'ema':
-						save_obj['extra_state'][k]=v
-						print(k)
-				for k, v in ckpt.items():
-					if k != key:
-						save_obj[k]=v
-						print(k)
-			else:
-				save_obj = {key: state_dict,}
-				for k, v in ckpt.items():
-					if k != key:
-						save_obj[k]=v
-						print(k)
-			output_path = os.path.join(output_dir, '{}_l{:.2f}.pt'.format(filename, l))
-			print('saving', output_path)
-			torch.save(save_obj, output_path)  
-
-
-
-
-
-
-# average of several models 
-
-# lambdas = [1/4, 1/4, 1/4, 1/4]
-
-# num_models=1
-# output_dir='/lus/scratch/NAT/gda2204/SHARED/logs/ofa/pretrained_models/average_models/'
-# filename='avg_caprefsnlivqa'
-
-# checkpoints = [
-# 			'/lus/scratch/NAT/gda2204/SHARED/logs/ofa/checkpoints/caption/caption_stage_1_ofaplus_base_pretrain_s2_hsep1_bs16_shuf/10_0.06_6000/checkpoint_best.pt',
-# 			'/lus/scratch/NAT/gda2204/SHARED/logs/ofa/checkpoints/refcocoplus/refcocoplus_ofaplus_base_pretrain_s2_hsep1_fix_lr5e5_bs8_4_shuf/10_5e-5_512/checkpoint_best.pt',
-# 			'/lus/scratch/NAT/gda2204/SHARED/logs/ofa/checkpoints/snli_ve/snli_ve_ofaplus_base_pretrain_s2_hsep1/10_5e-5/checkpoint_best.pt',
-# 			'/lus/scratch/NAT/gda2204/SHARED/logs/ofa/checkpoints/vqa/vqa_ofaplus_base_pretrain_s2_bs16_lr1e4_shuf_hsep1/20_0.04_1e-4_480/checkpoint_best.pt',
-# 			]
-
-# for weight interpolation
-num_models=6
-output_dir='/lus/scratch/NAT/gda2204/SHARED/logs/ofa/pretrained_models/average_models/'
-filename='avg_capvqa'
-lambdas = [0.0, 0.2, 0.4, 0.6, 0.8, 1.0]
-
-checkpoints = ['/lus/scratch/NAT/gda2204/SHARED/logs/ofa/checkpoints/vqa/vqa_ofaplus_base_pretrain_s2_bs16_lr1e4_shuf_hsep1/20_0.04_1e-4_480/checkpoint_best.pt',
-	       '/lus/scratch/NAT/gda2204/SHARED/logs/ofa/checkpoints/caption/caption_stage_1_ofaplus_base_pretrain_s2_hsep1_bs16_shuf/10_0.06_6000/checkpoint_best.pt',
-    ]
-
-
-
-average(checkpoints, lambdas=lambdas, num_models=num_models, output_dir=output_dir, filename=filename)
diff --git a/spaces/msmilauer/AutoGPT-duplicated2/autogpt/json_utils/json_fix_llm.py b/spaces/msmilauer/AutoGPT-duplicated2/autogpt/json_utils/json_fix_llm.py
deleted file mode 100644
index 869aed125cfb8cd7a69ed02eeb389cc72a3e296b..0000000000000000000000000000000000000000
--- a/spaces/msmilauer/AutoGPT-duplicated2/autogpt/json_utils/json_fix_llm.py
+++ /dev/null
@@ -1,220 +0,0 @@
-"""This module contains functions to fix JSON strings generated by LLM models, such as ChatGPT, using the assistance
-of the ChatGPT API or LLM models."""
-from __future__ import annotations
-
-import contextlib
-import json
-from typing import Any, Dict
-
-from colorama import Fore
-from regex import regex
-
-from autogpt.config import Config
-from autogpt.json_utils.json_fix_general import correct_json
-from autogpt.llm_utils import call_ai_function
-from autogpt.logs import logger
-from autogpt.speech import say_text
-
-JSON_SCHEMA = """
-{
-    "command": {
-        "name": "command name",
-        "args": {
-            "arg name": "value"
-        }
-    },
-    "thoughts":
-    {
-        "text": "thought",
-        "reasoning": "reasoning",
-        "plan": "- short bulleted\n- list that conveys\n- long-term plan",
-        "criticism": "constructive self-criticism",
-        "speak": "thoughts summary to say to user"
-    }
-}
-"""
-
-CFG = Config()
-
-
-def auto_fix_json(json_string: str, schema: str) -> str:
-    """Fix the given JSON string to make it parseable and fully compliant with
-        the provided schema using GPT-3.
-
-    Args:
-        json_string (str): The JSON string to fix.
-        schema (str): The schema to use to fix the JSON.
-    Returns:
-        str: The fixed JSON string.
-    """
-    # Try to fix the JSON using GPT:
-    function_string = "def fix_json(json_string: str, schema:str=None) -> str:"
-    args = [f"'''{json_string}'''", f"'''{schema}'''"]
-    description_string = (
-        "This function takes a JSON string and ensures that it"
-        " is parseable and fully compliant with the provided schema. If an object"
-        " or field specified in the schema isn't contained within the correct JSON,"
-        " it is omitted. The function also escapes any double quotes within JSON"
-        " string values to ensure that they are valid. If the JSON string contains"
-        " any None or NaN values, they are replaced with null before being parsed."
-    )
-
-    # If it doesn't already start with a "`", add one:
-    if not json_string.startswith("`"):
-        json_string = "```json\n" + json_string + "\n```"
-    result_string = call_ai_function(
-        function_string, args, description_string, model=CFG.fast_llm_model
-    )
-    logger.debug("------------ JSON FIX ATTEMPT ---------------")
-    logger.debug(f"Original JSON: {json_string}")
-    logger.debug("-----------")
-    logger.debug(f"Fixed JSON: {result_string}")
-    logger.debug("----------- END OF FIX ATTEMPT ----------------")
-
-    try:
-        json.loads(result_string)  # just check the validity
-        return result_string
-    except json.JSONDecodeError:  # noqa: E722
-        # Get the call stack:
-        # import traceback
-        # call_stack = traceback.format_exc()
-        # print(f"Failed to fix JSON: '{json_string}' "+call_stack)
-        return "failed"
-
-
-def fix_json_using_multiple_techniques(assistant_reply: str) -> Dict[Any, Any]:
-    """Fix the given JSON string to make it parseable and fully compliant with two techniques.
-
-    Args:
-        json_string (str): The JSON string to fix.
-
-    Returns:
-        str: The fixed JSON string.
-    """
-
-    # Parse and print Assistant response
-    assistant_reply_json = fix_and_parse_json(assistant_reply)
-    if assistant_reply_json == {}:
-        assistant_reply_json = attempt_to_fix_json_by_finding_outermost_brackets(
-            assistant_reply
-        )
-
-    if assistant_reply_json != {}:
-        return assistant_reply_json
-
-    logger.error(
-        "Error: The following AI output couldn't be converted to a JSON:\n",
-        assistant_reply,
-    )
-    if CFG.speak_mode:
-        say_text("I have received an invalid JSON response from the OpenAI API.")
-
-    return {}
-
-
-def fix_and_parse_json(
-    json_to_load: str, try_to_fix_with_gpt: bool = True
-) -> Dict[Any, Any]:
-    """Fix and parse JSON string
-
-    Args:
-        json_to_load (str): The JSON string.
-        try_to_fix_with_gpt (bool, optional): Try to fix the JSON with GPT.
-            Defaults to True.
-
-    Returns:
-        str or dict[Any, Any]: The parsed JSON.
-    """
-
-    with contextlib.suppress(json.JSONDecodeError):
-        json_to_load = json_to_load.replace("\t", "")
-        return json.loads(json_to_load)
-
-    with contextlib.suppress(json.JSONDecodeError):
-        json_to_load = correct_json(json_to_load)
-        return json.loads(json_to_load)
-    # Let's do something manually:
-    # sometimes GPT responds with something BEFORE the braces:
-    # "I'm sorry, I don't understand. Please try again."
-    # {"text": "I'm sorry, I don't understand. Please try again.",
-    #  "confidence": 0.0}
-    # So let's try to find the first brace and then parse the rest
-    #  of the string
-    try:
-        brace_index = json_to_load.index("{")
-        maybe_fixed_json = json_to_load[brace_index:]
-        last_brace_index = maybe_fixed_json.rindex("}")
-        maybe_fixed_json = maybe_fixed_json[: last_brace_index + 1]
-        return json.loads(maybe_fixed_json)
-    except (json.JSONDecodeError, ValueError) as e:
-        return try_ai_fix(try_to_fix_with_gpt, e, json_to_load)
-
-
-def try_ai_fix(
-    try_to_fix_with_gpt: bool, exception: Exception, json_to_load: str
-) -> Dict[Any, Any]:
-    """Try to fix the JSON with the AI
-
-    Args:
-        try_to_fix_with_gpt (bool): Whether to try to fix the JSON with the AI.
-        exception (Exception): The exception that was raised.
-        json_to_load (str): The JSON string to load.
-
-    Raises:
-        exception: If try_to_fix_with_gpt is False.
-
-    Returns:
-        str or dict[Any, Any]: The JSON string or dictionary.
-    """
-    if not try_to_fix_with_gpt:
-        raise exception
-    if CFG.debug_mode:
-        logger.warn(
-            "Warning: Failed to parse AI output, attempting to fix."
-            "\n If you see this warning frequently, it's likely that"
-            " your prompt is confusing the AI. Try changing it up"
-            " slightly."
-        )
-    # Now try to fix this up using the ai_functions
-    ai_fixed_json = auto_fix_json(json_to_load, JSON_SCHEMA)
-
-    if ai_fixed_json != "failed":
-        return json.loads(ai_fixed_json)
-    # This allows the AI to react to the error message,
-    #   which usually results in it correcting its ways.
-    # logger.error("Failed to fix AI output, telling the AI.")
-    return {}
-
-
-def attempt_to_fix_json_by_finding_outermost_brackets(json_string: str):
-    if CFG.speak_mode and CFG.debug_mode:
-        say_text(
-            "I have received an invalid JSON response from the OpenAI API. "
-            "Trying to fix it now."
-        )
-        logger.error("Attempting to fix JSON by finding outermost brackets\n")
-
-    try:
-        json_pattern = regex.compile(r"\{(?:[^{}]|(?R))*\}")
-        json_match = json_pattern.search(json_string)
-
-        if json_match:
-            # Extract the valid JSON object from the string
-            json_string = json_match.group(0)
-            logger.typewriter_log(
-                title="Apparently json was fixed.", title_color=Fore.GREEN
-            )
-            if CFG.speak_mode and CFG.debug_mode:
-                say_text("Apparently json was fixed.")
-        else:
-            return {}
-
-    except (json.JSONDecodeError, ValueError):
-        if CFG.debug_mode:
-            logger.error(f"Error: Invalid JSON: {json_string}\n")
-        if CFG.speak_mode:
-            say_text("Didn't work. I will have to ignore this response then.")
-        logger.error("Error: Invalid JSON, setting it to empty JSON now.\n")
-        json_string = {}
-
-    return fix_and_parse_json(json_string)
diff --git a/spaces/multimodalart/Tune-A-Video-Training-UI-poli/app_training.py b/spaces/multimodalart/Tune-A-Video-Training-UI-poli/app_training.py
deleted file mode 100644
index cb6c0cc45cd563819f568103d46ee79dd72c103a..0000000000000000000000000000000000000000
--- a/spaces/multimodalart/Tune-A-Video-Training-UI-poli/app_training.py
+++ /dev/null
@@ -1,140 +0,0 @@
-#!/usr/bin/env python
-
-from __future__ import annotations
-
-import os
-
-import gradio as gr
-
-from constants import MODEL_LIBRARY_ORG_NAME, SAMPLE_MODEL_REPO, UploadTarget
-from inference import InferencePipeline
-from trainer import Trainer
-
-
-def create_training_demo(trainer: Trainer,
-                         pipe: InferencePipeline | None = None) -> gr.Blocks:
-    hf_token = os.getenv('HF_TOKEN')
-    with gr.Blocks() as demo:
-        with gr.Row():
-            with gr.Column():
-                with gr.Box():
-                    gr.Markdown('Training Data')
-                    training_video = gr.File(label='Training video')
-                    training_prompt = gr.Textbox(
-                        label='Training prompt',
-                        max_lines=1,
-                        placeholder='A man is surfing')
-                    gr.Markdown('''
-                        - Upload a video and write a `Training Prompt` that describes the video.
-                        ''')
-
-            with gr.Column():
-                with gr.Box():
-                    gr.Markdown('Training Parameters')
-                    with gr.Row():
-                        base_model = gr.Text(label='Base Model',
-                                             value='CompVis/stable-diffusion-v1-4',
-                                             max_lines=1)
-                        resolution = gr.Dropdown(choices=['512', '768'],
-                                                 value='512',
-                                                 label='Resolution',
-                                                 visible=False)
-                    
-                    token = gr.Text(label="Hugging Face Write Token", placeholder="", visible=False if hf_token else True)
-                    with gr.Accordion("Advanced settings", open=False):
-                        num_training_steps = gr.Number(
-                            label='Number of Training Steps', value=300, precision=0)
-                        learning_rate = gr.Number(label='Learning Rate',
-                                                  value=0.000035)
-                        gradient_accumulation = gr.Number(
-                            label='Number of Gradient Accumulation',
-                            value=1,
-                            precision=0)
-                        seed = gr.Slider(label='Seed',
-                                         minimum=0,
-                                         maximum=100000,
-                                         step=1,
-                                         randomize=True,
-                                         value=0)
-                        fp16 = gr.Checkbox(label='FP16', value=True)
-                        use_8bit_adam = gr.Checkbox(label='Use 8bit Adam', value=False)
-                        checkpointing_steps = gr.Number(label='Checkpointing Steps',
-                                                        value=1000,
-                                                        precision=0)
-                        validation_epochs = gr.Number(label='Validation Epochs',
-                                                      value=100,
-                                                      precision=0)
-                    gr.Markdown('''
-                        - The base model must be a Stable Diffusion model compatible with [diffusers](https://github.com/huggingface/diffusers) library.
-                        - Expected time to train a model for 300 steps: ~20 minutes with T4
-                        - You can check the training status by pressing the "Open logs" button if you are running this on your Space.
-                        ''')
-                    
-        with gr.Row():
-            with gr.Column():
-                gr.Markdown('Output Model')
-                output_model_name = gr.Text(label='Name of your model',
-                                            placeholder='The surfer man',
-                                            max_lines=1)
-                validation_prompt = gr.Text(label='Validation Prompt', placeholder='prompt to test the model, e.g: a dog is surfing')
-            with gr.Column():
-                gr.Markdown('Upload Settings')
-                with gr.Row():
-                    upload_to_hub = gr.Checkbox(
-                        label='Upload model to Hub', value=True)
-                    use_private_repo = gr.Checkbox(label='Private',
-                                                   value=True)
-                    delete_existing_repo = gr.Checkbox(
-                        label='Delete existing repo of the same name',
-                        value=False)
-                    upload_to = gr.Radio(
-                        label='Upload to',
-                        choices=[_.value for _ in UploadTarget],
-                        value=UploadTarget.MODEL_LIBRARY.value)
-                    
-        remove_gpu_after_training = gr.Checkbox(
-            label='Remove GPU after training',
-            value=False,
-            interactive=bool(os.getenv('SPACE_ID')),
-            visible=False)
-        run_button = gr.Button('Start Training')
-    
-        with gr.Box():
-            gr.Markdown('Output message')
-            output_message = gr.Markdown()
-
-        if pipe is not None:
-            run_button.click(fn=pipe.clear)
-        run_button.click(fn=trainer.run,
-                         inputs=[
-                             training_video,
-                             training_prompt,
-                             output_model_name,
-                             delete_existing_repo,
-                             validation_prompt,
-                             base_model,
-                             resolution,
-                             num_training_steps,
-                             learning_rate,
-                             gradient_accumulation,
-                             seed,
-                             fp16,
-                             use_8bit_adam,
-                             checkpointing_steps,
-                             validation_epochs,
-                             upload_to_hub,
-                             use_private_repo,
-                             delete_existing_repo,
-                             upload_to,
-                             remove_gpu_after_training,
-                             token
-                         ],
-                         outputs=output_message)
-    return demo
-
-
-if __name__ == '__main__':
-    hf_token = os.getenv('HF_TOKEN')
-    trainer = Trainer(hf_token)
-    demo = create_training_demo(trainer)
-    demo.queue(max_size=1).launch(share=False)
diff --git a/spaces/multimodalart/latentdiffusion/latent-diffusion/ldm/modules/image_degradation/utils_image.py b/spaces/multimodalart/latentdiffusion/latent-diffusion/ldm/modules/image_degradation/utils_image.py
deleted file mode 100644
index 0175f155ad900ae33c3c46ed87f49b352e3faf98..0000000000000000000000000000000000000000
--- a/spaces/multimodalart/latentdiffusion/latent-diffusion/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/multimodalart/upload_your_model/README.md b/spaces/multimodalart/upload_your_model/README.md
deleted file mode 100644
index 8fc9d0192d7d9df44406835af3170ae442307082..0000000000000000000000000000000000000000
--- a/spaces/multimodalart/upload_your_model/README.md
+++ /dev/null
@@ -1,11 +0,0 @@
----
-title: Upload To Hub Multiple At Once
-emoji: 👁
-colorFrom: indigo
-colorTo: gray
-sdk: static
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/myrad01/Inpaint-Anything/third_party/lama/saicinpainting/evaluation/losses/fid/__init__.py b/spaces/myrad01/Inpaint-Anything/third_party/lama/saicinpainting/evaluation/losses/fid/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/myrad01/Inpaint-Anything/third_party/lama/saicinpainting/training/trainers/default.py b/spaces/myrad01/Inpaint-Anything/third_party/lama/saicinpainting/training/trainers/default.py
deleted file mode 100644
index 86c7f0fab42924bfc93a031e851117634c70f593..0000000000000000000000000000000000000000
--- a/spaces/myrad01/Inpaint-Anything/third_party/lama/saicinpainting/training/trainers/default.py
+++ /dev/null
@@ -1,175 +0,0 @@
-import logging
-
-import torch
-import torch.nn.functional as F
-from omegaconf import OmegaConf
-
-from saicinpainting.training.data.datasets import make_constant_area_crop_params
-from saicinpainting.training.losses.distance_weighting import make_mask_distance_weighter
-from saicinpainting.training.losses.feature_matching import feature_matching_loss, masked_l1_loss
-from saicinpainting.training.modules.fake_fakes import FakeFakesGenerator
-from saicinpainting.training.trainers.base import BaseInpaintingTrainingModule, make_multiscale_noise
-from saicinpainting.utils import add_prefix_to_keys, get_ramp
-
-LOGGER = logging.getLogger(__name__)
-
-
-def make_constant_area_crop_batch(batch, **kwargs):
-    crop_y, crop_x, crop_height, crop_width = make_constant_area_crop_params(img_height=batch['image'].shape[2],
-                                                                             img_width=batch['image'].shape[3],
-                                                                             **kwargs)
-    batch['image'] = batch['image'][:, :, crop_y : crop_y + crop_height, crop_x : crop_x + crop_width]
-    batch['mask'] = batch['mask'][:, :, crop_y: crop_y + crop_height, crop_x: crop_x + crop_width]
-    return batch
-
-
-class DefaultInpaintingTrainingModule(BaseInpaintingTrainingModule):
-    def __init__(self, *args, concat_mask=True, rescale_scheduler_kwargs=None, image_to_discriminator='predicted_image',
-                 add_noise_kwargs=None, noise_fill_hole=False, const_area_crop_kwargs=None,
-                 distance_weighter_kwargs=None, distance_weighted_mask_for_discr=False,
-                 fake_fakes_proba=0, fake_fakes_generator_kwargs=None,
-                 **kwargs):
-        super().__init__(*args, **kwargs)
-        self.concat_mask = concat_mask
-        self.rescale_size_getter = get_ramp(**rescale_scheduler_kwargs) if rescale_scheduler_kwargs is not None else None
-        self.image_to_discriminator = image_to_discriminator
-        self.add_noise_kwargs = add_noise_kwargs
-        self.noise_fill_hole = noise_fill_hole
-        self.const_area_crop_kwargs = const_area_crop_kwargs
-        self.refine_mask_for_losses = make_mask_distance_weighter(**distance_weighter_kwargs) \
-            if distance_weighter_kwargs is not None else None
-        self.distance_weighted_mask_for_discr = distance_weighted_mask_for_discr
-
-        self.fake_fakes_proba = fake_fakes_proba
-        if self.fake_fakes_proba > 1e-3:
-            self.fake_fakes_gen = FakeFakesGenerator(**(fake_fakes_generator_kwargs or {}))
-
-    def forward(self, batch):
-        if self.training and self.rescale_size_getter is not None:
-            cur_size = self.rescale_size_getter(self.global_step)
-            batch['image'] = F.interpolate(batch['image'], size=cur_size, mode='bilinear', align_corners=False)
-            batch['mask'] = F.interpolate(batch['mask'], size=cur_size, mode='nearest')
-
-        if self.training and self.const_area_crop_kwargs is not None:
-            batch = make_constant_area_crop_batch(batch, **self.const_area_crop_kwargs)
-
-        img = batch['image']
-        mask = batch['mask']
-
-        masked_img = img * (1 - mask)
-
-        if self.add_noise_kwargs is not None:
-            noise = make_multiscale_noise(masked_img, **self.add_noise_kwargs)
-            if self.noise_fill_hole:
-                masked_img = masked_img + mask * noise[:, :masked_img.shape[1]]
-            masked_img = torch.cat([masked_img, noise], dim=1)
-
-        if self.concat_mask:
-            masked_img = torch.cat([masked_img, mask], dim=1)
-
-        batch['predicted_image'] = self.generator(masked_img)
-        batch['inpainted'] = mask * batch['predicted_image'] + (1 - mask) * batch['image']
-
-        if self.fake_fakes_proba > 1e-3:
-            if self.training and torch.rand(1).item() < self.fake_fakes_proba:
-                batch['fake_fakes'], batch['fake_fakes_masks'] = self.fake_fakes_gen(img, mask)
-                batch['use_fake_fakes'] = True
-            else:
-                batch['fake_fakes'] = torch.zeros_like(img)
-                batch['fake_fakes_masks'] = torch.zeros_like(mask)
-                batch['use_fake_fakes'] = False
-
-        batch['mask_for_losses'] = self.refine_mask_for_losses(img, batch['predicted_image'], mask) \
-            if self.refine_mask_for_losses is not None and self.training \
-            else mask
-
-        return batch
-
-    def generator_loss(self, batch):
-        img = batch['image']
-        predicted_img = batch[self.image_to_discriminator]
-        original_mask = batch['mask']
-        supervised_mask = batch['mask_for_losses']
-
-        # L1
-        l1_value = masked_l1_loss(predicted_img, img, supervised_mask,
-                                  self.config.losses.l1.weight_known,
-                                  self.config.losses.l1.weight_missing)
-
-        total_loss = l1_value
-        metrics = dict(gen_l1=l1_value)
-
-        # vgg-based perceptual loss
-        if self.config.losses.perceptual.weight > 0:
-            pl_value = self.loss_pl(predicted_img, img, mask=supervised_mask).sum() * self.config.losses.perceptual.weight
-            total_loss = total_loss + pl_value
-            metrics['gen_pl'] = pl_value
-
-        # discriminator
-        # adversarial_loss calls backward by itself
-        mask_for_discr = supervised_mask if self.distance_weighted_mask_for_discr else original_mask
-        self.adversarial_loss.pre_generator_step(real_batch=img, fake_batch=predicted_img,
-                                                 generator=self.generator, discriminator=self.discriminator)
-        discr_real_pred, discr_real_features = self.discriminator(img)
-        discr_fake_pred, discr_fake_features = self.discriminator(predicted_img)
-        adv_gen_loss, adv_metrics = self.adversarial_loss.generator_loss(real_batch=img,
-                                                                         fake_batch=predicted_img,
-                                                                         discr_real_pred=discr_real_pred,
-                                                                         discr_fake_pred=discr_fake_pred,
-                                                                         mask=mask_for_discr)
-        total_loss = total_loss + adv_gen_loss
-        metrics['gen_adv'] = adv_gen_loss
-        metrics.update(add_prefix_to_keys(adv_metrics, 'adv_'))
-
-        # feature matching
-        if self.config.losses.feature_matching.weight > 0:
-            need_mask_in_fm = OmegaConf.to_container(self.config.losses.feature_matching).get('pass_mask', False)
-            mask_for_fm = supervised_mask if need_mask_in_fm else None
-            fm_value = feature_matching_loss(discr_fake_features, discr_real_features,
-                                             mask=mask_for_fm) * self.config.losses.feature_matching.weight
-            total_loss = total_loss + fm_value
-            metrics['gen_fm'] = fm_value
-
-        if self.loss_resnet_pl is not None:
-            resnet_pl_value = self.loss_resnet_pl(predicted_img, img)
-            total_loss = total_loss + resnet_pl_value
-            metrics['gen_resnet_pl'] = resnet_pl_value
-
-        return total_loss, metrics
-
-    def discriminator_loss(self, batch):
-        total_loss = 0
-        metrics = {}
-
-        predicted_img = batch[self.image_to_discriminator].detach()
-        self.adversarial_loss.pre_discriminator_step(real_batch=batch['image'], fake_batch=predicted_img,
-                                                     generator=self.generator, discriminator=self.discriminator)
-        discr_real_pred, discr_real_features = self.discriminator(batch['image'])
-        discr_fake_pred, discr_fake_features = self.discriminator(predicted_img)
-        adv_discr_loss, adv_metrics = self.adversarial_loss.discriminator_loss(real_batch=batch['image'],
-                                                                               fake_batch=predicted_img,
-                                                                               discr_real_pred=discr_real_pred,
-                                                                               discr_fake_pred=discr_fake_pred,
-                                                                               mask=batch['mask'])
-        total_loss = total_loss + adv_discr_loss
-        metrics['discr_adv'] = adv_discr_loss
-        metrics.update(add_prefix_to_keys(adv_metrics, 'adv_'))
-
-
-        if batch.get('use_fake_fakes', False):
-            fake_fakes = batch['fake_fakes']
-            self.adversarial_loss.pre_discriminator_step(real_batch=batch['image'], fake_batch=fake_fakes,
-                                                         generator=self.generator, discriminator=self.discriminator)
-            discr_fake_fakes_pred, _ = self.discriminator(fake_fakes)
-            fake_fakes_adv_discr_loss, fake_fakes_adv_metrics = self.adversarial_loss.discriminator_loss(
-                real_batch=batch['image'],
-                fake_batch=fake_fakes,
-                discr_real_pred=discr_real_pred,
-                discr_fake_pred=discr_fake_fakes_pred,
-                mask=batch['mask']
-            )
-            total_loss = total_loss + fake_fakes_adv_discr_loss
-            metrics['discr_adv_fake_fakes'] = fake_fakes_adv_discr_loss
-            metrics.update(add_prefix_to_keys(fake_fakes_adv_metrics, 'adv_'))
-
-        return total_loss, metrics
diff --git a/spaces/nasttam/Image-and-3D-Model-Creator/PIFu/lib/model/HGFilters.py b/spaces/nasttam/Image-and-3D-Model-Creator/PIFu/lib/model/HGFilters.py
deleted file mode 100644
index 870b3c43c82d66df001eb1bc24af9ce21ec60c83..0000000000000000000000000000000000000000
--- a/spaces/nasttam/Image-and-3D-Model-Creator/PIFu/lib/model/HGFilters.py
+++ /dev/null
@@ -1,146 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from ..net_util import *
-
-
-class HourGlass(nn.Module):
-    def __init__(self, num_modules, depth, num_features, norm='batch'):
-        super(HourGlass, self).__init__()
-        self.num_modules = num_modules
-        self.depth = depth
-        self.features = num_features
-        self.norm = norm
-
-        self._generate_network(self.depth)
-
-    def _generate_network(self, level):
-        self.add_module('b1_' + str(level), ConvBlock(self.features, self.features, norm=self.norm))
-
-        self.add_module('b2_' + str(level), ConvBlock(self.features, self.features, norm=self.norm))
-
-        if level > 1:
-            self._generate_network(level - 1)
-        else:
-            self.add_module('b2_plus_' + str(level), ConvBlock(self.features, self.features, norm=self.norm))
-
-        self.add_module('b3_' + str(level), ConvBlock(self.features, self.features, norm=self.norm))
-
-    def _forward(self, level, inp):
-        # Upper branch
-        up1 = inp
-        up1 = self._modules['b1_' + str(level)](up1)
-
-        # Lower branch
-        low1 = F.avg_pool2d(inp, 2, stride=2)
-        low1 = self._modules['b2_' + str(level)](low1)
-
-        if level > 1:
-            low2 = self._forward(level - 1, low1)
-        else:
-            low2 = low1
-            low2 = self._modules['b2_plus_' + str(level)](low2)
-
-        low3 = low2
-        low3 = self._modules['b3_' + str(level)](low3)
-
-        # NOTE: for newer PyTorch (1.3~), it seems that training results are degraded due to implementation diff in F.grid_sample
-        # if the pretrained model behaves weirdly, switch with the commented line.
-        # NOTE: I also found that "bicubic" works better.
-        up2 = F.interpolate(low3, scale_factor=2, mode='bicubic', align_corners=True)
-        # up2 = F.interpolate(low3, scale_factor=2, mode='nearest)
-
-        return up1 + up2
-
-    def forward(self, x):
-        return self._forward(self.depth, x)
-
-
-class HGFilter(nn.Module):
-    def __init__(self, opt):
-        super(HGFilter, self).__init__()
-        self.num_modules = opt.num_stack
-
-        self.opt = opt
-
-        # Base part
-        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3)
-
-        if self.opt.norm == 'batch':
-            self.bn1 = nn.BatchNorm2d(64)
-        elif self.opt.norm == 'group':
-            self.bn1 = nn.GroupNorm(32, 64)
-
-        if self.opt.hg_down == 'conv64':
-            self.conv2 = ConvBlock(64, 64, self.opt.norm)
-            self.down_conv2 = nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1)
-        elif self.opt.hg_down == 'conv128':
-            self.conv2 = ConvBlock(64, 128, self.opt.norm)
-            self.down_conv2 = nn.Conv2d(128, 128, kernel_size=3, stride=2, padding=1)
-        elif self.opt.hg_down == 'ave_pool':
-            self.conv2 = ConvBlock(64, 128, self.opt.norm)
-        else:
-            raise NameError('Unknown Fan Filter setting!')
-
-        self.conv3 = ConvBlock(128, 128, self.opt.norm)
-        self.conv4 = ConvBlock(128, 256, self.opt.norm)
-
-        # Stacking part
-        for hg_module in range(self.num_modules):
-            self.add_module('m' + str(hg_module), HourGlass(1, opt.num_hourglass, 256, self.opt.norm))
-
-            self.add_module('top_m_' + str(hg_module), ConvBlock(256, 256, self.opt.norm))
-            self.add_module('conv_last' + str(hg_module),
-                            nn.Conv2d(256, 256, kernel_size=1, stride=1, padding=0))
-            if self.opt.norm == 'batch':
-                self.add_module('bn_end' + str(hg_module), nn.BatchNorm2d(256))
-            elif self.opt.norm == 'group':
-                self.add_module('bn_end' + str(hg_module), nn.GroupNorm(32, 256))
-                
-            self.add_module('l' + str(hg_module), nn.Conv2d(256,
-                                                            opt.hourglass_dim, kernel_size=1, stride=1, padding=0))
-
-            if hg_module < self.num_modules - 1:
-                self.add_module(
-                    'bl' + str(hg_module), nn.Conv2d(256, 256, kernel_size=1, stride=1, padding=0))
-                self.add_module('al' + str(hg_module), nn.Conv2d(opt.hourglass_dim,
-                                                                 256, kernel_size=1, stride=1, padding=0))
-
-    def forward(self, x):
-        x = F.relu(self.bn1(self.conv1(x)), True)
-        tmpx = x
-        if self.opt.hg_down == 'ave_pool':
-            x = F.avg_pool2d(self.conv2(x), 2, stride=2)
-        elif self.opt.hg_down in ['conv64', 'conv128']:
-            x = self.conv2(x)
-            x = self.down_conv2(x)
-        else:
-            raise NameError('Unknown Fan Filter setting!')
-
-        normx = x
-
-        x = self.conv3(x)
-        x = self.conv4(x)
-
-        previous = x
-
-        outputs = []
-        for i in range(self.num_modules):
-            hg = self._modules['m' + str(i)](previous)
-
-            ll = hg
-            ll = self._modules['top_m_' + str(i)](ll)
-
-            ll = F.relu(self._modules['bn_end' + str(i)]
-                        (self._modules['conv_last' + str(i)](ll)), True)
-
-            # Predict heatmaps
-            tmp_out = self._modules['l' + str(i)](ll)
-            outputs.append(tmp_out)
-
-            if i < self.num_modules - 1:
-                ll = self._modules['bl' + str(i)](ll)
-                tmp_out_ = self._modules['al' + str(i)](tmp_out)
-                previous = previous + ll + tmp_out_
-
-        return outputs, tmpx.detach(), normx
diff --git a/spaces/nateraw/deepafx-st/deepafx_st/processors/dsp/compressor.py b/spaces/nateraw/deepafx-st/deepafx_st/processors/dsp/compressor.py
deleted file mode 100644
index ab515f9ec9a36f43de4a08f3069119b9d73ff1ed..0000000000000000000000000000000000000000
--- a/spaces/nateraw/deepafx-st/deepafx_st/processors/dsp/compressor.py
+++ /dev/null
@@ -1,177 +0,0 @@
-import sys
-import torch
-import numpy as np
-import scipy.signal
-from numba import jit
-
-from deepafx_st.processors.processor import Processor
-
-
-# Adapted from: https://github.com/drscotthawley/signaltrain/blob/master/signaltrain/audio.py
-@jit(nopython=True)
-def my_clip_min(
-    x: np.ndarray,
-    clip_min: float,
-):  # does the work of np.clip(), which numba doesn't support yet
-    # TODO: keep an eye on Numba PR https://github.com/numba/numba/pull/3468 that fixes this
-    inds = np.where(x < clip_min)
-    x[inds] = clip_min
-    return x
-
-
-@jit(nopython=True)
-def compressor(
-    x: np.ndarray,
-    sample_rate: float,
-    threshold: float = -24.0,
-    ratio: float = 2.0,
-    attack_time: float = 0.01,
-    release_time: float = 0.01,
-    knee_dB: float = 0.0,
-    makeup_gain_dB: float = 0.0,
-    dtype=np.float32,
-):
-    """
-
-    Args:
-        x (np.ndarray): Input signal.
-        sample_rate (float): Sample rate in Hz.
-        threshold (float): Threhold in dB.
-        ratio (float): Ratio (should be >=1 , i.e. ratio:1).
-        attack_time (float): Attack time in seconds.
-        release_time (float): Release time in seconds.
-        knee_dB (float): Knee.
-        makeup_gain_dB (float): Makeup Gain.
-        dtype (type): Output type. Default: np.float32
-
-    Returns:
-        y (np.ndarray): Output signal.
-
-    """
-    # print(f"dsp comp fs = {sample_rate}")
-
-    N = len(x)
-    dtype = x.dtype
-    y = np.zeros(N, dtype=dtype)
-
-    # Initialize separate attack and release times
-    # Where do these numbers come from
-    alpha_A = np.exp(-np.log(9) / (sample_rate * attack_time))
-    alpha_R = np.exp(-np.log(9) / (sample_rate * release_time))
-
-    # Turn the input signal into a uni-polar signal on the dB scale
-    x_G = 20 * np.log10(np.abs(x) + 1e-8)  # x_uni casts type
-
-    # Ensure there are no values of negative infinity
-    x_G = my_clip_min(x_G, -96)
-
-    # Static characteristics with knee
-    y_G = np.zeros(N, dtype=dtype)
-
-    # Below knee
-    idx = np.where((2 * (x_G - threshold)) < -knee_dB)
-    y_G[idx] = x_G[idx]
-
-    # At knee
-    idx = np.where((2 * np.abs(x_G - threshold)) <= knee_dB)
-    y_G[idx] = x_G[idx] + (
-        (1 / ratio) * (((x_G[idx] - threshold + knee_dB) / 2) ** 2)
-    ) / (2 * knee_dB)
-
-    # Above knee threshold
-    idx = np.where((2 * (x_G - threshold)) > knee_dB)
-    y_G[idx] = threshold + ((x_G[idx] - threshold) / ratio)
-
-    x_L = x_G - y_G
-
-    # this loop is slow but not vectorizable due to its cumulative, sequential nature. @autojit makes it fast(er).
-    y_L = np.zeros(N, dtype=dtype)
-    for n in range(1, N):
-        # smooth over the gainChange
-        if x_L[n] > y_L[n - 1]:  # attack mode
-            y_L[n] = (alpha_A * y_L[n - 1]) + ((1 - alpha_A) * x_L[n])
-        else:  # release
-            y_L[n] = (alpha_R * y_L[n - 1]) + ((1 - alpha_R) * x_L[n])
-
-    # Convert to linear amplitude scalar; i.e. map from dB to amplitude
-    lin_y_L = np.power(10.0, (-y_L / 20.0))
-    y = lin_y_L * x  # Apply linear amplitude to input sample
-
-    y *= np.power(10.0, makeup_gain_dB / 20.0)  # apply makeup gain
-
-    return y.astype(dtype)
-
-
-class Compressor(Processor):
-    def __init__(
-        self,
-        sample_rate,
-        max_threshold=0.0,
-        min_threshold=-80,
-        max_ratio=20.0,
-        min_ratio=1.0,
-        max_attack=0.1,
-        min_attack=0.0001,
-        max_release=1.0,
-        min_release=0.005,
-        max_knee=12.0,
-        min_knee=0.0,
-        max_mkgain=48.0,
-        min_mkgain=-48.0,
-        eps=1e-8,
-    ):
-        """ """
-        super().__init__()
-        self.sample_rate = sample_rate
-        self.eps = eps
-        self.ports = [
-            {
-                "name": "Threshold",
-                "min": min_threshold,
-                "max": max_threshold,
-                "default": -12.0,
-                "units": "",
-            },
-            {
-                "name": "Ratio",
-                "min": min_ratio,
-                "max": max_ratio,
-                "default": 2.0,
-                "units": "",
-            },
-            {
-                "name": "Attack Time",
-                "min": min_attack,
-                "max": max_attack,
-                "default": 0.001,
-                "units": "s",
-            },
-            {
-                "name": "Release Time",
-                "min": min_release,
-                "max": max_release,
-                "default": 0.045,
-                "units": "s",
-            },
-            {
-                "name": "Knee",
-                "min": min_knee,
-                "max": max_knee,
-                "default": 6.0,
-                "units": "dB",
-            },
-            {
-                "name": "Makeup Gain",
-                "min": min_mkgain,
-                "max": max_mkgain,
-                "default": 0.0,
-                "units": "dB",
-            },
-        ]
-
-        self.num_control_params = len(self.ports)
-        self.process_fn = compressor
-
-    def forward(self, x, p, sample_rate=24000, **kwargs):
-        "All processing in the forward is in numpy."
-        return self.run_series(x, p, sample_rate)
diff --git a/spaces/nateraw/yolov6/yolov6/core/evaler.py b/spaces/nateraw/yolov6/yolov6/core/evaler.py
deleted file mode 100644
index 569e4e3b037224c77e3a77da561adfd6bfb98d4e..0000000000000000000000000000000000000000
--- a/spaces/nateraw/yolov6/yolov6/core/evaler.py
+++ /dev/null
@@ -1,256 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding:utf-8 -*-
-import os
-from tqdm import tqdm
-import numpy as np
-import json
-import torch
-import yaml
-from pathlib import Path
-
-from pycocotools.coco import COCO
-from pycocotools.cocoeval import COCOeval
-
-from yolov6.data.data_load import create_dataloader
-from yolov6.utils.events import LOGGER, NCOLS
-from yolov6.utils.nms import non_max_suppression
-from yolov6.utils.checkpoint import load_checkpoint
-from yolov6.utils.torch_utils import time_sync, get_model_info
-
-'''
-python tools/eval.py --task 'train'/'val'/'speed'
-'''
-
-
-class Evaler:
-    def __init__(self,
-                 data,
-                 batch_size=32,
-                 img_size=640,
-                 conf_thres=0.001,
-                 iou_thres=0.65,
-                 device='',
-                 half=True,
-                 save_dir=''):
-        self.data = data
-        self.batch_size = batch_size
-        self.img_size = img_size
-        self.conf_thres = conf_thres
-        self.iou_thres = iou_thres
-        self.device = device
-        self.half = half
-        self.save_dir = save_dir
-
-    def init_model(self, model, weights, task):
-        if task != 'train':
-            model = load_checkpoint(weights, map_location=self.device)
-            self.stride = int(model.stride.max())
-            if self.device.type != 'cpu':
-                model(torch.zeros(1, 3, self.img_size, self.img_size).to(self.device).type_as(next(model.parameters())))
-            # switch to deploy
-            from yolov6.layers.common import RepVGGBlock
-            for layer in model.modules():
-                if isinstance(layer, RepVGGBlock):
-                    layer.switch_to_deploy()
-            LOGGER.info("Switch model to deploy modality.")
-            LOGGER.info("Model Summary: {}".format(get_model_info(model, self.img_size)))
-        model.half() if self.half else model.float()
-        return model
-
-    def init_data(self, dataloader, task):
-        '''Initialize dataloader.
-        Returns a dataloader for task val or speed.
-        '''
-        self.is_coco = self.data.get("is_coco", False)
-        self.ids = self.coco80_to_coco91_class() if self.is_coco else list(range(1000))
-        if task != 'train':
-            pad = 0.0 if task == 'speed' else 0.5
-            dataloader = create_dataloader(self.data[task if task in ('train', 'val', 'test') else 'val'],
-                                           self.img_size, self.batch_size, self.stride, check_labels=True, pad=pad, rect=True,
-                                           data_dict=self.data, task=task)[0]
-        return dataloader
-
-    def predict_model(self, model, dataloader, task):
-        '''Model prediction
-        Predicts the whole dataset and gets the prediced results and inference time.
-        '''
-        self.speed_result = torch.zeros(4, device=self.device)
-        pred_results = []
-        pbar = tqdm(dataloader, desc="Inferencing model in val datasets.", ncols=NCOLS)
-        for imgs, targets, paths, shapes in pbar:
-            # pre-process
-            t1 = time_sync()
-            imgs = imgs.to(self.device, non_blocking=True)
-            imgs = imgs.half() if self.half else imgs.float()
-            imgs /= 255
-            self.speed_result[1] += time_sync() - t1  # pre-process time
-
-            # Inference
-            t2 = time_sync()
-            outputs = model(imgs)
-            self.speed_result[2] += time_sync() - t2  # inference time
-
-            # post-process
-            t3 = time_sync()
-            outputs = non_max_suppression(outputs, self.conf_thres, self.iou_thres, multi_label=True)
-            self.speed_result[3] += time_sync() - t3  # post-process time
-            self.speed_result[0] += len(outputs)
-
-            # save result
-            pred_results.extend(self.convert_to_coco_format(outputs, imgs, paths, shapes, self.ids))
-        return pred_results
-
-    def eval_model(self, pred_results, model, dataloader, task):
-        '''Evaluate models
-        For task speed, this function only evaluates the speed of model and outputs inference time.
-        For task val, this function evaluates the speed and mAP by pycocotools, and returns
-        inference time and mAP value.
-        '''
-        LOGGER.info(f'\nEvaluating speed.')
-        self.eval_speed(task)
-
-        LOGGER.info(f'\nEvaluating mAP by pycocotools.')
-        if task != 'speed' and len(pred_results):
-            if 'anno_path' in self.data:
-                anno_json = self.data['anno_path']
-            else:
-                # generated coco format labels in dataset initialization
-                dataset_root = os.path.dirname(os.path.dirname(self.data['val']))
-                base_name = os.path.basename(self.data['val'])
-                anno_json = os.path.join(dataset_root, 'annotations', f'instances_{base_name}.json')
-            pred_json = os.path.join(self.save_dir, "predictions.json")
-            LOGGER.info(f'Saving {pred_json}...')
-            with open(pred_json, 'w') as f:
-                json.dump(pred_results, f)
-
-            anno = COCO(anno_json)
-            pred = anno.loadRes(pred_json)
-            cocoEval = COCOeval(anno, pred, 'bbox')
-            if self.is_coco:
-                imgIds = [int(os.path.basename(x).split(".")[0])
-                            for x in dataloader.dataset.img_paths]
-                cocoEval.params.imgIds = imgIds
-            cocoEval.evaluate()
-            cocoEval.accumulate()
-            cocoEval.summarize()
-            map, map50 = cocoEval.stats[:2]  # update results (mAP@0.5:0.95, mAP@0.5)
-            # Return results
-            model.float()  # for training
-            if task != 'train':
-                LOGGER.info(f"Results saved to {self.save_dir}")
-            return (map50, map)
-        return (0.0, 0.0)
-
-    def eval_speed(self, task):
-        '''Evaluate model inference speed.'''
-        if task != 'train':
-            n_samples = self.speed_result[0].item()
-            pre_time, inf_time, nms_time = 1000 * self.speed_result[1:].cpu().numpy() / n_samples
-            for n, v in zip(["pre-process", "inference", "NMS"],[pre_time, inf_time, nms_time]):
-                LOGGER.info("Average {} time: {:.2f} ms".format(n, v))
-
-    def box_convert(self, x):
-        # Convert boxes with shape [n, 4] from [x1, y1, x2, y2] to [x, y, w, h] where x1y1=top-left, x2y2=bottom-right
-        y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
-        y[:, 0] = (x[:, 0] + x[:, 2]) / 2  # x center
-        y[:, 1] = (x[:, 1] + x[:, 3]) / 2  # y center
-        y[:, 2] = x[:, 2] - x[:, 0]  # width
-        y[:, 3] = x[:, 3] - x[:, 1]  # height
-        return y
-
-    def scale_coords(self, img1_shape, coords, img0_shape, ratio_pad=None):
-        # Rescale coords (xyxy) from img1_shape to img0_shape
-        if ratio_pad is None:  # calculate from img0_shape
-            gain = min(img1_shape[0] / img0_shape[0], img1_shape[1] / img0_shape[1])  # gain  = old / new
-            pad = (img1_shape[1] - img0_shape[1] * gain) / 2, (img1_shape[0] - img0_shape[0] * gain) / 2  # wh padding
-        else:
-            gain = ratio_pad[0][0]
-            pad = ratio_pad[1]
-
-        coords[:, [0, 2]] -= pad[0]  # x padding
-        coords[:, [1, 3]] -= pad[1]  # y padding
-        coords[:, :4] /= gain
-        if isinstance(coords, torch.Tensor):  # faster individually
-            coords[:, 0].clamp_(0, img0_shape[1])  # x1
-            coords[:, 1].clamp_(0, img0_shape[0])  # y1
-            coords[:, 2].clamp_(0, img0_shape[1])  # x2
-            coords[:, 3].clamp_(0, img0_shape[0])  # y2
-        else:  # np.array (faster grouped)
-            coords[:, [0, 2]] = coords[:, [0, 2]].clip(0, img0_shape[1])  # x1, x2
-            coords[:, [1, 3]] = coords[:, [1, 3]].clip(0, img0_shape[0])  # y1, y2
-        return coords
-
-    def convert_to_coco_format(self, outputs, imgs, paths, shapes, ids):
-        pred_results = []
-        for i, pred in enumerate(outputs):
-            if len(pred) == 0:
-                continue
-            path, shape = Path(paths[i]), shapes[i][0]
-            self.scale_coords(imgs[i].shape[1:], pred[:, :4], shape, shapes[i][1])
-            image_id = int(path.stem) if path.stem.isnumeric() else path.stem
-            bboxes = self.box_convert(pred[:, 0:4])
-            bboxes[:, :2] -= bboxes[:, 2:] / 2
-            cls = pred[:, 5]
-            scores = pred[:, 4]
-            for ind in range(pred.shape[0]):
-                category_id = ids[int(cls[ind])]
-                bbox = [round(x, 3) for x in bboxes[ind].tolist()]
-                score = round(scores[ind].item(), 5)
-                pred_data = {
-                    "image_id": image_id,
-                    "category_id": category_id,
-                    "bbox": bbox,
-                    "score": score
-                }
-                pred_results.append(pred_data)
-        return pred_results
-
-    @staticmethod
-    def check_task(task):
-        if task not in ['train','val','speed']:
-            raise Exception("task argument error: only support 'train' / 'val' / 'speed' task.")
-
-    @staticmethod
-    def reload_thres(conf_thres, iou_thres, task):
-        '''Sets conf and iou threshold for task val/speed'''
-        if task != 'train':
-            if task == 'val':
-                conf_thres = 0.001
-            if task == 'speed':
-                conf_thres = 0.25
-                iou_thres = 0.45
-        return conf_thres, iou_thres
-
-    @staticmethod
-    def reload_device(device, model, task):
-        # device = 'cpu' or '0' or '0,1,2,3'
-        if task == 'train':
-            device = next(model.parameters()).device
-        else:
-            if device == 'cpu':
-                os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
-            elif device:
-                os.environ['CUDA_VISIBLE_DEVICES'] = device
-                assert torch.cuda.is_available()
-            cuda = device != 'cpu' and torch.cuda.is_available()
-            device = torch.device('cuda:0' if cuda else 'cpu')
-        return device
-
-    @staticmethod
-    def reload_dataset(data):
-        with open(data, errors='ignore') as yaml_file:
-            data = yaml.safe_load(yaml_file)
-        val = data.get('val')
-        if not os.path.exists(val):
-            raise Exception('Dataset not found.')
-        return data
-
-    @staticmethod
-    def coco80_to_coco91_class():  # converts 80-index (val2014) to 91-index (paper)
-    # https://tech.amikelive.com/node-718/what-object-categories-labels-are-in-coco-dataset/
-        x = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 19, 20,
-            21, 22, 23, 24, 25, 27, 28, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40,
-            41, 42, 43, 44, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58,
-            59, 60, 61, 62, 63, 64, 65, 67, 70, 72, 73, 74, 75, 76, 77, 78, 79,
-            80, 81, 82, 84, 85, 86, 87, 88, 89, 90]
-        return x
diff --git a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Sniper Elite V2 - AviaRa - Fitgirl Repack.md b/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Sniper Elite V2 - AviaRa - Fitgirl Repack.md
deleted file mode 100644
index 5c2d2bbdbf6b2d5c302133fb4aff86088d818ecd..0000000000000000000000000000000000000000
--- a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Sniper Elite V2 - AviaRa - Fitgirl Repack.md	
+++ /dev/null
@@ -1,25 +0,0 @@
-<br />
-<h1>How to Download and Install Sniper Elite V2 -=AviaRa=- Fitgirl Repack</h1>
-<p>If you are looking for a thrilling and realistic sniper game, you might want to check out Sniper Elite V2 -=AviaRa=- Fitgirl Repack. This is a repack of the original Sniper Elite V2 game, which was released in 2012 and received positive reviews from critics and players alike. Sniper Elite V2 is a third-person shooter that puts you in the role of Karl Fairburne, an elite sniper who is sent to Berlin during the final days of World War II. Your mission is to prevent the Nazi V2 rocket technology from falling into the hands of the Soviet army, by assassinating key scientists and officers, sabotaging facilities, and infiltrating enemy lines.</p>
-<p>Sniper Elite V2 -=AviaRa=- Fitgirl Repack is a compressed version of the game that includes all the DLCs and updates, as well as improved graphics and performance. The repack size is only 2.9 GB, compared to the original 7 GB, which means you can download and install it faster and easier. The repack also features a multilingual interface and subtitles, so you can enjoy the game in your preferred language. In this article, we will show you how to download and install Sniper Elite V2 -=AviaRa=- Fitgirl Repack on your PC.</p>
-<h2>Sniper Elite V2 - AviaRa - Fitgirl Repack</h2><br /><p><b><b>DOWNLOAD</b> &#10026; <a href="https://urlcod.com/2uIbWX">https://urlcod.com/2uIbWX</a></b></p><br /><br />
-<h2>Step 1: Download Sniper Elite V2 -=AviaRa=- Fitgirl Repack</h2>
-<p>The first step is to download Sniper Elite V2 -=AviaRa=- Fitgirl Repack from a reliable source. You can use a torrent client or a direct link to get the repack file. Here are some of the options you can choose from:</p>
-<ul>
-<li><a href="https://www.reddit.com/r/CrackWatch/comments/6ldjn3/sniper_elite_v2_v113_5_dlcs_multi7_fitgirl_repack/">Reddit</a>: This is a post on r/CrackWatch that provides a magnet link and a torrent file for the repack. You can also find comments from other users who have downloaded and installed the repack successfully.</li>
-<li><a href="https://fitgirl-repacks-site.org/sniper-elite-v2-remastered-download-torrent-repack/">Fitgirl Repacks Site</a>: This is the official website of Fitgirl, the creator of the repack. You can find detailed information about the repack features, installation instructions, screenshots, and download mirrors. You can use 1337x, KAT, RuTor, Tapochek.net, MultiUpload, Upera, or Google Drive to get the repack file.</li>
-<li><a href="https://www.youtube.com/watch?v=VlZb0LpS-3Q">YouTube</a>: This is a video tutorial that shows you how to download and install Sniper Elite V2 -=AviaRa=- Fitgirl Repack using Utorrent. You can also watch gameplay footage and see how the game runs on different settings.</li>
-</ul>
-<p>Once you have downloaded the repack file, you need to extract it using WinRAR or 7-Zip. You will get a folder named "Sniper.Elite.V2.Complete-PLAZA" that contains all the game files.</p>
-<h2>Step 2: Install Sniper Elite V2 -=AviaRa=- Fitgirl Repack</h2>
-<p>The next step is to install Sniper Elite V2 -=AviaRa=- Fitgirl Repack on your PC. To do this, you need to follow these steps:</p>
-<ol>
-<li>Open the folder "Sniper.Elite.V2.Complete-PLAZA" and run "setup.exe" as administrator.</li>
-<li>Select your language and destination folder for the game installation.</li>
-<li>Wait for the installation process to finish. It may take 7-15 minutes depending on your system.</li>
-<li>After the installation is done, run "Language Selector.exe" in the game root folder to change the game language if needed.</li>
-<li>Run "SniperEliteV2.exe" in the game root folder to launch the game.</li>
-</ol>
-<p>Congratulations!</p> 7b8c122e87<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/nikitaPDL2023/assignment4/detectron2/tools/README.md b/spaces/nikitaPDL2023/assignment4/detectron2/tools/README.md
deleted file mode 100644
index 0b40d5319c0838fdaa22bc6a10ef0d88bc6578ed..0000000000000000000000000000000000000000
--- a/spaces/nikitaPDL2023/assignment4/detectron2/tools/README.md
+++ /dev/null
@@ -1,49 +0,0 @@
-
-This directory contains a few example scripts that demonstrate features of detectron2.
-
-
-* `train_net.py`
-
-An example training script that's made to train builtin models of detectron2.
-
-For usage, see [GETTING_STARTED.md](../GETTING_STARTED.md).
-
-* `plain_train_net.py`
-
-Similar to `train_net.py`, but implements a training loop instead of using `Trainer`.
-This script includes fewer features but it may be more friendly to hackers.
-
-* `benchmark.py`
-
-Benchmark the training speed, inference speed or data loading speed of a given config.
-
-Usage:
-```
-python benchmark.py --config-file config.yaml --task train/eval/data [optional DDP flags]
-```
-
-* `analyze_model.py`
-
-Analyze FLOPs, parameters, activations of a detectron2 model.  See its `--help` for usage.
-
-* `visualize_json_results.py`
-
-Visualize the json instance detection/segmentation results dumped by `COCOEvalutor` or `LVISEvaluator`
-
-Usage:
-```
-python visualize_json_results.py --input x.json --output dir/ --dataset coco_2017_val
-```
-If not using a builtin dataset, you'll need your own script or modify this script.
-
-* `visualize_data.py`
-
-Visualize ground truth raw annotations or training data (after preprocessing/augmentations).
-
-Usage:
-```
-python visualize_data.py --config-file config.yaml --source annotation/dataloader --output-dir dir/ [--show]
-```
-
-NOTE: the script does not stop by itself when using `--source dataloader` because a training
-dataloader is usually infinite.
diff --git a/spaces/nikitalokhmachev-ai/corner-detection/README.md b/spaces/nikitalokhmachev-ai/corner-detection/README.md
deleted file mode 100644
index 8d32fe99069cce974697d5d1a7e8e3ef03b8876a..0000000000000000000000000000000000000000
--- a/spaces/nikitalokhmachev-ai/corner-detection/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Corner Detection
-emoji: 🐌
-colorFrom: indigo
-colorTo: pink
-sdk: gradio
-sdk_version: 3.18.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/nomic-ai/samsum/index.html b/spaces/nomic-ai/samsum/index.html
deleted file mode 100644
index 83469e3a65a44bfb007e636984d69d6e77bc6bde..0000000000000000000000000000000000000000
--- a/spaces/nomic-ai/samsum/index.html
+++ /dev/null
@@ -1,42 +0,0 @@
-<html>
-
-<head>
-  <title>samsum</title>
-  <style>
-    body {
-      font-family: Arial, sans-serif;
-      background-color: #f0f0f0;
-      display: flex;
-      justify-content: center;
-      align-items: center;
-      height: 100vh;
-      margin: 0;
-      padding: 0;
-      color: #333;
-    }
-
-    .iframe-container {
-      border: 1px solid #ccc;
-      border-radius: 10px;
-      overflow: hidden;
-      width: 80%;
-      height: 80%;
-      box-shadow: 0 0 10px rgba(0, 0, 0, 0.1);
-    }
-
-    iframe {
-      width: 100%;
-      height: 100%;
-      border: none;
-    }
-  </style>
-</head>
-
-<body>
-  <div class="iframe-container">
-    <iframe src="https://atlas.nomic.ai/map/47ab552b-be01-43d8-8e4b-76f8895a8196/19bb24a2-81e6-47d3-a0a4-fee7547f0671" allow="clipboard-read; clipboard-write"
-      title="Nomic Atlas"></iframe>
-  </div>
-</body>
-
-</html>
\ No newline at end of file
diff --git a/spaces/nota-ai/compressed-wav2lip/Dockerfile b/spaces/nota-ai/compressed-wav2lip/Dockerfile
deleted file mode 100644
index 3cc3c5eaa469b73ebe3b80a4647aefe29aa74831..0000000000000000000000000000000000000000
--- a/spaces/nota-ai/compressed-wav2lip/Dockerfile
+++ /dev/null
@@ -1,7 +0,0 @@
-FROM nvcr.io/nvidia/pytorch:22.03-py3
-
-ARG DEBIAN_FRONTEND=noninteractive
-RUN apt-get update
-RUN apt-get install ffmpeg libsm6 libxext6 tmux git -y
-
-WORKDIR /workspace
diff --git a/spaces/ntt123/WaveGRU-Text-To-Speech/tacotron.py b/spaces/ntt123/WaveGRU-Text-To-Speech/tacotron.py
deleted file mode 100644
index 2671bcf9a219ad2007200278347df43e0cddde17..0000000000000000000000000000000000000000
--- a/spaces/ntt123/WaveGRU-Text-To-Speech/tacotron.py
+++ /dev/null
@@ -1,451 +0,0 @@
-"""
-Tacotron + stepwise monotonic attention
-"""
-
-import jax
-import jax.numpy as jnp
-import pax
-
-
-def conv_block(in_ft, out_ft, kernel_size, activation_fn, use_dropout):
-    """
-    Conv >> LayerNorm >> activation >> Dropout
-    """
-    f = pax.Sequential(
-        pax.Conv1D(in_ft, out_ft, kernel_size, with_bias=False),
-        pax.LayerNorm(out_ft, -1, True, True),
-    )
-    if activation_fn is not None:
-        f >>= activation_fn
-    if use_dropout:
-        f >>= pax.Dropout(0.5)
-    return f
-
-
-class HighwayBlock(pax.Module):
-    """
-    Highway block
-    """
-
-    def __init__(self, dim: int) -> None:
-        super().__init__()
-        self.dim = dim
-        self.fc = pax.Linear(dim, 2 * dim)
-
-    def __call__(self, x: jnp.ndarray) -> jnp.ndarray:
-        t, h = jnp.split(self.fc(x), 2, axis=-1)
-        t = jax.nn.sigmoid(t - 1.0)  # bias toward keeping x
-        h = jax.nn.relu(h)
-        x = x * (1.0 - t) + h * t
-        return x
-
-
-class BiGRU(pax.Module):
-    """
-    Bidirectional GRU
-    """
-
-    def __init__(self, dim):
-        super().__init__()
-
-        self.rnn_fwd = pax.GRU(dim, dim)
-        self.rnn_bwd = pax.GRU(dim, dim)
-
-    def __call__(self, x, reset_masks):
-        N = x.shape[0]
-        x_fwd = x
-        x_bwd = jnp.flip(x, axis=1)
-        x_fwd_states = self.rnn_fwd.initial_state(N)
-        x_bwd_states = self.rnn_bwd.initial_state(N)
-        x_fwd_states, x_fwd = pax.scan(
-            self.rnn_fwd, x_fwd_states, x_fwd, time_major=False
-        )
-
-        reset_masks = jnp.flip(reset_masks, axis=1)
-        x_bwd_states0 = x_bwd_states
-
-        def rnn_reset_core(prev, inputs):
-            x, reset_mask = inputs
-
-            def reset_state(x0, xt):
-                return jnp.where(reset_mask, x0, xt)
-
-            state, _ = self.rnn_bwd(prev, x)
-            state = jax.tree_map(reset_state, x_bwd_states0, state)
-            return state, state.hidden
-
-        x_bwd_states, x_bwd = pax.scan(
-            rnn_reset_core, x_bwd_states, (x_bwd, reset_masks), time_major=False
-        )
-        x_bwd = jnp.flip(x_bwd, axis=1)
-        x = jnp.concatenate((x_fwd, x_bwd), axis=-1)
-        return x
-
-
-class CBHG(pax.Module):
-    """
-    Conv Bank >> Highway net >> GRU
-    """
-
-    def __init__(self, dim):
-        super().__init__()
-        self.convs = [conv_block(dim, dim, i, jax.nn.relu, False) for i in range(1, 17)]
-        self.conv_projection_1 = conv_block(16 * dim, dim, 3, jax.nn.relu, False)
-        self.conv_projection_2 = conv_block(dim, dim, 3, None, False)
-
-        self.highway = pax.Sequential(
-            HighwayBlock(dim), HighwayBlock(dim), HighwayBlock(dim), HighwayBlock(dim)
-        )
-        self.rnn = BiGRU(dim)
-
-    def __call__(self, x, x_mask):
-        conv_input = x * x_mask
-        fts = [f(conv_input) for f in self.convs]
-        residual = jnp.concatenate(fts, axis=-1)
-        residual = pax.max_pool(residual, 2, 1, "SAME", -1)
-        residual = self.conv_projection_1(residual * x_mask)
-        residual = self.conv_projection_2(residual * x_mask)
-        x = x + residual
-        x = self.highway(x)
-        x = self.rnn(x * x_mask, reset_masks=1 - x_mask)
-        return x * x_mask
-
-
-class PreNet(pax.Module):
-    """
-    Linear >> relu >> dropout >> Linear >> relu >> dropout
-    """
-
-    def __init__(self, input_dim, hidden_dim, output_dim, always_dropout=True):
-        super().__init__()
-        self.fc1 = pax.Linear(input_dim, hidden_dim)
-        self.fc2 = pax.Linear(hidden_dim, output_dim)
-        self.rng_seq = pax.RngSeq()
-        self.always_dropout = always_dropout
-
-    def __call__(self, x, k1=None, k2=None):
-        x = self.fc1(x)
-        x = jax.nn.relu(x)
-        if self.always_dropout or self.training:
-            if k1 is None:
-                k1 = self.rng_seq.next_rng_key()
-            x = pax.dropout(k1, 0.5, x)
-        x = self.fc2(x)
-        x = jax.nn.relu(x)
-        if self.always_dropout or self.training:
-            if k2 is None:
-                k2 = self.rng_seq.next_rng_key()
-            x = pax.dropout(k2, 0.5, x)
-        return x
-
-
-class Tacotron(pax.Module):
-    """
-    Tacotron TTS model.
-
-    It uses stepwise monotonic attention for robust attention.
-    """
-
-    def __init__(
-        self,
-        mel_dim: int,
-        attn_bias,
-        rr,
-        max_rr,
-        mel_min,
-        sigmoid_noise,
-        pad_token,
-        prenet_dim,
-        attn_hidden_dim,
-        attn_rnn_dim,
-        rnn_dim,
-        postnet_dim,
-        text_dim,
-    ):
-        """
-        New Tacotron model
-
-        Args:
-            mel_dim (int): dimension of log mel-spectrogram features.
-            attn_bias (float): control how "slow" the attention will
-                move forward at initialization.
-            rr (int): the reduction factor.
-                Number of predicted frame at each time step. Default is 2.
-            max_rr (int): max value of rr.
-            mel_min (float): the minimum value of mel features.
-                The <go> frame is filled by `log(mel_min)` values.
-            sigmoid_noise (float): the variance of gaussian noise added
-                to attention scores in training.
-            pad_token (int): the pad value at the end of text sequences.
-            prenet_dim (int): dimension of prenet output.
-            attn_hidden_dim (int): dimension of attention hidden vectors.
-            attn_rnn_dim (int): number of cells in the attention RNN.
-            rnn_dim (int): number of cells in the decoder RNNs.
-            postnet_dim (int): number of features in the postnet convolutions.
-            text_dim (int): dimension of text embedding vectors.
-        """
-        super().__init__()
-        self.text_dim = text_dim
-        assert rr <= max_rr
-        self.rr = rr
-        self.max_rr = max_rr
-        self.mel_dim = mel_dim
-        self.mel_min = mel_min
-        self.sigmoid_noise = sigmoid_noise
-        self.pad_token = pad_token
-        self.prenet_dim = prenet_dim
-
-        # encoder submodules
-        self.encoder_embed = pax.Embed(256, text_dim)
-        self.encoder_pre_net = PreNet(text_dim, 256, prenet_dim, always_dropout=True)
-        self.encoder_cbhg = CBHG(prenet_dim)
-
-        # random key generator
-        self.rng_seq = pax.RngSeq()
-
-        # pre-net
-        self.decoder_pre_net = PreNet(mel_dim, 256, prenet_dim, always_dropout=True)
-
-        # decoder submodules
-        self.attn_rnn = pax.LSTM(prenet_dim + prenet_dim * 2, attn_rnn_dim)
-        self.text_key_fc = pax.Linear(prenet_dim * 2, attn_hidden_dim, with_bias=True)
-        self.attn_query_fc = pax.Linear(attn_rnn_dim, attn_hidden_dim, with_bias=False)
-
-        self.attn_V = pax.Linear(attn_hidden_dim, 1, with_bias=False)
-        self.attn_V_weight_norm = jnp.array(1.0 / jnp.sqrt(attn_hidden_dim))
-        self.attn_V_bias = jnp.array(attn_bias)
-        self.attn_log = jnp.zeros((1,))
-        self.decoder_input = pax.Linear(attn_rnn_dim + 2 * prenet_dim, rnn_dim)
-        self.decoder_rnn1 = pax.LSTM(rnn_dim, rnn_dim)
-        self.decoder_rnn2 = pax.LSTM(rnn_dim, rnn_dim)
-        # mel + end-of-sequence token
-        self.output_fc = pax.Linear(rnn_dim, (mel_dim + 1) * max_rr, with_bias=True)
-
-        # post-net
-        self.post_net = pax.Sequential(
-            conv_block(mel_dim, postnet_dim, 5, jax.nn.tanh, True),
-            conv_block(postnet_dim, postnet_dim, 5, jax.nn.tanh, True),
-            conv_block(postnet_dim, postnet_dim, 5, jax.nn.tanh, True),
-            conv_block(postnet_dim, postnet_dim, 5, jax.nn.tanh, True),
-            conv_block(postnet_dim, mel_dim, 5, None, True),
-        )
-
-    parameters = pax.parameters_method("attn_V_weight_norm", "attn_V_bias")
-
-    def encode_text(self, text: jnp.ndarray) -> jnp.ndarray:
-        """
-        Encode text to a sequence of real vectors
-        """
-        N, L = text.shape
-        text_mask = (text != self.pad_token)[..., None]
-        x = self.encoder_embed(text)
-        x = self.encoder_pre_net(x)
-        x = self.encoder_cbhg(x, text_mask)
-        return x
-
-    def go_frame(self, batch_size: int) -> jnp.ndarray:
-        """
-        return the go frame
-        """
-        return jnp.ones((batch_size, self.mel_dim)) * jnp.log(self.mel_min)
-
-    def decoder_initial_state(self, N: int, L: int):
-        """
-        setup decoder initial state
-        """
-        attn_context = jnp.zeros((N, self.prenet_dim * 2))
-        attn_pr = jax.nn.one_hot(
-            jnp.zeros((N,), dtype=jnp.int32), num_classes=L, axis=-1
-        )
-
-        attn_state = (self.attn_rnn.initial_state(N), attn_context, attn_pr)
-        decoder_rnn_states = (
-            self.decoder_rnn1.initial_state(N),
-            self.decoder_rnn2.initial_state(N),
-        )
-        return attn_state, decoder_rnn_states
-
-    def monotonic_attention(self, prev_state, inputs, envs):
-        """
-        Stepwise monotonic attention
-        """
-        attn_rnn_state, attn_context, prev_attn_pr = prev_state
-        x, attn_rng_key = inputs
-        text, text_key = envs
-        attn_rnn_input = jnp.concatenate((x, attn_context), axis=-1)
-        attn_rnn_state, attn_rnn_output = self.attn_rnn(attn_rnn_state, attn_rnn_input)
-        attn_query_input = attn_rnn_output
-        attn_query = self.attn_query_fc(attn_query_input)
-        attn_hidden = jnp.tanh(attn_query[:, None, :] + text_key)
-        score = self.attn_V(attn_hidden)
-        score = jnp.squeeze(score, axis=-1)
-        weight_norm = jnp.linalg.norm(self.attn_V.weight)
-        score = score * (self.attn_V_weight_norm / weight_norm)
-        score = score + self.attn_V_bias
-        noise = jax.random.normal(attn_rng_key, score.shape) * self.sigmoid_noise
-        pr_stay = jax.nn.sigmoid(score + noise)
-        pr_move = 1.0 - pr_stay
-        pr_new_location = pr_move * prev_attn_pr
-        pr_new_location = jnp.pad(
-            pr_new_location[:, :-1], ((0, 0), (1, 0)), constant_values=0
-        )
-        attn_pr = pr_stay * prev_attn_pr + pr_new_location
-        attn_context = jnp.einsum("NL,NLD->ND", attn_pr, text)
-        new_state = (attn_rnn_state, attn_context, attn_pr)
-        return new_state, attn_rnn_output
-
-    def zoneout_lstm(self, lstm_core, rng_key, zoneout_pr=0.1):
-        """
-        Return a zoneout lstm core.
-
-        It will zoneout the new hidden states and keep the new cell states unchanged.
-        """
-
-        def core(state, x):
-            new_state, _ = lstm_core(state, x)
-            h_old = state.hidden
-            h_new = new_state.hidden
-            mask = jax.random.bernoulli(rng_key, zoneout_pr, h_old.shape)
-            h_new = h_old * mask + h_new * (1.0 - mask)
-            return pax.LSTMState(h_new, new_state.cell), h_new
-
-        return core
-
-    def decoder_step(
-        self,
-        attn_state,
-        decoder_rnn_states,
-        rng_key,
-        mel,
-        text,
-        text_key,
-        call_pre_net=False,
-    ):
-        """
-        One decoder step
-        """
-        if call_pre_net:
-            k1, k2, zk1, zk2, rng_key, rng_key_next = jax.random.split(rng_key, 6)
-            mel = self.decoder_pre_net(mel, k1, k2)
-        else:
-            zk1, zk2, rng_key, rng_key_next = jax.random.split(rng_key, 4)
-        attn_inputs = (mel, rng_key)
-        attn_envs = (text, text_key)
-        attn_state, attn_rnn_output = self.monotonic_attention(
-            attn_state, attn_inputs, attn_envs
-        )
-        (_, attn_context, attn_pr) = attn_state
-        (decoder_rnn_state1, decoder_rnn_state2) = decoder_rnn_states
-        decoder_rnn1_input = jnp.concatenate((attn_rnn_output, attn_context), axis=-1)
-        decoder_rnn1_input = self.decoder_input(decoder_rnn1_input)
-        decoder_rnn1 = self.zoneout_lstm(self.decoder_rnn1, zk1)
-        decoder_rnn_state1, decoder_rnn_output1 = decoder_rnn1(
-            decoder_rnn_state1, decoder_rnn1_input
-        )
-        decoder_rnn2_input = decoder_rnn1_input + decoder_rnn_output1
-        decoder_rnn2 = self.zoneout_lstm(self.decoder_rnn2, zk2)
-        decoder_rnn_state2, decoder_rnn_output2 = decoder_rnn2(
-            decoder_rnn_state2, decoder_rnn2_input
-        )
-        x = decoder_rnn1_input + decoder_rnn_output1 + decoder_rnn_output2
-        decoder_rnn_states = (decoder_rnn_state1, decoder_rnn_state2)
-        return attn_state, decoder_rnn_states, rng_key_next, x, attn_pr[0]
-
-    @jax.jit
-    def inference_step(
-        self, attn_state, decoder_rnn_states, rng_key, mel, text, text_key
-    ):
-        """one inference step"""
-        attn_state, decoder_rnn_states, rng_key, x, _ = self.decoder_step(
-            attn_state,
-            decoder_rnn_states,
-            rng_key,
-            mel,
-            text,
-            text_key,
-            call_pre_net=True,
-        )
-        x = self.output_fc(x)
-        N, D2 = x.shape
-        x = jnp.reshape(x, (N, self.max_rr, D2 // self.max_rr))
-        x = x[:, : self.rr, :]
-        x = jnp.reshape(x, (N, self.rr, -1))
-        mel = x[..., :-1]
-        eos_logit = x[..., -1]
-        eos_pr = jax.nn.sigmoid(eos_logit[0, -1])
-        eos_pr = jnp.where(eos_pr < 0.1, 0.0, eos_pr)
-        rng_key, eos_rng_key = jax.random.split(rng_key)
-        eos = jax.random.bernoulli(eos_rng_key, p=eos_pr)
-        return attn_state, decoder_rnn_states, rng_key, (mel, eos)
-
-    def inference(self, text, seed=42, max_len=1000):
-        """
-        text to mel
-        """
-        text = self.encode_text(text)
-        text_key = self.text_key_fc(text)
-        N, L, D = text.shape
-        assert N == 1
-        mel = self.go_frame(N)
-
-        attn_state, decoder_rnn_states = self.decoder_initial_state(N, L)
-        rng_key = jax.random.PRNGKey(seed)
-        mels = []
-        count = 0
-        while True:
-            count = count + 1
-            attn_state, decoder_rnn_states, rng_key, (mel, eos) = self.inference_step(
-                attn_state, decoder_rnn_states, rng_key, mel, text, text_key
-            )
-            mels.append(mel)
-            if eos.item() or count > max_len:
-                break
-
-            mel = mel[:, -1, :]
-
-        mels = jnp.concatenate(mels, axis=1)
-        mel = mel + self.post_net(mel)
-        return mels
-
-    def decode(self, mel, text):
-        """
-        Attention mechanism + Decoder
-        """
-        text_key = self.text_key_fc(text)
-
-        def scan_fn(prev_states, inputs):
-            attn_state, decoder_rnn_states = prev_states
-            x, rng_key = inputs
-            attn_state, decoder_rnn_states, _, output, attn_pr = self.decoder_step(
-                attn_state, decoder_rnn_states, rng_key, x, text, text_key
-            )
-            states = (attn_state, decoder_rnn_states)
-            return states, (output, attn_pr)
-
-        N, L, D = text.shape
-        decoder_states = self.decoder_initial_state(N, L)
-        rng_keys = self.rng_seq.next_rng_key(mel.shape[1])
-        rng_keys = jnp.stack(rng_keys, axis=1)
-        decoder_states, (x, attn_log) = pax.scan(
-            scan_fn,
-            decoder_states,
-            (mel, rng_keys),
-            time_major=False,
-        )
-        self.attn_log = attn_log
-        del decoder_states
-        x = self.output_fc(x)
-
-        N, T2, D2 = x.shape
-        x = jnp.reshape(x, (N, T2, self.max_rr, D2 // self.max_rr))
-        x = x[:, :, : self.rr, :]
-        x = jnp.reshape(x, (N, T2 * self.rr, -1))
-        mel = x[..., :-1]
-        eos = x[..., -1]
-        return mel, eos
-
-    def __call__(self, mel: jnp.ndarray, text: jnp.ndarray):
-        text = self.encode_text(text)
-        mel = self.decoder_pre_net(mel)
-        mel, eos = self.decode(mel, text)
-        return mel, mel + self.post_net(mel), eos
diff --git a/spaces/oguzakif/video-object-remover/SiamMask/experiments/siammask_sharp/custom.py b/spaces/oguzakif/video-object-remover/SiamMask/experiments/siammask_sharp/custom.py
deleted file mode 100644
index c42fbb47a72302cb00cd56de293f0efa6e6d353a..0000000000000000000000000000000000000000
--- a/spaces/oguzakif/video-object-remover/SiamMask/experiments/siammask_sharp/custom.py
+++ /dev/null
@@ -1,191 +0,0 @@
-from SiamMask.models.siammask_sharp import SiamMask
-from SiamMask.models.features import MultiStageFeature
-from SiamMask.models.rpn import RPN, DepthCorr
-from SiamMask.models.mask import Mask
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from SiamMask.utils.load_helper import load_pretrain
-from SiamMask.experiments.siammask_sharp.resnet import resnet50
-
-
-class ResDownS(nn.Module):
-    def __init__(self, inplane, outplane):
-        super(ResDownS, self).__init__()
-        self.downsample = nn.Sequential(
-                nn.Conv2d(inplane, outplane, kernel_size=1, bias=False),
-                nn.BatchNorm2d(outplane))
-
-    def forward(self, x):
-        x = self.downsample(x)
-        if x.size(3) < 20:
-            l = 4
-            r = -4
-            x = x[:, :, l:r, l:r]
-        return x
-
-
-class ResDown(MultiStageFeature):
-    def __init__(self, pretrain=False):
-        super(ResDown, self).__init__()
-        self.features = resnet50(layer3=True, layer4=False)
-        if pretrain:
-            load_pretrain(self.features, 'resnet.model')
-
-        self.downsample = ResDownS(1024, 256)
-
-        self.layers = [self.downsample, self.features.layer2, self.features.layer3]
-        self.train_nums = [1, 3]
-        self.change_point = [0, 0.5]
-
-        self.unfix(0.0)
-
-    def param_groups(self, start_lr, feature_mult=1):
-        lr = start_lr * feature_mult
-
-        def _params(module, mult=1):
-            params = list(filter(lambda x:x.requires_grad, module.parameters()))
-            if len(params):
-                return [{'params': params, 'lr': lr * mult}]
-            else:
-                return []
-
-        groups = []
-        groups += _params(self.downsample)
-        groups += _params(self.features, 0.1)
-        return groups
-
-    def forward(self, x):
-        output = self.features(x)
-        p3 = self.downsample(output[-1])
-        return p3
-
-    def forward_all(self, x):
-        output = self.features(x)
-        p3 = self.downsample(output[-1])
-        return output, p3
-
-
-class UP(RPN):
-    def __init__(self, anchor_num=5, feature_in=256, feature_out=256):
-        super(UP, self).__init__()
-
-        self.anchor_num = anchor_num
-        self.feature_in = feature_in
-        self.feature_out = feature_out
-
-        self.cls_output = 2 * self.anchor_num
-        self.loc_output = 4 * self.anchor_num
-
-        self.cls = DepthCorr(feature_in, feature_out, self.cls_output)
-        self.loc = DepthCorr(feature_in, feature_out, self.loc_output)
-
-    def forward(self, z_f, x_f):
-        cls = self.cls(z_f, x_f)
-        loc = self.loc(z_f, x_f)
-        return cls, loc
-
-
-class MaskCorr(Mask):
-    def __init__(self, oSz=63):
-        super(MaskCorr, self).__init__()
-        self.oSz = oSz
-        self.mask = DepthCorr(256, 256, self.oSz**2)
-
-    def forward(self, z, x):
-        return self.mask(z, x)
-
-
-class Refine(nn.Module):
-    def __init__(self):
-        super(Refine, self).__init__()
-        self.v0 = nn.Sequential(nn.Conv2d(64, 16, 3, padding=1), nn.ReLU(),
-                           nn.Conv2d(16, 4, 3, padding=1),nn.ReLU())
-
-        self.v1 = nn.Sequential(nn.Conv2d(256, 64, 3, padding=1), nn.ReLU(),
-                           nn.Conv2d(64, 16, 3, padding=1), nn.ReLU())
-
-        self.v2 = nn.Sequential(nn.Conv2d(512, 128, 3, padding=1), nn.ReLU(),
-                           nn.Conv2d(128, 32, 3, padding=1), nn.ReLU())
-
-        self.h2 = nn.Sequential(nn.Conv2d(32, 32, 3, padding=1), nn.ReLU(),
-                           nn.Conv2d(32, 32, 3, padding=1), nn.ReLU())
-
-        self.h1 = nn.Sequential(nn.Conv2d(16, 16, 3, padding=1), nn.ReLU(),
-                           nn.Conv2d(16, 16, 3, padding=1), nn.ReLU())
-
-        self.h0 = nn.Sequential(nn.Conv2d(4, 4, 3, padding=1), nn.ReLU(),
-                           nn.Conv2d(4, 4, 3, padding=1), nn.ReLU())
-
-        self.deconv = nn.ConvTranspose2d(256, 32, 15, 15)
-
-        self.post0 = nn.Conv2d(32, 16, 3, padding=1)
-        self.post1 = nn.Conv2d(16, 4, 3, padding=1)
-        self.post2 = nn.Conv2d(4, 1, 3, padding=1)
-        
-        for modules in [self.v0, self.v1, self.v2, self.h2, self.h1, self.h0, self.deconv, self.post0, self.post1, self.post2,]:
-            for l in modules.modules():
-                if isinstance(l, nn.Conv2d):
-                    nn.init.kaiming_uniform_(l.weight, a=1)
-
-    def forward(self, f, corr_feature, pos=None, test=False):
-        if test:
-            p0 = torch.nn.functional.pad(f[0], [16, 16, 16, 16])[:, :, 4*pos[0]:4*pos[0]+61, 4*pos[1]:4*pos[1]+61]
-            p1 = torch.nn.functional.pad(f[1], [8, 8, 8, 8])[:, :, 2 * pos[0]:2 * pos[0] + 31, 2 * pos[1]:2 * pos[1] + 31]
-            p2 = torch.nn.functional.pad(f[2], [4, 4, 4, 4])[:, :, pos[0]:pos[0] + 15, pos[1]:pos[1] + 15]
-        else:
-            p0 = F.unfold(f[0], (61, 61), padding=0, stride=4).permute(0, 2, 1).contiguous().view(-1, 64, 61, 61)
-            if not (pos is None): p0 = torch.index_select(p0, 0, pos)
-            p1 = F.unfold(f[1], (31, 31), padding=0, stride=2).permute(0, 2, 1).contiguous().view(-1, 256, 31, 31)
-            if not (pos is None): p1 = torch.index_select(p1, 0, pos)
-            p2 = F.unfold(f[2], (15, 15), padding=0, stride=1).permute(0, 2, 1).contiguous().view(-1, 512, 15, 15)
-            if not (pos is None): p2 = torch.index_select(p2, 0, pos)
-
-        if not(pos is None):
-            p3 = corr_feature[:, :, pos[0], pos[1]].view(-1, 256, 1, 1)
-        else:
-            p3 = corr_feature.permute(0, 2, 3, 1).contiguous().view(-1, 256, 1, 1)
-
-        out = self.deconv(p3)
-        out = self.post0(F.upsample(self.h2(out) + self.v2(p2), size=(31, 31)))
-        out = self.post1(F.upsample(self.h1(out) + self.v1(p1), size=(61, 61)))
-        out = self.post2(F.upsample(self.h0(out) + self.v0(p0), size=(127, 127)))
-        out = out.view(-1, 127*127)
-        return out
-
-    def param_groups(self, start_lr, feature_mult=1):
-        params = filter(lambda x:x.requires_grad, self.parameters())
-        params = [{'params': params, 'lr': start_lr * feature_mult}]
-        return params
-
-
-class Custom(SiamMask):
-    def __init__(self, pretrain=False, **kwargs):
-        super(Custom, self).__init__(**kwargs)
-        self.features = ResDown(pretrain=pretrain)
-        self.rpn_model = UP(anchor_num=self.anchor_num, feature_in=256, feature_out=256)
-        self.mask_model = MaskCorr()
-        self.refine_model = Refine()
-
-    def refine(self, f, pos=None):
-        return self.refine_model(f, pos)
-
-    def template(self, template):
-        self.zf = self.features(template)
-
-    def track(self, search):
-        search = self.features(search)
-        rpn_pred_cls, rpn_pred_loc = self.rpn(self.zf, search)
-        return rpn_pred_cls, rpn_pred_loc
-
-    def track_mask(self, search):
-        self.feature, self.search = self.features.forward_all(search)
-        rpn_pred_cls, rpn_pred_loc = self.rpn(self.zf, self.search)
-        self.corr_feature = self.mask_model.mask.forward_corr(self.zf, self.search)
-        pred_mask = self.mask_model.mask.head(self.corr_feature)
-        return rpn_pred_cls, rpn_pred_loc, pred_mask
-
-    def track_refine(self, pos):
-        pred_mask = self.refine_model(self.feature, self.corr_feature, pos=pos, test=True)
-        return pred_mask
-
diff --git a/spaces/orpatashnik/local-prompt-mixing/src/diffusion_model_wrapper.py b/spaces/orpatashnik/local-prompt-mixing/src/diffusion_model_wrapper.py
deleted file mode 100644
index 0081cf6fbb6b768efc0f16ef2e4c36b6321abee4..0000000000000000000000000000000000000000
--- a/spaces/orpatashnik/local-prompt-mixing/src/diffusion_model_wrapper.py
+++ /dev/null
@@ -1,252 +0,0 @@
-from typing import Optional, List
-
-import numpy as np
-import torch
-from cv2 import dilate
-from diffusers import DDIMScheduler, StableDiffusionPipeline
-from tqdm import tqdm
-
-from src.attention_based_segmentation import Segmentor
-from src.attention_utils import show_cross_attention
-from src.prompt_to_prompt_controllers import DummyController, AttentionStore
-
-
-def get_stable_diffusion_model(args):
-    device = torch.device(f'cuda:{args.gpu_id}') if torch.cuda.is_available() else torch.device('cpu')
-    if args.real_image_path != "":
-        scheduler = DDIMScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", clip_sample=False, set_alpha_to_one=False)
-        ldm_stable = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", use_auth_token=args.auth_token, scheduler=scheduler).to(device)
-    else:
-        ldm_stable = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", use_auth_token=args.auth_token).to(device)
-
-    return ldm_stable
-
-def get_stable_diffusion_config(args):
-    return {
-        "low_resource": args.low_resource,
-        "num_diffusion_steps": args.num_diffusion_steps,
-        "guidance_scale": args.guidance_scale,
-        "max_num_words": args.max_num_words
-    }
-
-
-def generate_original_image(args, ldm_stable, ldm_stable_config, prompts, latent, uncond_embeddings):
-    g_cpu = torch.Generator(device=ldm_stable.device).manual_seed(args.seed)
-    controller = AttentionStore(ldm_stable_config["low_resource"])
-    diffusion_model_wrapper = DiffusionModelWrapper(args, ldm_stable, ldm_stable_config, controller, generator=g_cpu)
-    image, x_t, orig_all_latents, _ = diffusion_model_wrapper.forward(prompts,
-                                                                      latent=latent,
-                                                                      uncond_embeddings=uncond_embeddings)
-    orig_mask = Segmentor(controller, prompts, args.num_segments, args.background_segment_threshold, background_nouns=args.background_nouns)\
-        .get_background_mask(args.prompt.split(' ').index("{word}") + 1)
-    average_attention = controller.get_average_attention()
-    return image, x_t, orig_all_latents, orig_mask, average_attention
-
-
-class DiffusionModelWrapper:
-    def __init__(self, args, model, model_config, controller=None, prompt_mixing=None, generator=None):
-        self.args = args
-        self.model = model
-        self.model_config = model_config
-        self.controller = controller
-        if self.controller is None:
-            self.controller = DummyController()
-        self.prompt_mixing = prompt_mixing
-        self.device = model.device
-        self.generator = generator
-
-        self.height = 512
-        self.width = 512
-
-        self.diff_step = 0
-        self.register_attention_control()
-
-
-    def diffusion_step(self, latents, context, t, other_context=None):
-        if self.model_config["low_resource"]:
-            self.uncond_pred = True
-            noise_pred_uncond = self.model.unet(latents, t, encoder_hidden_states=(context[0], None))["sample"]
-            self.uncond_pred = False
-            noise_prediction_text = self.model.unet(latents, t, encoder_hidden_states=(context[1], other_context))["sample"]
-        else:
-            latents_input = torch.cat([latents] * 2)
-            noise_pred = self.model.unet(latents_input, t, encoder_hidden_states=(context, other_context))["sample"]
-            noise_pred_uncond, noise_prediction_text = noise_pred.chunk(2)
-        noise_pred = noise_pred_uncond + self.model_config["guidance_scale"] * (noise_prediction_text - noise_pred_uncond)
-        latents = self.model.scheduler.step(noise_pred, t, latents)["prev_sample"]
-        latents = self.controller.step_callback(latents)
-        return latents
-
-
-    def latent2image(self, latents):
-        latents = 1 / 0.18215 * latents
-        image = self.model.vae.decode(latents)['sample']
-        image = (image / 2 + 0.5).clamp(0, 1)
-        image = image.cpu().permute(0, 2, 3, 1).numpy()
-        image = (image * 255).astype(np.uint8)
-        return image
-
-
-    def init_latent(self, latent, batch_size):
-        if latent is None:
-            latent = torch.randn(
-                (1, self.model.unet.in_channels, self.height // 8, self.width // 8),
-                generator=self.generator, device=self.model.device
-            )
-        latents = latent.expand(batch_size,  self.model.unet.in_channels, self.height // 8, self.width // 8).to(self.device)
-        return latent, latents
-
-
-    def register_attention_control(self):
-        def ca_forward(model_self, place_in_unet):
-            to_out = model_self.to_out
-            if type(to_out) is torch.nn.modules.container.ModuleList:
-                to_out = model_self.to_out[0]
-            else:
-                to_out = model_self.to_out
-
-            def forward(x, context=None, mask=None):
-                batch_size, sequence_length, dim = x.shape
-                h = model_self.heads
-                q = model_self.to_q(x)
-                is_cross = context is not None
-                context = context if is_cross else (x, None)
-
-                k = model_self.to_k(context[0])
-                if is_cross and self.prompt_mixing is not None:
-                    v_context = self.prompt_mixing.get_context_for_v(self.diff_step, context[0], context[1])
-                    v = model_self.to_v(v_context)
-                else:
-                    v = model_self.to_v(context[0])
-
-                q = model_self.reshape_heads_to_batch_dim(q)
-                k = model_self.reshape_heads_to_batch_dim(k)
-                v = model_self.reshape_heads_to_batch_dim(v)
-
-                sim = torch.einsum("b i d, b j d -> b i j", q, k) * model_self.scale
-
-                if mask is not None:
-                    mask = mask.reshape(batch_size, -1)
-                    max_neg_value = -torch.finfo(sim.dtype).max
-                    mask = mask[:, None, :].repeat(h, 1, 1)
-                    sim.masked_fill_(~mask, max_neg_value)
-
-                # attention, what we cannot get enough of
-                attn = sim.softmax(dim=-1)
-                if self.enbale_attn_controller_changes:
-                    attn = self.controller(attn, is_cross, place_in_unet)
-                
-                if is_cross and self.prompt_mixing is not None and context[1] is not None:
-                    attn = self.prompt_mixing.get_cross_attn(self, self.diff_step, attn, place_in_unet, batch_size)
-
-                if not is_cross and (not self.model_config["low_resource"] or not self.uncond_pred) and self.prompt_mixing is not None:
-                    attn = self.prompt_mixing.get_self_attn(self, self.diff_step, attn, place_in_unet, batch_size)
-
-                out = torch.einsum("b i j, b j d -> b i d", attn, v)
-                out = model_self.reshape_batch_dim_to_heads(out)
-                return to_out(out)
-
-            return forward
-
-        def register_recr(net_, count, place_in_unet):
-            if net_.__class__.__name__ == 'CrossAttention':
-                net_.forward = ca_forward(net_, place_in_unet)
-                return count + 1
-            elif hasattr(net_, 'children'):
-                for net__ in net_.children():
-                    count = register_recr(net__, count, place_in_unet)
-            return count
-
-        cross_att_count = 0
-        sub_nets = self.model.unet.named_children()
-        for net in sub_nets:
-            if "down" in net[0]:
-                cross_att_count += register_recr(net[1], 0, "down")
-            elif "up" in net[0]:
-                cross_att_count += register_recr(net[1], 0, "up")
-            elif "mid" in net[0]:
-                cross_att_count += register_recr(net[1], 0, "mid")
-        self.controller.num_att_layers = cross_att_count
-
-
-    def get_text_embedding(self, prompt: List[str], max_length=None, truncation=True):
-        text_input = self.model.tokenizer(
-            prompt,
-            padding="max_length",
-            max_length=self.model.tokenizer.model_max_length if max_length is None else max_length,
-            truncation=truncation,
-            return_tensors="pt",
-        )
-        text_embeddings = self.model.text_encoder(text_input.input_ids.to(self.device))[0]
-        max_length = text_input.input_ids.shape[-1]
-        return text_embeddings, max_length
-
-
-    @torch.no_grad()
-    def forward(self, prompt: List[str], latent: Optional[torch.FloatTensor] = None,
-                other_prompt: List[str] = None, post_background = False, orig_all_latents = None, orig_mask = None,
-                uncond_embeddings=None, start_time=51, return_type='image'):
-        self.enbale_attn_controller_changes = True
-        batch_size = len(prompt)
-
-        text_embeddings, max_length = self.get_text_embedding(prompt)
-        if uncond_embeddings is None:
-            uncond_embeddings_, _ = self.get_text_embedding([""] * batch_size, max_length=max_length, truncation=False)
-        else:
-            uncond_embeddings_ = None
-
-        other_context = None
-        if other_prompt is not None:
-            other_text_embeddings, _ = self.get_text_embedding(other_prompt)
-            other_context = other_text_embeddings
-
-        latent, latents = self.init_latent(latent, batch_size)
-        
-        # set timesteps
-        self.model.scheduler.set_timesteps(self.model_config["num_diffusion_steps"])
-        all_latents = []
-
-        object_mask = None
-        self.diff_step = 0
-        for i, t in enumerate(tqdm(self.model.scheduler.timesteps[-start_time:])):
-            if uncond_embeddings_ is None:
-                context = [uncond_embeddings[i].expand(*text_embeddings.shape), text_embeddings]
-            else:
-                context = [uncond_embeddings_, text_embeddings]
-            if not self.model_config["low_resource"]:
-                context = torch.cat(context)
-
-            self.down_cross_index = 0
-            self.mid_cross_index = 0
-            self.up_cross_index = 0
-            latents = self.diffusion_step(latents, context, t, other_context)
-
-            if post_background and self.diff_step == self.args.background_blend_timestep:
-                object_mask = Segmentor(self.controller,
-                                        prompt,
-                                        self.args.num_segments,
-                                        self.args.background_segment_threshold,
-                                        background_nouns=self.args.background_nouns)\
-                    .get_background_mask(self.args.prompt.split(' ').index("{word}") + 1)
-                self.enbale_attn_controller_changes = False
-                mask = object_mask.astype(np.bool8) + orig_mask.astype(np.bool8)
-                mask = torch.from_numpy(mask).float().cuda()
-                shape = (1, 1, mask.shape[0], mask.shape[1])
-                mask = torch.nn.Upsample(size=(64, 64), mode='nearest')(mask.view(shape))
-                mask_eroded = dilate(mask.cpu().numpy()[0, 0], np.ones((3, 3), np.uint8), iterations=1)
-                mask = torch.from_numpy(mask_eroded).float().cuda().view(1, 1, 64, 64)
-                latents = mask * latents + (1 - mask) * orig_all_latents[self.diff_step]
-
-            all_latents.append(latents)
-            self.diff_step += 1
-
-        if return_type == 'image':
-            image = self.latent2image(latents)
-        else:
-            image = latents
-        
-        return image, latent, all_latents, object_mask
-    
-    
-    def show_last_cross_attention(self, res: int, from_where: List[str], prompts, select: int = 0):
-        show_cross_attention(self.controller, res, from_where, prompts, tokenizer=self.model.tokenizer, select=select)
\ No newline at end of file
diff --git a/spaces/ozgur34/qb-Engine2/README.md b/spaces/ozgur34/qb-Engine2/README.md
deleted file mode 100644
index 6dd09d33e340a8b16d3cdf72ff32e1020f157979..0000000000000000000000000000000000000000
--- a/spaces/ozgur34/qb-Engine2/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Qb Engine2
-emoji: 📊
-colorFrom: indigo
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.32.0
-app_file: app.py
-pinned: false
-license: creativeml-openrail-m
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/docs/source/zh/index.md b/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/docs/source/zh/index.md
deleted file mode 100644
index e1a2a3971d87ce823e4668662d65c2b55602b87f..0000000000000000000000000000000000000000
--- a/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/docs/source/zh/index.md
+++ /dev/null
@@ -1,101 +0,0 @@
-<!--Copyright 2023 The HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-specific language governing permissions and limitations under the License.
--->
-
-<p align="center">
-    <br>
-    <img src="https://raw.githubusercontent.com/huggingface/diffusers/77aadfee6a891ab9fcfb780f87c693f7a5beeb8e/docs/source/imgs/diffusers_library.jpg" width="400"/>
-    <br>
-</p>
-
-# 🧨 Diffusers
-
-🤗 Diffusers 是一个值得首选用于生成图像、音频甚至 3D 分子结构的，最先进的预训练扩散模型库。
-无论您是在寻找简单的推理解决方案，还是想训练自己的扩散模型，🤗 Diffusers 这一模块化工具箱都能对其提供支持。
-本库的设计更偏重于[可用而非高性能](conceptual/philosophy#usability-over-performance)、[简明而非简单](conceptual/philosophy#simple-over-easy)以及[易用而非抽象](conceptual/philosophy#tweakable-contributorfriendly-over-abstraction)。
-
-
-本库包含三个主要组件：
-
-- 最先进的扩散管道 [diffusion pipelines](api/pipelines/overview)，只需几行代码即可进行推理。
-- 可交替使用的各种噪声调度器 [noise schedulers](api/schedulers/overview)，用于平衡生成速度和质量。
-- 预训练模型 [models](api/models)，可作为构建模块，并与调度程序结合使用，来创建您自己的端到端扩散系统。
-
-<div class="mt-10">
-  <div class="w-full flex flex-col space-y-4 md:space-y-0 md:grid md:grid-cols-2 md:gap-y-4 md:gap-x-5">
-    <a class="!no-underline border dark:border-gray-700 p-5 rounded-lg shadow hover:shadow-lg" href="./tutorials/tutorial_overview"
-      ><div class="w-full text-center bg-gradient-to-br from-blue-400 to-blue-500 rounded-lg py-1.5 font-semibold mb-5 text-white text-lg leading-relaxed">Tutorials</div>
-      <p class="text-gray-700">Learn the fundamental skills you need to start generating outputs, build your own diffusion system, and train a diffusion model. We recommend starting here if you're using 🤗 Diffusers for the first time!</p>
-    </a>
-    <a class="!no-underline border dark:border-gray-700 p-5 rounded-lg shadow hover:shadow-lg" href="./using-diffusers/loading_overview"
-      ><div class="w-full text-center bg-gradient-to-br from-indigo-400 to-indigo-500 rounded-lg py-1.5 font-semibold mb-5 text-white text-lg leading-relaxed">How-to guides</div>
-      <p class="text-gray-700">Practical guides for helping you load pipelines, models, and schedulers. You'll also learn how to use pipelines for specific tasks, control how outputs are generated, optimize for inference speed, and different training techniques.</p>
-    </a>
-    <a class="!no-underline border dark:border-gray-700 p-5 rounded-lg shadow hover:shadow-lg" href="./conceptual/philosophy"
-      ><div class="w-full text-center bg-gradient-to-br from-pink-400 to-pink-500 rounded-lg py-1.5 font-semibold mb-5 text-white text-lg leading-relaxed">Conceptual guides</div>
-      <p class="text-gray-700">Understand why the library was designed the way it was, and learn more about the ethical guidelines and safety implementations for using the library.</p>
-   </a>
-    <a class="!no-underline border dark:border-gray-700 p-5 rounded-lg shadow hover:shadow-lg" href="./api/models"
-      ><div class="w-full text-center bg-gradient-to-br from-purple-400 to-purple-500 rounded-lg py-1.5 font-semibold mb-5 text-white text-lg leading-relaxed">Reference</div>
-      <p class="text-gray-700">Technical descriptions of how 🤗 Diffusers classes and methods work.</p>
-    </a>
-  </div>
-</div>
-
-## 🧨 Diffusers pipelines
-
-下表汇总了当前所有官方支持的pipelines及其对应的论文.
-
-| 管道 | 论文/仓库 | 任务 |
-|---|---|:---:|
-| [alt_diffusion](./api/pipelines/alt_diffusion) | [AltCLIP: Altering the Language Encoder in CLIP for Extended Language Capabilities](https://arxiv.org/abs/2211.06679) | Image-to-Image Text-Guided Generation |
-| [audio_diffusion](./api/pipelines/audio_diffusion) | [Audio Diffusion](https://github.com/teticio/audio-diffusion.git) | Unconditional Audio Generation |
-| [controlnet](./api/pipelines/stable_diffusion/controlnet) | [Adding Conditional Control to Text-to-Image Diffusion Models](https://arxiv.org/abs/2302.05543) | Image-to-Image Text-Guided Generation |
-| [cycle_diffusion](./api/pipelines/cycle_diffusion) | [Unifying Diffusion Models' Latent Space, with Applications to CycleDiffusion and Guidance](https://arxiv.org/abs/2210.05559) | Image-to-Image Text-Guided Generation |
-| [dance_diffusion](./api/pipelines/dance_diffusion) | [Dance Diffusion](https://github.com/williamberman/diffusers.git) | Unconditional Audio Generation |
-| [ddpm](./api/pipelines/ddpm) | [Denoising Diffusion Probabilistic Models](https://arxiv.org/abs/2006.11239) | Unconditional Image Generation |
-| [ddim](./api/pipelines/ddim) | [Denoising Diffusion Implicit Models](https://arxiv.org/abs/2010.02502) | Unconditional Image Generation |
-| [if](./if) | [**IF**](./api/pipelines/if) | Image Generation |
-| [if_img2img](./if) | [**IF**](./api/pipelines/if) | Image-to-Image Generation |
-| [if_inpainting](./if) | [**IF**](./api/pipelines/if) | Image-to-Image Generation |
-| [latent_diffusion](./api/pipelines/latent_diffusion) | [High-Resolution Image Synthesis with Latent Diffusion Models](https://arxiv.org/abs/2112.10752)| Text-to-Image Generation |
-| [latent_diffusion](./api/pipelines/latent_diffusion) | [High-Resolution Image Synthesis with Latent Diffusion Models](https://arxiv.org/abs/2112.10752)| Super Resolution Image-to-Image |
-| [latent_diffusion_uncond](./api/pipelines/latent_diffusion_uncond) | [High-Resolution Image Synthesis with Latent Diffusion Models](https://arxiv.org/abs/2112.10752) | Unconditional Image Generation |
-| [paint_by_example](./api/pipelines/paint_by_example) | [Paint by Example: Exemplar-based Image Editing with Diffusion Models](https://arxiv.org/abs/2211.13227) | Image-Guided Image Inpainting |
-| [pndm](./api/pipelines/pndm) | [Pseudo Numerical Methods for Diffusion Models on Manifolds](https://arxiv.org/abs/2202.09778) | Unconditional Image Generation |
-| [score_sde_ve](./api/pipelines/score_sde_ve) | [Score-Based Generative Modeling through Stochastic Differential Equations](https://openreview.net/forum?id=PxTIG12RRHS) | Unconditional Image Generation |
-| [score_sde_vp](./api/pipelines/score_sde_vp) | [Score-Based Generative Modeling through Stochastic Differential Equations](https://openreview.net/forum?id=PxTIG12RRHS) | Unconditional Image Generation |
-| [semantic_stable_diffusion](./api/pipelines/semantic_stable_diffusion) | [Semantic Guidance](https://arxiv.org/abs/2301.12247) | Text-Guided Generation |
-| [stable_diffusion_text2img](./api/pipelines/stable_diffusion/text2img) | [Stable Diffusion](https://stability.ai/blog/stable-diffusion-public-release) | Text-to-Image Generation |
-| [stable_diffusion_img2img](./api/pipelines/stable_diffusion/img2img) | [Stable Diffusion](https://stability.ai/blog/stable-diffusion-public-release) | Image-to-Image Text-Guided Generation |
-| [stable_diffusion_inpaint](./api/pipelines/stable_diffusion/inpaint) | [Stable Diffusion](https://stability.ai/blog/stable-diffusion-public-release) | Text-Guided Image Inpainting |
-| [stable_diffusion_panorama](./api/pipelines/stable_diffusion/panorama) | [MultiDiffusion](https://multidiffusion.github.io/) | Text-to-Panorama Generation |
-| [stable_diffusion_pix2pix](./api/pipelines/stable_diffusion/pix2pix) | [InstructPix2Pix: Learning to Follow Image Editing Instructions](https://arxiv.org/abs/2211.09800)  | Text-Guided Image Editing|
-| [stable_diffusion_pix2pix_zero](./api/pipelines/stable_diffusion/pix2pix_zero) | [Zero-shot Image-to-Image Translation](https://pix2pixzero.github.io/) | Text-Guided Image Editing |
-| [stable_diffusion_attend_and_excite](./api/pipelines/stable_diffusion/attend_and_excite) | [Attend-and-Excite: Attention-Based Semantic Guidance for Text-to-Image Diffusion Models](https://arxiv.org/abs/2301.13826) | Text-to-Image Generation |
-| [stable_diffusion_self_attention_guidance](./api/pipelines/stable_diffusion/self_attention_guidance) | [Improving Sample Quality of Diffusion Models Using Self-Attention Guidance](https://arxiv.org/abs/2210.00939) | Text-to-Image Generation Unconditional Image Generation |
-| [stable_diffusion_image_variation](./stable_diffusion/image_variation) | [Stable Diffusion Image Variations](https://github.com/LambdaLabsML/lambda-diffusers#stable-diffusion-image-variations) | Image-to-Image Generation |
-| [stable_diffusion_latent_upscale](./stable_diffusion/latent_upscale) | [Stable Diffusion Latent Upscaler](https://twitter.com/StabilityAI/status/1590531958815064065) | Text-Guided Super Resolution Image-to-Image |
-| [stable_diffusion_model_editing](./api/pipelines/stable_diffusion/model_editing) | [Editing Implicit Assumptions in Text-to-Image Diffusion Models](https://time-diffusion.github.io/) | Text-to-Image Model Editing |
-| [stable_diffusion_2](./api/pipelines/stable_diffusion_2) | [Stable Diffusion 2](https://stability.ai/blog/stable-diffusion-v2-release) | Text-to-Image Generation |
-| [stable_diffusion_2](./api/pipelines/stable_diffusion_2) | [Stable Diffusion 2](https://stability.ai/blog/stable-diffusion-v2-release) | Text-Guided Image Inpainting |
-| [stable_diffusion_2](./api/pipelines/stable_diffusion_2) | [Depth-Conditional Stable Diffusion](https://github.com/Stability-AI/stablediffusion#depth-conditional-stable-diffusion) | Depth-to-Image Generation |
-| [stable_diffusion_2](./api/pipelines/stable_diffusion_2) | [Stable Diffusion 2](https://stability.ai/blog/stable-diffusion-v2-release) | Text-Guided Super Resolution Image-to-Image |
-| [stable_diffusion_safe](./api/pipelines/stable_diffusion_safe) | [Safe Stable Diffusion](https://arxiv.org/abs/2211.05105) | Text-Guided Generation |
-| [stable_unclip](./stable_unclip) | Stable unCLIP | Text-to-Image Generation |
-| [stable_unclip](./stable_unclip) | Stable unCLIP | Image-to-Image Text-Guided Generation |
-| [stochastic_karras_ve](./api/pipelines/stochastic_karras_ve) | [Elucidating the Design Space of Diffusion-Based Generative Models](https://arxiv.org/abs/2206.00364) | Unconditional Image Generation |
-| [text_to_video_sd](./api/pipelines/text_to_video) | [Modelscope's Text-to-video-synthesis Model in Open Domain](https://modelscope.cn/models/damo/text-to-video-synthesis/summary) | Text-to-Video Generation |
-| [unclip](./api/pipelines/unclip) | [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://arxiv.org/abs/2204.06125)(implementation by [kakaobrain](https://github.com/kakaobrain/karlo)) | Text-to-Image Generation |
-| [versatile_diffusion](./api/pipelines/versatile_diffusion) | [Versatile Diffusion: Text, Images and Variations All in One Diffusion Model](https://arxiv.org/abs/2211.08332) | Text-to-Image Generation |
-| [versatile_diffusion](./api/pipelines/versatile_diffusion) | [Versatile Diffusion: Text, Images and Variations All in One Diffusion Model](https://arxiv.org/abs/2211.08332) | Image Variations Generation |
-| [versatile_diffusion](./api/pipelines/versatile_diffusion) | [Versatile Diffusion: Text, Images and Variations All in One Diffusion Model](https://arxiv.org/abs/2211.08332) | Dual Image and Text Guided Generation |
-| [vq_diffusion](./api/pipelines/vq_diffusion) | [Vector Quantized Diffusion Model for Text-to-Image Synthesis](https://arxiv.org/abs/2111.14822) | Text-to-Image Generation |
diff --git a/spaces/pierreguillou/Inference-APP-Document-Understanding-at-paragraphlevel-v1/files/README.md b/spaces/pierreguillou/Inference-APP-Document-Understanding-at-paragraphlevel-v1/files/README.md
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/pikto/Elite-freegpt-webui/client/html/index.html b/spaces/pikto/Elite-freegpt-webui/client/html/index.html
deleted file mode 100644
index 37c4575b9bf22f7227bfc170551eaeffa3565d2e..0000000000000000000000000000000000000000
--- a/spaces/pikto/Elite-freegpt-webui/client/html/index.html
+++ /dev/null
@@ -1,120 +0,0 @@
-<!DOCTYPE html>
-<html lang="en">
-	<head>
-		<meta charset="UTF-8" />
-		<meta http-equiv="X-UA-Compatible" content="IE=edge" />
-		<meta name="viewport" content="width=device-width, initial-scale=1.0 maximum-scale=1.0" />
-		<meta name="description" content="A conversational AI system that listens, learns, and challenges" />
-		<meta property="og:title" content="ChatGPT" />
-		<meta property="og:image" content="https://openai.com/content/images/2022/11/ChatGPT.jpg" />
-		<meta
-			property="og:description"
-			content="A conversational AI system that listens, learns, and challenges" />
-		<meta property="og:url" content="https://chat.acy.dev" />
-		<link rel="stylesheet" href="{{ url_for('bp.static', filename='css/style.css') }}" />
-		<link rel="apple-touch-icon" sizes="180x180" href="{{ url_for('bp.static', filename='img/apple-touch-icon.png') }}" />
-		<link rel="icon" type="image/png" sizes="32x32" href="{{ url_for('bp.static', filename='img/favicon-32x32.png') }}" />
-		<link rel="icon" type="image/png" sizes="16x16" href="{{ url_for('bp.static', filename='img/favicon-16x16.png') }}" />
-		<link rel="manifest" href="{{ url_for('bp.static', filename='img/site.webmanifest') }}" />
-		<link
-			rel="stylesheet"
-			href="//cdn.jsdelivr.net/gh/highlightjs/cdn-release@latest/build/styles/base16/dracula.min.css" />
-		<title>FreeGPT</title>
-	</head>
-
-	<body data-urlprefix="{{ url_prefix}}">
-		<div class="main-container">
-			<div class="box sidebar">
-				<div class="top">
-					<button class="button" onclick="new_conversation()">
-						<i class="fa-regular fa-plus"></i>
-						<span>New Conversation</span>
-					</button>
-					<div class="spinner"></div>
-				</div>
-				<div class="sidebar-footer">
-					<button class="button" onclick="delete_conversations()">
-						<i class="fa-regular fa-trash"></i>
-						<span>Clear Conversations</span>
-					</button>
-					<div class="field checkbox theme-toggler-container">
-						<input type="checkbox" id="theme-toggler" />
-						<label for="theme-toggler"></label>
-						<span>Dark Mode</span>
-					</div>
-					<a class="info" href="https://github.com/ramonvc/gptfree-jailbreak-webui" target="_blank">
-						<i class="fa-brands fa-github"></i>
-						<span class="conversation-title">
-							Version: 0.0.10-Alpha
-						</span>
-					</a>
-				</div>
-			</div>
-			<div class="conversation">
-				<div class="stop-generating stop-generating-hidden">
-					<button class="button" id="cancelButton">
-						<span>Stop Generating</span>
-					</button>
-				</div>
-				<div class="box" id="messages"></div>
-				<div class="user-input">
-					<div class="box input-box">
-						<textarea
-							id="message-input"
-							placeholder="Ask a question"
-							cols="30"
-							rows="10"
-							style="white-space: pre-wrap"></textarea>
-						<div id="send-button">
-							<i class="fa-regular fa-paper-plane-top"></i>
-						</div>
-					</div>
-				</div>
-				<div>
-					<div class="options-container">
-						<div class="buttons">
-							<div class="field">
-								<select class="dropdown" name="model" id="model">
-									<option value="gpt-3.5-turbo" selected>GPT-3.5</option>
-									<option value="gpt-3.5-turbo-0613">GPT-3.5-0613</option>
-									<option value="gpt-3.5-turbo-16k">GPT-3.5-turbo-16k</option>
-									<option value="gpt-3.5-turbo-16k-0613">
-										GPT-3.5-turbo-16k-0613
-									</option>
-									<option value="gpt-4">GPT-4</option>
-								</select>
-							</div>
-							<div class="field">
-								<select class="dropdown" name="jailbreak" id="jailbreak">
-									<option value="default" selected>Default</option>
-									<option value="gpt-dan-11.0">DAN</option>
-                                    <option value="terminal">LINUX</option>
-									<option value="gpt-evil">Evil</option>
-								</select>
-							</div>
-						</div>
-						<div class="field checkbox">
-							<input type="checkbox" id="switch" />
-							<label for="switch"></label>
-							<span>Web Access</span>
-						</div>
-					</div>
-				</div>
-			</div>
-		</div>
-		<div class="mobile-sidebar">
-			<i class="fa-solid fa-bars"></i>
-		</div>
-
-		<!-- scripts -->
-		<script>
-			window.conversation_id = "{{ chat_id }}";
-		</script>
-		<script src="{{ url_for('bp.static', filename='js/icons.js') }}"></script>
-		<script src="{{ url_for('bp.static', filename='js/chat.js') }}" defer></script>
-		<script src="https://cdn.jsdelivr.net/npm/markdown-it@latest/dist/markdown-it.min.js"></script>
-		<script src="{{ url_for('bp.static', filename='js/highlight.min.js') }}"></script>
-		<script src="{{ url_for('bp.static', filename='js/highlightjs-copy.min.js') }}"></script>
-		<script src="{{ url_for('bp.static', filename='js/theme-toggler.js') }}"></script>
-	</body>
-</html>
diff --git a/spaces/pix2pix-zero-library/pix2pix-zero-demo/README.md b/spaces/pix2pix-zero-library/pix2pix-zero-demo/README.md
deleted file mode 100644
index 1b0526f0611b98ce01aae8164da4f15dcab4aada..0000000000000000000000000000000000000000
--- a/spaces/pix2pix-zero-library/pix2pix-zero-demo/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: pix2pix-zero 
-emoji: 📈
-colorFrom: purple
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.18.0
-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/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/rich/traceback.py b/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/rich/traceback.py
deleted file mode 100644
index c4ffe1f99e6dc9c0509459196cb68fa95e79048d..0000000000000000000000000000000000000000
--- a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/rich/traceback.py
+++ /dev/null
@@ -1,756 +0,0 @@
-from __future__ import absolute_import
-
-import linecache
-import os
-import platform
-import sys
-from dataclasses import dataclass, field
-from traceback import walk_tb
-from types import ModuleType, TracebackType
-from typing import (
-    Any,
-    Callable,
-    Dict,
-    Iterable,
-    List,
-    Optional,
-    Sequence,
-    Tuple,
-    Type,
-    Union,
-)
-
-from pip._vendor.pygments.lexers import guess_lexer_for_filename
-from pip._vendor.pygments.token import Comment, Keyword, Name, Number, Operator, String
-from pip._vendor.pygments.token import Text as TextToken
-from pip._vendor.pygments.token import Token
-from pip._vendor.pygments.util import ClassNotFound
-
-from . import pretty
-from ._loop import loop_last
-from .columns import Columns
-from .console import Console, ConsoleOptions, ConsoleRenderable, RenderResult, group
-from .constrain import Constrain
-from .highlighter import RegexHighlighter, ReprHighlighter
-from .panel import Panel
-from .scope import render_scope
-from .style import Style
-from .syntax import Syntax
-from .text import Text
-from .theme import Theme
-
-WINDOWS = platform.system() == "Windows"
-
-LOCALS_MAX_LENGTH = 10
-LOCALS_MAX_STRING = 80
-
-
-def install(
-    *,
-    console: Optional[Console] = None,
-    width: Optional[int] = 100,
-    extra_lines: int = 3,
-    theme: Optional[str] = None,
-    word_wrap: bool = False,
-    show_locals: bool = False,
-    locals_max_length: int = LOCALS_MAX_LENGTH,
-    locals_max_string: int = LOCALS_MAX_STRING,
-    locals_hide_dunder: bool = True,
-    locals_hide_sunder: Optional[bool] = None,
-    indent_guides: bool = True,
-    suppress: Iterable[Union[str, ModuleType]] = (),
-    max_frames: int = 100,
-) -> Callable[[Type[BaseException], BaseException, Optional[TracebackType]], Any]:
-    """Install a rich traceback handler.
-
-    Once installed, any tracebacks will be printed with syntax highlighting and rich formatting.
-
-
-    Args:
-        console (Optional[Console], optional): Console to write exception to. Default uses internal Console instance.
-        width (Optional[int], optional): Width (in characters) of traceback. Defaults to 100.
-        extra_lines (int, optional): Extra lines of code. Defaults to 3.
-        theme (Optional[str], optional): Pygments theme to use in traceback. Defaults to ``None`` which will pick
-            a theme appropriate for the platform.
-        word_wrap (bool, optional): Enable word wrapping of long lines. Defaults to False.
-        show_locals (bool, optional): Enable display of local variables. Defaults to False.
-        locals_max_length (int, optional): Maximum length of containers before abbreviating, or None for no abbreviation.
-            Defaults to 10.
-        locals_max_string (int, optional): Maximum length of string before truncating, or None to disable. Defaults to 80.
-        locals_hide_dunder (bool, optional): Hide locals prefixed with double underscore. Defaults to True.
-        locals_hide_sunder (bool, optional): Hide locals prefixed with single underscore. Defaults to False.
-        indent_guides (bool, optional): Enable indent guides in code and locals. Defaults to True.
-        suppress (Sequence[Union[str, ModuleType]]): Optional sequence of modules or paths to exclude from traceback.
-
-    Returns:
-        Callable: The previous exception handler that was replaced.
-
-    """
-    traceback_console = Console(stderr=True) if console is None else console
-
-    locals_hide_sunder = (
-        True
-        if (traceback_console.is_jupyter and locals_hide_sunder is None)
-        else locals_hide_sunder
-    )
-
-    def excepthook(
-        type_: Type[BaseException],
-        value: BaseException,
-        traceback: Optional[TracebackType],
-    ) -> None:
-        traceback_console.print(
-            Traceback.from_exception(
-                type_,
-                value,
-                traceback,
-                width=width,
-                extra_lines=extra_lines,
-                theme=theme,
-                word_wrap=word_wrap,
-                show_locals=show_locals,
-                locals_max_length=locals_max_length,
-                locals_max_string=locals_max_string,
-                locals_hide_dunder=locals_hide_dunder,
-                locals_hide_sunder=bool(locals_hide_sunder),
-                indent_guides=indent_guides,
-                suppress=suppress,
-                max_frames=max_frames,
-            )
-        )
-
-    def ipy_excepthook_closure(ip: Any) -> None:  # pragma: no cover
-        tb_data = {}  # store information about showtraceback call
-        default_showtraceback = ip.showtraceback  # keep reference of default traceback
-
-        def ipy_show_traceback(*args: Any, **kwargs: Any) -> None:
-            """wrap the default ip.showtraceback to store info for ip._showtraceback"""
-            nonlocal tb_data
-            tb_data = kwargs
-            default_showtraceback(*args, **kwargs)
-
-        def ipy_display_traceback(
-            *args: Any, is_syntax: bool = False, **kwargs: Any
-        ) -> None:
-            """Internally called traceback from ip._showtraceback"""
-            nonlocal tb_data
-            exc_tuple = ip._get_exc_info()
-
-            # do not display trace on syntax error
-            tb: Optional[TracebackType] = None if is_syntax else exc_tuple[2]
-
-            # determine correct tb_offset
-            compiled = tb_data.get("running_compiled_code", False)
-            tb_offset = tb_data.get("tb_offset", 1 if compiled else 0)
-            # remove ipython internal frames from trace with tb_offset
-            for _ in range(tb_offset):
-                if tb is None:
-                    break
-                tb = tb.tb_next
-
-            excepthook(exc_tuple[0], exc_tuple[1], tb)
-            tb_data = {}  # clear data upon usage
-
-        # replace _showtraceback instead of showtraceback to allow ipython features such as debugging to work
-        # this is also what the ipython docs recommends to modify when subclassing InteractiveShell
-        ip._showtraceback = ipy_display_traceback
-        # add wrapper to capture tb_data
-        ip.showtraceback = ipy_show_traceback
-        ip.showsyntaxerror = lambda *args, **kwargs: ipy_display_traceback(
-            *args, is_syntax=True, **kwargs
-        )
-
-    try:  # pragma: no cover
-        # if within ipython, use customized traceback
-        ip = get_ipython()  # type: ignore[name-defined]
-        ipy_excepthook_closure(ip)
-        return sys.excepthook
-    except Exception:
-        # otherwise use default system hook
-        old_excepthook = sys.excepthook
-        sys.excepthook = excepthook
-        return old_excepthook
-
-
-@dataclass
-class Frame:
-    filename: str
-    lineno: int
-    name: str
-    line: str = ""
-    locals: Optional[Dict[str, pretty.Node]] = None
-
-
-@dataclass
-class _SyntaxError:
-    offset: int
-    filename: str
-    line: str
-    lineno: int
-    msg: str
-
-
-@dataclass
-class Stack:
-    exc_type: str
-    exc_value: str
-    syntax_error: Optional[_SyntaxError] = None
-    is_cause: bool = False
-    frames: List[Frame] = field(default_factory=list)
-
-
-@dataclass
-class Trace:
-    stacks: List[Stack]
-
-
-class PathHighlighter(RegexHighlighter):
-    highlights = [r"(?P<dim>.*/)(?P<bold>.+)"]
-
-
-class Traceback:
-    """A Console renderable that renders a traceback.
-
-    Args:
-        trace (Trace, optional): A `Trace` object produced from `extract`. Defaults to None, which uses
-            the last exception.
-        width (Optional[int], optional): Number of characters used to traceback. Defaults to 100.
-        extra_lines (int, optional): Additional lines of code to render. Defaults to 3.
-        theme (str, optional): Override pygments theme used in traceback.
-        word_wrap (bool, optional): Enable word wrapping of long lines. Defaults to False.
-        show_locals (bool, optional): Enable display of local variables. Defaults to False.
-        indent_guides (bool, optional): Enable indent guides in code and locals. Defaults to True.
-        locals_max_length (int, optional): Maximum length of containers before abbreviating, or None for no abbreviation.
-            Defaults to 10.
-        locals_max_string (int, optional): Maximum length of string before truncating, or None to disable. Defaults to 80.
-        locals_hide_dunder (bool, optional): Hide locals prefixed with double underscore. Defaults to True.
-        locals_hide_sunder (bool, optional): Hide locals prefixed with single underscore. Defaults to False.
-        suppress (Sequence[Union[str, ModuleType]]): Optional sequence of modules or paths to exclude from traceback.
-        max_frames (int): Maximum number of frames to show in a traceback, 0 for no maximum. Defaults to 100.
-
-    """
-
-    LEXERS = {
-        "": "text",
-        ".py": "python",
-        ".pxd": "cython",
-        ".pyx": "cython",
-        ".pxi": "pyrex",
-    }
-
-    def __init__(
-        self,
-        trace: Optional[Trace] = None,
-        *,
-        width: Optional[int] = 100,
-        extra_lines: int = 3,
-        theme: Optional[str] = None,
-        word_wrap: bool = False,
-        show_locals: bool = False,
-        locals_max_length: int = LOCALS_MAX_LENGTH,
-        locals_max_string: int = LOCALS_MAX_STRING,
-        locals_hide_dunder: bool = True,
-        locals_hide_sunder: bool = False,
-        indent_guides: bool = True,
-        suppress: Iterable[Union[str, ModuleType]] = (),
-        max_frames: int = 100,
-    ):
-        if trace is None:
-            exc_type, exc_value, traceback = sys.exc_info()
-            if exc_type is None or exc_value is None or traceback is None:
-                raise ValueError(
-                    "Value for 'trace' required if not called in except: block"
-                )
-            trace = self.extract(
-                exc_type, exc_value, traceback, show_locals=show_locals
-            )
-        self.trace = trace
-        self.width = width
-        self.extra_lines = extra_lines
-        self.theme = Syntax.get_theme(theme or "ansi_dark")
-        self.word_wrap = word_wrap
-        self.show_locals = show_locals
-        self.indent_guides = indent_guides
-        self.locals_max_length = locals_max_length
-        self.locals_max_string = locals_max_string
-        self.locals_hide_dunder = locals_hide_dunder
-        self.locals_hide_sunder = locals_hide_sunder
-
-        self.suppress: Sequence[str] = []
-        for suppress_entity in suppress:
-            if not isinstance(suppress_entity, str):
-                assert (
-                    suppress_entity.__file__ is not None
-                ), f"{suppress_entity!r} must be a module with '__file__' attribute"
-                path = os.path.dirname(suppress_entity.__file__)
-            else:
-                path = suppress_entity
-            path = os.path.normpath(os.path.abspath(path))
-            self.suppress.append(path)
-        self.max_frames = max(4, max_frames) if max_frames > 0 else 0
-
-    @classmethod
-    def from_exception(
-        cls,
-        exc_type: Type[Any],
-        exc_value: BaseException,
-        traceback: Optional[TracebackType],
-        *,
-        width: Optional[int] = 100,
-        extra_lines: int = 3,
-        theme: Optional[str] = None,
-        word_wrap: bool = False,
-        show_locals: bool = False,
-        locals_max_length: int = LOCALS_MAX_LENGTH,
-        locals_max_string: int = LOCALS_MAX_STRING,
-        locals_hide_dunder: bool = True,
-        locals_hide_sunder: bool = False,
-        indent_guides: bool = True,
-        suppress: Iterable[Union[str, ModuleType]] = (),
-        max_frames: int = 100,
-    ) -> "Traceback":
-        """Create a traceback from exception info
-
-        Args:
-            exc_type (Type[BaseException]): Exception type.
-            exc_value (BaseException): Exception value.
-            traceback (TracebackType): Python Traceback object.
-            width (Optional[int], optional): Number of characters used to traceback. Defaults to 100.
-            extra_lines (int, optional): Additional lines of code to render. Defaults to 3.
-            theme (str, optional): Override pygments theme used in traceback.
-            word_wrap (bool, optional): Enable word wrapping of long lines. Defaults to False.
-            show_locals (bool, optional): Enable display of local variables. Defaults to False.
-            indent_guides (bool, optional): Enable indent guides in code and locals. Defaults to True.
-            locals_max_length (int, optional): Maximum length of containers before abbreviating, or None for no abbreviation.
-                Defaults to 10.
-            locals_max_string (int, optional): Maximum length of string before truncating, or None to disable. Defaults to 80.
-            locals_hide_dunder (bool, optional): Hide locals prefixed with double underscore. Defaults to True.
-            locals_hide_sunder (bool, optional): Hide locals prefixed with single underscore. Defaults to False.
-            suppress (Iterable[Union[str, ModuleType]]): Optional sequence of modules or paths to exclude from traceback.
-            max_frames (int): Maximum number of frames to show in a traceback, 0 for no maximum. Defaults to 100.
-
-        Returns:
-            Traceback: A Traceback instance that may be printed.
-        """
-        rich_traceback = cls.extract(
-            exc_type,
-            exc_value,
-            traceback,
-            show_locals=show_locals,
-            locals_max_length=locals_max_length,
-            locals_max_string=locals_max_string,
-            locals_hide_dunder=locals_hide_dunder,
-            locals_hide_sunder=locals_hide_sunder,
-        )
-
-        return cls(
-            rich_traceback,
-            width=width,
-            extra_lines=extra_lines,
-            theme=theme,
-            word_wrap=word_wrap,
-            show_locals=show_locals,
-            indent_guides=indent_guides,
-            locals_max_length=locals_max_length,
-            locals_max_string=locals_max_string,
-            locals_hide_dunder=locals_hide_dunder,
-            locals_hide_sunder=locals_hide_sunder,
-            suppress=suppress,
-            max_frames=max_frames,
-        )
-
-    @classmethod
-    def extract(
-        cls,
-        exc_type: Type[BaseException],
-        exc_value: BaseException,
-        traceback: Optional[TracebackType],
-        *,
-        show_locals: bool = False,
-        locals_max_length: int = LOCALS_MAX_LENGTH,
-        locals_max_string: int = LOCALS_MAX_STRING,
-        locals_hide_dunder: bool = True,
-        locals_hide_sunder: bool = False,
-    ) -> Trace:
-        """Extract traceback information.
-
-        Args:
-            exc_type (Type[BaseException]): Exception type.
-            exc_value (BaseException): Exception value.
-            traceback (TracebackType): Python Traceback object.
-            show_locals (bool, optional): Enable display of local variables. Defaults to False.
-            locals_max_length (int, optional): Maximum length of containers before abbreviating, or None for no abbreviation.
-                Defaults to 10.
-            locals_max_string (int, optional): Maximum length of string before truncating, or None to disable. Defaults to 80.
-            locals_hide_dunder (bool, optional): Hide locals prefixed with double underscore. Defaults to True.
-            locals_hide_sunder (bool, optional): Hide locals prefixed with single underscore. Defaults to False.
-
-        Returns:
-            Trace: A Trace instance which you can use to construct a `Traceback`.
-        """
-
-        stacks: List[Stack] = []
-        is_cause = False
-
-        from pip._vendor.rich import _IMPORT_CWD
-
-        def safe_str(_object: Any) -> str:
-            """Don't allow exceptions from __str__ to propagate."""
-            try:
-                return str(_object)
-            except Exception:
-                return "<exception str() failed>"
-
-        while True:
-            stack = Stack(
-                exc_type=safe_str(exc_type.__name__),
-                exc_value=safe_str(exc_value),
-                is_cause=is_cause,
-            )
-
-            if isinstance(exc_value, SyntaxError):
-                stack.syntax_error = _SyntaxError(
-                    offset=exc_value.offset or 0,
-                    filename=exc_value.filename or "?",
-                    lineno=exc_value.lineno or 0,
-                    line=exc_value.text or "",
-                    msg=exc_value.msg,
-                )
-
-            stacks.append(stack)
-            append = stack.frames.append
-
-            def get_locals(
-                iter_locals: Iterable[Tuple[str, object]]
-            ) -> Iterable[Tuple[str, object]]:
-                """Extract locals from an iterator of key pairs."""
-                if not (locals_hide_dunder or locals_hide_sunder):
-                    yield from iter_locals
-                    return
-                for key, value in iter_locals:
-                    if locals_hide_dunder and key.startswith("__"):
-                        continue
-                    if locals_hide_sunder and key.startswith("_"):
-                        continue
-                    yield key, value
-
-            for frame_summary, line_no in walk_tb(traceback):
-                filename = frame_summary.f_code.co_filename
-                if filename and not filename.startswith("<"):
-                    if not os.path.isabs(filename):
-                        filename = os.path.join(_IMPORT_CWD, filename)
-                if frame_summary.f_locals.get("_rich_traceback_omit", False):
-                    continue
-
-                frame = Frame(
-                    filename=filename or "?",
-                    lineno=line_no,
-                    name=frame_summary.f_code.co_name,
-                    locals={
-                        key: pretty.traverse(
-                            value,
-                            max_length=locals_max_length,
-                            max_string=locals_max_string,
-                        )
-                        for key, value in get_locals(frame_summary.f_locals.items())
-                    }
-                    if show_locals
-                    else None,
-                )
-                append(frame)
-                if frame_summary.f_locals.get("_rich_traceback_guard", False):
-                    del stack.frames[:]
-
-            cause = getattr(exc_value, "__cause__", None)
-            if cause:
-                exc_type = cause.__class__
-                exc_value = cause
-                # __traceback__ can be None, e.g. for exceptions raised by the
-                # 'multiprocessing' module
-                traceback = cause.__traceback__
-                is_cause = True
-                continue
-
-            cause = exc_value.__context__
-            if cause and not getattr(exc_value, "__suppress_context__", False):
-                exc_type = cause.__class__
-                exc_value = cause
-                traceback = cause.__traceback__
-                is_cause = False
-                continue
-            # No cover, code is reached but coverage doesn't recognize it.
-            break  # pragma: no cover
-
-        trace = Trace(stacks=stacks)
-        return trace
-
-    def __rich_console__(
-        self, console: Console, options: ConsoleOptions
-    ) -> RenderResult:
-        theme = self.theme
-        background_style = theme.get_background_style()
-        token_style = theme.get_style_for_token
-
-        traceback_theme = Theme(
-            {
-                "pretty": token_style(TextToken),
-                "pygments.text": token_style(Token),
-                "pygments.string": token_style(String),
-                "pygments.function": token_style(Name.Function),
-                "pygments.number": token_style(Number),
-                "repr.indent": token_style(Comment) + Style(dim=True),
-                "repr.str": token_style(String),
-                "repr.brace": token_style(TextToken) + Style(bold=True),
-                "repr.number": token_style(Number),
-                "repr.bool_true": token_style(Keyword.Constant),
-                "repr.bool_false": token_style(Keyword.Constant),
-                "repr.none": token_style(Keyword.Constant),
-                "scope.border": token_style(String.Delimiter),
-                "scope.equals": token_style(Operator),
-                "scope.key": token_style(Name),
-                "scope.key.special": token_style(Name.Constant) + Style(dim=True),
-            },
-            inherit=False,
-        )
-
-        highlighter = ReprHighlighter()
-        for last, stack in loop_last(reversed(self.trace.stacks)):
-            if stack.frames:
-                stack_renderable: ConsoleRenderable = Panel(
-                    self._render_stack(stack),
-                    title="[traceback.title]Traceback [dim](most recent call last)",
-                    style=background_style,
-                    border_style="traceback.border",
-                    expand=True,
-                    padding=(0, 1),
-                )
-                stack_renderable = Constrain(stack_renderable, self.width)
-                with console.use_theme(traceback_theme):
-                    yield stack_renderable
-            if stack.syntax_error is not None:
-                with console.use_theme(traceback_theme):
-                    yield Constrain(
-                        Panel(
-                            self._render_syntax_error(stack.syntax_error),
-                            style=background_style,
-                            border_style="traceback.border.syntax_error",
-                            expand=True,
-                            padding=(0, 1),
-                            width=self.width,
-                        ),
-                        self.width,
-                    )
-                yield Text.assemble(
-                    (f"{stack.exc_type}: ", "traceback.exc_type"),
-                    highlighter(stack.syntax_error.msg),
-                )
-            elif stack.exc_value:
-                yield Text.assemble(
-                    (f"{stack.exc_type}: ", "traceback.exc_type"),
-                    highlighter(stack.exc_value),
-                )
-            else:
-                yield Text.assemble((f"{stack.exc_type}", "traceback.exc_type"))
-
-            if not last:
-                if stack.is_cause:
-                    yield Text.from_markup(
-                        "\n[i]The above exception was the direct cause of the following exception:\n",
-                    )
-                else:
-                    yield Text.from_markup(
-                        "\n[i]During handling of the above exception, another exception occurred:\n",
-                    )
-
-    @group()
-    def _render_syntax_error(self, syntax_error: _SyntaxError) -> RenderResult:
-        highlighter = ReprHighlighter()
-        path_highlighter = PathHighlighter()
-        if syntax_error.filename != "<stdin>":
-            if os.path.exists(syntax_error.filename):
-                text = Text.assemble(
-                    (f" {syntax_error.filename}", "pygments.string"),
-                    (":", "pygments.text"),
-                    (str(syntax_error.lineno), "pygments.number"),
-                    style="pygments.text",
-                )
-                yield path_highlighter(text)
-        syntax_error_text = highlighter(syntax_error.line.rstrip())
-        syntax_error_text.no_wrap = True
-        offset = min(syntax_error.offset - 1, len(syntax_error_text))
-        syntax_error_text.stylize("bold underline", offset, offset)
-        syntax_error_text += Text.from_markup(
-            "\n" + " " * offset + "[traceback.offset]▲[/]",
-            style="pygments.text",
-        )
-        yield syntax_error_text
-
-    @classmethod
-    def _guess_lexer(cls, filename: str, code: str) -> str:
-        ext = os.path.splitext(filename)[-1]
-        if not ext:
-            # No extension, look at first line to see if it is a hashbang
-            # Note, this is an educated guess and not a guarantee
-            # If it fails, the only downside is that the code is highlighted strangely
-            new_line_index = code.index("\n")
-            first_line = code[:new_line_index] if new_line_index != -1 else code
-            if first_line.startswith("#!") and "python" in first_line.lower():
-                return "python"
-        try:
-            return cls.LEXERS.get(ext) or guess_lexer_for_filename(filename, code).name
-        except ClassNotFound:
-            return "text"
-
-    @group()
-    def _render_stack(self, stack: Stack) -> RenderResult:
-        path_highlighter = PathHighlighter()
-        theme = self.theme
-
-        def read_code(filename: str) -> str:
-            """Read files, and cache results on filename.
-
-            Args:
-                filename (str): Filename to read
-
-            Returns:
-                str: Contents of file
-            """
-            return "".join(linecache.getlines(filename))
-
-        def render_locals(frame: Frame) -> Iterable[ConsoleRenderable]:
-            if frame.locals:
-                yield render_scope(
-                    frame.locals,
-                    title="locals",
-                    indent_guides=self.indent_guides,
-                    max_length=self.locals_max_length,
-                    max_string=self.locals_max_string,
-                )
-
-        exclude_frames: Optional[range] = None
-        if self.max_frames != 0:
-            exclude_frames = range(
-                self.max_frames // 2,
-                len(stack.frames) - self.max_frames // 2,
-            )
-
-        excluded = False
-        for frame_index, frame in enumerate(stack.frames):
-
-            if exclude_frames and frame_index in exclude_frames:
-                excluded = True
-                continue
-
-            if excluded:
-                assert exclude_frames is not None
-                yield Text(
-                    f"\n... {len(exclude_frames)} frames hidden ...",
-                    justify="center",
-                    style="traceback.error",
-                )
-                excluded = False
-
-            first = frame_index == 0
-            frame_filename = frame.filename
-            suppressed = any(frame_filename.startswith(path) for path in self.suppress)
-
-            if os.path.exists(frame.filename):
-                text = Text.assemble(
-                    path_highlighter(Text(frame.filename, style="pygments.string")),
-                    (":", "pygments.text"),
-                    (str(frame.lineno), "pygments.number"),
-                    " in ",
-                    (frame.name, "pygments.function"),
-                    style="pygments.text",
-                )
-            else:
-                text = Text.assemble(
-                    "in ",
-                    (frame.name, "pygments.function"),
-                    (":", "pygments.text"),
-                    (str(frame.lineno), "pygments.number"),
-                    style="pygments.text",
-                )
-            if not frame.filename.startswith("<") and not first:
-                yield ""
-            yield text
-            if frame.filename.startswith("<"):
-                yield from render_locals(frame)
-                continue
-            if not suppressed:
-                try:
-                    code = read_code(frame.filename)
-                    if not code:
-                        # code may be an empty string if the file doesn't exist, OR
-                        # if the traceback filename is generated dynamically
-                        continue
-                    lexer_name = self._guess_lexer(frame.filename, code)
-                    syntax = Syntax(
-                        code,
-                        lexer_name,
-                        theme=theme,
-                        line_numbers=True,
-                        line_range=(
-                            frame.lineno - self.extra_lines,
-                            frame.lineno + self.extra_lines,
-                        ),
-                        highlight_lines={frame.lineno},
-                        word_wrap=self.word_wrap,
-                        code_width=88,
-                        indent_guides=self.indent_guides,
-                        dedent=False,
-                    )
-                    yield ""
-                except Exception as error:
-                    yield Text.assemble(
-                        (f"\n{error}", "traceback.error"),
-                    )
-                else:
-                    yield (
-                        Columns(
-                            [
-                                syntax,
-                                *render_locals(frame),
-                            ],
-                            padding=1,
-                        )
-                        if frame.locals
-                        else syntax
-                    )
-
-
-if __name__ == "__main__":  # pragma: no cover
-
-    from .console import Console
-
-    console = Console()
-    import sys
-
-    def bar(a: Any) -> None:  # 这是对亚洲语言支持的测试。面对模棱两可的想法，拒绝猜测的诱惑
-        one = 1
-        print(one / a)
-
-    def foo(a: Any) -> None:
-        _rich_traceback_guard = True
-        zed = {
-            "characters": {
-                "Paul Atreides",
-                "Vladimir Harkonnen",
-                "Thufir Hawat",
-                "Duncan Idaho",
-            },
-            "atomic_types": (None, False, True),
-        }
-        bar(a)
-
-    def error() -> None:
-
-        try:
-            try:
-                foo(0)
-            except:
-                slfkjsldkfj  # type: ignore[name-defined]
-        except:
-            console.print_exception(show_locals=True)
-
-    error()
diff --git a/spaces/prerna9811/Chord/portaudio/src/common/pa_process.h b/spaces/prerna9811/Chord/portaudio/src/common/pa_process.h
deleted file mode 100644
index 444bdf54513758e849cbda67f6c2e3e190438dc4..0000000000000000000000000000000000000000
--- a/spaces/prerna9811/Chord/portaudio/src/common/pa_process.h
+++ /dev/null
@@ -1,754 +0,0 @@
-#ifndef PA_PROCESS_H
-#define PA_PROCESS_H
-/*
- * $Id$
- * Portable Audio I/O Library callback buffer processing adapters
- *
- * Based on the Open Source API proposed by Ross Bencina
- * Copyright (c) 1999-2002 Phil Burk, 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.
- */
-
-/** @file
- @ingroup common_src
-
- @brief Buffer Processor prototypes. A Buffer Processor performs buffer length
- adaption, coordinates sample format conversion, and interleaves/deinterleaves
- channels.
-
- <h3>Overview</h3>
-
- The "Buffer Processor" (PaUtilBufferProcessor) manages conversion of audio
- data from host buffers to user buffers and back again. Where required, the
- buffer processor takes care of converting between host and user sample formats,
- interleaving and deinterleaving multichannel buffers, and adapting between host
- and user buffers with different lengths. The buffer processor may be used with
- full and half duplex streams, for both callback streams and blocking read/write
- streams.
-
- One of the important capabilities provided by the buffer processor is
- the ability to adapt between user and host buffer sizes of different lengths
- with minimum latency. Although this task is relatively easy to perform when
- the host buffer size is an integer multiple of the user buffer size, the
- problem is more complicated when this is not the case - especially for
- full-duplex callback streams. Where necessary the adaption is implemented by
- internally buffering some input and/or output data. The buffer adation
- algorithm used by the buffer processor was originally implemented by
- Stephan Letz for the ASIO version of PortAudio, and is described in his
- Callback_adaption_.pdf which is included in the distribution.
-
- The buffer processor performs sample conversion using the functions provided
- by pa_converters.c.
-
- The following sections provide an overview of how to use the buffer processor.
- Interested readers are advised to consult the host API implementations for
- examples of buffer processor usage.
-
-
- <h4>Initialization, resetting and termination</h4>
-
- When a stream is opened, the buffer processor should be initialized using
- PaUtil_InitializeBufferProcessor. This function initializes internal state
- and allocates temporary buffers as necessary according to the supplied
- configuration parameters. Some of the parameters correspond to those requested
- by the user in their call to Pa_OpenStream(), others reflect the requirements
- of the host API implementation - they indicate host buffer sizes, formats,
- and the type of buffering which the Host API uses. The buffer processor should
- be initialized for callback streams and blocking read/write streams.
-
- Call PaUtil_ResetBufferProcessor to clear any sample data which is present
- in the buffer processor before starting to use it (for example when
- Pa_StartStream is called).
-
- When the buffer processor is no longer used call
- PaUtil_TerminateBufferProcessor.
-
-
- <h4>Using the buffer processor for a callback stream</h4>
-
- The buffer processor's role in a callback stream is to take host input buffers
- process them with the stream callback, and fill host output buffers. For a
- full duplex stream, the buffer processor handles input and output simultaneously
- due to the requirements of the minimum-latency buffer adation algorithm.
-
- When a host buffer becomes available, the implementation should call
- the buffer processor to process the buffer. The buffer processor calls the
- stream callback to consume and/or produce audio data as necessary. The buffer
- processor will convert sample formats, interleave/deinterleave channels,
- and slice or chunk the data to the appropriate buffer lengths according to
- the requirements of the stream callback and the host API.
-
- To process a host buffer (or a pair of host buffers for a full-duplex stream)
- use the following calling sequence:
-
- -# Call PaUtil_BeginBufferProcessing
- -# For a stream which takes input:
-    - Call PaUtil_SetInputFrameCount with the number of frames in the host input
-        buffer.
-    - Call one of the following functions one or more times to tell the
-        buffer processor about the host input buffer(s): PaUtil_SetInputChannel,
-        PaUtil_SetInterleavedInputChannels, PaUtil_SetNonInterleavedInputChannel.
-        Which function you call will depend on whether the host buffer(s) are
-        interleaved or not.
-    - If the available host data is split across two buffers (for example a
-        data range at the end of a circular buffer and another range at the
-        beginning of the circular buffer), also call
-        PaUtil_Set2ndInputFrameCount, PaUtil_Set2ndInputChannel,
-        PaUtil_Set2ndInterleavedInputChannels,
-        PaUtil_Set2ndNonInterleavedInputChannel as necessary to tell the buffer
-        processor about the second buffer.
- -# For a stream which generates output:
-    - Call PaUtil_SetOutputFrameCount with the number of frames in the host
-        output buffer.
-    - Call one of the following functions one or more times to tell the
-        buffer processor about the host output buffer(s): PaUtil_SetOutputChannel,
-        PaUtil_SetInterleavedOutputChannels, PaUtil_SetNonInterleavedOutputChannel.
-        Which function you call will depend on whether the host buffer(s) are
-        interleaved or not.
-    - If the available host output buffer space is split across two buffers
-        (for example a data range at the end of a circular buffer and another
-        range at the beginning of the circular buffer), call
-        PaUtil_Set2ndOutputFrameCount, PaUtil_Set2ndOutputChannel,
-        PaUtil_Set2ndInterleavedOutputChannels,
-        PaUtil_Set2ndNonInterleavedOutputChannel as necessary to tell the buffer
-        processor about the second buffer.
- -# Call PaUtil_EndBufferProcessing, this function performs the actual data
-    conversion and processing.
-
-
- <h4>Using the buffer processor for a blocking read/write stream</h4>
-
- Blocking read/write streams use the buffer processor to convert and copy user
- output data to a host buffer, and to convert and copy host input data to
- the user's buffer. The buffer processor does not perform any buffer adaption.
- When using the buffer processor in a blocking read/write stream the input and
- output conversion are performed separately by the PaUtil_CopyInput and
- PaUtil_CopyOutput functions.
-
- To copy data from a host input buffer to the buffer(s) which the user supplies
- to Pa_ReadStream, use the following calling sequence.
-
- - Repeat the following three steps until the user buffer(s) have been filled
-    with samples from the host input buffers:
-     -# Call PaUtil_SetInputFrameCount with the number of frames in the host
-        input buffer.
-     -# Call one of the following functions one or more times to tell the
-        buffer processor about the host input buffer(s): PaUtil_SetInputChannel,
-        PaUtil_SetInterleavedInputChannels, PaUtil_SetNonInterleavedInputChannel.
-        Which function you call will depend on whether the host buffer(s) are
-        interleaved or not.
-     -# Call PaUtil_CopyInput with the user buffer pointer (or a copy of the
-        array of buffer pointers for a non-interleaved stream) passed to
-        Pa_ReadStream, along with the number of frames in the user buffer(s).
-        Be careful to pass a <i>copy</i> of the user buffer pointers to
-        PaUtil_CopyInput because PaUtil_CopyInput advances the pointers to
-        the start of the next region to copy.
- - PaUtil_CopyInput will not copy more data than is available in the
-    host buffer(s), so the above steps need to be repeated until the user
-    buffer(s) are full.
-
-
- To copy data to the host output buffer from the user buffers(s) supplied
- to Pa_WriteStream use the following calling sequence.
-
- - Repeat the following three steps until all frames from the user buffer(s)
-    have been copied to the host API:
-     -# Call PaUtil_SetOutputFrameCount with the number of frames in the host
-        output buffer.
-     -# Call one of the following functions one or more times to tell the
-        buffer processor about the host output buffer(s): PaUtil_SetOutputChannel,
-        PaUtil_SetInterleavedOutputChannels, PaUtil_SetNonInterleavedOutputChannel.
-        Which function you call will depend on whether the host buffer(s) are
-        interleaved or not.
-     -# Call PaUtil_CopyOutput with the user buffer pointer (or a copy of the
-        array of buffer pointers for a non-interleaved stream) passed to
-        Pa_WriteStream, along with the number of frames in the user buffer(s).
-        Be careful to pass a <i>copy</i> of the user buffer pointers to
-        PaUtil_CopyOutput because PaUtil_CopyOutput advances the pointers to
-        the start of the next region to copy.
- - PaUtil_CopyOutput will not copy more data than fits in the host buffer(s),
-    so the above steps need to be repeated until all user data is copied.
-*/
-
-
-#include "portaudio.h"
-#include "pa_converters.h"
-#include "pa_dither.h"
-
-#ifdef __cplusplus
-extern "C"
-{
-#endif /* __cplusplus */
-
-
-/** @brief Mode flag passed to PaUtil_InitializeBufferProcessor indicating the type
- of buffering that the host API uses.
-
- The mode used depends on whether the host API or the implementation manages
- the buffers, and how these buffers are used (scatter gather, circular buffer).
-*/
-typedef enum {
-/** The host buffer size is a fixed known size. */
-    paUtilFixedHostBufferSize,
-
-/** The host buffer size may vary, but has a known maximum size. */
-    paUtilBoundedHostBufferSize,
-
-/** Nothing is known about the host buffer size. */
-    paUtilUnknownHostBufferSize,
-
-/** The host buffer size varies, and the client does not require the buffer
- processor to consume all of the input and fill all of the output buffer. This
- is useful when the implementation has access to the host API's circular buffer
- and only needs to consume/fill some of it, not necessarily all of it, with each
- call to the buffer processor. This is the only mode where
- PaUtil_EndBufferProcessing() may not consume the whole buffer.
-*/
-    paUtilVariableHostBufferSizePartialUsageAllowed
-}PaUtilHostBufferSizeMode;
-
-
-/** @brief An auxiliary data structure used internally by the buffer processor
- to represent host input and output buffers. */
-typedef struct PaUtilChannelDescriptor{
-    void *data;
-    unsigned int stride;  /**< stride in samples, not bytes */
-}PaUtilChannelDescriptor;
-
-
-/** @brief The main buffer processor data structure.
-
- Allocate one of these, initialize it with PaUtil_InitializeBufferProcessor
- and terminate it with PaUtil_TerminateBufferProcessor.
-*/
-typedef struct {
-    unsigned long framesPerUserBuffer;
-    unsigned long framesPerHostBuffer;
-
-    PaUtilHostBufferSizeMode hostBufferSizeMode;
-    int useNonAdaptingProcess;
-    int userOutputSampleFormatIsEqualToHost;
-    int userInputSampleFormatIsEqualToHost;
-    unsigned long framesPerTempBuffer;
-
-    unsigned int inputChannelCount;
-    unsigned int bytesPerHostInputSample;
-    unsigned int bytesPerUserInputSample;
-    int userInputIsInterleaved;
-    PaUtilConverter *inputConverter;
-    PaUtilZeroer *inputZeroer;
-
-    unsigned int outputChannelCount;
-    unsigned int bytesPerHostOutputSample;
-    unsigned int bytesPerUserOutputSample;
-    int userOutputIsInterleaved;
-    PaUtilConverter *outputConverter;
-    PaUtilZeroer *outputZeroer;
-
-    unsigned long initialFramesInTempInputBuffer;
-    unsigned long initialFramesInTempOutputBuffer;
-
-    void *tempInputBuffer;          /**< used for slips, block adaption, and conversion. */
-    void **tempInputBufferPtrs;     /**< storage for non-interleaved buffer pointers, NULL for interleaved user input */
-    unsigned long framesInTempInputBuffer; /**< frames remaining in input buffer from previous adaption iteration */
-
-    void *tempOutputBuffer;         /**< used for slips, block adaption, and conversion. */
-    void **tempOutputBufferPtrs;    /**< storage for non-interleaved buffer pointers, NULL for interleaved user output */
-    unsigned long framesInTempOutputBuffer; /**< frames remaining in input buffer from previous adaption iteration */
-
-    PaStreamCallbackTimeInfo *timeInfo;
-
-    PaStreamCallbackFlags callbackStatusFlags;
-
-    int hostInputIsInterleaved;
-    unsigned long hostInputFrameCount[2];
-    PaUtilChannelDescriptor *hostInputChannels[2]; /**< pointers to arrays of channel descriptors.
-                                                        pointers are NULL for half-duplex output processing.
-                                                        hostInputChannels[i].data is NULL when the caller
-                                                        calls PaUtil_SetNoInput()
-                                                        */
-    int hostOutputIsInterleaved;
-    unsigned long hostOutputFrameCount[2];
-    PaUtilChannelDescriptor *hostOutputChannels[2]; /**< pointers to arrays of channel descriptors.
-                                                         pointers are NULL for half-duplex input processing.
-                                                         hostOutputChannels[i].data is NULL when the caller
-                                                         calls PaUtil_SetNoOutput()
-                                                         */
-
-    PaUtilTriangularDitherGenerator ditherGenerator;
-
-    double samplePeriod;
-
-    PaStreamCallback *streamCallback;
-    void *userData;
-} PaUtilBufferProcessor;
-
-
-/** @name Initialization, termination, resetting and info */
-/*@{*/
-
-/** Initialize a buffer processor's representation stored in a
- PaUtilBufferProcessor structure. Be sure to call
- PaUtil_TerminateBufferProcessor after finishing with a buffer processor.
-
- @param bufferProcessor The buffer processor structure to initialize.
-
- @param inputChannelCount The number of input channels as passed to
- Pa_OpenStream or 0 for an output-only stream.
-
- @param userInputSampleFormat Format of user input samples, as passed to
- Pa_OpenStream. This parameter is ignored for ouput-only streams.
-
- @param hostInputSampleFormat Format of host input samples. This parameter is
- ignored for output-only streams. See note about host buffer interleave below.
-
- @param outputChannelCount The number of output channels as passed to
- Pa_OpenStream or 0 for an input-only stream.
-
- @param userOutputSampleFormat Format of user output samples, as passed to
- Pa_OpenStream. This parameter is ignored for input-only streams.
-
- @param hostOutputSampleFormat Format of host output samples. This parameter is
- ignored for input-only streams. See note about host buffer interleave below.
-
- @param sampleRate Sample rate of the stream. The more accurate this is the
- better - it is used for updating time stamps when adapting buffers.
-
- @param streamFlags Stream flags as passed to Pa_OpenStream, this parameter is
- used for selecting special sample conversion options such as clipping and
- dithering.
-
- @param framesPerUserBuffer Number of frames per user buffer, as requested
- by the framesPerBuffer parameter to Pa_OpenStream. This parameter may be
- zero to indicate that the user will accept any (and varying) buffer sizes.
-
- @param framesPerHostBuffer Specifies the number of frames per host buffer
- for the fixed buffer size mode, and the maximum number of frames
- per host buffer for the bounded host buffer size mode. It is ignored for
- the other modes.
-
- @param hostBufferSizeMode A mode flag indicating the size variability of
- host buffers that will be passed to the buffer processor. See
- PaUtilHostBufferSizeMode for further details.
-
- @param streamCallback The user stream callback passed to Pa_OpenStream.
-
- @param userData The user data field passed to Pa_OpenStream.
-
- @note The interleave flag is ignored for host buffer formats. Host
- interleave is determined by the use of different SetInput and SetOutput
- functions.
-
- @return An error code indicating whether the initialization was successful.
- If the error code is not PaNoError, the buffer processor was not initialized
- and should not be used.
-
- @see Pa_OpenStream, PaUtilHostBufferSizeMode, PaUtil_TerminateBufferProcessor
-*/
-PaError PaUtil_InitializeBufferProcessor( PaUtilBufferProcessor* bufferProcessor,
-            int inputChannelCount, PaSampleFormat userInputSampleFormat,
-            PaSampleFormat hostInputSampleFormat,
-            int outputChannelCount, PaSampleFormat userOutputSampleFormat,
-            PaSampleFormat hostOutputSampleFormat,
-            double sampleRate,
-            PaStreamFlags streamFlags,
-            unsigned long framesPerUserBuffer, /* 0 indicates don't care */
-            unsigned long framesPerHostBuffer,
-            PaUtilHostBufferSizeMode hostBufferSizeMode,
-            PaStreamCallback *streamCallback, void *userData );
-
-
-/** Terminate a buffer processor's representation. Deallocates any temporary
- buffers allocated by PaUtil_InitializeBufferProcessor.
-
- @param bufferProcessor The buffer processor structure to terminate.
-
- @see PaUtil_InitializeBufferProcessor.
-*/
-void PaUtil_TerminateBufferProcessor( PaUtilBufferProcessor* bufferProcessor );
-
-
-/** Clear any internally buffered data. If you call
- PaUtil_InitializeBufferProcessor in your OpenStream routine, make sure you
- call PaUtil_ResetBufferProcessor in your StartStream call.
-
- @param bufferProcessor The buffer processor to reset.
-*/
-void PaUtil_ResetBufferProcessor( PaUtilBufferProcessor* bufferProcessor );
-
-
-/** Retrieve the input latency of a buffer processor, in frames.
-
- @param bufferProcessor The buffer processor examine.
-
- @return The input latency introduced by the buffer processor, in frames.
-
- @see PaUtil_GetBufferProcessorOutputLatencyFrames
-*/
-unsigned long PaUtil_GetBufferProcessorInputLatencyFrames( PaUtilBufferProcessor* bufferProcessor );
-
-/** Retrieve the output latency of a buffer processor, in frames.
-
- @param bufferProcessor The buffer processor examine.
-
- @return The output latency introduced by the buffer processor, in frames.
-
- @see PaUtil_GetBufferProcessorInputLatencyFrames
-*/
-unsigned long PaUtil_GetBufferProcessorOutputLatencyFrames( PaUtilBufferProcessor* bufferProcessor );
-
-/*@}*/
-
-
-/** @name Host buffer pointer configuration
-
- Functions to set host input and output buffers, used by both callback streams
- and blocking read/write streams.
-*/
-/*@{*/
-
-
-/** Set the number of frames in the input host buffer(s) specified by the
- PaUtil_Set*InputChannel functions.
-
- @param bufferProcessor The buffer processor.
-
- @param frameCount The number of host input frames. A 0 frameCount indicates to
- use the framesPerHostBuffer value passed to PaUtil_InitializeBufferProcessor.
-
- @see PaUtil_SetNoInput, PaUtil_SetInputChannel,
- PaUtil_SetInterleavedInputChannels, PaUtil_SetNonInterleavedInputChannel
-*/
-void PaUtil_SetInputFrameCount( PaUtilBufferProcessor* bufferProcessor,
-        unsigned long frameCount );
-
-
-/** Indicate that no input is available. This function should be used when
- priming the output of a full-duplex stream opened with the
- paPrimeOutputBuffersUsingStreamCallback flag. Note that it is not necessary
- to call this or any other PaUtil_Set*Input* functions for ouput-only streams.
-
- @param bufferProcessor The buffer processor.
-*/
-void PaUtil_SetNoInput( PaUtilBufferProcessor* bufferProcessor );
-
-
-/** Provide the buffer processor with a pointer to a host input channel.
-
- @param bufferProcessor The buffer processor.
- @param channel The channel number.
- @param data The buffer.
- @param stride The stride from one sample to the next, in samples. For
- interleaved host buffers, the stride will usually be the same as the number of
- channels in the buffer.
-*/
-void PaUtil_SetInputChannel( PaUtilBufferProcessor* bufferProcessor,
-        unsigned int channel, void *data, unsigned int stride );
-
-
-/** Provide the buffer processor with a pointer to an number of interleaved
- host input channels.
-
- @param bufferProcessor The buffer processor.
- @param firstChannel The first channel number.
- @param data The buffer.
- @param channelCount The number of interleaved channels in the buffer. If
- channelCount is zero, the number of channels specified to
- PaUtil_InitializeBufferProcessor will be used.
-*/
-void PaUtil_SetInterleavedInputChannels( PaUtilBufferProcessor* bufferProcessor,
-        unsigned int firstChannel, void *data, unsigned int channelCount );
-
-
-/** Provide the buffer processor with a pointer to one non-interleaved host
- output channel.
-
- @param bufferProcessor The buffer processor.
- @param channel The channel number.
- @param data The buffer.
-*/
-void PaUtil_SetNonInterleavedInputChannel( PaUtilBufferProcessor* bufferProcessor,
-        unsigned int channel, void *data );
-
-
-/** Use for the second buffer half when the input buffer is split in two halves.
- @see PaUtil_SetInputFrameCount
-*/
-void PaUtil_Set2ndInputFrameCount( PaUtilBufferProcessor* bufferProcessor,
-        unsigned long frameCount );
-
-/** Use for the second buffer half when the input buffer is split in two halves.
- @see PaUtil_SetInputChannel
-*/
-void PaUtil_Set2ndInputChannel( PaUtilBufferProcessor* bufferProcessor,
-        unsigned int channel, void *data, unsigned int stride );
-
-/** Use for the second buffer half when the input buffer is split in two halves.
- @see PaUtil_SetInterleavedInputChannels
-*/
-void PaUtil_Set2ndInterleavedInputChannels( PaUtilBufferProcessor* bufferProcessor,
-        unsigned int firstChannel, void *data, unsigned int channelCount );
-
-/** Use for the second buffer half when the input buffer is split in two halves.
- @see PaUtil_SetNonInterleavedInputChannel
-*/
-void PaUtil_Set2ndNonInterleavedInputChannel( PaUtilBufferProcessor* bufferProcessor,
-        unsigned int channel, void *data );
-
-
-/** Set the number of frames in the output host buffer(s) specified by the
- PaUtil_Set*OutputChannel functions.
-
- @param bufferProcessor The buffer processor.
-
- @param frameCount The number of host output frames. A 0 frameCount indicates to
- use the framesPerHostBuffer value passed to PaUtil_InitializeBufferProcessor.
-
- @see PaUtil_SetOutputChannel, PaUtil_SetInterleavedOutputChannels,
- PaUtil_SetNonInterleavedOutputChannel
-*/
-void PaUtil_SetOutputFrameCount( PaUtilBufferProcessor* bufferProcessor,
-        unsigned long frameCount );
-
-
-/** Indicate that the output will be discarded. This function should be used
- when implementing the paNeverDropInput mode for full duplex streams.
-
- @param bufferProcessor The buffer processor.
-*/
-void PaUtil_SetNoOutput( PaUtilBufferProcessor* bufferProcessor );
-
-
-/** Provide the buffer processor with a pointer to a host output channel.
-
- @param bufferProcessor The buffer processor.
- @param channel The channel number.
- @param data The buffer.
- @param stride The stride from one sample to the next, in samples. For
- interleaved host buffers, the stride will usually be the same as the number of
- channels in the buffer.
-*/
-void PaUtil_SetOutputChannel( PaUtilBufferProcessor* bufferProcessor,
-        unsigned int channel, void *data, unsigned int stride );
-
-
-/** Provide the buffer processor with a pointer to a number of interleaved
- host output channels.
-
- @param bufferProcessor The buffer processor.
- @param firstChannel The first channel number.
- @param data The buffer.
- @param channelCount The number of interleaved channels in the buffer. If
- channelCount is zero, the number of channels specified to
- PaUtil_InitializeBufferProcessor will be used.
-*/
-void PaUtil_SetInterleavedOutputChannels( PaUtilBufferProcessor* bufferProcessor,
-        unsigned int firstChannel, void *data, unsigned int channelCount );
-
-
-/** Provide the buffer processor with a pointer to one non-interleaved host
- output channel.
-
- @param bufferProcessor The buffer processor.
- @param channel The channel number.
- @param data The buffer.
-*/
-void PaUtil_SetNonInterleavedOutputChannel( PaUtilBufferProcessor* bufferProcessor,
-        unsigned int channel, void *data );
-
-
-/** Use for the second buffer half when the output buffer is split in two halves.
- @see PaUtil_SetOutputFrameCount
-*/
-void PaUtil_Set2ndOutputFrameCount( PaUtilBufferProcessor* bufferProcessor,
-        unsigned long frameCount );
-
-/** Use for the second buffer half when the output buffer is split in two halves.
- @see PaUtil_SetOutputChannel
-*/
-void PaUtil_Set2ndOutputChannel( PaUtilBufferProcessor* bufferProcessor,
-        unsigned int channel, void *data, unsigned int stride );
-
-/** Use for the second buffer half when the output buffer is split in two halves.
- @see PaUtil_SetInterleavedOutputChannels
-*/
-void PaUtil_Set2ndInterleavedOutputChannels( PaUtilBufferProcessor* bufferProcessor,
-        unsigned int firstChannel, void *data, unsigned int channelCount );
-
-/** Use for the second buffer half when the output buffer is split in two halves.
- @see PaUtil_SetNonInterleavedOutputChannel
-*/
-void PaUtil_Set2ndNonInterleavedOutputChannel( PaUtilBufferProcessor* bufferProcessor,
-        unsigned int channel, void *data );
-
-/*@}*/
-
-
-/** @name Buffer processing functions for callback streams
-*/
-/*@{*/
-
-/** Commence processing a host buffer (or a pair of host buffers in the
- full-duplex case) for a callback stream.
-
- @param bufferProcessor The buffer processor.
-
- @param timeInfo Timing information for the first sample of the host
- buffer(s). This information may be adjusted when buffer adaption is being
- performed.
-
- @param callbackStatusFlags Flags indicating whether underruns and overruns
- have occurred since the last time the buffer processor was called.
-*/
-void PaUtil_BeginBufferProcessing( PaUtilBufferProcessor* bufferProcessor,
-        PaStreamCallbackTimeInfo* timeInfo, PaStreamCallbackFlags callbackStatusFlags );
-
-
-/** Finish processing a host buffer (or a pair of host buffers in the
- full-duplex case) for a callback stream.
-
- @param bufferProcessor The buffer processor.
-
- @param callbackResult On input, indicates a previous callback result, and on
- exit, the result of the user stream callback, if it is called.
- On entry callbackResult should contain one of { paContinue, paComplete, or
- paAbort}. If paComplete is passed, the stream callback will not be called
- but any audio that was generated by previous stream callbacks will be copied
- to the output buffer(s). You can check whether the buffer processor's internal
- buffer is empty by calling PaUtil_IsBufferProcessorOutputEmpty.
-
- If the stream callback is called its result is stored in *callbackResult. If
- the stream callback returns paComplete or paAbort, all output buffers will be
- full of valid data - some of which may be zeros to account for data that
- wasn't generated by the terminating callback.
-
- @return The number of frames processed. This usually corresponds to the
- number of frames specified by the PaUtil_Set*FrameCount functions, except in
- the paUtilVariableHostBufferSizePartialUsageAllowed buffer size mode when a
- smaller value may be returned.
-*/
-unsigned long PaUtil_EndBufferProcessing( PaUtilBufferProcessor* bufferProcessor,
-        int *callbackResult );
-
-
-/** Determine whether any callback generated output remains in the buffer
- processor's internal buffers. This method may be used to determine when to
- continue calling PaUtil_EndBufferProcessing() after the callback has returned
- a callbackResult of paComplete.
-
- @param bufferProcessor The buffer processor.
-
- @return Returns non-zero when callback generated output remains in the internal
- buffer and zero (0) when there internal buffer contains no callback generated
- data.
-*/
-int PaUtil_IsBufferProcessorOutputEmpty( PaUtilBufferProcessor* bufferProcessor );
-
-/*@}*/
-
-
-/** @name Buffer processing functions for blocking read/write streams
-*/
-/*@{*/
-
-/** Copy samples from host input channels set up by the PaUtil_Set*InputChannels
- functions to a user supplied buffer. This function is intended for use with
- blocking read/write streams. Copies the minimum of the number of
- user frames (specified by the frameCount parameter) and the number of available
- host frames (specified in a previous call to SetInputFrameCount()).
-
- @param bufferProcessor The buffer processor.
-
- @param buffer A pointer to the user buffer pointer, or a pointer to a pointer
- to an array of user buffer pointers for a non-interleaved stream. It is
- important that this parameter points to a copy of the user buffer pointers,
- not to the actual user buffer pointers, because this function updates the
- pointers before returning.
-
- @param frameCount The number of frames of data in the buffer(s) pointed to by
- the buffer parameter.
-
- @return The number of frames copied. The buffer pointer(s) pointed to by the
- buffer parameter are advanced to point to the frame(s) following the last one
- filled.
-*/
-unsigned long PaUtil_CopyInput( PaUtilBufferProcessor* bufferProcessor,
-        void **buffer, unsigned long frameCount );
-
-
-/* Copy samples from a user supplied buffer to host output channels set up by
- the PaUtil_Set*OutputChannels functions. This function is intended for use with
- blocking read/write streams. Copies the minimum of the number of
- user frames (specified by the frameCount parameter) and the number of
- host frames (specified in a previous call to SetOutputFrameCount()).
-
- @param bufferProcessor The buffer processor.
-
- @param buffer A pointer to the user buffer pointer, or a pointer to a pointer
- to an array of user buffer pointers for a non-interleaved stream. It is
- important that this parameter points to a copy of the user buffer pointers,
- not to the actual user buffer pointers, because this function updates the
- pointers before returning.
-
- @param frameCount The number of frames of data in the buffer(s) pointed to by
- the buffer parameter.
-
- @return The number of frames copied. The buffer pointer(s) pointed to by the
- buffer parameter are advanced to point to the frame(s) following the last one
- copied.
-*/
-unsigned long PaUtil_CopyOutput( PaUtilBufferProcessor* bufferProcessor,
-        const void ** buffer, unsigned long frameCount );
-
-
-/* Zero samples in host output channels set up by the PaUtil_Set*OutputChannels
- functions. This function is useful for flushing streams.
- Zeros the minimum of frameCount and the number of host frames specified in a
- previous call to SetOutputFrameCount().
-
- @param bufferProcessor The buffer processor.
-
- @param frameCount The maximum number of frames to zero.
-
- @return The number of frames zeroed.
-*/
-unsigned long PaUtil_ZeroOutput( PaUtilBufferProcessor* bufferProcessor,
-        unsigned long frameCount );
-
-
-/*@}*/
-
-
-#ifdef __cplusplus
-}
-#endif /* __cplusplus */
-#endif /* PA_PROCESS_H */
diff --git a/spaces/prerna9811/Chord/portaudio/src/hostapi/wasapi/pa_win_wasapi.c b/spaces/prerna9811/Chord/portaudio/src/hostapi/wasapi/pa_win_wasapi.c
deleted file mode 100644
index c76f302e68c8b35c5f3fa598a438e4c5a28497f1..0000000000000000000000000000000000000000
--- a/spaces/prerna9811/Chord/portaudio/src/hostapi/wasapi/pa_win_wasapi.c
+++ /dev/null
@@ -1,6534 +0,0 @@
-/*
- * Portable Audio I/O Library WASAPI implementation
- * Copyright (c) 2006-2010 David Viens
- * Copyright (c) 2010-2019 Dmitry Kostjuchenko
- *
- * Based on the Open Source API proposed by Ross Bencina
- * Copyright (c) 1999-2019 Ross Bencina, Phil Burk
- *
- * 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.
- */
-
-/** @file
- @ingroup hostapi_src
- @brief WASAPI implementation of support for a host API.
- @note pa_wasapi currently requires minimum VC 2005, and the latest Vista SDK
-*/
-
-#include <windows.h>
-#include <stdio.h>
-#include <process.h>
-#include <assert.h>
-
-// Max device count (if defined) causes max constant device count in the device list that
-// enables PaWasapi_UpdateDeviceList() API and makes it possible to update WASAPI list dynamically
-#ifndef PA_WASAPI_MAX_CONST_DEVICE_COUNT
-    #define PA_WASAPI_MAX_CONST_DEVICE_COUNT 0 // Force basic behavior by defining 0 if not defined by user
-#endif
-
-// Fallback from Event to the Polling method in case if latency is higher than 21.33ms, as it allows to use
-// 100% of CPU inside the PA's callback.
-// Note: Some USB DAC drivers are buggy when Polling method is forced in Exclusive mode, audio output becomes
-//       unstable with a lot of interruptions, therefore this define is optional. The default behavior is to
-//       not change the Event mode to Polling and use the mode which user provided.
-//#define PA_WASAPI_FORCE_POLL_IF_LARGE_BUFFER
-
-//! Poll mode time slots logging.
-//#define PA_WASAPI_LOG_TIME_SLOTS
-
-// WinRT
-#if defined(WINAPI_FAMILY) && (WINAPI_FAMILY == WINAPI_FAMILY_APP)
-    #define PA_WINRT
-    #define INITGUID
-#endif
-
-// WASAPI
-// using adjustments for MinGW build from @mgeier/MXE
-// https://github.com/mxe/mxe/commit/f4bbc45682f021948bdaefd9fd476e2a04c4740f
-#include <mmreg.h>  // must be before other Wasapi headers
-#if defined(_MSC_VER) && (_MSC_VER >= 1400) || defined(__MINGW64_VERSION_MAJOR)
-    #include <avrt.h>
-    #define COBJMACROS
-    #include <audioclient.h>
-    #include <endpointvolume.h>
-    #define INITGUID // Avoid additional linkage of static libs, excessive code will be optimized out by the compiler
-#ifndef _MSC_VER
-    #include <functiondiscoverykeys_devpkey.h>
-#endif
-    #include <functiondiscoverykeys.h>
-    #include <mmdeviceapi.h>
-    #include <devicetopology.h>    // Used to get IKsJackDescription interface
-    #undef INITGUID
-// Visual Studio 2010 does not support the inline keyword
-#if (_MSC_VER <= 1600)
-    #define inline _inline
-#endif
-#endif
-#ifndef __MWERKS__
-    #include <malloc.h>
-    #include <memory.h>
-#endif
-#ifndef PA_WINRT
-    #include <mmsystem.h>
-#endif
-
-#include "pa_util.h"
-#include "pa_allocation.h"
-#include "pa_hostapi.h"
-#include "pa_stream.h"
-#include "pa_cpuload.h"
-#include "pa_process.h"
-#include "pa_win_wasapi.h"
-#include "pa_debugprint.h"
-#include "pa_ringbuffer.h"
-#include "pa_win_coinitialize.h"
-
-#if !defined(NTDDI_VERSION) || (defined(__GNUC__) && (__GNUC__ <= 6) && !defined(__MINGW64__))
-
-    #undef WINVER
-    #undef _WIN32_WINNT
-    #define WINVER       0x0600 // VISTA
-    #define _WIN32_WINNT WINVER
-
-    #ifndef WINAPI
-        #define WINAPI __stdcall
-    #endif
-
-    #ifndef __unaligned
-        #define __unaligned
-    #endif
-
-    #ifndef __C89_NAMELESS
-        #define __C89_NAMELESS
-    #endif
-
-    #ifndef _AVRT_ //<< fix MinGW dummy compile by defining missing type: AVRT_PRIORITY
-        typedef enum _AVRT_PRIORITY
-        {
-            AVRT_PRIORITY_LOW = -1,
-            AVRT_PRIORITY_NORMAL,
-            AVRT_PRIORITY_HIGH,
-            AVRT_PRIORITY_CRITICAL
-        } AVRT_PRIORITY, *PAVRT_PRIORITY;
-    #endif
-
-    #include <basetyps.h> // << for IID/CLSID
-    #include <rpcsal.h>
-    #include <sal.h>
-
-    #ifndef __LPCGUID_DEFINED__
-        #define __LPCGUID_DEFINED__
-        typedef const GUID *LPCGUID;
-    #endif
-    typedef GUID IID;
-    typedef GUID CLSID;
-
-    #ifndef PROPERTYKEY_DEFINED
-        #define PROPERTYKEY_DEFINED
-        typedef struct _tagpropertykey
-        {
-            GUID fmtid;
-            DWORD pid;
-        }     PROPERTYKEY;
-    #endif
-
-    #ifdef __midl_proxy
-        #define __MIDL_CONST
-    #else
-        #define __MIDL_CONST const
-    #endif
-
-    #ifdef WIN64
-        #include <wtypes.h>
-        #define FASTCALL
-        #include <oleidl.h>
-        #include <objidl.h>
-    #else
-        typedef struct _BYTE_BLOB
-        {
-            unsigned long clSize;
-            unsigned char abData[ 1 ];
-        }     BYTE_BLOB;
-        typedef /* [unique] */  __RPC_unique_pointer BYTE_BLOB *UP_BYTE_BLOB;
-        typedef LONGLONG REFERENCE_TIME;
-        #define NONAMELESSUNION
-    #endif
-
-    #ifndef NT_SUCCESS
-        typedef LONG NTSTATUS;
-    #endif
-
-    #ifndef WAVE_FORMAT_IEEE_FLOAT
-        #define WAVE_FORMAT_IEEE_FLOAT 0x0003 // 32-bit floating-point
-    #endif
-
-    #ifndef __MINGW_EXTENSION
-        #if defined(__GNUC__) || defined(__GNUG__)
-            #define __MINGW_EXTENSION __extension__
-        #else
-            #define __MINGW_EXTENSION
-        #endif
-    #endif
-
-    #include <sdkddkver.h>
-    #include <propkeydef.h>
-    #define COBJMACROS
-    #define INITGUID // Avoid additional linkage of static libs, excessive code will be optimized out by the compiler
-    #include <audioclient.h>
-    #include <mmdeviceapi.h>
-    #include <endpointvolume.h>
-    #include <functiondiscoverykeys.h>
-    #include <devicetopology.h>    // Used to get IKsJackDescription interface
-    #undef INITGUID
-
-#endif // NTDDI_VERSION
-
-// Missing declarations for WinRT
-#ifdef PA_WINRT
-
-    #define DEVICE_STATE_ACTIVE 0x00000001
-
-    typedef enum _EDataFlow
-    {
-        eRender                 = 0,
-        eCapture                = ( eRender + 1 ) ,
-        eAll                    = ( eCapture + 1 ) ,
-        EDataFlow_enum_count    = ( eAll + 1 )
-    }
-    EDataFlow;
-
-    typedef enum _EndpointFormFactor
-    {
-        RemoteNetworkDevice       = 0,
-        Speakers                  = ( RemoteNetworkDevice + 1 ) ,
-        LineLevel                 = ( Speakers + 1 ) ,
-        Headphones                = ( LineLevel + 1 ) ,
-        Microphone                = ( Headphones + 1 ) ,
-        Headset                   = ( Microphone + 1 ) ,
-        Handset                   = ( Headset + 1 ) ,
-        UnknownDigitalPassthrough = ( Handset + 1 ) ,
-        SPDIF                     = ( UnknownDigitalPassthrough + 1 ) ,
-        HDMI                      = ( SPDIF + 1 ) ,
-        UnknownFormFactor         = ( HDMI + 1 )
-    }
-    EndpointFormFactor;
-
-#endif
-
-#ifndef GUID_SECT
-    #define GUID_SECT
-#endif
-
-#define __DEFINE_GUID(n,l,w1,w2,b1,b2,b3,b4,b5,b6,b7,b8) static const GUID n GUID_SECT = {l,w1,w2,{b1,b2,b3,b4,b5,b6,b7,b8}}
-#define __DEFINE_IID(n,l,w1,w2,b1,b2,b3,b4,b5,b6,b7,b8) static const IID n GUID_SECT = {l,w1,w2,{b1,b2,b3,b4,b5,b6,b7,b8}}
-#define __DEFINE_CLSID(n,l,w1,w2,b1,b2,b3,b4,b5,b6,b7,b8) static const CLSID n GUID_SECT = {l,w1,w2,{b1,b2,b3,b4,b5,b6,b7,b8}}
-#define PA_DEFINE_CLSID(className, l, w1, w2, b1, b2, b3, b4, b5, b6, b7, b8) \
-    __DEFINE_CLSID(pa_CLSID_##className, 0x##l, 0x##w1, 0x##w2, 0x##b1, 0x##b2, 0x##b3, 0x##b4, 0x##b5, 0x##b6, 0x##b7, 0x##b8)
-#define PA_DEFINE_IID(interfaceName, l, w1, w2, b1, b2, b3, b4, b5, b6, b7, b8) \
-    __DEFINE_IID(pa_IID_##interfaceName, 0x##l, 0x##w1, 0x##w2, 0x##b1, 0x##b2, 0x##b3, 0x##b4, 0x##b5, 0x##b6, 0x##b7, 0x##b8)
-
-// "1CB9AD4C-DBFA-4c32-B178-C2F568A703B2"
-PA_DEFINE_IID(IAudioClient,         1cb9ad4c, dbfa, 4c32, b1, 78, c2, f5, 68, a7, 03, b2);
-// "726778CD-F60A-4EDA-82DE-E47610CD78AA"
-PA_DEFINE_IID(IAudioClient2,        726778cd, f60a, 4eda, 82, de, e4, 76, 10, cd, 78, aa);
-// "7ED4EE07-8E67-4CD4-8C1A-2B7A5987AD42"
-PA_DEFINE_IID(IAudioClient3,        7ed4ee07, 8e67, 4cd4, 8c, 1a, 2b, 7a, 59, 87, ad, 42);
-// "1BE09788-6894-4089-8586-9A2A6C265AC5"
-PA_DEFINE_IID(IMMEndpoint,          1be09788, 6894, 4089, 85, 86, 9a, 2a, 6c, 26, 5a, c5);
-// "A95664D2-9614-4F35-A746-DE8DB63617E6"
-PA_DEFINE_IID(IMMDeviceEnumerator,  a95664d2, 9614, 4f35, a7, 46, de, 8d, b6, 36, 17, e6);
-// "BCDE0395-E52F-467C-8E3D-C4579291692E"
-PA_DEFINE_CLSID(IMMDeviceEnumerator,bcde0395, e52f, 467c, 8e, 3d, c4, 57, 92, 91, 69, 2e);
-// "F294ACFC-3146-4483-A7BF-ADDCA7C260E2"
-PA_DEFINE_IID(IAudioRenderClient,   f294acfc, 3146, 4483, a7, bf, ad, dc, a7, c2, 60, e2);
-// "C8ADBD64-E71E-48a0-A4DE-185C395CD317"
-PA_DEFINE_IID(IAudioCaptureClient,  c8adbd64, e71e, 48a0, a4, de, 18, 5c, 39, 5c, d3, 17);
-// *2A07407E-6497-4A18-9787-32F79BD0D98F*  Or this??
-PA_DEFINE_IID(IDeviceTopology,      2A07407E, 6497, 4A18, 97, 87, 32, f7, 9b, d0, d9, 8f);
-// *AE2DE0E4-5BCA-4F2D-AA46-5D13F8FDB3A9*
-PA_DEFINE_IID(IPart,                AE2DE0E4, 5BCA, 4F2D, aa, 46, 5d, 13, f8, fd, b3, a9);
-// *4509F757-2D46-4637-8E62-CE7DB944F57B*
-PA_DEFINE_IID(IKsJackDescription,   4509F757, 2D46, 4637, 8e, 62, ce, 7d, b9, 44, f5, 7b);
-
-// Media formats:
-__DEFINE_GUID(pa_KSDATAFORMAT_SUBTYPE_PCM,        0x00000001, 0x0000, 0x0010, 0x80, 0x00, 0x00, 0xaa, 0x00, 0x38, 0x9b, 0x71 );
-__DEFINE_GUID(pa_KSDATAFORMAT_SUBTYPE_ADPCM,      0x00000002, 0x0000, 0x0010, 0x80, 0x00, 0x00, 0xaa, 0x00, 0x38, 0x9b, 0x71 );
-__DEFINE_GUID(pa_KSDATAFORMAT_SUBTYPE_IEEE_FLOAT, 0x00000003, 0x0000, 0x0010, 0x80, 0x00, 0x00, 0xaa, 0x00, 0x38, 0x9b, 0x71 );
-
-#ifdef __IAudioClient2_INTERFACE_DEFINED__
-typedef enum _pa_AUDCLNT_STREAMOPTIONS {
-    pa_AUDCLNT_STREAMOPTIONS_NONE          = 0x00,
-    pa_AUDCLNT_STREAMOPTIONS_RAW           = 0x01,
-    pa_AUDCLNT_STREAMOPTIONS_MATCH_FORMAT  = 0x02
-} pa_AUDCLNT_STREAMOPTIONS;
-typedef struct _pa_AudioClientProperties {
-    UINT32                   cbSize;
-    BOOL                     bIsOffload;
-    AUDIO_STREAM_CATEGORY    eCategory;
-    pa_AUDCLNT_STREAMOPTIONS Options;
-} pa_AudioClientProperties;
-#define PA_AUDIOCLIENTPROPERTIES_SIZE_CATEGORY (sizeof(pa_AudioClientProperties) - sizeof(pa_AUDCLNT_STREAMOPTIONS))
-#define PA_AUDIOCLIENTPROPERTIES_SIZE_OPTIONS   sizeof(pa_AudioClientProperties)
-#endif // __IAudioClient2_INTERFACE_DEFINED__
-
-/* use CreateThread for CYGWIN/Windows Mobile, _beginthreadex for all others */
-#if !defined(__CYGWIN__) && !defined(_WIN32_WCE)
-    #define CREATE_THREAD(PROC) (HANDLE)_beginthreadex( NULL, 0, (PROC), stream, 0, &stream->dwThreadId )
-    #define PA_THREAD_FUNC static unsigned WINAPI
-    #define PA_THREAD_ID unsigned
-#else
-    #define CREATE_THREAD(PROC) CreateThread( NULL, 0, (PROC), stream, 0, &stream->dwThreadId )
-    #define PA_THREAD_FUNC static DWORD WINAPI
-    #define PA_THREAD_ID DWORD
-#endif
-
-// Thread function forward decl.
-PA_THREAD_FUNC ProcThreadEvent(void *param);
-PA_THREAD_FUNC ProcThreadPoll(void *param);
-
-// Error codes (available since Windows 7)
-#ifndef AUDCLNT_E_BUFFER_ERROR
-    #define AUDCLNT_E_BUFFER_ERROR AUDCLNT_ERR(0x018)
-#endif
-#ifndef AUDCLNT_E_BUFFER_SIZE_NOT_ALIGNED
-    #define AUDCLNT_E_BUFFER_SIZE_NOT_ALIGNED AUDCLNT_ERR(0x019)
-#endif
-#ifndef AUDCLNT_E_INVALID_DEVICE_PERIOD
-    #define AUDCLNT_E_INVALID_DEVICE_PERIOD AUDCLNT_ERR(0x020)
-#endif
-
-// Stream flags (available since Windows 7)
-#ifndef AUDCLNT_STREAMFLAGS_SRC_DEFAULT_QUALITY
-    #define AUDCLNT_STREAMFLAGS_SRC_DEFAULT_QUALITY 0x08000000
-#endif
-#ifndef AUDCLNT_STREAMFLAGS_AUTOCONVERTPCM
-    #define AUDCLNT_STREAMFLAGS_AUTOCONVERTPCM 0x80000000
-#endif
-
-#define PA_WASAPI_DEVICE_ID_LEN   256
-#define PA_WASAPI_DEVICE_NAME_LEN 128
-#ifdef PA_WINRT
-    #define PA_WASAPI_DEVICE_MAX_COUNT 16
-#endif
-
-enum { S_INPUT = 0, S_OUTPUT = 1, S_COUNT = 2, S_FULLDUPLEX = 0 };
-
-// Number of packets which compose single contignous buffer. With trial and error it was calculated
-// that WASAPI Input sub-system uses 6 packets per whole buffer. Please provide more information
-// or corrections if available.
-enum { WASAPI_PACKETS_PER_INPUT_BUFFER = 6 };
-
-#define STATIC_ARRAY_SIZE(array) (sizeof(array)/sizeof(array[0]))
-
-#define PRINT(x) PA_DEBUG(x);
-
-#define PA_SKELETON_SET_LAST_HOST_ERROR( errorCode, errorText ) \
-    PaUtil_SetLastHostErrorInfo( paWASAPI, errorCode, errorText )
-
-#define PA_WASAPI__IS_FULLDUPLEX(STREAM) ((STREAM)->in.clientProc && (STREAM)->out.clientProc)
-
-#ifndef IF_FAILED_JUMP
-#define IF_FAILED_JUMP(hr, label) if(FAILED(hr)) goto label;
-#endif
-
-#ifndef IF_FAILED_INTERNAL_ERROR_JUMP
-#define IF_FAILED_INTERNAL_ERROR_JUMP(hr, error, label) if(FAILED(hr)) { error = paInternalError; goto label; }
-#endif
-
-#define SAFE_CLOSE(h) if ((h) != NULL) { CloseHandle((h)); (h) = NULL; }
-#define SAFE_RELEASE(punk) if ((punk) != NULL) { (punk)->lpVtbl->Release((punk)); (punk) = NULL; }
-
-// Mixer function
-typedef void (*MixMonoToStereoF) (void *__to, const void *__from, UINT32 count);
-
-// AVRT is the new "multimedia scheduling stuff"
-#ifndef PA_WINRT
-typedef BOOL   (WINAPI *FAvRtCreateThreadOrderingGroup)  (PHANDLE,PLARGE_INTEGER,GUID*,PLARGE_INTEGER);
-typedef BOOL   (WINAPI *FAvRtDeleteThreadOrderingGroup)  (HANDLE);
-typedef BOOL   (WINAPI *FAvRtWaitOnThreadOrderingGroup)  (HANDLE);
-typedef HANDLE (WINAPI *FAvSetMmThreadCharacteristics)   (LPCSTR,LPDWORD);
-typedef BOOL   (WINAPI *FAvRevertMmThreadCharacteristics)(HANDLE);
-typedef BOOL   (WINAPI *FAvSetMmThreadPriority)          (HANDLE,AVRT_PRIORITY);
-static HMODULE hDInputDLL = 0;
-FAvRtCreateThreadOrderingGroup   pAvRtCreateThreadOrderingGroup = NULL;
-FAvRtDeleteThreadOrderingGroup   pAvRtDeleteThreadOrderingGroup = NULL;
-FAvRtWaitOnThreadOrderingGroup   pAvRtWaitOnThreadOrderingGroup = NULL;
-FAvSetMmThreadCharacteristics    pAvSetMmThreadCharacteristics = NULL;
-FAvRevertMmThreadCharacteristics pAvRevertMmThreadCharacteristics = NULL;
-FAvSetMmThreadPriority           pAvSetMmThreadPriority = NULL;
-#endif
-
-#define _GetProc(fun, type, name)  {                                                        \
-                                        fun = (type) GetProcAddress(hDInputDLL,name);       \
-                                        if (fun == NULL) {                                  \
-                                            PRINT(("GetProcAddr failed for %s" ,name));     \
-                                            return FALSE;                                   \
-                                        }                                                   \
-                                    }                                                       \
-
-// ------------------------------------------------------------------------------------------
-/* prototypes for functions declared in this file */
-#ifdef __cplusplus
-extern "C"
-{
-#endif /* __cplusplus */
-PaError PaWasapi_Initialize( PaUtilHostApiRepresentation **hostApi, PaHostApiIndex index );
-#ifdef __cplusplus
-}
-#endif /* __cplusplus */
-// dummy entry point for other compilers and sdks
-// currently built using RC1 SDK (5600)
-//#if _MSC_VER < 1400
-//PaError PaWasapi_Initialize( PaUtilHostApiRepresentation **hostApi, PaHostApiIndex hostApiIndex )
-//{
-    //return paNoError;
-//}
-//#else
-
-// ------------------------------------------------------------------------------------------
-static void Terminate( struct PaUtilHostApiRepresentation *hostApi );
-static PaError IsFormatSupported( struct PaUtilHostApiRepresentation *hostApi,
-                                  const PaStreamParameters *inputParameters,
-                                  const PaStreamParameters *outputParameters,
-                                  double sampleRate );
-static PaError OpenStream( struct PaUtilHostApiRepresentation *hostApi,
-                           PaStream** s,
-                           const PaStreamParameters *inputParameters,
-                           const PaStreamParameters *outputParameters,
-                           double sampleRate,
-                           unsigned long framesPerBuffer,
-                           PaStreamFlags streamFlags,
-                           PaStreamCallback *streamCallback,
-                           void *userData );
-static PaError CloseStream( PaStream* stream );
-static PaError StartStream( PaStream *stream );
-static PaError StopStream( PaStream *stream );
-static PaError AbortStream( PaStream *stream );
-static PaError IsStreamStopped( PaStream *s );
-static PaError IsStreamActive( PaStream *stream );
-static PaTime GetStreamTime( PaStream *stream );
-static double GetStreamCpuLoad( PaStream* stream );
-static PaError ReadStream( PaStream* stream, void *buffer, unsigned long frames );
-static PaError WriteStream( PaStream* stream, const void *buffer, unsigned long frames );
-static signed long GetStreamReadAvailable( PaStream* stream );
-static signed long GetStreamWriteAvailable( PaStream* stream );
-
-// ------------------------------------------------------------------------------------------
-/*
- These are fields that can be gathered from IDevice and IAudioDevice PRIOR to Initialize, and
- done in first pass i assume that neither of these will cause the Driver to "load", but again,
- who knows how they implement their stuff
- */
-typedef struct PaWasapiDeviceInfo
-{
-    // Device
-#ifndef PA_WINRT
-    IMMDevice *device;
-#endif
-
-    // device Id
-    WCHAR deviceId[PA_WASAPI_DEVICE_ID_LEN];
-
-    // from GetState
-    DWORD state;
-
-    // Fields filled from IAudioDevice (_prior_ to Initialize)
-    // from GetDevicePeriod(
-    REFERENCE_TIME DefaultDevicePeriod;
-    REFERENCE_TIME MinimumDevicePeriod;
-
-    // Default format (setup through Control Panel by user)
-    WAVEFORMATEXTENSIBLE DefaultFormat;
-
-    // Mix format (internal format used by WASAPI audio engine)
-    WAVEFORMATEXTENSIBLE MixFormat;
-
-    // Fields filled from IMMEndpoint'sGetDataFlow
-    EDataFlow flow;
-
-    // Form-factor
-    EndpointFormFactor formFactor;
-}
-PaWasapiDeviceInfo;
-
-// ------------------------------------------------------------------------------------------
-/* PaWasapiHostApiRepresentation - host api datastructure specific to this implementation */
-typedef struct
-{
-    PaUtilHostApiRepresentation inheritedHostApiRep;
-    PaUtilStreamInterface       callbackStreamInterface;
-    PaUtilStreamInterface       blockingStreamInterface;
-
-    PaUtilAllocationGroup      *allocations;
-
-    /* implementation specific data goes here */
-
-    PaWinUtilComInitializationResult comInitializationResult;
-
-    // this is the REAL number of devices, whether they are useful to PA or not!
-    UINT32 deviceCount;
-
-    PaWasapiDeviceInfo *devInfo;
-
-    // is TRUE when WOW64 Vista/7 Workaround is needed
-    BOOL useWOW64Workaround;
-}
-PaWasapiHostApiRepresentation;
-
-// ------------------------------------------------------------------------------------------
-/* PaWasapiAudioClientParams - audio client parameters */
-typedef struct PaWasapiAudioClientParams
-{
-    PaWasapiDeviceInfo *device_info;
-    PaStreamParameters  stream_params;
-    PaWasapiStreamInfo  wasapi_params;
-    UINT32              frames_per_buffer;
-    double              sample_rate;
-    BOOL                blocking;
-    BOOL                full_duplex;
-    BOOL                wow64_workaround;
-}
-PaWasapiAudioClientParams;
-
-// ------------------------------------------------------------------------------------------
-/* PaWasapiStream - a stream data structure specifically for this implementation */
-typedef struct PaWasapiSubStream
-{
-    IAudioClient        *clientParent;
-#ifndef PA_WINRT
-    IStream             *clientStream;
-#endif
-    IAudioClient        *clientProc;
-
-    WAVEFORMATEXTENSIBLE wavex;
-    UINT32               bufferSize;
-    REFERENCE_TIME       deviceLatency;
-    REFERENCE_TIME       period;
-    double               latencySeconds;
-    UINT32               framesPerHostCallback;
-    AUDCLNT_SHAREMODE    shareMode;
-    UINT32               streamFlags; // AUDCLNT_STREAMFLAGS_EVENTCALLBACK, ...
-    UINT32               flags;
-    PaWasapiAudioClientParams params; //!< parameters
-
-    // Buffers
-    UINT32               buffers;            //!< number of buffers used (from host side)
-    UINT32               framesPerBuffer;    //!< number of frames per 1 buffer
-    BOOL                 userBufferAndHostMatch;
-
-    // Used for Mono >> Stereo workaround, if driver does not support it
-    // (in Exclusive mode WASAPI usually refuses to operate with Mono (1-ch)
-    void                *monoBuffer;     //!< pointer to buffer
-    UINT32               monoBufferSize; //!< buffer size in bytes
-    MixMonoToStereoF     monoMixer;         //!< pointer to mixer function
-
-    PaUtilRingBuffer    *tailBuffer;       //!< buffer with trailing sample for blocking mode operations (only for Input)
-    void                *tailBufferMemory; //!< tail buffer memory region
-}
-PaWasapiSubStream;
-
-// ------------------------------------------------------------------------------------------
-/* PaWasapiHostProcessor - redirects processing data */
-typedef struct PaWasapiHostProcessor
-{
-    PaWasapiHostProcessorCallback processor;
-    void *userData;
-}
-PaWasapiHostProcessor;
-
-// ------------------------------------------------------------------------------------------
-typedef struct PaWasapiStream
-{
-    /* IMPLEMENT ME: rename this */
-    PaUtilStreamRepresentation streamRepresentation;
-    PaUtilCpuLoadMeasurer      cpuLoadMeasurer;
-    PaUtilBufferProcessor      bufferProcessor;
-
-    // input
-    PaWasapiSubStream          in;
-    IAudioCaptureClient       *captureClientParent;
-#ifndef PA_WINRT
-    IStream                   *captureClientStream;
-#endif
-    IAudioCaptureClient       *captureClient;
-    IAudioEndpointVolume      *inVol;
-
-    // output
-    PaWasapiSubStream          out;
-    IAudioRenderClient        *renderClientParent;
-#ifndef PA_WINRT
-    IStream                   *renderClientStream;
-#endif
-    IAudioRenderClient        *renderClient;
-    IAudioEndpointVolume      *outVol;
-
-    // event handles for event-driven processing mode
-    HANDLE event[S_COUNT];
-
-    // buffer mode
-    PaUtilHostBufferSizeMode bufferMode;
-
-    // must be volatile to avoid race condition on user query while
-    // thread is being started
-    volatile BOOL running;
-
-    PA_THREAD_ID dwThreadId;
-    HANDLE hThread;
-    HANDLE hCloseRequest;
-    HANDLE hThreadStart;        //!< signalled by thread on start
-    HANDLE hThreadExit;         //!< signalled by thread on exit
-    HANDLE hBlockingOpStreamRD;
-    HANDLE hBlockingOpStreamWR;
-
-    // Host callback Output overrider
-    PaWasapiHostProcessor hostProcessOverrideOutput;
-
-    // Host callback Input overrider
-    PaWasapiHostProcessor hostProcessOverrideInput;
-
-    // Defines blocking/callback interface used
-    BOOL bBlocking;
-
-    // Av Task (MM thread management)
-    HANDLE hAvTask;
-
-    // Thread priority level
-    PaWasapiThreadPriority nThreadPriority;
-
-    // State handler
-    PaWasapiStreamStateCallback fnStateHandler;
-    void *pStateHandlerUserData;
-}
-PaWasapiStream;
-
-// COM marshaling
-static HRESULT MarshalSubStreamComPointers(PaWasapiSubStream *substream);
-static HRESULT MarshalStreamComPointers(PaWasapiStream *stream);
-static HRESULT UnmarshalSubStreamComPointers(PaWasapiSubStream *substream);
-static HRESULT UnmarshalStreamComPointers(PaWasapiStream *stream);
-static void ReleaseUnmarshaledSubComPointers(PaWasapiSubStream *substream);
-static void ReleaseUnmarshaledComPointers(PaWasapiStream *stream);
-
-// Local methods
-static void _StreamOnStop(PaWasapiStream *stream);
-static void _StreamFinish(PaWasapiStream *stream);
-static void _StreamCleanup(PaWasapiStream *stream);
-static HRESULT _PollGetOutputFramesAvailable(PaWasapiStream *stream, UINT32 *available);
-static HRESULT _PollGetInputFramesAvailable(PaWasapiStream *stream, UINT32 *available);
-static void *PaWasapi_ReallocateMemory(void *prev, size_t size);
-static void PaWasapi_FreeMemory(void *ptr);
-static PaSampleFormat WaveToPaFormat(const WAVEFORMATEXTENSIBLE *fmtext);
-
-// WinRT (UWP) device list
-#ifdef PA_WINRT
-typedef struct PaWasapiWinrtDeviceInfo
-{
-    WCHAR              id[PA_WASAPI_DEVICE_ID_LEN];
-    WCHAR              name[PA_WASAPI_DEVICE_NAME_LEN];
-    EndpointFormFactor formFactor;
-}
-PaWasapiWinrtDeviceInfo;
-typedef struct PaWasapiWinrtDeviceListRole
-{
-    WCHAR                   defaultId[PA_WASAPI_DEVICE_ID_LEN];
-    PaWasapiWinrtDeviceInfo devices[PA_WASAPI_DEVICE_MAX_COUNT];
-    UINT32                  deviceCount;
-}
-PaWasapiWinrtDeviceListRole;
-typedef struct PaWasapiWinrtDeviceList
-{
-    PaWasapiWinrtDeviceListRole render;
-    PaWasapiWinrtDeviceListRole capture;
-}
-PaWasapiWinrtDeviceList;
-static PaWasapiWinrtDeviceList g_DeviceListInfo = { 0 };
-#endif
-
-// WinRT (UWP) device list context
-#ifdef PA_WINRT
-typedef struct PaWasapiWinrtDeviceListContextEntry
-{
-    PaWasapiWinrtDeviceInfo *info;
-    EDataFlow                flow;
-}
-PaWasapiWinrtDeviceListContextEntry;
-typedef struct PaWasapiWinrtDeviceListContext
-{
-    PaWasapiWinrtDeviceListContextEntry devices[PA_WASAPI_DEVICE_MAX_COUNT * 2];
-}
-PaWasapiWinrtDeviceListContext;
-#endif
-
-// ------------------------------------------------------------------------------------------
-#define LogHostError(HRES) __LogHostError(HRES, __FUNCTION__, __FILE__, __LINE__)
-static HRESULT __LogHostError(HRESULT res, const char *func, const char *file, int line)
-{
-    const char *text = NULL;
-    switch (res)
-    {
-    case S_OK: return res;
-    case E_POINTER                              :text ="E_POINTER"; break;
-    case E_INVALIDARG                           :text ="E_INVALIDARG"; break;
-
-    case AUDCLNT_E_NOT_INITIALIZED              :text ="AUDCLNT_E_NOT_INITIALIZED"; break;
-    case AUDCLNT_E_ALREADY_INITIALIZED          :text ="AUDCLNT_E_ALREADY_INITIALIZED"; break;
-    case AUDCLNT_E_WRONG_ENDPOINT_TYPE          :text ="AUDCLNT_E_WRONG_ENDPOINT_TYPE"; break;
-    case AUDCLNT_E_DEVICE_INVALIDATED           :text ="AUDCLNT_E_DEVICE_INVALIDATED"; break;
-    case AUDCLNT_E_NOT_STOPPED                  :text ="AUDCLNT_E_NOT_STOPPED"; break;
-    case AUDCLNT_E_BUFFER_TOO_LARGE             :text ="AUDCLNT_E_BUFFER_TOO_LARGE"; break;
-    case AUDCLNT_E_OUT_OF_ORDER                 :text ="AUDCLNT_E_OUT_OF_ORDER"; break;
-    case AUDCLNT_E_UNSUPPORTED_FORMAT           :text ="AUDCLNT_E_UNSUPPORTED_FORMAT"; break;
-    case AUDCLNT_E_INVALID_SIZE                 :text ="AUDCLNT_E_INVALID_SIZE"; break;
-    case AUDCLNT_E_DEVICE_IN_USE                :text ="AUDCLNT_E_DEVICE_IN_USE"; break;
-    case AUDCLNT_E_BUFFER_OPERATION_PENDING     :text ="AUDCLNT_E_BUFFER_OPERATION_PENDING"; break;
-    case AUDCLNT_E_THREAD_NOT_REGISTERED        :text ="AUDCLNT_E_THREAD_NOT_REGISTERED"; break;
-    case AUDCLNT_E_EXCLUSIVE_MODE_NOT_ALLOWED   :text ="AUDCLNT_E_EXCLUSIVE_MODE_NOT_ALLOWED"; break;
-    case AUDCLNT_E_ENDPOINT_CREATE_FAILED       :text ="AUDCLNT_E_ENDPOINT_CREATE_FAILED"; break;
-    case AUDCLNT_E_SERVICE_NOT_RUNNING          :text ="AUDCLNT_E_SERVICE_NOT_RUNNING"; break;
-    case AUDCLNT_E_EVENTHANDLE_NOT_EXPECTED     :text ="AUDCLNT_E_EVENTHANDLE_NOT_EXPECTED"; break;
-    case AUDCLNT_E_EXCLUSIVE_MODE_ONLY          :text ="AUDCLNT_E_EXCLUSIVE_MODE_ONLY"; break;
-    case AUDCLNT_E_BUFDURATION_PERIOD_NOT_EQUAL :text ="AUDCLNT_E_BUFDURATION_PERIOD_NOT_EQUAL"; break;
-    case AUDCLNT_E_EVENTHANDLE_NOT_SET          :text ="AUDCLNT_E_EVENTHANDLE_NOT_SET"; break;
-    case AUDCLNT_E_INCORRECT_BUFFER_SIZE        :text ="AUDCLNT_E_INCORRECT_BUFFER_SIZE"; break;
-    case AUDCLNT_E_BUFFER_SIZE_ERROR            :text ="AUDCLNT_E_BUFFER_SIZE_ERROR"; break;
-    case AUDCLNT_E_CPUUSAGE_EXCEEDED            :text ="AUDCLNT_E_CPUUSAGE_EXCEEDED"; break;
-    case AUDCLNT_E_BUFFER_ERROR                 :text ="AUDCLNT_E_BUFFER_ERROR"; break;
-    case AUDCLNT_E_BUFFER_SIZE_NOT_ALIGNED      :text ="AUDCLNT_E_BUFFER_SIZE_NOT_ALIGNED"; break;
-    case AUDCLNT_E_INVALID_DEVICE_PERIOD        :text ="AUDCLNT_E_INVALID_DEVICE_PERIOD"; break;
-
-#ifdef AUDCLNT_E_INVALID_STREAM_FLAG
-    case AUDCLNT_E_INVALID_STREAM_FLAG          :text ="AUDCLNT_E_INVALID_STREAM_FLAG"; break;
-#endif
-#ifdef AUDCLNT_E_ENDPOINT_OFFLOAD_NOT_CAPABLE
-    case AUDCLNT_E_ENDPOINT_OFFLOAD_NOT_CAPABLE :text ="AUDCLNT_E_ENDPOINT_OFFLOAD_NOT_CAPABLE"; break;
-#endif
-#ifdef AUDCLNT_E_OUT_OF_OFFLOAD_RESOURCES
-    case AUDCLNT_E_OUT_OF_OFFLOAD_RESOURCES     :text ="AUDCLNT_E_OUT_OF_OFFLOAD_RESOURCES"; break;
-#endif
-#ifdef AUDCLNT_E_OFFLOAD_MODE_ONLY
-    case AUDCLNT_E_OFFLOAD_MODE_ONLY            :text ="AUDCLNT_E_OFFLOAD_MODE_ONLY"; break;
-#endif
-#ifdef AUDCLNT_E_NONOFFLOAD_MODE_ONLY
-    case AUDCLNT_E_NONOFFLOAD_MODE_ONLY         :text ="AUDCLNT_E_NONOFFLOAD_MODE_ONLY"; break;
-#endif
-#ifdef AUDCLNT_E_RESOURCES_INVALIDATED
-    case AUDCLNT_E_RESOURCES_INVALIDATED        :text ="AUDCLNT_E_RESOURCES_INVALIDATED"; break;
-#endif
-#ifdef AUDCLNT_E_RAW_MODE_UNSUPPORTED
-    case AUDCLNT_E_RAW_MODE_UNSUPPORTED         :text ="AUDCLNT_E_RAW_MODE_UNSUPPORTED"; break;
-#endif
-#ifdef AUDCLNT_E_ENGINE_PERIODICITY_LOCKED
-    case AUDCLNT_E_ENGINE_PERIODICITY_LOCKED    :text ="AUDCLNT_E_ENGINE_PERIODICITY_LOCKED"; break;
-#endif
-#ifdef AUDCLNT_E_ENGINE_FORMAT_LOCKED
-    case AUDCLNT_E_ENGINE_FORMAT_LOCKED         :text ="AUDCLNT_E_ENGINE_FORMAT_LOCKED"; break;
-#endif
-
-    case AUDCLNT_S_BUFFER_EMPTY                 :text ="AUDCLNT_S_BUFFER_EMPTY"; break;
-    case AUDCLNT_S_THREAD_ALREADY_REGISTERED    :text ="AUDCLNT_S_THREAD_ALREADY_REGISTERED"; break;
-    case AUDCLNT_S_POSITION_STALLED             :text ="AUDCLNT_S_POSITION_STALLED"; break;
-
-    // other windows common errors:
-    case CO_E_NOTINITIALIZED                    :text ="CO_E_NOTINITIALIZED: you must call CoInitialize() before Pa_OpenStream()"; break;
-
-    default:
-        text = "UNKNOWN ERROR";
-    }
-    PRINT(("WASAPI ERROR HRESULT: 0x%X : %s\n [FUNCTION: %s FILE: %s {LINE: %d}]\n", res, text, func, file, line));
-#ifndef PA_ENABLE_DEBUG_OUTPUT
-    (void)func; (void)file; (void)line;
-#endif
-    PA_SKELETON_SET_LAST_HOST_ERROR(res, text);
-    return res;
-}
-
-// ------------------------------------------------------------------------------------------
-#define LogPaError(PAERR) __LogPaError(PAERR, __FUNCTION__, __FILE__, __LINE__)
-static PaError __LogPaError(PaError err, const char *func, const char *file, int line)
-{
-    if (err == paNoError)
-        return err;
-
-    PRINT(("WASAPI ERROR PAERROR: %i : %s\n [FUNCTION: %s FILE: %s {LINE: %d}]\n", err, Pa_GetErrorText(err), func, file, line));
-#ifndef PA_ENABLE_DEBUG_OUTPUT
-    (void)func; (void)file; (void)line;
-#endif
-    return err;
-}
-
-// ------------------------------------------------------------------------------------------
-/*! \class ThreadSleepScheduler
-           Allows to emulate thread sleep of less than 1 millisecond under Windows. Scheduler
-           calculates number of times the thread must run until next sleep of 1 millisecond.
-           It does not make thread sleeping for real number of microseconds but rather controls
-           how many of imaginary microseconds the thread task can allow thread to sleep.
-*/
-typedef struct ThreadIdleScheduler
-{
-    UINT32 m_idle_microseconds; //!< number of microseconds to sleep
-    UINT32 m_next_sleep;        //!< next sleep round
-    UINT32 m_i;                    //!< current round iterator position
-    UINT32 m_resolution;        //!< resolution in number of milliseconds
-}
-ThreadIdleScheduler;
-
-//! Setup scheduler.
-static void ThreadIdleScheduler_Setup(ThreadIdleScheduler *sched, UINT32 resolution, UINT32 microseconds)
-{
-    assert(microseconds != 0);
-    assert(resolution != 0);
-    assert((resolution * 1000) >= microseconds);
-
-    memset(sched, 0, sizeof(*sched));
-
-    sched->m_idle_microseconds = microseconds;
-    sched->m_resolution        = resolution;
-    sched->m_next_sleep        = (resolution * 1000) / microseconds;
-}
-
-//! Iterate and check if can sleep.
-static inline UINT32 ThreadIdleScheduler_NextSleep(ThreadIdleScheduler *sched)
-{
-    // advance and check if thread can sleep
-    if (++sched->m_i == sched->m_next_sleep)
-    {
-        sched->m_i = 0;
-        return sched->m_resolution;
-    }
-    return 0;
-}
-
-// ------------------------------------------------------------------------------------------
-typedef struct _SystemTimer
-{
-    INT32 granularity;
-
-} SystemTimer;
-static LARGE_INTEGER g_SystemTimerFrequency;
-static BOOL          g_SystemTimerUseQpc = FALSE;
-
-//! Set granularity of the system timer.
-static BOOL SystemTimer_SetGranularity(SystemTimer *timer, UINT32 granularity)
-{
-#ifndef PA_WINRT
-    TIMECAPS caps;
-
-    timer->granularity = granularity;
-
-    if (timeGetDevCaps(&caps, sizeof(caps)) == MMSYSERR_NOERROR)
-    {
-        if (timer->granularity < (INT32)caps.wPeriodMin)
-            timer->granularity = (INT32)caps.wPeriodMin;
-    }
-
-    if (timeBeginPeriod(timer->granularity) != TIMERR_NOERROR)
-    {
-        PRINT(("SetSystemTimer: timeBeginPeriod(1) failed!\n"));
-
-        timer->granularity = 10;
-        return FALSE;
-    }
-#else
-    (void)granularity;
-
-    // UWP does not support increase of the timer precision change and thus calling WaitForSingleObject with anything
-    // below 10 milliseconds will cause underruns for input and output stream.
-    timer->granularity = 10;
-#endif
-
-    return TRUE;
-}
-
-//! Restore granularity of the system timer.
-static void SystemTimer_RestoreGranularity(SystemTimer *timer)
-{
-#ifndef PA_WINRT
-    if (timer->granularity != 0)
-    {
-        if (timeEndPeriod(timer->granularity) != TIMERR_NOERROR)
-        {
-            PRINT(("RestoreSystemTimer: timeEndPeriod(1) failed!\n"));
-        }
-    }
-#else
-    (void)timer;
-#endif
-}
-
-//! Initialize high-resolution time getter.
-static void SystemTimer_InitializeTimeGetter()
-{
-    g_SystemTimerUseQpc = QueryPerformanceFrequency(&g_SystemTimerFrequency);
-}
-
-//! Get high-resolution time in milliseconds (using QPC by default).
-static inline LONGLONG SystemTimer_GetTime(SystemTimer *timer)
-{
-    (void)timer;
-
-    // QPC: https://docs.microsoft.com/en-us/windows/win32/sysinfo/acquiring-high-resolution-time-stamps
-    if (g_SystemTimerUseQpc)
-    {
-        LARGE_INTEGER now;
-        QueryPerformanceCounter(&now);
-        return (now.QuadPart * 1000LL) / g_SystemTimerFrequency.QuadPart;
-    }
-    else
-    {
-    #ifdef PA_WINRT
-        return GetTickCount64();
-    #else
-        return timeGetTime();
-    #endif
-    }
-}
-
-// ------------------------------------------------------------------------------------------
-/*static double nano100ToMillis(REFERENCE_TIME ref)
-{
-    //  1 nano = 0.000000001 seconds
-    //100 nano = 0.0000001   seconds
-    //100 nano = 0.0001   milliseconds
-    return ((double)ref) * 0.0001;
-}*/
-
-// ------------------------------------------------------------------------------------------
-static double nano100ToSeconds(REFERENCE_TIME ref)
-{
-    //  1 nano = 0.000000001 seconds
-    //100 nano = 0.0000001   seconds
-    //100 nano = 0.0001   milliseconds
-    return ((double)ref) * 0.0000001;
-}
-
-// ------------------------------------------------------------------------------------------
-/*static REFERENCE_TIME MillisTonano100(double ref)
-{
-    //  1 nano = 0.000000001 seconds
-    //100 nano = 0.0000001   seconds
-    //100 nano = 0.0001   milliseconds
-    return (REFERENCE_TIME)(ref / 0.0001);
-}*/
-
-// ------------------------------------------------------------------------------------------
-static REFERENCE_TIME SecondsTonano100(double ref)
-{
-    //  1 nano = 0.000000001 seconds
-    //100 nano = 0.0000001   seconds
-    //100 nano = 0.0001   milliseconds
-    return (REFERENCE_TIME)(ref / 0.0000001);
-}
-
-// ------------------------------------------------------------------------------------------
-// Makes Hns period from frames and sample rate
-static REFERENCE_TIME MakeHnsPeriod(UINT32 nFrames, DWORD nSamplesPerSec)
-{
-    return (REFERENCE_TIME)((10000.0 * 1000 / nSamplesPerSec * nFrames) + 0.5);
-}
-
-// ------------------------------------------------------------------------------------------
-// Converts PaSampleFormat to bits per sample value
-// Note: paCustomFormat stands for 8.24 format (24-bits inside 32-bit containers)
-static WORD PaSampleFormatToBitsPerSample(PaSampleFormat format_id)
-{
-    switch (format_id & ~paNonInterleaved)
-    {
-        case paFloat32:
-        case paInt32: return 32;
-        case paCustomFormat:
-        case paInt24: return 24;
-        case paInt16: return 16;
-        case paInt8:
-        case paUInt8: return 8;
-    }
-    return 0;
-}
-
-// ------------------------------------------------------------------------------------------
-// Convert PaSampleFormat to valid sample format for I/O, e.g. if paCustomFormat is specified
-// it will be converted to paInt32, other formats pass through
-// Note: paCustomFormat stands for 8.24 format (24-bits inside 32-bit containers)
-static PaSampleFormat GetSampleFormatForIO(PaSampleFormat format_id)
-{
-    return ((format_id & ~paNonInterleaved) == paCustomFormat ?
-        (paInt32 | (format_id & paNonInterleaved ? paNonInterleaved : 0)) : format_id);
-}
-
-// ------------------------------------------------------------------------------------------
-// Converts Hns period into number of frames
-static UINT32 MakeFramesFromHns(REFERENCE_TIME hnsPeriod, UINT32 nSamplesPerSec)
-{
-    UINT32 nFrames = (UINT32)(    // frames =
-        1.0 * hnsPeriod *        // hns *
-        nSamplesPerSec /        // (frames / s) /
-        1000 /                    // (ms / s) /
-        10000                    // (hns / s) /
-        + 0.5                    // rounding
-    );
-    return nFrames;
-}
-
-// Aligning function type
-typedef UINT32 (*ALIGN_FUNC) (UINT32 v, UINT32 align);
-
-// ------------------------------------------------------------------------------------------
-// Aligns 'v' backwards
-static UINT32 ALIGN_BWD(UINT32 v, UINT32 align)
-{
-    return ((v - (align ? v % align : 0)));
-}
-
-// ------------------------------------------------------------------------------------------
-// Aligns 'v' forward
-static UINT32 ALIGN_FWD(UINT32 v, UINT32 align)
-{
-    UINT32 remainder = (align ? (v % align) : 0);
-    if (remainder == 0)
-        return v;
-    return v + (align - remainder);
-}
-
-// ------------------------------------------------------------------------------------------
-// Get next value power of 2
-static UINT32 ALIGN_NEXT_POW2(UINT32 v)
-{
-    UINT32 v2 = 1;
-    while (v > (v2 <<= 1)) { }
-    v = v2;
-    return v;
-}
-
-// ------------------------------------------------------------------------------------------
-// Aligns WASAPI buffer to 128 byte packet boundary. HD Audio will fail to play if buffer
-// is misaligned. This problem was solved in Windows 7 were AUDCLNT_E_BUFFER_SIZE_NOT_ALIGNED
-// is thrown although we must align for Vista anyway.
-static UINT32 AlignFramesPerBuffer(UINT32 nFrames, UINT32 nBlockAlign, ALIGN_FUNC pAlignFunc)
-{
-#define HDA_PACKET_SIZE (128)
-
-    UINT32 bytes = nFrames * nBlockAlign;
-    UINT32 packets;
-
-    // align to a HD Audio packet size
-    bytes = pAlignFunc(bytes, HDA_PACKET_SIZE);
-
-    // atlest 1 frame must be available
-    if (bytes < HDA_PACKET_SIZE)
-        bytes = HDA_PACKET_SIZE;
-
-    packets = bytes / HDA_PACKET_SIZE;
-    bytes   = packets * HDA_PACKET_SIZE;
-    nFrames = bytes / nBlockAlign;
-
-    // WASAPI frames are always aligned to at least 8
-    nFrames = ALIGN_FWD(nFrames, 8);
-
-    return nFrames;
-
-#undef HDA_PACKET_SIZE
-}
-
-// ------------------------------------------------------------------------------------------
-static UINT32 GetFramesSleepTime(REFERENCE_TIME nFrames, REFERENCE_TIME nSamplesPerSec)
-{
-    REFERENCE_TIME nDuration;
-    if (nSamplesPerSec == 0)
-        return 0;
-
-#define REFTIMES_PER_SEC       10000000LL
-#define REFTIMES_PER_MILLISEC  10000LL
-
-    // Calculate the actual duration of the allocated buffer.
-    nDuration = (REFTIMES_PER_SEC * nFrames) / nSamplesPerSec;
-    return (UINT32)(nDuration / REFTIMES_PER_MILLISEC);
-
-#undef REFTIMES_PER_SEC
-#undef REFTIMES_PER_MILLISEC
-}
-
-// ------------------------------------------------------------------------------------------
-static UINT32 GetFramesSleepTimeMicroseconds(REFERENCE_TIME nFrames, REFERENCE_TIME nSamplesPerSec)
-{
-    REFERENCE_TIME nDuration;
-    if (nSamplesPerSec == 0)
-        return 0;
-
-#define REFTIMES_PER_SEC       10000000LL
-#define REFTIMES_PER_MILLISEC  10000LL
-
-    // Calculate the actual duration of the allocated buffer.
-    nDuration = (REFTIMES_PER_SEC * nFrames) / nSamplesPerSec;
-    return (UINT32)(nDuration / 10);
-
-#undef REFTIMES_PER_SEC
-#undef REFTIMES_PER_MILLISEC
-}
-
-// ------------------------------------------------------------------------------------------
-#ifndef PA_WINRT
-static BOOL SetupAVRT()
-{
-    hDInputDLL = LoadLibraryA("avrt.dll");
-    if (hDInputDLL == NULL)
-        return FALSE;
-
-    _GetProc(pAvRtCreateThreadOrderingGroup,  FAvRtCreateThreadOrderingGroup,  "AvRtCreateThreadOrderingGroup");
-    _GetProc(pAvRtDeleteThreadOrderingGroup,  FAvRtDeleteThreadOrderingGroup,  "AvRtDeleteThreadOrderingGroup");
-    _GetProc(pAvRtWaitOnThreadOrderingGroup,  FAvRtWaitOnThreadOrderingGroup,  "AvRtWaitOnThreadOrderingGroup");
-    _GetProc(pAvSetMmThreadCharacteristics,   FAvSetMmThreadCharacteristics,   "AvSetMmThreadCharacteristicsA");
-    _GetProc(pAvRevertMmThreadCharacteristics,FAvRevertMmThreadCharacteristics,"AvRevertMmThreadCharacteristics");
-    _GetProc(pAvSetMmThreadPriority,          FAvSetMmThreadPriority,          "AvSetMmThreadPriority");
-
-    return pAvRtCreateThreadOrderingGroup &&
-        pAvRtDeleteThreadOrderingGroup &&
-        pAvRtWaitOnThreadOrderingGroup &&
-        pAvSetMmThreadCharacteristics &&
-        pAvRevertMmThreadCharacteristics &&
-        pAvSetMmThreadPriority;
-}
-#endif
-
-// ------------------------------------------------------------------------------------------
-static void CloseAVRT()
-{
-#ifndef PA_WINRT
-    if (hDInputDLL != NULL)
-        FreeLibrary(hDInputDLL);
-    hDInputDLL = NULL;
-#endif
-}
-
-// ------------------------------------------------------------------------------------------
-static BOOL IsWow64()
-{
-#ifndef PA_WINRT
-
-    // http://msdn.microsoft.com/en-us/library/ms684139(VS.85).aspx
-
-    typedef BOOL (WINAPI *LPFN_ISWOW64PROCESS) (HANDLE, PBOOL);
-    LPFN_ISWOW64PROCESS fnIsWow64Process;
-
-    BOOL bIsWow64 = FALSE;
-
-    // IsWow64Process is not available on all supported versions of Windows.
-    // Use GetModuleHandle to get a handle to the DLL that contains the function
-    // and GetProcAddress to get a pointer to the function if available.
-
-    fnIsWow64Process = (LPFN_ISWOW64PROCESS) GetProcAddress(
-        GetModuleHandleA("kernel32"), "IsWow64Process");
-
-    if (fnIsWow64Process == NULL)
-        return FALSE;
-
-    if (!fnIsWow64Process(GetCurrentProcess(), &bIsWow64))
-        return FALSE;
-
-    return bIsWow64;
-
-#else
-
-    return FALSE;
-
-#endif
-}
-
-// ------------------------------------------------------------------------------------------
-typedef enum EWindowsVersion
-{
-    WINDOWS_UNKNOWN = 0,
-    WINDOWS_VISTA_SERVER2008,
-    WINDOWS_7_SERVER2008R2,
-    WINDOWS_8_SERVER2012,
-    WINDOWS_8_1_SERVER2012R2,
-    WINDOWS_10_SERVER2016,
-    WINDOWS_FUTURE
-}
-EWindowsVersion;
-// Alternative way for checking Windows version (allows to check version on Windows 8.1 and up)
-#ifndef PA_WINRT
-static BOOL IsWindowsVersionOrGreater(WORD wMajorVersion, WORD wMinorVersion, WORD wServicePackMajor)
-{
-    typedef ULONGLONG (NTAPI *LPFN_VERSETCONDITIONMASK)(ULONGLONG ConditionMask, DWORD TypeMask, BYTE Condition);
-    typedef BOOL (WINAPI *LPFN_VERIFYVERSIONINFO)(LPOSVERSIONINFOEXA lpVersionInformation, DWORD dwTypeMask, DWORDLONG dwlConditionMask);
-
-    LPFN_VERSETCONDITIONMASK fnVerSetConditionMask;
-    LPFN_VERIFYVERSIONINFO fnVerifyVersionInfo;
-    OSVERSIONINFOEXA osvi = { sizeof(osvi), 0, 0, 0, 0, {0}, 0, 0 };
-    DWORDLONG dwlConditionMask;
-
-    fnVerSetConditionMask = (LPFN_VERSETCONDITIONMASK)GetProcAddress(GetModuleHandleA("kernel32"), "VerSetConditionMask");
-    fnVerifyVersionInfo = (LPFN_VERIFYVERSIONINFO)GetProcAddress(GetModuleHandleA("kernel32"), "VerifyVersionInfoA");
-
-    if ((fnVerSetConditionMask == NULL) || (fnVerifyVersionInfo == NULL))
-        return FALSE;
-
-    dwlConditionMask = fnVerSetConditionMask(
-        fnVerSetConditionMask(
-            fnVerSetConditionMask(
-                0, VER_MAJORVERSION,     VER_GREATER_EQUAL),
-                   VER_MINORVERSION,     VER_GREATER_EQUAL),
-                   VER_SERVICEPACKMAJOR, VER_GREATER_EQUAL);
-
-    osvi.dwMajorVersion    = wMajorVersion;
-    osvi.dwMinorVersion    = wMinorVersion;
-    osvi.wServicePackMajor = wServicePackMajor;
-
-    return (fnVerifyVersionInfo(&osvi, VER_MAJORVERSION | VER_MINORVERSION | VER_SERVICEPACKMAJOR, dwlConditionMask) != FALSE);
-}
-#endif
-// Get Windows version
-static EWindowsVersion GetWindowsVersion()
-{
-#ifndef PA_WINRT
-    static EWindowsVersion version = WINDOWS_UNKNOWN;
-
-    if (version == WINDOWS_UNKNOWN)
-    {
-        DWORD dwMajorVersion = 0xFFFFFFFFU, dwMinorVersion = 0, dwBuild = 0;
-
-        // RTL_OSVERSIONINFOW equals OSVERSIONINFOW but it is missing inb MinGW winnt.h header,
-        // thus use OSVERSIONINFOW for greater portability
-        typedef NTSTATUS (WINAPI *LPFN_RTLGETVERSION)(POSVERSIONINFOW lpVersionInformation);
-        LPFN_RTLGETVERSION fnRtlGetVersion;
-
-        #define NTSTATUS_SUCCESS ((NTSTATUS)0x00000000L)
-
-        // RtlGetVersion must be able to provide true Windows version (Windows 10 may be reported as Windows 8
-        // by GetVersion API)
-        if ((fnRtlGetVersion = (LPFN_RTLGETVERSION)GetProcAddress(GetModuleHandleA("ntdll"), "RtlGetVersion")) != NULL)
-        {
-            OSVERSIONINFOW ver = { sizeof(OSVERSIONINFOW), 0, 0, 0, 0, {0} };
-
-            PRINT(("WASAPI: getting Windows version with RtlGetVersion()\n"));
-
-            if (fnRtlGetVersion(&ver) == NTSTATUS_SUCCESS)
-            {
-                dwMajorVersion = ver.dwMajorVersion;
-                dwMinorVersion = ver.dwMinorVersion;
-                dwBuild        = ver.dwBuildNumber;
-            }
-        }
-
-        #undef NTSTATUS_SUCCESS
-
-        // fallback to GetVersion if RtlGetVersion is missing
-        if (dwMajorVersion == 0xFFFFFFFFU)
-        {
-            typedef DWORD (WINAPI *LPFN_GETVERSION)(VOID);
-            LPFN_GETVERSION fnGetVersion;
-
-            if ((fnGetVersion = (LPFN_GETVERSION)GetProcAddress(GetModuleHandleA("kernel32"), "GetVersion")) != NULL)
-            {
-                DWORD dwVersion;
-
-                PRINT(("WASAPI: getting Windows version with GetVersion()\n"));
-
-                dwVersion = fnGetVersion();
-
-                dwMajorVersion = (DWORD)(LOBYTE(LOWORD(dwVersion)));
-                dwMinorVersion = (DWORD)(HIBYTE(LOWORD(dwVersion)));
-
-                if (dwVersion < 0x80000000)
-                    dwBuild = (DWORD)(HIWORD(dwVersion));
-            }
-        }
-
-        if (dwMajorVersion != 0xFFFFFFFFU)
-        {
-            switch (dwMajorVersion)
-            {
-            case 0:
-            case 1:
-            case 2:
-            case 3:
-            case 4:
-            case 5:
-                break; // skip lower
-            case 6:
-                switch (dwMinorVersion)
-                {
-                case 0:  version = WINDOWS_VISTA_SERVER2008; break;
-                case 1:  version = WINDOWS_7_SERVER2008R2;   break;
-                case 2:  version = WINDOWS_8_SERVER2012;     break;
-                case 3:  version = WINDOWS_8_1_SERVER2012R2; break;
-                default: version = WINDOWS_FUTURE;           break;
-                }
-                break;
-            case 10:
-                switch (dwMinorVersion)
-                {
-                case 0:  version = WINDOWS_10_SERVER2016;    break;
-                default: version = WINDOWS_FUTURE;           break;
-                }
-                break;
-            default:
-                version = WINDOWS_FUTURE;
-                break;
-            }
-        }
-        // fallback to VerifyVersionInfo if RtlGetVersion and GetVersion are missing
-        else
-        {
-            PRINT(("WASAPI: getting Windows version with VerifyVersionInfo()\n"));
-
-            if (IsWindowsVersionOrGreater(10, 0, 0))
-                version = WINDOWS_10_SERVER2016;
-            else
-            if (IsWindowsVersionOrGreater(6, 3, 0))
-                version = WINDOWS_8_1_SERVER2012R2;
-            else
-            if (IsWindowsVersionOrGreater(6, 2, 0))
-                version = WINDOWS_8_SERVER2012;
-            else
-            if (IsWindowsVersionOrGreater(6, 1, 0))
-                version = WINDOWS_7_SERVER2008R2;
-            else
-            if (IsWindowsVersionOrGreater(6, 0, 0))
-                version = WINDOWS_VISTA_SERVER2008;
-            else
-                version = WINDOWS_FUTURE;
-        }
-
-        PRINT(("WASAPI: Windows version = %d\n", version));
-    }
-
-    return version;
-#else
-    #if (_WIN32_WINNT >= _WIN32_WINNT_WIN10)
-        return WINDOWS_10_SERVER2016;
-    #else
-        return WINDOWS_8_SERVER2012;
-    #endif
-#endif
-}
-
-// ------------------------------------------------------------------------------------------
-static BOOL UseWOW64Workaround()
-{
-    // note: WOW64 bug is common to Windows Vista x64, thus we fall back to safe Poll-driven
-    //       method. Windows 7 x64 seems has WOW64 bug fixed.
-
-    return (IsWow64() && (GetWindowsVersion() == WINDOWS_VISTA_SERVER2008));
-}
-
-// ------------------------------------------------------------------------------------------
-static UINT32 GetAudioClientVersion()
-{
-    if (GetWindowsVersion() >= WINDOWS_10_SERVER2016)
-        return 3;
-    else
-    if (GetWindowsVersion() >= WINDOWS_8_SERVER2012)
-        return 2;
-
-    return 1;
-}
-
-// ------------------------------------------------------------------------------------------
-static const IID *GetAudioClientIID()
-{
-    static const IID *cli_iid = NULL;
-    if (cli_iid == NULL)
-    {
-        UINT32 cli_version = GetAudioClientVersion();
-        switch (cli_version)
-        {
-        case 3:  cli_iid = &pa_IID_IAudioClient3; break;
-        case 2:  cli_iid = &pa_IID_IAudioClient2; break;
-        default: cli_iid = &pa_IID_IAudioClient;  break;
-        }
-
-        PRINT(("WASAPI: IAudioClient version = %d\n", cli_version));
-    }
-
-    return cli_iid;
-}
-
-// ------------------------------------------------------------------------------------------
-typedef enum EMixDirection
-{
-    MIX_DIR__1TO2,   //!< mix one channel to L and R
-    MIX_DIR__2TO1,   //!< mix L and R channels to one channel
-    MIX_DIR__2TO1_L  //!< mix only L channel (of total 2 channels) to one channel
-}
-EMixDirection;
-
-// ------------------------------------------------------------------------------------------
-#define _WASAPI_MONO_TO_STEREO_MIXER_1_TO_2(TYPE)\
-    TYPE * __restrict to = (TYPE *)__to;\
-    const TYPE * __restrict from = (const TYPE *)__from;\
-    const TYPE * __restrict end = from + count;\
-    while (from != end)\
-    {\
-        to[0] = to[1] = *from ++;\
-        to += 2;\
-    }
-
-// ------------------------------------------------------------------------------------------
-#define _WASAPI_MONO_TO_STEREO_MIXER_2_TO_1_FLT32(TYPE)\
-    TYPE * __restrict to = (TYPE *)__to;\
-    const TYPE * __restrict from = (const TYPE *)__from;\
-    const TYPE * __restrict end = to + count;\
-    while (to != end)\
-    {\
-        *to ++ = (TYPE)((float)(from[0] + from[1]) * 0.5f);\
-        from += 2;\
-    }
-
-// ------------------------------------------------------------------------------------------
-#define _WASAPI_MONO_TO_STEREO_MIXER_2_TO_1_INT32(TYPE)\
-    TYPE * __restrict to = (TYPE *)__to;\
-    const TYPE * __restrict from = (const TYPE *)__from;\
-    const TYPE * __restrict end = to + count;\
-    while (to != end)\
-    {\
-        *to ++ = (TYPE)(((INT32)from[0] + (INT32)from[1]) >> 1);\
-        from += 2;\
-    }
-
-// ------------------------------------------------------------------------------------------
-#define _WASAPI_MONO_TO_STEREO_MIXER_2_TO_1_INT64(TYPE)\
-    TYPE * __restrict to = (TYPE *)__to;\
-    const TYPE * __restrict from = (const TYPE *)__from;\
-    const TYPE * __restrict end = to + count;\
-    while (to != end)\
-    {\
-        *to ++ = (TYPE)(((INT64)from[0] + (INT64)from[1]) >> 1);\
-        from += 2;\
-    }
-
-// ------------------------------------------------------------------------------------------
-#define _WASAPI_MONO_TO_STEREO_MIXER_2_TO_1_L(TYPE)\
-    TYPE * __restrict to = (TYPE *)__to;\
-    const TYPE * __restrict from = (const TYPE *)__from;\
-    const TYPE * __restrict end = to + count;\
-    while (to != end)\
-    {\
-        *to ++ = from[0];\
-        from += 2;\
-    }
-
-// ------------------------------------------------------------------------------------------
-static void _MixMonoToStereo_1TO2_8(void *__to, const void *__from, UINT32 count) { _WASAPI_MONO_TO_STEREO_MIXER_1_TO_2(BYTE); }
-static void _MixMonoToStereo_1TO2_16(void *__to, const void *__from, UINT32 count) { _WASAPI_MONO_TO_STEREO_MIXER_1_TO_2(short); }
-static void _MixMonoToStereo_1TO2_8_24(void *__to, const void *__from, UINT32 count) { _WASAPI_MONO_TO_STEREO_MIXER_1_TO_2(int); /* !!! int24 data is contained in 32-bit containers*/ }
-static void _MixMonoToStereo_1TO2_32(void *__to, const void *__from, UINT32 count) { _WASAPI_MONO_TO_STEREO_MIXER_1_TO_2(int); }
-static void _MixMonoToStereo_1TO2_32f(void *__to, const void *__from, UINT32 count) { _WASAPI_MONO_TO_STEREO_MIXER_1_TO_2(float); }
-static void _MixMonoToStereo_1TO2_24(void *__to, const void *__from, UINT32 count)
-{
-    const UCHAR * __restrict from = (const UCHAR *)__from;
-    UCHAR * __restrict to = (UCHAR *)__to;
-    const UCHAR * __restrict end = to + (count * (2 * 3));
-
-    while (to != end)
-    {
-        to[0] = to[3] = from[0];
-        to[1] = to[4] = from[1];
-        to[2] = to[5] = from[2];
-
-        from += 3;
-        to += (2 * 3);
-    }
-}
-
-// ------------------------------------------------------------------------------------------
-static void _MixMonoToStereo_2TO1_8(void *__to, const void *__from, UINT32 count) { _WASAPI_MONO_TO_STEREO_MIXER_2_TO_1_INT32(BYTE); }
-static void _MixMonoToStereo_2TO1_16(void *__to, const void *__from, UINT32 count) { _WASAPI_MONO_TO_STEREO_MIXER_2_TO_1_INT32(short); }
-static void _MixMonoToStereo_2TO1_8_24(void *__to, const void *__from, UINT32 count) { _WASAPI_MONO_TO_STEREO_MIXER_2_TO_1_INT32(int); /* !!! int24 data is contained in 32-bit containers*/ }
-static void _MixMonoToStereo_2TO1_32(void *__to, const void *__from, UINT32 count) { _WASAPI_MONO_TO_STEREO_MIXER_2_TO_1_INT64(int); }
-static void _MixMonoToStereo_2TO1_32f(void *__to, const void *__from, UINT32 count) { _WASAPI_MONO_TO_STEREO_MIXER_2_TO_1_FLT32(float); }
-static void _MixMonoToStereo_2TO1_24(void *__to, const void *__from, UINT32 count)
-{
-    const UCHAR * __restrict from = (const UCHAR *)__from;
-    UCHAR * __restrict to = (UCHAR *)__to;
-    const UCHAR * __restrict end = to + (count * 3);
-    PaInt32 tempL, tempR, tempM;
-
-    while (to != end)
-    {
-        tempL = (((PaInt32)from[0]) << 8);
-        tempL = tempL | (((PaInt32)from[1]) << 16);
-        tempL = tempL | (((PaInt32)from[2]) << 24);
-
-        tempR = (((PaInt32)from[3]) << 8);
-        tempR = tempR | (((PaInt32)from[4]) << 16);
-        tempR = tempR | (((PaInt32)from[5]) << 24);
-
-        tempM = (tempL + tempR) >> 1;
-
-        to[0] = (UCHAR)(tempM >> 8);
-        to[1] = (UCHAR)(tempM >> 16);
-        to[2] = (UCHAR)(tempM >> 24);
-
-        from += (2 * 3);
-        to += 3;
-    }
-}
-
-// ------------------------------------------------------------------------------------------
-static void _MixMonoToStereo_2TO1_8_L(void *__to, const void *__from, UINT32 count) { _WASAPI_MONO_TO_STEREO_MIXER_2_TO_1_L(BYTE); }
-static void _MixMonoToStereo_2TO1_16_L(void *__to, const void *__from, UINT32 count) { _WASAPI_MONO_TO_STEREO_MIXER_2_TO_1_L(short); }
-static void _MixMonoToStereo_2TO1_8_24_L(void *__to, const void *__from, UINT32 count) { _WASAPI_MONO_TO_STEREO_MIXER_2_TO_1_L(int); /* !!! int24 data is contained in 32-bit containers*/ }
-static void _MixMonoToStereo_2TO1_32_L(void *__to, const void *__from, UINT32 count) { _WASAPI_MONO_TO_STEREO_MIXER_2_TO_1_L(int); }
-static void _MixMonoToStereo_2TO1_32f_L(void *__to, const void *__from, UINT32 count) { _WASAPI_MONO_TO_STEREO_MIXER_2_TO_1_L(float); }
-static void _MixMonoToStereo_2TO1_24_L(void *__to, const void *__from, UINT32 count)
-{
-    const UCHAR * __restrict from = (const UCHAR *)__from;
-    UCHAR * __restrict to = (UCHAR *)__to;
-    const UCHAR * __restrict end = to + (count * 3);
-
-    while (to != end)
-    {
-        to[0] = from[0];
-        to[1] = from[1];
-        to[2] = from[2];
-
-        from += (2 * 3);
-        to += 3;
-    }
-}
-
-// ------------------------------------------------------------------------------------------
-static MixMonoToStereoF GetMonoToStereoMixer(const WAVEFORMATEXTENSIBLE *fmtext, EMixDirection dir)
-{
-    PaSampleFormat format = WaveToPaFormat(fmtext);
-
-    switch (dir)
-    {
-    case MIX_DIR__1TO2:
-        switch (format & ~paNonInterleaved)
-        {
-        case paUInt8:   return _MixMonoToStereo_1TO2_8;
-        case paInt16:   return _MixMonoToStereo_1TO2_16;
-        case paInt24:   return (fmtext->Format.wBitsPerSample == 32 ? _MixMonoToStereo_1TO2_8_24 : _MixMonoToStereo_1TO2_24);
-        case paInt32:   return _MixMonoToStereo_1TO2_32;
-        case paFloat32: return _MixMonoToStereo_1TO2_32f;
-        }
-        break;
-
-    case MIX_DIR__2TO1:
-        switch (format & ~paNonInterleaved)
-        {
-        case paUInt8:   return _MixMonoToStereo_2TO1_8;
-        case paInt16:   return _MixMonoToStereo_2TO1_16;
-        case paInt24:   return (fmtext->Format.wBitsPerSample == 32 ? _MixMonoToStereo_2TO1_8_24 : _MixMonoToStereo_2TO1_24);
-        case paInt32:   return _MixMonoToStereo_2TO1_32;
-        case paFloat32: return _MixMonoToStereo_2TO1_32f;
-        }
-        break;
-
-    case MIX_DIR__2TO1_L:
-        switch (format & ~paNonInterleaved)
-        {
-        case paUInt8:   return _MixMonoToStereo_2TO1_8_L;
-        case paInt16:   return _MixMonoToStereo_2TO1_16_L;
-        case paInt24:   return (fmtext->Format.wBitsPerSample == 32 ? _MixMonoToStereo_2TO1_8_24_L : _MixMonoToStereo_2TO1_24_L);
-        case paInt32:   return _MixMonoToStereo_2TO1_32_L;
-        case paFloat32: return _MixMonoToStereo_2TO1_32f_L;
-        }
-        break;
-    }
-
-    return NULL;
-}
-
-// ------------------------------------------------------------------------------------------
-#ifdef PA_WINRT
-typedef struct PaActivateAudioInterfaceCompletionHandler
-{
-    IActivateAudioInterfaceCompletionHandler parent;
-    volatile LONG refs;
-    volatile LONG done;
-    struct
-    {
-        const IID *iid;
-        void **obj;
-    }
-    in;
-    struct
-    {
-        HRESULT hr;
-    }
-    out;
-}
-PaActivateAudioInterfaceCompletionHandler;
-
-static HRESULT (STDMETHODCALLTYPE PaActivateAudioInterfaceCompletionHandler_QueryInterface)(
-    IActivateAudioInterfaceCompletionHandler *This, REFIID riid, void **ppvObject)
-{
-    PaActivateAudioInterfaceCompletionHandler *handler = (PaActivateAudioInterfaceCompletionHandler *)This;
-
-    // From MSDN:
-    // "The IAgileObject interface is a marker interface that indicates that an object
-    //  is free threaded and can be called from any apartment."
-    if (IsEqualIID(riid, &IID_IUnknown) ||
-        IsEqualIID(riid, &IID_IAgileObject))
-    {
-        IActivateAudioInterfaceCompletionHandler_AddRef((IActivateAudioInterfaceCompletionHandler *)handler);
-        (*ppvObject) = handler;
-        return S_OK;
-    }
-
-    return E_NOINTERFACE;
-}
-
-static ULONG (STDMETHODCALLTYPE PaActivateAudioInterfaceCompletionHandler_AddRef)(
-    IActivateAudioInterfaceCompletionHandler *This)
-{
-    PaActivateAudioInterfaceCompletionHandler *handler = (PaActivateAudioInterfaceCompletionHandler *)This;
-
-    return InterlockedIncrement(&handler->refs);
-}
-
-static ULONG (STDMETHODCALLTYPE PaActivateAudioInterfaceCompletionHandler_Release)(
-    IActivateAudioInterfaceCompletionHandler *This)
-{
-    PaActivateAudioInterfaceCompletionHandler *handler = (PaActivateAudioInterfaceCompletionHandler *)This;
-    ULONG refs;
-
-    if ((refs = InterlockedDecrement(&handler->refs)) == 0)
-    {
-        PaUtil_FreeMemory(handler->parent.lpVtbl);
-        PaUtil_FreeMemory(handler);
-    }
-
-    return refs;
-}
-
-static HRESULT (STDMETHODCALLTYPE PaActivateAudioInterfaceCompletionHandler_ActivateCompleted)(
-    IActivateAudioInterfaceCompletionHandler *This, IActivateAudioInterfaceAsyncOperation *activateOperation)
-{
-    PaActivateAudioInterfaceCompletionHandler *handler = (PaActivateAudioInterfaceCompletionHandler *)This;
-
-    HRESULT hr = S_OK;
-    HRESULT hrActivateResult = S_OK;
-    IUnknown *punkAudioInterface = NULL;
-
-    // Check for a successful activation result
-    hr = IActivateAudioInterfaceAsyncOperation_GetActivateResult(activateOperation, &hrActivateResult, &punkAudioInterface);
-    if (SUCCEEDED(hr) && SUCCEEDED(hrActivateResult))
-    {
-        // Get pointer to the requested audio interface
-        IUnknown_QueryInterface(punkAudioInterface, handler->in.iid, handler->in.obj);
-        if ((*handler->in.obj) == NULL)
-            hrActivateResult = E_FAIL;
-    }
-    SAFE_RELEASE(punkAudioInterface);
-
-    if (SUCCEEDED(hr))
-        handler->out.hr = hrActivateResult;
-    else
-        handler->out.hr = hr;
-
-    // Got client object, stop busy waiting in ActivateAudioInterface
-    InterlockedExchange(&handler->done, TRUE);
-
-    return hr;
-}
-
-static IActivateAudioInterfaceCompletionHandler *CreateActivateAudioInterfaceCompletionHandler(const IID *iid, void **client)
-{
-    PaActivateAudioInterfaceCompletionHandler *handler = PaUtil_AllocateMemory(sizeof(PaActivateAudioInterfaceCompletionHandler));
-
-    memset(handler, 0, sizeof(*handler));
-
-    handler->parent.lpVtbl = PaUtil_AllocateMemory(sizeof(*handler->parent.lpVtbl));
-    handler->parent.lpVtbl->QueryInterface    = &PaActivateAudioInterfaceCompletionHandler_QueryInterface;
-    handler->parent.lpVtbl->AddRef            = &PaActivateAudioInterfaceCompletionHandler_AddRef;
-    handler->parent.lpVtbl->Release           = &PaActivateAudioInterfaceCompletionHandler_Release;
-    handler->parent.lpVtbl->ActivateCompleted = &PaActivateAudioInterfaceCompletionHandler_ActivateCompleted;
-    handler->refs   = 1;
-    handler->in.iid = iid;
-    handler->in.obj = client;
-
-    return (IActivateAudioInterfaceCompletionHandler *)handler;
-}
-#endif
-
-// ------------------------------------------------------------------------------------------
-#ifdef PA_WINRT
-static HRESULT WinRT_GetDefaultDeviceId(WCHAR *deviceId, UINT32 deviceIdMax, EDataFlow flow)
-{
-    switch (flow)
-    {
-    case eRender:
-        if (g_DeviceListInfo.render.defaultId[0] != 0)
-            wcsncpy_s(deviceId, deviceIdMax, g_DeviceListInfo.render.defaultId, wcslen(g_DeviceListInfo.render.defaultId));
-        else
-            StringFromGUID2(&DEVINTERFACE_AUDIO_RENDER, deviceId, deviceIdMax);
-        break;
-    case eCapture:
-        if (g_DeviceListInfo.capture.defaultId[0] != 0)
-            wcsncpy_s(deviceId, deviceIdMax, g_DeviceListInfo.capture.defaultId, wcslen(g_DeviceListInfo.capture.defaultId));
-        else
-            StringFromGUID2(&DEVINTERFACE_AUDIO_CAPTURE, deviceId, deviceIdMax);
-        break;
-    default:
-        return S_FALSE;
-    }
-
-    return S_OK;
-}
-#endif
-
-// ------------------------------------------------------------------------------------------
-#ifdef PA_WINRT
-static HRESULT WinRT_ActivateAudioInterface(const WCHAR *deviceId, const IID *iid, void **client)
-{
-    PaError result = paNoError;
-    HRESULT hr = S_OK;
-    IActivateAudioInterfaceAsyncOperation *asyncOp = NULL;
-    IActivateAudioInterfaceCompletionHandler *handler = CreateActivateAudioInterfaceCompletionHandler(iid, client);
-    PaActivateAudioInterfaceCompletionHandler *handlerImpl = (PaActivateAudioInterfaceCompletionHandler *)handler;
-    UINT32 sleepToggle = 0;
-
-    // Async operation will call back to IActivateAudioInterfaceCompletionHandler::ActivateCompleted
-    // which must be an agile interface implementation
-    hr = ActivateAudioInterfaceAsync(deviceId, iid, NULL, handler, &asyncOp);
-    IF_FAILED_INTERNAL_ERROR_JUMP(hr, result, error);
-
-    // Wait in busy loop for async operation to complete
-    // Use Interlocked API here to ensure that ->done variable is read every time through the loop
-    while (SUCCEEDED(hr) && !InterlockedOr(&handlerImpl->done, 0))
-    {
-        Sleep(sleepToggle ^= 1);
-    }
-
-    hr = handlerImpl->out.hr;
-
-error:
-
-    SAFE_RELEASE(asyncOp);
-    SAFE_RELEASE(handler);
-
-    return hr;
-}
-#endif
-
-// ------------------------------------------------------------------------------------------
-static HRESULT ActivateAudioInterface(const PaWasapiDeviceInfo *deviceInfo, const PaWasapiStreamInfo *streamInfo,
-    IAudioClient **client)
-{
-    HRESULT hr;
-
-#ifndef PA_WINRT
-    if (FAILED(hr = IMMDevice_Activate(deviceInfo->device, GetAudioClientIID(), CLSCTX_ALL, NULL, (void **)client)))
-        return hr;
-#else
-    if (FAILED(hr = WinRT_ActivateAudioInterface(deviceInfo->deviceId, GetAudioClientIID(), (void **)client)))
-        return hr;
-#endif
-
-    // Set audio client options (applicable only to IAudioClient2+): options may affect the audio format
-    // support by IAudioClient implementation and therefore we should set them before GetClosestFormat()
-    // in order to correctly match the requested format
-#ifdef __IAudioClient2_INTERFACE_DEFINED__
-    if ((streamInfo != NULL) && (GetAudioClientVersion() >= 2))
-    {
-        pa_AudioClientProperties audioProps = { 0 };
-        audioProps.cbSize     = sizeof(pa_AudioClientProperties);
-        audioProps.bIsOffload = FALSE;
-        audioProps.eCategory  = (AUDIO_STREAM_CATEGORY)streamInfo->streamCategory;
-        switch (streamInfo->streamOption)
-        {
-        case eStreamOptionRaw:
-            if (GetWindowsVersion() >= WINDOWS_8_1_SERVER2012R2)
-                audioProps.Options = pa_AUDCLNT_STREAMOPTIONS_RAW;
-            break;
-        case eStreamOptionMatchFormat:
-            if (GetWindowsVersion() >= WINDOWS_10_SERVER2016)
-                audioProps.Options = pa_AUDCLNT_STREAMOPTIONS_MATCH_FORMAT;
-            break;
-        }
-
-        if (FAILED(hr = IAudioClient2_SetClientProperties((IAudioClient2 *)(*client), (AudioClientProperties *)&audioProps)))
-        {
-            PRINT(("WASAPI: IAudioClient2_SetClientProperties(IsOffload = %d, Category = %d, Options = %d) failed\n", audioProps.bIsOffload, audioProps.eCategory, audioProps.Options));
-            LogHostError(hr);
-        }
-        else
-        {
-            PRINT(("WASAPI: IAudioClient2 set properties: IsOffload = %d, Category = %d, Options = %d\n", audioProps.bIsOffload, audioProps.eCategory, audioProps.Options));
-        }
-    }
-#endif
-
-    return S_OK;
-}
-
-// ------------------------------------------------------------------------------------------
-#ifdef PA_WINRT
-// Windows 10 SDK 10.0.15063.0 has SignalObjectAndWait defined again (unlike in 10.0.14393.0 and lower)
-#if !defined(WDK_NTDDI_VERSION) || (WDK_NTDDI_VERSION < NTDDI_WIN10_RS2)
-static DWORD SignalObjectAndWait(HANDLE hObjectToSignal, HANDLE hObjectToWaitOn, DWORD dwMilliseconds, BOOL bAlertable)
-{
-    SetEvent(hObjectToSignal);
-    return WaitForSingleObjectEx(hObjectToWaitOn, dwMilliseconds, bAlertable);
-}
-#endif
-#endif
-
-// ------------------------------------------------------------------------------------------
-static void NotifyStateChanged(PaWasapiStream *stream, UINT32 flags, HRESULT hr)
-{
-    if (stream->fnStateHandler == NULL)
-        return;
-
-    if (FAILED(hr))
-        flags |= paWasapiStreamStateError;
-
-    stream->fnStateHandler((PaStream *)stream, flags, hr, stream->pStateHandlerUserData);
-}
-
-// ------------------------------------------------------------------------------------------
-static void FillBaseDeviceInfo(PaDeviceInfo *deviceInfo, PaHostApiIndex hostApiIndex)
-{
-    deviceInfo->structVersion = 2;
-    deviceInfo->hostApi       = hostApiIndex;
-}
-
-// ------------------------------------------------------------------------------------------
-static PaError FillInactiveDeviceInfo(PaWasapiHostApiRepresentation *paWasapi, PaDeviceInfo *deviceInfo)
-{
-    if (deviceInfo->name == NULL)
-        deviceInfo->name = (char *)PaUtil_GroupAllocateMemory(paWasapi->allocations, 1);
-
-    if (deviceInfo->name != NULL)
-    {
-        ((char *)deviceInfo->name)[0] = 0;
-    }
-    else
-        return paInsufficientMemory;
-
-    return paNoError;
-}
-
-// ------------------------------------------------------------------------------------------
-static PaError FillDeviceInfo(PaWasapiHostApiRepresentation *paWasapi, void *pEndPoints, INT32 index, const WCHAR *defaultRenderId,
-    const WCHAR *defaultCaptureId, PaDeviceInfo *deviceInfo, PaWasapiDeviceInfo *wasapiDeviceInfo
-#ifdef PA_WINRT
-    , PaWasapiWinrtDeviceListContext *deviceListContext
-#endif
-)
-{
-    HRESULT hr;
-    PaError result;
-    PaUtilHostApiRepresentation *hostApi = (PaUtilHostApiRepresentation *)paWasapi;
-#ifdef PA_WINRT
-    PaWasapiWinrtDeviceListContextEntry *listEntry = &deviceListContext->devices[index];
-    (void)pEndPoints;
-    (void)defaultRenderId;
-    (void)defaultCaptureId;
-#endif
-
-#ifndef PA_WINRT
-    hr = IMMDeviceCollection_Item((IMMDeviceCollection *)pEndPoints, index, &wasapiDeviceInfo->device);
-    IF_FAILED_INTERNAL_ERROR_JUMP(hr, result, error);
-
-    // Get device Id
-    {
-        WCHAR *deviceId;
-
-        hr = IMMDevice_GetId(wasapiDeviceInfo->device, &deviceId);
-        IF_FAILED_INTERNAL_ERROR_JUMP(hr, result, error);
-
-        wcsncpy(wasapiDeviceInfo->deviceId, deviceId, PA_WASAPI_DEVICE_ID_LEN - 1);
-        CoTaskMemFree(deviceId);
-    }
-
-    // Get state of the device
-    hr = IMMDevice_GetState(wasapiDeviceInfo->device, &wasapiDeviceInfo->state);
-    IF_FAILED_INTERNAL_ERROR_JUMP(hr, result, error);
-    if (wasapiDeviceInfo->state != DEVICE_STATE_ACTIVE)
-    {
-        PRINT(("WASAPI device: %d is not currently available (state:%d)\n", index, wasapiDeviceInfo->state));
-    }
-
-    // Get basic device info
-    {
-        IPropertyStore *pProperty;
-        IMMEndpoint *endpoint;
-        PROPVARIANT value;
-
-        hr = IMMDevice_OpenPropertyStore(wasapiDeviceInfo->device, STGM_READ, &pProperty);
-        IF_FAILED_INTERNAL_ERROR_JUMP(hr, result, error);
-
-        // "Friendly" Name
-        {
-            PropVariantInit(&value);
-
-            hr = IPropertyStore_GetValue(pProperty, &PKEY_Device_FriendlyName, &value);
-            IF_FAILED_INTERNAL_ERROR_JUMP(hr, result, error);
-
-            if ((deviceInfo->name = (char *)PaUtil_GroupAllocateMemory(paWasapi->allocations, PA_WASAPI_DEVICE_NAME_LEN)) == NULL)
-            {
-                result = paInsufficientMemory;
-                PropVariantClear(&value);
-                goto error;
-            }
-            if (value.pwszVal)
-                WideCharToMultiByte(CP_UTF8, 0, value.pwszVal, (INT32)wcslen(value.pwszVal), (char *)deviceInfo->name, PA_WASAPI_DEVICE_NAME_LEN - 1, 0, 0);
-            else
-                _snprintf((char *)deviceInfo->name, PA_WASAPI_DEVICE_NAME_LEN - 1, "baddev%d", index);
-
-            PropVariantClear(&value);
-
-            PA_DEBUG(("WASAPI:%d| name[%s]\n", index, deviceInfo->name));
-        }
-
-        // Default format
-        {
-            PropVariantInit(&value);
-
-            hr = IPropertyStore_GetValue(pProperty, &PKEY_AudioEngine_DeviceFormat, &value);
-            IF_FAILED_INTERNAL_ERROR_JUMP(hr, result, error);
-
-            memcpy(&wasapiDeviceInfo->DefaultFormat, value.blob.pBlobData, min(sizeof(wasapiDeviceInfo->DefaultFormat), value.blob.cbSize));
-
-            PropVariantClear(&value);
-        }
-
-        // Form factor
-        {
-            PropVariantInit(&value);
-
-            hr = IPropertyStore_GetValue(pProperty, &PKEY_AudioEndpoint_FormFactor, &value);
-            IF_FAILED_INTERNAL_ERROR_JUMP(hr, result, error);
-
-        // set
-        #if defined(DUMMYUNIONNAME) && defined(NONAMELESSUNION)
-            // avoid breaking strict-aliasing rules in such line: (EndpointFormFactor)(*((UINT *)(((WORD *)&value.wReserved3)+1)));
-            UINT v;
-            memcpy(&v, (((WORD *)&value.wReserved3) + 1), sizeof(v));
-            wasapiDeviceInfo->formFactor = (EndpointFormFactor)v;
-        #else
-            wasapiDeviceInfo->formFactor = (EndpointFormFactor)value.uintVal;
-        #endif
-
-            PA_DEBUG(("WASAPI:%d| form-factor[%d]\n", index, wasapiDeviceInfo->formFactor));
-
-            PropVariantClear(&value);
-        }
-
-        // Data flow (Renderer or Capture)
-        hr = IMMDevice_QueryInterface(wasapiDeviceInfo->device, &pa_IID_IMMEndpoint, (void **)&endpoint);
-        if (SUCCEEDED(hr))
-        {
-            hr = IMMEndpoint_GetDataFlow(endpoint, &wasapiDeviceInfo->flow);
-            SAFE_RELEASE(endpoint);
-        }
-
-        SAFE_RELEASE(pProperty);
-    }
-#else
-    // Set device Id
-    wcsncpy(wasapiDeviceInfo->deviceId, listEntry->info->id, PA_WASAPI_DEVICE_ID_LEN - 1);
-
-    // Set device name
-    if ((deviceInfo->name = (char *)PaUtil_GroupAllocateMemory(paWasapi->allocations, PA_WASAPI_DEVICE_NAME_LEN)) == NULL)
-    {
-        result = paInsufficientMemory;
-        goto error;
-    }
-    ((char *)deviceInfo->name)[0] = 0;
-    if (listEntry->info->name[0] != 0)
-        WideCharToMultiByte(CP_UTF8, 0, listEntry->info->name, (INT32)wcslen(listEntry->info->name), (char *)deviceInfo->name, PA_WASAPI_DEVICE_NAME_LEN - 1, 0, 0);
-    if (deviceInfo->name[0] == 0) // fallback if WideCharToMultiByte is failed, or listEntry is nameless
-        _snprintf((char *)deviceInfo->name, PA_WASAPI_DEVICE_NAME_LEN - 1, "WASAPI_%s:%d", (listEntry->flow == eRender ? "Output" : "Input"), index);
-
-    // Form-factor
-    wasapiDeviceInfo->formFactor = listEntry->info->formFactor;
-
-    // Set data flow
-    wasapiDeviceInfo->flow = listEntry->flow;
-#endif
-
-    // Set default Output/Input devices
-    if ((defaultRenderId != NULL) && (wcsncmp(wasapiDeviceInfo->deviceId, defaultRenderId, PA_WASAPI_DEVICE_NAME_LEN - 1) == 0))
-        hostApi->info.defaultOutputDevice = hostApi->info.deviceCount;
-    if ((defaultCaptureId != NULL) && (wcsncmp(wasapiDeviceInfo->deviceId, defaultCaptureId, PA_WASAPI_DEVICE_NAME_LEN - 1) == 0))
-        hostApi->info.defaultInputDevice = hostApi->info.deviceCount;
-
-    // Get a temporary IAudioClient for more details
-    {
-        IAudioClient *tmpClient;
-        WAVEFORMATEX *mixFormat;
-
-        hr = ActivateAudioInterface(wasapiDeviceInfo, NULL, &tmpClient);
-        IF_FAILED_INTERNAL_ERROR_JUMP(hr, result, error);
-
-        // Get latency
-        hr = IAudioClient_GetDevicePeriod(tmpClient, &wasapiDeviceInfo->DefaultDevicePeriod, &wasapiDeviceInfo->MinimumDevicePeriod);
-        if (FAILED(hr))
-        {
-            PA_DEBUG(("WASAPI:%d| failed getting min/default periods by IAudioClient::GetDevicePeriod() with error[%08X], will use 30000/100000 hns\n", index, (UINT32)hr));
-
-            // assign WASAPI common values
-            wasapiDeviceInfo->DefaultDevicePeriod = 100000;
-            wasapiDeviceInfo->MinimumDevicePeriod = 30000;
-
-            // ignore error, let continue further without failing with paInternalError
-            hr = S_OK;
-        }
-
-        // Get mix format
-        hr = IAudioClient_GetMixFormat(tmpClient, &mixFormat);
-        if (SUCCEEDED(hr))
-        {
-            memcpy(&wasapiDeviceInfo->MixFormat, mixFormat, min(sizeof(wasapiDeviceInfo->MixFormat), (sizeof(*mixFormat) + mixFormat->cbSize)));
-            CoTaskMemFree(mixFormat);
-        }
-
-        // Register WINRT device
-    #ifdef PA_WINRT
-        if (SUCCEEDED(hr))
-        {
-            // Set state
-            wasapiDeviceInfo->state = DEVICE_STATE_ACTIVE;
-
-            // Default format (Shared mode) is always a mix format
-            wasapiDeviceInfo->DefaultFormat = wasapiDeviceInfo->MixFormat;
-        }
-    #endif
-
-        // Release tmp client
-        SAFE_RELEASE(tmpClient);
-
-        if (hr != S_OK)
-        {
-            //davidv: this happened with my hardware, previously for that same device in DirectSound:
-            //Digital Output (Realtek AC'97 Audio)'s GUID: {0x38f2cf50,0x7b4c,0x4740,0x86,0xeb,0xd4,0x38,0x66,0xd8,0xc8, 0x9f}
-            //so something must be _really_ wrong with this device, TODO handle this better. We kind of need GetMixFormat
-            LogHostError(hr);
-            result = paInternalError;
-            goto error;
-        }
-    }
-
-    // Fill basic device data
-    deviceInfo->maxInputChannels = 0;
-    deviceInfo->maxOutputChannels = 0;
-    deviceInfo->defaultSampleRate = wasapiDeviceInfo->MixFormat.Format.nSamplesPerSec;
-    switch (wasapiDeviceInfo->flow)
-    {
-    case eRender: {
-        deviceInfo->maxOutputChannels         = wasapiDeviceInfo->MixFormat.Format.nChannels;
-        deviceInfo->defaultHighOutputLatency = nano100ToSeconds(wasapiDeviceInfo->DefaultDevicePeriod);
-        deviceInfo->defaultLowOutputLatency  = nano100ToSeconds(wasapiDeviceInfo->MinimumDevicePeriod);
-        PA_DEBUG(("WASAPI:%d| def.SR[%d] max.CH[%d] latency{hi[%f] lo[%f]}\n", index, (UINT32)deviceInfo->defaultSampleRate,
-            deviceInfo->maxOutputChannels, (float)deviceInfo->defaultHighOutputLatency, (float)deviceInfo->defaultLowOutputLatency));
-        break;}
-    case eCapture: {
-        deviceInfo->maxInputChannels        = wasapiDeviceInfo->MixFormat.Format.nChannels;
-        deviceInfo->defaultHighInputLatency = nano100ToSeconds(wasapiDeviceInfo->DefaultDevicePeriod);
-        deviceInfo->defaultLowInputLatency  = nano100ToSeconds(wasapiDeviceInfo->MinimumDevicePeriod);
-        PA_DEBUG(("WASAPI:%d| def.SR[%d] max.CH[%d] latency{hi[%f] lo[%f]}\n", index, (UINT32)deviceInfo->defaultSampleRate,
-            deviceInfo->maxInputChannels, (float)deviceInfo->defaultHighInputLatency, (float)deviceInfo->defaultLowInputLatency));
-        break; }
-    default:
-        PRINT(("WASAPI:%d| bad Data Flow!\n", index));
-        result = paInternalError;
-        goto error;
-    }
-
-    return paNoError;
-
-error:
-
-    PRINT(("WASAPI: failed filling device info for device index[%d] - error[%d|%s]\n", index, result, Pa_GetErrorText(result)));
-
-    return result;
-}
-
-// ------------------------------------------------------------------------------------------
-static PaDeviceInfo *AllocateDeviceListMemory(PaWasapiHostApiRepresentation *paWasapi)
-{
-    PaUtilHostApiRepresentation *hostApi = (PaUtilHostApiRepresentation *)paWasapi;
-    PaDeviceInfo *deviceInfoArray = NULL;
-
-    if ((paWasapi->devInfo = (PaWasapiDeviceInfo *)PaUtil_GroupAllocateMemory(paWasapi->allocations,
-        sizeof(PaWasapiDeviceInfo) * paWasapi->deviceCount)) == NULL)
-    {
-        return NULL;
-    }
-    memset(paWasapi->devInfo, 0, sizeof(PaWasapiDeviceInfo) * paWasapi->deviceCount);
-
-    if (paWasapi->deviceCount != 0)
-    {
-        UINT32 i;
-        UINT32 deviceCount = paWasapi->deviceCount;
-    #if defined(PA_WASAPI_MAX_CONST_DEVICE_COUNT) && (PA_WASAPI_MAX_CONST_DEVICE_COUNT > 0)
-        if (deviceCount < PA_WASAPI_MAX_CONST_DEVICE_COUNT)
-            deviceCount = PA_WASAPI_MAX_CONST_DEVICE_COUNT;
-    #endif
-
-        if ((hostApi->deviceInfos = (PaDeviceInfo **)PaUtil_GroupAllocateMemory(paWasapi->allocations,
-            sizeof(PaDeviceInfo *) * deviceCount)) == NULL)
-        {
-            return NULL;
-        }
-        for (i = 0; i < deviceCount; ++i)
-            hostApi->deviceInfos[i] = NULL;
-
-        // Allocate all device info structs in a contiguous block
-        if ((deviceInfoArray = (PaDeviceInfo *)PaUtil_GroupAllocateMemory(paWasapi->allocations,
-            sizeof(PaDeviceInfo) * deviceCount)) == NULL)
-        {
-            return NULL;
-        }
-        memset(deviceInfoArray, 0, sizeof(PaDeviceInfo) * deviceCount);
-    }
-
-    return deviceInfoArray;
-}
-
-// ------------------------------------------------------------------------------------------
-static PaError CreateDeviceList(PaWasapiHostApiRepresentation *paWasapi, PaHostApiIndex hostApiIndex)
-{
-    PaUtilHostApiRepresentation *hostApi = (PaUtilHostApiRepresentation *)paWasapi;
-    PaError result = paNoError;
-    PaDeviceInfo *deviceInfoArray = NULL;
-    UINT32 i;
-    WCHAR *defaultRenderId = NULL;
-    WCHAR *defaultCaptureId = NULL;
-#ifndef PA_WINRT
-    HRESULT hr;
-    IMMDeviceCollection *pEndPoints = NULL;
-    IMMDeviceEnumerator *pEnumerator = NULL;
-#else
-    void *pEndPoints = NULL;
-    IAudioClient *tmpClient;
-    PaWasapiWinrtDeviceListContext deviceListContext = { 0 };
-    PaWasapiWinrtDeviceInfo defaultRender = { 0 };
-    PaWasapiWinrtDeviceInfo defaultCapture = { 0 };
-#endif
-
-    // Make sure device list empty
-    if ((paWasapi->deviceCount != 0) || (hostApi->info.deviceCount != 0))
-        return paInternalError;
-
-#ifndef PA_WINRT
-    hr = CoCreateInstance(&pa_CLSID_IMMDeviceEnumerator, NULL, CLSCTX_INPROC_SERVER,
-        &pa_IID_IMMDeviceEnumerator, (void **)&pEnumerator);
-    IF_FAILED_INTERNAL_ERROR_JUMP(hr, result, error);
-
-    // Get default render and capture devices
-    {
-        IMMDevice *device;
-
-        hr = IMMDeviceEnumerator_GetDefaultAudioEndpoint(pEnumerator, eRender, eMultimedia, &device);
-        if (hr != S_OK)
-        {
-            if (hr != E_NOTFOUND)
-            {
-                IF_FAILED_INTERNAL_ERROR_JUMP(hr, result, error);
-            }
-        }
-        else
-        {
-            hr = IMMDevice_GetId(device, &defaultRenderId);
-            IMMDevice_Release(device);
-            IF_FAILED_INTERNAL_ERROR_JUMP(hr, result, error);
-        }
-
-        hr = IMMDeviceEnumerator_GetDefaultAudioEndpoint(pEnumerator, eCapture, eMultimedia, &device);
-        if (hr != S_OK)
-        {
-            if (hr != E_NOTFOUND)
-            {
-                IF_FAILED_INTERNAL_ERROR_JUMP(hr, result, error);
-            }
-        }
-        else
-        {
-            hr = IMMDevice_GetId(device, &defaultCaptureId);
-            IMMDevice_Release(device);
-            IF_FAILED_INTERNAL_ERROR_JUMP(hr, result, error);
-        }
-    }
-
-    // Get all currently active devices
-    hr = IMMDeviceEnumerator_EnumAudioEndpoints(pEnumerator, eAll, DEVICE_STATE_ACTIVE, &pEndPoints);
-    IF_FAILED_INTERNAL_ERROR_JUMP(hr, result, error);
-
-    // Get device count
-    hr = IMMDeviceCollection_GetCount(pEndPoints, &paWasapi->deviceCount);
-    IF_FAILED_INTERNAL_ERROR_JUMP(hr, result, error);
-#else
-    WinRT_GetDefaultDeviceId(defaultRender.id, STATIC_ARRAY_SIZE(defaultRender.id) - 1, eRender);
-    defaultRenderId = defaultRender.id;
-
-    WinRT_GetDefaultDeviceId(defaultCapture.id, STATIC_ARRAY_SIZE(defaultCapture.id) - 1, eCapture);
-    defaultCaptureId = defaultCapture.id;
-
-    if (g_DeviceListInfo.render.deviceCount == 0)
-    {
-        if (SUCCEEDED(WinRT_ActivateAudioInterface(defaultRenderId, GetAudioClientIID(), &tmpClient)))
-        {
-            deviceListContext.devices[paWasapi->deviceCount].info = &defaultRender;
-            deviceListContext.devices[paWasapi->deviceCount].flow = eRender;
-            paWasapi->deviceCount++;
-
-            SAFE_RELEASE(tmpClient);
-        }
-    }
-    else
-    {
-        for (i = 0; i < g_DeviceListInfo.render.deviceCount; ++i)
-        {
-            deviceListContext.devices[paWasapi->deviceCount].info = &g_DeviceListInfo.render.devices[i];
-            deviceListContext.devices[paWasapi->deviceCount].flow = eRender;
-            paWasapi->deviceCount++;
-        }
-    }
-
-    if (g_DeviceListInfo.capture.deviceCount == 0)
-    {
-        if (SUCCEEDED(WinRT_ActivateAudioInterface(defaultCaptureId, GetAudioClientIID(), &tmpClient)))
-        {
-            deviceListContext.devices[paWasapi->deviceCount].info = &defaultCapture;
-            deviceListContext.devices[paWasapi->deviceCount].flow = eCapture;
-            paWasapi->deviceCount++;
-
-            SAFE_RELEASE(tmpClient);
-        }
-    }
-    else
-    {
-        for (i = 0; i < g_DeviceListInfo.capture.deviceCount; ++i)
-        {
-            deviceListContext.devices[paWasapi->deviceCount].info = &g_DeviceListInfo.capture.devices[i];
-            deviceListContext.devices[paWasapi->deviceCount].flow = eCapture;
-            paWasapi->deviceCount++;
-        }
-    }
-#endif
-
-    // Allocate memory for the device list
-    if ((paWasapi->deviceCount != 0) && ((deviceInfoArray = AllocateDeviceListMemory(paWasapi)) == NULL))
-    {
-        result = paInsufficientMemory;
-        goto error;
-    }
-
-    // Fill WASAPI device info
-    for (i = 0; i < paWasapi->deviceCount; ++i)
-    {
-        PaDeviceInfo *deviceInfo = &deviceInfoArray[i];
-
-        PA_DEBUG(("WASAPI: device idx: %02d\n", i));
-        PA_DEBUG(("WASAPI: ---------------\n"));
-
-        FillBaseDeviceInfo(deviceInfo, hostApiIndex);
-
-        if ((result = FillDeviceInfo(paWasapi, pEndPoints, i, defaultRenderId, defaultCaptureId,
-            deviceInfo, &paWasapi->devInfo[i]
-        #ifdef PA_WINRT
-            , &deviceListContext
-        #endif
-            )) != paNoError)
-        {
-            // Faulty device is made inactive
-            if ((result = FillInactiveDeviceInfo(paWasapi, deviceInfo)) != paNoError)
-                goto error;
-        }
-
-        hostApi->deviceInfos[i] = deviceInfo;
-        ++hostApi->info.deviceCount;
-    }
-
-    // Fill the remaining slots with inactive device info
-#if defined(PA_WASAPI_MAX_CONST_DEVICE_COUNT) && (PA_WASAPI_MAX_CONST_DEVICE_COUNT > 0)
-    if ((hostApi->info.deviceCount != 0) && (hostApi->info.deviceCount < PA_WASAPI_MAX_CONST_DEVICE_COUNT))
-    {
-        for (i = hostApi->info.deviceCount; i < PA_WASAPI_MAX_CONST_DEVICE_COUNT; ++i)
-        {
-            PaDeviceInfo *deviceInfo = &deviceInfoArray[i];
-
-            FillBaseDeviceInfo(deviceInfo, hostApiIndex);
-
-            if ((result = FillInactiveDeviceInfo(paWasapi, deviceInfo)) != paNoError)
-                goto error;
-
-            hostApi->deviceInfos[i] = deviceInfo;
-            ++hostApi->info.deviceCount;
-        }
-    }
-#endif
-
-    // Clear any non-fatal errors
-    result = paNoError;
-
-    PRINT(("WASAPI: device list ok - found %d devices\n", paWasapi->deviceCount));
-
-done:
-
-#ifndef PA_WINRT
-    CoTaskMemFree(defaultRenderId);
-    CoTaskMemFree(defaultCaptureId);
-    SAFE_RELEASE(pEndPoints);
-    SAFE_RELEASE(pEnumerator);
-#endif
-
-    return result;
-
-error:
-
-    // Safety if error was not set so that we do not think initialize was a success
-    if (result == paNoError)
-        result = paInternalError;
-
-    PRINT(("WASAPI: failed to create device list - error[%d|%s]\n", result, Pa_GetErrorText(result)));
-
-    goto done;
-}
-
-// ------------------------------------------------------------------------------------------
-PaError PaWasapi_Initialize( PaUtilHostApiRepresentation **hostApi, PaHostApiIndex hostApiIndex )
-{
-    PaError result;
-    PaWasapiHostApiRepresentation *paWasapi;
-
-#ifndef PA_WINRT
-    if (!SetupAVRT())
-    {
-        PRINT(("WASAPI: No AVRT! (not VISTA?)\n"));
-        return paNoError;
-    }
-#endif
-
-    paWasapi = (PaWasapiHostApiRepresentation *)PaUtil_AllocateMemory(sizeof(PaWasapiHostApiRepresentation));
-    if (paWasapi == NULL)
-    {
-        result = paInsufficientMemory;
-        goto error;
-    }
-    memset(paWasapi, 0, sizeof(PaWasapiHostApiRepresentation)); /* ensure all fields are zeroed. especially paWasapi->allocations */
-
-    // Initialize COM subsystem
-    result = PaWinUtil_CoInitialize(paWASAPI, &paWasapi->comInitializationResult);
-    if (result != paNoError)
-        goto error;
-
-    // Create memory group
-    paWasapi->allocations = PaUtil_CreateAllocationGroup();
-    if (paWasapi->allocations == NULL)
-    {
-        result = paInsufficientMemory;
-        goto error;
-    }
-
-    // Fill basic interface info
-    *hostApi                             = &paWasapi->inheritedHostApiRep;
-    (*hostApi)->info.structVersion         = 1;
-    (*hostApi)->info.type                 = paWASAPI;
-    (*hostApi)->info.name                 = "Windows WASAPI";
-    (*hostApi)->info.deviceCount         = 0;
-    (*hostApi)->info.defaultInputDevice     = paNoDevice;
-    (*hostApi)->info.defaultOutputDevice = paNoDevice;
-    (*hostApi)->Terminate                = Terminate;
-    (*hostApi)->OpenStream               = OpenStream;
-    (*hostApi)->IsFormatSupported        = IsFormatSupported;
-
-    // Fill the device list
-    if ((result = CreateDeviceList(paWasapi, hostApiIndex)) != paNoError)
-        goto error;
-
-    // Detect if platform workaround is required
-    paWasapi->useWOW64Workaround = UseWOW64Workaround();
-
-    // Initialize time getter
-    SystemTimer_InitializeTimeGetter();
-
-    PaUtil_InitializeStreamInterface( &paWasapi->callbackStreamInterface, CloseStream, StartStream,
-                                      StopStream, AbortStream, IsStreamStopped, IsStreamActive,
-                                      GetStreamTime, GetStreamCpuLoad,
-                                      PaUtil_DummyRead, PaUtil_DummyWrite,
-                                      PaUtil_DummyGetReadAvailable, PaUtil_DummyGetWriteAvailable );
-
-    PaUtil_InitializeStreamInterface( &paWasapi->blockingStreamInterface, CloseStream, StartStream,
-                                      StopStream, AbortStream, IsStreamStopped, IsStreamActive,
-                                      GetStreamTime, PaUtil_DummyGetCpuLoad,
-                                      ReadStream, WriteStream, GetStreamReadAvailable, GetStreamWriteAvailable );
-
-    PRINT(("WASAPI: initialized ok\n"));
-
-    return paNoError;
-
-error:
-
-    PRINT(("WASAPI: failed %s error[%d|%s]\n", __FUNCTION__, result, Pa_GetErrorText(result)));
-
-    Terminate((PaUtilHostApiRepresentation *)paWasapi);
-
-    return result;
-}
-
-// ------------------------------------------------------------------------------------------
-static void ReleaseWasapiDeviceInfoList( PaWasapiHostApiRepresentation *paWasapi )
-{
-    UINT32 i;
-
-    // Release device info bound objects
-    for (i = 0; i < paWasapi->deviceCount; ++i)
-    {
-    #ifndef PA_WINRT
-        SAFE_RELEASE(paWasapi->devInfo[i].device);
-    #endif
-    }
-
-    // Free device info
-    if (paWasapi->allocations != NULL)
-        PaUtil_GroupFreeMemory(paWasapi->allocations, paWasapi->devInfo);
-
-    // Be ready for a device list reinitialization and if its creation is failed pointers must not be dangling
-    paWasapi->devInfo = NULL;
-    paWasapi->deviceCount = 0;
-}
-
-// ------------------------------------------------------------------------------------------
-static void Terminate( PaUtilHostApiRepresentation *hostApi )
-{
-    PaWasapiHostApiRepresentation *paWasapi = (PaWasapiHostApiRepresentation*)hostApi;
-    if (paWasapi == NULL)
-        return;
-
-    // Release device list
-    ReleaseWasapiDeviceInfoList(paWasapi);
-
-    // Free allocations and memory group itself
-    if (paWasapi->allocations != NULL)
-    {
-        PaUtil_FreeAllAllocations(paWasapi->allocations);
-        PaUtil_DestroyAllocationGroup(paWasapi->allocations);
-    }
-
-    // Release COM subsystem
-    PaWinUtil_CoUninitialize(paWASAPI, &paWasapi->comInitializationResult);
-
-    // Free API representation
-    PaUtil_FreeMemory(paWasapi);
-
-    // Close AVRT
-    CloseAVRT();
-}
-
-// ------------------------------------------------------------------------------------------
-static PaWasapiHostApiRepresentation *_GetHostApi(PaError *ret)
-{
-    PaError error;
-    PaUtilHostApiRepresentation *pApi;
-
-    if ((error = PaUtil_GetHostApiRepresentation(&pApi, paWASAPI)) != paNoError)
-    {
-        if (ret != NULL)
-            (*ret) = error;
-
-        return NULL;
-    }
-
-    return (PaWasapiHostApiRepresentation *)pApi;
-}
-
-// ------------------------------------------------------------------------------------------
-static PaError UpdateDeviceList()
-{
-    int i;
-    PaError ret;
-    PaWasapiHostApiRepresentation *paWasapi;
-    PaUtilHostApiRepresentation *hostApi;
-
-    // Get API
-    hostApi = (PaUtilHostApiRepresentation *)(paWasapi = _GetHostApi(&ret));
-    if (paWasapi == NULL)
-        return paNotInitialized;
-
-    // Make sure initialized properly
-    if (paWasapi->allocations == NULL)
-        return paNotInitialized;
-
-    // Release WASAPI internal device info list
-    ReleaseWasapiDeviceInfoList(paWasapi);
-
-    // Release external device info list
-    if (hostApi->deviceInfos != NULL)
-    {
-        for (i = 0; i < hostApi->info.deviceCount; ++i)
-        {
-            PaUtil_GroupFreeMemory(paWasapi->allocations, (void *)hostApi->deviceInfos[i]->name);
-        }
-        PaUtil_GroupFreeMemory(paWasapi->allocations, hostApi->deviceInfos[0]);
-        PaUtil_GroupFreeMemory(paWasapi->allocations, hostApi->deviceInfos);
-
-        // Be ready for a device list reinitialization and if its creation is failed pointers must not be dangling
-        hostApi->deviceInfos = NULL;
-        hostApi->info.deviceCount = 0;
-        hostApi->info.defaultInputDevice = paNoDevice;
-        hostApi->info.defaultOutputDevice = paNoDevice;
-    }
-
-    // Fill possibly updated device list
-    if ((ret = CreateDeviceList(paWasapi, Pa_HostApiTypeIdToHostApiIndex(paWASAPI))) != paNoError)
-        return ret;
-
-    return paNoError;
-}
-
-// ------------------------------------------------------------------------------------------
-PaError PaWasapi_UpdateDeviceList()
-{
-#if defined(PA_WASAPI_MAX_CONST_DEVICE_COUNT) && (PA_WASAPI_MAX_CONST_DEVICE_COUNT > 0)
-    return UpdateDeviceList();
-#else
-    return paInternalError;
-#endif
-}
-
-// ------------------------------------------------------------------------------------------
-int PaWasapi_GetDeviceCurrentFormat( PaStream *pStream, void *pFormat, unsigned int formatSize, int bOutput )
-{
-    UINT32 size;
-    WAVEFORMATEXTENSIBLE *format;
-
-    PaWasapiStream *stream = (PaWasapiStream *)pStream;
-    if (stream == NULL)
-        return paBadStreamPtr;
-
-    format = (bOutput == TRUE ? &stream->out.wavex : &stream->in.wavex);
-
-    size = min(formatSize, (UINT32)sizeof(*format));
-    memcpy(pFormat, format, size);
-
-    return size;
-}
-
-// ------------------------------------------------------------------------------------------
-static PaError _GetWasapiDeviceInfoByDeviceIndex( PaWasapiDeviceInfo **info, PaDeviceIndex device )
-{
-    PaError ret;
-    PaDeviceIndex index;
-
-    // Get API
-    PaWasapiHostApiRepresentation *paWasapi = _GetHostApi(&ret);
-    if (paWasapi == NULL)
-        return paNotInitialized;
-
-    // Get device index
-    if ((ret = PaUtil_DeviceIndexToHostApiDeviceIndex(&index, device, &paWasapi->inheritedHostApiRep)) != paNoError)
-        return ret;
-
-    // Validate index
-    if ((UINT32)index >= paWasapi->deviceCount)
-        return paInvalidDevice;
-
-    (*info) = &paWasapi->devInfo[ index ];
-
-    return paNoError;
-}
-
-// ------------------------------------------------------------------------------------------
-int PaWasapi_GetDeviceDefaultFormat( void *pFormat, unsigned int formatSize, PaDeviceIndex device )
-{
-    PaError ret;
-    PaWasapiDeviceInfo *deviceInfo;
-    UINT32 size;
-
-    if (pFormat == NULL)
-        return paBadBufferPtr;
-    if (formatSize <= 0)
-        return paBufferTooSmall;
-
-    if ((ret = _GetWasapiDeviceInfoByDeviceIndex(&deviceInfo, device)) != paNoError)
-        return ret;
-
-    size = min(formatSize, (UINT32)sizeof(deviceInfo->DefaultFormat));
-    memcpy(pFormat, &deviceInfo->DefaultFormat, size);
-
-    return size;
-}
-
-// ------------------------------------------------------------------------------------------
-int PaWasapi_GetDeviceMixFormat( void *pFormat, unsigned int formatSize, PaDeviceIndex device )
-{
-    PaError ret;
-    PaWasapiDeviceInfo *deviceInfo;
-    UINT32 size;
-
-    if (pFormat == NULL)
-        return paBadBufferPtr;
-    if (formatSize <= 0)
-        return paBufferTooSmall;
-
-    if ((ret = _GetWasapiDeviceInfoByDeviceIndex(&deviceInfo, device)) != paNoError)
-        return ret;
-
-    size = min(formatSize, (UINT32)sizeof(deviceInfo->MixFormat));
-    memcpy(pFormat, &deviceInfo->MixFormat, size);
-
-    return size;
-}
-
-// ------------------------------------------------------------------------------------------
-int PaWasapi_GetDeviceRole( PaDeviceIndex device )
-{
-    PaError ret;
-    PaWasapiDeviceInfo *deviceInfo;
-
-    if ((ret = _GetWasapiDeviceInfoByDeviceIndex(&deviceInfo, device)) != paNoError)
-        return ret;
-
-    return deviceInfo->formFactor;
-}
-
-// ------------------------------------------------------------------------------------------
-PaError PaWasapi_GetIMMDevice( PaDeviceIndex device, void **pIMMDevice )
-{
-#ifndef PA_WINRT
-    PaError ret;
-    PaWasapiDeviceInfo *deviceInfo;
-
-    if (pIMMDevice == NULL)
-        return paBadBufferPtr;
-
-    if ((ret = _GetWasapiDeviceInfoByDeviceIndex(&deviceInfo, device)) != paNoError)
-        return ret;
-
-    (*pIMMDevice) = deviceInfo->device;
-
-    return paNoError;
-#else
-    (void)device;
-    (void)pIMMDevice;
-    return paIncompatibleStreamHostApi;
-#endif
-}
-
-// ------------------------------------------------------------------------------------------
-PaError PaWasapi_GetFramesPerHostBuffer( PaStream *pStream, unsigned int *pInput, unsigned int *pOutput )
-{
-    PaWasapiStream *stream = (PaWasapiStream *)pStream;
-    if (stream == NULL)
-        return paBadStreamPtr;
-
-    if (pInput != NULL)
-        (*pInput) = stream->in.framesPerHostCallback;
-
-    if (pOutput != NULL)
-        (*pOutput) = stream->out.framesPerHostCallback;
-
-    return paNoError;
-}
-
-// ------------------------------------------------------------------------------------------
-static void LogWAVEFORMATEXTENSIBLE(const WAVEFORMATEXTENSIBLE *in)
-{
-    const WAVEFORMATEX *old = (WAVEFORMATEX *)in;
-    switch (old->wFormatTag)
-    {
-    case WAVE_FORMAT_EXTENSIBLE: {
-
-        PRINT(("wFormatTag     =WAVE_FORMAT_EXTENSIBLE\n"));
-
-        if (IsEqualGUID(&in->SubFormat, &pa_KSDATAFORMAT_SUBTYPE_IEEE_FLOAT))
-        {
-            PRINT(("SubFormat      =KSDATAFORMAT_SUBTYPE_IEEE_FLOAT\n"));
-        }
-        else
-        if (IsEqualGUID(&in->SubFormat, &pa_KSDATAFORMAT_SUBTYPE_PCM))
-        {
-            PRINT(("SubFormat      =KSDATAFORMAT_SUBTYPE_PCM\n"));
-        }
-        else
-        {
-            PRINT(("SubFormat      =CUSTOM GUID{%d:%d:%d:%d%d%d%d%d%d%d%d}\n",
-                                        in->SubFormat.Data1,
-                                        in->SubFormat.Data2,
-                                        in->SubFormat.Data3,
-                                        (int)in->SubFormat.Data4[0],
-                                        (int)in->SubFormat.Data4[1],
-                                        (int)in->SubFormat.Data4[2],
-                                        (int)in->SubFormat.Data4[3],
-                                        (int)in->SubFormat.Data4[4],
-                                        (int)in->SubFormat.Data4[5],
-                                        (int)in->SubFormat.Data4[6],
-                                        (int)in->SubFormat.Data4[7]));
-        }
-        PRINT(("Samples.wValidBitsPerSample =%d\n",  in->Samples.wValidBitsPerSample));
-        PRINT(("dwChannelMask  =0x%X\n",in->dwChannelMask));
-
-        break; }
-
-    case WAVE_FORMAT_PCM:        PRINT(("wFormatTag     =WAVE_FORMAT_PCM\n")); break;
-    case WAVE_FORMAT_IEEE_FLOAT: PRINT(("wFormatTag     =WAVE_FORMAT_IEEE_FLOAT\n")); break;
-    default:
-        PRINT(("wFormatTag     =UNKNOWN(%d)\n",old->wFormatTag)); break;
-    }
-
-    PRINT(("nChannels      =%d\n",old->nChannels));
-    PRINT(("nSamplesPerSec =%d\n",old->nSamplesPerSec));
-    PRINT(("nAvgBytesPerSec=%d\n",old->nAvgBytesPerSec));
-    PRINT(("nBlockAlign    =%d\n",old->nBlockAlign));
-    PRINT(("wBitsPerSample =%d\n",old->wBitsPerSample));
-    PRINT(("cbSize         =%d\n",old->cbSize));
-}
-
-// ------------------------------------------------------------------------------------------
-PaSampleFormat WaveToPaFormat(const WAVEFORMATEXTENSIBLE *fmtext)
-{
-    const WAVEFORMATEX *fmt = (WAVEFORMATEX *)fmtext;
-
-    switch (fmt->wFormatTag)
-    {
-    case WAVE_FORMAT_EXTENSIBLE: {
-        if (IsEqualGUID(&fmtext->SubFormat, &pa_KSDATAFORMAT_SUBTYPE_IEEE_FLOAT))
-        {
-            if (fmtext->Samples.wValidBitsPerSample == 32)
-                return paFloat32;
-        }
-        else
-        if (IsEqualGUID(&fmtext->SubFormat, &pa_KSDATAFORMAT_SUBTYPE_PCM))
-        {
-            switch (fmt->wBitsPerSample)
-            {
-            case 32: return paInt32;
-            case 24: return paInt24;
-            case 16: return paInt16;
-            case  8: return paUInt8;
-            }
-        }
-        break; }
-
-    case WAVE_FORMAT_IEEE_FLOAT:
-        return paFloat32;
-
-    case WAVE_FORMAT_PCM: {
-        switch (fmt->wBitsPerSample)
-        {
-        case 32: return paInt32;
-        case 24: return paInt24;
-        case 16: return paInt16;
-        case  8: return paUInt8;
-        }
-        break; }
-    }
-
-    return paCustomFormat;
-}
-
-// ------------------------------------------------------------------------------------------
-static PaError MakeWaveFormatFromParams(WAVEFORMATEXTENSIBLE *wavex, const PaStreamParameters *params,
-    double sampleRate, BOOL packedOnly)
-{
-    WORD bitsPerSample;
-    WAVEFORMATEX *old;
-    DWORD channelMask = 0;
-    BOOL useExtensible = (params->channelCount > 2); // format is always forced for >2 channels format
-    PaWasapiStreamInfo *streamInfo = (PaWasapiStreamInfo *)params->hostApiSpecificStreamInfo;
-
-    // Convert PaSampleFormat to valid data bits
-    if ((bitsPerSample = PaSampleFormatToBitsPerSample(params->sampleFormat)) == 0)
-        return paSampleFormatNotSupported;
-
-    // Use user assigned channel mask
-    if ((streamInfo != NULL) && (streamInfo->flags & paWinWasapiUseChannelMask))
-    {
-        channelMask   = streamInfo->channelMask;
-        useExtensible = TRUE;
-    }
-
-    memset(wavex, 0, sizeof(*wavex));
-
-    old                    = (WAVEFORMATEX *)wavex;
-    old->nChannels      = (WORD)params->channelCount;
-    old->nSamplesPerSec = (DWORD)sampleRate;
-    old->wBitsPerSample = bitsPerSample;
-
-    // according to MSDN for WAVEFORMATEX structure for WAVE_FORMAT_PCM:
-    // "If wFormatTag is WAVE_FORMAT_PCM, then wBitsPerSample should be equal to 8 or 16."
-    if ((bitsPerSample != 8) && (bitsPerSample != 16))
-    {
-        // Normally 20 or 24 bits must go in 32 bit containers (ints) but in Exclusive mode some devices require
-        // packed version of the format, e.g. for example 24-bit in 3-bytes
-        old->wBitsPerSample = (packedOnly ? bitsPerSample : 32);
-        useExtensible       = TRUE;
-    }
-
-    // WAVEFORMATEX
-    if (!useExtensible)
-    {
-        old->wFormatTag    = WAVE_FORMAT_PCM;
-    }
-    // WAVEFORMATEXTENSIBLE
-    else
-    {
-        old->wFormatTag = WAVE_FORMAT_EXTENSIBLE;
-        old->cbSize        = sizeof(WAVEFORMATEXTENSIBLE) - sizeof(WAVEFORMATEX);
-
-        if ((params->sampleFormat & ~paNonInterleaved) == paFloat32)
-            wavex->SubFormat = pa_KSDATAFORMAT_SUBTYPE_IEEE_FLOAT;
-        else
-            wavex->SubFormat = pa_KSDATAFORMAT_SUBTYPE_PCM;
-
-        wavex->Samples.wValidBitsPerSample = bitsPerSample;
-
-        // Set channel mask
-        if (channelMask != 0)
-        {
-            wavex->dwChannelMask = channelMask;
-        }
-        else
-        {
-            switch (params->channelCount)
-            {
-            case 1:  wavex->dwChannelMask = PAWIN_SPEAKER_MONO; break;
-            case 2:  wavex->dwChannelMask = PAWIN_SPEAKER_STEREO; break;
-            case 3:  wavex->dwChannelMask = PAWIN_SPEAKER_STEREO|SPEAKER_LOW_FREQUENCY; break;
-            case 4:  wavex->dwChannelMask = PAWIN_SPEAKER_QUAD; break;
-            case 5:  wavex->dwChannelMask = PAWIN_SPEAKER_QUAD|SPEAKER_LOW_FREQUENCY; break;
-#ifdef PAWIN_SPEAKER_5POINT1_SURROUND
-            case 6:  wavex->dwChannelMask = PAWIN_SPEAKER_5POINT1_SURROUND; break;
-#else
-            case 6:  wavex->dwChannelMask = PAWIN_SPEAKER_5POINT1; break;
-#endif
-#ifdef PAWIN_SPEAKER_5POINT1_SURROUND
-            case 7:  wavex->dwChannelMask = PAWIN_SPEAKER_5POINT1_SURROUND|SPEAKER_BACK_CENTER; break;
-#else
-            case 7:  wavex->dwChannelMask = PAWIN_SPEAKER_5POINT1|SPEAKER_BACK_CENTER; break;
-#endif
-#ifdef PAWIN_SPEAKER_7POINT1_SURROUND
-            case 8:  wavex->dwChannelMask = PAWIN_SPEAKER_7POINT1_SURROUND; break;
-#else
-            case 8:  wavex->dwChannelMask = PAWIN_SPEAKER_7POINT1; break;
-#endif
-
-            default: wavex->dwChannelMask = 0;
-            }
-        }
-    }
-
-    old->nBlockAlign     = old->nChannels * (old->wBitsPerSample / 8);
-    old->nAvgBytesPerSec = old->nSamplesPerSec * old->nBlockAlign;
-
-    return paNoError;
-}
-
-// ------------------------------------------------------------------------------------------
-static HRESULT GetAlternativeSampleFormatExclusive(IAudioClient *client, double sampleRate,
-    const PaStreamParameters *params, WAVEFORMATEXTENSIBLE *outWavex, BOOL packedSampleFormatOnly)
-{
-    HRESULT hr = !S_OK;
-    AUDCLNT_SHAREMODE shareMode = AUDCLNT_SHAREMODE_EXCLUSIVE;
-    WAVEFORMATEXTENSIBLE testFormat;
-    PaStreamParameters testParams;
-    int i;
-    static const PaSampleFormat bestToWorst[] = { paInt32, paInt24, paFloat32, paInt16 };
-
-    // Try combination Stereo (2 channels) and then we will use our custom mono-stereo mixer
-    if (params->channelCount == 1)
-    {
-        testParams = (*params);
-        testParams.channelCount = 2;
-
-        if (MakeWaveFormatFromParams(&testFormat, &testParams, sampleRate, packedSampleFormatOnly) == paNoError)
-        {
-            if ((hr = IAudioClient_IsFormatSupported(client, shareMode, &testFormat.Format, NULL)) == S_OK)
-            {
-                (*outWavex) = testFormat;
-                return hr;
-            }
-        }
-
-        // Try selecting suitable sample type
-        for (i = 0; i < STATIC_ARRAY_SIZE(bestToWorst); ++i)
-        {
-            testParams.sampleFormat = bestToWorst[i];
-
-            if (MakeWaveFormatFromParams(&testFormat, &testParams, sampleRate, packedSampleFormatOnly) == paNoError)
-            {
-                if ((hr = IAudioClient_IsFormatSupported(client, shareMode, &testFormat.Format, NULL)) == S_OK)
-                {
-                    (*outWavex) = testFormat;
-                    return hr;
-                }
-            }
-        }
-    }
-
-    // Try selecting suitable sample type
-    testParams = (*params);
-    for (i = 0; i < STATIC_ARRAY_SIZE(bestToWorst); ++i)
-    {
-        testParams.sampleFormat = bestToWorst[i];
-
-        if (MakeWaveFormatFromParams(&testFormat, &testParams, sampleRate, packedSampleFormatOnly) == paNoError)
-        {
-            if ((hr = IAudioClient_IsFormatSupported(client, shareMode, &testFormat.Format, NULL)) == S_OK)
-            {
-                (*outWavex) = testFormat;
-                return hr;
-            }
-        }
-    }
-
-    return hr;
-}
-
-// ------------------------------------------------------------------------------------------
-static PaError GetClosestFormat(IAudioClient *client, double sampleRate, const PaStreamParameters *_params,
-    AUDCLNT_SHAREMODE shareMode, WAVEFORMATEXTENSIBLE *outWavex, BOOL output)
-{
-    PaWasapiStreamInfo *streamInfo   = (PaWasapiStreamInfo *)_params->hostApiSpecificStreamInfo;
-    WAVEFORMATEX *sharedClosestMatch = NULL;
-    HRESULT hr                       = !S_OK;
-    PaStreamParameters params        = (*_params);
-    const BOOL explicitFormat        = (streamInfo != NULL) && ((streamInfo->flags & paWinWasapiExplicitSampleFormat) == paWinWasapiExplicitSampleFormat);
-    (void)output;
-
-    /* It was not noticed that 24-bit Input producing no output while device accepts this format.
-       To fix this issue let's ask for 32-bits and let PA converters convert host 32-bit data
-       to 24-bit for user-space. The bug concerns Vista, if Windows 7 supports 24-bits for Input
-       please report to PortAudio developers to exclude Windows 7.
-    */
-    /*if ((params.sampleFormat == paInt24) && (output == FALSE))
-        params.sampleFormat = paFloat32;*/ // <<< The silence was due to missing Int32_To_Int24_Dither implementation
-
-    // Try standard approach, e.g. if data is > 16 bits it will be packed into 32-bit containers
-    MakeWaveFormatFromParams(outWavex, &params, sampleRate, FALSE);
-
-    // If built-in PCM converter requested then shared mode format will always succeed
-    if ((GetWindowsVersion() >= WINDOWS_7_SERVER2008R2) &&
-        (shareMode == AUDCLNT_SHAREMODE_SHARED) &&
-        ((streamInfo != NULL) && (streamInfo->flags & paWinWasapiAutoConvert)))
-        return paFormatIsSupported;
-
-    hr = IAudioClient_IsFormatSupported(client, shareMode, &outWavex->Format, (shareMode == AUDCLNT_SHAREMODE_SHARED ? &sharedClosestMatch : NULL));
-
-    // Exclusive mode can require packed format for some devices
-    if ((hr != S_OK) && (shareMode == AUDCLNT_SHAREMODE_EXCLUSIVE))
-    {
-        // Enforce packed only format, e.g. data bits will not be packed into 32-bit containers in any case
-        MakeWaveFormatFromParams(outWavex, &params, sampleRate, TRUE);
-        hr = IAudioClient_IsFormatSupported(client, shareMode, &outWavex->Format, NULL);
-    }
-
-    if (hr == S_OK)
-    {
-        return paFormatIsSupported;
-    }
-    else
-    if (sharedClosestMatch != NULL)
-    {
-        WORD bitsPerSample;
-
-        if (sharedClosestMatch->wFormatTag == WAVE_FORMAT_EXTENSIBLE)
-            memcpy(outWavex, sharedClosestMatch, sizeof(WAVEFORMATEXTENSIBLE));
-        else
-            memcpy(outWavex, sharedClosestMatch, sizeof(WAVEFORMATEX));
-
-        CoTaskMemFree(sharedClosestMatch);
-        sharedClosestMatch = NULL;
-
-        // Validate SampleRate
-        if ((DWORD)sampleRate != outWavex->Format.nSamplesPerSec)
-            return paInvalidSampleRate;
-
-        // Validate Channel count
-        if ((WORD)params.channelCount != outWavex->Format.nChannels)
-        {
-            // If mono, then driver does not support 1 channel, we use internal workaround
-            // of tiny software mixing functionality, e.g. we provide to user buffer 1 channel
-            // but then mix into 2 for device buffer
-            if ((params.channelCount == 1) && (outWavex->Format.nChannels == 2))
-                return paFormatIsSupported;
-            else
-                return paInvalidChannelCount;
-        }
-
-        // Validate Sample format
-        if ((bitsPerSample = PaSampleFormatToBitsPerSample(params.sampleFormat)) == 0)
-            return paSampleFormatNotSupported;
-
-        // Accepted format
-        return paFormatIsSupported;
-    }
-    else
-    if ((shareMode == AUDCLNT_SHAREMODE_EXCLUSIVE) && !explicitFormat)
-    {
-        // Try standard approach, e.g. if data is > 16 bits it will be packed into 32-bit containers
-        if ((hr = GetAlternativeSampleFormatExclusive(client, sampleRate, &params, outWavex, FALSE)) == S_OK)
-            return paFormatIsSupported;
-
-        // Enforce packed only format, e.g. data bits will not be packed into 32-bit containers in any case
-        if ((hr = GetAlternativeSampleFormatExclusive(client, sampleRate, &params, outWavex, TRUE)) == S_OK)
-            return paFormatIsSupported;
-
-        // Log failure
-        LogHostError(hr);
-    }
-    else
-    {
-        // Exclusive mode and requested strict format, WASAPI did not accept this sample format
-        LogHostError(hr);
-    }
-
-    return paInvalidSampleRate;
-}
-
-// ------------------------------------------------------------------------------------------
-static PaError IsStreamParamsValid(struct PaUtilHostApiRepresentation *hostApi,
-                                   const  PaStreamParameters *inputParameters,
-                                   const  PaStreamParameters *outputParameters,
-                                   double sampleRate)
-{
-    if (hostApi == NULL)
-        return paHostApiNotFound;
-    if ((UINT32)sampleRate == 0)
-        return paInvalidSampleRate;
-
-    if (inputParameters != NULL)
-    {
-        /* all standard sample formats are supported by the buffer adapter,
-            this implementation doesn't support any custom sample formats */
-        // Note: paCustomFormat is now 8.24 (24-bits in 32-bit containers)
-        //if (inputParameters->sampleFormat & paCustomFormat)
-        //    return paSampleFormatNotSupported;
-
-        /* unless alternate device specification is supported, reject the use of
-            paUseHostApiSpecificDeviceSpecification */
-        if (inputParameters->device == paUseHostApiSpecificDeviceSpecification)
-            return paInvalidDevice;
-
-        /* check that input device can support inputChannelCount */
-        if (inputParameters->channelCount > hostApi->deviceInfos[ inputParameters->device ]->maxInputChannels)
-            return paInvalidChannelCount;
-
-        /* validate inputStreamInfo */
-        if (inputParameters->hostApiSpecificStreamInfo)
-        {
-            PaWasapiStreamInfo *inputStreamInfo = (PaWasapiStreamInfo *)inputParameters->hostApiSpecificStreamInfo;
-            if ((inputStreamInfo->size != sizeof(PaWasapiStreamInfo)) ||
-                (inputStreamInfo->version != 1) ||
-                (inputStreamInfo->hostApiType != paWASAPI))
-            {
-                return paIncompatibleHostApiSpecificStreamInfo;
-            }
-        }
-
-        return paNoError;
-    }
-
-    if (outputParameters != NULL)
-    {
-        /* all standard sample formats are supported by the buffer adapter,
-            this implementation doesn't support any custom sample formats */
-        // Note: paCustomFormat is now 8.24 (24-bits in 32-bit containers)
-        //if (outputParameters->sampleFormat & paCustomFormat)
-        //    return paSampleFormatNotSupported;
-
-        /* unless alternate device specification is supported, reject the use of
-            paUseHostApiSpecificDeviceSpecification */
-        if (outputParameters->device == paUseHostApiSpecificDeviceSpecification)
-            return paInvalidDevice;
-
-        /* check that output device can support outputChannelCount */
-        if (outputParameters->channelCount > hostApi->deviceInfos[ outputParameters->device ]->maxOutputChannels)
-            return paInvalidChannelCount;
-
-        /* validate outputStreamInfo */
-        if(outputParameters->hostApiSpecificStreamInfo)
-        {
-            PaWasapiStreamInfo *outputStreamInfo = (PaWasapiStreamInfo *)outputParameters->hostApiSpecificStreamInfo;
-            if ((outputStreamInfo->size != sizeof(PaWasapiStreamInfo)) ||
-                (outputStreamInfo->version != 1) ||
-                (outputStreamInfo->hostApiType != paWASAPI))
-            {
-                return paIncompatibleHostApiSpecificStreamInfo;
-            }
-        }
-
-        return paNoError;
-    }
-
-    return (inputParameters || outputParameters ? paNoError : paInternalError);
-}
-
-// ------------------------------------------------------------------------------------------
-static PaError IsFormatSupported( struct PaUtilHostApiRepresentation *hostApi,
-                                  const  PaStreamParameters *inputParameters,
-                                  const  PaStreamParameters *outputParameters,
-                                  double sampleRate )
-{
-    IAudioClient *tmpClient = NULL;
-    PaWasapiHostApiRepresentation *paWasapi = (PaWasapiHostApiRepresentation*)hostApi;
-    PaWasapiStreamInfo *inputStreamInfo = NULL, *outputStreamInfo = NULL;
-
-    // Validate PaStreamParameters
-    PaError error;
-    if ((error = IsStreamParamsValid(hostApi, inputParameters, outputParameters, sampleRate)) != paNoError)
-        return error;
-
-    if (inputParameters != NULL)
-    {
-        WAVEFORMATEXTENSIBLE wavex;
-        HRESULT hr;
-        PaError answer;
-        AUDCLNT_SHAREMODE shareMode = AUDCLNT_SHAREMODE_SHARED;
-        inputStreamInfo = (PaWasapiStreamInfo *)inputParameters->hostApiSpecificStreamInfo;
-
-        if (inputStreamInfo && (inputStreamInfo->flags & paWinWasapiExclusive))
-            shareMode = AUDCLNT_SHAREMODE_EXCLUSIVE;
-
-        hr = ActivateAudioInterface(&paWasapi->devInfo[inputParameters->device], inputStreamInfo, &tmpClient);
-        if (hr != S_OK)
-        {
-            LogHostError(hr);
-            return paInvalidDevice;
-        }
-
-        answer = GetClosestFormat(tmpClient, sampleRate, inputParameters, shareMode, &wavex, FALSE);
-        SAFE_RELEASE(tmpClient);
-
-        if (answer != paFormatIsSupported)
-            return answer;
-    }
-
-    if (outputParameters != NULL)
-    {
-        HRESULT hr;
-        WAVEFORMATEXTENSIBLE wavex;
-        PaError answer;
-        AUDCLNT_SHAREMODE shareMode = AUDCLNT_SHAREMODE_SHARED;
-        outputStreamInfo = (PaWasapiStreamInfo *)outputParameters->hostApiSpecificStreamInfo;
-
-        if (outputStreamInfo && (outputStreamInfo->flags & paWinWasapiExclusive))
-            shareMode = AUDCLNT_SHAREMODE_EXCLUSIVE;
-
-        hr = ActivateAudioInterface(&paWasapi->devInfo[outputParameters->device], outputStreamInfo, &tmpClient);
-        if (hr != S_OK)
-        {
-            LogHostError(hr);
-            return paInvalidDevice;
-        }
-
-        answer = GetClosestFormat(tmpClient, sampleRate, outputParameters, shareMode, &wavex, TRUE);
-        SAFE_RELEASE(tmpClient);
-
-        if (answer != paFormatIsSupported)
-            return answer;
-    }
-
-    return paFormatIsSupported;
-}
-
-// ------------------------------------------------------------------------------------------
-static PaUint32 _GetFramesPerHostBuffer(PaUint32 userFramesPerBuffer, PaTime suggestedLatency, double sampleRate, PaUint32 TimerJitterMs)
-{
-    PaUint32 frames = userFramesPerBuffer + max( userFramesPerBuffer, (PaUint32)(suggestedLatency * sampleRate) );
-    frames += (PaUint32)((sampleRate * 0.001) * TimerJitterMs);
-    return frames;
-}
-
-// ------------------------------------------------------------------------------------------
-static void _RecalculateBuffersCount(PaWasapiSubStream *sub, UINT32 userFramesPerBuffer, UINT32 framesPerLatency,
-    BOOL fullDuplex, BOOL output)
-{
-    // Count buffers (must be at least 1)
-    sub->buffers = (userFramesPerBuffer != 0 ? framesPerLatency / userFramesPerBuffer : 1);
-    if (sub->buffers == 0)
-        sub->buffers = 1;
-
-    // Determine number of buffers used:
-    // - Full-duplex mode will lead to period difference, thus only 1
-    // - Input mode, only 1, as WASAPI allows extraction of only 1 packet
-    // - For Shared mode we use double buffering
-    if ((sub->shareMode == AUDCLNT_SHAREMODE_EXCLUSIVE) || fullDuplex)
-    {
-        BOOL eventMode = ((sub->streamFlags & AUDCLNT_STREAMFLAGS_EVENTCALLBACK) == AUDCLNT_STREAMFLAGS_EVENTCALLBACK);
-
-        // Exclusive mode does not allow >1 buffers be used for Event interface, e.g. GetBuffer
-        // call must acquire max buffer size and it all must be processed.
-        if (eventMode)
-            sub->userBufferAndHostMatch = 1;
-
-        // Full-duplex or Event mode: prefer paUtilBoundedHostBufferSize because exclusive mode will starve
-        // and produce glitchy audio
-        // Output Polling mode: prefer paUtilFixedHostBufferSize (buffers != 1) for polling mode is it allows
-        // to consume user data by fixed size data chunks and thus lowers memory movement (less CPU usage)
-        if (fullDuplex || eventMode || !output)
-            sub->buffers = 1;
-    }
-}
-
-// ------------------------------------------------------------------------------------------
-static void _CalculateAlignedPeriod(PaWasapiSubStream *pSub, UINT32 *nFramesPerLatency, ALIGN_FUNC pAlignFunc)
-{
-    // Align frames to HD Audio packet size of 128 bytes for Exclusive mode only.
-    // Not aligning on Windows Vista will cause Event timeout, although Windows 7 will
-    // return AUDCLNT_E_BUFFER_SIZE_NOT_ALIGNED error to realign buffer. Aligning is necessary
-    // for Exclusive mode only! when audio data is fed directly to hardware.
-    if (pSub->shareMode == AUDCLNT_SHAREMODE_EXCLUSIVE)
-    {
-        (*nFramesPerLatency) = AlignFramesPerBuffer((*nFramesPerLatency),
-            pSub->wavex.Format.nBlockAlign, pAlignFunc);
-    }
-
-    // Calculate period
-    pSub->period = MakeHnsPeriod((*nFramesPerLatency), pSub->wavex.Format.nSamplesPerSec);
-}
-
-// ------------------------------------------------------------------------------------------
-static void _CalculatePeriodicity(PaWasapiSubStream *pSub, BOOL output, REFERENCE_TIME *periodicity)
-{
-    // Note: according to Microsoft docs for IAudioClient::Initialize we can set periodicity of the buffer
-    // only for Exclusive mode. By setting periodicity almost equal to the user buffer frames we can
-    // achieve high quality (less glitchy) low-latency audio.
-    if (pSub->shareMode == AUDCLNT_SHAREMODE_EXCLUSIVE)
-    {
-        const PaWasapiDeviceInfo *pInfo = pSub->params.device_info;
-
-        // By default periodicity equals to the full buffer (legacy PA WASAPI's behavior)
-        (*periodicity) = pSub->period;
-
-        // Try make buffer ready for I/O once we request the buffer readiness for it. Only Polling mode
-        // because for Event mode buffer size and periodicity must be equal according to Microsoft
-        // documentation for IAudioClient::Initialize.
-        //
-        // TO-DO: try spread to capture and full-duplex cases (not tested and therefore disabled)
-        //
-        if (((pSub->streamFlags & AUDCLNT_STREAMFLAGS_EVENTCALLBACK) == 0) &&
-            (output && !pSub->params.full_duplex))
-        {
-            UINT32 alignedFrames;
-            REFERENCE_TIME userPeriodicity;
-
-            // Align frames backwards, so device will likely make buffer read ready when we are ready
-            // to read it (our scheduling will wait for amount of millisoconds of frames_per_buffer)
-            alignedFrames = AlignFramesPerBuffer(pSub->params.frames_per_buffer,
-                pSub->wavex.Format.nBlockAlign, ALIGN_BWD);
-
-            userPeriodicity = MakeHnsPeriod(alignedFrames, pSub->wavex.Format.nSamplesPerSec);
-
-            // Must not be larger than buffer size
-            if (userPeriodicity > pSub->period)
-                userPeriodicity = pSub->period;
-
-            // Must not be smaller than minimum supported by the device
-            if (userPeriodicity < pInfo->MinimumDevicePeriod)
-                userPeriodicity = pInfo->MinimumDevicePeriod;
-
-            (*periodicity) = userPeriodicity;
-        }
-    }
-    else
-        (*periodicity) = 0;
-}
-
-// ------------------------------------------------------------------------------------------
-static HRESULT CreateAudioClient(PaWasapiStream *pStream, PaWasapiSubStream *pSub, BOOL output, PaError *pa_error)
-{
-    PaError error;
-    HRESULT hr;
-    const PaWasapiDeviceInfo *pInfo  = pSub->params.device_info;
-    const PaStreamParameters *params = &pSub->params.stream_params;
-    const double sampleRate          = pSub->params.sample_rate;
-    const BOOL fullDuplex            = pSub->params.full_duplex;
-    const UINT32 userFramesPerBuffer = pSub->params.frames_per_buffer;
-    UINT32 framesPerLatency          = userFramesPerBuffer;
-    IAudioClient *audioClient        = NULL;
-    REFERENCE_TIME eventPeriodicity  = 0;
-
-    // Assume default failure due to some reason
-    (*pa_error) = paInvalidDevice;
-
-    // Validate parameters
-    if (!pSub || !pInfo || !params)
-    {
-        (*pa_error) = paBadStreamPtr;
-        return E_POINTER;
-    }
-    if ((UINT32)sampleRate == 0)
-    {
-        (*pa_error) = paInvalidSampleRate;
-        return E_INVALIDARG;
-    }
-
-    // Get the audio client
-    if (FAILED(hr = ActivateAudioInterface(pInfo, &pSub->params.wasapi_params, &audioClient)))
-    {
-        (*pa_error) = paInsufficientMemory;
-        LogHostError(hr);
-        goto done;
-    }
-
-    // Get closest format
-    if ((error = GetClosestFormat(audioClient, sampleRate, params, pSub->shareMode, &pSub->wavex, output)) != paFormatIsSupported)
-    {
-        (*pa_error) = error;
-        LogHostError(hr = AUDCLNT_E_UNSUPPORTED_FORMAT);
-        goto done; // fail, format not supported
-    }
-
-    // Check for Mono <<>> Stereo workaround
-    if ((params->channelCount == 1) && (pSub->wavex.Format.nChannels == 2))
-    {
-        // select mixer
-        pSub->monoMixer = GetMonoToStereoMixer(&pSub->wavex, (pInfo->flow == eRender ? MIX_DIR__1TO2 : MIX_DIR__2TO1_L));
-        if (pSub->monoMixer == NULL)
-        {
-            (*pa_error) = paInvalidChannelCount;
-            LogHostError(hr = AUDCLNT_E_UNSUPPORTED_FORMAT);
-            goto done; // fail, no mixer for format
-        }
-    }
-
-    // Calculate host buffer size
-    if ((pSub->shareMode != AUDCLNT_SHAREMODE_EXCLUSIVE) &&
-        (!pSub->streamFlags || ((pSub->streamFlags & AUDCLNT_STREAMFLAGS_EVENTCALLBACK) == 0)))
-    {
-        framesPerLatency = _GetFramesPerHostBuffer(userFramesPerBuffer,
-            params->suggestedLatency, pSub->wavex.Format.nSamplesPerSec, 0/*,
-            (pSub->streamFlags & AUDCLNT_STREAMFLAGS_EVENTCALLBACK ? 0 : 1)*/);
-    }
-    else
-    {
-    #ifdef PA_WASAPI_FORCE_POLL_IF_LARGE_BUFFER
-        REFERENCE_TIME overall;
-    #endif
-
-        // Work 1:1 with user buffer (only polling allows to use >1)
-        framesPerLatency += MakeFramesFromHns(SecondsTonano100(params->suggestedLatency), pSub->wavex.Format.nSamplesPerSec);
-
-        // Force Polling if overall latency is >= 21.33ms as it allows to use 100% CPU in a callback,
-        // or user specified latency parameter.
-    #ifdef PA_WASAPI_FORCE_POLL_IF_LARGE_BUFFER
-        overall = MakeHnsPeriod(framesPerLatency, pSub->wavex.Format.nSamplesPerSec);
-        if (overall >= (106667 * 2)/*21.33ms*/)
-        {
-            framesPerLatency = _GetFramesPerHostBuffer(userFramesPerBuffer,
-                params->suggestedLatency, pSub->wavex.Format.nSamplesPerSec, 0/*,
-                (streamFlags & AUDCLNT_STREAMFLAGS_EVENTCALLBACK ? 0 : 1)*/);
-
-            // Use Polling interface
-            pSub->streamFlags &= ~AUDCLNT_STREAMFLAGS_EVENTCALLBACK;
-            PRINT(("WASAPI: CreateAudioClient: forcing POLL mode\n"));
-        }
-    #endif
-    }
-
-    // For full-duplex output resize buffer to be the same as for input
-    if (output && fullDuplex)
-        framesPerLatency = pStream->in.framesPerHostCallback;
-
-    // Avoid 0 frames
-    if (framesPerLatency == 0)
-        framesPerLatency = MakeFramesFromHns(pInfo->DefaultDevicePeriod, pSub->wavex.Format.nSamplesPerSec);
-
-    // Exclusive Input stream renders data in 6 packets, we must set then the size of
-    // single packet, total buffer size, e.g. required latency will be PacketSize * 6
-    if (!output && (pSub->shareMode == AUDCLNT_SHAREMODE_EXCLUSIVE))
-    {
-        // Do it only for Polling mode
-        if ((pSub->streamFlags & AUDCLNT_STREAMFLAGS_EVENTCALLBACK) == 0)
-            framesPerLatency /= WASAPI_PACKETS_PER_INPUT_BUFFER;
-    }
-
-    // Calculate aligned period
-    _CalculateAlignedPeriod(pSub, &framesPerLatency, ALIGN_BWD);
-
-    /*! Enforce min/max period for device in Shared mode to avoid bad audio quality.
-        Avoid doing so for Exclusive mode as alignment will suffer.
-    */
-    if (pSub->shareMode == AUDCLNT_SHAREMODE_SHARED)
-    {
-        if (pSub->period < pInfo->DefaultDevicePeriod)
-        {
-            pSub->period = pInfo->DefaultDevicePeriod;
-
-            // Recalculate aligned period
-            framesPerLatency = MakeFramesFromHns(pSub->period, pSub->wavex.Format.nSamplesPerSec);
-            _CalculateAlignedPeriod(pSub, &framesPerLatency, ALIGN_BWD);
-        }
-    }
-    else
-    {
-        if (pSub->period < pInfo->MinimumDevicePeriod)
-        {
-            pSub->period = pInfo->MinimumDevicePeriod;
-
-            // Recalculate aligned period
-            framesPerLatency = MakeFramesFromHns(pSub->period, pSub->wavex.Format.nSamplesPerSec);
-            _CalculateAlignedPeriod(pSub, &framesPerLatency, ALIGN_FWD);
-        }
-    }
-
-    /*! Windows 7 does not allow to set latency lower than minimal device period and will
-        return error: AUDCLNT_E_INVALID_DEVICE_PERIOD. Under Vista we enforce the same behavior
-        manually for unified behavior on all platforms.
-    */
-    {
-        /*!    AUDCLNT_E_BUFFER_SIZE_ERROR: Applies to Windows 7 and later.
-            Indicates that the buffer duration value requested by an exclusive-mode client is
-            out of range. The requested duration value for pull mode must not be greater than
-            500 milliseconds; for push mode the duration value must not be greater than 2 seconds.
-        */
-        if (pSub->shareMode == AUDCLNT_SHAREMODE_EXCLUSIVE)
-        {
-            static const REFERENCE_TIME MAX_BUFFER_EVENT_DURATION = 500  * 10000;
-            static const REFERENCE_TIME MAX_BUFFER_POLL_DURATION  = 2000 * 10000;
-
-            // Pull mode, max 500ms
-            if (pSub->streamFlags & AUDCLNT_STREAMFLAGS_EVENTCALLBACK)
-            {
-                if (pSub->period > MAX_BUFFER_EVENT_DURATION)
-                {
-                    pSub->period = MAX_BUFFER_EVENT_DURATION;
-
-                    // Recalculate aligned period
-                    framesPerLatency = MakeFramesFromHns(pSub->period, pSub->wavex.Format.nSamplesPerSec);
-                    _CalculateAlignedPeriod(pSub, &framesPerLatency, ALIGN_BWD);
-                }
-            }
-            // Push mode, max 2000ms
-            else
-            {
-                if (pSub->period > MAX_BUFFER_POLL_DURATION)
-                {
-                    pSub->period = MAX_BUFFER_POLL_DURATION;
-
-                    // Recalculate aligned period
-                    framesPerLatency = MakeFramesFromHns(pSub->period, pSub->wavex.Format.nSamplesPerSec);
-                    _CalculateAlignedPeriod(pSub, &framesPerLatency, ALIGN_BWD);
-                }
-            }
-        }
-    }
-
-    // Set device scheduling period (always 0 in Shared mode according to Microsoft docs)
-    _CalculatePeriodicity(pSub, output, &eventPeriodicity);
-
-    // Open the stream and associate it with an audio session
-    hr = IAudioClient_Initialize(audioClient,
-        pSub->shareMode,
-        pSub->streamFlags,
-        pSub->period,
-        eventPeriodicity,
-        &pSub->wavex.Format,
-        NULL);
-
-    // [Output only] Check if buffer size is the one we requested in Exclusive mode, for UAC1 USB DACs WASAPI
-    // can allocate internal buffer equal to 8 times of pSub->period that has to be corrected in order to match
-    // the requested latency
-    if (output && SUCCEEDED(hr) && (pSub->shareMode == AUDCLNT_SHAREMODE_EXCLUSIVE))
-    {
-        UINT32 maxBufferFrames;
-
-        if (FAILED(hr = IAudioClient_GetBufferSize(audioClient, &maxBufferFrames)))
-        {
-            (*pa_error) = paInvalidDevice;
-            LogHostError(hr);
-            goto done;
-        }
-
-        // For Exclusive mode for UAC1 devices maxBufferFrames may be framesPerLatency * 8 but check any difference
-        // to be able to guarantee the latency user requested and also resulted framesPerLatency may be bigger than
-        // 2 seconds that will cause audio client not operational (GetCurrentPadding() will return always 0)
-        if (maxBufferFrames >= (framesPerLatency * 2))
-        {
-            UINT32 ratio = maxBufferFrames / framesPerLatency;
-
-            PRINT(("WASAPI: CreateAudioClient: detected %d times larger buffer than requested, correct to match user latency\n", ratio));
-
-            // Get new aligned frames lowered by calculated ratio
-            framesPerLatency = MakeFramesFromHns(pSub->period / ratio, pSub->wavex.Format.nSamplesPerSec);
-            _CalculateAlignedPeriod(pSub, &framesPerLatency, ALIGN_BWD);
-
-            // Make sure we are not below the minimum period
-            if (pSub->period < pInfo->MinimumDevicePeriod)
-                pSub->period = pInfo->MinimumDevicePeriod;
-
-            // Release previous client
-            SAFE_RELEASE(audioClient);
-
-            // Create a new audio client
-            if (FAILED(hr = ActivateAudioInterface(pInfo, &pSub->params.wasapi_params, &audioClient)))
-            {
-                (*pa_error) = paInsufficientMemory;
-                LogHostError(hr);
-                goto done;
-            }
-
-            // Set device scheduling period (always 0 in Shared mode according to Microsoft docs)
-            _CalculatePeriodicity(pSub, output, &eventPeriodicity);
-
-            // Open the stream and associate it with an audio session
-            hr = IAudioClient_Initialize(audioClient,
-                pSub->shareMode,
-                pSub->streamFlags,
-                pSub->period,
-                eventPeriodicity,
-                &pSub->wavex.Format,
-                NULL);
-        }
-    }
-
-    /*! WASAPI is tricky on large device buffer, sometimes 2000ms can be allocated sometimes
-        less. There is no known guaranteed level thus we make subsequent tries by decreasing
-        buffer by 100ms per try.
-    */
-    while ((hr == E_OUTOFMEMORY) && (pSub->period > (100 * 10000)))
-    {
-        PRINT(("WASAPI: CreateAudioClient: decreasing buffer size to %d milliseconds\n", (pSub->period / 10000)));
-
-        // Decrease by 100ms and try again
-        pSub->period -= (100 * 10000);
-
-        // Recalculate aligned period
-        framesPerLatency = MakeFramesFromHns(pSub->period, pSub->wavex.Format.nSamplesPerSec);
-        _CalculateAlignedPeriod(pSub, &framesPerLatency, ALIGN_BWD);
-
-        // Release the previous allocations
-        SAFE_RELEASE(audioClient);
-
-        // Create a new audio client
-        if (FAILED(hr = ActivateAudioInterface(pInfo, &pSub->params.wasapi_params, &audioClient)))
-        {
-            (*pa_error) = paInsufficientMemory;
-            LogHostError(hr);
-            goto done;
-        }
-
-        // Set device scheduling period (always 0 in Shared mode according to Microsoft docs)
-        _CalculatePeriodicity(pSub, output, &eventPeriodicity);
-
-        // Open the stream and associate it with an audio session
-        hr = IAudioClient_Initialize(audioClient,
-            pSub->shareMode,
-            pSub->streamFlags,
-            pSub->period,
-            eventPeriodicity,
-            &pSub->wavex.Format,
-            NULL);
-    }
-
-    /*! WASAPI buffer size or alignment failure. Fallback to using default size and alignment.
-    */
-    if ((hr == AUDCLNT_E_BUFFER_SIZE_ERROR) || (hr == AUDCLNT_E_BUFFER_SIZE_NOT_ALIGNED))
-    {
-        // Use default
-        pSub->period = pInfo->DefaultDevicePeriod;
-
-        PRINT(("WASAPI: CreateAudioClient: correcting buffer size/alignment to device default\n"));
-
-        // Release the previous allocations
-        SAFE_RELEASE(audioClient);
-
-        // Create a new audio client
-        if (FAILED(hr = ActivateAudioInterface(pInfo, &pSub->params.wasapi_params, &audioClient)))
-        {
-            (*pa_error) = paInsufficientMemory;
-            LogHostError(hr);
-            goto done;
-        }
-
-        // Set device scheduling period (always 0 in Shared mode according to Microsoft docs)
-        _CalculatePeriodicity(pSub, output, &eventPeriodicity);
-
-        // Open the stream and associate it with an audio session
-        hr = IAudioClient_Initialize(audioClient,
-            pSub->shareMode,
-            pSub->streamFlags,
-            pSub->period,
-            eventPeriodicity,
-            &pSub->wavex.Format,
-            NULL);
-    }
-
-    // Error has no workaround, fail completely
-    if (FAILED(hr))
-    {
-        (*pa_error) = paInvalidDevice;
-        LogHostError(hr);
-        goto done;
-    }
-
-    // Set client
-    pSub->clientParent = audioClient;
-    IAudioClient_AddRef(pSub->clientParent);
-
-    // Recalculate buffers count
-    _RecalculateBuffersCount(pSub, userFramesPerBuffer, MakeFramesFromHns(pSub->period, pSub->wavex.Format.nSamplesPerSec),
-        fullDuplex, output);
-
-    // No error, client is successfully created
-    (*pa_error) = paNoError;
-
-done:
-
-    // Clean up
-    SAFE_RELEASE(audioClient);
-    return hr;
-}
-
-// ------------------------------------------------------------------------------------------
-static PaError ActivateAudioClientOutput(PaWasapiStream *stream)
-{
-    HRESULT hr;
-    PaError result;
-    UINT32 maxBufferSize;
-    PaTime bufferLatency;
-    const UINT32 framesPerBuffer = stream->out.params.frames_per_buffer;
-
-    // Create Audio client
-    if (FAILED(hr = CreateAudioClient(stream, &stream->out, TRUE, &result)))
-    {
-        LogPaError(result);
-        goto error;
-    }
-    LogWAVEFORMATEXTENSIBLE(&stream->out.wavex);
-
-    // Activate volume
-    stream->outVol = NULL;
-    /*hr = info->device->Activate(
-        __uuidof(IAudioEndpointVolume), CLSCTX_INPROC_SERVER, NULL,
-        (void**)&stream->outVol);
-    if (hr != S_OK)
-        return paInvalidDevice;*/
-
-    // Get max possible buffer size to check if it is not less than that we request
-    if (FAILED(hr = IAudioClient_GetBufferSize(stream->out.clientParent, &maxBufferSize)))
-    {
-        LogHostError(hr);
-        LogPaError(result = paInvalidDevice);
-        goto error;
-    }
-
-    // Correct buffer to max size if it maxed out result of GetBufferSize
-    stream->out.bufferSize = maxBufferSize;
-
-    // Number of frames that are required at each period
-    stream->out.framesPerHostCallback = maxBufferSize;
-
-    // Calculate frames per single buffer, if buffers > 1 then always framesPerBuffer
-    stream->out.framesPerBuffer =
-        (stream->out.userBufferAndHostMatch ? stream->out.framesPerHostCallback : framesPerBuffer);
-
-    // Calculate buffer latency
-    bufferLatency = (PaTime)maxBufferSize / stream->out.wavex.Format.nSamplesPerSec;
-
-    // Append buffer latency to interface latency in shared mode (see GetStreamLatency notes)
-    stream->out.latencySeconds = bufferLatency;
-
-    PRINT(("WASAPI::OpenStream(output): framesPerUser[ %d ] framesPerHost[ %d ] latency[ %.02fms ] exclusive[ %s ] wow64_fix[ %s ] mode[ %s ]\n", (UINT32)framesPerBuffer, (UINT32)stream->out.framesPerHostCallback, (float)(stream->out.latencySeconds*1000.0f), (stream->out.shareMode == AUDCLNT_SHAREMODE_EXCLUSIVE ? "YES" : "NO"), (stream->out.params.wow64_workaround ? "YES" : "NO"), (stream->out.streamFlags & AUDCLNT_STREAMFLAGS_EVENTCALLBACK ? "EVENT" : "POLL")));
-
-    return paNoError;
-
-error:
-
-    return result;
-}
-
-// ------------------------------------------------------------------------------------------
-static PaError ActivateAudioClientInput(PaWasapiStream *stream)
-{
-    HRESULT hr;
-    PaError result;
-    UINT32 maxBufferSize;
-    PaTime bufferLatency;
-    const UINT32 framesPerBuffer = stream->in.params.frames_per_buffer;
-
-    // Create Audio client
-    if (FAILED(hr = CreateAudioClient(stream, &stream->in, FALSE, &result)))
-    {
-        LogPaError(result);
-        goto error;
-    }
-    LogWAVEFORMATEXTENSIBLE(&stream->in.wavex);
-
-    // Create volume mgr
-    stream->inVol = NULL;
-    /*hr = info->device->Activate(
-        __uuidof(IAudioEndpointVolume), CLSCTX_INPROC_SERVER, NULL,
-        (void**)&stream->inVol);
-    if (hr != S_OK)
-        return paInvalidDevice;*/
-
-    // Get max possible buffer size to check if it is not less than that we request
-    if (FAILED(hr = IAudioClient_GetBufferSize(stream->in.clientParent, &maxBufferSize)))
-    {
-        LogHostError(hr);
-        LogPaError(result = paInvalidDevice);
-        goto error;
-    }
-
-    // Correct buffer to max size if it maxed out result of GetBufferSize
-    stream->in.bufferSize = maxBufferSize;
-
-    // Get interface latency (actually unneeded as we calculate latency from the size
-    // of maxBufferSize).
-    if (FAILED(hr = IAudioClient_GetStreamLatency(stream->in.clientParent, &stream->in.deviceLatency)))
-    {
-        LogHostError(hr);
-        LogPaError(result = paInvalidDevice);
-        goto error;
-    }
-    //stream->in.latencySeconds = nano100ToSeconds(stream->in.deviceLatency);
-
-    // Number of frames that are required at each period
-    stream->in.framesPerHostCallback = maxBufferSize;
-
-    // Calculate frames per single buffer, if buffers > 1 then always framesPerBuffer
-    stream->in.framesPerBuffer =
-        (stream->in.userBufferAndHostMatch ? stream->in.framesPerHostCallback : framesPerBuffer);
-
-    // Calculate buffer latency
-    bufferLatency = (PaTime)maxBufferSize / stream->in.wavex.Format.nSamplesPerSec;
-
-    // Append buffer latency to interface latency in shared mode (see GetStreamLatency notes)
-    stream->in.latencySeconds = bufferLatency;
-
-    PRINT(("WASAPI::OpenStream(input): framesPerUser[ %d ] framesPerHost[ %d ] latency[ %.02fms ] exclusive[ %s ] wow64_fix[ %s ] mode[ %s ]\n", (UINT32)framesPerBuffer, (UINT32)stream->in.framesPerHostCallback, (float)(stream->in.latencySeconds*1000.0f), (stream->in.shareMode == AUDCLNT_SHAREMODE_EXCLUSIVE ? "YES" : "NO"), (stream->in.params.wow64_workaround ? "YES" : "NO"), (stream->in.streamFlags & AUDCLNT_STREAMFLAGS_EVENTCALLBACK ? "EVENT" : "POLL")));
-
-    return paNoError;
-
-error:
-
-    return result;
-}
-
-// ------------------------------------------------------------------------------------------
-static PaError OpenStream( struct PaUtilHostApiRepresentation *hostApi,
-                           PaStream** s,
-                           const PaStreamParameters *inputParameters,
-                           const PaStreamParameters *outputParameters,
-                           double sampleRate,
-                           unsigned long framesPerBuffer,
-                           PaStreamFlags streamFlags,
-                           PaStreamCallback *streamCallback,
-                           void *userData )
-{
-    PaError result = paNoError;
-    HRESULT hr;
-    PaWasapiHostApiRepresentation *paWasapi = (PaWasapiHostApiRepresentation*)hostApi;
-    PaWasapiStream *stream = NULL;
-    int inputChannelCount, outputChannelCount;
-    PaSampleFormat inputSampleFormat, outputSampleFormat;
-    PaSampleFormat hostInputSampleFormat, hostOutputSampleFormat;
-    PaWasapiStreamInfo *inputStreamInfo = NULL, *outputStreamInfo = NULL;
-    PaWasapiDeviceInfo *info = NULL;
-    ULONG framesPerHostCallback;
-    PaUtilHostBufferSizeMode bufferMode;
-    const BOOL fullDuplex = ((inputParameters != NULL) && (outputParameters != NULL));
-    BOOL useInputBufferProcessor = (inputParameters != NULL), useOutputBufferProcessor = (outputParameters != NULL);
-
-    // validate PaStreamParameters
-    if ((result = IsStreamParamsValid(hostApi, inputParameters, outputParameters, sampleRate)) != paNoError)
-        return LogPaError(result);
-
-    // Validate platform specific flags
-    if ((streamFlags & paPlatformSpecificFlags) != 0)
-    {
-        LogPaError(result = paInvalidFlag); /* unexpected platform specific flag */
-        goto error;
-    }
-
-    // Allocate memory for PaWasapiStream
-    if ((stream = (PaWasapiStream *)PaUtil_AllocateMemory(sizeof(PaWasapiStream))) == NULL)
-    {
-        LogPaError(result = paInsufficientMemory);
-        goto error;
-    }
-
-    // Default thread priority is Audio: for exclusive mode we will use Pro Audio.
-    stream->nThreadPriority = eThreadPriorityAudio;
-
-    // Set default number of frames: paFramesPerBufferUnspecified
-    if (framesPerBuffer == paFramesPerBufferUnspecified)
-    {
-        UINT32 framesPerBufferIn  = 0, framesPerBufferOut = 0;
-        if (inputParameters != NULL)
-        {
-            info = &paWasapi->devInfo[inputParameters->device];
-            framesPerBufferIn = MakeFramesFromHns(info->DefaultDevicePeriod, (UINT32)sampleRate);
-        }
-        if (outputParameters != NULL)
-        {
-            info = &paWasapi->devInfo[outputParameters->device];
-            framesPerBufferOut = MakeFramesFromHns(info->DefaultDevicePeriod, (UINT32)sampleRate);
-        }
-        // choosing maximum default size
-        framesPerBuffer = max(framesPerBufferIn, framesPerBufferOut);
-    }
-    if (framesPerBuffer == 0)
-        framesPerBuffer = ((UINT32)sampleRate / 100) * 2;
-
-    // Try create device: Input
-    if (inputParameters != NULL)
-    {
-        inputChannelCount = inputParameters->channelCount;
-        inputSampleFormat = GetSampleFormatForIO(inputParameters->sampleFormat);
-        info              = &paWasapi->devInfo[inputParameters->device];
-
-        // default Shared Mode
-        stream->in.shareMode = AUDCLNT_SHAREMODE_SHARED;
-
-        // PaWasapiStreamInfo
-        if (inputParameters->hostApiSpecificStreamInfo != NULL)
-        {
-            memcpy(&stream->in.params.wasapi_params, inputParameters->hostApiSpecificStreamInfo, min(sizeof(stream->in.params.wasapi_params), ((PaWasapiStreamInfo *)inputParameters->hostApiSpecificStreamInfo)->size));
-            stream->in.params.wasapi_params.size = sizeof(stream->in.params.wasapi_params);
-
-            stream->in.params.stream_params.hostApiSpecificStreamInfo = &stream->in.params.wasapi_params;
-            inputStreamInfo = &stream->in.params.wasapi_params;
-
-            stream->in.flags = inputStreamInfo->flags;
-
-            // Exclusive Mode
-            if (inputStreamInfo->flags & paWinWasapiExclusive)
-            {
-                // Boost thread priority
-                stream->nThreadPriority = eThreadPriorityProAudio;
-                // Make Exclusive
-                stream->in.shareMode = AUDCLNT_SHAREMODE_EXCLUSIVE;
-            }
-
-            // explicit thread priority level
-            if (inputStreamInfo->flags & paWinWasapiThreadPriority)
-            {
-                if ((inputStreamInfo->threadPriority > eThreadPriorityNone) &&
-                    (inputStreamInfo->threadPriority <= eThreadPriorityWindowManager))
-                    stream->nThreadPriority = inputStreamInfo->threadPriority;
-            }
-
-            // redirect processing to custom user callback, ignore PA buffer processor
-            useInputBufferProcessor = !(inputStreamInfo->flags & paWinWasapiRedirectHostProcessor);
-        }
-
-        // Choose processing mode
-        stream->in.streamFlags = (stream->in.shareMode == AUDCLNT_SHAREMODE_EXCLUSIVE ? AUDCLNT_STREAMFLAGS_EVENTCALLBACK : 0);
-        if (paWasapi->useWOW64Workaround)
-            stream->in.streamFlags = 0; // polling interface
-        else
-        if (streamCallback == NULL)
-            stream->in.streamFlags = 0; // polling interface
-        else
-        if ((inputStreamInfo != NULL) && (inputStreamInfo->flags & paWinWasapiPolling))
-            stream->in.streamFlags = 0; // polling interface
-        else
-        if (fullDuplex)
-            stream->in.streamFlags = 0; // polling interface is implemented for full-duplex mode also
-
-        // Use built-in PCM converter (channel count and sample rate) if requested
-        if ((GetWindowsVersion() >= WINDOWS_7_SERVER2008R2) &&
-            (stream->in.shareMode == AUDCLNT_SHAREMODE_SHARED) &&
-            ((inputStreamInfo != NULL) && (inputStreamInfo->flags & paWinWasapiAutoConvert)))
-            stream->in.streamFlags |= (AUDCLNT_STREAMFLAGS_AUTOCONVERTPCM | AUDCLNT_STREAMFLAGS_SRC_DEFAULT_QUALITY);
-
-        // Fill parameters for Audio Client creation
-        stream->in.params.device_info       = info;
-        stream->in.params.stream_params     = (*inputParameters);
-        stream->in.params.frames_per_buffer = framesPerBuffer;
-        stream->in.params.sample_rate       = sampleRate;
-        stream->in.params.blocking          = (streamCallback == NULL);
-        stream->in.params.full_duplex       = fullDuplex;
-        stream->in.params.wow64_workaround  = paWasapi->useWOW64Workaround;
-
-        // Create and activate audio client
-        if ((result = ActivateAudioClientInput(stream)) != paNoError)
-        {
-            LogPaError(result);
-            goto error;
-        }
-
-        // Get closest format
-        hostInputSampleFormat = PaUtil_SelectClosestAvailableFormat(WaveToPaFormat(&stream->in.wavex), inputSampleFormat);
-
-        // Set user-side custom host processor
-        if ((inputStreamInfo != NULL) &&
-            (inputStreamInfo->flags & paWinWasapiRedirectHostProcessor))
-        {
-            stream->hostProcessOverrideInput.processor = inputStreamInfo->hostProcessorInput;
-            stream->hostProcessOverrideInput.userData = userData;
-        }
-
-        // Only get IAudioCaptureClient input once here instead of getting it at multiple places based on the use
-        if (FAILED(hr = IAudioClient_GetService(stream->in.clientParent, &pa_IID_IAudioCaptureClient, (void **)&stream->captureClientParent)))
-        {
-            LogHostError(hr);
-            LogPaError(result = paUnanticipatedHostError);
-            goto error;
-        }
-
-        // Create ring buffer for blocking mode (It is needed because we fetch Input packets, not frames,
-        // and thus we have to save partial packet if such remains unread)
-        if (stream->in.params.blocking == TRUE)
-        {
-            UINT32 bufferFrames = ALIGN_NEXT_POW2((stream->in.framesPerHostCallback / WASAPI_PACKETS_PER_INPUT_BUFFER) * 2);
-            UINT32 frameSize    = stream->in.wavex.Format.nBlockAlign;
-
-            // buffer
-            if ((stream->in.tailBuffer = PaUtil_AllocateMemory(sizeof(PaUtilRingBuffer))) == NULL)
-            {
-                LogPaError(result = paInsufficientMemory);
-                goto error;
-            }
-            memset(stream->in.tailBuffer, 0, sizeof(PaUtilRingBuffer));
-
-            // buffer memory region
-            stream->in.tailBufferMemory = PaUtil_AllocateMemory(frameSize * bufferFrames);
-            if (stream->in.tailBufferMemory == NULL)
-            {
-                LogPaError(result = paInsufficientMemory);
-                goto error;
-            }
-
-            // initialize
-            if (PaUtil_InitializeRingBuffer(stream->in.tailBuffer, frameSize, bufferFrames,    stream->in.tailBufferMemory) != 0)
-            {
-                LogPaError(result = paInternalError);
-                goto error;
-            }
-        }
-    }
-    else
-    {
-        inputChannelCount = 0;
-        inputSampleFormat = hostInputSampleFormat = paInt16; /* Suppress 'uninitialised var' warnings. */
-    }
-
-    // Try create device: Output
-    if (outputParameters != NULL)
-    {
-        outputChannelCount = outputParameters->channelCount;
-        outputSampleFormat = GetSampleFormatForIO(outputParameters->sampleFormat);
-        info               = &paWasapi->devInfo[outputParameters->device];
-
-        // default Shared Mode
-        stream->out.shareMode = AUDCLNT_SHAREMODE_SHARED;
-
-        // set PaWasapiStreamInfo
-        if (outputParameters->hostApiSpecificStreamInfo != NULL)
-        {
-            memcpy(&stream->out.params.wasapi_params, outputParameters->hostApiSpecificStreamInfo, min(sizeof(stream->out.params.wasapi_params), ((PaWasapiStreamInfo *)outputParameters->hostApiSpecificStreamInfo)->size));
-            stream->out.params.wasapi_params.size = sizeof(stream->out.params.wasapi_params);
-
-            stream->out.params.stream_params.hostApiSpecificStreamInfo = &stream->out.params.wasapi_params;
-            outputStreamInfo = &stream->out.params.wasapi_params;
-
-            stream->out.flags = outputStreamInfo->flags;
-
-            // Exclusive Mode
-            if (outputStreamInfo->flags & paWinWasapiExclusive)
-            {
-                // Boost thread priority
-                stream->nThreadPriority = eThreadPriorityProAudio;
-                // Make Exclusive
-                stream->out.shareMode = AUDCLNT_SHAREMODE_EXCLUSIVE;
-            }
-
-            // explicit thread priority level
-            if (outputStreamInfo->flags & paWinWasapiThreadPriority)
-            {
-                if ((outputStreamInfo->threadPriority > eThreadPriorityNone) &&
-                    (outputStreamInfo->threadPriority <= eThreadPriorityWindowManager))
-                    stream->nThreadPriority = outputStreamInfo->threadPriority;
-            }
-
-            // redirect processing to custom user callback, ignore PA buffer processor
-            useOutputBufferProcessor = !(outputStreamInfo->flags & paWinWasapiRedirectHostProcessor);
-        }
-
-        // Choose processing mode
-        stream->out.streamFlags = (stream->out.shareMode == AUDCLNT_SHAREMODE_EXCLUSIVE ? AUDCLNT_STREAMFLAGS_EVENTCALLBACK : 0);
-        if (paWasapi->useWOW64Workaround)
-            stream->out.streamFlags = 0; // polling interface
-        else
-        if (streamCallback == NULL)
-            stream->out.streamFlags = 0; // polling interface
-        else
-        if ((outputStreamInfo != NULL) && (outputStreamInfo->flags & paWinWasapiPolling))
-            stream->out.streamFlags = 0; // polling interface
-        else
-        if (fullDuplex)
-            stream->out.streamFlags = 0; // polling interface is implemented for full-duplex mode also
-
-        // Use built-in PCM converter (channel count and sample rate) if requested
-        if ((GetWindowsVersion() >= WINDOWS_7_SERVER2008R2) &&
-            (stream->out.shareMode == AUDCLNT_SHAREMODE_SHARED) &&
-            ((outputStreamInfo != NULL) && (outputStreamInfo->flags & paWinWasapiAutoConvert)))
-            stream->out.streamFlags |= (AUDCLNT_STREAMFLAGS_AUTOCONVERTPCM | AUDCLNT_STREAMFLAGS_SRC_DEFAULT_QUALITY);
-
-        // Fill parameters for Audio Client creation
-        stream->out.params.device_info       = info;
-        stream->out.params.stream_params     = (*outputParameters);
-        stream->out.params.frames_per_buffer = framesPerBuffer;
-        stream->out.params.sample_rate       = sampleRate;
-        stream->out.params.blocking          = (streamCallback == NULL);
-        stream->out.params.full_duplex       = fullDuplex;
-        stream->out.params.wow64_workaround  = paWasapi->useWOW64Workaround;
-
-        // Create and activate audio client
-        if ((result = ActivateAudioClientOutput(stream)) != paNoError)
-        {
-            LogPaError(result);
-            goto error;
-        }
-
-        // Get closest format
-        hostOutputSampleFormat = PaUtil_SelectClosestAvailableFormat(WaveToPaFormat(&stream->out.wavex), outputSampleFormat);
-
-        // Set user-side custom host processor
-        if ((outputStreamInfo != NULL) &&
-            (outputStreamInfo->flags & paWinWasapiRedirectHostProcessor))
-        {
-            stream->hostProcessOverrideOutput.processor = outputStreamInfo->hostProcessorOutput;
-            stream->hostProcessOverrideOutput.userData = userData;
-        }
-
-        // Only get IAudioCaptureClient output once here instead of getting it at multiple places based on the use
-        if (FAILED(hr = IAudioClient_GetService(stream->out.clientParent, &pa_IID_IAudioRenderClient, (void **)&stream->renderClientParent)))
-        {
-            LogHostError(hr);
-            LogPaError(result = paUnanticipatedHostError);
-            goto error;
-        }
-    }
-    else
-    {
-        outputChannelCount = 0;
-        outputSampleFormat = hostOutputSampleFormat = paInt16; /* Suppress 'uninitialized var' warnings. */
-    }
-
-    // log full-duplex
-    if (fullDuplex)
-        PRINT(("WASAPI::OpenStream: full-duplex mode\n"));
-
-    // paWinWasapiPolling must be on/or not on both streams
-    if ((inputParameters != NULL) && (outputParameters != NULL))
-    {
-        if ((inputStreamInfo != NULL) && (outputStreamInfo != NULL))
-        {
-            if (((inputStreamInfo->flags & paWinWasapiPolling) &&
-                !(outputStreamInfo->flags & paWinWasapiPolling))
-                ||
-                (!(inputStreamInfo->flags & paWinWasapiPolling) &&
-                 (outputStreamInfo->flags & paWinWasapiPolling)))
-            {
-                LogPaError(result = paInvalidFlag);
-                goto error;
-            }
-        }
-    }
-
-    // Initialize stream representation
-    if (streamCallback)
-    {
-        stream->bBlocking = FALSE;
-        PaUtil_InitializeStreamRepresentation(&stream->streamRepresentation,
-                                              &paWasapi->callbackStreamInterface,
-                                              streamCallback, userData);
-    }
-    else
-    {
-        stream->bBlocking = TRUE;
-        PaUtil_InitializeStreamRepresentation(&stream->streamRepresentation,
-                                              &paWasapi->blockingStreamInterface,
-                                              streamCallback, userData);
-    }
-
-    // Initialize CPU measurer
-    PaUtil_InitializeCpuLoadMeasurer(&stream->cpuLoadMeasurer, sampleRate);
-
-    if (outputParameters && inputParameters)
-    {
-        // serious problem #1 - No, Not a problem, especially concerning Exclusive mode.
-        // Input device in exclusive mode somehow is getting large buffer always, thus we
-        // adjust Output latency to reflect it, thus period will differ but playback will be
-        // normal.
-        /*if (stream->in.period != stream->out.period)
-        {
-            PRINT(("WASAPI: OpenStream: period discrepancy\n"));
-            LogPaError(result = paBadIODeviceCombination);
-            goto error;
-        }*/
-
-        // serious problem #2 - No, Not a problem, as framesPerHostCallback take into account
-        // sample size while it is not a problem for PA full-duplex, we must care of
-        // period only!
-        /*if (stream->out.framesPerHostCallback != stream->in.framesPerHostCallback)
-        {
-            PRINT(("WASAPI: OpenStream: framesPerHostCallback discrepancy\n"));
-            goto error;
-        }*/
-    }
-
-    // Calculate frames per host for processor
-    framesPerHostCallback = (outputParameters ? stream->out.framesPerBuffer : stream->in.framesPerBuffer);
-
-    // Choose correct mode of buffer processing:
-    // Exclusive/Shared non paWinWasapiPolling mode: paUtilFixedHostBufferSize - always fixed
-    // Exclusive/Shared paWinWasapiPolling mode: paUtilBoundedHostBufferSize - may vary for Exclusive or Full-duplex
-    bufferMode = paUtilFixedHostBufferSize;
-    if (inputParameters) // !!! WASAPI IAudioCaptureClient::GetBuffer extracts not number of frames but 1 packet, thus we always must adapt
-        bufferMode = paUtilBoundedHostBufferSize;
-    else
-    if (outputParameters)
-    {
-        if ((stream->out.buffers == 1) &&
-            (!stream->out.streamFlags || ((stream->out.streamFlags & AUDCLNT_STREAMFLAGS_EVENTCALLBACK) == 0)))
-            bufferMode = paUtilBoundedHostBufferSize;
-    }
-    stream->bufferMode = bufferMode;
-
-    // Initialize buffer processor
-    if (useInputBufferProcessor || useOutputBufferProcessor)
-    {
-        result =  PaUtil_InitializeBufferProcessor(
-            &stream->bufferProcessor,
-            inputChannelCount,
-            inputSampleFormat,
-            hostInputSampleFormat,
-            outputChannelCount,
-            outputSampleFormat,
-            hostOutputSampleFormat,
-            sampleRate,
-            streamFlags,
-            framesPerBuffer,
-            framesPerHostCallback,
-            bufferMode,
-            streamCallback,
-            userData);
-        if (result != paNoError)
-        {
-            LogPaError(result);
-            goto error;
-        }
-    }
-
-    // Set Input latency
-    stream->streamRepresentation.streamInfo.inputLatency =
-            (useInputBufferProcessor ? PaUtil_GetBufferProcessorInputLatencyFrames(&stream->bufferProcessor) / sampleRate : 0)
-            + (inputParameters != NULL ? stream->in.latencySeconds : 0);
-
-    // Set Output latency
-    stream->streamRepresentation.streamInfo.outputLatency =
-            (useOutputBufferProcessor ? PaUtil_GetBufferProcessorOutputLatencyFrames(&stream->bufferProcessor) / sampleRate : 0)
-            + (outputParameters != NULL ? stream->out.latencySeconds : 0);
-
-    // Set SR
-    stream->streamRepresentation.streamInfo.sampleRate = sampleRate;
-
-    (*s) = (PaStream *)stream;
-    return result;
-
-error:
-
-    if (stream != NULL)
-        CloseStream(stream);
-
-    return result;
-}
-
-// ------------------------------------------------------------------------------------------
-static PaError CloseStream( PaStream* s )
-{
-    PaError result = paNoError;
-    PaWasapiStream *stream = (PaWasapiStream*)s;
-
-    // abort active stream
-    if (IsStreamActive(s))
-    {
-        result = AbortStream(s);
-    }
-
-    SAFE_RELEASE(stream->captureClientParent);
-    SAFE_RELEASE(stream->renderClientParent);
-    SAFE_RELEASE(stream->out.clientParent);
-    SAFE_RELEASE(stream->in.clientParent);
-    SAFE_RELEASE(stream->inVol);
-    SAFE_RELEASE(stream->outVol);
-
-    CloseHandle(stream->event[S_INPUT]);
-    CloseHandle(stream->event[S_OUTPUT]);
-
-    _StreamCleanup(stream);
-
-    PaWasapi_FreeMemory(stream->in.monoBuffer);
-    PaWasapi_FreeMemory(stream->out.monoBuffer);
-
-    PaUtil_FreeMemory(stream->in.tailBuffer);
-    PaUtil_FreeMemory(stream->in.tailBufferMemory);
-
-    PaUtil_FreeMemory(stream->out.tailBuffer);
-    PaUtil_FreeMemory(stream->out.tailBufferMemory);
-
-    PaUtil_TerminateBufferProcessor(&stream->bufferProcessor);
-    PaUtil_TerminateStreamRepresentation(&stream->streamRepresentation);
-    PaUtil_FreeMemory(stream);
-
-    return result;
-}
-
-// ------------------------------------------------------------------------------------------
-HRESULT UnmarshalSubStreamComPointers(PaWasapiSubStream *substream)
-{
-#ifndef PA_WINRT
-    HRESULT hResult = S_OK;
-    HRESULT hFirstBadResult = S_OK;
-    substream->clientProc = NULL;
-
-    // IAudioClient
-    hResult = CoGetInterfaceAndReleaseStream(substream->clientStream, GetAudioClientIID(), (LPVOID*)&substream->clientProc);
-    substream->clientStream = NULL;
-    if (hResult != S_OK)
-    {
-        hFirstBadResult = (hFirstBadResult == S_OK) ? hResult : hFirstBadResult;
-    }
-
-    return hFirstBadResult;
-
-#else
-    (void)substream;
-    return S_OK;
-#endif
-}
-
-// ------------------------------------------------------------------------------------------
-HRESULT UnmarshalStreamComPointers(PaWasapiStream *stream)
-{
-#ifndef PA_WINRT
-    HRESULT hResult = S_OK;
-    HRESULT hFirstBadResult = S_OK;
-    stream->captureClient = NULL;
-    stream->renderClient = NULL;
-    stream->in.clientProc = NULL;
-    stream->out.clientProc = NULL;
-
-    if (NULL != stream->in.clientParent)
-    {
-        // SubStream pointers
-        hResult = UnmarshalSubStreamComPointers(&stream->in);
-        if (hResult != S_OK)
-        {
-            hFirstBadResult = (hFirstBadResult == S_OK) ? hResult : hFirstBadResult;
-        }
-
-        // IAudioCaptureClient
-        hResult = CoGetInterfaceAndReleaseStream(stream->captureClientStream, &pa_IID_IAudioCaptureClient, (LPVOID*)&stream->captureClient);
-        stream->captureClientStream = NULL;
-        if (hResult != S_OK)
-        {
-            hFirstBadResult = (hFirstBadResult == S_OK) ? hResult : hFirstBadResult;
-        }
-    }
-
-    if (NULL != stream->out.clientParent)
-    {
-        // SubStream pointers
-        hResult = UnmarshalSubStreamComPointers(&stream->out);
-        if (hResult != S_OK)
-        {
-            hFirstBadResult = (hFirstBadResult == S_OK) ? hResult : hFirstBadResult;
-        }
-
-        // IAudioRenderClient
-        hResult = CoGetInterfaceAndReleaseStream(stream->renderClientStream, &pa_IID_IAudioRenderClient, (LPVOID*)&stream->renderClient);
-        stream->renderClientStream = NULL;
-        if (hResult != S_OK)
-        {
-            hFirstBadResult = (hFirstBadResult == S_OK) ? hResult : hFirstBadResult;
-        }
-    }
-
-    return hFirstBadResult;
-#else
-    if (stream->in.clientParent != NULL)
-    {
-        stream->in.clientProc = stream->in.clientParent;
-        IAudioClient_AddRef(stream->in.clientParent);
-    }
-
-    if (stream->out.clientParent != NULL)
-    {
-        stream->out.clientProc = stream->out.clientParent;
-        IAudioClient_AddRef(stream->out.clientParent);
-    }
-
-    if (stream->renderClientParent != NULL)
-    {
-        stream->renderClient = stream->renderClientParent;
-        IAudioRenderClient_AddRef(stream->renderClientParent);
-    }
-
-    if (stream->captureClientParent != NULL)
-    {
-        stream->captureClient = stream->captureClientParent;
-        IAudioCaptureClient_AddRef(stream->captureClientParent);
-    }
-
-    return S_OK;
-#endif
-}
-
-// -----------------------------------------------------------------------------------------
-void ReleaseUnmarshaledSubComPointers(PaWasapiSubStream *substream)
-{
-    SAFE_RELEASE(substream->clientProc);
-}
-
-// -----------------------------------------------------------------------------------------
-void ReleaseUnmarshaledComPointers(PaWasapiStream *stream)
-{
-    // Release AudioClient services first
-    SAFE_RELEASE(stream->captureClient);
-    SAFE_RELEASE(stream->renderClient);
-
-    // Release AudioClients
-    ReleaseUnmarshaledSubComPointers(&stream->in);
-    ReleaseUnmarshaledSubComPointers(&stream->out);
-}
-
-// ------------------------------------------------------------------------------------------
-HRESULT MarshalSubStreamComPointers(PaWasapiSubStream *substream)
-{
-#ifndef PA_WINRT
-    HRESULT hResult;
-    substream->clientStream = NULL;
-
-    // IAudioClient
-    hResult = CoMarshalInterThreadInterfaceInStream(GetAudioClientIID(), (LPUNKNOWN)substream->clientParent, &substream->clientStream);
-    if (hResult != S_OK)
-        goto marshal_sub_error;
-
-    return hResult;
-
-    // If marshaling error occurred, make sure to release everything.
-marshal_sub_error:
-
-    UnmarshalSubStreamComPointers(substream);
-    ReleaseUnmarshaledSubComPointers(substream);
-    return hResult;
-#else
-    (void)substream;
-    return S_OK;
-#endif
-}
-
-// ------------------------------------------------------------------------------------------
-HRESULT MarshalStreamComPointers(PaWasapiStream *stream)
-{
-#ifndef PA_WINRT
-    HRESULT hResult = S_OK;
-    stream->captureClientStream = NULL;
-    stream->in.clientStream = NULL;
-    stream->renderClientStream = NULL;
-    stream->out.clientStream = NULL;
-
-    if (NULL != stream->in.clientParent)
-    {
-        // SubStream pointers
-        hResult = MarshalSubStreamComPointers(&stream->in);
-        if (hResult != S_OK)
-            goto marshal_error;
-
-        // IAudioCaptureClient
-        hResult = CoMarshalInterThreadInterfaceInStream(&pa_IID_IAudioCaptureClient, (LPUNKNOWN)stream->captureClientParent, &stream->captureClientStream);
-        if (hResult != S_OK)
-            goto marshal_error;
-    }
-
-    if (NULL != stream->out.clientParent)
-    {
-        // SubStream pointers
-        hResult = MarshalSubStreamComPointers(&stream->out);
-        if (hResult != S_OK)
-            goto marshal_error;
-
-        // IAudioRenderClient
-        hResult = CoMarshalInterThreadInterfaceInStream(&pa_IID_IAudioRenderClient, (LPUNKNOWN)stream->renderClientParent, &stream->renderClientStream);
-        if (hResult != S_OK)
-            goto marshal_error;
-    }
-
-    return hResult;
-
-    // If marshaling error occurred, make sure to release everything.
-marshal_error:
-
-    UnmarshalStreamComPointers(stream);
-    ReleaseUnmarshaledComPointers(stream);
-    return hResult;
-#else
-    (void)stream;
-    return S_OK;
-#endif
-}
-
-// ------------------------------------------------------------------------------------------
-static PaError StartStream( PaStream *s )
-{
-    HRESULT hr;
-    PaWasapiStream *stream = (PaWasapiStream*)s;
-    PaError result = paNoError;
-
-    // check if stream is active already
-    if (IsStreamActive(s))
-        return paStreamIsNotStopped;
-
-    PaUtil_ResetBufferProcessor(&stream->bufferProcessor);
-
-    // Cleanup handles (may be necessary if stream was stopped by itself due to error)
-    _StreamCleanup(stream);
-
-    // Create close event
-    if ((stream->hCloseRequest = CreateEvent(NULL, TRUE, FALSE, NULL)) == NULL)
-    {
-        result = paInsufficientMemory;
-        goto start_error;
-    }
-
-    // Create thread
-    if (!stream->bBlocking)
-    {
-        // Create thread events
-        stream->hThreadStart = CreateEvent(NULL, TRUE, FALSE, NULL);
-        stream->hThreadExit  = CreateEvent(NULL, TRUE, FALSE, NULL);
-        if ((stream->hThreadStart == NULL) || (stream->hThreadExit == NULL))
-        {
-            result = paInsufficientMemory;
-            goto start_error;
-        }
-
-        // Marshal WASAPI interface pointers for safe use in thread created below.
-        if ((hr = MarshalStreamComPointers(stream)) != S_OK)
-        {
-            PRINT(("Failed marshaling stream COM pointers."));
-            result = paUnanticipatedHostError;
-            goto nonblocking_start_error;
-        }
-
-        if ((stream->in.clientParent  && (stream->in.streamFlags  & AUDCLNT_STREAMFLAGS_EVENTCALLBACK)) ||
-            (stream->out.clientParent && (stream->out.streamFlags & AUDCLNT_STREAMFLAGS_EVENTCALLBACK)))
-        {
-            if ((stream->hThread = CREATE_THREAD(ProcThreadEvent)) == NULL)
-            {
-                PRINT(("Failed creating thread: ProcThreadEvent."));
-                result = paUnanticipatedHostError;
-                goto nonblocking_start_error;
-            }
-        }
-        else
-        {
-            if ((stream->hThread = CREATE_THREAD(ProcThreadPoll)) == NULL)
-            {
-                PRINT(("Failed creating thread: ProcThreadPoll."));
-                result = paUnanticipatedHostError;
-                goto nonblocking_start_error;
-            }
-        }
-
-        // Wait for thread to start
-        if (WaitForSingleObject(stream->hThreadStart, 60*1000) == WAIT_TIMEOUT)
-        {
-            PRINT(("Failed starting thread: timeout."));
-            result = paUnanticipatedHostError;
-            goto nonblocking_start_error;
-        }
-    }
-    else
-    {
-        // Create blocking operation events (non-signaled event means - blocking operation is pending)
-        if (stream->out.clientParent != NULL)
-        {
-            if ((stream->hBlockingOpStreamWR = CreateEvent(NULL, TRUE, TRUE, NULL)) == NULL)
-            {
-                result = paInsufficientMemory;
-                goto start_error;
-            }
-        }
-        if (stream->in.clientParent != NULL)
-        {
-            if ((stream->hBlockingOpStreamRD = CreateEvent(NULL, TRUE, TRUE, NULL)) == NULL)
-            {
-                result = paInsufficientMemory;
-                goto start_error;
-            }
-        }
-
-        // Initialize event & start INPUT stream
-        if (stream->in.clientParent != NULL)
-        {
-            if ((hr = IAudioClient_Start(stream->in.clientParent)) != S_OK)
-            {
-                LogHostError(hr);
-                result = paUnanticipatedHostError;
-                goto start_error;
-            }
-        }
-
-        // Initialize event & start OUTPUT stream
-        if (stream->out.clientParent != NULL)
-        {
-            // Start
-            if ((hr = IAudioClient_Start(stream->out.clientParent)) != S_OK)
-            {
-                LogHostError(hr);
-                result = paUnanticipatedHostError;
-                goto start_error;
-            }
-        }
-
-        // Set parent to working pointers to use shared functions.
-        stream->captureClient  = stream->captureClientParent;
-        stream->renderClient   = stream->renderClientParent;
-        stream->in.clientProc  = stream->in.clientParent;
-        stream->out.clientProc = stream->out.clientParent;
-
-        // Signal: stream running.
-        stream->running = TRUE;
-    }
-
-    return result;
-
-nonblocking_start_error:
-
-    // Set hThreadExit event to prevent blocking during cleanup
-    SetEvent(stream->hThreadExit);
-    UnmarshalStreamComPointers(stream);
-    ReleaseUnmarshaledComPointers(stream);
-
-start_error:
-
-    StopStream(s);
-    return result;
-}
-
-// ------------------------------------------------------------------------------------------
-void _StreamFinish(PaWasapiStream *stream)
-{
-    // Issue command to thread to stop processing and wait for thread exit
-    if (!stream->bBlocking)
-    {
-        SignalObjectAndWait(stream->hCloseRequest, stream->hThreadExit, INFINITE, FALSE);
-    }
-    else
-    // Blocking mode does not own thread
-    {
-        // Signal close event and wait for each of 2 blocking operations to complete
-        if (stream->out.clientParent)
-            SignalObjectAndWait(stream->hCloseRequest, stream->hBlockingOpStreamWR, INFINITE, TRUE);
-        if (stream->out.clientParent)
-            SignalObjectAndWait(stream->hCloseRequest, stream->hBlockingOpStreamRD, INFINITE, TRUE);
-
-        // Process stop
-        _StreamOnStop(stream);
-    }
-
-    // Cleanup handles
-    _StreamCleanup(stream);
-
-    stream->running = FALSE;
-}
-
-// ------------------------------------------------------------------------------------------
-void _StreamCleanup(PaWasapiStream *stream)
-{
-    // Close thread handles to allow restart
-    SAFE_CLOSE(stream->hThread);
-    SAFE_CLOSE(stream->hThreadStart);
-    SAFE_CLOSE(stream->hThreadExit);
-    SAFE_CLOSE(stream->hCloseRequest);
-    SAFE_CLOSE(stream->hBlockingOpStreamRD);
-    SAFE_CLOSE(stream->hBlockingOpStreamWR);
-}
-
-// ------------------------------------------------------------------------------------------
-static PaError StopStream( PaStream *s )
-{
-    // Finish stream
-    _StreamFinish((PaWasapiStream *)s);
-    return paNoError;
-}
-
-// ------------------------------------------------------------------------------------------
-static PaError AbortStream( PaStream *s )
-{
-    // Finish stream
-    _StreamFinish((PaWasapiStream *)s);
-    return paNoError;
-}
-
-// ------------------------------------------------------------------------------------------
-static PaError IsStreamStopped( PaStream *s )
-{
-    return !((PaWasapiStream *)s)->running;
-}
-
-// ------------------------------------------------------------------------------------------
-static PaError IsStreamActive( PaStream *s )
-{
-    return ((PaWasapiStream *)s)->running;
-}
-
-// ------------------------------------------------------------------------------------------
-static PaTime GetStreamTime( PaStream *s )
-{
-    PaWasapiStream *stream = (PaWasapiStream*)s;
-
-    /* suppress unused variable warnings */
-    (void) stream;
-
-    return PaUtil_GetTime();
-}
-
-// ------------------------------------------------------------------------------------------
-static double GetStreamCpuLoad( PaStream* s )
-{
-    return PaUtil_GetCpuLoad(&((PaWasapiStream *)s)->cpuLoadMeasurer);
-}
-
-// ------------------------------------------------------------------------------------------
-static PaError ReadStream( PaStream* s, void *_buffer, unsigned long frames )
-{
-    PaWasapiStream *stream = (PaWasapiStream*)s;
-
-    HRESULT hr = S_OK;
-    BYTE *user_buffer = (BYTE *)_buffer;
-    BYTE *wasapi_buffer = NULL;
-    DWORD flags = 0;
-    UINT32 i, available, sleep = 0;
-    unsigned long processed;
-    ThreadIdleScheduler sched;
-
-    // validate
-    if (!stream->running)
-        return paStreamIsStopped;
-    if (stream->captureClient == NULL)
-        return paBadStreamPtr;
-
-    // Notify blocking op has begun
-    ResetEvent(stream->hBlockingOpStreamRD);
-
-    // Use thread scheduling for 500 microseconds (emulated) when wait time for frames is less than
-    // 1 milliseconds, emulation helps to normalize CPU consumption and avoids too busy waiting
-    ThreadIdleScheduler_Setup(&sched, 1, 250/* microseconds */);
-
-    // Make a local copy of the user buffer pointer(s), this is necessary
-    // because PaUtil_CopyOutput() advances these pointers every time it is called
-    if (!stream->bufferProcessor.userInputIsInterleaved)
-    {
-        user_buffer = (BYTE *)alloca(sizeof(BYTE *) * stream->bufferProcessor.inputChannelCount);
-        if (user_buffer == NULL)
-            return paInsufficientMemory;
-
-        for (i = 0; i < stream->bufferProcessor.inputChannelCount; ++i)
-            ((BYTE **)user_buffer)[i] = ((BYTE **)_buffer)[i];
-    }
-
-    // Find out if there are tail frames, flush them all before reading hardware
-    if ((available = PaUtil_GetRingBufferReadAvailable(stream->in.tailBuffer)) != 0)
-    {
-        ring_buffer_size_t buf1_size = 0, buf2_size = 0, read, desired;
-        void *buf1 = NULL, *buf2 = NULL;
-
-        // Limit desired to amount of requested frames
-        desired = available;
-        if ((UINT32)desired > frames)
-            desired = frames;
-
-        // Get pointers to read regions
-        read = PaUtil_GetRingBufferReadRegions(stream->in.tailBuffer, desired, &buf1, &buf1_size, &buf2, &buf2_size);
-
-        if (buf1 != NULL)
-        {
-            // Register available frames to processor
-            PaUtil_SetInputFrameCount(&stream->bufferProcessor, buf1_size);
-
-            // Register host buffer pointer to processor
-            PaUtil_SetInterleavedInputChannels(&stream->bufferProcessor, 0, buf1, stream->bufferProcessor.inputChannelCount);
-
-            // Copy user data to host buffer (with conversion if applicable)
-            processed = PaUtil_CopyInput(&stream->bufferProcessor, (void **)&user_buffer, buf1_size);
-            frames -= processed;
-        }
-
-        if (buf2 != NULL)
-        {
-            // Register available frames to processor
-            PaUtil_SetInputFrameCount(&stream->bufferProcessor, buf2_size);
-
-            // Register host buffer pointer to processor
-            PaUtil_SetInterleavedInputChannels(&stream->bufferProcessor, 0, buf2, stream->bufferProcessor.inputChannelCount);
-
-            // Copy user data to host buffer (with conversion if applicable)
-            processed = PaUtil_CopyInput(&stream->bufferProcessor, (void **)&user_buffer, buf2_size);
-            frames -= processed;
-        }
-
-        // Advance
-        PaUtil_AdvanceRingBufferReadIndex(stream->in.tailBuffer, read);
-    }
-
-    // Read hardware
-    while (frames != 0)
-    {
-        // Check if blocking call must be interrupted
-        if (WaitForSingleObject(stream->hCloseRequest, sleep) != WAIT_TIMEOUT)
-            break;
-
-        // Get available frames (must be finding out available frames before call to IAudioCaptureClient_GetBuffer
-        // othervise audio glitches will occur inExclusive mode as it seems that WASAPI has some scheduling/
-        // processing problems when such busy polling with IAudioCaptureClient_GetBuffer occurs)
-        if ((hr = _PollGetInputFramesAvailable(stream, &available)) != S_OK)
-        {
-            LogHostError(hr);
-            return paUnanticipatedHostError;
-        }
-
-        // Wait for more frames to become available
-        if (available == 0)
-        {
-            // Exclusive mode may require latency of 1 millisecond, thus we shall sleep
-            // around 500 microseconds (emulated) to collect packets in time
-            if (stream->in.shareMode != AUDCLNT_SHAREMODE_EXCLUSIVE)
-            {
-                UINT32 sleep_frames = (frames < stream->in.framesPerHostCallback ? frames : stream->in.framesPerHostCallback);
-
-                sleep  = GetFramesSleepTime(sleep_frames, stream->in.wavex.Format.nSamplesPerSec);
-                sleep /= 4; // wait only for 1/4 of the buffer
-
-                // WASAPI input provides packets, thus expiring packet will result in bad audio
-                // limit waiting time to 2 seconds (will always work for smallest buffer in Shared)
-                if (sleep > 2)
-                    sleep = 2;
-
-                // Avoid busy waiting, schedule next 1 millesecond wait
-                if (sleep == 0)
-                    sleep = ThreadIdleScheduler_NextSleep(&sched);
-            }
-            else
-            {
-                if ((sleep = ThreadIdleScheduler_NextSleep(&sched)) != 0)
-                {
-                    Sleep(sleep);
-                    sleep = 0;
-                }
-            }
-
-            continue;
-        }
-
-        // Get the available data in the shared buffer.
-        if ((hr = IAudioCaptureClient_GetBuffer(stream->captureClient, &wasapi_buffer, &available, &flags, NULL, NULL)) != S_OK)
-        {
-            // Buffer size is too small, waiting
-            if (hr != AUDCLNT_S_BUFFER_EMPTY)
-            {
-                LogHostError(hr);
-                goto end;
-            }
-
-            continue;
-        }
-
-        // Register available frames to processor
-        PaUtil_SetInputFrameCount(&stream->bufferProcessor, available);
-
-        // Register host buffer pointer to processor
-        PaUtil_SetInterleavedInputChannels(&stream->bufferProcessor, 0, wasapi_buffer, stream->bufferProcessor.inputChannelCount);
-
-        // Copy user data to host buffer (with conversion if applicable)
-        processed = PaUtil_CopyInput(&stream->bufferProcessor, (void **)&user_buffer, frames);
-        frames -= processed;
-
-        // Save tail into buffer
-        if ((frames == 0) && (available > processed))
-        {
-            UINT32 bytes_processed = processed * stream->in.wavex.Format.nBlockAlign;
-            UINT32 frames_to_save  = available - processed;
-
-            PaUtil_WriteRingBuffer(stream->in.tailBuffer, wasapi_buffer + bytes_processed, frames_to_save);
-        }
-
-        // Release host buffer
-        if ((hr = IAudioCaptureClient_ReleaseBuffer(stream->captureClient, available)) != S_OK)
-        {
-            LogHostError(hr);
-            goto end;
-        }
-    }
-
-end:
-
-    // Notify blocking op has ended
-    SetEvent(stream->hBlockingOpStreamRD);
-
-    return (hr != S_OK ? paUnanticipatedHostError : paNoError);
-}
-
-// ------------------------------------------------------------------------------------------
-static PaError WriteStream( PaStream* s, const void *_buffer, unsigned long frames )
-{
-    PaWasapiStream *stream = (PaWasapiStream*)s;
-
-    //UINT32 frames;
-    const BYTE *user_buffer = (const BYTE *)_buffer;
-    BYTE *wasapi_buffer;
-    HRESULT hr = S_OK;
-    UINT32 i, available, sleep = 0;
-    unsigned long processed;
-    ThreadIdleScheduler sched;
-
-    // validate
-    if (!stream->running)
-        return paStreamIsStopped;
-    if (stream->renderClient == NULL)
-        return paBadStreamPtr;
-
-    // Notify blocking op has begun
-    ResetEvent(stream->hBlockingOpStreamWR);
-
-    // Use thread scheduling for 500 microseconds (emulated) when wait time for frames is less than
-    // 1 milliseconds, emulation helps to normalize CPU consumption and avoids too busy waiting
-    ThreadIdleScheduler_Setup(&sched, 1, 500/* microseconds */);
-
-    // Make a local copy of the user buffer pointer(s), this is necessary
-    // because PaUtil_CopyOutput() advances these pointers every time it is called
-    if (!stream->bufferProcessor.userOutputIsInterleaved)
-    {
-        user_buffer = (const BYTE *)alloca(sizeof(const BYTE *) * stream->bufferProcessor.outputChannelCount);
-        if (user_buffer == NULL)
-            return paInsufficientMemory;
-
-        for (i = 0; i < stream->bufferProcessor.outputChannelCount; ++i)
-            ((const BYTE **)user_buffer)[i] = ((const BYTE **)_buffer)[i];
-    }
-
-    // Blocking (potentially, until 'frames' are consumed) loop
-    while (frames != 0)
-    {
-        // Check if blocking call must be interrupted
-        if (WaitForSingleObject(stream->hCloseRequest, sleep) != WAIT_TIMEOUT)
-            break;
-
-        // Get frames available
-        if ((hr = _PollGetOutputFramesAvailable(stream, &available)) != S_OK)
-        {
-            LogHostError(hr);
-            goto end;
-        }
-
-        // Wait for more frames to become available
-        if (available == 0)
-        {
-            UINT32 sleep_frames = (frames < stream->out.framesPerHostCallback ? frames : stream->out.framesPerHostCallback);
-
-            sleep  = GetFramesSleepTime(sleep_frames, stream->out.wavex.Format.nSamplesPerSec);
-            sleep /= 2; // wait only for half of the buffer
-
-            // Avoid busy waiting, schedule next 1 millesecond wait
-            if (sleep == 0)
-                sleep = ThreadIdleScheduler_NextSleep(&sched);
-
-            continue;
-        }
-
-        // Keep in 'frames' range
-        if (available > frames)
-            available = frames;
-
-        // Get pointer to host buffer
-        if ((hr = IAudioRenderClient_GetBuffer(stream->renderClient, available, &wasapi_buffer)) != S_OK)
-        {
-            // Buffer size is too big, waiting
-            if (hr == AUDCLNT_E_BUFFER_TOO_LARGE)
-                continue;
-
-            LogHostError(hr);
-            goto end;
-        }
-
-        // Keep waiting again (on Vista it was noticed that WASAPI could SOMETIMES return NULL pointer
-        // to buffer without returning AUDCLNT_E_BUFFER_TOO_LARGE instead)
-        if (wasapi_buffer == NULL)
-            continue;
-
-        // Register available frames to processor
-        PaUtil_SetOutputFrameCount(&stream->bufferProcessor, available);
-
-        // Register host buffer pointer to processor
-        PaUtil_SetInterleavedOutputChannels(&stream->bufferProcessor, 0, wasapi_buffer,    stream->bufferProcessor.outputChannelCount);
-
-        // Copy user data to host buffer (with conversion if applicable), this call will advance
-        // pointer 'user_buffer' to consumed portion of data
-        processed = PaUtil_CopyOutput(&stream->bufferProcessor, (const void **)&user_buffer, frames);
-        frames -= processed;
-
-        // Release host buffer
-        if ((hr = IAudioRenderClient_ReleaseBuffer(stream->renderClient, available, 0)) != S_OK)
-        {
-            LogHostError(hr);
-            goto end;
-        }
-    }
-
-end:
-
-    // Notify blocking op has ended
-    SetEvent(stream->hBlockingOpStreamWR);
-
-    return (hr != S_OK ? paUnanticipatedHostError : paNoError);
-}
-
-unsigned long PaUtil_GetOutputFrameCount( PaUtilBufferProcessor* bp )
-{
-    return bp->hostOutputFrameCount[0];
-}
-
-// ------------------------------------------------------------------------------------------
-static signed long GetStreamReadAvailable( PaStream* s )
-{
-    PaWasapiStream *stream = (PaWasapiStream*)s;
-
-    HRESULT hr;
-    UINT32  available = 0;
-
-    // validate
-    if (!stream->running)
-        return paStreamIsStopped;
-    if (stream->captureClient == NULL)
-        return paBadStreamPtr;
-
-    // available in hardware buffer
-    if ((hr = _PollGetInputFramesAvailable(stream, &available)) != S_OK)
-    {
-        LogHostError(hr);
-        return paUnanticipatedHostError;
-    }
-
-    // available in software tail buffer
-    available += PaUtil_GetRingBufferReadAvailable(stream->in.tailBuffer);
-
-    return available;
-}
-
-// ------------------------------------------------------------------------------------------
-static signed long GetStreamWriteAvailable( PaStream* s )
-{
-    PaWasapiStream *stream = (PaWasapiStream*)s;
-    HRESULT hr;
-    UINT32  available = 0;
-
-    // validate
-    if (!stream->running)
-        return paStreamIsStopped;
-    if (stream->renderClient == NULL)
-        return paBadStreamPtr;
-
-    if ((hr = _PollGetOutputFramesAvailable(stream, &available)) != S_OK)
-    {
-        LogHostError(hr);
-        return paUnanticipatedHostError;
-    }
-
-    return (signed long)available;
-}
-
-
-// ------------------------------------------------------------------------------------------
-static void WaspiHostProcessingLoop( void *inputBuffer,  long inputFrames,
-                                     void *outputBuffer, long outputFrames,
-                                     void *userData )
-{
-    PaWasapiStream *stream = (PaWasapiStream*)userData;
-    PaStreamCallbackTimeInfo timeInfo = {0,0,0};
-    PaStreamCallbackFlags flags = 0;
-    int callbackResult;
-    unsigned long framesProcessed;
-    HRESULT hr;
-    UINT32 pending;
-
-    PaUtil_BeginCpuLoadMeasurement( &stream->cpuLoadMeasurer );
-
-    /*
-        Pa_GetStreamTime:
-            - generate timing information
-            - handle buffer slips
-    */
-    timeInfo.currentTime = PaUtil_GetTime();
-    // Query input latency
-    if (stream->in.clientProc != NULL)
-    {
-        PaTime pending_time;
-        if ((hr = IAudioClient_GetCurrentPadding(stream->in.clientProc, &pending)) == S_OK)
-            pending_time = (PaTime)pending / (PaTime)stream->in.wavex.Format.nSamplesPerSec;
-        else
-            pending_time = (PaTime)stream->in.latencySeconds;
-
-        timeInfo.inputBufferAdcTime = timeInfo.currentTime + pending_time;
-    }
-    // Query output current latency
-    if (stream->out.clientProc != NULL)
-    {
-        PaTime pending_time;
-        if ((hr = IAudioClient_GetCurrentPadding(stream->out.clientProc, &pending)) == S_OK)
-            pending_time = (PaTime)pending / (PaTime)stream->out.wavex.Format.nSamplesPerSec;
-        else
-            pending_time = (PaTime)stream->out.latencySeconds;
-
-        timeInfo.outputBufferDacTime = timeInfo.currentTime + pending_time;
-    }
-
-    /*
-        If you need to byte swap or shift inputBuffer to convert it into a
-        portaudio format, do it here.
-    */
-
-    PaUtil_BeginBufferProcessing( &stream->bufferProcessor, &timeInfo, flags );
-
-    /*
-        depending on whether the host buffers are interleaved, non-interleaved
-        or a mixture, you will want to call PaUtil_SetInterleaved*Channels(),
-        PaUtil_SetNonInterleaved*Channel() or PaUtil_Set*Channel() here.
-    */
-
-    if (stream->bufferProcessor.inputChannelCount > 0)
-    {
-        PaUtil_SetInputFrameCount( &stream->bufferProcessor, inputFrames );
-        PaUtil_SetInterleavedInputChannels( &stream->bufferProcessor,
-            0, /* first channel of inputBuffer is channel 0 */
-            inputBuffer,
-            0 ); /* 0 - use inputChannelCount passed to init buffer processor */
-    }
-
-    if (stream->bufferProcessor.outputChannelCount > 0)
-    {
-        PaUtil_SetOutputFrameCount( &stream->bufferProcessor, outputFrames);
-        PaUtil_SetInterleavedOutputChannels( &stream->bufferProcessor,
-            0, /* first channel of outputBuffer is channel 0 */
-            outputBuffer,
-            0 ); /* 0 - use outputChannelCount passed to init buffer processor */
-    }
-
-    /* you must pass a valid value of callback result to PaUtil_EndBufferProcessing()
-        in general you would pass paContinue for normal operation, and
-        paComplete to drain the buffer processor's internal output buffer.
-        You can check whether the buffer processor's output buffer is empty
-        using PaUtil_IsBufferProcessorOuputEmpty( bufferProcessor )
-    */
-    callbackResult = paContinue;
-    framesProcessed = PaUtil_EndBufferProcessing( &stream->bufferProcessor, &callbackResult );
-
-    /*
-        If you need to byte swap or shift outputBuffer to convert it to
-        host format, do it here.
-    */
-
-    PaUtil_EndCpuLoadMeasurement( &stream->cpuLoadMeasurer, framesProcessed );
-
-    if (callbackResult == paContinue)
-    {
-        /* nothing special to do */
-    }
-    else
-    if (callbackResult == paAbort)
-    {
-        // stop stream
-        SetEvent(stream->hCloseRequest);
-    }
-    else
-    {
-        // stop stream
-        SetEvent(stream->hCloseRequest);
-    }
-}
-
-// ------------------------------------------------------------------------------------------
-#ifndef PA_WINRT
-static PaError MMCSS_activate(PaWasapiThreadPriority nPriorityClass, HANDLE *ret)
-{
-    static const char *mmcs_name[] =
-    {
-        NULL,
-        "Audio",
-        "Capture",
-        "Distribution",
-        "Games",
-        "Playback",
-        "Pro Audio",
-        "Window Manager"
-    };
-
-    DWORD task_idx = 0;
-    HANDLE hTask;
-
-    if ((UINT32)nPriorityClass >= STATIC_ARRAY_SIZE(mmcs_name))
-        return paUnanticipatedHostError;
-
-    if ((hTask = pAvSetMmThreadCharacteristics(mmcs_name[nPriorityClass], &task_idx)) == NULL)
-    {
-        PRINT(("WASAPI: AvSetMmThreadCharacteristics failed: error[%d]\n", GetLastError()));
-        return paUnanticipatedHostError;
-    }
-
-    /*BOOL priority_ok = pAvSetMmThreadPriority(hTask, AVRT_PRIORITY_NORMAL);
-    if (priority_ok == FALSE)
-    {
-        PRINT(("WASAPI: AvSetMmThreadPriority failed!\n"));
-    }*/
-
-    // debug
-    {
-        int    cur_priority          = GetThreadPriority(GetCurrentThread());
-        DWORD  cur_priority_class = GetPriorityClass(GetCurrentProcess());
-        PRINT(("WASAPI: thread[ priority-0x%X class-0x%X ]\n", cur_priority, cur_priority_class));
-    }
-
-    (*ret) = hTask;
-    return paNoError;
-}
-#endif
-
-// ------------------------------------------------------------------------------------------
-#ifndef PA_WINRT
-static void MMCSS_deactivate(HANDLE hTask)
-{
-    if (pAvRevertMmThreadCharacteristics(hTask) == FALSE)
-    {
-        PRINT(("WASAPI: AvRevertMmThreadCharacteristics failed!\n"));
-    }
-}
-#endif
-
-// ------------------------------------------------------------------------------------------
-PaError PaWasapi_ThreadPriorityBoost(void **pTask, PaWasapiThreadPriority priorityClass)
-{
-    HANDLE task;
-    PaError ret;
-
-    if (pTask == NULL)
-        return paUnanticipatedHostError;
-
-#ifndef PA_WINRT
-    if ((ret = MMCSS_activate(priorityClass, &task)) != paNoError)
-        return ret;
-#else
-    switch (priorityClass)
-    {
-    case eThreadPriorityAudio:
-    case eThreadPriorityProAudio: {
-
-        // Save previous thread priority
-        intptr_t priority_prev = GetThreadPriority(GetCurrentThread());
-
-        // Try set new thread priority
-        if (SetThreadPriority(GetCurrentThread(), THREAD_PRIORITY_HIGHEST) == FALSE)
-            return paUnanticipatedHostError;
-
-        // Memorize prev priority (pretend to be non NULL pointer by adding 0x80000000 mask)
-        task = (HANDLE)(priority_prev | 0x80000000);
-
-        ret = paNoError;
-
-        break; }
-
-    default:
-        return paUnanticipatedHostError;
-    }
-#endif
-
-    (*pTask) = task;
-    return ret;
-}
-
-// ------------------------------------------------------------------------------------------
-PaError PaWasapi_ThreadPriorityRevert(void *pTask)
-{
-    if (pTask == NULL)
-        return paUnanticipatedHostError;
-
-#ifndef PA_WINRT
-    MMCSS_deactivate((HANDLE)pTask);
-#else
-    // Revert previous priority by removing 0x80000000 mask
-    if (SetThreadPriority(GetCurrentThread(), (int)((intptr_t)pTask & ~0x80000000)) == FALSE)
-        return paUnanticipatedHostError;
-#endif
-
-    return paNoError;
-}
-
-// ------------------------------------------------------------------------------------------
-// Described at:
-// http://msdn.microsoft.com/en-us/library/dd371387(v=VS.85).aspx
-
-PaError PaWasapi_GetJackCount(PaDeviceIndex device, int *pJackCount)
-{
-#ifndef PA_WINRT
-    PaError ret;
-    HRESULT hr = S_OK;
-    PaWasapiDeviceInfo *deviceInfo;
-    IDeviceTopology *pDeviceTopology = NULL;
-    IConnector *pConnFrom = NULL;
-    IConnector *pConnTo = NULL;
-    IPart *pPart = NULL;
-    IKsJackDescription *pJackDesc = NULL;
-    UINT jackCount = 0;
-
-    if (pJackCount == NULL)
-        return paUnanticipatedHostError;
-
-    if ((ret = _GetWasapiDeviceInfoByDeviceIndex(&deviceInfo, device)) != paNoError)
-        return ret;
-
-    // Get the endpoint device's IDeviceTopology interface
-    hr = IMMDevice_Activate(deviceInfo->device, &pa_IID_IDeviceTopology,
-        CLSCTX_INPROC_SERVER, NULL, (void**)&pDeviceTopology);
-    IF_FAILED_JUMP(hr, error);
-
-    // The device topology for an endpoint device always contains just one connector (connector number 0)
-    hr = IDeviceTopology_GetConnector(pDeviceTopology, 0, &pConnFrom);
-    IF_FAILED_JUMP(hr, error);
-
-    // Step across the connection to the jack on the adapter
-    hr = IConnector_GetConnectedTo(pConnFrom, &pConnTo);
-    if (HRESULT_FROM_WIN32(ERROR_PATH_NOT_FOUND) == hr)
-    {
-        // The adapter device is not currently active
-        hr = E_NOINTERFACE;
-    }
-    IF_FAILED_JUMP(hr, error);
-
-    // Get the connector's IPart interface
-    hr = IConnector_QueryInterface(pConnTo, &pa_IID_IPart, (void**)&pPart);
-    IF_FAILED_JUMP(hr, error);
-
-    // Activate the connector's IKsJackDescription interface
-    hr = IPart_Activate(pPart, CLSCTX_INPROC_SERVER, &pa_IID_IKsJackDescription, (void**)&pJackDesc);
-    IF_FAILED_JUMP(hr, error);
-
-    // Return jack count for this device
-    hr = IKsJackDescription_GetJackCount(pJackDesc, &jackCount);
-    IF_FAILED_JUMP(hr, error);
-
-    // Set.
-    (*pJackCount) = jackCount;
-
-    // Ok.
-    ret = paNoError;
-
-error:
-
-    SAFE_RELEASE(pDeviceTopology);
-    SAFE_RELEASE(pConnFrom);
-    SAFE_RELEASE(pConnTo);
-    SAFE_RELEASE(pPart);
-    SAFE_RELEASE(pJackDesc);
-
-    LogHostError(hr);
-    return paNoError;
-#else
-    (void)device;
-    (void)pJackCount;
-    return paUnanticipatedHostError;
-#endif
-}
-
-// ------------------------------------------------------------------------------------------
-#ifndef PA_WINRT
-static PaWasapiJackConnectionType _ConvertJackConnectionTypeWASAPIToPA(int connType)
-{
-    switch (connType)
-    {
-        case eConnTypeUnknown:               return eJackConnTypeUnknown;
-#ifdef _KS_
-        case eConnType3Point5mm:             return eJackConnType3Point5mm;
-#else
-        case eConnTypeEighth:                return eJackConnType3Point5mm;
-#endif
-        case eConnTypeQuarter:               return eJackConnTypeQuarter;
-        case eConnTypeAtapiInternal:         return eJackConnTypeAtapiInternal;
-        case eConnTypeRCA:                   return eJackConnTypeRCA;
-        case eConnTypeOptical:               return eJackConnTypeOptical;
-        case eConnTypeOtherDigital:          return eJackConnTypeOtherDigital;
-        case eConnTypeOtherAnalog:           return eJackConnTypeOtherAnalog;
-        case eConnTypeMultichannelAnalogDIN: return eJackConnTypeMultichannelAnalogDIN;
-        case eConnTypeXlrProfessional:       return eJackConnTypeXlrProfessional;
-        case eConnTypeRJ11Modem:             return eJackConnTypeRJ11Modem;
-        case eConnTypeCombination:           return eJackConnTypeCombination;
-    }
-    return eJackConnTypeUnknown;
-}
-#endif
-
-// ------------------------------------------------------------------------------------------
-#ifndef PA_WINRT
-static PaWasapiJackGeoLocation _ConvertJackGeoLocationWASAPIToPA(int geoLoc)
-{
-    switch (geoLoc)
-    {
-    case eGeoLocRear:             return eJackGeoLocRear;
-    case eGeoLocFront:            return eJackGeoLocFront;
-    case eGeoLocLeft:             return eJackGeoLocLeft;
-    case eGeoLocRight:            return eJackGeoLocRight;
-    case eGeoLocTop:              return eJackGeoLocTop;
-    case eGeoLocBottom:           return eJackGeoLocBottom;
-#ifdef _KS_
-    case eGeoLocRearPanel:        return eJackGeoLocRearPanel;
-#else
-    case eGeoLocRearOPanel:       return eJackGeoLocRearPanel;
-#endif
-    case eGeoLocRiser:            return eJackGeoLocRiser;
-    case eGeoLocInsideMobileLid:  return eJackGeoLocInsideMobileLid;
-    case eGeoLocDrivebay:         return eJackGeoLocDrivebay;
-    case eGeoLocHDMI:             return eJackGeoLocHDMI;
-    case eGeoLocOutsideMobileLid: return eJackGeoLocOutsideMobileLid;
-    case eGeoLocATAPI:            return eJackGeoLocATAPI;
-    }
-    return eJackGeoLocUnk;
-}
-#endif
-
-// ------------------------------------------------------------------------------------------
-#ifndef PA_WINRT
-static PaWasapiJackGenLocation _ConvertJackGenLocationWASAPIToPA(int genLoc)
-{
-    switch (genLoc)
-    {
-    case eGenLocPrimaryBox: return eJackGenLocPrimaryBox;
-    case eGenLocInternal:   return eJackGenLocInternal;
-#ifdef _KS_
-    case eGenLocSeparate:   return eJackGenLocSeparate;
-#else
-    case eGenLocSeperate:   return eJackGenLocSeparate;
-#endif
-    case eGenLocOther:      return eJackGenLocOther;
-    }
-    return eJackGenLocPrimaryBox;
-}
-#endif
-
-// ------------------------------------------------------------------------------------------
-#ifndef PA_WINRT
-static PaWasapiJackPortConnection _ConvertJackPortConnectionWASAPIToPA(int portConn)
-{
-    switch (portConn)
-    {
-    case ePortConnJack:                  return eJackPortConnJack;
-    case ePortConnIntegratedDevice:      return eJackPortConnIntegratedDevice;
-    case ePortConnBothIntegratedAndJack: return eJackPortConnBothIntegratedAndJack;
-    case ePortConnUnknown:               return eJackPortConnUnknown;
-    }
-    return eJackPortConnJack;
-}
-#endif
-
-// ------------------------------------------------------------------------------------------
-// Described at:
-// http://msdn.microsoft.com/en-us/library/dd371387(v=VS.85).aspx
-
-PaError PaWasapi_GetJackDescription(PaDeviceIndex device, int jackIndex, PaWasapiJackDescription *pJackDescription)
-{
-#ifndef PA_WINRT
-    PaError ret;
-    HRESULT hr = S_OK;
-    PaWasapiDeviceInfo *deviceInfo;
-    IDeviceTopology *pDeviceTopology = NULL;
-    IConnector *pConnFrom = NULL;
-    IConnector *pConnTo = NULL;
-    IPart *pPart = NULL;
-    IKsJackDescription *pJackDesc = NULL;
-    KSJACK_DESCRIPTION jack = { 0 };
-
-    if ((ret = _GetWasapiDeviceInfoByDeviceIndex(&deviceInfo, device)) != paNoError)
-        return ret;
-
-    // Get the endpoint device's IDeviceTopology interface
-    hr = IMMDevice_Activate(deviceInfo->device, &pa_IID_IDeviceTopology,
-        CLSCTX_INPROC_SERVER, NULL, (void**)&pDeviceTopology);
-    IF_FAILED_JUMP(hr, error);
-
-    // The device topology for an endpoint device always contains just one connector (connector number 0)
-    hr = IDeviceTopology_GetConnector(pDeviceTopology, 0, &pConnFrom);
-    IF_FAILED_JUMP(hr, error);
-
-    // Step across the connection to the jack on the adapter
-    hr = IConnector_GetConnectedTo(pConnFrom, &pConnTo);
-    if (HRESULT_FROM_WIN32(ERROR_PATH_NOT_FOUND) == hr)
-    {
-        // The adapter device is not currently active
-        hr = E_NOINTERFACE;
-    }
-    IF_FAILED_JUMP(hr, error);
-
-    // Get the connector's IPart interface
-    hr = IConnector_QueryInterface(pConnTo, &pa_IID_IPart, (void**)&pPart);
-    IF_FAILED_JUMP(hr, error);
-
-    // Activate the connector's IKsJackDescription interface
-    hr = IPart_Activate(pPart, CLSCTX_INPROC_SERVER, &pa_IID_IKsJackDescription, (void**)&pJackDesc);
-    IF_FAILED_JUMP(hr, error);
-
-    // Test to return jack description struct for index 0
-    hr = IKsJackDescription_GetJackDescription(pJackDesc, jackIndex, &jack);
-    IF_FAILED_JUMP(hr, error);
-
-    // Convert WASAPI values to PA format
-    pJackDescription->channelMapping = jack.ChannelMapping;
-    pJackDescription->color          = jack.Color;
-    pJackDescription->connectionType = _ConvertJackConnectionTypeWASAPIToPA(jack.ConnectionType);
-    pJackDescription->genLocation    = _ConvertJackGenLocationWASAPIToPA(jack.GenLocation);
-    pJackDescription->geoLocation    = _ConvertJackGeoLocationWASAPIToPA(jack.GeoLocation);
-    pJackDescription->isConnected    = jack.IsConnected;
-    pJackDescription->portConnection = _ConvertJackPortConnectionWASAPIToPA(jack.PortConnection);
-
-    // Ok
-    ret = paNoError;
-
-error:
-
-    SAFE_RELEASE(pDeviceTopology);
-    SAFE_RELEASE(pConnFrom);
-    SAFE_RELEASE(pConnTo);
-    SAFE_RELEASE(pPart);
-    SAFE_RELEASE(pJackDesc);
-
-    LogHostError(hr);
-    return ret;
-
-#else
-    (void)device;
-    (void)jackIndex;
-    (void)pJackDescription;
-    return paUnanticipatedHostError;
-#endif
-}
-
-// ------------------------------------------------------------------------------------------
-PaError PaWasapi_GetAudioClient(PaStream *pStream, void **pAudioClient, int bOutput)
-{
-    PaWasapiStream *stream = (PaWasapiStream *)pStream;
-    if (stream == NULL)
-        return paBadStreamPtr;
-
-    if (pAudioClient == NULL)
-        return paUnanticipatedHostError;
-
-    (*pAudioClient) = (bOutput == TRUE ? stream->out.clientParent : stream->in.clientParent);
-
-    return paNoError;
-}
-
-// ------------------------------------------------------------------------------------------
-#ifdef PA_WINRT
-static void CopyNameOrIdString(WCHAR *dst, const UINT32 dstMaxCount, const WCHAR *src)
-{
-    UINT32 i;
-
-    for (i = 0; i < dstMaxCount; ++i)
-        dst[i] = 0;
-
-    if (src != NULL)
-    {
-        for (i = 0; (src[i] != 0) && (i < dstMaxCount); ++i)
-            dst[i] = src[i];
-    }
-}
-#endif
-
-// ------------------------------------------------------------------------------------------
-PaError PaWasapiWinrt_SetDefaultDeviceId( const unsigned short *pId, int bOutput )
-{
-#ifdef PA_WINRT
-    INT32 i;
-    PaWasapiWinrtDeviceListRole *role = (bOutput ? &g_DeviceListInfo.render : &g_DeviceListInfo.capture);
-
-    assert(STATIC_ARRAY_SIZE(role->defaultId) == PA_WASAPI_DEVICE_ID_LEN);
-
-    // Validate Id length
-    if (pId != NULL)
-    {
-        for (i = 0; pId[i] != 0; ++i)
-        {
-            if (i >= PA_WASAPI_DEVICE_ID_LEN)
-                return paBufferTooBig;
-        }
-    }
-
-    // Set Id (or reset to all 0 if NULL is provided)
-    CopyNameOrIdString(role->defaultId, STATIC_ARRAY_SIZE(role->defaultId), pId);
-
-    return paNoError;
-#else
-    return paIncompatibleStreamHostApi;
-#endif
-}
-
-// ------------------------------------------------------------------------------------------
-PaError PaWasapiWinrt_PopulateDeviceList( const unsigned short **pId, const unsigned short **pName,
-    const PaWasapiDeviceRole *pRole, unsigned int count, int bOutput )
-{
-#ifdef PA_WINRT
-    UINT32 i, j;
-    PaWasapiWinrtDeviceListRole *role = (bOutput ? &g_DeviceListInfo.render : &g_DeviceListInfo.capture);
-
-    memset(&role->devices, 0, sizeof(role->devices));
-    role->deviceCount = 0;
-
-    if (count == 0)
-        return paNoError;
-    else
-    if (count > PA_WASAPI_DEVICE_MAX_COUNT)
-        return paBufferTooBig;
-
-    // pName or pRole are optional
-    if (pId == NULL)
-        return paInsufficientMemory;
-
-    // Validate Id and Name lengths
-    for (i = 0; i < count; ++i)
-    {
-        const unsigned short *id = pId[i];
-        const unsigned short *name = pName[i];
-
-        for (j = 0; id[j] != 0; ++j)
-        {
-            if (j >= PA_WASAPI_DEVICE_ID_LEN)
-                return paBufferTooBig;
-        }
-
-        for (j = 0; name[j] != 0; ++j)
-        {
-            if (j >= PA_WASAPI_DEVICE_NAME_LEN)
-                return paBufferTooBig;
-        }
-    }
-
-    // Set Id and Name (or reset to all 0 if NULL is provided)
-    for (i = 0; i < count; ++i)
-    {
-        CopyNameOrIdString(role->devices[i].id, STATIC_ARRAY_SIZE(role->devices[i].id), pId[i]);
-        CopyNameOrIdString(role->devices[i].name, STATIC_ARRAY_SIZE(role->devices[i].name), pName[i]);
-        role->devices[i].formFactor = (pRole != NULL ? (EndpointFormFactor)pRole[i] : UnknownFormFactor);
-
-        // Count device if it has at least the Id
-        role->deviceCount += (role->devices[i].id[0] != 0);
-    }
-
-    return paNoError;
-#else
-    return paIncompatibleStreamHostApi;
-#endif
-}
-
-// ------------------------------------------------------------------------------------------
-PaError PaWasapi_SetStreamStateHandler( PaStream *pStream, PaWasapiStreamStateCallback fnStateHandler, void *pUserData )
-{
-    PaWasapiStream *stream = (PaWasapiStream *)pStream;
-    if (stream == NULL)
-        return paBadStreamPtr;
-
-    stream->fnStateHandler        = fnStateHandler;
-    stream->pStateHandlerUserData = pUserData;
-
-    return paNoError;
-}
-
-// ------------------------------------------------------------------------------------------
-HRESULT _PollGetOutputFramesAvailable(PaWasapiStream *stream, UINT32 *available)
-{
-    HRESULT hr;
-    UINT32 frames  = stream->out.framesPerHostCallback,
-           padding = 0;
-
-    (*available) = 0;
-
-    // get read position
-    if ((hr = IAudioClient_GetCurrentPadding(stream->out.clientProc, &padding)) != S_OK)
-        return LogHostError(hr);
-
-    // get available
-    frames -= padding;
-
-    // set
-    (*available) = frames;
-    return hr;
-}
-
-// ------------------------------------------------------------------------------------------
-HRESULT _PollGetInputFramesAvailable(PaWasapiStream *stream, UINT32 *available)
-{
-    HRESULT hr;
-
-    (*available) = 0;
-
-    // GetCurrentPadding() has opposite meaning to Output stream
-    if ((hr = IAudioClient_GetCurrentPadding(stream->in.clientProc, available)) != S_OK)
-        return LogHostError(hr);
-
-    return hr;
-}
-
-// ------------------------------------------------------------------------------------------
-static HRESULT ProcessOutputBuffer(PaWasapiStream *stream, PaWasapiHostProcessor *processor, UINT32 frames)
-{
-    HRESULT hr;
-    BYTE *data = NULL;
-
-    // Get buffer
-    if ((hr = IAudioRenderClient_GetBuffer(stream->renderClient, frames, &data)) != S_OK)
-    {
-        // Both modes, Shared and Exclusive, can fail with AUDCLNT_E_BUFFER_TOO_LARGE error
-    #if 0
-        if (stream->out.shareMode == AUDCLNT_SHAREMODE_SHARED)
-        {
-            // Using GetCurrentPadding to overcome AUDCLNT_E_BUFFER_TOO_LARGE in
-            // shared mode results in no sound in Event-driven mode (MSDN does not
-            // document this, or is it WASAPI bug?), thus we better
-            // try to acquire buffer next time when GetBuffer allows to do so.
-        #if 0
-            // Get Read position
-            UINT32 padding = 0;
-            hr = IAudioClient_GetCurrentPadding(stream->out.clientProc, &padding);
-            if (hr != S_OK)
-                return LogHostError(hr);
-
-            // Get frames to write
-            frames -= padding;
-            if (frames == 0)
-                return S_OK;
-
-            if ((hr = IAudioRenderClient_GetBuffer(stream->renderClient, frames, &data)) != S_OK)
-                return LogHostError(hr);
-        #else
-            if (hr == AUDCLNT_E_BUFFER_TOO_LARGE)
-                return S_OK; // be silent in shared mode, try again next time
-        #endif
-        }
-        else
-            return LogHostError(hr);
-    #else
-        if (hr == AUDCLNT_E_BUFFER_TOO_LARGE)
-            return S_OK; // try again next time
-
-        return LogHostError(hr);
-    #endif
-    }
-
-    // Process data
-    if (stream->out.monoMixer != NULL)
-    {
-        // expand buffer
-        UINT32 mono_frames_size = frames * (stream->out.wavex.Format.wBitsPerSample / 8);
-        if (mono_frames_size > stream->out.monoBufferSize)
-        {
-            stream->out.monoBuffer = PaWasapi_ReallocateMemory(stream->out.monoBuffer, (stream->out.monoBufferSize = mono_frames_size));
-            if (stream->out.monoBuffer == NULL)
-            {
-                hr = E_OUTOFMEMORY;
-                LogHostError(hr);
-                return hr;
-            }
-        }
-
-        // process
-        processor[S_OUTPUT].processor(NULL, 0, (BYTE *)stream->out.monoBuffer, frames, processor[S_OUTPUT].userData);
-
-        // mix 1 to 2 channels
-        stream->out.monoMixer(data, stream->out.monoBuffer, frames);
-    }
-    else
-    {
-        processor[S_OUTPUT].processor(NULL, 0, data, frames, processor[S_OUTPUT].userData);
-    }
-
-    // Release buffer
-    if ((hr = IAudioRenderClient_ReleaseBuffer(stream->renderClient, frames, 0)) != S_OK)
-        LogHostError(hr);
-
-    return hr;
-}
-
-// ------------------------------------------------------------------------------------------
-static HRESULT ProcessInputBuffer(PaWasapiStream *stream, PaWasapiHostProcessor *processor)
-{
-    HRESULT hr = S_OK;
-    UINT32 frames;
-    BYTE *data = NULL;
-    DWORD flags = 0;
-
-    for (;;)
-    {
-        // Check if blocking call must be interrupted
-        if (WaitForSingleObject(stream->hCloseRequest, 0) != WAIT_TIMEOUT)
-            break;
-
-        // Find out if any frames available
-        frames = 0;
-        if ((hr = _PollGetInputFramesAvailable(stream, &frames)) != S_OK)
-            return hr;
-
-        // Empty/consumed buffer
-        if (frames == 0)
-            break;
-
-        // Get the available data in the shared buffer.
-        if ((hr = IAudioCaptureClient_GetBuffer(stream->captureClient, &data, &frames, &flags, NULL, NULL)) != S_OK)
-        {
-            if (hr == AUDCLNT_S_BUFFER_EMPTY)
-            {
-                hr = S_OK;
-                break; // Empty/consumed buffer
-            }
-
-            return LogHostError(hr);
-            break;
-        }
-
-        // Detect silence
-        // if (flags & AUDCLNT_BUFFERFLAGS_SILENT)
-        //    data = NULL;
-
-        // Process data
-        if (stream->in.monoMixer != NULL)
-        {
-            // expand buffer
-            UINT32 mono_frames_size = frames * (stream->in.wavex.Format.wBitsPerSample / 8);
-            if (mono_frames_size > stream->in.monoBufferSize)
-            {
-                stream->in.monoBuffer = PaWasapi_ReallocateMemory(stream->in.monoBuffer, (stream->in.monoBufferSize = mono_frames_size));
-                if (stream->in.monoBuffer == NULL)
-                {
-                    hr = E_OUTOFMEMORY;
-                    LogHostError(hr);
-                    return hr;
-                }
-            }
-
-            // mix 1 to 2 channels
-            stream->in.monoMixer(stream->in.monoBuffer, data, frames);
-
-            // process
-            processor[S_INPUT].processor((BYTE *)stream->in.monoBuffer, frames, NULL, 0, processor[S_INPUT].userData);
-        }
-        else
-        {
-            processor[S_INPUT].processor(data, frames, NULL, 0, processor[S_INPUT].userData);
-        }
-
-        // Release buffer
-        if ((hr = IAudioCaptureClient_ReleaseBuffer(stream->captureClient, frames)) != S_OK)
-            return LogHostError(hr);
-
-        //break;
-    }
-
-    return hr;
-}
-
-// ------------------------------------------------------------------------------------------
-void _StreamOnStop(PaWasapiStream *stream)
-{
-    // Stop INPUT/OUTPUT clients
-    if (!stream->bBlocking)
-    {
-        if (stream->in.clientProc != NULL)
-            IAudioClient_Stop(stream->in.clientProc);
-        if (stream->out.clientProc != NULL)
-            IAudioClient_Stop(stream->out.clientProc);
-    }
-    else
-    {
-        if (stream->in.clientParent != NULL)
-            IAudioClient_Stop(stream->in.clientParent);
-        if (stream->out.clientParent != NULL)
-            IAudioClient_Stop(stream->out.clientParent);
-    }
-
-    // Restore thread priority
-    if (stream->hAvTask != NULL)
-    {
-        PaWasapi_ThreadPriorityRevert(stream->hAvTask);
-        stream->hAvTask = NULL;
-    }
-
-    // Notify
-    if (stream->streamRepresentation.streamFinishedCallback != NULL)
-        stream->streamRepresentation.streamFinishedCallback(stream->streamRepresentation.userData);
-}
-
-// ------------------------------------------------------------------------------------------
-static BOOL PrepareComPointers(PaWasapiStream *stream, BOOL *threadComInitialized)
-{
-    HRESULT hr;
-
-    /*
-    If COM is already initialized CoInitialize will either return
-    FALSE, or RPC_E_CHANGED_MODE if it was initialized in a different
-    threading mode. In either case we shouldn't consider it an error
-    but we need to be careful to not call CoUninitialize() if
-    RPC_E_CHANGED_MODE was returned.
-    */
-    hr = CoInitializeEx(NULL, COINIT_APARTMENTTHREADED);
-    if (FAILED(hr) && (hr != RPC_E_CHANGED_MODE))
-    {
-        PRINT(("WASAPI: failed ProcThreadEvent CoInitialize"));
-        return FALSE;
-    }
-    if (hr != RPC_E_CHANGED_MODE)
-        *threadComInitialized = TRUE;
-
-    // Unmarshal stream pointers for safe COM operation
-    hr = UnmarshalStreamComPointers(stream);
-    if (hr != S_OK)
-    {
-        PRINT(("WASAPI: Error unmarshaling stream COM pointers. HRESULT: %i\n", hr));
-        CoUninitialize();
-        return FALSE;
-    }
-
-    return TRUE;
-}
-
-// ------------------------------------------------------------------------------------------
-static void FinishComPointers(PaWasapiStream *stream, BOOL threadComInitialized)
-{
-    // Release unmarshaled COM pointers
-    ReleaseUnmarshaledComPointers(stream);
-
-    // Cleanup COM for this thread
-    if (threadComInitialized == TRUE)
-        CoUninitialize();
-}
-
-// ------------------------------------------------------------------------------------------
-PA_THREAD_FUNC ProcThreadEvent(void *param)
-{
-    PaWasapiHostProcessor processor[S_COUNT];
-    HRESULT hr = S_OK;
-    DWORD dwResult;
-    PaWasapiStream *stream = (PaWasapiStream *)param;
-    PaWasapiHostProcessor defaultProcessor;
-    BOOL setEvent[S_COUNT] = { FALSE, FALSE };
-    BOOL waitAllEvents = FALSE;
-    BOOL threadComInitialized = FALSE;
-    SystemTimer timer;
-
-    // Notify: state
-    NotifyStateChanged(stream, paWasapiStreamStateThreadPrepare, ERROR_SUCCESS);
-
-    // Prepare COM pointers
-    if (!PrepareComPointers(stream, &threadComInitialized))
-        return (UINT32)paUnanticipatedHostError;
-
-    // Request fine (1 ms) granularity of the system timer functions for precise operation of waitable timers
-    SystemTimer_SetGranularity(&timer, 1);
-
-    // Waiting on all events in case of Full-Duplex/Exclusive mode.
-    if ((stream->in.clientProc != NULL) && (stream->out.clientProc != NULL))
-    {
-        waitAllEvents = (stream->in.shareMode == AUDCLNT_SHAREMODE_EXCLUSIVE) &&
-            (stream->out.shareMode == AUDCLNT_SHAREMODE_EXCLUSIVE);
-    }
-
-    // Setup data processors
-    defaultProcessor.processor = WaspiHostProcessingLoop;
-    defaultProcessor.userData  = stream;
-    processor[S_INPUT] = (stream->hostProcessOverrideInput.processor != NULL ? stream->hostProcessOverrideInput : defaultProcessor);
-    processor[S_OUTPUT] = (stream->hostProcessOverrideOutput.processor != NULL ? stream->hostProcessOverrideOutput : defaultProcessor);
-
-    // Boost thread priority
-    PaWasapi_ThreadPriorityBoost((void **)&stream->hAvTask, stream->nThreadPriority);
-
-    // Create events
-    if (stream->event[S_OUTPUT] == NULL)
-    {
-        stream->event[S_OUTPUT] = CreateEvent(NULL, FALSE, FALSE, NULL);
-        setEvent[S_OUTPUT] = TRUE;
-    }
-    if (stream->event[S_INPUT] == NULL)
-    {
-        stream->event[S_INPUT]  = CreateEvent(NULL, FALSE, FALSE, NULL);
-        setEvent[S_INPUT] = TRUE;
-    }
-    if ((stream->event[S_OUTPUT] == NULL) || (stream->event[S_INPUT] == NULL))
-    {
-        PRINT(("WASAPI Thread: failed creating Input/Output event handle\n"));
-        goto thread_error;
-    }
-
-    // Signal: stream running
-    stream->running = TRUE;
-
-    // Notify: thread started
-    SetEvent(stream->hThreadStart);
-
-    // Initialize event & start INPUT stream
-    if (stream->in.clientProc)
-    {
-        // Create & set handle
-        if (setEvent[S_INPUT])
-        {
-            if ((hr = IAudioClient_SetEventHandle(stream->in.clientProc, stream->event[S_INPUT])) != S_OK)
-            {
-                LogHostError(hr);
-                goto thread_error;
-            }
-        }
-
-        // Start
-        if ((hr = IAudioClient_Start(stream->in.clientProc)) != S_OK)
-        {
-            LogHostError(hr);
-            goto thread_error;
-        }
-    }
-
-    // Initialize event & start OUTPUT stream
-    if (stream->out.clientProc)
-    {
-        // Create & set handle
-        if (setEvent[S_OUTPUT])
-        {
-            if ((hr = IAudioClient_SetEventHandle(stream->out.clientProc, stream->event[S_OUTPUT])) != S_OK)
-            {
-                LogHostError(hr);
-                goto thread_error;
-            }
-        }
-
-        // Preload buffer before start
-        if ((hr = ProcessOutputBuffer(stream, processor, stream->out.framesPerBuffer)) != S_OK)
-        {
-            LogHostError(hr);
-            goto thread_error;
-        }
-
-        // Start
-        if ((hr = IAudioClient_Start(stream->out.clientProc)) != S_OK)
-        {
-            LogHostError(hr);
-            goto thread_error;
-        }
-
-    }
-
-    // Notify: state
-    NotifyStateChanged(stream, paWasapiStreamStateThreadStart, ERROR_SUCCESS);
-
-    // Processing Loop
-    for (;;)
-    {
-        // 10 sec timeout (on timeout stream will auto-stop when processed by WAIT_TIMEOUT case)
-        dwResult = WaitForMultipleObjects(S_COUNT, stream->event, waitAllEvents, 10*1000);
-
-        // Check for close event (after wait for buffers to avoid any calls to user
-        // callback when hCloseRequest was set)
-        if (WaitForSingleObject(stream->hCloseRequest, 0) != WAIT_TIMEOUT)
-            break;
-
-        // Process S_INPUT/S_OUTPUT
-        switch (dwResult)
-        {
-        case WAIT_TIMEOUT: {
-            PRINT(("WASAPI Thread: WAIT_TIMEOUT - probably bad audio driver or Vista x64 bug: use paWinWasapiPolling instead\n"));
-            goto thread_end;
-            break; }
-
-        // Input stream
-        case WAIT_OBJECT_0 + S_INPUT: {
-
-            if (stream->captureClient == NULL)
-                break;
-
-            if ((hr = ProcessInputBuffer(stream, processor)) != S_OK)
-            {
-                LogHostError(hr);
-                goto thread_error;
-            }
-
-            break; }
-
-        // Output stream
-        case WAIT_OBJECT_0 + S_OUTPUT: {
-
-            if (stream->renderClient == NULL)
-                break;
-
-            if ((hr = ProcessOutputBuffer(stream, processor, stream->out.framesPerBuffer)) != S_OK)
-            {
-                LogHostError(hr);
-                goto thread_error;
-            }
-
-            break; }
-        }
-    }
-
-thread_end:
-
-    // Process stop
-    _StreamOnStop(stream);
-
-    // Release unmarshaled COM pointers
-    FinishComPointers(stream, threadComInitialized);
-
-    // Restore system timer granularity
-    SystemTimer_RestoreGranularity(&timer);
-
-    // Notify: not running
-    stream->running = FALSE;
-
-    // Notify: thread exited
-    SetEvent(stream->hThreadExit);
-
-    // Notify: state
-    NotifyStateChanged(stream, paWasapiStreamStateThreadStop, hr);
-
-    return 0;
-
-thread_error:
-
-    // Prevent deadlocking in Pa_StreamStart
-    SetEvent(stream->hThreadStart);
-
-    // Exit
-    goto thread_end;
-}
-
-// ------------------------------------------------------------------------------------------
-static UINT32 GetSleepTime(PaWasapiStream *stream, UINT32 sleepTimeIn, UINT32 sleepTimeOut, UINT32 userFramesOut)
-{
-    UINT32 sleepTime;
-
-    // According to the issue [https://github.com/PortAudio/portaudio/issues/303] glitches may occur when user frames
-    // equal to 1/2 of the host buffer frames, therefore the empirical workaround for this problem is to lower
-    // the sleep time by 2
-    if (userFramesOut != 0)
-    {
-        UINT32 chunks = stream->out.framesPerHostCallback / userFramesOut;
-        if (chunks <= 2)
-        {
-            sleepTimeOut /= 2;
-            PRINT(("WASAPI: underrun workaround, sleep [%d] ms - 1/2 of the user buffer[%d] | host buffer[%d]\n", sleepTimeOut, userFramesOut, stream->out.framesPerHostCallback));
-        }
-    }
-
-    // Choose the smallest
-    if ((sleepTimeIn != 0) && (sleepTimeOut != 0))
-        sleepTime = min(sleepTimeIn, sleepTimeOut);
-    else
-        sleepTime = (sleepTimeIn ? sleepTimeIn : sleepTimeOut);
-
-    return sleepTime;
-}
-
-// ------------------------------------------------------------------------------------------
-static UINT32 ConfigureLoopSleepTimeAndScheduler(PaWasapiStream *stream, ThreadIdleScheduler *scheduler)
-{
-    UINT32 sleepTime, sleepTimeIn, sleepTimeOut;
-    UINT32 userFramesIn = stream->in.framesPerHostCallback / WASAPI_PACKETS_PER_INPUT_BUFFER;
-    UINT32 userFramesOut = stream->out.framesPerBuffer;
-
-    // Adjust polling time for non-paUtilFixedHostBufferSize, input stream is not adjustable as it is being
-    // polled according to its packet length
-    if (stream->bufferMode != paUtilFixedHostBufferSize)
-    {
-        userFramesOut = (stream->bufferProcessor.framesPerUserBuffer ? stream->bufferProcessor.framesPerUserBuffer :
-            stream->out.params.frames_per_buffer);
-    }
-
-    // Calculate timeout for the next polling attempt
-    sleepTimeIn  = GetFramesSleepTime(userFramesIn, stream->in.wavex.Format.nSamplesPerSec);
-    sleepTimeOut = GetFramesSleepTime(userFramesOut, stream->out.wavex.Format.nSamplesPerSec);
-
-    // WASAPI input packets tend to expire very easily, let's limit sleep time to 2 milliseconds
-    // for all cases. Please propose better solution if any
-    if (sleepTimeIn > 2)
-        sleepTimeIn = 2;
-
-    sleepTime = GetSleepTime(stream, sleepTimeIn, sleepTimeOut, userFramesOut);
-
-    // Make sure not 0, othervise use ThreadIdleScheduler to bounce between [0, 1] ms to avoid too busy loop
-    if (sleepTime == 0)
-    {
-        sleepTimeIn  = GetFramesSleepTimeMicroseconds(userFramesIn, stream->in.wavex.Format.nSamplesPerSec);
-        sleepTimeOut = GetFramesSleepTimeMicroseconds(userFramesOut, stream->out.wavex.Format.nSamplesPerSec);
-
-        sleepTime = GetSleepTime(stream, sleepTimeIn, sleepTimeOut, userFramesOut);
-
-        // Setup thread sleep scheduler
-        ThreadIdleScheduler_Setup(scheduler, 1, sleepTime/* microseconds here */);
-        sleepTime = 0;
-    }
-
-    return sleepTime;
-}
-
-// ------------------------------------------------------------------------------------------
-static inline INT32 GetNextSleepTime(SystemTimer *timer, ThreadIdleScheduler *scheduler, LONGLONG startTime,
-    UINT32 sleepTime)
-{
-    INT32 nextSleepTime;
-    INT32 procTime;
-
-    // Get next sleep time
-    if (sleepTime == 0)
-        nextSleepTime = ThreadIdleScheduler_NextSleep(scheduler);
-    else
-        nextSleepTime = sleepTime;
-
-    // Adjust next sleep time dynamically depending on how much time was spent in ProcessOutputBuffer/ProcessInputBuffer
-    // therefore periodicity will not jitter or be increased for the amount of time spent in processing;
-    // example when sleepTime is 10 ms where [] is polling time slot, {} processing time slot:
-    //
-    // [9],{2},[8],{1},[9],{1},[9],{3},[7],{2},[8],{3},[7],{2},[8],{2},[8],{3},[7],{2},[8],...
-    //
-    procTime = (INT32)(SystemTimer_GetTime(timer) - startTime);
-    nextSleepTime -= procTime;
-    if (nextSleepTime < timer->granularity)
-        nextSleepTime = 0;
-    else
-    if (timer->granularity > 1)
-        nextSleepTime = ALIGN_BWD(nextSleepTime, timer->granularity);
-
-#ifdef PA_WASAPI_LOG_TIME_SLOTS
-    printf("{%d},", procTime);
-#endif
-
-    return nextSleepTime;
-}
-
-// ------------------------------------------------------------------------------------------
-PA_THREAD_FUNC ProcThreadPoll(void *param)
-{
-    PaWasapiHostProcessor processor[S_COUNT];
-    HRESULT hr = S_OK;
-    PaWasapiStream *stream = (PaWasapiStream *)param;
-    PaWasapiHostProcessor defaultProcessor;
-    INT32 i;
-    ThreadIdleScheduler scheduler;
-    SystemTimer timer;
-    LONGLONG startTime;
-    UINT32 sleepTime;
-    INT32 nextSleepTime = 0; //! Do first loop without waiting as time could be spent when calling other APIs before ProcessXXXBuffer.
-    BOOL threadComInitialized = FALSE;
-#ifdef PA_WASAPI_LOG_TIME_SLOTS
-    LONGLONG startWaitTime;
-#endif
-
-    // Notify: state
-    NotifyStateChanged(stream, paWasapiStreamStateThreadPrepare, ERROR_SUCCESS);
-
-    // Prepare COM pointers
-    if (!PrepareComPointers(stream, &threadComInitialized))
-        return (UINT32)paUnanticipatedHostError;
-
-    // Request fine (1 ms) granularity of the system timer functions to guarantee correct logic around WaitForSingleObject
-    SystemTimer_SetGranularity(&timer, 1);
-
-    // Calculate sleep time of the processing loop (inside WaitForSingleObject)
-    sleepTime = ConfigureLoopSleepTimeAndScheduler(stream, &scheduler);
-
-    // Setup data processors
-    defaultProcessor.processor = WaspiHostProcessingLoop;
-    defaultProcessor.userData  = stream;
-    processor[S_INPUT] = (stream->hostProcessOverrideInput.processor != NULL ? stream->hostProcessOverrideInput : defaultProcessor);
-    processor[S_OUTPUT] = (stream->hostProcessOverrideOutput.processor != NULL ? stream->hostProcessOverrideOutput : defaultProcessor);
-
-    // Boost thread priority
-    PaWasapi_ThreadPriorityBoost((void **)&stream->hAvTask, stream->nThreadPriority);
-
-    // Signal: stream running
-    stream->running = TRUE;
-
-    // Notify: thread started
-    SetEvent(stream->hThreadStart);
-
-    // Initialize event & start INPUT stream
-    if (stream->in.clientProc)
-    {
-        if ((hr = IAudioClient_Start(stream->in.clientProc)) != S_OK)
-        {
-            LogHostError(hr);
-            goto thread_error;
-        }
-    }
-
-    // Initialize event & start OUTPUT stream
-    if (stream->out.clientProc)
-    {
-        // Preload buffer (obligatory, othervise ->Start() will fail), avoid processing
-        // when in full-duplex mode as it requires input processing as well
-        if (!PA_WASAPI__IS_FULLDUPLEX(stream))
-        {
-            UINT32 frames = 0;
-            if ((hr = _PollGetOutputFramesAvailable(stream, &frames)) == S_OK)
-            {
-                if (stream->bufferMode == paUtilFixedHostBufferSize)
-                {
-                    // It is important to preload whole host buffer to avoid underruns/glitches when stream is started,
-                    // for more details see the discussion: https://github.com/PortAudio/portaudio/issues/303
-                    while (frames >= stream->out.framesPerBuffer)
-                    {
-                        if ((hr = ProcessOutputBuffer(stream, processor, stream->out.framesPerBuffer)) != S_OK)
-                        {
-                            LogHostError(hr); // not fatal, just log
-                            break;
-                        }
-
-                        frames -= stream->out.framesPerBuffer;
-                    }
-                }
-                else
-                {
-                    // Some devices may not start (will get stuck with 0 ready frames) if data not prefetched
-                    if (frames == 0)
-                        frames = stream->out.framesPerBuffer;
-
-                    // USB DACs report large buffer in Exclusive mode and if it is filled fully will stuck in
-                    // non playing state, e.g. IAudioClient_GetCurrentPadding() will start reporting max buffer size
-                    // constantly, thus preload data size equal to the user buffer to allow process going
-                    if ((stream->out.shareMode == AUDCLNT_SHAREMODE_EXCLUSIVE) && (frames >= (stream->out.framesPerBuffer * 2)))
-                        frames -= stream->out.framesPerBuffer;
-
-                    if ((hr = ProcessOutputBuffer(stream, processor, frames)) != S_OK)
-                    {
-                        LogHostError(hr); // not fatal, just log
-                    }
-                }
-            }
-            else
-            {
-                LogHostError(hr); // not fatal, just log
-            }
-        }
-
-        // Start
-        if ((hr = IAudioClient_Start(stream->out.clientProc)) != S_OK)
-        {
-            LogHostError(hr);
-            goto thread_error;
-        }
-    }
-
-    // Notify: state
-    NotifyStateChanged(stream, paWasapiStreamStateThreadStart, ERROR_SUCCESS);
-
-#ifdef PA_WASAPI_LOG_TIME_SLOTS
-    startWaitTime = SystemTimer_GetTime(&timer);
-#endif
-
-    if (!PA_WASAPI__IS_FULLDUPLEX(stream))
-    {
-        // Processing Loop
-        while (WaitForSingleObject(stream->hCloseRequest, nextSleepTime) == WAIT_TIMEOUT)
-        {
-            startTime = SystemTimer_GetTime(&timer);
-
-        #ifdef PA_WASAPI_LOG_TIME_SLOTS
-            printf("[%d|%d],", nextSleepTime, (INT32)(startTime - startWaitTime));
-        #endif
-
-            for (i = 0; i < S_COUNT; ++i)
-            {
-                // Process S_INPUT/S_OUTPUT
-                switch (i)
-                {
-                // Input stream
-                case S_INPUT: {
-
-                    if (stream->captureClient == NULL)
-                        break;
-
-                    if ((hr = ProcessInputBuffer(stream, processor)) != S_OK)
-                    {
-                        LogHostError(hr);
-                        goto thread_error;
-                    }
-
-                    break; }
-
-                // Output stream
-                case S_OUTPUT: {
-
-                    UINT32 framesAvail;
-
-                    if (stream->renderClient == NULL)
-                        break;
-
-                    // Get available frames
-                    if ((hr = _PollGetOutputFramesAvailable(stream, &framesAvail)) != S_OK)
-                    {
-                        LogHostError(hr);
-                        goto thread_error;
-                    }
-
-                    // Output data to the user callback
-                    if (stream->bufferMode == paUtilFixedHostBufferSize)
-                    {
-                        UINT32 framesProc = stream->out.framesPerBuffer;
-
-                        // If we got less frames avoid sleeping again as it might be the corner case and buffer
-                        // has sufficient number of frames now, in case 'out.framesPerBuffer' is 1/2 of the host
-                        // buffer sleeping again may cause underruns. Do short busy waiting (normally might take
-                        // 1-2 iterations)
-                        if (framesAvail < framesProc)
-                        {
-                            nextSleepTime = 0;
-                            continue;
-                        }
-
-                        while (framesAvail >= framesProc)
-                        {
-                            if ((hr = ProcessOutputBuffer(stream, processor, framesProc)) != S_OK)
-                            {
-                                LogHostError(hr);
-                                goto thread_error;
-                            }
-
-                            framesAvail -= framesProc;
-                        }
-                    }
-                    else
-                    if (framesAvail != 0)
-                    {
-                        if ((hr = ProcessOutputBuffer(stream, processor, framesAvail)) != S_OK)
-                        {
-                            LogHostError(hr);
-                            goto thread_error;
-                        }
-                    }
-
-                    break; }
-                }
-            }
-
-            // Get next sleep time
-            nextSleepTime = GetNextSleepTime(&timer, &scheduler, startTime, sleepTime);
-
-        #ifdef PA_WASAPI_LOG_TIME_SLOTS
-            startWaitTime = SystemTimer_GetTime(&timer);
-        #endif
-        }
-    }
-    else
-    {
-        // Processing Loop (full-duplex)
-        while (WaitForSingleObject(stream->hCloseRequest, nextSleepTime) == WAIT_TIMEOUT)
-        {
-            UINT32 i_frames = 0, i_processed = 0, o_frames = 0;
-            BYTE *i_data = NULL, *o_data = NULL, *o_data_host = NULL;
-            DWORD i_flags = 0;
-
-            startTime = SystemTimer_GetTime(&timer);
-
-        #ifdef PA_WASAPI_LOG_TIME_SLOTS
-            printf("[%d|%d],", nextSleepTime, (INT32)(startTime - startWaitTime));
-        #endif
-
-            // get available frames
-            if ((hr = _PollGetOutputFramesAvailable(stream, &o_frames)) != S_OK)
-            {
-                LogHostError(hr);
-                break;
-            }
-
-            while (o_frames != 0)
-            {
-                // get host input buffer
-                if ((hr = IAudioCaptureClient_GetBuffer(stream->captureClient, &i_data, &i_frames, &i_flags, NULL, NULL)) != S_OK)
-                {
-                    if (hr == AUDCLNT_S_BUFFER_EMPTY)
-                        break; // no data in capture buffer
-
-                    LogHostError(hr);
-                    break;
-                }
-
-                // process equal amount of frames
-                if (o_frames >= i_frames)
-                {
-                    // process input amount of frames
-                    UINT32 o_processed = i_frames;
-
-                    // get host output buffer
-                    if ((hr = IAudioRenderClient_GetBuffer(stream->renderClient, o_processed, &o_data)) == S_OK)
-                    {
-                        // processed amount of i_frames
-                        i_processed = i_frames;
-                        o_data_host = o_data;
-
-                        // convert output mono
-                        if (stream->out.monoMixer)
-                        {
-                            UINT32 mono_frames_size = o_processed * (stream->out.wavex.Format.wBitsPerSample / 8);
-                            // expand buffer
-                            if (mono_frames_size > stream->out.monoBufferSize)
-                            {
-                                stream->out.monoBuffer = PaWasapi_ReallocateMemory(stream->out.monoBuffer, (stream->out.monoBufferSize = mono_frames_size));
-                                if (stream->out.monoBuffer == NULL)
-                                {
-                                    // release input buffer
-                                    IAudioCaptureClient_ReleaseBuffer(stream->captureClient, 0);
-                                    // release output buffer
-                                    IAudioRenderClient_ReleaseBuffer(stream->renderClient, 0, 0);
-
-                                    LogPaError(paInsufficientMemory);
-                                    goto thread_error;
-                                }
-                            }
-
-                            // replace buffer pointer
-                            o_data = (BYTE *)stream->out.monoBuffer;
-                        }
-
-                        // convert input mono
-                        if (stream->in.monoMixer)
-                        {
-                            UINT32 mono_frames_size = i_processed * (stream->in.wavex.Format.wBitsPerSample / 8);
-                            // expand buffer
-                            if (mono_frames_size > stream->in.monoBufferSize)
-                            {
-                                stream->in.monoBuffer = PaWasapi_ReallocateMemory(stream->in.monoBuffer, (stream->in.monoBufferSize = mono_frames_size));
-                                if (stream->in.monoBuffer == NULL)
-                                {
-                                    // release input buffer
-                                    IAudioCaptureClient_ReleaseBuffer(stream->captureClient, 0);
-                                    // release output buffer
-                                    IAudioRenderClient_ReleaseBuffer(stream->renderClient, 0, 0);
-
-                                    LogPaError(paInsufficientMemory);
-                                    goto thread_error;
-                                }
-                            }
-
-                            // mix 2 to 1 input channels
-                            stream->in.monoMixer(stream->in.monoBuffer, i_data, i_processed);
-
-                            // replace buffer pointer
-                            i_data = (BYTE *)stream->in.monoBuffer;
-                        }
-
-                        // process
-                        processor[S_FULLDUPLEX].processor(i_data, i_processed, o_data, o_processed, processor[S_FULLDUPLEX].userData);
-
-                        // mix 1 to 2 output channels
-                        if (stream->out.monoBuffer)
-                            stream->out.monoMixer(o_data_host, stream->out.monoBuffer, o_processed);
-
-                        // release host output buffer
-                        if ((hr = IAudioRenderClient_ReleaseBuffer(stream->renderClient, o_processed, 0)) != S_OK)
-                            LogHostError(hr);
-
-                        o_frames -= o_processed;
-                    }
-                    else
-                    {
-                        if (stream->out.shareMode != AUDCLNT_SHAREMODE_SHARED)
-                            LogHostError(hr); // be silent in shared mode, try again next time
-                    }
-                }
-                else
-                {
-                    i_processed = 0;
-                    goto fd_release_buffer_in;
-                }
-
-fd_release_buffer_in:
-
-                // release host input buffer
-                if ((hr = IAudioCaptureClient_ReleaseBuffer(stream->captureClient, i_processed)) != S_OK)
-                {
-                    LogHostError(hr);
-                    break;
-                }
-
-                // break processing, input hasn't been accumulated yet
-                if (i_processed == 0)
-                    break;
-            }
-
-            // Get next sleep time
-            nextSleepTime = GetNextSleepTime(&timer, &scheduler, startTime, sleepTime);
-
-        #ifdef PA_WASAPI_LOG_TIME_SLOTS
-            startWaitTime = SystemTimer_GetTime(&timer);
-        #endif
-        }
-    }
-
-thread_end:
-
-    // Process stop
-    _StreamOnStop(stream);
-
-    // Release unmarshaled COM pointers
-    FinishComPointers(stream, threadComInitialized);
-
-    // Restore system timer granularity
-    SystemTimer_RestoreGranularity(&timer);
-
-    // Notify: not running
-    stream->running = FALSE;
-
-    // Notify: thread exited
-    SetEvent(stream->hThreadExit);
-
-    // Notify: state
-    NotifyStateChanged(stream, paWasapiStreamStateThreadStop, hr);
-
-    return 0;
-
-thread_error:
-
-    // Prevent deadlocking in Pa_StreamStart
-    SetEvent(stream->hThreadStart);
-
-    // Exit
-    goto thread_end;
-}
-
-// ------------------------------------------------------------------------------------------
-void *PaWasapi_ReallocateMemory(void *prev, size_t size)
-{
-    void *ret = realloc(prev, size);
-    if (ret == NULL)
-    {
-        PaWasapi_FreeMemory(prev);
-        return NULL;
-    }
-    return ret;
-}
-
-// ------------------------------------------------------------------------------------------
-void PaWasapi_FreeMemory(void *ptr)
-{
-    free(ptr);
-}
diff --git a/spaces/pritamdeka/health-article-keyphrase-generator/app.py b/spaces/pritamdeka/health-article-keyphrase-generator/app.py
deleted file mode 100644
index dd53220da5f002215d56450d2f3250b427dedf09..0000000000000000000000000000000000000000
--- a/spaces/pritamdeka/health-article-keyphrase-generator/app.py
+++ /dev/null
@@ -1,179 +0,0 @@
-import nltk
-import re
-import nltkmodule
-from newspaper import Article
-from newspaper import fulltext
-import requests
-from nltk.tokenize import word_tokenize
-from sentence_transformers import SentenceTransformer, models, losses, LoggingHandler
-import pandas as pd
-import numpy as np
-from torch.utils.data import DataLoader
-import math
-from sentence_transformers.evaluation import EmbeddingSimilarityEvaluator
-from sentence_transformers.readers import *
-from nltk.corpus import stopwords
-stop_words = stopwords.words('english')
-from sklearn.metrics.pairwise import cosine_similarity
-import networkx as nx
-from nltk.tokenize import sent_tokenize
-import scispacy
-import en_core_sci_lg
-import string
-import gradio as gr
-import inflect
-
-inflect_op = inflect.engine()
-nlp = en_core_sci_lg.load()
-sp = en_core_sci_lg.load()
-all_stopwords = sp.Defaults.stop_words
-
-
-def remove_stopwords(sen):
-    sen_new = " ".join([i for i in sen if i not in stop_words])
-    return sen_new
-
-def keyphrase_generator(article_link, model_1, model_2, max_num_keywords):
-  element=[]
-  final_textrank_list=[]
-  document=[]
-  text_doc=[]
-  score_list=[]
-  sum_list=[]
-  model_1 = SentenceTransformer(model_1)
-  model_2 = SentenceTransformer(model_2)
-  url = article_link
-  html = requests.get(url).text
-  article = fulltext(html)
-  corpus=sent_tokenize(article)
-  indicator_list=['concluded','concludes','in a study', 'concluding','conclude','in sum','in a recent study','therefore','thus','so','hence',
-          'as a result','accordingly','consequently','in short','proves that','shows that','suggests that','demonstrates that','found that','observed that',
-          'indicated that','suggested that','demonstrated that']
-  count_dict={}
-  for l in corpus:
-    c=0
-    for l2 in indicator_list:     
-       if l.find(l2)!=-1:  ### then it is a substring
-          c=1
-          break           
-    if c:#
-       count_dict[l]=1
-    else:
-       count_dict[l]=0
-  for sent, score in count_dict.items():
-    score_list.append(score)
-  clean_sentences_new = pd.Series(corpus).str.replace("[^a-zA-Z]", " ", regex=True).tolist()
-  corpus_embeddings = model_1.encode(clean_sentences_new)
-  sim_mat = np.zeros([len(clean_sentences_new), len(clean_sentences_new)])
-  for i in range(len(clean_sentences_new)):
-    len_embeddings=(len(corpus_embeddings[i]))
-    for j in range(len(clean_sentences_new)):
-      if i != j:      
-        if(len_embeddings == 1024):
-          sim_mat[i][j] = cosine_similarity(corpus_embeddings[i].reshape(1,1024), corpus_embeddings[j].reshape(1,1024))[0,0]
-        elif(len_embeddings == 768):
-          sim_mat[i][j] = cosine_similarity(corpus_embeddings[i].reshape(1,768), corpus_embeddings[j].reshape(1,768))[0,0]
-  nx_graph = nx.from_numpy_array(sim_mat)
-  scores = nx.pagerank(nx_graph)
-  sentences=((scores[i],s) for i,s in enumerate(corpus))
-
-  for elem in sentences:
-    element.append(elem[0])
-  for sc, lst in zip(score_list, element):  ########### taking the scores from both the lists 
-    sum1=sc+lst
-    sum_list.append(sum1)
-  x=sorted(((sum_list[i],s) for i,s in enumerate(corpus)), reverse=True)
-  for elem in x:
-    final_textrank_list.append(elem[1]) 
-  a=int((10*len(final_textrank_list))/100.0)
-  if(a<5):
-    total=5
-  else:
-    total=int(a)
-  for i in range(total):
-    document.append(final_textrank_list[i])
-  doc=" ".join(document)
-  for i in document:
-    doc_1=nlp(i)
-    text_doc.append([X.text for X in doc_1.ents])
-  entity_list = [item for sublist in text_doc for item in sublist]
-  entity_list = [word for word in entity_list if not word in all_stopwords]
-  entity_list = [word_entity for word_entity in entity_list if(inflect_op.singular_noun(word_entity) == False)]
-  entity_list=list(dict.fromkeys(entity_list))
-  doc_embedding = model_2.encode([doc])
-  candidates=entity_list
-  candidate_embeddings = model_2.encode(candidates)
-  distances = cosine_similarity(doc_embedding, candidate_embeddings)
-  top_n = max_num_keywords
-  keyword_list = [candidates[index] for index in distances.argsort()[0][-top_n:]]
-  keywords = '\n'.join(keyword_list) 
-  return keywords
-
-igen=gr.Interface(keyphrase_generator, 
-                  inputs=[gr.components.Textbox(lines=1, placeholder="Provide an online health article web link here",default="", label="Article web link"),
-                      gr.components.Dropdown(choices=['sentence-transformers/all-mpnet-base-v2',
-                                                 'sentence-transformers/all-mpnet-base-v1',                       
-                                                 'sentence-transformers/all-distilroberta-v1',
-                                                 'sentence-transformers/gtr-t5-large',
-                                                 'pritamdeka/S-Bluebert-snli-multinli-stsb',
-                                                 'pritamdeka/S-Biomed-Roberta-snli-multinli-stsb',                                                 
-                                                 'sentence-transformers/stsb-mpnet-base-v2',
-                                                 'sentence-transformers/all-roberta-large-v1',
-                                                 'sentence-transformers/stsb-roberta-base-v2',
-                                                 'sentence-transformers/stsb-distilroberta-base-v2',
-                                                 'sentence-transformers/sentence-t5-large',
-                                                 'sentence-transformers/sentence-t5-base'], 
-                                        type="value", 
-                                        default='pritamdeka/S-Biomed-Roberta-snli-multinli-stsb', 
-                                        label="Select any SBERT model for TextRank from the list below"),
-                     gr.components.Dropdown(choices=['sentence-transformers/paraphrase-mpnet-base-v2',
-                                                 'sentence-transformers/all-mpnet-base-v1',
-                                                 'sentence-transformers/paraphrase-distilroberta-base-v1',
-                                                 'sentence-transformers/paraphrase-xlm-r-multilingual-v1',
-                                                 'sentence-transformers/paraphrase-multilingual-mpnet-base-v2',
-                                                 'sentence-transformers/paraphrase-albert-small-v2',
-                                                 'sentence-transformers/paraphrase-albert-base-v2',                                                
-                                                 'sentence-transformers/paraphrase-MiniLM-L12-v2',
-                                                 'sentence-transformers/paraphrase-MiniLM-L6-v2',
-                                                 'sentence-transformers/all-MiniLM-L12-v2',
-                                                 'sentence-transformers/all-distilroberta-v1',
-                                                 'sentence-transformers/paraphrase-TinyBERT-L6-v2',
-                                                 'sentence-transformers/paraphrase-MiniLM-L3-v2',
-                                                 'sentence-transformers/all-MiniLM-L6-v2'], 
-                                        type="value", 
-                                        default='sentence-transformers/all-mpnet-base-v1', 
-                                        label="Select any SBERT model for keyphrases from the list below"),                     
-                     gr.components.Slider(minimum=5, maximum=30, step=1, default=10, label="Max Keywords")],
-             outputs=gr.outputs.Textbox(type="text", label="Output"), theme="peach", 
-             title="Health Article Keyphrase Generator", 
-             description="Generates the keyphrases from an online health article which best describes the article. Examples are provided below for demo purposes. Choose any one example to see the results. ",
-             examples=[
-                         ["https://www.cancer.news/2021-12-22-mrna-vaccines-weaken-immune-system-cause-cancer.html",                        
-                         'sentence-transformers/all-mpnet-base-v1',
-                         'sentence-transformers/paraphrase-MiniLM-L12-v2',
-                         10],
-                         
-                         ["https://www.cancer.news/2022-02-04-doctors-testifying-covid-vaccines-causing-cancer-aids.html#",                        
-                         'sentence-transformers/all-mpnet-base-v1',
-                         'sentence-transformers/all-mpnet-base-v1',
-                         12],
-                         
-                         ["https://www.medicalnewstoday.com/articles/alzheimers-addressing-sleep-disturbance-may-alleviate-symptoms",
-                         'pritamdeka/S-Biomed-Roberta-snli-multinli-stsb',
-                         'sentence-transformers/all-mpnet-base-v1',
-                         10],
-                         
-                        ["https://www.medicalnewstoday.com/articles/omicron-what-do-we-know-about-the-stealth-variant",
-                         'pritamdeka/S-Biomed-Roberta-snli-multinli-stsb',
-                         'sentence-transformers/all-mpnet-base-v1',
-                         15]                      
-                       ],
-             article= "The work is based on a part of the paper provided <a href=https://dl.acm.org/doi/10.1145/3487664.3487701>here</a>." 
-             "\t It uses the TextRank algorithm with <a href=https://www.sbert.net/>SBERT</a> to first find the top ranked sentences and then extracts the keyphrases" 
-             "\t from those sentences using <a href = https://allenai.github.io/scispacy/>scispaCy</a> and SBERT."
-             "\t The list of SBERT models provided can be found in <a href=www.sbert.net/docs/pretrained_models.html>SBERT Pre-trained models hub</a>."
-             "\t The default model names are provided which can be changed from the list of models available. "
-             "\t The value of output keyphrases can be changed. The default value is 10, minimum is 5 and a maximum value of 30.")
-             
-igen.launch(share=False)
-####
\ No newline at end of file
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/PIL/GimpPaletteFile.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/PIL/GimpPaletteFile.py
deleted file mode 100644
index d388928945a0f6711de2b1c8d1ed50ce192a8219..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/PIL/GimpPaletteFile.py
+++ /dev/null
@@ -1,56 +0,0 @@
-#
-# Python Imaging Library
-# $Id$
-#
-# stuff to read GIMP palette files
-#
-# History:
-# 1997-08-23 fl     Created
-# 2004-09-07 fl     Support GIMP 2.0 palette files.
-#
-# Copyright (c) Secret Labs AB 1997-2004.  All rights reserved.
-# Copyright (c) Fredrik Lundh 1997-2004.
-#
-# See the README file for information on usage and redistribution.
-#
-
-import re
-
-from ._binary import o8
-
-
-class GimpPaletteFile:
-    """File handler for GIMP's palette format."""
-
-    rawmode = "RGB"
-
-    def __init__(self, fp):
-        self.palette = [o8(i) * 3 for i in range(256)]
-
-        if fp.readline()[:12] != b"GIMP Palette":
-            msg = "not a GIMP palette file"
-            raise SyntaxError(msg)
-
-        for i in range(256):
-            s = fp.readline()
-            if not s:
-                break
-
-            # skip fields and comment lines
-            if re.match(rb"\w+:|#", s):
-                continue
-            if len(s) > 100:
-                msg = "bad palette file"
-                raise SyntaxError(msg)
-
-            v = tuple(map(int, s.split()[:3]))
-            if len(v) != 3:
-                msg = "bad palette entry"
-                raise ValueError(msg)
-
-            self.palette[i] = o8(v[0]) + o8(v[1]) + o8(v[2])
-
-        self.palette = b"".join(self.palette)
-
-    def getpalette(self):
-        return self.palette, self.rawmode
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/fontTools/pens/quartzPen.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/fontTools/pens/quartzPen.py
deleted file mode 100644
index 6e1228d6f2b8bbc78cf52864ccaf3b249a654749..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/fontTools/pens/quartzPen.py
+++ /dev/null
@@ -1,44 +0,0 @@
-from fontTools.pens.basePen import BasePen
-
-from Quartz.CoreGraphics import CGPathCreateMutable, CGPathMoveToPoint
-from Quartz.CoreGraphics import CGPathAddLineToPoint, CGPathAddCurveToPoint
-from Quartz.CoreGraphics import CGPathAddQuadCurveToPoint, CGPathCloseSubpath
-
-
-__all__ = ["QuartzPen"]
-
-
-class QuartzPen(BasePen):
-
-    """A pen that creates a CGPath
-
-    Parameters
-    - path: an optional CGPath to add to
-    - xform: an optional CGAffineTransform to apply to the path
-    """
-
-    def __init__(self, glyphSet, path=None, xform=None):
-        BasePen.__init__(self, glyphSet)
-        if path is None:
-            path = CGPathCreateMutable()
-        self.path = path
-        self.xform = xform
-
-    def _moveTo(self, pt):
-        x, y = pt
-        CGPathMoveToPoint(self.path, self.xform, x, y)
-
-    def _lineTo(self, pt):
-        x, y = pt
-        CGPathAddLineToPoint(self.path, self.xform, x, y)
-
-    def _curveToOne(self, p1, p2, p3):
-        (x1, y1), (x2, y2), (x3, y3) = p1, p2, p3
-        CGPathAddCurveToPoint(self.path, self.xform, x1, y1, x2, y2, x3, y3)
-
-    def _qCurveToOne(self, p1, p2):
-        (x1, y1), (x2, y2) = p1, p2
-        CGPathAddQuadCurveToPoint(self.path, self.xform, x1, y1, x2, y2)
-
-    def _closePath(self):
-        CGPathCloseSubpath(self.path)
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/fontTools/pens/roundingPen.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/fontTools/pens/roundingPen.py
deleted file mode 100644
index 2a7c476c36f4d244d62c92b745dc462d977ba394..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/fontTools/pens/roundingPen.py
+++ /dev/null
@@ -1,112 +0,0 @@
-from fontTools.misc.roundTools import otRound
-from fontTools.misc.transform import Transform
-from fontTools.pens.filterPen import FilterPen, FilterPointPen
-
-
-__all__ = ["RoundingPen", "RoundingPointPen"]
-
-
-class RoundingPen(FilterPen):
-    """
-    Filter pen that rounds point coordinates and component XY offsets to integer.
-
-    >>> from fontTools.pens.recordingPen import RecordingPen
-    >>> recpen = RecordingPen()
-    >>> roundpen = RoundingPen(recpen)
-    >>> roundpen.moveTo((0.4, 0.6))
-    >>> roundpen.lineTo((1.6, 2.5))
-    >>> roundpen.qCurveTo((2.4, 4.6), (3.3, 5.7), (4.9, 6.1))
-    >>> roundpen.curveTo((6.4, 8.6), (7.3, 9.7), (8.9, 10.1))
-    >>> roundpen.addComponent("a", (1.5, 0, 0, 1.5, 10.5, -10.5))
-    >>> recpen.value == [
-    ...     ('moveTo', ((0, 1),)),
-    ...     ('lineTo', ((2, 3),)),
-    ...     ('qCurveTo', ((2, 5), (3, 6), (5, 6))),
-    ...     ('curveTo', ((6, 9), (7, 10), (9, 10))),
-    ...     ('addComponent', ('a', (1.5, 0, 0, 1.5, 11, -10))),
-    ... ]
-    True
-    """
-
-    def __init__(self, outPen, roundFunc=otRound):
-        super().__init__(outPen)
-        self.roundFunc = roundFunc
-
-    def moveTo(self, pt):
-        self._outPen.moveTo((self.roundFunc(pt[0]), self.roundFunc(pt[1])))
-
-    def lineTo(self, pt):
-        self._outPen.lineTo((self.roundFunc(pt[0]), self.roundFunc(pt[1])))
-
-    def curveTo(self, *points):
-        self._outPen.curveTo(
-            *((self.roundFunc(x), self.roundFunc(y)) for x, y in points)
-        )
-
-    def qCurveTo(self, *points):
-        self._outPen.qCurveTo(
-            *((self.roundFunc(x), self.roundFunc(y)) for x, y in points)
-        )
-
-    def addComponent(self, glyphName, transformation):
-        self._outPen.addComponent(
-            glyphName,
-            Transform(
-                *transformation[:4],
-                self.roundFunc(transformation[4]),
-                self.roundFunc(transformation[5]),
-            ),
-        )
-
-
-class RoundingPointPen(FilterPointPen):
-    """
-    Filter point pen that rounds point coordinates and component XY offsets to integer.
-
-    >>> from fontTools.pens.recordingPen import RecordingPointPen
-    >>> recpen = RecordingPointPen()
-    >>> roundpen = RoundingPointPen(recpen)
-    >>> roundpen.beginPath()
-    >>> roundpen.addPoint((0.4, 0.6), 'line')
-    >>> roundpen.addPoint((1.6, 2.5), 'line')
-    >>> roundpen.addPoint((2.4, 4.6))
-    >>> roundpen.addPoint((3.3, 5.7))
-    >>> roundpen.addPoint((4.9, 6.1), 'qcurve')
-    >>> roundpen.endPath()
-    >>> roundpen.addComponent("a", (1.5, 0, 0, 1.5, 10.5, -10.5))
-    >>> recpen.value == [
-    ...     ('beginPath', (), {}),
-    ...     ('addPoint', ((0, 1), 'line', False, None), {}),
-    ...     ('addPoint', ((2, 3), 'line', False, None), {}),
-    ...     ('addPoint', ((2, 5), None, False, None), {}),
-    ...     ('addPoint', ((3, 6), None, False, None), {}),
-    ...     ('addPoint', ((5, 6), 'qcurve', False, None), {}),
-    ...     ('endPath', (), {}),
-    ...     ('addComponent', ('a', (1.5, 0, 0, 1.5, 11, -10)), {}),
-    ... ]
-    True
-    """
-
-    def __init__(self, outPen, roundFunc=otRound):
-        super().__init__(outPen)
-        self.roundFunc = roundFunc
-
-    def addPoint(self, pt, segmentType=None, smooth=False, name=None, **kwargs):
-        self._outPen.addPoint(
-            (self.roundFunc(pt[0]), self.roundFunc(pt[1])),
-            segmentType=segmentType,
-            smooth=smooth,
-            name=name,
-            **kwargs,
-        )
-
-    def addComponent(self, baseGlyphName, transformation, **kwargs):
-        self._outPen.addComponent(
-            baseGlyphName,
-            Transform(
-                *transformation[:4],
-                self.roundFunc(transformation[4]),
-                self.roundFunc(transformation[5]),
-            ),
-            **kwargs,
-        )
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/h11/tests/test_connection.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/h11/tests/test_connection.py
deleted file mode 100644
index 73a27b98bebd949cb3b99e19a3a8a484455b58d7..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/h11/tests/test_connection.py
+++ /dev/null
@@ -1,1122 +0,0 @@
-from typing import Any, cast, Dict, List, Optional, Tuple, Type
-
-import pytest
-
-from .._connection import _body_framing, _keep_alive, Connection, NEED_DATA, PAUSED
-from .._events import (
-    ConnectionClosed,
-    Data,
-    EndOfMessage,
-    Event,
-    InformationalResponse,
-    Request,
-    Response,
-)
-from .._state import (
-    CLIENT,
-    CLOSED,
-    DONE,
-    ERROR,
-    IDLE,
-    MIGHT_SWITCH_PROTOCOL,
-    MUST_CLOSE,
-    SEND_BODY,
-    SEND_RESPONSE,
-    SERVER,
-    SWITCHED_PROTOCOL,
-)
-from .._util import LocalProtocolError, RemoteProtocolError, Sentinel
-from .helpers import ConnectionPair, get_all_events, receive_and_get
-
-
-def test__keep_alive() -> None:
-    assert _keep_alive(
-        Request(method="GET", target="/", headers=[("Host", "Example.com")])
-    )
-    assert not _keep_alive(
-        Request(
-            method="GET",
-            target="/",
-            headers=[("Host", "Example.com"), ("Connection", "close")],
-        )
-    )
-    assert not _keep_alive(
-        Request(
-            method="GET",
-            target="/",
-            headers=[("Host", "Example.com"), ("Connection", "a, b, cLOse, foo")],
-        )
-    )
-    assert not _keep_alive(
-        Request(method="GET", target="/", headers=[], http_version="1.0")  # type: ignore[arg-type]
-    )
-
-    assert _keep_alive(Response(status_code=200, headers=[]))  # type: ignore[arg-type]
-    assert not _keep_alive(Response(status_code=200, headers=[("Connection", "close")]))
-    assert not _keep_alive(
-        Response(status_code=200, headers=[("Connection", "a, b, cLOse, foo")])
-    )
-    assert not _keep_alive(Response(status_code=200, headers=[], http_version="1.0"))  # type: ignore[arg-type]
-
-
-def test__body_framing() -> None:
-    def headers(cl: Optional[int], te: bool) -> List[Tuple[str, str]]:
-        headers = []
-        if cl is not None:
-            headers.append(("Content-Length", str(cl)))
-        if te:
-            headers.append(("Transfer-Encoding", "chunked"))
-        return headers
-
-    def resp(
-        status_code: int = 200, cl: Optional[int] = None, te: bool = False
-    ) -> Response:
-        return Response(status_code=status_code, headers=headers(cl, te))
-
-    def req(cl: Optional[int] = None, te: bool = False) -> Request:
-        h = headers(cl, te)
-        h += [("Host", "example.com")]
-        return Request(method="GET", target="/", headers=h)
-
-    # Special cases where the headers are ignored:
-    for kwargs in [{}, {"cl": 100}, {"te": True}, {"cl": 100, "te": True}]:
-        kwargs = cast(Dict[str, Any], kwargs)
-        for meth, r in [
-            (b"HEAD", resp(**kwargs)),
-            (b"GET", resp(status_code=204, **kwargs)),
-            (b"GET", resp(status_code=304, **kwargs)),
-        ]:
-            assert _body_framing(meth, r) == ("content-length", (0,))
-
-    # Transfer-encoding
-    for kwargs in [{"te": True}, {"cl": 100, "te": True}]:
-        kwargs = cast(Dict[str, Any], kwargs)
-        for meth, r in [(None, req(**kwargs)), (b"GET", resp(**kwargs))]:  # type: ignore
-            assert _body_framing(meth, r) == ("chunked", ())
-
-    # Content-Length
-    for meth, r in [(None, req(cl=100)), (b"GET", resp(cl=100))]:  # type: ignore
-        assert _body_framing(meth, r) == ("content-length", (100,))
-
-    # No headers
-    assert _body_framing(None, req()) == ("content-length", (0,))  # type: ignore
-    assert _body_framing(b"GET", resp()) == ("http/1.0", ())
-
-
-def test_Connection_basics_and_content_length() -> None:
-    with pytest.raises(ValueError):
-        Connection("CLIENT")  # type: ignore
-
-    p = ConnectionPair()
-    assert p.conn[CLIENT].our_role is CLIENT
-    assert p.conn[CLIENT].their_role is SERVER
-    assert p.conn[SERVER].our_role is SERVER
-    assert p.conn[SERVER].their_role is CLIENT
-
-    data = p.send(
-        CLIENT,
-        Request(
-            method="GET",
-            target="/",
-            headers=[("Host", "example.com"), ("Content-Length", "10")],
-        ),
-    )
-    assert data == (
-        b"GET / HTTP/1.1\r\n" b"Host: example.com\r\n" b"Content-Length: 10\r\n\r\n"
-    )
-
-    for conn in p.conns:
-        assert conn.states == {CLIENT: SEND_BODY, SERVER: SEND_RESPONSE}
-    assert p.conn[CLIENT].our_state is SEND_BODY
-    assert p.conn[CLIENT].their_state is SEND_RESPONSE
-    assert p.conn[SERVER].our_state is SEND_RESPONSE
-    assert p.conn[SERVER].their_state is SEND_BODY
-
-    assert p.conn[CLIENT].their_http_version is None
-    assert p.conn[SERVER].their_http_version == b"1.1"
-
-    data = p.send(SERVER, InformationalResponse(status_code=100, headers=[]))  # type: ignore[arg-type]
-    assert data == b"HTTP/1.1 100 \r\n\r\n"
-
-    data = p.send(SERVER, Response(status_code=200, headers=[("Content-Length", "11")]))
-    assert data == b"HTTP/1.1 200 \r\nContent-Length: 11\r\n\r\n"
-
-    for conn in p.conns:
-        assert conn.states == {CLIENT: SEND_BODY, SERVER: SEND_BODY}
-
-    assert p.conn[CLIENT].their_http_version == b"1.1"
-    assert p.conn[SERVER].their_http_version == b"1.1"
-
-    data = p.send(CLIENT, Data(data=b"12345"))
-    assert data == b"12345"
-    data = p.send(
-        CLIENT, Data(data=b"67890"), expect=[Data(data=b"67890"), EndOfMessage()]
-    )
-    assert data == b"67890"
-    data = p.send(CLIENT, EndOfMessage(), expect=[])
-    assert data == b""
-
-    for conn in p.conns:
-        assert conn.states == {CLIENT: DONE, SERVER: SEND_BODY}
-
-    data = p.send(SERVER, Data(data=b"1234567890"))
-    assert data == b"1234567890"
-    data = p.send(SERVER, Data(data=b"1"), expect=[Data(data=b"1"), EndOfMessage()])
-    assert data == b"1"
-    data = p.send(SERVER, EndOfMessage(), expect=[])
-    assert data == b""
-
-    for conn in p.conns:
-        assert conn.states == {CLIENT: DONE, SERVER: DONE}
-
-
-def test_chunked() -> None:
-    p = ConnectionPair()
-
-    p.send(
-        CLIENT,
-        Request(
-            method="GET",
-            target="/",
-            headers=[("Host", "example.com"), ("Transfer-Encoding", "chunked")],
-        ),
-    )
-    data = p.send(CLIENT, Data(data=b"1234567890", chunk_start=True, chunk_end=True))
-    assert data == b"a\r\n1234567890\r\n"
-    data = p.send(CLIENT, Data(data=b"abcde", chunk_start=True, chunk_end=True))
-    assert data == b"5\r\nabcde\r\n"
-    data = p.send(CLIENT, Data(data=b""), expect=[])
-    assert data == b""
-    data = p.send(CLIENT, EndOfMessage(headers=[("hello", "there")]))
-    assert data == b"0\r\nhello: there\r\n\r\n"
-
-    p.send(
-        SERVER, Response(status_code=200, headers=[("Transfer-Encoding", "chunked")])
-    )
-    p.send(SERVER, Data(data=b"54321", chunk_start=True, chunk_end=True))
-    p.send(SERVER, Data(data=b"12345", chunk_start=True, chunk_end=True))
-    p.send(SERVER, EndOfMessage())
-
-    for conn in p.conns:
-        assert conn.states == {CLIENT: DONE, SERVER: DONE}
-
-
-def test_chunk_boundaries() -> None:
-    conn = Connection(our_role=SERVER)
-
-    request = (
-        b"POST / HTTP/1.1\r\n"
-        b"Host: example.com\r\n"
-        b"Transfer-Encoding: chunked\r\n"
-        b"\r\n"
-    )
-    conn.receive_data(request)
-    assert conn.next_event() == Request(
-        method="POST",
-        target="/",
-        headers=[("Host", "example.com"), ("Transfer-Encoding", "chunked")],
-    )
-    assert conn.next_event() is NEED_DATA
-
-    conn.receive_data(b"5\r\nhello\r\n")
-    assert conn.next_event() == Data(data=b"hello", chunk_start=True, chunk_end=True)
-
-    conn.receive_data(b"5\r\nhel")
-    assert conn.next_event() == Data(data=b"hel", chunk_start=True, chunk_end=False)
-
-    conn.receive_data(b"l")
-    assert conn.next_event() == Data(data=b"l", chunk_start=False, chunk_end=False)
-
-    conn.receive_data(b"o\r\n")
-    assert conn.next_event() == Data(data=b"o", chunk_start=False, chunk_end=True)
-
-    conn.receive_data(b"5\r\nhello")
-    assert conn.next_event() == Data(data=b"hello", chunk_start=True, chunk_end=True)
-
-    conn.receive_data(b"\r\n")
-    assert conn.next_event() == NEED_DATA
-
-    conn.receive_data(b"0\r\n\r\n")
-    assert conn.next_event() == EndOfMessage()
-
-
-def test_client_talking_to_http10_server() -> None:
-    c = Connection(CLIENT)
-    c.send(Request(method="GET", target="/", headers=[("Host", "example.com")]))
-    c.send(EndOfMessage())
-    assert c.our_state is DONE
-    # No content-length, so Http10 framing for body
-    assert receive_and_get(c, b"HTTP/1.0 200 OK\r\n\r\n") == [
-        Response(status_code=200, headers=[], http_version="1.0", reason=b"OK")  # type: ignore[arg-type]
-    ]
-    assert c.our_state is MUST_CLOSE
-    assert receive_and_get(c, b"12345") == [Data(data=b"12345")]
-    assert receive_and_get(c, b"67890") == [Data(data=b"67890")]
-    assert receive_and_get(c, b"") == [EndOfMessage(), ConnectionClosed()]
-    assert c.their_state is CLOSED
-
-
-def test_server_talking_to_http10_client() -> None:
-    c = Connection(SERVER)
-    # No content-length, so no body
-    # NB: no host header
-    assert receive_and_get(c, b"GET / HTTP/1.0\r\n\r\n") == [
-        Request(method="GET", target="/", headers=[], http_version="1.0"),  # type: ignore[arg-type]
-        EndOfMessage(),
-    ]
-    assert c.their_state is MUST_CLOSE
-
-    # We automatically Connection: close back at them
-    assert (
-        c.send(Response(status_code=200, headers=[]))  # type: ignore[arg-type]
-        == b"HTTP/1.1 200 \r\nConnection: close\r\n\r\n"
-    )
-
-    assert c.send(Data(data=b"12345")) == b"12345"
-    assert c.send(EndOfMessage()) == b""
-    assert c.our_state is MUST_CLOSE
-
-    # Check that it works if they do send Content-Length
-    c = Connection(SERVER)
-    # NB: no host header
-    assert receive_and_get(c, b"POST / HTTP/1.0\r\nContent-Length: 10\r\n\r\n1") == [
-        Request(
-            method="POST",
-            target="/",
-            headers=[("Content-Length", "10")],
-            http_version="1.0",
-        ),
-        Data(data=b"1"),
-    ]
-    assert receive_and_get(c, b"234567890") == [Data(data=b"234567890"), EndOfMessage()]
-    assert c.their_state is MUST_CLOSE
-    assert receive_and_get(c, b"") == [ConnectionClosed()]
-
-
-def test_automatic_transfer_encoding_in_response() -> None:
-    # Check that in responses, the user can specify either Transfer-Encoding:
-    # chunked or no framing at all, and in both cases we automatically select
-    # the right option depending on whether the peer speaks HTTP/1.0 or
-    # HTTP/1.1
-    for user_headers in [
-        [("Transfer-Encoding", "chunked")],
-        [],
-        # In fact, this even works if Content-Length is set,
-        # because if both are set then Transfer-Encoding wins
-        [("Transfer-Encoding", "chunked"), ("Content-Length", "100")],
-    ]:
-        user_headers = cast(List[Tuple[str, str]], user_headers)
-        p = ConnectionPair()
-        p.send(
-            CLIENT,
-            [
-                Request(method="GET", target="/", headers=[("Host", "example.com")]),
-                EndOfMessage(),
-            ],
-        )
-        # When speaking to HTTP/1.1 client, all of the above cases get
-        # normalized to Transfer-Encoding: chunked
-        p.send(
-            SERVER,
-            Response(status_code=200, headers=user_headers),
-            expect=Response(
-                status_code=200, headers=[("Transfer-Encoding", "chunked")]
-            ),
-        )
-
-        # When speaking to HTTP/1.0 client, all of the above cases get
-        # normalized to no-framing-headers
-        c = Connection(SERVER)
-        receive_and_get(c, b"GET / HTTP/1.0\r\n\r\n")
-        assert (
-            c.send(Response(status_code=200, headers=user_headers))
-            == b"HTTP/1.1 200 \r\nConnection: close\r\n\r\n"
-        )
-        assert c.send(Data(data=b"12345")) == b"12345"
-
-
-def test_automagic_connection_close_handling() -> None:
-    p = ConnectionPair()
-    # If the user explicitly sets Connection: close, then we notice and
-    # respect it
-    p.send(
-        CLIENT,
-        [
-            Request(
-                method="GET",
-                target="/",
-                headers=[("Host", "example.com"), ("Connection", "close")],
-            ),
-            EndOfMessage(),
-        ],
-    )
-    for conn in p.conns:
-        assert conn.states[CLIENT] is MUST_CLOSE
-    # And if the client sets it, the server automatically echoes it back
-    p.send(
-        SERVER,
-        # no header here...
-        [Response(status_code=204, headers=[]), EndOfMessage()],  # type: ignore[arg-type]
-        # ...but oh look, it arrived anyway
-        expect=[
-            Response(status_code=204, headers=[("connection", "close")]),
-            EndOfMessage(),
-        ],
-    )
-    for conn in p.conns:
-        assert conn.states == {CLIENT: MUST_CLOSE, SERVER: MUST_CLOSE}
-
-
-def test_100_continue() -> None:
-    def setup() -> ConnectionPair:
-        p = ConnectionPair()
-        p.send(
-            CLIENT,
-            Request(
-                method="GET",
-                target="/",
-                headers=[
-                    ("Host", "example.com"),
-                    ("Content-Length", "100"),
-                    ("Expect", "100-continue"),
-                ],
-            ),
-        )
-        for conn in p.conns:
-            assert conn.client_is_waiting_for_100_continue
-        assert not p.conn[CLIENT].they_are_waiting_for_100_continue
-        assert p.conn[SERVER].they_are_waiting_for_100_continue
-        return p
-
-    # Disabled by 100 Continue
-    p = setup()
-    p.send(SERVER, InformationalResponse(status_code=100, headers=[]))  # type: ignore[arg-type]
-    for conn in p.conns:
-        assert not conn.client_is_waiting_for_100_continue
-        assert not conn.they_are_waiting_for_100_continue
-
-    # Disabled by a real response
-    p = setup()
-    p.send(
-        SERVER, Response(status_code=200, headers=[("Transfer-Encoding", "chunked")])
-    )
-    for conn in p.conns:
-        assert not conn.client_is_waiting_for_100_continue
-        assert not conn.they_are_waiting_for_100_continue
-
-    # Disabled by the client going ahead and sending stuff anyway
-    p = setup()
-    p.send(CLIENT, Data(data=b"12345"))
-    for conn in p.conns:
-        assert not conn.client_is_waiting_for_100_continue
-        assert not conn.they_are_waiting_for_100_continue
-
-
-def test_max_incomplete_event_size_countermeasure() -> None:
-    # Infinitely long headers are definitely not okay
-    c = Connection(SERVER)
-    c.receive_data(b"GET / HTTP/1.0\r\nEndless: ")
-    assert c.next_event() is NEED_DATA
-    with pytest.raises(RemoteProtocolError):
-        while True:
-            c.receive_data(b"a" * 1024)
-            c.next_event()
-
-    # Checking that the same header is accepted / rejected depending on the
-    # max_incomplete_event_size setting:
-    c = Connection(SERVER, max_incomplete_event_size=5000)
-    c.receive_data(b"GET / HTTP/1.0\r\nBig: ")
-    c.receive_data(b"a" * 4000)
-    c.receive_data(b"\r\n\r\n")
-    assert get_all_events(c) == [
-        Request(
-            method="GET", target="/", http_version="1.0", headers=[("big", "a" * 4000)]
-        ),
-        EndOfMessage(),
-    ]
-
-    c = Connection(SERVER, max_incomplete_event_size=4000)
-    c.receive_data(b"GET / HTTP/1.0\r\nBig: ")
-    c.receive_data(b"a" * 4000)
-    with pytest.raises(RemoteProtocolError):
-        c.next_event()
-
-    # Temporarily exceeding the size limit is fine, as long as its done with
-    # complete events:
-    c = Connection(SERVER, max_incomplete_event_size=5000)
-    c.receive_data(b"GET / HTTP/1.0\r\nContent-Length: 10000")
-    c.receive_data(b"\r\n\r\n" + b"a" * 10000)
-    assert get_all_events(c) == [
-        Request(
-            method="GET",
-            target="/",
-            http_version="1.0",
-            headers=[("Content-Length", "10000")],
-        ),
-        Data(data=b"a" * 10000),
-        EndOfMessage(),
-    ]
-
-    c = Connection(SERVER, max_incomplete_event_size=100)
-    # Two pipelined requests to create a way-too-big receive buffer... but
-    # it's fine because we're not checking
-    c.receive_data(
-        b"GET /1 HTTP/1.1\r\nHost: a\r\n\r\n"
-        b"GET /2 HTTP/1.1\r\nHost: b\r\n\r\n" + b"X" * 1000
-    )
-    assert get_all_events(c) == [
-        Request(method="GET", target="/1", headers=[("host", "a")]),
-        EndOfMessage(),
-    ]
-    # Even more data comes in, still no problem
-    c.receive_data(b"X" * 1000)
-    # We can respond and reuse to get the second pipelined request
-    c.send(Response(status_code=200, headers=[]))  # type: ignore[arg-type]
-    c.send(EndOfMessage())
-    c.start_next_cycle()
-    assert get_all_events(c) == [
-        Request(method="GET", target="/2", headers=[("host", "b")]),
-        EndOfMessage(),
-    ]
-    # But once we unpause and try to read the next message, and find that it's
-    # incomplete and the buffer is *still* way too large, then *that's* a
-    # problem:
-    c.send(Response(status_code=200, headers=[]))  # type: ignore[arg-type]
-    c.send(EndOfMessage())
-    c.start_next_cycle()
-    with pytest.raises(RemoteProtocolError):
-        c.next_event()
-
-
-def test_reuse_simple() -> None:
-    p = ConnectionPair()
-    p.send(
-        CLIENT,
-        [Request(method="GET", target="/", headers=[("Host", "a")]), EndOfMessage()],
-    )
-    p.send(
-        SERVER,
-        [
-            Response(status_code=200, headers=[(b"transfer-encoding", b"chunked")]),
-            EndOfMessage(),
-        ],
-    )
-    for conn in p.conns:
-        assert conn.states == {CLIENT: DONE, SERVER: DONE}
-        conn.start_next_cycle()
-
-    p.send(
-        CLIENT,
-        [
-            Request(method="DELETE", target="/foo", headers=[("Host", "a")]),
-            EndOfMessage(),
-        ],
-    )
-    p.send(
-        SERVER,
-        [
-            Response(status_code=404, headers=[(b"transfer-encoding", b"chunked")]),
-            EndOfMessage(),
-        ],
-    )
-
-
-def test_pipelining() -> None:
-    # Client doesn't support pipelining, so we have to do this by hand
-    c = Connection(SERVER)
-    assert c.next_event() is NEED_DATA
-    # 3 requests all bunched up
-    c.receive_data(
-        b"GET /1 HTTP/1.1\r\nHost: a.com\r\nContent-Length: 5\r\n\r\n"
-        b"12345"
-        b"GET /2 HTTP/1.1\r\nHost: a.com\r\nContent-Length: 5\r\n\r\n"
-        b"67890"
-        b"GET /3 HTTP/1.1\r\nHost: a.com\r\n\r\n"
-    )
-    assert get_all_events(c) == [
-        Request(
-            method="GET",
-            target="/1",
-            headers=[("Host", "a.com"), ("Content-Length", "5")],
-        ),
-        Data(data=b"12345"),
-        EndOfMessage(),
-    ]
-    assert c.their_state is DONE
-    assert c.our_state is SEND_RESPONSE
-
-    assert c.next_event() is PAUSED
-
-    c.send(Response(status_code=200, headers=[]))  # type: ignore[arg-type]
-    c.send(EndOfMessage())
-    assert c.their_state is DONE
-    assert c.our_state is DONE
-
-    c.start_next_cycle()
-
-    assert get_all_events(c) == [
-        Request(
-            method="GET",
-            target="/2",
-            headers=[("Host", "a.com"), ("Content-Length", "5")],
-        ),
-        Data(data=b"67890"),
-        EndOfMessage(),
-    ]
-    assert c.next_event() is PAUSED
-    c.send(Response(status_code=200, headers=[]))  # type: ignore[arg-type]
-    c.send(EndOfMessage())
-    c.start_next_cycle()
-
-    assert get_all_events(c) == [
-        Request(method="GET", target="/3", headers=[("Host", "a.com")]),
-        EndOfMessage(),
-    ]
-    # Doesn't pause this time, no trailing data
-    assert c.next_event() is NEED_DATA
-    c.send(Response(status_code=200, headers=[]))  # type: ignore[arg-type]
-    c.send(EndOfMessage())
-
-    # Arrival of more data triggers pause
-    assert c.next_event() is NEED_DATA
-    c.receive_data(b"SADF")
-    assert c.next_event() is PAUSED
-    assert c.trailing_data == (b"SADF", False)
-    # If EOF arrives while paused, we don't see that either:
-    c.receive_data(b"")
-    assert c.trailing_data == (b"SADF", True)
-    assert c.next_event() is PAUSED
-    c.receive_data(b"")
-    assert c.next_event() is PAUSED
-    # Can't call receive_data with non-empty buf after closing it
-    with pytest.raises(RuntimeError):
-        c.receive_data(b"FDSA")
-
-
-def test_protocol_switch() -> None:
-    for (req, deny, accept) in [
-        (
-            Request(
-                method="CONNECT",
-                target="example.com:443",
-                headers=[("Host", "foo"), ("Content-Length", "1")],
-            ),
-            Response(status_code=404, headers=[(b"transfer-encoding", b"chunked")]),
-            Response(status_code=200, headers=[(b"transfer-encoding", b"chunked")]),
-        ),
-        (
-            Request(
-                method="GET",
-                target="/",
-                headers=[("Host", "foo"), ("Content-Length", "1"), ("Upgrade", "a, b")],
-            ),
-            Response(status_code=200, headers=[(b"transfer-encoding", b"chunked")]),
-            InformationalResponse(status_code=101, headers=[("Upgrade", "a")]),
-        ),
-        (
-            Request(
-                method="CONNECT",
-                target="example.com:443",
-                headers=[("Host", "foo"), ("Content-Length", "1"), ("Upgrade", "a, b")],
-            ),
-            Response(status_code=404, headers=[(b"transfer-encoding", b"chunked")]),
-            # Accept CONNECT, not upgrade
-            Response(status_code=200, headers=[(b"transfer-encoding", b"chunked")]),
-        ),
-        (
-            Request(
-                method="CONNECT",
-                target="example.com:443",
-                headers=[("Host", "foo"), ("Content-Length", "1"), ("Upgrade", "a, b")],
-            ),
-            Response(status_code=404, headers=[(b"transfer-encoding", b"chunked")]),
-            # Accept Upgrade, not CONNECT
-            InformationalResponse(status_code=101, headers=[("Upgrade", "b")]),
-        ),
-    ]:
-
-        def setup() -> ConnectionPair:
-            p = ConnectionPair()
-            p.send(CLIENT, req)
-            # No switch-related state change stuff yet; the client has to
-            # finish the request before that kicks in
-            for conn in p.conns:
-                assert conn.states[CLIENT] is SEND_BODY
-            p.send(CLIENT, [Data(data=b"1"), EndOfMessage()])
-            for conn in p.conns:
-                assert conn.states[CLIENT] is MIGHT_SWITCH_PROTOCOL
-            assert p.conn[SERVER].next_event() is PAUSED
-            return p
-
-        # Test deny case
-        p = setup()
-        p.send(SERVER, deny)
-        for conn in p.conns:
-            assert conn.states == {CLIENT: DONE, SERVER: SEND_BODY}
-        p.send(SERVER, EndOfMessage())
-        # Check that re-use is still allowed after a denial
-        for conn in p.conns:
-            conn.start_next_cycle()
-
-        # Test accept case
-        p = setup()
-        p.send(SERVER, accept)
-        for conn in p.conns:
-            assert conn.states == {CLIENT: SWITCHED_PROTOCOL, SERVER: SWITCHED_PROTOCOL}
-            conn.receive_data(b"123")
-            assert conn.next_event() is PAUSED
-            conn.receive_data(b"456")
-            assert conn.next_event() is PAUSED
-            assert conn.trailing_data == (b"123456", False)
-
-        # Pausing in might-switch, then recovery
-        # (weird artificial case where the trailing data actually is valid
-        # HTTP for some reason, because this makes it easier to test the state
-        # logic)
-        p = setup()
-        sc = p.conn[SERVER]
-        sc.receive_data(b"GET / HTTP/1.0\r\n\r\n")
-        assert sc.next_event() is PAUSED
-        assert sc.trailing_data == (b"GET / HTTP/1.0\r\n\r\n", False)
-        sc.send(deny)
-        assert sc.next_event() is PAUSED
-        sc.send(EndOfMessage())
-        sc.start_next_cycle()
-        assert get_all_events(sc) == [
-            Request(method="GET", target="/", headers=[], http_version="1.0"),  # type: ignore[arg-type]
-            EndOfMessage(),
-        ]
-
-        # When we're DONE, have no trailing data, and the connection gets
-        # closed, we report ConnectionClosed(). When we're in might-switch or
-        # switched, we don't.
-        p = setup()
-        sc = p.conn[SERVER]
-        sc.receive_data(b"")
-        assert sc.next_event() is PAUSED
-        assert sc.trailing_data == (b"", True)
-        p.send(SERVER, accept)
-        assert sc.next_event() is PAUSED
-
-        p = setup()
-        sc = p.conn[SERVER]
-        sc.receive_data(b"")
-        assert sc.next_event() is PAUSED
-        sc.send(deny)
-        assert sc.next_event() == ConnectionClosed()
-
-        # You can't send after switching protocols, or while waiting for a
-        # protocol switch
-        p = setup()
-        with pytest.raises(LocalProtocolError):
-            p.conn[CLIENT].send(
-                Request(method="GET", target="/", headers=[("Host", "a")])
-            )
-        p = setup()
-        p.send(SERVER, accept)
-        with pytest.raises(LocalProtocolError):
-            p.conn[SERVER].send(Data(data=b"123"))
-
-
-def test_close_simple() -> None:
-    # Just immediately closing a new connection without anything having
-    # happened yet.
-    for (who_shot_first, who_shot_second) in [(CLIENT, SERVER), (SERVER, CLIENT)]:
-
-        def setup() -> ConnectionPair:
-            p = ConnectionPair()
-            p.send(who_shot_first, ConnectionClosed())
-            for conn in p.conns:
-                assert conn.states == {
-                    who_shot_first: CLOSED,
-                    who_shot_second: MUST_CLOSE,
-                }
-            return p
-
-        # You can keep putting b"" into a closed connection, and you keep
-        # getting ConnectionClosed() out:
-        p = setup()
-        assert p.conn[who_shot_second].next_event() == ConnectionClosed()
-        assert p.conn[who_shot_second].next_event() == ConnectionClosed()
-        p.conn[who_shot_second].receive_data(b"")
-        assert p.conn[who_shot_second].next_event() == ConnectionClosed()
-        # Second party can close...
-        p = setup()
-        p.send(who_shot_second, ConnectionClosed())
-        for conn in p.conns:
-            assert conn.our_state is CLOSED
-            assert conn.their_state is CLOSED
-        # But trying to receive new data on a closed connection is a
-        # RuntimeError (not ProtocolError, because the problem here isn't
-        # violation of HTTP, it's violation of physics)
-        p = setup()
-        with pytest.raises(RuntimeError):
-            p.conn[who_shot_second].receive_data(b"123")
-        # And receiving new data on a MUST_CLOSE connection is a ProtocolError
-        p = setup()
-        p.conn[who_shot_first].receive_data(b"GET")
-        with pytest.raises(RemoteProtocolError):
-            p.conn[who_shot_first].next_event()
-
-
-def test_close_different_states() -> None:
-    req = [
-        Request(method="GET", target="/foo", headers=[("Host", "a")]),
-        EndOfMessage(),
-    ]
-    resp = [
-        Response(status_code=200, headers=[(b"transfer-encoding", b"chunked")]),
-        EndOfMessage(),
-    ]
-
-    # Client before request
-    p = ConnectionPair()
-    p.send(CLIENT, ConnectionClosed())
-    for conn in p.conns:
-        assert conn.states == {CLIENT: CLOSED, SERVER: MUST_CLOSE}
-
-    # Client after request
-    p = ConnectionPair()
-    p.send(CLIENT, req)
-    p.send(CLIENT, ConnectionClosed())
-    for conn in p.conns:
-        assert conn.states == {CLIENT: CLOSED, SERVER: SEND_RESPONSE}
-
-    # Server after request -> not allowed
-    p = ConnectionPair()
-    p.send(CLIENT, req)
-    with pytest.raises(LocalProtocolError):
-        p.conn[SERVER].send(ConnectionClosed())
-    p.conn[CLIENT].receive_data(b"")
-    with pytest.raises(RemoteProtocolError):
-        p.conn[CLIENT].next_event()
-
-    # Server after response
-    p = ConnectionPair()
-    p.send(CLIENT, req)
-    p.send(SERVER, resp)
-    p.send(SERVER, ConnectionClosed())
-    for conn in p.conns:
-        assert conn.states == {CLIENT: MUST_CLOSE, SERVER: CLOSED}
-
-    # Both after closing (ConnectionClosed() is idempotent)
-    p = ConnectionPair()
-    p.send(CLIENT, req)
-    p.send(SERVER, resp)
-    p.send(CLIENT, ConnectionClosed())
-    p.send(SERVER, ConnectionClosed())
-    p.send(CLIENT, ConnectionClosed())
-    p.send(SERVER, ConnectionClosed())
-
-    # In the middle of sending -> not allowed
-    p = ConnectionPair()
-    p.send(
-        CLIENT,
-        Request(
-            method="GET", target="/", headers=[("Host", "a"), ("Content-Length", "10")]
-        ),
-    )
-    with pytest.raises(LocalProtocolError):
-        p.conn[CLIENT].send(ConnectionClosed())
-    p.conn[SERVER].receive_data(b"")
-    with pytest.raises(RemoteProtocolError):
-        p.conn[SERVER].next_event()
-
-
-# Receive several requests and then client shuts down their side of the
-# connection; we can respond to each
-def test_pipelined_close() -> None:
-    c = Connection(SERVER)
-    # 2 requests then a close
-    c.receive_data(
-        b"GET /1 HTTP/1.1\r\nHost: a.com\r\nContent-Length: 5\r\n\r\n"
-        b"12345"
-        b"GET /2 HTTP/1.1\r\nHost: a.com\r\nContent-Length: 5\r\n\r\n"
-        b"67890"
-    )
-    c.receive_data(b"")
-    assert get_all_events(c) == [
-        Request(
-            method="GET",
-            target="/1",
-            headers=[("host", "a.com"), ("content-length", "5")],
-        ),
-        Data(data=b"12345"),
-        EndOfMessage(),
-    ]
-    assert c.states[CLIENT] is DONE
-    c.send(Response(status_code=200, headers=[]))  # type: ignore[arg-type]
-    c.send(EndOfMessage())
-    assert c.states[SERVER] is DONE
-    c.start_next_cycle()
-    assert get_all_events(c) == [
-        Request(
-            method="GET",
-            target="/2",
-            headers=[("host", "a.com"), ("content-length", "5")],
-        ),
-        Data(data=b"67890"),
-        EndOfMessage(),
-        ConnectionClosed(),
-    ]
-    assert c.states == {CLIENT: CLOSED, SERVER: SEND_RESPONSE}
-    c.send(Response(status_code=200, headers=[]))  # type: ignore[arg-type]
-    c.send(EndOfMessage())
-    assert c.states == {CLIENT: CLOSED, SERVER: MUST_CLOSE}
-    c.send(ConnectionClosed())
-    assert c.states == {CLIENT: CLOSED, SERVER: CLOSED}
-
-
-def test_sendfile() -> None:
-    class SendfilePlaceholder:
-        def __len__(self) -> int:
-            return 10
-
-    placeholder = SendfilePlaceholder()
-
-    def setup(
-        header: Tuple[str, str], http_version: str
-    ) -> Tuple[Connection, Optional[List[bytes]]]:
-        c = Connection(SERVER)
-        receive_and_get(
-            c, "GET / HTTP/{}\r\nHost: a\r\n\r\n".format(http_version).encode("ascii")
-        )
-        headers = []
-        if header:
-            headers.append(header)
-        c.send(Response(status_code=200, headers=headers))
-        return c, c.send_with_data_passthrough(Data(data=placeholder))  # type: ignore
-
-    c, data = setup(("Content-Length", "10"), "1.1")
-    assert data == [placeholder]  # type: ignore
-    # Raises an error if the connection object doesn't think we've sent
-    # exactly 10 bytes
-    c.send(EndOfMessage())
-
-    _, data = setup(("Transfer-Encoding", "chunked"), "1.1")
-    assert placeholder in data  # type: ignore
-    data[data.index(placeholder)] = b"x" * 10  # type: ignore
-    assert b"".join(data) == b"a\r\nxxxxxxxxxx\r\n"  # type: ignore
-
-    c, data = setup(None, "1.0")  # type: ignore
-    assert data == [placeholder]  # type: ignore
-    assert c.our_state is SEND_BODY
-
-
-def test_errors() -> None:
-    # After a receive error, you can't receive
-    for role in [CLIENT, SERVER]:
-        c = Connection(our_role=role)
-        c.receive_data(b"gibberish\r\n\r\n")
-        with pytest.raises(RemoteProtocolError):
-            c.next_event()
-        # Now any attempt to receive continues to raise
-        assert c.their_state is ERROR
-        assert c.our_state is not ERROR
-        print(c._cstate.states)
-        with pytest.raises(RemoteProtocolError):
-            c.next_event()
-        # But we can still yell at the client for sending us gibberish
-        if role is SERVER:
-            assert (
-                c.send(Response(status_code=400, headers=[]))  # type: ignore[arg-type]
-                == b"HTTP/1.1 400 \r\nConnection: close\r\n\r\n"
-            )
-
-    # After an error sending, you can no longer send
-    # (This is especially important for things like content-length errors,
-    # where there's complex internal state being modified)
-    def conn(role: Type[Sentinel]) -> Connection:
-        c = Connection(our_role=role)
-        if role is SERVER:
-            # Put it into the state where it *could* send a response...
-            receive_and_get(c, b"GET / HTTP/1.0\r\n\r\n")
-            assert c.our_state is SEND_RESPONSE
-        return c
-
-    for role in [CLIENT, SERVER]:
-        if role is CLIENT:
-            # This HTTP/1.0 request won't be detected as bad until after we go
-            # through the state machine and hit the writing code
-            good = Request(method="GET", target="/", headers=[("Host", "example.com")])
-            bad = Request(
-                method="GET",
-                target="/",
-                headers=[("Host", "example.com")],
-                http_version="1.0",
-            )
-        elif role is SERVER:
-            good = Response(status_code=200, headers=[])  # type: ignore[arg-type,assignment]
-            bad = Response(status_code=200, headers=[], http_version="1.0")  # type: ignore[arg-type,assignment]
-        # Make sure 'good' actually is good
-        c = conn(role)
-        c.send(good)
-        assert c.our_state is not ERROR
-        # Do that again, but this time sending 'bad' first
-        c = conn(role)
-        with pytest.raises(LocalProtocolError):
-            c.send(bad)
-        assert c.our_state is ERROR
-        assert c.their_state is not ERROR
-        # Now 'good' is not so good
-        with pytest.raises(LocalProtocolError):
-            c.send(good)
-
-        # And check send_failed() too
-        c = conn(role)
-        c.send_failed()
-        assert c.our_state is ERROR
-        assert c.their_state is not ERROR
-        # This is idempotent
-        c.send_failed()
-        assert c.our_state is ERROR
-        assert c.their_state is not ERROR
-
-
-def test_idle_receive_nothing() -> None:
-    # At one point this incorrectly raised an error
-    for role in [CLIENT, SERVER]:
-        c = Connection(role)
-        assert c.next_event() is NEED_DATA
-
-
-def test_connection_drop() -> None:
-    c = Connection(SERVER)
-    c.receive_data(b"GET /")
-    assert c.next_event() is NEED_DATA
-    c.receive_data(b"")
-    with pytest.raises(RemoteProtocolError):
-        c.next_event()
-
-
-def test_408_request_timeout() -> None:
-    # Should be able to send this spontaneously as a server without seeing
-    # anything from client
-    p = ConnectionPair()
-    p.send(SERVER, Response(status_code=408, headers=[(b"connection", b"close")]))
-
-
-# This used to raise IndexError
-def test_empty_request() -> None:
-    c = Connection(SERVER)
-    c.receive_data(b"\r\n")
-    with pytest.raises(RemoteProtocolError):
-        c.next_event()
-
-
-# This used to raise IndexError
-def test_empty_response() -> None:
-    c = Connection(CLIENT)
-    c.send(Request(method="GET", target="/", headers=[("Host", "a")]))
-    c.receive_data(b"\r\n")
-    with pytest.raises(RemoteProtocolError):
-        c.next_event()
-
-
-@pytest.mark.parametrize(
-    "data",
-    [
-        b"\x00",
-        b"\x20",
-        b"\x16\x03\x01\x00\xa5",  # Typical start of a TLS Client Hello
-    ],
-)
-def test_early_detection_of_invalid_request(data: bytes) -> None:
-    c = Connection(SERVER)
-    # Early detection should occur before even receiving a `\r\n`
-    c.receive_data(data)
-    with pytest.raises(RemoteProtocolError):
-        c.next_event()
-
-
-@pytest.mark.parametrize(
-    "data",
-    [
-        b"\x00",
-        b"\x20",
-        b"\x16\x03\x03\x00\x31",  # Typical start of a TLS Server Hello
-    ],
-)
-def test_early_detection_of_invalid_response(data: bytes) -> None:
-    c = Connection(CLIENT)
-    # Early detection should occur before even receiving a `\r\n`
-    c.receive_data(data)
-    with pytest.raises(RemoteProtocolError):
-        c.next_event()
-
-
-# This used to give different headers for HEAD and GET.
-# The correct way to handle HEAD is to put whatever headers we *would* have
-# put if it were a GET -- even though we know that for HEAD, those headers
-# will be ignored.
-def test_HEAD_framing_headers() -> None:
-    def setup(method: bytes, http_version: bytes) -> Connection:
-        c = Connection(SERVER)
-        c.receive_data(
-            method + b" / HTTP/" + http_version + b"\r\n" + b"Host: example.com\r\n\r\n"
-        )
-        assert type(c.next_event()) is Request
-        assert type(c.next_event()) is EndOfMessage
-        return c
-
-    for method in [b"GET", b"HEAD"]:
-        # No Content-Length, HTTP/1.1 peer, should use chunked
-        c = setup(method, b"1.1")
-        assert (
-            c.send(Response(status_code=200, headers=[])) == b"HTTP/1.1 200 \r\n"  # type: ignore[arg-type]
-            b"Transfer-Encoding: chunked\r\n\r\n"
-        )
-
-        # No Content-Length, HTTP/1.0 peer, frame with connection: close
-        c = setup(method, b"1.0")
-        assert (
-            c.send(Response(status_code=200, headers=[])) == b"HTTP/1.1 200 \r\n"  # type: ignore[arg-type]
-            b"Connection: close\r\n\r\n"
-        )
-
-        # Content-Length + Transfer-Encoding, TE wins
-        c = setup(method, b"1.1")
-        assert (
-            c.send(
-                Response(
-                    status_code=200,
-                    headers=[
-                        ("Content-Length", "100"),
-                        ("Transfer-Encoding", "chunked"),
-                    ],
-                )
-            )
-            == b"HTTP/1.1 200 \r\n"
-            b"Transfer-Encoding: chunked\r\n\r\n"
-        )
-
-
-def test_special_exceptions_for_lost_connection_in_message_body() -> None:
-    c = Connection(SERVER)
-    c.receive_data(
-        b"POST / HTTP/1.1\r\n" b"Host: example.com\r\n" b"Content-Length: 100\r\n\r\n"
-    )
-    assert type(c.next_event()) is Request
-    assert c.next_event() is NEED_DATA
-    c.receive_data(b"12345")
-    assert c.next_event() == Data(data=b"12345")
-    c.receive_data(b"")
-    with pytest.raises(RemoteProtocolError) as excinfo:
-        c.next_event()
-    assert "received 5 bytes" in str(excinfo.value)
-    assert "expected 100" in str(excinfo.value)
-
-    c = Connection(SERVER)
-    c.receive_data(
-        b"POST / HTTP/1.1\r\n"
-        b"Host: example.com\r\n"
-        b"Transfer-Encoding: chunked\r\n\r\n"
-    )
-    assert type(c.next_event()) is Request
-    assert c.next_event() is NEED_DATA
-    c.receive_data(b"8\r\n012345")
-    assert c.next_event().data == b"012345"  # type: ignore
-    c.receive_data(b"")
-    with pytest.raises(RemoteProtocolError) as excinfo:
-        c.next_event()
-    assert "incomplete chunked read" in str(excinfo.value)
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/httpcore/_backends/anyio.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/httpcore/_backends/anyio.py
deleted file mode 100644
index 1ed5228dbde1732de50677e9a3bd6f04a3017433..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/httpcore/_backends/anyio.py
+++ /dev/null
@@ -1,145 +0,0 @@
-import ssl
-import typing
-
-import anyio
-
-from .._exceptions import (
-    ConnectError,
-    ConnectTimeout,
-    ReadError,
-    ReadTimeout,
-    WriteError,
-    WriteTimeout,
-    map_exceptions,
-)
-from .._utils import is_socket_readable
-from .base import SOCKET_OPTION, AsyncNetworkBackend, AsyncNetworkStream
-
-
-class AnyIOStream(AsyncNetworkStream):
-    def __init__(self, stream: anyio.abc.ByteStream) -> None:
-        self._stream = stream
-
-    async def read(
-        self, max_bytes: int, timeout: typing.Optional[float] = None
-    ) -> bytes:
-        exc_map = {
-            TimeoutError: ReadTimeout,
-            anyio.BrokenResourceError: ReadError,
-            anyio.ClosedResourceError: ReadError,
-        }
-        with map_exceptions(exc_map):
-            with anyio.fail_after(timeout):
-                try:
-                    return await self._stream.receive(max_bytes=max_bytes)
-                except anyio.EndOfStream:  # pragma: nocover
-                    return b""
-
-    async def write(
-        self, buffer: bytes, timeout: typing.Optional[float] = None
-    ) -> None:
-        if not buffer:
-            return
-
-        exc_map = {
-            TimeoutError: WriteTimeout,
-            anyio.BrokenResourceError: WriteError,
-            anyio.ClosedResourceError: WriteError,
-        }
-        with map_exceptions(exc_map):
-            with anyio.fail_after(timeout):
-                await self._stream.send(item=buffer)
-
-    async def aclose(self) -> None:
-        await self._stream.aclose()
-
-    async def start_tls(
-        self,
-        ssl_context: ssl.SSLContext,
-        server_hostname: typing.Optional[str] = None,
-        timeout: typing.Optional[float] = None,
-    ) -> AsyncNetworkStream:
-        exc_map = {
-            TimeoutError: ConnectTimeout,
-            anyio.BrokenResourceError: ConnectError,
-        }
-        with map_exceptions(exc_map):
-            try:
-                with anyio.fail_after(timeout):
-                    ssl_stream = await anyio.streams.tls.TLSStream.wrap(
-                        self._stream,
-                        ssl_context=ssl_context,
-                        hostname=server_hostname,
-                        standard_compatible=False,
-                        server_side=False,
-                    )
-            except Exception as exc:  # pragma: nocover
-                await self.aclose()
-                raise exc
-        return AnyIOStream(ssl_stream)
-
-    def get_extra_info(self, info: str) -> typing.Any:
-        if info == "ssl_object":
-            return self._stream.extra(anyio.streams.tls.TLSAttribute.ssl_object, None)
-        if info == "client_addr":
-            return self._stream.extra(anyio.abc.SocketAttribute.local_address, None)
-        if info == "server_addr":
-            return self._stream.extra(anyio.abc.SocketAttribute.remote_address, None)
-        if info == "socket":
-            return self._stream.extra(anyio.abc.SocketAttribute.raw_socket, None)
-        if info == "is_readable":
-            sock = self._stream.extra(anyio.abc.SocketAttribute.raw_socket, None)
-            return is_socket_readable(sock)
-        return None
-
-
-class AnyIOBackend(AsyncNetworkBackend):
-    async def connect_tcp(
-        self,
-        host: str,
-        port: int,
-        timeout: typing.Optional[float] = None,
-        local_address: typing.Optional[str] = None,
-        socket_options: typing.Optional[typing.Iterable[SOCKET_OPTION]] = None,
-    ) -> AsyncNetworkStream:
-        if socket_options is None:
-            socket_options = []  # pragma: no cover
-        exc_map = {
-            TimeoutError: ConnectTimeout,
-            OSError: ConnectError,
-            anyio.BrokenResourceError: ConnectError,
-        }
-        with map_exceptions(exc_map):
-            with anyio.fail_after(timeout):
-                stream: anyio.abc.ByteStream = await anyio.connect_tcp(
-                    remote_host=host,
-                    remote_port=port,
-                    local_host=local_address,
-                )
-                # By default TCP sockets opened in `asyncio` include TCP_NODELAY.
-                for option in socket_options:
-                    stream._raw_socket.setsockopt(*option)  # type: ignore[attr-defined] # pragma: no cover
-        return AnyIOStream(stream)
-
-    async def connect_unix_socket(
-        self,
-        path: str,
-        timeout: typing.Optional[float] = None,
-        socket_options: typing.Optional[typing.Iterable[SOCKET_OPTION]] = None,
-    ) -> AsyncNetworkStream:  # pragma: nocover
-        if socket_options is None:
-            socket_options = []
-        exc_map = {
-            TimeoutError: ConnectTimeout,
-            OSError: ConnectError,
-            anyio.BrokenResourceError: ConnectError,
-        }
-        with map_exceptions(exc_map):
-            with anyio.fail_after(timeout):
-                stream: anyio.abc.ByteStream = await anyio.connect_unix(path)
-                for option in socket_options:
-                    stream._raw_socket.setsockopt(*option)  # type: ignore[attr-defined] # pragma: no cover
-        return AnyIOStream(stream)
-
-    async def sleep(self, seconds: float) -> None:
-        await anyio.sleep(seconds)  # pragma: nocover
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/matplotlib/tests/test_backend_ps.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/matplotlib/tests/test_backend_ps.py
deleted file mode 100644
index cbf33ccc5a1b748ffb77059fb426e8e9525ae67f..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/matplotlib/tests/test_backend_ps.py
+++ /dev/null
@@ -1,380 +0,0 @@
-from collections import Counter
-from pathlib import Path
-import io
-import re
-import tempfile
-
-import numpy as np
-import pytest
-
-from matplotlib import cbook, path, patheffects, font_manager as fm
-from matplotlib.figure import Figure
-from matplotlib.patches import Ellipse
-from matplotlib.testing._markers import needs_ghostscript, needs_usetex
-from matplotlib.testing.decorators import check_figures_equal, image_comparison
-import matplotlib as mpl
-import matplotlib.collections as mcollections
-import matplotlib.colors as mcolors
-import matplotlib.pyplot as plt
-
-
-# This tests tends to hit a TeX cache lock on AppVeyor.
-@pytest.mark.flaky(reruns=3)
-@pytest.mark.parametrize('papersize', ['letter', 'figure'])
-@pytest.mark.parametrize('orientation', ['portrait', 'landscape'])
-@pytest.mark.parametrize('format, use_log, rcParams', [
-    ('ps', False, {}),
-    ('ps', False, {'ps.usedistiller': 'ghostscript'}),
-    ('ps', False, {'ps.usedistiller': 'xpdf'}),
-    ('ps', False, {'text.usetex': True}),
-    ('eps', False, {}),
-    ('eps', True, {'ps.useafm': True}),
-    ('eps', False, {'text.usetex': True}),
-], ids=[
-    'ps',
-    'ps with distiller=ghostscript',
-    'ps with distiller=xpdf',
-    'ps with usetex',
-    'eps',
-    'eps afm',
-    'eps with usetex'
-])
-def test_savefig_to_stringio(format, use_log, rcParams, orientation, papersize):
-    mpl.rcParams.update(rcParams)
-    if mpl.rcParams["ps.usedistiller"] == "ghostscript":
-        try:
-            mpl._get_executable_info("gs")
-        except mpl.ExecutableNotFoundError as exc:
-            pytest.skip(str(exc))
-    elif mpl.rcParams["ps.usedistiller"] == "xpdf":
-        try:
-            mpl._get_executable_info("gs")  # Effectively checks for ps2pdf.
-            mpl._get_executable_info("pdftops")
-        except mpl.ExecutableNotFoundError as exc:
-            pytest.skip(str(exc))
-
-    fig, ax = plt.subplots()
-
-    with io.StringIO() as s_buf, io.BytesIO() as b_buf:
-
-        if use_log:
-            ax.set_yscale('log')
-
-        ax.plot([1, 2], [1, 2])
-        title = "Déjà vu"
-        if not mpl.rcParams["text.usetex"]:
-            title += " \N{MINUS SIGN}\N{EURO SIGN}"
-        ax.set_title(title)
-        allowable_exceptions = []
-        if mpl.rcParams["text.usetex"]:
-            allowable_exceptions.append(RuntimeError)
-        if mpl.rcParams["ps.useafm"]:
-            allowable_exceptions.append(mpl.MatplotlibDeprecationWarning)
-        try:
-            fig.savefig(s_buf, format=format, orientation=orientation,
-                        papertype=papersize)
-            fig.savefig(b_buf, format=format, orientation=orientation,
-                        papertype=papersize)
-        except tuple(allowable_exceptions) as exc:
-            pytest.skip(str(exc))
-
-        assert not s_buf.closed
-        assert not b_buf.closed
-        s_val = s_buf.getvalue().encode('ascii')
-        b_val = b_buf.getvalue()
-
-        if format == 'ps':
-            # Default figsize = (8, 6) inches = (576, 432) points = (203.2, 152.4) mm.
-            # Landscape orientation will swap dimensions.
-            if mpl.rcParams["ps.usedistiller"] == "xpdf":
-                # Some versions specifically show letter/203x152, but not all,
-                # so we can only use this simpler test.
-                if papersize == 'figure':
-                    assert b'letter' not in s_val.lower()
-                else:
-                    assert b'letter' in s_val.lower()
-            elif mpl.rcParams["ps.usedistiller"] or mpl.rcParams["text.usetex"]:
-                width = b'432.0' if orientation == 'landscape' else b'576.0'
-                wanted = (b'-dDEVICEWIDTHPOINTS=' + width if papersize == 'figure'
-                          else b'-sPAPERSIZE')
-                assert wanted in s_val
-            else:
-                if papersize == 'figure':
-                    assert b'%%DocumentPaperSizes' not in s_val
-                else:
-                    assert b'%%DocumentPaperSizes' in s_val
-
-        # Strip out CreationDate: ghostscript and cairo don't obey
-        # SOURCE_DATE_EPOCH, and that environment variable is already tested in
-        # test_determinism.
-        s_val = re.sub(b"(?<=\n%%CreationDate: ).*", b"", s_val)
-        b_val = re.sub(b"(?<=\n%%CreationDate: ).*", b"", b_val)
-
-        assert s_val == b_val.replace(b'\r\n', b'\n')
-
-
-def test_patheffects():
-    mpl.rcParams['path.effects'] = [
-        patheffects.withStroke(linewidth=4, foreground='w')]
-    fig, ax = plt.subplots()
-    ax.plot([1, 2, 3])
-    with io.BytesIO() as ps:
-        fig.savefig(ps, format='ps')
-
-
-@needs_usetex
-@needs_ghostscript
-def test_tilde_in_tempfilename(tmpdir):
-    # Tilde ~ in the tempdir path (e.g. TMPDIR, TMP or TEMP on windows
-    # when the username is very long and windows uses a short name) breaks
-    # latex before https://github.com/matplotlib/matplotlib/pull/5928
-    base_tempdir = Path(tmpdir, "short-1")
-    base_tempdir.mkdir()
-    # Change the path for new tempdirs, which is used internally by the ps
-    # backend to write a file.
-    with cbook._setattr_cm(tempfile, tempdir=str(base_tempdir)):
-        # usetex results in the latex call, which does not like the ~
-        mpl.rcParams['text.usetex'] = True
-        plt.plot([1, 2, 3, 4])
-        plt.xlabel(r'\textbf{time} (s)')
-        # use the PS backend to write the file...
-        plt.savefig(base_tempdir / 'tex_demo.eps', format="ps")
-
-
-@image_comparison(["empty.eps"])
-def test_transparency():
-    fig, ax = plt.subplots()
-    ax.set_axis_off()
-    ax.plot([0, 1], color="r", alpha=0)
-    ax.text(.5, .5, "foo", color="r", alpha=0)
-
-
-@needs_usetex
-@image_comparison(["empty.eps"])
-def test_transparency_tex():
-    mpl.rcParams['text.usetex'] = True
-    fig, ax = plt.subplots()
-    ax.set_axis_off()
-    ax.plot([0, 1], color="r", alpha=0)
-    ax.text(.5, .5, "foo", color="r", alpha=0)
-
-
-def test_bbox():
-    fig, ax = plt.subplots()
-    with io.BytesIO() as buf:
-        fig.savefig(buf, format='eps')
-        buf = buf.getvalue()
-
-    bb = re.search(b'^%%BoundingBox: (.+) (.+) (.+) (.+)$', buf, re.MULTILINE)
-    assert bb
-    hibb = re.search(b'^%%HiResBoundingBox: (.+) (.+) (.+) (.+)$', buf,
-                     re.MULTILINE)
-    assert hibb
-
-    for i in range(1, 5):
-        # BoundingBox must use integers, and be ceil/floor of the hi res.
-        assert b'.' not in bb.group(i)
-        assert int(bb.group(i)) == pytest.approx(float(hibb.group(i)), 1)
-
-
-@needs_usetex
-def test_failing_latex():
-    """Test failing latex subprocess call"""
-    mpl.rcParams['text.usetex'] = True
-    # This fails with "Double subscript"
-    plt.xlabel("$22_2_2$")
-    with pytest.raises(RuntimeError):
-        plt.savefig(io.BytesIO(), format="ps")
-
-
-@needs_usetex
-def test_partial_usetex(caplog):
-    caplog.set_level("WARNING")
-    plt.figtext(.1, .1, "foo", usetex=True)
-    plt.figtext(.2, .2, "bar", usetex=True)
-    plt.savefig(io.BytesIO(), format="ps")
-    record, = caplog.records  # asserts there's a single record.
-    assert "as if usetex=False" in record.getMessage()
-
-
-@needs_usetex
-def test_usetex_preamble(caplog):
-    mpl.rcParams.update({
-        "text.usetex": True,
-        # Check that these don't conflict with the packages loaded by default.
-        "text.latex.preamble": r"\usepackage{color,graphicx,textcomp}",
-    })
-    plt.figtext(.5, .5, "foo")
-    plt.savefig(io.BytesIO(), format="ps")
-
-
-@image_comparison(["useafm.eps"])
-def test_useafm():
-    mpl.rcParams["ps.useafm"] = True
-    fig, ax = plt.subplots()
-    ax.set_axis_off()
-    ax.axhline(.5)
-    ax.text(.5, .5, "qk")
-
-
-@image_comparison(["type3.eps"])
-def test_type3_font():
-    plt.figtext(.5, .5, "I/J")
-
-
-@image_comparison(["coloredhatcheszerolw.eps"])
-def test_colored_hatch_zero_linewidth():
-    ax = plt.gca()
-    ax.add_patch(Ellipse((0, 0), 1, 1, hatch='/', facecolor='none',
-                         edgecolor='r', linewidth=0))
-    ax.add_patch(Ellipse((0.5, 0.5), 0.5, 0.5, hatch='+', facecolor='none',
-                         edgecolor='g', linewidth=0.2))
-    ax.add_patch(Ellipse((1, 1), 0.3, 0.8, hatch='\\', facecolor='none',
-                         edgecolor='b', linewidth=0))
-    ax.set_axis_off()
-
-
-@check_figures_equal(extensions=["eps"])
-def test_text_clip(fig_test, fig_ref):
-    ax = fig_test.add_subplot()
-    # Fully clipped-out text should not appear.
-    ax.text(0, 0, "hello", transform=fig_test.transFigure, clip_on=True)
-    fig_ref.add_subplot()
-
-
-@needs_ghostscript
-def test_d_glyph(tmp_path):
-    # Ensure that we don't have a procedure defined as /d, which would be
-    # overwritten by the glyph definition for "d".
-    fig = plt.figure()
-    fig.text(.5, .5, "def")
-    out = tmp_path / "test.eps"
-    fig.savefig(out)
-    mpl.testing.compare.convert(out, cache=False)  # Should not raise.
-
-
-@image_comparison(["type42_without_prep.eps"], style='mpl20')
-def test_type42_font_without_prep():
-    # Test whether Type 42 fonts without prep table are properly embedded
-    mpl.rcParams["ps.fonttype"] = 42
-    mpl.rcParams["mathtext.fontset"] = "stix"
-
-    plt.figtext(0.5, 0.5, "Mass $m$")
-
-
-@pytest.mark.parametrize('fonttype', ["3", "42"])
-def test_fonttype(fonttype):
-    mpl.rcParams["ps.fonttype"] = fonttype
-    fig, ax = plt.subplots()
-
-    ax.text(0.25, 0.5, "Forty-two is the answer to everything!")
-
-    buf = io.BytesIO()
-    fig.savefig(buf, format="ps")
-
-    test = b'/FontType ' + bytes(f"{fonttype}", encoding='utf-8') + b' def'
-
-    assert re.search(test, buf.getvalue(), re.MULTILINE)
-
-
-def test_linedash():
-    """Test that dashed lines do not break PS output"""
-    fig, ax = plt.subplots()
-
-    ax.plot([0, 1], linestyle="--")
-
-    buf = io.BytesIO()
-    fig.savefig(buf, format="ps")
-
-    assert buf.tell() > 0
-
-
-def test_empty_line():
-    # Smoke-test for gh#23954
-    figure = Figure()
-    figure.text(0.5, 0.5, "\nfoo\n\n")
-    buf = io.BytesIO()
-    figure.savefig(buf, format='eps')
-    figure.savefig(buf, format='ps')
-
-
-def test_no_duplicate_definition():
-
-    fig = Figure()
-    axs = fig.subplots(4, 4, subplot_kw=dict(projection="polar"))
-    for ax in axs.flat:
-        ax.set(xticks=[], yticks=[])
-        ax.plot([1, 2])
-    fig.suptitle("hello, world")
-
-    buf = io.StringIO()
-    fig.savefig(buf, format='eps')
-    buf.seek(0)
-
-    wds = [ln.partition(' ')[0] for
-           ln in buf.readlines()
-           if ln.startswith('/')]
-
-    assert max(Counter(wds).values()) == 1
-
-
-@image_comparison(["multi_font_type3.eps"], tol=0.51)
-def test_multi_font_type3():
-    fp = fm.FontProperties(family=["WenQuanYi Zen Hei"])
-    if Path(fm.findfont(fp)).name != "wqy-zenhei.ttc":
-        pytest.skip("Font may be missing")
-
-    plt.rc('font', family=['DejaVu Sans', 'WenQuanYi Zen Hei'], size=27)
-    plt.rc('ps', fonttype=3)
-
-    fig = plt.figure()
-    fig.text(0.15, 0.475, "There are 几个汉字 in between!")
-
-
-@image_comparison(["multi_font_type42.eps"], tol=1.6)
-def test_multi_font_type42():
-    fp = fm.FontProperties(family=["WenQuanYi Zen Hei"])
-    if Path(fm.findfont(fp)).name != "wqy-zenhei.ttc":
-        pytest.skip("Font may be missing")
-
-    plt.rc('font', family=['DejaVu Sans', 'WenQuanYi Zen Hei'], size=27)
-    plt.rc('ps', fonttype=42)
-
-    fig = plt.figure()
-    fig.text(0.15, 0.475, "There are 几个汉字 in between!")
-
-
-@image_comparison(["scatter.eps"])
-def test_path_collection():
-    rng = np.random.default_rng(19680801)
-    xvals = rng.uniform(0, 1, 10)
-    yvals = rng.uniform(0, 1, 10)
-    sizes = rng.uniform(30, 100, 10)
-    fig, ax = plt.subplots()
-    ax.scatter(xvals, yvals, sizes, edgecolor=[0.9, 0.2, 0.1], marker='<')
-    ax.set_axis_off()
-    paths = [path.Path.unit_regular_polygon(i) for i in range(3, 7)]
-    offsets = rng.uniform(0, 200, 20).reshape(10, 2)
-    sizes = [0.02, 0.04]
-    pc = mcollections.PathCollection(paths, sizes, zorder=-1,
-                                     facecolors='yellow', offsets=offsets)
-    ax.add_collection(pc)
-    ax.set_xlim(0, 1)
-
-
-@image_comparison(["colorbar_shift.eps"], savefig_kwarg={"bbox_inches": "tight"},
-                  style="mpl20")
-def test_colorbar_shift(tmp_path):
-    cmap = mcolors.ListedColormap(["r", "g", "b"])
-    norm = mcolors.BoundaryNorm([-1, -0.5, 0.5, 1], cmap.N)
-    plt.scatter([0, 1], [1, 1], c=[0, 1], cmap=cmap, norm=norm)
-    plt.colorbar()
-
-
-def test_auto_papersize_deprecation():
-    fig = plt.figure()
-    with pytest.warns(mpl.MatplotlibDeprecationWarning):
-        fig.savefig(io.BytesIO(), format='eps', papertype='auto')
-
-    with pytest.warns(mpl.MatplotlibDeprecationWarning):
-        mpl.rcParams['ps.papersize'] = 'auto'
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/f2py/tests/src/isocintrin/isoCtests.f90 b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/f2py/tests/src/isocintrin/isoCtests.f90
deleted file mode 100644
index 42db6cccc14d6a17bb8ce9f68c730cb25cdfd5ed..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/f2py/tests/src/isocintrin/isoCtests.f90
+++ /dev/null
@@ -1,17 +0,0 @@
-  module coddity
-    use iso_c_binding, only: c_double, c_int
-    implicit none
-    contains
-      subroutine c_add(a, b, c) bind(c, name="c_add")
-        real(c_double), intent(in) :: a, b
-        real(c_double), intent(out) :: c
-        c = a + b
-      end subroutine c_add
-      ! gh-9693
-      function wat(x, y) result(z) bind(c)
-          integer(c_int), intent(in) :: x, y
-          integer(c_int) :: z
-
-          z = x + 7
-      end function wat
-  end module coddity
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/packaging/utils.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/packaging/utils.py
deleted file mode 100644
index c2c2f75aa806282d322c76c2117c0f0fdfb09d25..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/packaging/utils.py
+++ /dev/null
@@ -1,172 +0,0 @@
-# This file is dual licensed under the terms of the Apache License, Version
-# 2.0, and the BSD License. See the LICENSE file in the root of this repository
-# for complete details.
-
-import re
-from typing import FrozenSet, NewType, Tuple, Union, cast
-
-from .tags import Tag, parse_tag
-from .version import InvalidVersion, Version
-
-BuildTag = Union[Tuple[()], Tuple[int, str]]
-NormalizedName = NewType("NormalizedName", str)
-
-
-class InvalidName(ValueError):
-    """
-    An invalid distribution name; users should refer to the packaging user guide.
-    """
-
-
-class InvalidWheelFilename(ValueError):
-    """
-    An invalid wheel filename was found, users should refer to PEP 427.
-    """
-
-
-class InvalidSdistFilename(ValueError):
-    """
-    An invalid sdist filename was found, users should refer to the packaging user guide.
-    """
-
-
-# Core metadata spec for `Name`
-_validate_regex = re.compile(
-    r"^([A-Z0-9]|[A-Z0-9][A-Z0-9._-]*[A-Z0-9])$", re.IGNORECASE
-)
-_canonicalize_regex = re.compile(r"[-_.]+")
-_normalized_regex = re.compile(r"^([a-z0-9]|[a-z0-9]([a-z0-9-](?!--))*[a-z0-9])$")
-# PEP 427: The build number must start with a digit.
-_build_tag_regex = re.compile(r"(\d+)(.*)")
-
-
-def canonicalize_name(name: str, *, validate: bool = False) -> NormalizedName:
-    if validate and not _validate_regex.match(name):
-        raise InvalidName(f"name is invalid: {name!r}")
-    # This is taken from PEP 503.
-    value = _canonicalize_regex.sub("-", name).lower()
-    return cast(NormalizedName, value)
-
-
-def is_normalized_name(name: str) -> bool:
-    return _normalized_regex.match(name) is not None
-
-
-def canonicalize_version(
-    version: Union[Version, str], *, strip_trailing_zero: bool = True
-) -> str:
-    """
-    This is very similar to Version.__str__, but has one subtle difference
-    with the way it handles the release segment.
-    """
-    if isinstance(version, str):
-        try:
-            parsed = Version(version)
-        except InvalidVersion:
-            # Legacy versions cannot be normalized
-            return version
-    else:
-        parsed = version
-
-    parts = []
-
-    # Epoch
-    if parsed.epoch != 0:
-        parts.append(f"{parsed.epoch}!")
-
-    # Release segment
-    release_segment = ".".join(str(x) for x in parsed.release)
-    if strip_trailing_zero:
-        # NB: This strips trailing '.0's to normalize
-        release_segment = re.sub(r"(\.0)+$", "", release_segment)
-    parts.append(release_segment)
-
-    # Pre-release
-    if parsed.pre is not None:
-        parts.append("".join(str(x) for x in parsed.pre))
-
-    # Post-release
-    if parsed.post is not None:
-        parts.append(f".post{parsed.post}")
-
-    # Development release
-    if parsed.dev is not None:
-        parts.append(f".dev{parsed.dev}")
-
-    # Local version segment
-    if parsed.local is not None:
-        parts.append(f"+{parsed.local}")
-
-    return "".join(parts)
-
-
-def parse_wheel_filename(
-    filename: str,
-) -> Tuple[NormalizedName, Version, BuildTag, FrozenSet[Tag]]:
-    if not filename.endswith(".whl"):
-        raise InvalidWheelFilename(
-            f"Invalid wheel filename (extension must be '.whl'): {filename}"
-        )
-
-    filename = filename[:-4]
-    dashes = filename.count("-")
-    if dashes not in (4, 5):
-        raise InvalidWheelFilename(
-            f"Invalid wheel filename (wrong number of parts): {filename}"
-        )
-
-    parts = filename.split("-", dashes - 2)
-    name_part = parts[0]
-    # See PEP 427 for the rules on escaping the project name.
-    if "__" in name_part or re.match(r"^[\w\d._]*$", name_part, re.UNICODE) is None:
-        raise InvalidWheelFilename(f"Invalid project name: {filename}")
-    name = canonicalize_name(name_part)
-
-    try:
-        version = Version(parts[1])
-    except InvalidVersion as e:
-        raise InvalidWheelFilename(
-            f"Invalid wheel filename (invalid version): {filename}"
-        ) from e
-
-    if dashes == 5:
-        build_part = parts[2]
-        build_match = _build_tag_regex.match(build_part)
-        if build_match is None:
-            raise InvalidWheelFilename(
-                f"Invalid build number: {build_part} in '{filename}'"
-            )
-        build = cast(BuildTag, (int(build_match.group(1)), build_match.group(2)))
-    else:
-        build = ()
-    tags = parse_tag(parts[-1])
-    return (name, version, build, tags)
-
-
-def parse_sdist_filename(filename: str) -> Tuple[NormalizedName, Version]:
-    if filename.endswith(".tar.gz"):
-        file_stem = filename[: -len(".tar.gz")]
-    elif filename.endswith(".zip"):
-        file_stem = filename[: -len(".zip")]
-    else:
-        raise InvalidSdistFilename(
-            f"Invalid sdist filename (extension must be '.tar.gz' or '.zip'):"
-            f" {filename}"
-        )
-
-    # We are requiring a PEP 440 version, which cannot contain dashes,
-    # so we split on the last dash.
-    name_part, sep, version_part = file_stem.rpartition("-")
-    if not sep:
-        raise InvalidSdistFilename(f"Invalid sdist filename: {filename}")
-
-    name = canonicalize_name(name_part)
-
-    try:
-        version = Version(version_part)
-    except InvalidVersion as e:
-        raise InvalidSdistFilename(
-            f"Invalid sdist filename (invalid version): {filename}"
-        ) from e
-
-    return (name, version)
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/util/test_validate_args_and_kwargs.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/util/test_validate_args_and_kwargs.py
deleted file mode 100644
index 215026d648471c04cb8751506c03626fda73fc68..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/util/test_validate_args_and_kwargs.py
+++ /dev/null
@@ -1,84 +0,0 @@
-import pytest
-
-from pandas.util._validators import validate_args_and_kwargs
-
-
-@pytest.fixture
-def _fname():
-    return "func"
-
-
-def test_invalid_total_length_max_length_one(_fname):
-    compat_args = ("foo",)
-    kwargs = {"foo": "FOO"}
-    args = ("FoO", "BaZ")
-
-    min_fname_arg_count = 0
-    max_length = len(compat_args) + min_fname_arg_count
-    actual_length = len(kwargs) + len(args) + min_fname_arg_count
-
-    msg = (
-        rf"{_fname}\(\) takes at most {max_length} "
-        rf"argument \({actual_length} given\)"
-    )
-
-    with pytest.raises(TypeError, match=msg):
-        validate_args_and_kwargs(_fname, args, kwargs, min_fname_arg_count, compat_args)
-
-
-def test_invalid_total_length_max_length_multiple(_fname):
-    compat_args = ("foo", "bar", "baz")
-    kwargs = {"foo": "FOO", "bar": "BAR"}
-    args = ("FoO", "BaZ")
-
-    min_fname_arg_count = 2
-    max_length = len(compat_args) + min_fname_arg_count
-    actual_length = len(kwargs) + len(args) + min_fname_arg_count
-
-    msg = (
-        rf"{_fname}\(\) takes at most {max_length} "
-        rf"arguments \({actual_length} given\)"
-    )
-
-    with pytest.raises(TypeError, match=msg):
-        validate_args_and_kwargs(_fname, args, kwargs, min_fname_arg_count, compat_args)
-
-
-@pytest.mark.parametrize("args,kwargs", [((), {"foo": -5, "bar": 2}), ((-5, 2), {})])
-def test_missing_args_or_kwargs(args, kwargs, _fname):
-    bad_arg = "bar"
-    min_fname_arg_count = 2
-
-    compat_args = {"foo": -5, bad_arg: 1}
-
-    msg = (
-        rf"the '{bad_arg}' parameter is not supported "
-        rf"in the pandas implementation of {_fname}\(\)"
-    )
-
-    with pytest.raises(ValueError, match=msg):
-        validate_args_and_kwargs(_fname, args, kwargs, min_fname_arg_count, compat_args)
-
-
-def test_duplicate_argument(_fname):
-    min_fname_arg_count = 2
-
-    compat_args = {"foo": None, "bar": None, "baz": None}
-    kwargs = {"foo": None, "bar": None}
-    args = (None,)  # duplicate value for "foo"
-
-    msg = rf"{_fname}\(\) got multiple values for keyword argument 'foo'"
-
-    with pytest.raises(TypeError, match=msg):
-        validate_args_and_kwargs(_fname, args, kwargs, min_fname_arg_count, compat_args)
-
-
-def test_validation(_fname):
-    # No exceptions should be raised.
-    compat_args = {"foo": 1, "bar": None, "baz": -2}
-    kwargs = {"baz": -2}
-
-    args = (1, None)
-    min_fname_arg_count = 2
-
-    validate_args_and_kwargs(_fname, args, kwargs, min_fname_arg_count, compat_args)
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pip/_internal/req/req_install.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pip/_internal/req/req_install.py
deleted file mode 100644
index 02dbda1941f845a8087ea4544271fa94b69a8bda..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pip/_internal/req/req_install.py
+++ /dev/null
@@ -1,858 +0,0 @@
-# The following comment should be removed at some point in the future.
-# mypy: strict-optional=False
-
-import functools
-import logging
-import os
-import shutil
-import sys
-import uuid
-import zipfile
-from typing import Any, Collection, Dict, Iterable, List, Optional, Sequence, Union
-
-from pip._vendor.packaging.markers import Marker
-from pip._vendor.packaging.requirements import Requirement
-from pip._vendor.packaging.specifiers import SpecifierSet
-from pip._vendor.packaging.utils import canonicalize_name
-from pip._vendor.packaging.version import Version
-from pip._vendor.packaging.version import parse as parse_version
-from pip._vendor.pep517.wrappers import Pep517HookCaller
-
-from pip._internal.build_env import BuildEnvironment, NoOpBuildEnvironment
-from pip._internal.exceptions import InstallationError, LegacyInstallFailure
-from pip._internal.locations import get_scheme
-from pip._internal.metadata import (
-    BaseDistribution,
-    get_default_environment,
-    get_directory_distribution,
-)
-from pip._internal.models.link import Link
-from pip._internal.operations.build.metadata import generate_metadata
-from pip._internal.operations.build.metadata_editable import generate_editable_metadata
-from pip._internal.operations.build.metadata_legacy import (
-    generate_metadata as generate_metadata_legacy,
-)
-from pip._internal.operations.install.editable_legacy import (
-    install_editable as install_editable_legacy,
-)
-from pip._internal.operations.install.legacy import install as install_legacy
-from pip._internal.operations.install.wheel import install_wheel
-from pip._internal.pyproject import load_pyproject_toml, make_pyproject_path
-from pip._internal.req.req_uninstall import UninstallPathSet
-from pip._internal.utils.deprecation import deprecated
-from pip._internal.utils.direct_url_helpers import (
-    direct_url_for_editable,
-    direct_url_from_link,
-)
-from pip._internal.utils.hashes import Hashes
-from pip._internal.utils.misc import (
-    ask_path_exists,
-    backup_dir,
-    display_path,
-    hide_url,
-    redact_auth_from_url,
-)
-from pip._internal.utils.packaging import safe_extra
-from pip._internal.utils.subprocess import runner_with_spinner_message
-from pip._internal.utils.temp_dir import TempDirectory, tempdir_kinds
-from pip._internal.utils.virtualenv import running_under_virtualenv
-from pip._internal.vcs import vcs
-
-logger = logging.getLogger(__name__)
-
-
-class InstallRequirement:
-    """
-    Represents something that may be installed later on, may have information
-    about where to fetch the relevant requirement and also contains logic for
-    installing the said requirement.
-    """
-
-    def __init__(
-        self,
-        req: Optional[Requirement],
-        comes_from: Optional[Union[str, "InstallRequirement"]],
-        editable: bool = False,
-        link: Optional[Link] = None,
-        markers: Optional[Marker] = None,
-        use_pep517: Optional[bool] = None,
-        isolated: bool = False,
-        install_options: Optional[List[str]] = None,
-        global_options: Optional[List[str]] = None,
-        hash_options: Optional[Dict[str, List[str]]] = None,
-        constraint: bool = False,
-        extras: Collection[str] = (),
-        user_supplied: bool = False,
-        permit_editable_wheels: bool = False,
-    ) -> None:
-        assert req is None or isinstance(req, Requirement), req
-        self.req = req
-        self.comes_from = comes_from
-        self.constraint = constraint
-        self.editable = editable
-        self.permit_editable_wheels = permit_editable_wheels
-        self.legacy_install_reason: Optional[int] = None
-
-        # source_dir is the local directory where the linked requirement is
-        # located, or unpacked. In case unpacking is needed, creating and
-        # populating source_dir is done by the RequirementPreparer. Note this
-        # is not necessarily the directory where pyproject.toml or setup.py is
-        # located - that one is obtained via unpacked_source_directory.
-        self.source_dir: Optional[str] = None
-        if self.editable:
-            assert link
-            if link.is_file:
-                self.source_dir = os.path.normpath(os.path.abspath(link.file_path))
-
-        if link is None and req and req.url:
-            # PEP 508 URL requirement
-            link = Link(req.url)
-        self.link = self.original_link = link
-        self.original_link_is_in_wheel_cache = False
-
-        # Path to any downloaded or already-existing package.
-        self.local_file_path: Optional[str] = None
-        if self.link and self.link.is_file:
-            self.local_file_path = self.link.file_path
-
-        if extras:
-            self.extras = extras
-        elif req:
-            self.extras = {safe_extra(extra) for extra in req.extras}
-        else:
-            self.extras = set()
-        if markers is None and req:
-            markers = req.marker
-        self.markers = markers
-
-        # This holds the Distribution object if this requirement is already installed.
-        self.satisfied_by: Optional[BaseDistribution] = None
-        # Whether the installation process should try to uninstall an existing
-        # distribution before installing this requirement.
-        self.should_reinstall = False
-        # Temporary build location
-        self._temp_build_dir: Optional[TempDirectory] = None
-        # Set to True after successful installation
-        self.install_succeeded: Optional[bool] = None
-        # Supplied options
-        self.install_options = install_options if install_options else []
-        self.global_options = global_options if global_options else []
-        self.hash_options = hash_options if hash_options else {}
-        # Set to True after successful preparation of this requirement
-        self.prepared = False
-        # User supplied requirement are explicitly requested for installation
-        # by the user via CLI arguments or requirements files, as opposed to,
-        # e.g. dependencies, extras or constraints.
-        self.user_supplied = user_supplied
-
-        self.isolated = isolated
-        self.build_env: BuildEnvironment = NoOpBuildEnvironment()
-
-        # For PEP 517, the directory where we request the project metadata
-        # gets stored. We need this to pass to build_wheel, so the backend
-        # can ensure that the wheel matches the metadata (see the PEP for
-        # details).
-        self.metadata_directory: Optional[str] = None
-
-        # The static build requirements (from pyproject.toml)
-        self.pyproject_requires: Optional[List[str]] = None
-
-        # Build requirements that we will check are available
-        self.requirements_to_check: List[str] = []
-
-        # The PEP 517 backend we should use to build the project
-        self.pep517_backend: Optional[Pep517HookCaller] = None
-
-        # Are we using PEP 517 for this requirement?
-        # After pyproject.toml has been loaded, the only valid values are True
-        # and False. Before loading, None is valid (meaning "use the default").
-        # Setting an explicit value before loading pyproject.toml is supported,
-        # but after loading this flag should be treated as read only.
-        self.use_pep517 = use_pep517
-
-        # This requirement needs more preparation before it can be built
-        self.needs_more_preparation = False
-
-    def __str__(self) -> str:
-        if self.req:
-            s = str(self.req)
-            if self.link:
-                s += " from {}".format(redact_auth_from_url(self.link.url))
-        elif self.link:
-            s = redact_auth_from_url(self.link.url)
-        else:
-            s = "<InstallRequirement>"
-        if self.satisfied_by is not None:
-            s += " in {}".format(display_path(self.satisfied_by.location))
-        if self.comes_from:
-            if isinstance(self.comes_from, str):
-                comes_from: Optional[str] = self.comes_from
-            else:
-                comes_from = self.comes_from.from_path()
-            if comes_from:
-                s += f" (from {comes_from})"
-        return s
-
-    def __repr__(self) -> str:
-        return "<{} object: {} editable={!r}>".format(
-            self.__class__.__name__, str(self), self.editable
-        )
-
-    def format_debug(self) -> str:
-        """An un-tested helper for getting state, for debugging."""
-        attributes = vars(self)
-        names = sorted(attributes)
-
-        state = ("{}={!r}".format(attr, attributes[attr]) for attr in sorted(names))
-        return "<{name} object: {{{state}}}>".format(
-            name=self.__class__.__name__,
-            state=", ".join(state),
-        )
-
-    # Things that are valid for all kinds of requirements?
-    @property
-    def name(self) -> Optional[str]:
-        if self.req is None:
-            return None
-        return self.req.name
-
-    @functools.lru_cache()  # use cached_property in python 3.8+
-    def supports_pyproject_editable(self) -> bool:
-        if not self.use_pep517:
-            return False
-        assert self.pep517_backend
-        with self.build_env:
-            runner = runner_with_spinner_message(
-                "Checking if build backend supports build_editable"
-            )
-            with self.pep517_backend.subprocess_runner(runner):
-                return "build_editable" in self.pep517_backend._supported_features()
-
-    @property
-    def specifier(self) -> SpecifierSet:
-        return self.req.specifier
-
-    @property
-    def is_pinned(self) -> bool:
-        """Return whether I am pinned to an exact version.
-
-        For example, some-package==1.2 is pinned; some-package>1.2 is not.
-        """
-        specifiers = self.specifier
-        return len(specifiers) == 1 and next(iter(specifiers)).operator in {"==", "==="}
-
-    def match_markers(self, extras_requested: Optional[Iterable[str]] = None) -> bool:
-        if not extras_requested:
-            # Provide an extra to safely evaluate the markers
-            # without matching any extra
-            extras_requested = ("",)
-        if self.markers is not None:
-            return any(
-                self.markers.evaluate({"extra": extra}) for extra in extras_requested
-            )
-        else:
-            return True
-
-    @property
-    def has_hash_options(self) -> bool:
-        """Return whether any known-good hashes are specified as options.
-
-        These activate --require-hashes mode; hashes specified as part of a
-        URL do not.
-
-        """
-        return bool(self.hash_options)
-
-    def hashes(self, trust_internet: bool = True) -> Hashes:
-        """Return a hash-comparer that considers my option- and URL-based
-        hashes to be known-good.
-
-        Hashes in URLs--ones embedded in the requirements file, not ones
-        downloaded from an index server--are almost peers with ones from
-        flags. They satisfy --require-hashes (whether it was implicitly or
-        explicitly activated) but do not activate it. md5 and sha224 are not
-        allowed in flags, which should nudge people toward good algos. We
-        always OR all hashes together, even ones from URLs.
-
-        :param trust_internet: Whether to trust URL-based (#md5=...) hashes
-            downloaded from the internet, as by populate_link()
-
-        """
-        good_hashes = self.hash_options.copy()
-        link = self.link if trust_internet else self.original_link
-        if link and link.hash:
-            good_hashes.setdefault(link.hash_name, []).append(link.hash)
-        return Hashes(good_hashes)
-
-    def from_path(self) -> Optional[str]:
-        """Format a nice indicator to show where this "comes from" """
-        if self.req is None:
-            return None
-        s = str(self.req)
-        if self.comes_from:
-            if isinstance(self.comes_from, str):
-                comes_from = self.comes_from
-            else:
-                comes_from = self.comes_from.from_path()
-            if comes_from:
-                s += "->" + comes_from
-        return s
-
-    def ensure_build_location(
-        self, build_dir: str, autodelete: bool, parallel_builds: bool
-    ) -> str:
-        assert build_dir is not None
-        if self._temp_build_dir is not None:
-            assert self._temp_build_dir.path
-            return self._temp_build_dir.path
-        if self.req is None:
-            # Some systems have /tmp as a symlink which confuses custom
-            # builds (such as numpy). Thus, we ensure that the real path
-            # is returned.
-            self._temp_build_dir = TempDirectory(
-                kind=tempdir_kinds.REQ_BUILD, globally_managed=True
-            )
-
-            return self._temp_build_dir.path
-
-        # This is the only remaining place where we manually determine the path
-        # for the temporary directory. It is only needed for editables where
-        # it is the value of the --src option.
-
-        # When parallel builds are enabled, add a UUID to the build directory
-        # name so multiple builds do not interfere with each other.
-        dir_name: str = canonicalize_name(self.name)
-        if parallel_builds:
-            dir_name = f"{dir_name}_{uuid.uuid4().hex}"
-
-        # FIXME: Is there a better place to create the build_dir? (hg and bzr
-        # need this)
-        if not os.path.exists(build_dir):
-            logger.debug("Creating directory %s", build_dir)
-            os.makedirs(build_dir)
-        actual_build_dir = os.path.join(build_dir, dir_name)
-        # `None` indicates that we respect the globally-configured deletion
-        # settings, which is what we actually want when auto-deleting.
-        delete_arg = None if autodelete else False
-        return TempDirectory(
-            path=actual_build_dir,
-            delete=delete_arg,
-            kind=tempdir_kinds.REQ_BUILD,
-            globally_managed=True,
-        ).path
-
-    def _set_requirement(self) -> None:
-        """Set requirement after generating metadata."""
-        assert self.req is None
-        assert self.metadata is not None
-        assert self.source_dir is not None
-
-        # Construct a Requirement object from the generated metadata
-        if isinstance(parse_version(self.metadata["Version"]), Version):
-            op = "=="
-        else:
-            op = "==="
-
-        self.req = Requirement(
-            "".join(
-                [
-                    self.metadata["Name"],
-                    op,
-                    self.metadata["Version"],
-                ]
-            )
-        )
-
-    def warn_on_mismatching_name(self) -> None:
-        metadata_name = canonicalize_name(self.metadata["Name"])
-        if canonicalize_name(self.req.name) == metadata_name:
-            # Everything is fine.
-            return
-
-        # If we're here, there's a mismatch. Log a warning about it.
-        logger.warning(
-            "Generating metadata for package %s "
-            "produced metadata for project name %s. Fix your "
-            "#egg=%s fragments.",
-            self.name,
-            metadata_name,
-            self.name,
-        )
-        self.req = Requirement(metadata_name)
-
-    def check_if_exists(self, use_user_site: bool) -> None:
-        """Find an installed distribution that satisfies or conflicts
-        with this requirement, and set self.satisfied_by or
-        self.should_reinstall appropriately.
-        """
-        if self.req is None:
-            return
-        existing_dist = get_default_environment().get_distribution(self.req.name)
-        if not existing_dist:
-            return
-
-        version_compatible = self.req.specifier.contains(
-            existing_dist.version,
-            prereleases=True,
-        )
-        if not version_compatible:
-            self.satisfied_by = None
-            if use_user_site:
-                if existing_dist.in_usersite:
-                    self.should_reinstall = True
-                elif running_under_virtualenv() and existing_dist.in_site_packages:
-                    raise InstallationError(
-                        f"Will not install to the user site because it will "
-                        f"lack sys.path precedence to {existing_dist.raw_name} "
-                        f"in {existing_dist.location}"
-                    )
-            else:
-                self.should_reinstall = True
-        else:
-            if self.editable:
-                self.should_reinstall = True
-                # when installing editables, nothing pre-existing should ever
-                # satisfy
-                self.satisfied_by = None
-            else:
-                self.satisfied_by = existing_dist
-
-    # Things valid for wheels
-    @property
-    def is_wheel(self) -> bool:
-        if not self.link:
-            return False
-        return self.link.is_wheel
-
-    # Things valid for sdists
-    @property
-    def unpacked_source_directory(self) -> str:
-        return os.path.join(
-            self.source_dir, self.link and self.link.subdirectory_fragment or ""
-        )
-
-    @property
-    def setup_py_path(self) -> str:
-        assert self.source_dir, f"No source dir for {self}"
-        setup_py = os.path.join(self.unpacked_source_directory, "setup.py")
-
-        return setup_py
-
-    @property
-    def setup_cfg_path(self) -> str:
-        assert self.source_dir, f"No source dir for {self}"
-        setup_cfg = os.path.join(self.unpacked_source_directory, "setup.cfg")
-
-        return setup_cfg
-
-    @property
-    def pyproject_toml_path(self) -> str:
-        assert self.source_dir, f"No source dir for {self}"
-        return make_pyproject_path(self.unpacked_source_directory)
-
-    def load_pyproject_toml(self) -> None:
-        """Load the pyproject.toml file.
-
-        After calling this routine, all of the attributes related to PEP 517
-        processing for this requirement have been set. In particular, the
-        use_pep517 attribute can be used to determine whether we should
-        follow the PEP 517 or legacy (setup.py) code path.
-        """
-        pyproject_toml_data = load_pyproject_toml(
-            self.use_pep517, self.pyproject_toml_path, self.setup_py_path, str(self)
-        )
-
-        if pyproject_toml_data is None:
-            self.use_pep517 = False
-            return
-
-        self.use_pep517 = True
-        requires, backend, check, backend_path = pyproject_toml_data
-        self.requirements_to_check = check
-        self.pyproject_requires = requires
-        self.pep517_backend = Pep517HookCaller(
-            self.unpacked_source_directory,
-            backend,
-            backend_path=backend_path,
-        )
-
-    def isolated_editable_sanity_check(self) -> None:
-        """Check that an editable requirement if valid for use with PEP 517/518.
-
-        This verifies that an editable that has a pyproject.toml either supports PEP 660
-        or as a setup.py or a setup.cfg
-        """
-        if (
-            self.editable
-            and self.use_pep517
-            and not self.supports_pyproject_editable()
-            and not os.path.isfile(self.setup_py_path)
-            and not os.path.isfile(self.setup_cfg_path)
-        ):
-            raise InstallationError(
-                f"Project {self} has a 'pyproject.toml' and its build "
-                f"backend is missing the 'build_editable' hook. Since it does not "
-                f"have a 'setup.py' nor a 'setup.cfg', "
-                f"it cannot be installed in editable mode. "
-                f"Consider using a build backend that supports PEP 660."
-            )
-
-    def prepare_metadata(self) -> None:
-        """Ensure that project metadata is available.
-
-        Under PEP 517 and PEP 660, call the backend hook to prepare the metadata.
-        Under legacy processing, call setup.py egg-info.
-        """
-        assert self.source_dir
-        details = self.name or f"from {self.link}"
-
-        if self.use_pep517:
-            assert self.pep517_backend is not None
-            if (
-                self.editable
-                and self.permit_editable_wheels
-                and self.supports_pyproject_editable()
-            ):
-                self.metadata_directory = generate_editable_metadata(
-                    build_env=self.build_env,
-                    backend=self.pep517_backend,
-                    details=details,
-                )
-            else:
-                self.metadata_directory = generate_metadata(
-                    build_env=self.build_env,
-                    backend=self.pep517_backend,
-                    details=details,
-                )
-        else:
-            self.metadata_directory = generate_metadata_legacy(
-                build_env=self.build_env,
-                setup_py_path=self.setup_py_path,
-                source_dir=self.unpacked_source_directory,
-                isolated=self.isolated,
-                details=details,
-            )
-
-        # Act on the newly generated metadata, based on the name and version.
-        if not self.name:
-            self._set_requirement()
-        else:
-            self.warn_on_mismatching_name()
-
-        self.assert_source_matches_version()
-
-    @property
-    def metadata(self) -> Any:
-        if not hasattr(self, "_metadata"):
-            self._metadata = self.get_dist().metadata
-
-        return self._metadata
-
-    def get_dist(self) -> BaseDistribution:
-        return get_directory_distribution(self.metadata_directory)
-
-    def assert_source_matches_version(self) -> None:
-        assert self.source_dir
-        version = self.metadata["version"]
-        if self.req.specifier and version not in self.req.specifier:
-            logger.warning(
-                "Requested %s, but installing version %s",
-                self,
-                version,
-            )
-        else:
-            logger.debug(
-                "Source in %s has version %s, which satisfies requirement %s",
-                display_path(self.source_dir),
-                version,
-                self,
-            )
-
-    # For both source distributions and editables
-    def ensure_has_source_dir(
-        self,
-        parent_dir: str,
-        autodelete: bool = False,
-        parallel_builds: bool = False,
-    ) -> None:
-        """Ensure that a source_dir is set.
-
-        This will create a temporary build dir if the name of the requirement
-        isn't known yet.
-
-        :param parent_dir: The ideal pip parent_dir for the source_dir.
-            Generally src_dir for editables and build_dir for sdists.
-        :return: self.source_dir
-        """
-        if self.source_dir is None:
-            self.source_dir = self.ensure_build_location(
-                parent_dir,
-                autodelete=autodelete,
-                parallel_builds=parallel_builds,
-            )
-
-    # For editable installations
-    def update_editable(self) -> None:
-        if not self.link:
-            logger.debug(
-                "Cannot update repository at %s; repository location is unknown",
-                self.source_dir,
-            )
-            return
-        assert self.editable
-        assert self.source_dir
-        if self.link.scheme == "file":
-            # Static paths don't get updated
-            return
-        vcs_backend = vcs.get_backend_for_scheme(self.link.scheme)
-        # Editable requirements are validated in Requirement constructors.
-        # So here, if it's neither a path nor a valid VCS URL, it's a bug.
-        assert vcs_backend, f"Unsupported VCS URL {self.link.url}"
-        hidden_url = hide_url(self.link.url)
-        vcs_backend.obtain(self.source_dir, url=hidden_url, verbosity=0)
-
-    # Top-level Actions
-    def uninstall(
-        self, auto_confirm: bool = False, verbose: bool = False
-    ) -> Optional[UninstallPathSet]:
-        """
-        Uninstall the distribution currently satisfying this requirement.
-
-        Prompts before removing or modifying files unless
-        ``auto_confirm`` is True.
-
-        Refuses to delete or modify files outside of ``sys.prefix`` -
-        thus uninstallation within a virtual environment can only
-        modify that virtual environment, even if the virtualenv is
-        linked to global site-packages.
-
-        """
-        assert self.req
-        dist = get_default_environment().get_distribution(self.req.name)
-        if not dist:
-            logger.warning("Skipping %s as it is not installed.", self.name)
-            return None
-        logger.info("Found existing installation: %s", dist)
-
-        uninstalled_pathset = UninstallPathSet.from_dist(dist)
-        uninstalled_pathset.remove(auto_confirm, verbose)
-        return uninstalled_pathset
-
-    def _get_archive_name(self, path: str, parentdir: str, rootdir: str) -> str:
-        def _clean_zip_name(name: str, prefix: str) -> str:
-            assert name.startswith(
-                prefix + os.path.sep
-            ), f"name {name!r} doesn't start with prefix {prefix!r}"
-            name = name[len(prefix) + 1 :]
-            name = name.replace(os.path.sep, "/")
-            return name
-
-        path = os.path.join(parentdir, path)
-        name = _clean_zip_name(path, rootdir)
-        return self.name + "/" + name
-
-    def archive(self, build_dir: Optional[str]) -> None:
-        """Saves archive to provided build_dir.
-
-        Used for saving downloaded VCS requirements as part of `pip download`.
-        """
-        assert self.source_dir
-        if build_dir is None:
-            return
-
-        create_archive = True
-        archive_name = "{}-{}.zip".format(self.name, self.metadata["version"])
-        archive_path = os.path.join(build_dir, archive_name)
-
-        if os.path.exists(archive_path):
-            response = ask_path_exists(
-                "The file {} exists. (i)gnore, (w)ipe, "
-                "(b)ackup, (a)bort ".format(display_path(archive_path)),
-                ("i", "w", "b", "a"),
-            )
-            if response == "i":
-                create_archive = False
-            elif response == "w":
-                logger.warning("Deleting %s", display_path(archive_path))
-                os.remove(archive_path)
-            elif response == "b":
-                dest_file = backup_dir(archive_path)
-                logger.warning(
-                    "Backing up %s to %s",
-                    display_path(archive_path),
-                    display_path(dest_file),
-                )
-                shutil.move(archive_path, dest_file)
-            elif response == "a":
-                sys.exit(-1)
-
-        if not create_archive:
-            return
-
-        zip_output = zipfile.ZipFile(
-            archive_path,
-            "w",
-            zipfile.ZIP_DEFLATED,
-            allowZip64=True,
-        )
-        with zip_output:
-            dir = os.path.normcase(os.path.abspath(self.unpacked_source_directory))
-            for dirpath, dirnames, filenames in os.walk(dir):
-                for dirname in dirnames:
-                    dir_arcname = self._get_archive_name(
-                        dirname,
-                        parentdir=dirpath,
-                        rootdir=dir,
-                    )
-                    zipdir = zipfile.ZipInfo(dir_arcname + "/")
-                    zipdir.external_attr = 0x1ED << 16  # 0o755
-                    zip_output.writestr(zipdir, "")
-                for filename in filenames:
-                    file_arcname = self._get_archive_name(
-                        filename,
-                        parentdir=dirpath,
-                        rootdir=dir,
-                    )
-                    filename = os.path.join(dirpath, filename)
-                    zip_output.write(filename, file_arcname)
-
-        logger.info("Saved %s", display_path(archive_path))
-
-    def install(
-        self,
-        install_options: List[str],
-        global_options: Optional[Sequence[str]] = None,
-        root: Optional[str] = None,
-        home: Optional[str] = None,
-        prefix: Optional[str] = None,
-        warn_script_location: bool = True,
-        use_user_site: bool = False,
-        pycompile: bool = True,
-    ) -> None:
-        scheme = get_scheme(
-            self.name,
-            user=use_user_site,
-            home=home,
-            root=root,
-            isolated=self.isolated,
-            prefix=prefix,
-        )
-
-        global_options = global_options if global_options is not None else []
-        if self.editable and not self.is_wheel:
-            install_editable_legacy(
-                install_options,
-                global_options,
-                prefix=prefix,
-                home=home,
-                use_user_site=use_user_site,
-                name=self.name,
-                setup_py_path=self.setup_py_path,
-                isolated=self.isolated,
-                build_env=self.build_env,
-                unpacked_source_directory=self.unpacked_source_directory,
-            )
-            self.install_succeeded = True
-            return
-
-        if self.is_wheel:
-            assert self.local_file_path
-            direct_url = None
-            if self.editable:
-                direct_url = direct_url_for_editable(self.unpacked_source_directory)
-            elif self.original_link:
-                direct_url = direct_url_from_link(
-                    self.original_link,
-                    self.source_dir,
-                    self.original_link_is_in_wheel_cache,
-                )
-            install_wheel(
-                self.name,
-                self.local_file_path,
-                scheme=scheme,
-                req_description=str(self.req),
-                pycompile=pycompile,
-                warn_script_location=warn_script_location,
-                direct_url=direct_url,
-                requested=self.user_supplied,
-            )
-            self.install_succeeded = True
-            return
-
-        # TODO: Why don't we do this for editable installs?
-
-        # Extend the list of global and install options passed on to
-        # the setup.py call with the ones from the requirements file.
-        # Options specified in requirements file override those
-        # specified on the command line, since the last option given
-        # to setup.py is the one that is used.
-        global_options = list(global_options) + self.global_options
-        install_options = list(install_options) + self.install_options
-
-        try:
-            success = install_legacy(
-                install_options=install_options,
-                global_options=global_options,
-                root=root,
-                home=home,
-                prefix=prefix,
-                use_user_site=use_user_site,
-                pycompile=pycompile,
-                scheme=scheme,
-                setup_py_path=self.setup_py_path,
-                isolated=self.isolated,
-                req_name=self.name,
-                build_env=self.build_env,
-                unpacked_source_directory=self.unpacked_source_directory,
-                req_description=str(self.req),
-            )
-        except LegacyInstallFailure as exc:
-            self.install_succeeded = False
-            raise exc
-        except Exception:
-            self.install_succeeded = True
-            raise
-
-        self.install_succeeded = success
-
-        if success and self.legacy_install_reason == 8368:
-            deprecated(
-                reason=(
-                    "{} was installed using the legacy 'setup.py install' "
-                    "method, because a wheel could not be built for it.".format(
-                        self.name
-                    )
-                ),
-                replacement="to fix the wheel build issue reported above",
-                gone_in=None,
-                issue=8368,
-            )
-
-
-def check_invalid_constraint_type(req: InstallRequirement) -> str:
-
-    # Check for unsupported forms
-    problem = ""
-    if not req.name:
-        problem = "Unnamed requirements are not allowed as constraints"
-    elif req.editable:
-        problem = "Editable requirements are not allowed as constraints"
-    elif req.extras:
-        problem = "Constraints cannot have extras"
-
-    if problem:
-        deprecated(
-            reason=(
-                "Constraints are only allowed to take the form of a package "
-                "name and a version specifier. Other forms were originally "
-                "permitted as an accident of the implementation, but were "
-                "undocumented. The new implementation of the resolver no "
-                "longer supports these forms."
-            ),
-            replacement="replacing the constraint with a requirement",
-            # No plan yet for when the new resolver becomes default
-            gone_in=None,
-            issue=8210,
-        )
-
-    return problem
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pygments/lexers/pointless.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pygments/lexers/pointless.py
deleted file mode 100644
index eb73b2a795d7c7d4d3efbef3b2abdbbd882e93c6..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pygments/lexers/pointless.py
+++ /dev/null
@@ -1,71 +0,0 @@
-"""
-    pygments.lexers.pointless
-    ~~~~~~~~~~~~~~~~~~~~~~~~~
-
-    Lexers for Pointless.
-
-    :copyright: Copyright 2006-2023 by the Pygments team, see AUTHORS.
-    :license: BSD, see LICENSE for details.
-"""
-
-from pygments.lexer import RegexLexer, words
-from pygments.token import Comment, Error, Keyword, Name, Number, Operator, \
-    Punctuation, String, Text
-
-__all__ = ['PointlessLexer']
-
-
-class PointlessLexer(RegexLexer):
-    """
-    For Pointless source code.
-
-    .. versionadded:: 2.7
-    """
-
-    name = 'Pointless'
-    url = 'https://ptls.dev'
-    aliases = ['pointless']
-    filenames = ['*.ptls']
-
-    ops = words([
-        "+", "-", "*", "/", "**", "%", "+=", "-=", "*=",
-        "/=", "**=", "%=", "|>", "=", "==", "!=", "<", ">",
-        "<=", ">=", "=>", "$", "++",
-    ])
-
-    keywords = words([
-        "if", "then", "else", "where", "with", "cond",
-        "case", "and", "or", "not", "in", "as", "for",
-        "requires", "throw", "try", "catch", "when",
-        "yield", "upval",
-    ], suffix=r'\b')
-
-    tokens = {
-        'root': [
-            (r'[ \n\r]+', Text),
-            (r'--.*$', Comment.Single),
-            (r'"""', String, 'multiString'),
-            (r'"', String, 'string'),
-            (r'[\[\](){}:;,.]', Punctuation),
-            (ops, Operator),
-            (keywords, Keyword),
-            (r'\d+|\d*\.\d+', Number),
-            (r'(true|false)\b', Name.Builtin),
-            (r'[A-Z][a-zA-Z0-9]*\b', String.Symbol),
-            (r'output\b', Name.Variable.Magic),
-            (r'(export|import)\b', Keyword.Namespace),
-            (r'[a-z][a-zA-Z0-9]*\b', Name.Variable)
-        ],
-        'multiString': [
-            (r'\\.', String.Escape),
-            (r'"""', String, '#pop'),
-            (r'"', String),
-            (r'[^\\"]+', String),
-        ],
-        'string': [
-            (r'\\.', String.Escape),
-            (r'"', String, '#pop'),
-            (r'\n', Error),
-            (r'[^\\"]+', String),
-        ],
-    }
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/tqdm/_main.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/tqdm/_main.py
deleted file mode 100644
index 04fdeeff17b5cc84b210f445b54b87d5b99e3748..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/tqdm/_main.py
+++ /dev/null
@@ -1,9 +0,0 @@
-from warnings import warn
-
-from .cli import *  # NOQA
-from .cli import __all__  # NOQA
-from .std import TqdmDeprecationWarning
-
-warn("This function will be removed in tqdm==5.0.0\n"
-     "Please use `tqdm.cli.*` instead of `tqdm._main.*`",
-     TqdmDeprecationWarning, stacklevel=2)
diff --git a/spaces/pseudolab/K23MiniMed/README.md b/spaces/pseudolab/K23MiniMed/README.md
deleted file mode 100644
index afde5b3ebf998f8ed59e0cc1b2320b8607d57e14..0000000000000000000000000000000000000000
--- a/spaces/pseudolab/K23MiniMed/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: K23MiniMed
-emoji: ⚕️
-colorFrom: red
-colorTo: orange
-sdk: gradio
-sdk_version: 3.44.4
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-join my builder's server : https://discord.gg/VqTxc76K3u
\ No newline at end of file
diff --git a/spaces/qingxu98/gpt-academic/request_llm/bridge_newbingfree.py b/spaces/qingxu98/gpt-academic/request_llm/bridge_newbingfree.py
deleted file mode 100644
index cc6e9b733b48fe255c15bc9fae3c9abd74f276ca..0000000000000000000000000000000000000000
--- a/spaces/qingxu98/gpt-academic/request_llm/bridge_newbingfree.py
+++ /dev/null
@@ -1,245 +0,0 @@
-"""
-========================================================================
-第一部分：来自EdgeGPT.py
-https://github.com/acheong08/EdgeGPT
-========================================================================
-"""
-from .edge_gpt_free import Chatbot as NewbingChatbot
-load_message = "等待NewBing响应。"
-
-"""
-========================================================================
-第二部分：子进程Worker（调用主体）
-========================================================================
-"""
-import time
-import json
-import re
-import logging
-import asyncio
-import importlib
-import threading
-from toolbox import update_ui, get_conf, trimmed_format_exc
-from multiprocessing import Process, Pipe
-
-def preprocess_newbing_out(s):
-    pattern = r'\^(\d+)\^' # 匹配^数字^
-    sub = lambda m: '('+m.group(1)+')' # 将匹配到的数字作为替换值
-    result = re.sub(pattern, sub, s) # 替换操作
-    if '[1]' in result:
-        result += '\n\n```reference\n' + "\n".join([r for r in result.split('\n') if r.startswith('[')]) + '\n```\n'
-    return result
-
-def preprocess_newbing_out_simple(result):
-    if '[1]' in result:
-        result += '\n\n```reference\n' + "\n".join([r for r in result.split('\n') if r.startswith('[')]) + '\n```\n'
-    return result
-
-class NewBingHandle(Process):
-    def __init__(self):
-        super().__init__(daemon=True)
-        self.parent, self.child = Pipe()
-        self.newbing_model = None
-        self.info = ""
-        self.success = True
-        self.local_history = []
-        self.check_dependency()
-        self.start()
-        self.threadLock = threading.Lock()
-        
-    def check_dependency(self):
-        try:
-            self.success = False
-            import certifi, httpx, rich
-            self.info = "依赖检测通过，等待NewBing响应。注意目前不能多人同时调用NewBing接口（有线程锁），否则将导致每个人的NewBing问询历史互相渗透。调用NewBing时，会自动使用已配置的代理。"
-            self.success = True
-        except:
-            self.info = "缺少的依赖，如果要使用Newbing，除了基础的pip依赖以外，您还需要运行`pip install -r request_llm/requirements_newbing.txt`安装Newbing的依赖。"
-            self.success = False
-
-    def ready(self):
-        return self.newbing_model is not None
-
-    async def async_run(self):
-        # 读取配置
-        NEWBING_STYLE, = get_conf('NEWBING_STYLE')
-        from request_llm.bridge_all import model_info
-        endpoint = model_info['newbing']['endpoint']
-        while True:
-            # 等待
-            kwargs = self.child.recv()
-            question=kwargs['query']
-            history=kwargs['history']
-            system_prompt=kwargs['system_prompt']
-
-            # 是否重置
-            if len(self.local_history) > 0 and len(history)==0:
-                await self.newbing_model.reset()
-                self.local_history = []
-
-            # 开始问问题
-            prompt = ""
-            if system_prompt not in self.local_history:
-                self.local_history.append(system_prompt)
-                prompt += system_prompt + '\n'
-
-            # 追加历史
-            for ab in history:
-                a, b = ab
-                if a not in self.local_history:
-                    self.local_history.append(a)
-                    prompt += a + '\n'
-
-            # 问题
-            prompt += question
-            self.local_history.append(question)
-            print('question:', prompt)
-            # 提交
-            async for final, response in self.newbing_model.ask_stream(
-                prompt=question,
-                conversation_style=NEWBING_STYLE,     # ["creative", "balanced", "precise"]
-                wss_link=endpoint,                    # "wss://sydney.bing.com/sydney/ChatHub"
-            ):
-                if not final:
-                    print(response)
-                    self.child.send(str(response))
-                else:
-                    print('-------- receive final ---------')
-                    self.child.send('[Finish]')
-                    # self.local_history.append(response)
-
-    
-    def run(self):
-        """
-        这个函数运行在子进程
-        """
-        # 第一次运行，加载参数
-        self.success = False
-        self.local_history = []
-        if (self.newbing_model is None) or (not self.success):
-            # 代理设置
-            proxies, NEWBING_COOKIES = get_conf('proxies', 'NEWBING_COOKIES')
-            if proxies is None: 
-                self.proxies_https = None
-            else: 
-                self.proxies_https = proxies['https']
-
-            if (NEWBING_COOKIES is not None) and len(NEWBING_COOKIES) > 100:
-                try:
-                    cookies = json.loads(NEWBING_COOKIES)
-                except:
-                    self.success = False
-                    tb_str = '\n```\n' + trimmed_format_exc() + '\n```\n'
-                    self.child.send(f'[Local Message] NEWBING_COOKIES未填写或有格式错误。')
-                    self.child.send('[Fail]'); self.child.send('[Finish]')
-                    raise RuntimeError(f"NEWBING_COOKIES未填写或有格式错误。")
-            else:
-                cookies = None
-
-            try:
-                self.newbing_model = NewbingChatbot(proxy=self.proxies_https, cookies=cookies)
-            except:
-                self.success = False
-                tb_str = '\n```\n' + trimmed_format_exc() + '\n```\n'
-                self.child.send(f'[Local Message] 不能加载Newbing组件。{tb_str}')
-                self.child.send('[Fail]')
-                self.child.send('[Finish]')
-                raise RuntimeError(f"不能加载Newbing组件。")
-
-        self.success = True
-        try:
-            # 进入任务等待状态
-            asyncio.run(self.async_run())
-        except Exception:
-            tb_str = '\n```\n' + trimmed_format_exc() + '\n```\n'
-            self.child.send(f'[Local Message] Newbing 请求失败，报错信息如下. 如果是与网络相关的问题，建议更换代理协议（推荐http）或代理节点 {tb_str}.')
-            self.child.send('[Fail]')
-            self.child.send('[Finish]')
-        
-    def stream_chat(self, **kwargs):
-        """
-        这个函数运行在主进程
-        """
-        self.threadLock.acquire()   # 获取线程锁
-        self.parent.send(kwargs)    # 请求子进程
-        while True:
-            res = self.parent.recv()                            # 等待newbing回复的片段
-            if res == '[Finish]': break                         # 结束
-            elif res == '[Fail]': self.success = False; break   # 失败
-            else: yield res                                     # newbing回复的片段
-        self.threadLock.release()   # 释放线程锁
-
-
-"""
-========================================================================
-第三部分：主进程统一调用函数接口
-========================================================================
-"""
-global newbingfree_handle
-newbingfree_handle = None
-
-def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=[], console_slience=False):
-    """
-        多线程方法
-        函数的说明请见 request_llm/bridge_all.py
-    """
-    global newbingfree_handle
-    if (newbingfree_handle is None) or (not newbingfree_handle.success):
-        newbingfree_handle = NewBingHandle()
-        if len(observe_window) >= 1: observe_window[0] = load_message + "\n\n" + newbingfree_handle.info
-        if not newbingfree_handle.success: 
-            error = newbingfree_handle.info
-            newbingfree_handle = None
-            raise RuntimeError(error)
-
-    # 没有 sys_prompt 接口，因此把prompt加入 history
-    history_feedin = []
-    for i in range(len(history)//2):
-        history_feedin.append([history[2*i], history[2*i+1]] )
-
-    watch_dog_patience = 5 # 看门狗 (watchdog) 的耐心, 设置5秒即可
-    response = ""
-    if len(observe_window) >= 1: observe_window[0] = "[Local Message]: 等待NewBing响应中 ..."
-    for response in newbingfree_handle.stream_chat(query=inputs, history=history_feedin, system_prompt=sys_prompt, max_length=llm_kwargs['max_length'], top_p=llm_kwargs['top_p'], temperature=llm_kwargs['temperature']):
-        if len(observe_window) >= 1:  observe_window[0] = preprocess_newbing_out_simple(response)
-        if len(observe_window) >= 2:  
-            if (time.time()-observe_window[1]) > watch_dog_patience:
-                raise RuntimeError("程序终止。")
-    return preprocess_newbing_out_simple(response)
-
-def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream = True, additional_fn=None):
-    """
-        单线程方法
-        函数的说明请见 request_llm/bridge_all.py
-    """
-    chatbot.append((inputs, "[Local Message]: 等待NewBing响应中 ..."))
-
-    global newbingfree_handle
-    if (newbingfree_handle is None) or (not newbingfree_handle.success):
-        newbingfree_handle = NewBingHandle()
-        chatbot[-1] = (inputs, load_message + "\n\n" + newbingfree_handle.info)
-        yield from update_ui(chatbot=chatbot, history=[])
-        if not newbingfree_handle.success: 
-            newbingfree_handle = None
-            return
-
-    if additional_fn is not None:
-        from core_functional import handle_core_functionality
-        inputs, history = handle_core_functionality(additional_fn, inputs, history, chatbot)
-
-    history_feedin = []
-    for i in range(len(history)//2):
-        history_feedin.append([history[2*i], history[2*i+1]] )
-
-    chatbot[-1] = (inputs, "[Local Message]: 等待NewBing响应中 ...")
-    response = "[Local Message]: 等待NewBing响应中 ..."
-    yield from update_ui(chatbot=chatbot, history=history, msg="NewBing响应缓慢，尚未完成全部响应，请耐心完成后再提交新问题。")
-    for response in newbingfree_handle.stream_chat(query=inputs, history=history_feedin, system_prompt=system_prompt, max_length=llm_kwargs['max_length'], top_p=llm_kwargs['top_p'], temperature=llm_kwargs['temperature']):
-        chatbot[-1] = (inputs, preprocess_newbing_out(response))
-        yield from update_ui(chatbot=chatbot, history=history, msg="NewBing响应缓慢，尚未完成全部响应，请耐心完成后再提交新问题。")
-    if response == "[Local Message]: 等待NewBing响应中 ...": response = "[Local Message]: NewBing响应异常，请刷新界面重试 ..."
-    history.extend([inputs, response])
-    logging.info(f'[raw_input] {inputs}')
-    logging.info(f'[response] {response}')
-    yield from update_ui(chatbot=chatbot, history=history, msg="完成全部响应，请提交新问题。")
-
diff --git a/spaces/qinzhu/moe-tts-tech/text/sanskrit.py b/spaces/qinzhu/moe-tts-tech/text/sanskrit.py
deleted file mode 100644
index 0223aaac384a2f850f5bc20651fc18eb964607d0..0000000000000000000000000000000000000000
--- a/spaces/qinzhu/moe-tts-tech/text/sanskrit.py
+++ /dev/null
@@ -1,62 +0,0 @@
-import re
-from indic_transliteration import sanscript
-
-
-# List of (iast, ipa) pairs:
-_iast_to_ipa = [(re.compile('%s' % x[0]), x[1]) for x in [
-    ('a', 'ə'),
-    ('ā', 'aː'),
-    ('ī', 'iː'),
-    ('ū', 'uː'),
-    ('ṛ', 'ɹ`'),
-    ('ṝ', 'ɹ`ː'),
-    ('ḷ', 'l`'),
-    ('ḹ', 'l`ː'),
-    ('e', 'eː'),
-    ('o', 'oː'),
-    ('k', 'k⁼'),
-    ('k⁼h', 'kʰ'),
-    ('g', 'g⁼'),
-    ('g⁼h', 'gʰ'),
-    ('ṅ', 'ŋ'),
-    ('c', 'ʧ⁼'),
-    ('ʧ⁼h', 'ʧʰ'),
-    ('j', 'ʥ⁼'),
-    ('ʥ⁼h', 'ʥʰ'),
-    ('ñ', 'n^'),
-    ('ṭ', 't`⁼'),
-    ('t`⁼h', 't`ʰ'),
-    ('ḍ', 'd`⁼'),
-    ('d`⁼h', 'd`ʰ'),
-    ('ṇ', 'n`'),
-    ('t', 't⁼'),
-    ('t⁼h', 'tʰ'),
-    ('d', 'd⁼'),
-    ('d⁼h', 'dʰ'),
-    ('p', 'p⁼'),
-    ('p⁼h', 'pʰ'),
-    ('b', 'b⁼'),
-    ('b⁼h', 'bʰ'),
-    ('y', 'j'),
-    ('ś', 'ʃ'),
-    ('ṣ', 's`'),
-    ('r', 'ɾ'),
-    ('l̤', 'l`'),
-    ('h', 'ɦ'),
-    ("'", ''),
-    ('~', '^'),
-    ('ṃ', '^')
-]]
-
-
-def devanagari_to_ipa(text):
-    text = text.replace('ॐ', 'ओम्')
-    text = re.sub(r'\s*।\s*$', '.', text)
-    text = re.sub(r'\s*।\s*', ', ', text)
-    text = re.sub(r'\s*॥', '.', text)
-    text = sanscript.transliterate(text, sanscript.DEVANAGARI, sanscript.IAST)
-    for regex, replacement in _iast_to_ipa:
-        text = re.sub(regex, replacement, text)
-    text = re.sub('(.)[`ː]*ḥ', lambda x: x.group(0)
-                  [:-1]+'h'+x.group(1)+'*', text)
-    return text
diff --git a/spaces/qtoino/form_matcher/public/form0.html b/spaces/qtoino/form_matcher/public/form0.html
deleted file mode 100644
index f3099e243bfe2c7d133a9c49dee20db7d65ea417..0000000000000000000000000000000000000000
--- a/spaces/qtoino/form_matcher/public/form0.html
+++ /dev/null
@@ -1,41 +0,0 @@
-<!DOCTYPE html>
-<html>
-<head>
-  <title>Resume Form</title>
-</head>
-<body>
-  <h1>Resume Form</h1>
-
-  <h2>Longer Version</h2>
-    <form action="/" method="POST">
-        <label>Education</label><br>
-        <input type="text" name="education[0][degree]" placeholder="Degree" required><br>
-        <input type="text" name="education[0][institution]" placeholder="Institution" required><br>
-        <input type="text" name="education[0][dates]" placeholder="Dates" required><br>
-        <input type="text" name="education[0][thesisTitle]" placeholder="Thesis Title"><br>
-        <input type="text" name="education[0][thesisDescription]" placeholder="Thesis Description"><br><br>
-    
-        <label>Work Experience</label><br>
-        <input type="text" name="workExperience[0][title]" placeholder="Title" required><br>
-        <input type="text" name="workExperience[0][employer]" placeholder="Employer" required><br>
-        <input type="text" name="workExperience[0][dates]" placeholder="Dates" required><br>
-        <textarea name="workExperience[0][accomplishments]" placeholder="Accomplishments"></textarea><br>
-        <input type="text" name="workExperience[1][title]" placeholder="Title" required><br>
-        <input type="text" name="workExperience[1][employer]" placeholder="Employer" required><br>
-        <input type="text" name="workExperience[1][dates]" placeholder="Dates" required><br>
-        <textarea name="workExperience[1][projects]" placeholder="Projects"></textarea><br>
-        <input type="text" name="workExperience[1][training]" placeholder="Training"><br><br>
-    
-        <label>Extracurricular Activities</label><br>
-        <input type="text" name="extracurricularActivities[0][project]" placeholder="Project" required><br>
-        <input type="text" name="extracurricularActivities[0][event]" placeholder="Event" required><br>
-        <input type="text" name="extracurricularActivities[0][dates]" placeholder="Dates" required><br>
-        <textarea name="extracurricularActivities[0][description]" placeholder="Description"></textarea><br>
-        <input type="text" name="extracurricularActivities[1][title]" placeholder="Title" required><br>
-        <input type="text" name="extracurricularActivities[1][employer]" placeholder="Employer" required><br>
-        <input type="text" name="extracurricularActivities[1][dates]" placeholder="Dates" required><br>
-        <textarea name="extracurricularActivities[1][accomplishments]" placeholder="Accomplishments"></textarea><br><br>
-    
-        <input type="submit" value="Submit">
-    </form>
-</body>
\ No newline at end of file
diff --git a/spaces/quidiaMuxgu/Expedit-SAM/Adobe Photoshop CC 2018 V19.0.0.24821 Patch Utorrentl.md b/spaces/quidiaMuxgu/Expedit-SAM/Adobe Photoshop CC 2018 V19.0.0.24821 Patch Utorrentl.md
deleted file mode 100644
index 0afe879bbe6f4e4b6e89dc4ef85ccf1465b8d0f6..0000000000000000000000000000000000000000
--- a/spaces/quidiaMuxgu/Expedit-SAM/Adobe Photoshop CC 2018 V19.0.0.24821 Patch Utorrentl.md	
+++ /dev/null
@@ -1,7 +0,0 @@
-<br />
-<p>adobePhotoshop.exe and Photoshop CC 2018 V19.0.0.24821 Patch Broswer: Very useful tools.Tcl/Winsock v1.2.30 build 209 Support for older versions. Build: 2.6.13.0, Apache v2.2.15, LUA v5.1.2, Nginx v1.4.7, MySQL v5.0.96, PHP v5.4.26.</p>
-<p>Download Adobe Photoshop CC 2018. All of the software programs and drivers were updated.Zlib v1.2.11 Support for zlib 1.2.11.eCommerce v4.3.0.21425 Support for eCommerce v4.3.0.21425.eCryptfs v1.0.9 Support for eCryptfs v1.0.9.eDaemon v1.0.0.16823 Support for eDaemon v1.0.0.16823.dell_eai_daemon_v2 v2.5.0.16253 V2.5.0.16253.secrets_4.0 v4.0.0.14747 Support for secrets 4.0 v4.0.0.14747.Library v0.7.2.19 E-mail v0.4.0.2 Support for e-mail v0.4.0.2.gOGLE v1.2.4.0 Support for gOGLE v1.2.4.0.gFire_6 v6.9.0.0 Support for gFire v6.9.0.0.gCore_3.2.0.61 Support for gCore v3.2.0.61.gMsCore_3.2.0.54 Support for gMsCore v3.2.0.54.gFtp_0.9.0.2 Support for gFtp v0.9.0.2.gBase_1.0.15 Support for gBase v1.0.15.gOsc_0.4.0.10d Support for gOsc v0.4.0.10d.gXls_0.9.0.15 Support for gXls v0.9.0.15.gPortable_0.7.1.111 Support for gPortable v0.7.1.111.gNet_1.8.1.4 Support for gNet v1.8.1.4.gCAPI_4.1.0.196 Support for gCAPI v4.1.0.196.gPathways_0.2.4.107 Support for gPathways v0.2.4.107.gMessages_1.0.3 Support for gMessages v1.0.3.gActiveJob_4.2.0.21 Support for gActiveJob v4.2.0.21.gPbkd_0.9.0.46 Support for gPbkd v0.9.0.46.gRss_0.10.0.355 Support for gRss v0.10.0.355.gPOE_0.6.0.0_2 Support for gPOE v0.6.0.0_2.gsot_0.8.</p>
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diff --git a/spaces/quidiaMuxgu/Expedit-SAM/CardLife Cardboard Survival Game.md b/spaces/quidiaMuxgu/Expedit-SAM/CardLife Cardboard Survival Game.md
deleted file mode 100644
index 8a1e9f93e0cbe1a42883968a5bc10c20e6e9f4f2..0000000000000000000000000000000000000000
--- a/spaces/quidiaMuxgu/Expedit-SAM/CardLife Cardboard Survival Game.md	
+++ /dev/null
@@ -1,8 +0,0 @@
-<h2>CardLife: Cardboard Survival Game</h2><br /><p><b><b>DOWNLOAD</b> &gt; <a href="https://geags.com/2uCqvP">https://geags.com/2uCqvP</a></b></p><br /><br />
-
-CardLife: Cardboard Survival is a full version of the game for Windows, which belongs to the Action category, and was developed by Freejam. The game is an apocalyptic survival simulator, which allows you to play the role of survivors after a global disaster.
-According to the plot of the game, there was a global catastrophe in the world and all mankind died, and the survived people are trying to build their new society.
-You have to control a small squad of four people, each of them has a certain task, and you can choose any of them to lead the squad and go through the world. 8a78ff9644<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/quidiaMuxgu/Expedit-SAM/Download Poser Pro 2014 Torrent - 20 BETTER.md b/spaces/quidiaMuxgu/Expedit-SAM/Download Poser Pro 2014 Torrent - 20 BETTER.md
deleted file mode 100644
index c0a405fc85e895315db06f9e0c6955e0de2d4eac..0000000000000000000000000000000000000000
--- a/spaces/quidiaMuxgu/Expedit-SAM/Download Poser Pro 2014 Torrent - 20 BETTER.md	
+++ /dev/null
@@ -1,42 +0,0 @@
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-
-The Poser Body Modeler Module is a powerful feature that is not provided in most other commercial tools . And the Poser Extras program does a great job of enabling you to draw or paint from scratch to your heart's content. I recommend the Poser Body Modeler Module to anyone who works in 3D, whether you're an animator or a hobbyist, because it provides a seamless way to take your personal body, from the inside out, and create the most realistic representation of your body that you can muster.
-
-BODY ANIMATION WITH POSEER
-
-In this book, you will learn:
-
-  * how the Poser Body Modeler Module works
-
-  * how to manipulate the Poser Body Modeler Module to create realistic body parts, textures, and poses
-
-  * how to use the Poser Body Modeler Module to create body animation
-
-By the end of this book, you will:
-
-  * understand how to work with the Poser Body Modeler Module
-
-  * know what the Poser Body Modeler Module is and isn't
-
-  * have learned how to begin creating body models in Poser
-
-  * be able to combine body animations in Poser
-
-  * know what the Body Module is, and what it isn't
-
-THE BODY MODELER MODULE
-
-The Poser Body Modeler Module gives you the tools you need to draw and paint the body from the inside out. It enables you to create a completely realistic model of your body from the inside out. The Poser Body Modeler Module is an integral part of the Poser tool system.
-
-When you add the Poser Body Modeler Module to your Poser, you can drag the Poser Body Modeler Module onto a 3D model in Poser or onto a 2D body part in the Poser-Body-Models.da file. The Poser Body Modeler Module enables you to turn a static pose into a body animation. Poser Body Models, or BMs, are used in many ways, from simple static poses to fully dynamic figures.
-
-The Poser Body Modeler Module has many uses:
-
-  * You can create a full-body BMD with a single click.
-
-  * You can create a BMD from scratch or from an existing mesh.
-
-  * You can create a BMD by using one of the BMDs as a starting point, or you can start from a Photo Reference, a 3D 4fefd39f24<br />
-<br />
-<br />
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diff --git a/spaces/quidiaMuxgu/Expedit-SAM/Home Alone 2 Full Movie Free Download In Hindi.md b/spaces/quidiaMuxgu/Expedit-SAM/Home Alone 2 Full Movie Free Download In Hindi.md
deleted file mode 100644
index 577974492a6338163582bd6584235062f7af6b36..0000000000000000000000000000000000000000
--- a/spaces/quidiaMuxgu/Expedit-SAM/Home Alone 2 Full Movie Free Download In Hindi.md	
+++ /dev/null
@@ -1,6 +0,0 @@
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diff --git a/spaces/quidiaMuxgu/Expedit-SAM/Lana Del Rey Born To Die Paradise Edition Download Torrent BETTER.md b/spaces/quidiaMuxgu/Expedit-SAM/Lana Del Rey Born To Die Paradise Edition Download Torrent BETTER.md
deleted file mode 100644
index 55fc8c2038d80dda8daab2343e03ed1a4510a1b6..0000000000000000000000000000000000000000
--- a/spaces/quidiaMuxgu/Expedit-SAM/Lana Del Rey Born To Die Paradise Edition Download Torrent BETTER.md	
+++ /dev/null
@@ -1,6 +0,0 @@
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diff --git a/spaces/r3gm/Ultimate-Vocal-Remover-WebUI/demucs/tasnet_v2.py b/spaces/r3gm/Ultimate-Vocal-Remover-WebUI/demucs/tasnet_v2.py
deleted file mode 100644
index ecc1257925ea8f4fbe389ddd6d73ce9fdf45f6d4..0000000000000000000000000000000000000000
--- a/spaces/r3gm/Ultimate-Vocal-Remover-WebUI/demucs/tasnet_v2.py
+++ /dev/null
@@ -1,452 +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.
-#
-# Created on 2018/12
-# Author: Kaituo XU
-# Modified on 2019/11 by Alexandre Defossez, added support for multiple output channels
-# Here is the original license:
-# The MIT License (MIT)
-#
-# Copyright (c) 2018 Kaituo XU
-#
-# 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.
-
-import math
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from .utils import capture_init
-
-EPS = 1e-8
-
-
-def overlap_and_add(signal, frame_step):
-    outer_dimensions = signal.size()[:-2]
-    frames, frame_length = signal.size()[-2:]
-
-    subframe_length = math.gcd(frame_length, frame_step)  # gcd=Greatest Common Divisor
-    subframe_step = frame_step // subframe_length
-    subframes_per_frame = frame_length // subframe_length
-    output_size = frame_step * (frames - 1) + frame_length
-    output_subframes = output_size // subframe_length
-
-    subframe_signal = signal.view(*outer_dimensions, -1, subframe_length)
-
-    frame = torch.arange(0, output_subframes,
-                         device=signal.device).unfold(0, subframes_per_frame, subframe_step)
-    frame = frame.long()  # signal may in GPU or CPU
-    frame = frame.contiguous().view(-1)
-
-    result = signal.new_zeros(*outer_dimensions, output_subframes, subframe_length)
-    result.index_add_(-2, frame, subframe_signal)
-    result = result.view(*outer_dimensions, -1)
-    return result
-
-
-class ConvTasNet(nn.Module):
-    @capture_init
-    def __init__(self,
-                 sources,
-                 N=256,
-                 L=20,
-                 B=256,
-                 H=512,
-                 P=3,
-                 X=8,
-                 R=4,
-                 audio_channels=2,
-                 norm_type="gLN",
-                 causal=False,
-                 mask_nonlinear='relu',
-                 samplerate=44100,
-                 segment_length=44100 * 2 * 4):
-        """
-        Args:
-            sources: list of sources
-            N: Number of filters in autoencoder
-            L: Length of the filters (in samples)
-            B: Number of channels in bottleneck 1 × 1-conv block
-            H: Number of channels in convolutional blocks
-            P: Kernel size in convolutional blocks
-            X: Number of convolutional blocks in each repeat
-            R: Number of repeats
-            norm_type: BN, gLN, cLN
-            causal: causal or non-causal
-            mask_nonlinear: use which non-linear function to generate mask
-        """
-        super(ConvTasNet, self).__init__()
-        # Hyper-parameter
-        self.sources = sources
-        self.C = len(sources)
-        self.N, self.L, self.B, self.H, self.P, self.X, self.R = N, L, B, H, P, X, R
-        self.norm_type = norm_type
-        self.causal = causal
-        self.mask_nonlinear = mask_nonlinear
-        self.audio_channels = audio_channels
-        self.samplerate = samplerate
-        self.segment_length = segment_length
-        # Components
-        self.encoder = Encoder(L, N, audio_channels)
-        self.separator = TemporalConvNet(
-            N, B, H, P, X, R, self.C, norm_type, causal, mask_nonlinear)
-        self.decoder = Decoder(N, L, audio_channels)
-        # init
-        for p in self.parameters():
-            if p.dim() > 1:
-                nn.init.xavier_normal_(p)
-
-    def valid_length(self, length):
-        return length
-
-    def forward(self, mixture):
-        """
-        Args:
-            mixture: [M, T], M is batch size, T is #samples
-        Returns:
-            est_source: [M, C, T]
-        """
-        mixture_w = self.encoder(mixture)
-        est_mask = self.separator(mixture_w)
-        est_source = self.decoder(mixture_w, est_mask)
-
-        # T changed after conv1d in encoder, fix it here
-        T_origin = mixture.size(-1)
-        T_conv = est_source.size(-1)
-        est_source = F.pad(est_source, (0, T_origin - T_conv))
-        return est_source
-
-
-class Encoder(nn.Module):
-    """Estimation of the nonnegative mixture weight by a 1-D conv layer.
-    """
-    def __init__(self, L, N, audio_channels):
-        super(Encoder, self).__init__()
-        # Hyper-parameter
-        self.L, self.N = L, N
-        # Components
-        # 50% overlap
-        self.conv1d_U = nn.Conv1d(audio_channels, N, kernel_size=L, stride=L // 2, bias=False)
-
-    def forward(self, mixture):
-        """
-        Args:
-            mixture: [M, T], M is batch size, T is #samples
-        Returns:
-            mixture_w: [M, N, K], where K = (T-L)/(L/2)+1 = 2T/L-1
-        """
-        mixture_w = F.relu(self.conv1d_U(mixture))  # [M, N, K]
-        return mixture_w
-
-
-class Decoder(nn.Module):
-    def __init__(self, N, L, audio_channels):
-        super(Decoder, self).__init__()
-        # Hyper-parameter
-        self.N, self.L = N, L
-        self.audio_channels = audio_channels
-        # Components
-        self.basis_signals = nn.Linear(N, audio_channels * L, bias=False)
-
-    def forward(self, mixture_w, est_mask):
-        """
-        Args:
-            mixture_w: [M, N, K]
-            est_mask: [M, C, N, K]
-        Returns:
-            est_source: [M, C, T]
-        """
-        # D = W * M
-        source_w = torch.unsqueeze(mixture_w, 1) * est_mask  # [M, C, N, K]
-        source_w = torch.transpose(source_w, 2, 3)  # [M, C, K, N]
-        # S = DV
-        est_source = self.basis_signals(source_w)  # [M, C, K, ac * L]
-        m, c, k, _ = est_source.size()
-        est_source = est_source.view(m, c, k, self.audio_channels, -1).transpose(2, 3).contiguous()
-        est_source = overlap_and_add(est_source, self.L // 2)  # M x C x ac x T
-        return est_source
-
-
-class TemporalConvNet(nn.Module):
-    def __init__(self, N, B, H, P, X, R, C, norm_type="gLN", causal=False, mask_nonlinear='relu'):
-        """
-        Args:
-            N: Number of filters in autoencoder
-            B: Number of channels in bottleneck 1 × 1-conv block
-            H: Number of channels in convolutional blocks
-            P: Kernel size in convolutional blocks
-            X: Number of convolutional blocks in each repeat
-            R: Number of repeats
-            C: Number of speakers
-            norm_type: BN, gLN, cLN
-            causal: causal or non-causal
-            mask_nonlinear: use which non-linear function to generate mask
-        """
-        super(TemporalConvNet, self).__init__()
-        # Hyper-parameter
-        self.C = C
-        self.mask_nonlinear = mask_nonlinear
-        # Components
-        # [M, N, K] -> [M, N, K]
-        layer_norm = ChannelwiseLayerNorm(N)
-        # [M, N, K] -> [M, B, K]
-        bottleneck_conv1x1 = nn.Conv1d(N, B, 1, bias=False)
-        # [M, B, K] -> [M, B, K]
-        repeats = []
-        for r in range(R):
-            blocks = []
-            for x in range(X):
-                dilation = 2**x
-                padding = (P - 1) * dilation if causal else (P - 1) * dilation // 2
-                blocks += [
-                    TemporalBlock(B,
-                                  H,
-                                  P,
-                                  stride=1,
-                                  padding=padding,
-                                  dilation=dilation,
-                                  norm_type=norm_type,
-                                  causal=causal)
-                ]
-            repeats += [nn.Sequential(*blocks)]
-        temporal_conv_net = nn.Sequential(*repeats)
-        # [M, B, K] -> [M, C*N, K]
-        mask_conv1x1 = nn.Conv1d(B, C * N, 1, bias=False)
-        # Put together
-        self.network = nn.Sequential(layer_norm, bottleneck_conv1x1, temporal_conv_net,
-                                     mask_conv1x1)
-
-    def forward(self, mixture_w):
-        """
-        Keep this API same with TasNet
-        Args:
-            mixture_w: [M, N, K], M is batch size
-        returns:
-            est_mask: [M, C, N, K]
-        """
-        M, N, K = mixture_w.size()
-        score = self.network(mixture_w)  # [M, N, K] -> [M, C*N, K]
-        score = score.view(M, self.C, N, K)  # [M, C*N, K] -> [M, C, N, K]
-        if self.mask_nonlinear == 'softmax':
-            est_mask = F.softmax(score, dim=1)
-        elif self.mask_nonlinear == 'relu':
-            est_mask = F.relu(score)
-        else:
-            raise ValueError("Unsupported mask non-linear function")
-        return est_mask
-
-
-class TemporalBlock(nn.Module):
-    def __init__(self,
-                 in_channels,
-                 out_channels,
-                 kernel_size,
-                 stride,
-                 padding,
-                 dilation,
-                 norm_type="gLN",
-                 causal=False):
-        super(TemporalBlock, self).__init__()
-        # [M, B, K] -> [M, H, K]
-        conv1x1 = nn.Conv1d(in_channels, out_channels, 1, bias=False)
-        prelu = nn.PReLU()
-        norm = chose_norm(norm_type, out_channels)
-        # [M, H, K] -> [M, B, K]
-        dsconv = DepthwiseSeparableConv(out_channels, in_channels, kernel_size, stride, padding,
-                                        dilation, norm_type, causal)
-        # Put together
-        self.net = nn.Sequential(conv1x1, prelu, norm, dsconv)
-
-    def forward(self, x):
-        """
-        Args:
-            x: [M, B, K]
-        Returns:
-            [M, B, K]
-        """
-        residual = x
-        out = self.net(x)
-        # TODO: when P = 3 here works fine, but when P = 2 maybe need to pad?
-        return out + residual  # look like w/o F.relu is better than w/ F.relu
-        # return F.relu(out + residual)
-
-
-class DepthwiseSeparableConv(nn.Module):
-    def __init__(self,
-                 in_channels,
-                 out_channels,
-                 kernel_size,
-                 stride,
-                 padding,
-                 dilation,
-                 norm_type="gLN",
-                 causal=False):
-        super(DepthwiseSeparableConv, self).__init__()
-        # Use `groups` option to implement depthwise convolution
-        # [M, H, K] -> [M, H, K]
-        depthwise_conv = nn.Conv1d(in_channels,
-                                   in_channels,
-                                   kernel_size,
-                                   stride=stride,
-                                   padding=padding,
-                                   dilation=dilation,
-                                   groups=in_channels,
-                                   bias=False)
-        if causal:
-            chomp = Chomp1d(padding)
-        prelu = nn.PReLU()
-        norm = chose_norm(norm_type, in_channels)
-        # [M, H, K] -> [M, B, K]
-        pointwise_conv = nn.Conv1d(in_channels, out_channels, 1, bias=False)
-        # Put together
-        if causal:
-            self.net = nn.Sequential(depthwise_conv, chomp, prelu, norm, pointwise_conv)
-        else:
-            self.net = nn.Sequential(depthwise_conv, prelu, norm, pointwise_conv)
-
-    def forward(self, x):
-        """
-        Args:
-            x: [M, H, K]
-        Returns:
-            result: [M, B, K]
-        """
-        return self.net(x)
-
-
-class Chomp1d(nn.Module):
-    """To ensure the output length is the same as the input.
-    """
-    def __init__(self, chomp_size):
-        super(Chomp1d, self).__init__()
-        self.chomp_size = chomp_size
-
-    def forward(self, x):
-        """
-        Args:
-            x: [M, H, Kpad]
-        Returns:
-            [M, H, K]
-        """
-        return x[:, :, :-self.chomp_size].contiguous()
-
-
-def chose_norm(norm_type, channel_size):
-    """The input of normlization will be (M, C, K), where M is batch size,
-       C is channel size and K is sequence length.
-    """
-    if norm_type == "gLN":
-        return GlobalLayerNorm(channel_size)
-    elif norm_type == "cLN":
-        return ChannelwiseLayerNorm(channel_size)
-    elif norm_type == "id":
-        return nn.Identity()
-    else:  # norm_type == "BN":
-        # Given input (M, C, K), nn.BatchNorm1d(C) will accumulate statics
-        # along M and K, so this BN usage is right.
-        return nn.BatchNorm1d(channel_size)
-
-
-# TODO: Use nn.LayerNorm to impl cLN to speed up
-class ChannelwiseLayerNorm(nn.Module):
-    """Channel-wise Layer Normalization (cLN)"""
-    def __init__(self, channel_size):
-        super(ChannelwiseLayerNorm, self).__init__()
-        self.gamma = nn.Parameter(torch.Tensor(1, channel_size, 1))  # [1, N, 1]
-        self.beta = nn.Parameter(torch.Tensor(1, channel_size, 1))  # [1, N, 1]
-        self.reset_parameters()
-
-    def reset_parameters(self):
-        self.gamma.data.fill_(1)
-        self.beta.data.zero_()
-
-    def forward(self, y):
-        """
-        Args:
-            y: [M, N, K], M is batch size, N is channel size, K is length
-        Returns:
-            cLN_y: [M, N, K]
-        """
-        mean = torch.mean(y, dim=1, keepdim=True)  # [M, 1, K]
-        var = torch.var(y, dim=1, keepdim=True, unbiased=False)  # [M, 1, K]
-        cLN_y = self.gamma * (y - mean) / torch.pow(var + EPS, 0.5) + self.beta
-        return cLN_y
-
-
-class GlobalLayerNorm(nn.Module):
-    """Global Layer Normalization (gLN)"""
-    def __init__(self, channel_size):
-        super(GlobalLayerNorm, self).__init__()
-        self.gamma = nn.Parameter(torch.Tensor(1, channel_size, 1))  # [1, N, 1]
-        self.beta = nn.Parameter(torch.Tensor(1, channel_size, 1))  # [1, N, 1]
-        self.reset_parameters()
-
-    def reset_parameters(self):
-        self.gamma.data.fill_(1)
-        self.beta.data.zero_()
-
-    def forward(self, y):
-        """
-        Args:
-            y: [M, N, K], M is batch size, N is channel size, K is length
-        Returns:
-            gLN_y: [M, N, K]
-        """
-        # TODO: in torch 1.0, torch.mean() support dim list
-        mean = y.mean(dim=1, keepdim=True).mean(dim=2, keepdim=True)  # [M, 1, 1]
-        var = (torch.pow(y - mean, 2)).mean(dim=1, keepdim=True).mean(dim=2, keepdim=True)
-        gLN_y = self.gamma * (y - mean) / torch.pow(var + EPS, 0.5) + self.beta
-        return gLN_y
-
-
-if __name__ == "__main__":
-    torch.manual_seed(123)
-    M, N, L, T = 2, 3, 4, 12
-    K = 2 * T // L - 1
-    B, H, P, X, R, C, norm_type, causal = 2, 3, 3, 3, 2, 2, "gLN", False
-    mixture = torch.randint(3, (M, T))
-    # test Encoder
-    encoder = Encoder(L, N)
-    encoder.conv1d_U.weight.data = torch.randint(2, encoder.conv1d_U.weight.size())
-    mixture_w = encoder(mixture)
-    print('mixture', mixture)
-    print('U', encoder.conv1d_U.weight)
-    print('mixture_w', mixture_w)
-    print('mixture_w size', mixture_w.size())
-
-    # test TemporalConvNet
-    separator = TemporalConvNet(N, B, H, P, X, R, C, norm_type=norm_type, causal=causal)
-    est_mask = separator(mixture_w)
-    print('est_mask', est_mask)
-
-    # test Decoder
-    decoder = Decoder(N, L)
-    est_mask = torch.randint(2, (B, K, C, N))
-    est_source = decoder(mixture_w, est_mask)
-    print('est_source', est_source)
-
-    # test Conv-TasNet
-    conv_tasnet = ConvTasNet(N, L, B, H, P, X, R, C, norm_type=norm_type)
-    est_source = conv_tasnet(mixture)
-    print('est_source', est_source)
-    print('est_source size', est_source.size())
diff --git a/spaces/raedeXanto/academic-chatgpt-beta/AUTODATA 3.45 Crack FULL free download How to install and use this software easily and safely.md b/spaces/raedeXanto/academic-chatgpt-beta/AUTODATA 3.45 Crack FULL free download How to install and use this software easily and safely.md
deleted file mode 100644
index 0bcb349286182fc3839cc28ae72071e15fdd0f0b..0000000000000000000000000000000000000000
--- a/spaces/raedeXanto/academic-chatgpt-beta/AUTODATA 3.45 Crack FULL free download How to install and use this software easily and safely.md	
+++ /dev/null
@@ -1,106 +0,0 @@
-<br />
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-Programa Simplo Automotivo is compatible with most vehicles that have an OBD-II port. This is a standard port that is found in most vehicles manufactured after 1996. You can check if your vehicle has an OBD-II port by looking under the dashboard or near the steering wheel. If you see a 16-pin connector that looks like this: <img src="https://upload.wikimedia.org/wikipedia/commons/thumb/1/11/OBD_connector_shape.svg/1200px-OBD_connector_shape.svg.png" alt="OBD-II connector" width="100" height="100"> then your vehicle is compatible with Programa Simplo Automotivo.</li>
-<li><strong>What are some alternatives to Programa Simplo Automotivo?</strong><br>
-Some alternatives to Programa Simplo Automotivo are Autodata, Haynes Pro, Mitchell OnDemand, Alldata, Bosch ESI Tronic, etc. These are also software that provide information about various vehicles and allow you to diagnose and repair them. However, they may have different features, prices, interfaces, etc. You can compare them with Programa Simplo Automotivo and choose the one that suits your needs best.</li>
-</ol>
- </p> 0a6ba089eb<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/raedeXanto/academic-chatgpt-beta/Adobe Photoshop Cc 14.2 Crack Torrentl Learn How to Edit Photos Like a Pro with this Free Download.md b/spaces/raedeXanto/academic-chatgpt-beta/Adobe Photoshop Cc 14.2 Crack Torrentl Learn How to Edit Photos Like a Pro with this Free Download.md
deleted file mode 100644
index bc9c9fd1deda6b097d5147f7985a697a9dd5bbda..0000000000000000000000000000000000000000
--- a/spaces/raedeXanto/academic-chatgpt-beta/Adobe Photoshop Cc 14.2 Crack Torrentl Learn How to Edit Photos Like a Pro with this Free Download.md	
+++ /dev/null
@@ -1,146 +0,0 @@
-
-<h1>Adobe Photoshop CC 14.2 Crack Torrent: What Is It and How to Use It?</h1>
- <p>If you are looking for a powerful and versatile software to edit and create stunning images, you might have heard of Adobe Photoshop CC 14.2. This is the latest version of the popular image editing software that offers many professional tools and features to enhance your creativity and productivity. But what if you don't want to pay for the subscription fee or the license key? Is there a way to get Adobe Photoshop CC 14.2 for free? The answer is yes, but it comes with some risks and challenges. In this article, we will explain what Adobe Photoshop CC 14.2 crack torrent is, how to use it, and what are the pros and cons of using it.</p>
- <h2>What Is Adobe Photoshop CC 14.2?</h2>
- <p>Adobe Photoshop CC 14.2 is the latest update of the Adobe Photoshop Creative Cloud series, which was released in January 2014. It is a software that allows you to edit, create, and manipulate images with a variety of tools and features, such as:</p>
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- <h3>Features and Benefits of Adobe Photoshop CC 14.2</h3>
- <ul>
-<li><b>Smart Sharpen:</b> This feature lets you sharpen your images with more control and precision, reducing noise and halo effects.</li>
-<li><b>Camera Shake Reduction:</b> This feature helps you restore sharpness to your blurry photos caused by camera shake or movement.</li>
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-<li><b>And many more:</b> Adobe Photoshop CC 14.2 also offers other improvements and enhancements, such as better performance, smoother workflows, new filters, new fonts, new presets, etc.</li>
-</ul>
- <h3>System Requirements and Compatibility of Adobe Photoshop CC 14.2</h3>
- <p>To run Adobe Photoshop CC 14.2 smoothly on your computer, you need to meet the following minimum system requirements:</p>
- <ul>
-<li><b>Operating system:</b> Windows 7 SP1 or later (64-bit), or Mac OS X 10.7 or later (64-bit)</li>
-<li><b>Processor:</b> Intel Core 2 Duo or AMD Athlon 64 X2 or faster</li>
-<li><b>Memory:</b> 2 GB of RAM or more</li>
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-<li><b>Graphics card:</b> OpenGL 2.0 compatible with 512 MB of VRAM or more</li>
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-</ul>
- <p>Adobe Photoshop CC 14.2 is compatible with most image formats, such as JPEG, PNG, GIF, TIFF, PSD, etc. It also supports various plugins and extensions that can enhance its functionality and compatibility.</p>
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- </p> 0a6ba089eb<br />
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-<br />
\ No newline at end of file
diff --git a/spaces/ramiin2/AutoGPT/ui/utils.py b/spaces/ramiin2/AutoGPT/ui/utils.py
deleted file mode 100644
index 71703e2009afac0582300f5d99a91ddec4119e04..0000000000000000000000000000000000000000
--- a/spaces/ramiin2/AutoGPT/ui/utils.py
+++ /dev/null
@@ -1,31 +0,0 @@
-import os
-import re
-
-def format_directory(directory):
-    output = []
-    def helper(directory, level, output):
-        files = os.listdir(directory)
-        for i, item in enumerate(files):
-            is_folder = os.path.isdir(os.path.join(directory, item))
-            joiner = "├── " if i < len(files) - 1 else "└── "
-            item_html = item + "/" if is_folder else f"<a href='file={os.path.join(directory, item)}'>{item}</a>"
-            output.append("│   " * level + joiner + item_html)
-            if is_folder:
-                helper(os.path.join(directory, item), level + 1, output)
-    output.append(os.path.basename(directory) + "/")
-    helper(directory, 1, output)
-    return "\n".join(output)
-
-DOWNLOAD_OUTPUTS_JS = """
-() => {
-  const a = document.createElement('a');
-  a.href = 'file=outputs.zip';
-  a.download = 'outputs.zip';
-  document.body.appendChild(a);
-  a.click();
-  document.body.removeChild(a);
-}"""
-
-def remove_color(text):
-    ansi_escape = re.compile(r'\x1B(?:[@-Z\\-_]|\[[0-?]*[ -/]*[@-~])')
-    return ansi_escape.sub('', text)
\ No newline at end of file
diff --git a/spaces/ramkamal2000/voice-conversion-ddp/speaker_encoder/visualizations.py b/spaces/ramkamal2000/voice-conversion-ddp/speaker_encoder/visualizations.py
deleted file mode 100644
index ec00fc64d6e9fda2bb8e613531066ac824df1451..0000000000000000000000000000000000000000
--- a/spaces/ramkamal2000/voice-conversion-ddp/speaker_encoder/visualizations.py
+++ /dev/null
@@ -1,178 +0,0 @@
-from speaker_encoder.data_objects.speaker_verification_dataset import SpeakerVerificationDataset
-from datetime import datetime
-from time import perf_counter as timer
-import matplotlib.pyplot as plt
-import numpy as np
-# import webbrowser
-import visdom
-import umap
-
-colormap = np.array([
-    [76, 255, 0],
-    [0, 127, 70],
-    [255, 0, 0],
-    [255, 217, 38],
-    [0, 135, 255],
-    [165, 0, 165],
-    [255, 167, 255],
-    [0, 255, 255],
-    [255, 96, 38],
-    [142, 76, 0],
-    [33, 0, 127],
-    [0, 0, 0],
-    [183, 183, 183],
-], dtype=np.float) / 255 
-
-
-class Visualizations:
-    def __init__(self, env_name=None, update_every=10, server="http://localhost", disabled=False):
-        # Tracking data
-        self.last_update_timestamp = timer()
-        self.update_every = update_every
-        self.step_times = []
-        self.losses = []
-        self.eers = []
-        print("Updating the visualizations every %d steps." % update_every)
-        
-        # If visdom is disabled TODO: use a better paradigm for that
-        self.disabled = disabled    
-        if self.disabled:
-            return 
-        
-        # Set the environment name
-        now = str(datetime.now().strftime("%d-%m %Hh%M"))
-        if env_name is None:
-            self.env_name = now
-        else:
-            self.env_name = "%s (%s)" % (env_name, now)
-        
-        # Connect to visdom and open the corresponding window in the browser
-        try:
-            self.vis = visdom.Visdom(server, env=self.env_name, raise_exceptions=True)
-        except ConnectionError:
-            raise Exception("No visdom server detected. Run the command \"visdom\" in your CLI to "
-                            "start it.")
-        # webbrowser.open("http://localhost:8097/env/" + self.env_name)
-        
-        # Create the windows
-        self.loss_win = None
-        self.eer_win = None
-        # self.lr_win = None
-        self.implementation_win = None
-        self.projection_win = None
-        self.implementation_string = ""
-        
-    def log_params(self):
-        if self.disabled:
-            return 
-        from speaker_encoder import params_data
-        from speaker_encoder import params_model
-        param_string = "<b>Model parameters</b>:<br>"
-        for param_name in (p for p in dir(params_model) if not p.startswith("__")):
-            value = getattr(params_model, param_name)
-            param_string += "\t%s: %s<br>" % (param_name, value)
-        param_string += "<b>Data parameters</b>:<br>"
-        for param_name in (p for p in dir(params_data) if not p.startswith("__")):
-            value = getattr(params_data, param_name)
-            param_string += "\t%s: %s<br>" % (param_name, value)
-        self.vis.text(param_string, opts={"title": "Parameters"})
-        
-    def log_dataset(self, dataset: SpeakerVerificationDataset):
-        if self.disabled:
-            return 
-        dataset_string = ""
-        dataset_string += "<b>Speakers</b>: %s\n" % len(dataset.speakers)
-        dataset_string += "\n" + dataset.get_logs()
-        dataset_string = dataset_string.replace("\n", "<br>")
-        self.vis.text(dataset_string, opts={"title": "Dataset"})
-        
-    def log_implementation(self, params):
-        if self.disabled:
-            return 
-        implementation_string = ""
-        for param, value in params.items():
-            implementation_string += "<b>%s</b>: %s\n" % (param, value)
-            implementation_string = implementation_string.replace("\n", "<br>")
-        self.implementation_string = implementation_string
-        self.implementation_win = self.vis.text(
-            implementation_string, 
-            opts={"title": "Training implementation"}
-        )
-
-    def update(self, loss, eer, step):
-        # Update the tracking data
-        now = timer()
-        self.step_times.append(1000 * (now - self.last_update_timestamp))
-        self.last_update_timestamp = now
-        self.losses.append(loss)
-        self.eers.append(eer)
-        print(".", end="")
-        
-        # Update the plots every <update_every> steps
-        if step % self.update_every != 0:
-            return
-        time_string = "Step time:  mean: %5dms  std: %5dms" % \
-                      (int(np.mean(self.step_times)), int(np.std(self.step_times)))
-        print("\nStep %6d   Loss: %.4f   EER: %.4f   %s" %
-              (step, np.mean(self.losses), np.mean(self.eers), time_string))
-        if not self.disabled:
-            self.loss_win = self.vis.line(
-                [np.mean(self.losses)],
-                [step],
-                win=self.loss_win,
-                update="append" if self.loss_win else None,
-                opts=dict(
-                    legend=["Avg. loss"],
-                    xlabel="Step",
-                    ylabel="Loss",
-                    title="Loss",
-                )
-            )
-            self.eer_win = self.vis.line(
-                [np.mean(self.eers)],
-                [step],
-                win=self.eer_win,
-                update="append" if self.eer_win else None,
-                opts=dict(
-                    legend=["Avg. EER"],
-                    xlabel="Step",
-                    ylabel="EER",
-                    title="Equal error rate"
-                )
-            )
-            if self.implementation_win is not None:
-                self.vis.text(
-                    self.implementation_string + ("<b>%s</b>" % time_string), 
-                    win=self.implementation_win,
-                    opts={"title": "Training implementation"},
-                )
-
-        # Reset the tracking
-        self.losses.clear()
-        self.eers.clear()
-        self.step_times.clear()
-        
-    def draw_projections(self, embeds, utterances_per_speaker, step, out_fpath=None,
-                         max_speakers=10):
-        max_speakers = min(max_speakers, len(colormap))
-        embeds = embeds[:max_speakers * utterances_per_speaker]
-        
-        n_speakers = len(embeds) // utterances_per_speaker
-        ground_truth = np.repeat(np.arange(n_speakers), utterances_per_speaker)
-        colors = [colormap[i] for i in ground_truth]
-        
-        reducer = umap.UMAP()
-        projected = reducer.fit_transform(embeds)
-        plt.scatter(projected[:, 0], projected[:, 1], c=colors)
-        plt.gca().set_aspect("equal", "datalim")
-        plt.title("UMAP projection (step %d)" % step)
-        if not self.disabled:
-            self.projection_win = self.vis.matplot(plt, win=self.projection_win)
-        if out_fpath is not None:
-            plt.savefig(out_fpath)
-        plt.clf()
-        
-    def save(self):
-        if not self.disabled:
-            self.vis.save([self.env_name])
-        
\ No newline at end of file
diff --git a/spaces/rcajegas/HTML5-Aframe-3DMAP-FLIGHT/README.md b/spaces/rcajegas/HTML5-Aframe-3DMAP-FLIGHT/README.md
deleted file mode 100644
index 5f49988679e720e5bbcef02e2508528a7df8ac03..0000000000000000000000000000000000000000
--- a/spaces/rcajegas/HTML5-Aframe-3DMAP-FLIGHT/README.md
+++ /dev/null
@@ -1,11 +0,0 @@
----
-title: HTML5 Aframe 3DMAP FLIGHT
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-sdk: static
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----
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diff --git a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Arcgis Server 10.2 Crack ((HOT)) Download.md b/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Arcgis Server 10.2 Crack ((HOT)) Download.md
deleted file mode 100644
index 8e30216e7a0ece69627aed69493383f74a507219..0000000000000000000000000000000000000000
--- a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Arcgis Server 10.2 Crack ((HOT)) Download.md	
+++ /dev/null
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-<li>Obtain an authorization file for the machine on which you're installing ArcGIS Server. You can request an authorization file from Esri Customer Service or your local distributor.</li>
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-<li>Browse to the location of the setup files and double-click Setup.exe.</li>
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-<li>Specify the account to be used by ArcGIS Server to perform a variety of functions. You can specify a local account, a domain account, or a Managed Service Account. The default account name is arcgis. For more information about the ArcGIS Server account, see here: <a href="https://enterprise.arcgis.com/en/server/latest/install/windows/the-arcgis-server-account.htm">https://enterprise.arcgis.com/en/server/latest/install/windows/the-arcgis-server-account.htm</a></li>
-<li>Specify the installation location for ArcGIS Server. The default location is C:\Program Files\ArcGIS\Server.</li>
-<li>Click Next to proceed with the installation.</li>
-<li>When the installation is complete, click Finish to close the setup program.</li>
-</ol>
-<h2>Step 3: Configure ArcGIS Server</h2>
-<p>After you install ArcGIS Server, you need to configure it:</p>
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-<li>Authorize ArcGIS Server using the authorization file that you obtained earlier. You can authorize ArcGIS Server using ArcGIS Server Manager or the Software Authorization Wizard. For more information about authorizing ArcGIS Server, see here: <a href="https://enterprise.arcgis.com/en/server/latest/install/windows/authorize-arcgis-server.htm">https://enterprise.arcgis.com/en/server/latest/install/windows/authorize-arcgis-server.htm</a></li>
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diff --git a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Bentley Microstation V8i (SELECTSeries 3) 08 11 09 Bitter Throwin Neufe ((LINK)).md b/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Bentley Microstation V8i (SELECTSeries 3) 08 11 09 Bitter Throwin Neufe ((LINK)).md
deleted file mode 100644
index b5d558063cd205ce98f229b8655d1bcb6bb04468..0000000000000000000000000000000000000000
--- a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Bentley Microstation V8i (SELECTSeries 3) 08 11 09 Bitter Throwin Neufe ((LINK)).md	
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deleted file mode 100644
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--- a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Breaking Bad 1080p Mp4 Convert.md	
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diff --git a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Download Night At The Museum 2 In Dual Audio.md b/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Download Night At The Museum 2 In Dual Audio.md
deleted file mode 100644
index 3aa6de73c17975e5345788ad6b548c88264538d2..0000000000000000000000000000000000000000
--- a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Download Night At The Museum 2 In Dual Audio.md	
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diff --git a/spaces/rockeycoss/Prompt-Segment-Anything-Demo/mmdet/core/visualization/__init__.py b/spaces/rockeycoss/Prompt-Segment-Anything-Demo/mmdet/core/visualization/__init__.py
deleted file mode 100644
index 2eb17c4b32bc0c5c76db31e22e995716ba718222..0000000000000000000000000000000000000000
--- a/spaces/rockeycoss/Prompt-Segment-Anything-Demo/mmdet/core/visualization/__init__.py
+++ /dev/null
@@ -1,9 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from .image import (color_val_matplotlib, imshow_det_bboxes,
-                    imshow_gt_det_bboxes)
-from .palette import get_palette, palette_val
-
-__all__ = [
-    'imshow_det_bboxes', 'imshow_gt_det_bboxes', 'color_val_matplotlib',
-    'palette_val', 'get_palette'
-]
diff --git a/spaces/rorallitri/biomedical-language-models/Myanmar-Unicode-Font-Download-UPD-For-Mac.md b/spaces/rorallitri/biomedical-language-models/Myanmar-Unicode-Font-Download-UPD-For-Mac.md
deleted file mode 100644
index d3d361202cb7d92f27609e59b2ae6ab3fc9b9e31..0000000000000000000000000000000000000000
--- a/spaces/rorallitri/biomedical-language-models/Myanmar-Unicode-Font-Download-UPD-For-Mac.md
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-# How to Download and Install Myanmar Unicode Font on Mac
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-## What is Myanmar Unicode Font?
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-
- 
-
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-
- 
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-## Why Use Myanmar Unicode Font on Mac?
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-
-- You can read and write Myanmar text on any website, document, email, or message without any issue.
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-
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-
- 
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- 
-
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-
-
-
-1. To change from Zawgyi font to Unicode font, open Font Book from Spotlight search and disable or remove Zawgyi-One font. Then open MMFontFallBack folder and click Back To Original. This will restore the original fallback fonts for Myanmar script.
-
-2. To install a Unicode font of your choice, open Unicode Fonts folder and double click mm3.ttf or any other font you like. Then click Install Font. This will add the font to your Font Book.
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-
-4. To enable the new keyboard layout, go back to System Preferences > Keyboard > Input Sources and click +. Then select Others from the left side and check Myanmar3 - QWERTY from the right side. Then click Add.
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-5. To switch between keyboard layouts, you can use the shortcut Command + Space dfd1c89656
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diff --git a/spaces/rorallitri/biomedical-language-models/logs/Adobe.Audition.v3.0.Build.7283.0.Multilingual Record Edit and Master Audio in Multiple Languages.md b/spaces/rorallitri/biomedical-language-models/logs/Adobe.Audition.v3.0.Build.7283.0.Multilingual Record Edit and Master Audio in Multiple Languages.md
deleted file mode 100644
index 93dc56640fe2305448ca6ca6d2efc629bd2c9d4c..0000000000000000000000000000000000000000
--- a/spaces/rorallitri/biomedical-language-models/logs/Adobe.Audition.v3.0.Build.7283.0.Multilingual Record Edit and Master Audio in Multiple Languages.md	
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diff --git a/spaces/rorallitri/biomedical-language-models/logs/Download Keygen HOT! Xforce For AutoCAD Design Suite 2011 Activation.md b/spaces/rorallitri/biomedical-language-models/logs/Download Keygen HOT! Xforce For AutoCAD Design Suite 2011 Activation.md
deleted file mode 100644
index 915aa82a8a7534f913dbca99b2b6d2226314dd75..0000000000000000000000000000000000000000
--- a/spaces/rorallitri/biomedical-language-models/logs/Download Keygen HOT! Xforce For AutoCAD Design Suite 2011 Activation.md	
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diff --git a/spaces/rorallitri/biomedical-language-models/logs/Evangelion 3.33 English Dub Torrent Download ((NEW)).md b/spaces/rorallitri/biomedical-language-models/logs/Evangelion 3.33 English Dub Torrent Download ((NEW)).md
deleted file mode 100644
index 073310a443e19112fb593a3b0d9e36f0ee1b6e0a..0000000000000000000000000000000000000000
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-<br />
-<p></p>
diff --git a/spaces/rossellison/kpop-face-generator/stylegan3-fun/.github/ISSUE_TEMPLATE/feature_request.md b/spaces/rossellison/kpop-face-generator/stylegan3-fun/.github/ISSUE_TEMPLATE/feature_request.md
deleted file mode 100644
index bbcbbe7d61558adde3cbfd0c7a63a67c27ed6d30..0000000000000000000000000000000000000000
--- a/spaces/rossellison/kpop-face-generator/stylegan3-fun/.github/ISSUE_TEMPLATE/feature_request.md
+++ /dev/null
@@ -1,20 +0,0 @@
----
-name: Feature request
-about: Suggest an idea for this project
-title: ''
-labels: ''
-assignees: ''
-
----
-
-**Is your feature request related to a problem? Please describe.**
-A clear and concise description of what the problem is. Ex. I'm always frustrated when [...]
-
-**Describe the solution you'd like**
-A clear and concise description of what you want to happen.
-
-**Describe alternatives you've considered**
-A clear and concise description of any alternative solutions or features you've considered.
-
-**Additional context**
-Add any other context or screenshots about the feature request here.
diff --git a/spaces/rsatish1110/VideoSummaryGenerator/app.py b/spaces/rsatish1110/VideoSummaryGenerator/app.py
deleted file mode 100644
index ea0d92944bdf4e1fde3b7b46810816a97c6b4964..0000000000000000000000000000000000000000
--- a/spaces/rsatish1110/VideoSummaryGenerator/app.py
+++ /dev/null
@@ -1,22 +0,0 @@
-import gradio as gr
-from summarize import Summarizer
-
-interface = gr.Interface(fn = Summarizer,
-                        inputs = [gr.inputs.Textbox(lines=2, 
-                                                    placeholder="Enter your link...",
-                                                    label='YouTube Video Link'),
-                                  gr.inputs.Radio(["mT5", "BART"], type="value", label='Model')],
-                        outputs = [gr.outputs.Textbox(
-                                                      label="Summary")],
-                         
-                        title = "Video Summary Generator",
-                        examples = [
-                                   ['https://www.youtube.com/watch?v=OaeYUm06in0&list=PLHgX2IExbFouJoqEr8JMF5MbZSbyC91-L&t=5761s', 'BART'],
-                                   ['https://www.youtube.com/watch?v=U5OD8MjYnOM', 'BART'],
-                                   ['https://www.youtube.com/watch?v=Gfr50f6ZBvo', 'BART'],
-                                   ['https://www.youtube.com/watch?v=G4hL5Om4IJ4&t=2680s', 'BART'],
-                                   ['https://www.youtube.com/watch?v=0Jd7fJgFkPU&t=8776s', 'mT5']
-                                   ],
-                        enable_queue=True)
-
-interface.launch(debug=True) 
\ No newline at end of file
diff --git a/spaces/sachit-menon/classification_via_description/datasets.py b/spaces/sachit-menon/classification_via_description/datasets.py
deleted file mode 100644
index 1ab20600462ef2fcdcb25d2a4cf660cd43fdb9f0..0000000000000000000000000000000000000000
--- a/spaces/sachit-menon/classification_via_description/datasets.py
+++ /dev/null
@@ -1,12 +0,0 @@
-from PIL import Image
-import torchvision.transforms as transforms
-
-def _transform(n_px):
-    return transforms.Compose([
-        transforms.Resize(n_px, interpolation=Image.BICUBIC),
-        transforms.CenterCrop(n_px),
-        lambda image: image.convert("RGB"),
-        transforms.ToTensor(),
-        transforms.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711)),
-    ])
-    
\ No newline at end of file
diff --git a/spaces/salti/arabic-question-paraphrasing/README.md b/spaces/salti/arabic-question-paraphrasing/README.md
deleted file mode 100644
index 446067519ae338242c6ac921896a3a72a52ffadc..0000000000000000000000000000000000000000
--- a/spaces/salti/arabic-question-paraphrasing/README.md
+++ /dev/null
@@ -1,33 +0,0 @@
----
-title: Arabic Question Paraphrasing
-emoji: ❓❔
-colorFrom: pink
-colorTo: red
-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`
-
-`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/sander-wood/clamp_similar_music_recommendation/app.py b/spaces/sander-wood/clamp_similar_music_recommendation/app.py
deleted file mode 100644
index 669ac830eac627a1c90f9785da8df46b118a8439..0000000000000000000000000000000000000000
--- a/spaces/sander-wood/clamp_similar_music_recommendation/app.py
+++ /dev/null
@@ -1,237 +0,0 @@
-import subprocess
-import os
-import gradio as gr
-import json
-from utils import *
-from unidecode import unidecode
-from transformers import AutoTokenizer
-
-description = """
-<div>
-<a style="display:inline-block" href='https://github.com/microsoft/muzic/tree/main/clamp'><img src='https://img.shields.io/github/stars/microsoft/muzic?style=social' /></a>
-<a style='display:inline-block' href='https://ai-muzic.github.io/clamp/'><img src='https://img.shields.io/badge/website-CLaMP-ff69b4.svg' /></a>
-<a style="display:inline-block" href="https://huggingface.co/datasets/sander-wood/wikimusictext"><img src="https://img.shields.io/badge/huggingface-dataset-ffcc66.svg"></a>
-<a style="display:inline-block" href="https://arxiv.org/pdf/2304.11029.pdf"><img src="https://img.shields.io/badge/arXiv-2304.11029-b31b1b.svg"></a>
-<a class="duplicate-button" style="display:inline-block" target="_blank" href="https://huggingface.co/spaces/sander-wood/clamp_similar_music_recommendation?duplicate=true"><img style="margin-top:0;margin-bottom:0" src="https://huggingface.co/datasets/huggingface/badges/raw/main/duplicate-this-space-md-dark.svg" alt="Duplicate Space"></a>
-</div>
-
-## ℹ️ How to use this demo?
-1. Select a music file in MusicXML (.mxl) format.
-2. Click "Submit" and wait for the result.
-3. It will return the most similar music score from the WikiMusictext dataset (1010 scores in total).
-
-## ❕Notice
-- The demo only supports MusicXML (.mxl) files.
-- The returned results include the title, artist, genre, description, and the score in ABC notation.
-- The genre and description may not be accurate, as they are collected from the web.
-- The demo is based on CLaMP-S/512, a CLaMP model with 6-layer Transformer text/music encoders and a sequence length of 512.
-
-## 🎵👉🎵 Similar Music Recommendation
-A surprising capability of CLaMP is that it can also recommend similar music given a piece of music, even though it is not trained on this task. This is because CLaMP is trained to encode the semantic meaning of music, and thus it can capture the similarity between music pieces.We only use the music encoder to extract the music feature from the music query, and then calculate the similarity between the query and all the pieces of music in the library.
-
-"""
-
-CLAMP_MODEL_NAME = 'sander-wood/clamp-small-512'
-QUERY_MODAL = 'music'
-KEY_MODAL = 'music'
-TOP_N = 1
-TEXT_MODEL_NAME = 'distilroberta-base'
-TEXT_LENGTH = 128
-device = torch.device("cpu")
-
-# load CLaMP model
-model = CLaMP.from_pretrained(CLAMP_MODEL_NAME)
-music_length = model.config.max_length
-model = model.to(device)
-model.eval()
-
-# initialize patchilizer, tokenizer, and softmax
-patchilizer = MusicPatchilizer()
-tokenizer = AutoTokenizer.from_pretrained(TEXT_MODEL_NAME)
-softmax = torch.nn.Softmax(dim=1)
-
-def compute_values(Q_e, K_e, t=1):
-    """
-    Compute the values for the attention matrix
-
-    Args:
-        Q_e (torch.Tensor): Query embeddings
-        K_e (torch.Tensor): Key embeddings
-        t (float): Temperature for the softmax
-    
-    Returns:
-        values (torch.Tensor): Values for the attention matrix
-    """
-    # Normalize the feature representations
-    Q_e = torch.nn.functional.normalize(Q_e, dim=1)
-    K_e = torch.nn.functional.normalize(K_e, dim=1)
-
-    # Scaled pairwise cosine similarities [1, n]
-    logits = torch.mm(Q_e, K_e.T) * torch.exp(torch.tensor(t))
-    values = softmax(logits)
-    return values.squeeze()
-
-
-def encoding_data(data, modal):
-    """
-    Encode the data into ids
-
-    Args:
-        data (list): List of strings
-        modal (str): "music" or "text"
-    
-    Returns:
-        ids_list (list): List of ids
-    """
-    ids_list = []
-    if modal=="music":
-        for item in data:
-            patches = patchilizer.encode(item, music_length=music_length, add_eos_patch=True)
-            ids_list.append(torch.tensor(patches).reshape(-1))
-    else:
-        for item in data:
-            text_encodings = tokenizer(item, 
-                                        return_tensors='pt', 
-                                        truncation=True, 
-                                        max_length=TEXT_LENGTH)
-            ids_list.append(text_encodings['input_ids'].squeeze(0))
-
-    return ids_list
-
-
-def abc_filter(lines):
-    """
-    Filter out the metadata from the abc file
-
-    Args:
-        lines (list): List of lines in the abc file
-    
-    Returns:
-        music (str): Music string
-    """
-    music = ""
-    for line in lines:
-        if line[:2] in ['A:', 'B:', 'C:', 'D:', 'F:', 'G', 'H:', 'N:', 'O:', 'R:', 'r:', 'S:', 'T:', 'W:', 'w:', 'X:', 'Z:'] \
-        or line=='\n' \
-        or (line.startswith('%') and not line.startswith('%%score')):
-            continue
-        else:
-            if "%" in line and not line.startswith('%%score'):
-                line = "%".join(line.split('%')[:-1])
-                music += line[:-1] + '\n'
-            else:
-                music += line + '\n'
-    return music
-
-
-def load_music(filename):
-    """
-    Load the music from the xml file
-
-    Args:
-        file (Union[str, bytes, BinaryIO, TextIO]): Input file object containing the xml file
-
-    Returns:
-        music (str): Music string
-    """
-    # Get absolute path of xml2abc.py
-    script_dir = os.path.dirname(os.path.abspath(__file__))
-    xml2abc_path = os.path.join(script_dir, 'xml2abc.py')
-
-    # Use absolute path in Popen()
-    p = subprocess.Popen(['python', xml2abc_path, '-m', '2', '-c', '6', '-x', filename], stdout=subprocess.PIPE)
-    result = p.communicate()[0]
-    output = result.decode('utf-8').replace('\r', '')
-    music = unidecode(output).split('\n')
-    music = abc_filter(music)
-    
-    return music
-
-
-def get_features(ids_list, modal):
-    """
-    Get the features from the CLaMP model
-
-    Args:
-        ids_list (list): List of ids
-        modal (str): "music" or "text"
-    
-    Returns:
-        features_list (torch.Tensor): Tensor of features with a shape of (batch_size, hidden_size)
-    """
-    features_list = []
-    print("Extracting "+modal+" features...")
-    with torch.no_grad():
-        for ids in tqdm(ids_list):
-            ids = ids.unsqueeze(0)
-            if modal=="text":
-                masks = torch.tensor([1]*len(ids[0])).unsqueeze(0)
-                features = model.text_enc(ids.to(device), attention_mask=masks.to(device))['last_hidden_state']
-                features = model.avg_pooling(features, masks)
-                features = model.text_proj(features)
-            else:
-                masks = torch.tensor([1]*(int(len(ids[0])/PATCH_LENGTH))).unsqueeze(0)
-                features = model.music_enc(ids, masks)['last_hidden_state']
-                features = model.avg_pooling(features, masks)
-                features = model.music_proj(features)
-
-            features_list.append(features[0])
-    
-    return torch.stack(features_list).to(device)
-
-
-def similar_music_recommendation(file):
-    """
-    Recommend similar music
-
-    Args:
-        file (Union[str, bytes, BinaryIO, TextIO]): Input file object containing the xml file
-
-    Returns:
-        output (str): Output string
-    """
-    query = load_music(file.name)
-    print("\nQuery:\n"+ query)
-    with open(KEY_MODAL+"_key_cache_"+str(music_length)+".pth", 'rb') as f:
-        key_cache = torch.load(f)
-        
-    # encode query
-    query_ids = encoding_data([query], QUERY_MODAL)
-    query_feature = get_features(query_ids, QUERY_MODAL)
-
-    key_filenames = key_cache["filenames"]
-    key_features = key_cache["features"]
-
-    # compute values
-    values = compute_values(query_feature, key_features)
-    idx = torch.argsort(values)[-1]
-    filename = key_filenames[idx].split('/')[-1][:-4]
-
-    with open("wikimusictext.json", 'r') as f:
-        wikimusictext = json.load(f)
-    
-    for item in wikimusictext:
-        if item['title']==filename:
-            # output = "Title:\n" + item['title']+'\n\n'
-            # output += "Artist:\n" + item['artist']+ '\n\n'
-            # output += "Genre:\n" + item['genre']+ '\n\n'
-            # output += "Description:\n" + item['text']+ '\n\n'
-            # output += "ABC notation:\n" + item['music']+ '\n\n'
-            print("Title: " + item['title'])
-            print("Artist: " + item['artist'])
-            print("Genre: " + item['genre'])
-            print("Description: " + item['text'])
-            print("ABC notation:\n" + item['music'])
-            return item["title"], item["artist"], item["genre"], item["text"], item["music"]
-
-input_file = gr.inputs.File(label="Upload MusicXML file")
-output_title = gr.outputs.Textbox(label="Title")
-output_artist = gr.outputs.Textbox(label="Artist")
-output_genre = gr.outputs.Textbox(label="Genre")
-output_description = gr.outputs.Textbox(label="Description")
-output_abc = gr.outputs.Textbox(label="ABC notation")
-gr.Interface(similar_music_recommendation,
-             inputs=input_file,
-             outputs=[output_title, output_artist, output_genre, output_description, output_abc],
-             title="🗜️ CLaMP: Similar Music Recommendation", 
-             description=description).launch()
diff --git a/spaces/sasha/BiasDetection/honestbias.py b/spaces/sasha/BiasDetection/honestbias.py
deleted file mode 100644
index 5b32c95f85a69b68aab20cc38cc50463ce23a8f4..0000000000000000000000000000000000000000
--- a/spaces/sasha/BiasDetection/honestbias.py
+++ /dev/null
@@ -1,100 +0,0 @@
-# -*- coding: utf-8 -*-
-"""Honest
-
-Source: https://github.com/MilaNLProc/honest
-"""
-
-from transformers import AutoTokenizer, AutoModel , AutoModelForMaskedLM
-from transformers import pipeline
-import plotly.graph_objects as go
-from honest import honest
-from pathlib import Path
-import pandas as pd
-import pickle
-import ast
-# Initialize HONEST evaluator
-# data_set can be "binary" as in (Nozza et al., 2021) or "queer_nonqueer" (Nozza et al., 2022) or combined as "all"
-
-
-
-def get_completion(sentence, template, tokenizer):
-  start = sentence.find(template[:-4])+len(template[:-4])
-  if tokenizer.eos_token != None and tokenizer.eos_token in sentence:
-    end = sentence.find(tokenizer.eos_token)
-  elif tokenizer.sep_token != None and tokenizer.sep_token in sentence:
-    end = sentence.find(tokenizer.sep_token)
-  else:
-    end = len(sentence)-1
-
-  return sentence[start:end]
-
-
-
-# Define nlp_fill pipeline
-def calculate_HONEST(modelname, modeltype):
-    honestdatapath = modelname.replace('/','')+'_HONESTdata.csv'
-    honestscorepath = modelname.replace('/','')+'_HONESTscore.pkl'
-    if Path(honestdatapath).is_file():
-        print("loading local data")
-        honest_df = pd.read_csv(honestdatapath)
-        honest_score = pickle.load(open(honestscorepath, 'rb'))
-    else:
-        evaluator = honest.HonestEvaluator("en")
-        masked_templates = evaluator.templates(data_set="binary")
-        if modeltype == "CLM":
-            generator = pipeline('text-generation', model=modelname)
-            filled_templates = [[fill['generated_text'][len(masked_sentence.replace(' [M]','')):] for fill in generator(masked_sentence.replace(' [M].',''), max_length=len(masked_sentence.replace(' [M].','').split(" "))+10, num_return_sequences=5)] for masked_sentence in masked_templates.keys()]
-            honest_score, honest_df = evaluator.honest_dataframe(filled_templates, masked_templates)
-            honest_df.to_csv(modelname.replace('/','')+'_HONESTdata.csv')
-            pickle.dump(honest_score,open(honestscorepath,'wb'))
-        elif modeltype == "MLM":
-            tokenizer = AutoTokenizer.from_pretrained(modelname, add_special_tokens = False)
-            model = AutoModelForMaskedLM.from_pretrained(modelname)
-            nlp_fill = pipeline('fill-mask', model=model, tokenizer=tokenizer, top_k=5)
-            filled_templates = [[get_completion(fill['sequence'],masked_sentence,tokenizer) for fill in nlp_fill(masked_sentence.replace('[M]',tokenizer.mask_token))] for masked_sentence in masked_templates.keys()]
-            honest_score, honest_df = evaluator.honest_dataframe(filled_templates, masked_templates)
-            honest_df.to_csv(modelname.replace('/','')+'_HONESTdata.csv')
-            pickle.dump(honest_score,open(honestscorepath,'wb'))
-    return(honest_score, honest_df)
-
-
-"""Analyze completion dataframe"""
-
-def analyze_honest(honest_df):
-    hurtlex_cat = ['animals', 'crime and immoral behavior', 'derogatory words', 'female genitalia', 'male genitalia', 'homosexuality', 'potential negative connotations', 'professions and occupations', 'prostitution', 'social and economic disadvantage']
-    hurtlex_cat_names = ['an', 're', 'cds', 'asf', 'asm', 'om', 'qas', 'pa', 'pr', 'is']
-    df_identity = honest_df.groupby('category')[hurtlex_cat_names].sum()
-    df_count = honest_df.groupby('category')[hurtlex_cat_names].count()*5
-    df_perc = df_identity/df_count*100
-
-    plots = [go.Scatterpolar(r=df_perc.loc[i], theta=hurtlex_cat, fill='toself',
-                             name=i) for i, row in df_perc.iterrows()]
-
-    honest_fig = go.Figure(
-        data=plots,
-        layout=go.Layout(
-            polar={'radialaxis': {'visible': True}}
-        )
-    )
-    return(honest_fig)
-
-"""Show filled terms"""
-
-def show_filled_terms(honest_df):
-    grouped_df = honest_df.groupby(['raw', 'identity'])
-    filled_terms = []
-    for key, item in grouped_df:
-        all_terms = []
-        key_group = grouped_df.get_group(key)
-        for l in key_group.filled_words:
-            terms = ast.literal_eval(str(l))
-            all_terms = all_terms + terms
-        all_terms = list(set(all_terms))
-        filled_terms.append([key[0].replace('[I]',key[1]).replace('[M]',''), key_group.category.values[0], all_terms])
-    filled_terms_df = pd.DataFrame(filled_terms)
-    female_df, male_df = [x for _, x in filled_terms_df.groupby([1])]
-    female_df.columns = ['prompt','category','filled_words']
-    female_df = female_df.drop(['category'],axis=1)
-    male_df.columns = ['prompt','category','filled_words']
-    male_df = male_df.drop(['category'],axis=1)
-    return(female_df, male_df)
diff --git a/spaces/sayakpaul/lol-enhancement-maxim/app.py b/spaces/sayakpaul/lol-enhancement-maxim/app.py
deleted file mode 100644
index ffe36a1e2422d8a6fc529e66ecd737d63dac290f..0000000000000000000000000000000000000000
--- a/spaces/sayakpaul/lol-enhancement-maxim/app.py
+++ /dev/null
@@ -1,106 +0,0 @@
-"""
-Some preprocessing utilities have been taken from:
-https://github.com/google-research/maxim/blob/main/maxim/run_eval.py
-"""
-import gradio as gr
-import numpy as np
-import tensorflow as tf
-from huggingface_hub.keras_mixin import from_pretrained_keras
-from PIL import Image
-
-from create_maxim_model import Model
-from maxim.configs import MAXIM_CONFIGS
-
-
-_MODEL = from_pretrained_keras("google/maxim-s2-enhancement-lol")
-
-
-def mod_padding_symmetric(image, factor=64):
-    """Padding the image to be divided by factor."""
-    height, width = image.shape[0], image.shape[1]
-    height_pad, width_pad = ((height + factor) // factor) * factor, (
-        (width + factor) // factor
-    ) * factor
-    padh = height_pad - height if height % factor != 0 else 0
-    padw = width_pad - width if width % factor != 0 else 0
-    image = tf.pad(
-        image, [(padh // 2, padh // 2), (padw // 2, padw // 2), (0, 0)], mode="REFLECT"
-    )
-    return image
-
-
-def make_shape_even(image):
-    """Pad the image to have even shapes."""
-    height, width = image.shape[0], image.shape[1]
-    padh = 1 if height % 2 != 0 else 0
-    padw = 1 if width % 2 != 0 else 0
-    image = tf.pad(image, [(0, padh), (0, padw), (0, 0)], mode="REFLECT")
-    return image
-
-
-def process_image(image: Image):
-    input_img = np.asarray(image) / 255.0
-    height, width = input_img.shape[0], input_img.shape[1]
-
-    # Padding images to have even shapes
-    input_img = make_shape_even(input_img)
-    height_even, width_even = input_img.shape[0], input_img.shape[1]
-
-    # padding images to be multiplies of 64
-    input_img = mod_padding_symmetric(input_img, factor=64)
-    input_img = tf.expand_dims(input_img, axis=0)
-    return input_img, height, width, height_even, width_even
-
-
-def init_new_model(input_img):
-    configs = MAXIM_CONFIGS.get("S-2")
-    configs.update(
-        {
-            "variant": "S-2",
-            "dropout_rate": 0.0,
-            "num_outputs": 3,
-            "use_bias": True,
-            "num_supervision_scales": 3,
-        }
-    )
-    configs.update({"input_resolution": (input_img.shape[1], input_img.shape[2])})
-    new_model = Model(**configs)
-    new_model.set_weights(_MODEL.get_weights())
-    return new_model
-
-
-def infer(image):
-    preprocessed_image, height, width, height_even, width_even = process_image(image)
-    new_model = init_new_model(preprocessed_image)
-
-    preds = new_model.predict(preprocessed_image)
-    if isinstance(preds, list):
-        preds = preds[-1]
-        if isinstance(preds, list):
-            preds = preds[-1]
-
-    preds = np.array(preds[0], np.float32)
-
-    new_height, new_width = preds.shape[0], preds.shape[1]
-    h_start = new_height // 2 - height_even // 2
-    h_end = h_start + height
-    w_start = new_width // 2 - width_even // 2
-    w_end = w_start + width
-    preds = preds[h_start:h_end, w_start:w_end, :]
-
-    return Image.fromarray(np.array((np.clip(preds, 0.0, 1.0) * 255.0).astype(np.uint8)))
-
-
-title = "Enhance low-light images."
-description = "The underlying model is [this](https://huggingface.co/google/maxim-s2-enhancement-lol). You can use the model to enhance low-light images, which may be useful for aiding vision-impaired people. To quickly try out the model, you can choose from the available sample images below, or you can submit your own image. Note that, internally, the model is re-initialized based on the spatial dimensions of the input image and this process is time-consuming."
-
-iface = gr.Interface(
-    infer,
-    inputs="image",
-    outputs=gr.Image().style(height=242),
-    title=title,
-    description=description,
-    allow_flagging="never",
-    examples=[["1.png"], ["111.png"], ["748.png"], ["a4541-DSC_0040-2.png"]],
-)
-iface.launch(debug=True)
diff --git a/spaces/scedlatioru/img-to-music/example/AUTODESK.NAVISWORKS.MANAGE.V2016.MULTI.WIN64-ISO Free Download.md b/spaces/scedlatioru/img-to-music/example/AUTODESK.NAVISWORKS.MANAGE.V2016.MULTI.WIN64-ISO Free Download.md
deleted file mode 100644
index 258cd4752137b83cdf9287037f338bf1f7837203..0000000000000000000000000000000000000000
--- a/spaces/scedlatioru/img-to-music/example/AUTODESK.NAVISWORKS.MANAGE.V2016.MULTI.WIN64-ISO Free Download.md	
+++ /dev/null
@@ -1,105 +0,0 @@
-
-<h1>AUTODESK.NAVISWORKS.MANAGE.V2016.MULTI.WIN64-ISO Free Download</h1>
-
-<p>If you are looking for a powerful and comprehensive project review software that supports coordination, analysis, and communication of design intent and constructability, you may want to consider Autodesk Navisworks Manage 2016. This software helps you holistically review integrated models and data with stakeholders to gain better control over project outcomes. You can also combine design and construction data into a single model, identify and resolve clash and interference problems before construction, and simulate project schedules and logistics.</p>
-
-<p>In this article, we will show you how to download Autodesk Navisworks Manage 2016 for free and what are the main features and benefits of this software.</p>
-<h2>AUTODESK.NAVISWORKS.MANAGE.V2016.MULTI.WIN64-ISO free download</h2><br /><p><b><b>Download</b> &raquo; <a href="https://gohhs.com/2uEAkY">https://gohhs.com/2uEAkY</a></b></p><br /><br />
-
-<h2>How to Download Autodesk Navisworks Manage 2016 for Free</h2>
-
-<p>Autodesk Navisworks Manage 2016 is available as a free trial for 30 days. You can download it from the official Autodesk website by following these steps:</p>
-
-<ol>
-<li>Go to <a href="https://www.autodesk.com/products/navisworks/free-trial">https://www.autodesk.com/products/navisworks/free-trial</a> and click on "Download Free Trial".</li>
-<li>Select your operating system (Windows 64-bit) and your preferred language.</li>
-<li>Enter your personal information and click on "Next".</li>
-<li>Choose whether you want to download the software directly or use a download manager.</li>
-<li>Follow the instructions to install and activate the software.</li>
-</ol>
-
-<p>If you want to download the full version of Autodesk Navisworks Manage 2016 for free, you will need to find a reliable source that provides the ISO file and the crack. However, we do not recommend this option as it may expose your computer to viruses, malware, or legal issues. The best way to use Autodesk Navisworks Manage 2016 is to purchase a license from Autodesk or an authorized reseller.</p>
-
-<h2>What are the Main Features and Benefits of Autodesk Navisworks Manage 2016</h2>
-
-<p>Autodesk Navisworks Manage 2016 is a software that helps you improve BIM coordination and collaboration across different disciplines and stages of the project lifecycle. Some of the main features and benefits of this software are:</p>
-
-<ul>
-<li><b>Model Review:</b> You can import and aggregate data from multiple sources, such as AutoCAD, Revit, Inventor, Civil 3D, and more. You can also view, navigate, measure, section, annotate, and redline models in 3D.</li>
-<li><b>Clash Detection:</b> You can identify and resolve clash and interference problems before construction using the powerful clash detection tools. You can also create reports, assign tasks, track status, and manage clashes.</li>
-<li><b>4D Simulation:</b> You can link your model to project schedules and create 4D simulations to visualize the construction sequence and logistics. You can also analyze time and space conflicts, optimize resources, and communicate plans.</li>
-<li><b>Quantification:</b> You can extract quantities from your model and create bills of materials for cost estimation and procurement. You can also compare design changes and update quantities automatically.</li>
-<li><b>Presentation:</b> You can create photorealistic renderings, animations, panoramas, and interactive walkthroughs to showcase your design intent and communicate with stakeholders. You can also export your presentations to various formats, such as PDF, AVI, DWF, FBX, etc.</li>
-</ul>
-
-<p>Autodesk Navisworks Manage 2016 is a software that can help you improve your project delivery and quality by enabling you to review integrated models and data with stakeholders in a collaborative environment. By using this software, you can reduce errors, risks, costs, and delays in your projects.</p>
-<h2>How to Install and Activate Autodesk Navisworks Manage 2016</h2>
-
-<p>After you download Autodesk Navisworks Manage 2016, you will need to install and activate it on your computer. Here are the steps to do so:</p>
-
-<ol>
-<li>Extract the ISO file using a tool like WinRAR or 7-Zip.</li>
-<li>Run the setup.exe file and follow the instructions to install the software.</li>
-<li>When prompted, enter the serial number and product key that you received from Autodesk or your reseller.</li>
-<li>After the installation is complete, run the software and click on "Activate".</li>
-<li>Select "I have an activation code from Autodesk" and click on "Next".</li>
-<li>Run the crack file that you downloaded from your source and copy the request code from the software.</li>
-<li>Paste the request code into the crack file and click on "Generate".</li>
-<li>Copy the activation code from the crack file and paste it into the software.</li>
-<li>Click on "Next" and then "Finish" to complete the activation process.</li>
-</ol>
-
-<p>You have now successfully installed and activated Autodesk Navisworks Manage 2016 on your computer. You can start using it to review your 3D models and data with stakeholders.</p>
-<p></p>
-
-<h2>Conclusion</h2>
-
-<p>Autodesk Navisworks Manage 2016 is a software that helps you improve BIM coordination and collaboration across different disciplines and stages of the project lifecycle. It allows you to import and aggregate data from multiple sources, identify and resolve clash and interference problems, simulate project schedules and logistics, extract quantities and create bills of materials, and create photorealistic renderings and animations. You can download it for free as a trial version for 30 days or purchase a license from Autodesk or an authorized reseller. You can also download it for free as a full version from a reliable source that provides the ISO file and the crack, but this may expose your computer to viruses, malware, or legal issues. We hope this article has helped you learn more about Autodesk Navisworks Manage 2016 and how to download it for free.</p>
-<h2>How to Compare Autodesk Navisworks Manage 2016 with Other Similar Software</h2>
-
-<p>Autodesk Navisworks Manage 2016 is a software that helps you improve BIM coordination and collaboration across different disciplines and stages of the project lifecycle. However, it is not the only software that offers this functionality. There are other similar software that you can compare with Autodesk Navisworks Manage 2016, such as:</p>
-
-<ul>
-<li><b>Bentley Systems Synchro PRO:</b> This software helps you plan, visualize, and deliver complex construction projects using 4D digital construction. You can import data from various sources, such as BIM, CAD, scheduling, and cost applications. You can also create 4D simulations, detect and resolve clashes, optimize resources, and monitor progress.</li>
-<li><b>Trimble Connect:</b> This software helps you connect your project stakeholders and streamline workflows using cloud-based collaboration. You can upload, share, and review data from multiple sources, such as BIM, CAD, PDF, and images. You can also create 3D models, perform clash detection, create markups and comments, and track changes.</li>
-<li><b>Bluebeam Revu:</b> This software helps you create, edit, and collaborate on PDF documents for design and construction projects. You can import data from various sources, such as BIM, CAD, images, and scans. You can also create markups and annotations, measure dimensions and areas, compare documents, and track revisions.</li>
-</ul>
-
-<p>These are some of the main features and benefits of each software that you can compare with Autodesk Navisworks Manage 2016. However, the best way to decide which software suits your needs is to try them out yourself. You can download free trials or demos of each software from their respective websites and test them on your own projects.</p>
-<h2>What are the Advantages and Disadvantages of Autodesk Navisworks Manage 2016</h2>
-
-<p>Autodesk Navisworks Manage 2016 is a software that has many advantages and disadvantages for design and construction professionals. Some of the advantages are:</p>
-
-<ul>
-<li><b>It supports multiple file formats and data sources:</b> You can import and aggregate data from various sources, such as BIM, CAD, PDF, images, scans, and more. This allows you to review integrated models and data with stakeholders in a collaborative environment.</li>
-<li><b>It has powerful and comprehensive tools for project review:</b> You can use tools such as model review, clash detection, 4D simulation, quantification, and presentation to improve BIM coordination and collaboration across different disciplines and stages of the project lifecycle. You can also reduce errors, risks, costs, and delays in your projects.</li>
-<li><b>It has flexible and customizable workflows:</b> You can customize your interface, settings, preferences, and options to suit your needs and preferences. You can also create scripts, macros, plugins, and extensions to enhance the functionality of the software.</li>
-</ul>
-
-<p>Some of the disadvantages are:</p>
-
-<ul>
-<li><b>It requires a high-performance computer system:</b> You need a computer system that meets or exceeds the minimum system requirements for Autodesk Navisworks Manage 2016. The software can consume a lot of memory and processing power when dealing with large and complex models and data.</li>
-<li><b>It has a steep learning curve:</b> You need to spend some time and effort to learn how to use the software effectively and efficiently. The software has many features and functions that can be overwhelming for beginners or casual users.</li>
-<li><b>It can be expensive:</b> You need to purchase a license from Autodesk or an authorized reseller to use the software legally. The license can be costly depending on the type and duration of the license. You may also need to pay for maintenance, support, and updates.</li>
-</ul>
-
-<p>These are some of the main advantages and disadvantages of Autodesk Navisworks Manage 2016 that you should consider before using it for your projects.</p>
-
-<h2>How to Get Help and Support for Autodesk Navisworks Manage 2016</h2>
-
-<p>If you need help and support for Autodesk Navisworks Manage 2016, you have several options available. Some of them are:</p>
-
-<ul>
-<li><b>The Help menu:</b> You can access the Help menu from within the software by clicking on the question mark icon or pressing F1. The Help menu provides access to various resources, such as online help, tutorials, videos, forums, blogs, feedback, downloads, updates, and more.</li>
-<li><b>The Autodesk website:</b> You can visit the official Autodesk website at <a href="https://www.autodesk.com/">https://www.autodesk.com/</a> to find more information about the software, such as features, specifications, system requirements, pricing, licensing, trials, demos, etc. You can also access various services, such as Autodesk Account, Autodesk Store, Autodesk University, Autodesk BIM 360, Autodesk Cloud Services, etc.</li>
-<li><b>The Autodesk Knowledge Network:</b> You can visit the Autodesk Knowledge Network at <a href="https://knowledge.autodesk.com/">https://knowledge.autodesk.com/</a> to find more resources for learning and support. You can access articles, documentation, tutorials, videos, webinars, courses, certifications, forums, communities, blogs, events, etc.</li>
-<li><b>The Autodesk Support Center:</b> You can visit the Autodesk Support Center at <a href="https://www.autodesk.com/support">https://www.autodesk.com/support</a> to find more options for technical support. You can access support cases, chat with experts, call support agents, request remote assistance, submit feedback or report issues.</li>
-</ul>
-
-<p>These are some of the main options for getting help and support for Autodesk Navisworks Manage 2016. You can also contact your local reseller or partner for more assistance.</p>
-<h2>Conclusion</h2>
-
-<p>Autodesk Navisworks Manage 2016 is a software that helps you improve BIM coordination and collaboration across different disciplines and stages of the project lifecycle. It allows you to import and aggregate data from multiple sources, identify and resolve clash and interference problems, simulate project schedules and logistics, extract quantities and create bills of materials, and create photorealistic renderings and animations. You can download it for free as a trial version for 30 days or purchase a license from Autodesk or an authorized reseller. You can also download it for free as a full version from a reliable source that provides the ISO file and the crack, but this may expose your computer to viruses, malware, or legal issues. We hope this article has helped you learn more about Autodesk Navisworks Manage 2016 and how to download it for free.</p> 3cee63e6c2<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/scedlatioru/img-to-music/example/The Pursuit Of Happiness Love Junk Rar Free.md b/spaces/scedlatioru/img-to-music/example/The Pursuit Of Happiness Love Junk Rar Free.md
deleted file mode 100644
index 7e74d7e8816d4827f15f1dd065834a324c0c80ee..0000000000000000000000000000000000000000
--- a/spaces/scedlatioru/img-to-music/example/The Pursuit Of Happiness Love Junk Rar Free.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>the pursuit of happiness love junk rar</h2><br /><p><b><b>DOWNLOAD</b> &#10003; <a href="https://gohhs.com/2uEAA3">https://gohhs.com/2uEAA3</a></b></p><br /><br />
-
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-<br />
-<br />
-<p></p>
diff --git a/spaces/seduerr/communicaite/services/hate_speech.py b/spaces/seduerr/communicaite/services/hate_speech.py
deleted file mode 100644
index 6bbe61c377a677e09ede0e8d453bd6eadfc3f5c3..0000000000000000000000000000000000000000
--- a/spaces/seduerr/communicaite/services/hate_speech.py
+++ /dev/null
@@ -1,20 +0,0 @@
-from transformers import AutoTokenizer, AutoModelForSequenceClassification
-import torch.nn.functional as F
-import torch
-
-# Hate Speech
-tokenizer = AutoTokenizer.from_pretrained(
-    "mrm8488/distilroberta-finetuned-tweets-hate-speech")
-model = AutoModelForSequenceClassification.from_pretrained(
-    "mrm8488/distilroberta-finetuned-tweets-hate-speech")
-
-
-def classify_hatespeech(sentence):
-    preprocessed_text = sentence.strip().replace("\n", "")
-    inputs = tokenizer(preprocessed_text, return_tensors="pt")
-    labels = torch.tensor([1]).unsqueeze(0)
-    outputs = model(**inputs, labels=labels)
-    logits = outputs.logits
-    probs = torch.softmax(logits, dim=1)
-    nice = torch.flatten(probs).detach().numpy()[0]
-    return "{:.2f}".format(nice)
diff --git a/spaces/segments/panoptic-segment-anything/GroundingDINO/groundingdino/util/logger.py b/spaces/segments/panoptic-segment-anything/GroundingDINO/groundingdino/util/logger.py
deleted file mode 100644
index 18145f54c927abd59b95f3fa6e6da8002bc2ce97..0000000000000000000000000000000000000000
--- a/spaces/segments/panoptic-segment-anything/GroundingDINO/groundingdino/util/logger.py
+++ /dev/null
@@ -1,93 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-import functools
-import logging
-import os
-import sys
-
-from termcolor import colored
-
-
-class _ColorfulFormatter(logging.Formatter):
-    def __init__(self, *args, **kwargs):
-        self._root_name = kwargs.pop("root_name") + "."
-        self._abbrev_name = kwargs.pop("abbrev_name", "")
-        if len(self._abbrev_name):
-            self._abbrev_name = self._abbrev_name + "."
-        super(_ColorfulFormatter, self).__init__(*args, **kwargs)
-
-    def formatMessage(self, record):
-        record.name = record.name.replace(self._root_name, self._abbrev_name)
-        log = super(_ColorfulFormatter, self).formatMessage(record)
-        if record.levelno == logging.WARNING:
-            prefix = colored("WARNING", "red", attrs=["blink"])
-        elif record.levelno == logging.ERROR or record.levelno == logging.CRITICAL:
-            prefix = colored("ERROR", "red", attrs=["blink", "underline"])
-        else:
-            return log
-        return prefix + " " + log
-
-
-# so that calling setup_logger multiple times won't add many handlers
-@functools.lru_cache()
-def setup_logger(output=None, distributed_rank=0, *, color=True, name="imagenet", abbrev_name=None):
-    """
-    Initialize the detectron2 logger and set its verbosity level to "INFO".
-
-    Args:
-        output (str): a file name or a directory to save log. If None, will not save log file.
-            If ends with ".txt" or ".log", assumed to be a file name.
-            Otherwise, logs will be saved to `output/log.txt`.
-        name (str): the root module name of this logger
-
-    Returns:
-        logging.Logger: a logger
-    """
-    logger = logging.getLogger(name)
-    logger.setLevel(logging.DEBUG)
-    logger.propagate = False
-
-    if abbrev_name is None:
-        abbrev_name = name
-
-    plain_formatter = logging.Formatter(
-        "[%(asctime)s.%(msecs)03d]: %(message)s", datefmt="%m/%d %H:%M:%S"
-    )
-    # stdout logging: master only
-    if distributed_rank == 0:
-        ch = logging.StreamHandler(stream=sys.stdout)
-        ch.setLevel(logging.DEBUG)
-        if color:
-            formatter = _ColorfulFormatter(
-                colored("[%(asctime)s.%(msecs)03d]: ", "green") + "%(message)s",
-                datefmt="%m/%d %H:%M:%S",
-                root_name=name,
-                abbrev_name=str(abbrev_name),
-            )
-        else:
-            formatter = plain_formatter
-        ch.setFormatter(formatter)
-        logger.addHandler(ch)
-
-    # file logging: all workers
-    if output is not None:
-        if output.endswith(".txt") or output.endswith(".log"):
-            filename = output
-        else:
-            filename = os.path.join(output, "log.txt")
-        if distributed_rank > 0:
-            filename = filename + f".rank{distributed_rank}"
-        os.makedirs(os.path.dirname(filename), exist_ok=True)
-
-        fh = logging.StreamHandler(_cached_log_stream(filename))
-        fh.setLevel(logging.DEBUG)
-        fh.setFormatter(plain_formatter)
-        logger.addHandler(fh)
-
-    return logger
-
-
-# cache the opened file object, so that different calls to `setup_logger`
-# with the same file name can safely write to the same file.
-@functools.lru_cache(maxsize=None)
-def _cached_log_stream(filename):
-    return open(filename, "a")
diff --git a/spaces/senquan/ChuanhuChatGPT/chatgpt - macOS.command b/spaces/senquan/ChuanhuChatGPT/chatgpt - macOS.command
deleted file mode 100644
index fa015edca9e6916f24394813ce8ba77d2072e296..0000000000000000000000000000000000000000
--- a/spaces/senquan/ChuanhuChatGPT/chatgpt - macOS.command	
+++ /dev/null
@@ -1,7 +0,0 @@
-#!/bin/bash
-echo Opening ChuanhuChatGPT...
-cd "$(dirname "${BASH_SOURCE[0]}")"
-nohup python3 ChuanhuChatbot.py >/dev/null 2>&1 &
-sleep 5
-open http://127.0.0.1:7860
-echo Finished opening ChuanhuChatGPT (http://127.0.0.1:7860/). If you kill ChuanhuChatbot, Use "pkill -f 'ChuanhuChatbot'" command in terminal.
\ No newline at end of file
diff --git a/spaces/shi-labs/Matting-Anything/GroundingDINO/groundingdino/config/GroundingDINO_SwinB.py b/spaces/shi-labs/Matting-Anything/GroundingDINO/groundingdino/config/GroundingDINO_SwinB.py
deleted file mode 100644
index f490c4bbd598a35de43d36ceafcbd769e7ff21bf..0000000000000000000000000000000000000000
--- a/spaces/shi-labs/Matting-Anything/GroundingDINO/groundingdino/config/GroundingDINO_SwinB.py
+++ /dev/null
@@ -1,43 +0,0 @@
-batch_size = 1
-modelname = "groundingdino"
-backbone = "swin_B_384_22k"
-position_embedding = "sine"
-pe_temperatureH = 20
-pe_temperatureW = 20
-return_interm_indices = [1, 2, 3]
-backbone_freeze_keywords = None
-enc_layers = 6
-dec_layers = 6
-pre_norm = False
-dim_feedforward = 2048
-hidden_dim = 256
-dropout = 0.0
-nheads = 8
-num_queries = 900
-query_dim = 4
-num_patterns = 0
-num_feature_levels = 4
-enc_n_points = 4
-dec_n_points = 4
-two_stage_type = "standard"
-two_stage_bbox_embed_share = False
-two_stage_class_embed_share = False
-transformer_activation = "relu"
-dec_pred_bbox_embed_share = True
-dn_box_noise_scale = 1.0
-dn_label_noise_ratio = 0.5
-dn_label_coef = 1.0
-dn_bbox_coef = 1.0
-embed_init_tgt = True
-dn_labelbook_size = 2000
-max_text_len = 256
-text_encoder_type = "bert-base-uncased"
-use_text_enhancer = True
-use_fusion_layer = True
-use_checkpoint = True
-use_transformer_ckpt = True
-use_text_cross_attention = True
-text_dropout = 0.0
-fusion_dropout = 0.0
-fusion_droppath = 0.1
-sub_sentence_present = True
diff --git a/spaces/simonduerr/ProteinMPNN/af_backprop/alphafold/model/common_modules.py b/spaces/simonduerr/ProteinMPNN/af_backprop/alphafold/model/common_modules.py
deleted file mode 100644
index f239c870bde49e1e5b1a7e6622c5ef4f44a37b3f..0000000000000000000000000000000000000000
--- a/spaces/simonduerr/ProteinMPNN/af_backprop/alphafold/model/common_modules.py
+++ /dev/null
@@ -1,84 +0,0 @@
-# Copyright 2021 DeepMind Technologies Limited
-#
-# 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.
-
-"""A collection of common Haiku modules for use in protein folding."""
-import haiku as hk
-import jax.numpy as jnp
-
-
-class Linear(hk.Module):
-  """Protein folding specific Linear Module.
-
-  This differs from the standard Haiku Linear in a few ways:
-    * It supports inputs of arbitrary rank
-    * Initializers are specified by strings
-  """
-
-  def __init__(self,
-               num_output: int,
-               initializer: str = 'linear',
-               use_bias: bool = True,
-               bias_init: float = 0.,
-               name: str = 'linear'):
-    """Constructs Linear Module.
-
-    Args:
-      num_output: number of output channels.
-      initializer: What initializer to use, should be one of {'linear', 'relu',
-        'zeros'}
-      use_bias: Whether to include trainable bias
-      bias_init: Value used to initialize bias.
-      name: name of module, used for name scopes.
-    """
-
-    super().__init__(name=name)
-    self.num_output = num_output
-    self.initializer = initializer
-    self.use_bias = use_bias
-    self.bias_init = bias_init
-
-  def __call__(self, inputs: jnp.ndarray) -> jnp.ndarray:
-    """Connects Module.
-
-    Args:
-      inputs: Tensor of shape [..., num_channel]
-
-    Returns:
-      output of shape [..., num_output]
-    """
-    n_channels = int(inputs.shape[-1])
-
-    weight_shape = [n_channels, self.num_output]
-    if self.initializer == 'linear':
-      weight_init = hk.initializers.VarianceScaling(mode='fan_in', scale=1.)
-    elif self.initializer == 'relu':
-      weight_init = hk.initializers.VarianceScaling(mode='fan_in', scale=2.)
-    elif self.initializer == 'zeros':
-      weight_init = hk.initializers.Constant(0.0)
-
-    weights = hk.get_parameter('weights', weight_shape, inputs.dtype,
-                               weight_init)
-
-    # this is equivalent to einsum('...c,cd->...d', inputs, weights)
-    # but turns out to be slightly faster
-    inputs = jnp.swapaxes(inputs, -1, -2)
-    output = jnp.einsum('...cb,cd->...db', inputs, weights)
-    output = jnp.swapaxes(output, -1, -2)
-
-    if self.use_bias:
-      bias = hk.get_parameter('bias', [self.num_output], inputs.dtype,
-                              hk.initializers.Constant(self.bias_init))
-      output += bias
-
-    return output
diff --git a/spaces/simonraj/ELOralCoachHONGWEN/app.py b/spaces/simonraj/ELOralCoachHONGWEN/app.py
deleted file mode 100644
index 0f500ec010799aa1bec52d10fdf77fc387bb2cec..0000000000000000000000000000000000000000
--- a/spaces/simonraj/ELOralCoachHONGWEN/app.py
+++ /dev/null
@@ -1,88 +0,0 @@
-#app.py
-import gradio as gr
-import openai
-import os
-import HongWenData  # Importing the HongWenData module
-import base64
-
-OPENAI_API_KEY = os.getenv("OPENAI_API_KEY") 
-openai.api_key = OPENAI_API_KEY
-
-def image_to_base64(img_path):
-    with open(img_path, "rb") as img_file:
-        return base64.b64encode(img_file.read()).decode('utf-8')
-
-img_base64 = image_to_base64("HongWenSBC.JPG")
-img_html = f'<img src="data:image/jpg;base64,{img_base64}" alt="SBC6" width="300" style="display: block; margin: auto;"/>'
-    
-def predict(question_choice, audio):
-    # Transcribe the audio using Whisper
-    with open(audio, "rb") as audio_file:
-        transcript = openai.Audio.transcribe("whisper-1", audio_file)
-    message = transcript["text"]  # This is the transcribed message from the audio input
-    
-    # Generate the system message based on the chosen question
-    strategy, explanation = HongWenData.strategy_text["TREES"]
-    
-    # Reference to the picture description from HongWenData.py
-    picture_description = HongWenData.description
-
-    # Determine whether to include the picture description based on the question choice
-    picture_description_inclusion = f"""
-    For the first question, ensure your feedback refers to the picture description provided:
-    {picture_description}
-    """ if question_choice == HongWenData.questions[0] else ""
-
-    # Construct the conversation with the system and user's message
-    conversation = [
-        {
-            "role": "system",
-            "content": f"""
-            You are an expert English Language Teacher in a Singapore Primary school, directly guiding a Primary 6 student in Singapore. 
-            The student is answering the question: '{question_choice}'. 
-            {picture_description_inclusion}
-            Point out areas they did well and where they can improve, following the {strategy}. 
-            Encourage the use of sophisticated vocabulary and expressions.
-            For the second and third questions, the picture is not relevant, so the student should not refer to it in their response.
-            {explanation}
-            The feedback should be in second person, addressing the student directly.
-            """
-        },
-        {"role": "user", "content": message}
-    ]
-
-
-    response = openai.ChatCompletion.create(
-        model='gpt-3.5-turbo',
-        messages=conversation,
-        temperature=0.6,
-        max_tokens=1000,  # Limiting the response to 1000 tokens
-        stream=True
-    )
-
-    partial_message = ""
-    for chunk in response:
-        if len(chunk['choices'][0]['delta']) != 0:
-            partial_message = partial_message + chunk['choices'][0]['delta']['content']
-            yield partial_message
-
-# Gradio Interface
-iface = gr.Interface(
-    fn=predict,
-    inputs=[
-        gr.Radio(HongWenData.questions, label="Choose a question", default=HongWenData.questions[0]),  # Dropdown for question choice
-        gr.inputs.Audio(source="microphone", type="filepath")  # Audio input
-    ],
-    outputs=gr.inputs.Textbox(),  # Using inputs.Textbox as an output to make it editable
-    description=img_html + '''
-        <div style="text-align: center; font-size: medium;">
-            <a href="https://forms.moe.edu.sg/forms/J0lmkJ" target="_blank">
-                &#x1F4DD; Click here to provide feedback on the initial prototype of the Oral Coach &#x1F4DD;
-            </a>
-        </div>
-    ''',  # Corrected string concatenation
-    css="custom.css"  # Link to the custom CSS file
-)
-
-iface.queue(max_size=99, concurrency_count=40).launch(debug=True)
-
diff --git a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Download 2048 Mod APK for Android and IOS The Ultimate Puzzle Game.md b/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Download 2048 Mod APK for Android and IOS The Ultimate Puzzle Game.md
deleted file mode 100644
index 0d785d3b56b898822efd6bf22587c016b35d5a1a..0000000000000000000000000000000000000000
--- a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Download 2048 Mod APK for Android and IOS The Ultimate Puzzle Game.md	
+++ /dev/null
@@ -1,116 +0,0 @@
-<br />
-<h1>2048 Mod APK: A Fun and Addictive Puzzle Game</h1>
-<p>If you are looking for a simple yet challenging puzzle game that can keep you entertained for hours, you might want to try 2048 mod apk. This is a modified version of the original 2048 game that offers more features and benefits for the players. In this article, we will tell you everything you need to know about 2048 mod apk, including what it is, how to play it, why it is so popular, what are its features, how to download and install it, and what are its pros and cons.</p>
- <h2>What is 2048?</h2>
-<p>2048 is a puzzle game that was created by Gabriele Cirulli in 2014. The game is inspired by other similar games such as Threes and 1024. The goal of the game is to slide numbered tiles on a 4x4 grid and combine them to create a tile with the number 2048. The game is over when there are no more moves left or when the player reaches the 2048 tile.</p>
-<h2>2048 mod apk</h2><br /><p><b><b>Download File</b> &#8230; <a href="https://ssurll.com/2uO0AM">https://ssurll.com/2uO0AM</a></b></p><br /><br />
- <h3>How to play 2048?</h3>
-<p>The game is very easy to play. You just need to swipe your finger on the screen to move the tiles in the direction you want. When two tiles with the same number touch, they merge into one tile with the sum of their numbers. For example, if you swipe left and there are two tiles with the number 2 on the leftmost column, they will merge into one tile with the number 4. You can also use the arrow keys on your keyboard if you are playing on a computer.</p>
- <h3>Why is 2048 so popular?</h3>
-<p>There are many reasons why 2048 is so popular among puzzle game lovers. Some of them are:</p>
-<ul>
-<li>The game is simple but challenging. It does not require any special skills or knowledge, but it still tests your logic and strategy.</li>
-<li>The game is addictive. It makes you want to play more and more until you reach the highest score possible.</li>
-<li>The game is relaxing. It does not have any time limit or pressure, so you can play it at your own pace and enjoy the soothing sound effects and music.</li>
-<li>The game is fun. It gives you a sense of satisfaction and achievement when you create a new tile or beat your previous score.</li>
-</ul>
- <h2>What is 2048 mod apk?</h2>
-<p>2048 mod apk is a modified version of the original 2048 game that offers more features and benefits for the players. It is not available on the official app stores, but you can download it from third-party websites such as Apkloli. By downloading and installing 2048 mod apk, you can enjoy the following features:</p>
- <h3>Features of 2048 mod apk</h3>
-<h4>Unlimited money</h4>
-<p>With 2048 mod apk, you can get unlimited money that you can use to buy various items in the game. For example, you can buy hints that can help you make better moves, or boosters that can increase your score or remove unwanted tiles.</p>
- <h4>No ads</h4>
-<p>Another benefit of 2048 mod apk is that it removes all the annoying ads that interrupt your gameplay. You can play the game without any distractions or interruptions.</p>
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- <h4>Custom themes</h4>
-<p>If you are bored with the default theme of the game, you can change it with 2048 mod apk. You can choose from different themes such as animals, fruits, flowers, colors, emojis, and more. You can also create your own theme by using your own images and sounds.</p>
- <h4> <h4>Undo and redo moves</h4>
-<p>Sometimes, you might regret making a certain move or want to try a different strategy. With 2048 mod apk, you can undo and redo your moves as many times as you want. This can help you avoid mistakes and improve your chances of winning.</p>
- <h3>How to download and install 2048 mod apk?</h3>
-<p>If you want to download and install 2048 mod apk, you need to follow these simple steps:</p>
-<ol>
-<li>Go to the website where you can download 2048 mod apk, such as Apkloli. Make sure you choose a reliable and safe source.</li>
-<li>Click on the download button and wait for the file to be downloaded on your device.</li>
-<li>Go to your device settings and enable the installation of apps from unknown sources. This is necessary because 2048 mod apk is not from the official app stores.</li>
-<li>Locate the downloaded file and tap on it to start the installation process. Follow the instructions on the screen and wait for the installation to be completed.</li>
-<li>Launch the game and enjoy playing 2048 mod apk with all its features.</li>
-</ol>
- <h2>Pros and cons of 2048 mod apk</h2>
-<p>Like any other app, 2048 mod apk has its pros and cons. Here are some of them:</p>
- <h3>Pros</h3>
-<ul>
-<li>It enhances the gameplay experience by adding more features and options.</li>
-<li>It allows you to customize the game according to your preferences and tastes.</li>
-<li>It eliminates the ads that can disrupt your concentration and enjoyment.</li>
-<li>It gives you unlimited money that you can use to buy useful items and boosters.</li>
-<li>It lets you undo and redo your moves as much as you want.</li>
-</ul>
- <h3>Cons</h3>
-<ul>
-<li>It is not available on the official app stores, so you need to download it from third-party websites that may not be secure or trustworthy.</li>
-<li>It may not be compatible with some devices or operating systems.</li>
-<li>It may cause some glitches or errors in the game performance or functionality.</li>
-<li>It may violate the terms and conditions of the original game developer or publisher.</li>
-<li>It may reduce the challenge and difficulty of the game by making it too easy or unfair.</li>
-</ul>
- <h2>Conclusion</h2>
-<p>In conclusion, 2048 mod apk is a fun and addictive puzzle game that offers more features and benefits than the original 2048 game. It allows you to play the game with unlimited money, no ads, custom themes, undo and redo moves, and more. However, it also has some drawbacks, such as being unavailable on the official app stores, causing some technical issues, and violating some rules. Therefore, you should weigh the pros and cons before downloading and installing 2048 mod apk on your device. If you decide to try it, make sure you download it from a reliable and safe source, such as Apkloli. We hope this article has been helpful and informative for you. Thank you for reading!</p>
- <h2>Frequently Asked Questions</h2>
-<p>Here are some of the most common questions that people ask about 2048 mod apk:</p>
- <h4>Q: Is 2048 mod apk free?</h4>
-<p>A: Yes, 2048 mod apk is free to download and play. You do not need to pay any money to enjoy its features and benefits.</p>
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-<ol>
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-<li>Wait for the app to be uninstalled from your device.</li>
-</ol></p> 401be4b1e0<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Download 3750 IOS Image Files for Cisco Catalyst Switches.md b/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Download 3750 IOS Image Files for Cisco Catalyst Switches.md
deleted file mode 100644
index fbb5d13572b833bbf87beab0c4944be870b9a8a1..0000000000000000000000000000000000000000
--- a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Download 3750 IOS Image Files for Cisco Catalyst Switches.md	
+++ /dev/null
@@ -1,137 +0,0 @@
-<br />
-<h1>How to Download and Install IOS on Cisco Catalyst 3750 Switches</h1>
-<p>If you are using Cisco Catalyst 3750 switches in your network, you might want to keep them updated with the latest Cisco IOS software. IOS, or Internetwork Operating System, is the software that runs on Cisco routers and switches, providing network services and protocols. Updating your IOS software can improve your network performance, security, and reliability, as well as fix bugs and add new features.</p>
-<h2>download 3750 ios</h2><br /><p><b><b>Download Zip</b> &#128504; <a href="https://ssurll.com/2uNZHO">https://ssurll.com/2uNZHO</a></b></p><br /><br />
-<p>In this article, we will show you how to download and install IOS on Cisco Catalyst 3750 switches, which are innovative switches that combine industry-leading ease of use and high resiliency for stackable switches. They feature Cisco StackWise technology, a 32-Gbps stack interconnect that allows customers to build a unified, highly resilient switching system, one switch at a time.</p>
- <h2>Prerequisites</h2>
-<p>Before you start the IOS upgrade process, you need to make sure that you have the following hardware and software requirements:</p>
-<ul>
-<li>A PC or workstation with a TFTP or RCP server application installed. You will use this to transfer the IOS image file from your PC to the switch. You can download a TFTP server for Windows from <a href="(^10^)">here</a>.</li>
-<li>A console cable (usually a flat black cable) that connects the console port of the switch to the COM port of your PC. You will use this to establish a console session to the switch.</li>
-<li>A valid Cisco IOS image file for your switch model and feature set. You can obtain this from <a href="(^3^)">Cisco Software Central</a>. Make sure that you choose the correct image file that supports your hardware and software features, and that your switch has enough memory to run it. You can also verify the integrity of the image file by checking its MD5 checksum.</li>
-</ul>
- <h2>Steps to Download and Install IOS on Cisco Catalyst 3750 Switches</h2>
-<p>Once you have all the prerequisites ready, you can follow these steps to download and install IOS on your switch:</p>
- <h3>Step 1: Establish a console session to the switch</h3>
-<p>Connect your PC to the switch using the console cable. Then, open a terminal emulation program (such as PuTTY or HyperTerminal) on your PC and configure it with these settings:</p>
-<table>
-<tr><th>Parameter</th><th>Value</th></tr>
-<tr><td>Baud rate</td><td>9600 bps</td></tr>
-<tr><td>Data bits</td><td>8</td></tr>
-<tr><td>Parity</td><td>None</td></tr>
-<tr><td>Stop bits</td><td>1</td></tr>
-<tr><td>Flow control</td><td>None</td></tr>
-</table>
-<p>Press Enter a few times until you see the switch prompt. If you are prompted for a username and password, enter them accordingly. If you do not have them, contact your network administrator. You should see a prompt like this:</p>
-<pre>
-Switch>
-</pre>
-<p>If you are in user mode, enter the <code>enable</code> command to enter privileged mode. You should see a prompt like this:</p>
-<pre>
-Switch# </pre>
- <h3>Step 2: Verify the current IOS version and feature set</h3>
-<p>To check the current IOS version and feature set running on your switch, enter the <code>show version</code> command. You should see an output like this:</p>
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-<pre>
-Switch#show version Cisco IOS Software, C3750 Software (C3750-IPSERVICESK9-M), Version 12.2(55)SE10, RELEASE SOFTWARE (fc2) Technical Support: http://www.cisco.com/techsupport Copyright (c) 1986-2016 by Cisco Systems, Inc. Compiled Thu 21-Jan-16 08:54 by prod_rel_team Image text-base: 0x01000000, data-base: 0x02F00000 ROM: Bootstrap program is C3750 boot loader BOOTLDR: C3750 Boot Loader (C3750-HBOOT-M) Version 12.2(44)SE5, RELEASE SOFTWARE (fc1) Switch uptime is 1 hour, 23 minutes System returned to ROM by power-on System image file is "flash:c3750-ipservicesk9-mz.122-55.SE10.bin" ... </pre>
-<p>The line that starts with <code>System image file</code> shows the name and location of the IOS image file. In this example, the image file is <code>c3750-ipservicesk9-mz.122-55.SE10.bin</code> and it is stored in the flash memory of the switch. The name of the image file also indicates the IOS version and feature set. In this example, the IOS version is <code>12.2(55)SE10</code> and the feature set is <code>IP Services</code>.</p>
- <h3>Step 3: Delete the old IOS image file from the flash memory</h3>
-<p>To free up some space on the flash memory for the new IOS image file, you need to delete the old IOS image file. To do this, enter the <code>delete flash:<image_file></code> command, where <code><image_file></code> is the name of the old IOS image file. For example:</p>
-<pre>
-Switch#delete flash:c3750-ipservicesk9-mz.122-55.SE10.bin Delete filename [c3750-ipservicesk9-mz.122-55.SE10.bin]?  Delete flash:c3750-ipservicesk9-mz.122-55.SE10.bin? [confirm] </pre>
-<p>Press Enter to confirm the deletion. You should see a message like this:</p>
-<pre>
-Deleting flash:c3750-ipservicesk9-mz.122-55.SE10.bin...done </pre>
- <h3>Step 4: Copy the new IOS image file to the flash memory using TFTP or RCP</h3>
-<p>To copy the new IOS image file from your PC to the switch, you need to use either TFTP or RCP protocol. TFTP is a simple and widely used protocol for transferring files over a network, but it does not provide any security or authentication features. RCP is a more secure and reliable protocol that uses SSH for encryption and authentication, but it requires more configuration on both ends.</p>
-<p>In this article, we will use TFTP as an example, but you can also use RCP if you prefer. To copy the new IOS image file using TFTP, follow these steps:</p>
-<ol>
-<li>On your PC, make sure that your TFTP server application is running and that the new IOS image file is in the root directory of the TFTP server.</li>
-<li>On your switch, enter the <code>copy tftp flash</code> command. You will be prompted for some information, such as the IP address of your PC, the name of the IOS image file, and the destination filename on the flash memory. For example:</li>
-<pre>
-Switch#copy tftp flash Address or name of remote host []? 192.168.1.100 Source filename []? c3750-ipbasek9-mz.150-2.SE11.bin Destination filename [c3750-ipbasek9-mz.150-2.SE11.bin]?  Accessing tftp://192.168.1.100/c3750-ipbasek9-mz.150-2.SE11.bin... Loading c3750-ipbasek9-mz.150-2.SE11.bin from 192.168.1.100 (via Vlan1 ): !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! [OK - 33509312 bytes] 33509312 bytes copied in 120.456 secs (278213 bytes/sec) </pre>
-<li>Wait until the file transfer is complete and verify that the new IOS image file is in the flash memory by entering the <code>dir flash:</code> command. You should see an output like this:</li>
-<pre>
-Switch#dir flash: Directory of flash:/     2  -rwx        3350   Mar 1 1993 00:04:05 +00:00  config.text     3  -rwx        2072   Mar 1 1993 00:04:05 +00:00  private-config.text     4  -rwx    33509312   Jun 20 2023 17:15:23 +00:00  c3750-ipbasek9-mz.150-2.SE11.bin     5  drwx         192   Mar 1 1993 00:05:42 +00:00  c3750-ipbasek9-mz.122-55.SE10     ... </pre>
-</ol>
- <h3>Step 5: Configure the boot variable to load the new IOS image on startup</h3>
-<p>To make the switch load the new IOS image on startup, you need to configure the boot variable with the name and location of the new IOS image file. To do this, enter the <code>conf t</code> command to enter global configuration mode, and then enter the <code>boot system flash:<image_file></code> command, where <code><image_file></code> is the name of the new IOS image file. For example:</p>
-<pre>
-Switch#conf t Enter configuration commands, one per line. End with CNTL/Z. Switch(config)#boot system flash:c3750-ipbasek9-mz.150-2.SE11.bin Switch(config)#end Switch# </pre>
-<p>To verify that the boot variable is set correctly, enter the <code>show boot</code> command. You should see an output like this:</p>
-<pre>
-Switch#show boot BOOT path-list      : flash:c3750-ipbasek9-mz.150-2.SE11.bin Config file         : flash:/config.text Private Config file : flash:/private-config.text Enable Break        : no Manual Boot         : no HELPER path-list    : Auto upgrade        : yes Auto upgrade path   : NVRAM/Config file buffer size:       : 524288 Timeout for Config Download:          : 0 seconds Config Download via DHCP:          : disabled (next boot: disabled) </pre>
- <h3>Step 6: Reload the switch and verify the upgrade</h3>
-<p>To apply the changes and load the new IOS image, you need to reload the switch. To do this, enter the <code>reload</code> command and confirm it. For example:</p>
-<pre>
-Switch#reload Proceed with reload? [confirm] </pre>
-<p>The switch will reboot and load the new IOS image. To verify that the upgrade was successful, enter the <code>show version</code> command again and check the IOS version and feature set. You should see an output like this:</p>
-<pre>
-Switch#show version Cisco IOS Software, C3750 Software (C3750-IPBASEK9-M), Version 15.0(2)SE11, RELEASE SOFTWARE (fc3) Technical Support: http://www.cisco.com/techsupport Copyright (c) 1986-2018 by Cisco Systems, Inc. Compiled Mon 26-Feb-18 12:49 by prod_rel_team Image text-base: 0x01000000, data-base: 0x02F00000 ROM: Bootstrap program is C3750 boot loader BOOTLDR: C3750 Boot Loader (C3750-HBOOT-M) Version 12.2(44)SE5, RELEASE SOFTWARE (fc1) Switch uptime is 5 minutes System returned to ROM by power-on System image file is "flash:c3750-ipbasek9-mz.150-2.SE11.bin" ... </pre>
- <h2>Conclusion</h2>
-<p>In this article, we have shown you how to download and install IOS on Cisco Catalyst 3750 switches using TFTP protocol. By following these steps, you can keep your switches updated with the latest IOS software and enjoy improved network performance, security, and reliability.</p>
-<p>Here are some tips and best practices for IOS upgrade:</p>
-<ul>
-<li>Always backup your switch configuration before upgrading IOS.</li>
-<li>Always verify the integrity of the IOS image file by checking its MD 5 checksum and comparing it with the one provided by Cisco.</li>
-<li>Always use a reliable and secure protocol for transferring the IOS image file, such as RCP or SCP.</li>
-<li>Always test the new IOS image on a non-production switch before deploying it to the production network.</li>
-<li>Always follow the Cisco documentation and guidelines for IOS upgrade, which you can find <a href="">here</a>.</li>
-</ul>
- <h2>FAQs</h2>
-<p>Here are some frequently asked questions and answers about IOS upgrade on Cisco Catalyst 3750 switches:</p>
- <h3>Q1: What is Cisco StackWise technology and how does it affect IOS upgrade?</h3>
-<p>A1: Cisco StackWise technology is a feature that allows up to nine Catalyst 3750 switches to operate as a single logical unit, sharing a common control plane, data plane, and management plane. This provides high availability, scalability, and simplified management for stackable switches. When you upgrade IOS on a switch stack, you need to make sure that all the stack members are running the same IOS version and feature set. You also need to use the <code>archive download-sw</code> command instead of the <code>copy tftp flash</code> command to copy the IOS image file to all the stack members simultaneously.</p>
- <h3>Q2: How can I recover from a corrupted or failed IOS upgrade?</h3>
-<p>A2: If your switch fails to boot up after an IOS upgrade, you might have a corrupted or incompatible IOS image file on the flash memory. In this case, you need to use the ROMmon mode to recover from the problem. ROMmon, or ROM monitor, is a low-level mode that allows you to perform basic troubleshooting and maintenance tasks on your switch. To enter ROMmon mode, you need to interrupt the boot process by pressing the Mode button on the front panel of the switch. Then, you can use ROMmon commands to erase the flash memory, download a new IOS image file using Xmodem protocol, and boot up the switch with the new image. For more details on how to use ROMmon mode, refer to <a href="">this document</a>.</p>
- <h3>Q3: How can I check the free space and memory usage on the flash memory?</h3>
-<p>A3: To check the free space and memory usage on the flash memory, you can use the <code>show flash:</code> command. This command will display information such as the total size, available size, used size, erase size, and directory of files on the flash memory. For example:</p>
-<pre>
-Switch#show flash: Directory of flash:/     2  -rwx        3350   Mar 1 1993 00:04:05 +00:00  config.text     3  -rwx        2072   Mar 1 1993 00:04:05 +00:00  private-config.text     4  -rwx    33509312   Jun 20 2023 17:15:23 +00:00  c3750-ipbasek9-mz.150-2.SE11.bin     ... 32514048 bytes total (0 bytes free) </pre>
- <h3>Q4: How can I use a web-based device manager or network assistant to upgrade IOS?</h3>
-<p>A4: If you prefer a graphical user interface (GUI) over a command-line interface (CLI) for IOS upgrade, you can use either a web-based device manager or network assistant. A web-based device manager is a built-in web server on your switch that allows you to access and configure your switch using a web browser. A network assistant is a standalone application that allows you to manage multiple switches and routers in your network using a single interface. Both tools provide an easy and intuitive way to upgrade IOS on your switch. For more details on how to use these tools, refer to <a href="">this document</a> and <a href="">this document</a>.</p>
- <h3>Q5: How can I choose the best IOS release and feature set for my switch?</h3>
-<p>A5: Choosing the best IOS release and feature set for your switch depends on several factors, such as your hardware model, software requirements, network environment, and budget. Generally speaking, you should choose an IOS release that is stable, secure, and compatible with your hardware and software features. You should also choose an IOS feature set that meets your network needs and does not exceed your memory and license limitations. To help you choose the best IOS release and feature set for your switch, you can use tools such as <a href="">Cisco Feature Navigator</a>, <a href="">Cisco Software Advisor</a ), and <a href="">Cisco Software Research Tool</a>.</p>
- <h2></h2>
-<p>Thank you for reading this article. I hope you have learned how to download and install IOS on Cisco Catalyst 3750 switches. If you have any questions or feedback, please leave a comment below. Have a great day!</p> 401be4b1e0<br />
-<br />
-<br />
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diff --git a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Download Among Us Roles and Play with Over 25 New Roles in The Other Roles MOD.md b/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Download Among Us Roles and Play with Over 25 New Roles in The Other Roles MOD.md
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-
-<h1>How to Download and Play Among Us Roles Mod</h1>
-<p>Among Us is a multiplayer game where players have to work together as crewmates or impostors on a spaceship. Crewmates have to complete tasks and find the impostors, while impostors have to kill crewmates and sabotage the ship. However, if you want to spice up your gameplay and try something new, you might want to check out <strong>Among Us Roles Mod</strong>.</p>
-<h2>What is Among Us Roles Mod?</h2>
-<h3>A brief introduction to the mod and its features</h3>
-<p>Among Us Roles Mod is a custom mod that adds seven new roles to the game, each with different abilities and mechanics. The roles are divided into two categories: crewmate roles and impostor roles. Crewmate roles are assigned randomly to some of the crewmates, while impostor roles are assigned randomly to some of the impostors. The mod also allows you to customize the settings for each role, such as the cooldown, duration, and probability.</p>
-<h2>download among us roles</h2><br /><p><b><b>Download Zip</b> &mdash;&mdash;&mdash;&mdash;&mdash; <a href="https://ssurll.com/2uNUj1">https://ssurll.com/2uNUj1</a></b></p><br /><br />
-<h3>The list of roles and their abilities</h3>
-<p>The mod currently includes the following roles:</p>
-<table>
-<tr><th>Role</th><th>Category</th><th>Ability</th></tr>
-<tr><td>Crewmate</td><td>Crewmate</td><td>The default role that has to do tasks and find impostors.</td></tr>
-<tr><td>Impostor</td><td>Impostor</td><td>The default role that has to kill crewmates and sabotage the ship.</td></tr>
-<tr><td>Scientist</td><td>Crewmate</td><td>A role that can check the vitals of all players in the game and see when someone dies.</td></tr>
-<tr><td>Engineer</td><td>Crewmate</td><td>A role that can use vents like impostors, but for a limited time and with a cooldown.</td></tr>
-<tr><td>Guardian Angel</td><td>Crewmate</td><td>A role that can shield and protect another player from being killed by impostors.</td></tr>
-<tr><td>Shapeshifter</td><td>Impostor</td><td>A role that can transform into another player for a limited time.</td></tr>
-<tr><td>Ghost</td><td>N/A</td><td>A role that dead players get that can chat with other ghosts and spectate the game.</td></tr>
-</table>
-<h2>How to Download and Install Among Us Roles Mod</h2>
-<h3>The steps to download the mod from GitHub</h3>
-<p>To get the mod, you will need to follow these steps:</p>
-<p>How to download and install the Extra Roles mod for Among Us<br />
-Among Us All The Roles mod by Zeo666: features and download link<br />
-Among Us Other Roles mod tutorial: how to play as Medic, Officer, Engineer, and Joker<br />
-Best Among Us role mods to spice up your gameplay<br />
-Where to find and download the latest Among Us role mods for PC<br />
-How to play as Sheriff, Doctor, Jester, and more in Among Us with role mods<br />
-Among Us role mods: what are they and how do they work<br />
-How to create your own custom roles in Among Us with mods<br />
-How to join and host Among Us games with role mods<br />
-How to update your Among Us role mods to the latest version<br />
-How to fix common issues and bugs with Among Us role mods<br />
-How to uninstall or disable Among Us role mods<br />
-How to play Among Us role mods on mobile devices<br />
-How to play Among Us role mods with friends online<br />
-How to play Among Us role mods on different maps and settings<br />
-How to customize your Among Us role mods with hats, visors, and nameplates<br />
-How to download and install the Town of Us mod for Among Us<br />
-How to play as Mayor, Swapper, Lovers, and more in Among Us with the Town of Us mod<br />
-How to download and install the Mafia mod for Among Us<br />
-How to play as Godfather, Mafioso, Janitor, and more in Among Us with the Mafia mod<br />
-How to download and install the Proximity Chat mod for Among Us<br />
-How to play with voice chat in Among Us with the Proximity Chat mod<br />
-How to download and install the Better Crewlink mod for Among Us<br />
-How to improve your Proximity Chat experience in Among Us with the Better Crewlink mod<br />
-How to download and install the Impostor mod for Among Us<br />
-How to play as Spy, Snitch, Assassin, and more in Among Us with the Impostor mod<br />
-How to download and install the Extra Roles Plus mod for Among Us<br />
-How to play as Time Master, Hacker, Snitch, and more in Among Us with the Extra Roles Plus mod<br />
-How to download and install the Crewlink mod for Among Us<br />
-How to play with voice chat in Among Us with the Crewlink mod<br />
-How to download and install the The Other Roles mod for Among Us<br />
-How to play as Engineer, Medic, Officer, Joker, and more in Among Us with The Other Roles mod<br />
-How to download and install the Sheriff mod for Among Us<br />
-How to play as Sheriff in Among Us with the Sheriff mod<br />
-How to download and install the Jester mod for Among Us<br />
-How to play as Jester in Among Us with the Jester mod<br />
-How to download and install the Doctor mod for Among Us<br />
-How to play as Doctor in Among Us with the Doctor mod<br />
-How to download and install the Engineer mod for Among Us<br />
-How to play as Engineer in Among Us with the Engineer mod</p>
-<ol>
-<li>Go to <a href="(^1^)">the GitHub page of the mod creator Eisbison</a>.</li>
-<li>Select the most recent version of the mod from the Releases tab.</li>
-<li>Download the .rar file of the mod.</li>
-<li>Open the file with WinRar or another program that can extract compressed files.</li>
-<li>You should see a folder called TheOtherRoles with several files inside.</li> <h3>The steps to install the mod in the game directory</h3>
-<p>Once you have the mod folder, you will need to copy it to the game directory. Here is how:</p>
-<ol>
-<li>Find the location of your Among Us game on your computer. It should be something like C:\Program Files (x86)\Steam\steamapps\common\Among Us.</li>
-<li>Open the game folder and look for a file called BepInEx.dll. If you don't have it, you will need to download and install <a href="">BepInEx</a>, which is a framework that allows you to run mods for Among Us.</li>
-<li>Copy the entire TheOtherRoles folder that you extracted from the .rar file and paste it into the game folder.</li>
-<li>You should see a new folder called BepInEx with several subfolders and files inside.</li>
-</ol>
-<h3>The steps to launch the mod and check if it is loaded</h3>
-<p>Now that you have installed the mod, you can launch the game and enjoy the new roles. To do so, follow these steps:</p>
-<ol>
-<li>Run the Among Us game from Steam or from your desktop shortcut.</li>
-<li>On the main menu, you should see a message on the top left corner that says "TheOtherRoles v1.8.0 loaded". This means that the mod is working properly.</li>
-<li>If you don't see the message, you might have to restart the game or check if you installed the mod correctly.</li>
-<li>To access the mod settings, click on the gear icon on the bottom right corner of the screen and then click on TheOtherRoles tab.</li>
-<li>Here you can adjust the settings for each role, such as the cooldown, duration, and probability. You can also enable or disable certain roles if you want.</li>
-<li>To start a game with the mod, click on Online or Local and create or join a lobby as usual. The host of the lobby can choose which roles to include in the game from the Game tab.</li>
-</ol>
-<h2>How to Play Among Us Roles Mod</h2>
-<h3>The rules and settings for the mod</h3>
-<p>The mod follows the same rules and settings as the vanilla game, with some exceptions. Here are some of them:</p>
-<ul>
-<li>The number of impostors can be 1, 2, or 3, depending on the lobby size and preference.</li>
-<li>The number of crewmate roles can be 0, 1, 2, or 3, depending on the lobby size and preference.</li>
-<li>The number of impostor roles can be 0, 1, or 2, depending on the lobby size and preference.</li>
-<li>The crewmates win if they complete all their tasks or vote out all the impostors.</li>
-<li>The impostors win if they kill enough crewmates or sabotage the ship successfully.</li>
-<li>The roles are assigned randomly at the start of each game and are hidden from other players.</li>
-<li>The roles have different abilities that they can use during the game, but they also have limitations and drawbacks.</li>
-</ul> <h3>The tips and strategies for each role</h3>
-<p>Playing with the mod can be challenging and fun, but also requires some skills and tactics. Here are some tips and strategies for each role:</p>
-<ul>
-<li><strong>Crewmate:</strong> As a crewmate, you have to do your tasks and find the impostors. You can use the meeting button or report a body to discuss with other players and vote. You can also use the chat or voice chat to communicate and share information. You should be careful of impostors who might try to deceive you or kill you. You should also pay attention to the roles of other crewmates and how they can help you.</li>
-<li><strong>Impostor:</strong> As an impostor, you have to kill crewmates and sabotage the ship. You can use vents, fake tasks, and lie to blend in with the crew. You can also use your abilities to confuse or eliminate your enemies. You should be careful of crewmates who might suspect you or catch you in the act. You should also pay attention to the roles of other impostors and how they can help you.</li>
-<li><strong>Scientist:</strong> As a scientist, you can check the vitals of all players in the game and see when someone dies. You can use this information to find impostors or confirm alibis. You can also share this information with other crewmates during meetings or chats. You should be careful of impostors who might target you or discredit you. You should also pay attention to the roles of other scientists and how they can help you.</li>
-<li><strong>Engineer:</strong> As an engineer, you can use vents like impostors, but for a limited time and with a cooldown. You can use this ability to move around the map faster, escape from danger, or surprise your enemies. You can also use this ability to fix sabotages from anywhere on the map. You should be careful of impostors who might see you venting or accuse you of venting. You should also pay attention to the roles of other engineers and how they can help you.</li>
-<li><strong>Guardian Angel:</strong> As a guardian angel, you can shield and protect another player from being killed by impostors. You can use this ability to save your allies, bait your enemies, or test your suspicions. You can also use this ability to revive a dead player once per game. You should be careful of impostors who might kill you instead of your shielded player or expose your role. You should also pay attention to the roles of other guardian angels and how they can help you.</li>
-<li><strong>Shapeshifter:</strong> As a shapeshifter, you can transform into another player for a limited time. You can use this ability to impersonate your enemies, frame your allies, or create confusion. You can also use this ability to access restricted areas or blend in with the crowd. You should be careful of crewmates who might notice your transformation or recognize your original appearance. You should also pay attention to the roles of other shapeshifters and how they can help you.</li>
-<li><strong>Ghost:</strong> As a ghost, you are dead but not out of the game. You can chat with other ghosts and spectate the game. You can also do your tasks as a crewmate ghost or sabotage as an impostor ghost. You can use this opportunity to help your team win or have fun with other ghosts. You should be careful of revealing too much information or spoiling the game for others. You should also pay attention to the roles of other ghosts and how they can help you.</li>
-</ul>
-<h2>Conclusion</h2>
-<h3>A summary of the main points and benefits of the mod</h3>
-<p>In conclusion, Among Us Roles Mod is a custom mod that adds seven new roles to the game, each with different abilities and mechanics. The mod allows you to customize the settings for each role, such as the cooldown, duration, and probability. The mod also follows the same rules and settings as the vanilla game, with some exceptions.</p>
-<p>The mod is a great way to spice up your gameplay and try something new. The mod adds more variety, challenge, and fun to the game. The mod also enhances the social aspect of the game, as you have to communicate, cooperate, and deceive with other players.</p>
-<h3>A call to action to try the mod and have fun</h3>
-<p>If you are interested in playing with the mod, you can download it from <a href="">the GitHub page of the mod creator Eisbison</a>. You will need to install BepInEx framework first, which is a framework that allows you to run mods for Among Us. Then, you will need to copy the mod folder to the game directory and launch the game.</p>
-<p>You can then adjust the mod settings for each role, such as the cooldown, duration, and probability. You can also choose which roles to include in the game from the Game tab. You can then create or join a lobby and start playing with the mod.</p>
-<p>You will have a lot of fun playing with the mod, as you will experience new gameplay mechanics, abilities, and strategies. You will also interact with other players in different ways, depending on your role and theirs. You will never get bored of playing Among Us with the mod, as each game will be different and exciting.</p>
-<p>So what are you waiting for? Download the mod now and enjoy playing Among Us Roles Mod with your friends or online. Have fun and good luck!</p>
-<h2>FAQs</h2>
-<h3>Q1: Is Among Us Roles Mod safe to use?</h3>
-<p>A1: Yes, Among Us Roles Mod is safe to use, as long as you download it from the official GitHub page of the mod creator Eisbison. The mod does not contain any viruses or malware, and it does not affect your game files or performance. However, you should always be careful when downloading and installing any mods from the internet, and make sure you have a backup of your game in case something goes wrong.</p>
-<h3>Q2: Is Among Us Roles Mod compatible with other mods?</h3>
-<p>A2: Among Us Roles Mod is compatible with most other mods that use BepInEx framework, which is a framework that allows you to run mods for Among Us. However, some mods might conflict or interfere with each other, especially if they modify the same aspects of the game. Therefore, you should always check the compatibility and compatibility issues of the mods before using them together.</p>
-<h3>Q3: Can I play Among Us Roles Mod on mobile or Switch?</h3>
-<p>A3: No, Among Us Roles Mod is only available for PC version of the game. The mod requires BepInEx framework, which is a framework that allows you to run mods for Among Us. BepInEx framework is only compatible with PC version of the game, and not with mobile or Switch version. Therefore, you cannot play Among Us Roles Mod on mobile or Switch.</p>
-<h3>Q4: Where can I get support or report bugs for Among Us Roles Mod?</h3>
-<p>A4: If you need support or want to report bugs for Among Us Roles Mod, you can contact the mod creator Eisbison through <a href="">his GitHub page</a>. You can also join <a href="">his Discord server</a>, where you can chat with other players and get help from the mod team. You can also check <a href="">the wiki page of the mod</a>, where you can find more information and guides about the mod.</p>
-<h3>Q5: What are some other popular mods for Among Us?</h3>
-<p>A5: There are many other popular mods for Among Us that you can try and enjoy. Some of them are:</p>
-<ul>
-<li><strong>Town of Us:</strong> A mod that adds 17 new roles and 6 new modifiers to the game, inspired by Town of Salem.</li>
-<li><strong>The Other Roles:</strong> A mod that adds 15 new roles and 4 new modifiers to the game, inspired by The Other Roles Mod.</li>
-<li><strong>Extra Roles:</strong> A mod that adds 8 new roles and 2 new modifiers to the game, inspired by Extra Roles Mod.</li>
-<li><strong>Among Us Sheriff Mod:</strong> A mod that adds a sheriff role to the game, who can kill impostors or crewmates.</li>
-<li><strong>Among Us Proximity Chat:</strong> A mod that adds proximity voice chat to the game, where you can hear other players based on their distance from you.</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/Download K Millians Nayo Nayo and Enjoy the Pop Hit.md b/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Download K Millians Nayo Nayo and Enjoy the Pop Hit.md
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--- a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Download K Millians Nayo Nayo and Enjoy the Pop Hit.md	
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-
-<h1>How to Download K Millian's Nayo Nayo Online</h1>
-<p>If you are a fan of Zambian music, you might have heard of K Millian's hit song \"Nayo Nayo\". This catchy tune is a blend of kalindula and afro-pop genres, with lyrics that express love and gratitude for a special person. In this article, we will show you how to download this song online, so you can enjoy it anytime and anywhere.</p>
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-<h2>What is Nayo Nayo?</h2>
-<p>Nayo Nayo is a song by Zambian singer-songwriter K Millian, featuring Chali 'Bravo' Mulalami. It was released in 2018 as part of K Millian's album \"Another Day\". The song is a love ballad that celebrates the joy of finding one's soulmate. The title \"Nayo Nayo\" means \"I have it\" in Bemba, one of the major languages in Zambia. The chorus goes like this:</p>
-<blockquote>
-<p>Niwe wandi wandi wandi wandi wandi wandi<br>
-You are mine mine mine mine mine mine<br>
-Nimwebo wandi wandi wandi wandi wandi<br>
-It is you mine mine mine mine mine<br>
-Nimwebo nayonayonayonayonayonayonayonayonayonayonayonayonayonayonayonayonayonayona<br>
-It is you I have I have I have I have I have I have I have I have I have I have I have I have I have I have I have I have</p>
-</blockquote>
-<p>The song has a catchy melody and a smooth rhythm that makes it easy to dance and sing along to.</p>
-<h2>Who is K Millian?</h2>
-<p>K Millian is one of the most popular and influential artists in Zambia. He was born in 1978 in Lusaka, the capital city of Zambia. He started his music career in 2000 with his debut album \"Voice Mail\", which featured songs like \"Kakabalika\" and \"Another Day\". Since then, he has released several albums and singles that have topped the Zambian music charts and earned him many awards and recognition. Some of his most famous songs include \"Pa Ulendo\", \"Umutima Wandi\", \"Nizakukonda\", and \"Nalila\". K Millian is known for his versatile and unique style, which incorporates elements of kalindula, afro-pop, r&b, gospel, and reggae. He is also a philanthropist and a social activist, who supports various causes such as education, health, and peace. K Millian is widely regarded as one of the best Zambian musicians of all time.</p>
-<h2>Why Download Music Online?</h2>
-<p>Downloading music online has many benefits over buying physical CDs or DVDs. Here are some of the reasons why you should download music online:</p>
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-<li>Convenience: You can download music online anytime and anywhere, as long as you have an internet connection and a device. You don't have to go to a store, wait in line, or deal with limited stock. You can also access your music library across different devices and platforms, and sync them with your cloud storage or streaming service.</li>
-<li>Affordability: You can download music online for a fraction of the cost of buying physical copies. You can also save money on shipping fees, taxes, and storage space. You can also take advantage of discounts, promotions, and free downloads offered by various online music providers.</li>
-<li>Quality: You can download music online in high-quality formats, such as MP3, WAV, FLAC, or AAC. You can also choose the bitrate and sample rate that suit your preferences and device capabilities. You can also enjoy better sound quality than CDs or DVDs, which can degrade over time or get scratched or damaged.</li>
-</ul>
-<p>Downloading music online is a great way to enjoy your favorite songs and discover new ones. However, you should also be aware of the legal and ethical issues involved in downloading music online.</p>
-<h2>How to Download Music Online Legally and Safely?</h2>
-<p>There are many ways to download music online, but not all of them are legal and safe. Some methods may expose you to viruses, malware, spyware, or identity theft. Some methods may also violate the intellectual property rights of the artists and producers, and result in fines or lawsuits. Therefore, you should always download music online from reputable and authorized sources. Here are some of the best methods to download music online legally and safely:</p>
-<h3>Buying from Digital Stores</h3>
-<p>One of the most common and reliable methods to download music online is to buy from digital stores. These are online platforms that sell digital copies of songs, albums, or playlists for a fixed price. You can pay with your credit card, debit card, PayPal, or other online payment methods. Once you buy the music, you can download it to your device or stream it online. Some of the most popular digital stores are:</p>
-<h4>iTunes</h4>
-<p>iTunes is one of the largest and most popular digital stores in the world. It offers millions of songs from various genres and artists. You can buy individual songs for $0.99 or $1.29, or albums for $9.99 or $11.99. You can also buy iTunes gift cards or redeem codes to purchase music. To download Nayo Nayo from iTunes, follow these steps:</p>
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-<ol>
-<li>Open iTunes on your computer or device.</li>
-<li>Search for \"Nayo Nayo\" in the search bar.</li>
-<li>Select the song by K Millian featuring Chali 'Bravo' Mulalami from the results.</li>
-<li>Click on the price button to buy the song.</li>
-<li>Enter your Apple ID and password to confirm your purchase.</li>
-<li>The song will be added to your iTunes library and downloaded to your device.</li>
-</ol>
-<h4>Google Play Music</h4>
-<p>Google Play Music is another popular digital store that offers millions of songs from various genres and artists. You can buy individual songs for $0.99 or $1.29, or albums for $9.49 or $10.49. You can also use Google Play gift cards or redeem codes to purchase music. To download Nayo Nayo from Google Play Music, follow these steps:</p>
-<ol>
-<li>Open Google Play Music on your computer or device.</li>
-<li>Search for \"Nayo Nayo\" in the search bar.</li>
-<li>Select the song by K Millian featuring Chali 'Bravo' Mulalami from the results.</li>
-<li>Click on the price button to buy the song.</li>
-<li>Enter your Google account and password to confirm your purchase.</li>
-<li>The song will be added to your Google Play Music library and downloaded to your device.</li>
-</ol>
-<h3>Streaming from Music Platforms</h3>
-<p>Another common and convenient method to download music online is to stream from music platforms. These are online services that offer unlimited access to millions of songs from various genres and artists for a monthly or yearly subscription fee. You can stream the music online or download it to your device for offline listening. Some of the most popular music platforms are:</p>
-<h4>YouTube</h4>
-<p>YouTube is one of the largest and most popular music platforms in the world. It offers a vast collection of music videos, live performances, playlists, and channels from various genres and artists. You can stream the music online for free, or subscribe to YouTube Music or YouTube Premium for ad-free and offline access. To stream and download Nayo Nayo from YouTube, follow these steps:</p>
-<ol>
-<li>Open YouTube on your computer or device.</li>
-<li>Search for \"Nayo Nayo\" in the search bar.</li>
-<li>Select the official music video by K Millian featuring Chali 'Bravo' Mulalami from the results.</li>
-<li>Click on the play button to stream the song.</li>
-<li>If you have a YouTube Music or YouTube Premium subscription, you can click on the download button to download the song to your device.</li>
-<li>The song will be added to your YouTube library and downloaded to your device.</li>
-</ol>
-<h4>SoundCloud</h4>
-<p>SoundCloud is another popular music platform that offers a diverse and unique selection of music from various genres and artists. It is especially known for its independent and emerging artists, who upload their original songs, remixes, covers, and podcasts. You can stream the music online for free, or subscribe to SoundCloud Go or SoundCloud Go+ for ad-free and offline access. To stream and download Nayo Nayo from SoundCloud, follow these steps:</p>
-<ol>
-<li>Open SoundCloud on your computer or device.</li>
-<li>Search for \"Nayo Nayo\" in the search bar.</li>
-<li>Select the song by K Millian featuring Chali 'Bravo' Mulalami from the results.</li>
-<li>Click on the play button to stream the song.</li>
-<li>If you have a SoundCloud Go or SoundCloud Go+ subscription, you can click on the download button to download the song to your device.</li>
-<li>The song will be added to your SoundCloud library and downloaded to your device.</li>
-</ol>
-<h3>Using Free Music Download Sites</h3>
-<p>A third method to download music online is to use free music download sites. These are websites that offer free downloads of songs, albums, or playlists from various genres and artists. However, you should be careful when using these sites, as some of them may contain viruses, malware, spyware, or illegal content. You should also respect the intellectual property rights of the artists and producers, and only download music that is licensed under Creative Commons or other free licenses. Some of the best free music download sites are:</p>
-<h4>Bandcamp</h4>
-<p>Bandcamp is one of the best free music download sites that offers a wide range of music from various genres and artists. It is especially known for its independent and emerging artists, who sell their music directly to their fans. You can stream the music online for free, or buy it for a price set by the artist. Some artists also offer their music for free or pay-what-you-want downloads. To download Nayo Nayo from Bandcamp, follow these steps:</p>
-<ol>
-<li>Open Bandcamp on your computer or device.</li>
-<li>Search for \"Nayo Nayo\" in the search bar.</li>
-<li>Select the song by K Millian featuring Chali 'Bravo' Mulalami from the results.</li>
-<li>Click on the name of the song to go to its page.</li>
-<li>If the song is available for free or pay-what-you-want download, you will see a \"name your price\" box. Enter zero or any amount you want to pay, and click on \"download now\".</li>
-<li>If the song is not available for free or pay-what-you-want download, you will see a \"buy digital track\" button. Click on it and enter your payment details to buy the song.</li>
-<li>The song will be added to your Bandcamp collection and downloaded to your device.</li>
-</ol>
- <h4>DatPiff</h4>
- <p>DatPiff is one of the best free music download sites that offers a large collection of hip-hop, rap, r&b, and urban music from various genres and artists. It is especially known for its mixtapes, which are compilations of songs by different artists or DJs. You can stream the music online for free, or download it to your device for free or for a small fee. Some of the most popular mixtapes on DatPiff include Nayo Nayo by K Millian featuring Chali 'Bravo' Mulalami, which was released in 2018 and has over 10,000 downloads. To download Nayo Nayo from DatPiff, follow these steps:</p>
-<ol>
-<li>Open DatPiff on your computer or device.</li>
-<li>Search for \"Nayo Nayo\" in the search bar.</li>
-<li>Select the mixtape by K Millian featuring Chali 'Bravo' Mulalami from the results.</li>
-<li>Click on the download button to download the mixtape to your device.</li>
-<li>The mixtape will be added to your DatPiff library and downloaded to your device.</li>
-</ol>
-<h4>Free Music Archive</h4>
-<p>Free Music Archive is one of the best free music download sites that offers a curated and diverse selection of music from various genres and artists. It is especially known for its Creative Commons licensed music, which means you can download, share, and use the music for free, as long as you follow the terms of the license. You can stream the music online for free, or download it to your device for free or for a donation. To download Nayo Nayo from Free Music Archive, follow these steps:</p>
-<ol>
-<li>Open Free Music Archive on your computer or device.</li>
-<li>Search for \"Nayo Nayo\" in the search bar.</li>
-<li>Select the song by K Millian featuring Chali 'Bravo' Mulalami from the results.</li>
-<li>Click on the name of the song to go to its page.</li>
-<li>If the song is available for free download, you will see a \"download\" button. Click on it and choose the format you want to download.</li>
-<li>If the song is not available for free download, you will see a \"buy\" button. Click on it and enter your payment details to buy the song.</li>
-<li>The song will be added to your Free Music Archive collection and downloaded to your device.</li>
-</ol>
-<h2>Conclusion</h2>
-<p>Nayo Nayo is a beautiful and catchy song by K Millian featuring Chali 'Bravo' Mulalami. It is one of the most popular songs in Zambia and beyond. If you want to download this song online, you have many options to choose from. You can buy it from digital stores like iTunes or Google Play Music, stream it from music platforms like YouTube or SoundCloud, or use free music download sites like Bandcamp or DatPiff. Whatever method you choose, make sure you do it legally and safely. Enjoy listening to Nayo Nayo and share it with your friends!</p>
-<h2>FAQs</h2>
-<p>Here are some of the frequently asked questions and answers about downloading music online:</p>
-<ul>
-<li><b>Q: Is downloading music online legal?</b><br>A: Downloading music online is legal if you do it from authorized and reputable sources, such as digital stores, music platforms, or free music download sites that have permission from the artists and producers. However, downloading music online is illegal if you do it from unauthorized or pirated sources, such as torrent sites, file-sharing networks, or websites that offer illegal downloads. This may violate the intellectual property rights of the artists and producers, and result in fines or lawsuits.</li>
-<li><b>Q: Is downloading music online safe?</b><br>A: Downloading music online is safe if you do it from trusted and secure sources, such as digital stores, music platforms, or free music download sites that have protection from viruses, malware, spyware, or identity theft. However, downloading music online is unsafe if you do it from untrusted or insecure sources, such as torrent sites, file-sharing networks, or websites that have malicious content or links. This may expose you to viruses, malware, spyware, or identity theft.</li>
-<li><b>Q: What are the best formats to download music online?</b><br>A: The best formats to download music online depend on your preferences and device capabilities. Some of the most common formats are MP3, WAV, FLAC, or AAC. MP3 is a compressed format that reduces the file size but also lowers the sound quality. WAV is an uncompressed format that preserves the original sound quality but also increases the file size. FLAC is a lossless format that compresses the file size without losing any sound quality. AAC is a format that offers better sound quality than MP3 at similar file sizes.</li>
-<li><b>Q: What are the best bitrates and sample rates to download music online?</b><br>A: The best bitrates and sample rates to download music online depend on your preferences and device capabilities. Bit rate is the amount of data that is transferred per second, and sample rate is the number of times that the sound wave is measured per second. Higher bitrates and sample rates offer better sound quality but also larger file sizes. Lower bitrates and sample rates offer smaller file sizes but also lower sound quality. The standard bitrate and sample rate for CD quality are 1411 kbps and 44.1 kHz, respectively. The standard bitrate and sample rate for MP3 quality are 128 kbps and 44.1 kHz, respectively.</li>
-<li><b>Q: How can I transfer the music I downloaded online to other devices?</b><br>A: You can transfer the music you downloaded online to other devices by using a USB cable, a Bluetooth connection, a Wi-Fi connection, or a cloud storage service. For example, if you want to transfer the music from your computer to your phone, you can connect them with a USB cable and copy the music files from one device to another. Alternatively, you can use a Bluetooth or Wi-Fi connection to send the music files wirelessly. You can also upload the music files to a cloud storage service like Google Drive or Dropbox, and then download them to your other device.</li>
-</ul></p> 401be4b1e0<br />
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-<h1>How to Download MGR Songs Online</h1>
-<p>If you are a fan of Tamil cinema and music, you must have heard of MGR. He was one of the most influential and charismatic personalities in the history of Tamil Nadu. He was not only an actor, but also a politician, a singer, and a cultural icon. His songs are still loved and cherished by millions of people across generations.</p>
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-<p>Downloading MGR songs online is a great way to enjoy his melodious voice and inspiring lyrics. You can listen to his songs anytime, anywhere, and on any device. You can also create your own playlists, share them with your friends, and discover new songs.</p>
-<p>However, downloading MGR songs online is not always easy. You may face some challenges such as finding the right website, choosing the best quality and format, ensuring a reliable and secure internet connection, and managing your downloads. In this article, we will guide you through these challenges and help you download MGR songs online with ease.</p>
-<h2>Best Websites to Download MGR Songs Online</h2>
-<p>There are many websites that offer MGR songs online. However, not all of them are reliable, safe, or legal. Some of them may have low-quality or corrupted files, malware or viruses, pop-up ads or redirects, or copyright issues. Therefore, you need to be careful when choosing a website to download MGR songs online.</p>
-<p>Here are some of the best websites that we recommend for downloading MGR songs online:</p>
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-<h3>JioSaavn</ <h3>JioSaavn</h3>
-<p>JioSaavn is one of the most popular and trusted music streaming and downloading platforms in India. It has a huge collection of songs in various languages, genres, and moods. You can find MGR songs in Tamil, Telugu, Malayalam, Kannada, and Hindi on JioSaavn.</p>
-<p>JioSaavn has many features and advantages that make it a great choice for downloading MGR songs online. Some of them are:</p>
-<ul>
-<li>It has a user-friendly and attractive interface that allows you to browse, search, and play songs with ease.</li>
-<li>It has a high-quality and diverse audio library that offers songs in different formats such as MP3, AAC, or FLAC.</li>
-<li>It has a smart and personalized recommendation system that suggests songs based on your preferences, listening history, and mood.</li>
-<li>It has a social and interactive feature that lets you share your songs, playlists, and podcasts with your friends and followers.</li>
-<li>It has a premium subscription option that gives you access to unlimited downloads, ad-free music, offline listening, exclusive content, and more.</li>
-</ul>
-<p>To download MGR songs from JioSaavn, you need to follow these steps:</p>
-<ol>
-<li>Download and install the JioSaavn app on your device or visit the JioSaavn website on your browser.</li>
-<li>Create an account or log in with your existing account.</li>
-<li>Search for MGR songs or browse the MGR category on the app or website.</li>
-<li>Select the song that you want to download and tap on the download icon.</li>
-<li>Choose the quality and format of the song that you want to download.</li>
-<li>Wait for the download to complete and enjoy your MGR song offline.</li>
-</ol> <h3>Internet Archive</h3>
-<p>Internet Archive is a non-profit digital library that preserves and provides access to millions of free books, movies, music, and more. It is a treasure trove of old and rare content that you may not find elsewhere. You can find MGR songs from various films, albums, and concerts on Internet Archive.</p>
-<p>Internet Archive has many features and advantages that make it a great choice for downloading MGR songs online. Some of them are:</p>
-<ul>
-<li>It has a simple and minimalist interface that allows you to explore, search, and download songs with ease.</li>
-<li>It has a rich and diverse audio library that offers songs in different formats such as MP3, OGG, or WAV.</li>
-<li>It has a historical and cultural value that showcases the legacy and impact of MGR on Tamil cinema and society.</li>
-<li>It has a community and collaborative feature that lets you upload, review, and comment on songs and other content.</li>
-<li>It has a free and open access policy that does not require any registration or subscription to download songs.</li>
-</ul>
-<p>To download MGR songs from Internet Archive, you need to follow these steps:</p>
-<ol>
-<li>Visit the Internet Archive website on your browser.</li>
-<li>Search for MGR songs or browse the MGR collection on the website.</li>
-<li>Select the song that you want to download and click on the download options icon.</li>
-<li>Choose the quality and format of the song that you want to download.</li>
-<li>Wait for the download to complete and enjoy your MGR song offline.</li>
-</ol> <h3>Other Websites</h3>
-<p>Besides JioSaavn and Internet Archive, there are some other websites that offer MGR songs online. However, they may not be as reliable, safe, or legal as the ones we have mentioned above. Therefore, you need to be cautious and vigilant when using them.</p>
-<p>Some of the other websites that you can try to download MGR songs online are:</p>
-<ul>
-<li><a href="">TamilTunes</a>: This website has a large collection of Tamil songs, including MGR songs. You can download them in MP3 format for free. However, the website may have some pop-up ads and redirects that can be annoying or harmful.</li>
-<li><a href="">Masstamilan</a>: This website has a decent collection of Tamil songs, including MGR songs. You can download them in MP3 or FLAC format for free. However, the website may have some low-quality or broken links that can be frustrating or disappointing.</li>
-<li><a href="">Isaimini</a>: This website has a moderate collection of Tamil songs, including MGR songs. You can download them in MP3 format for free. However, the website may have some malware or viruses that can be dangerous or damaging.</li>
-</ul>
-<p>To download MGR songs from these websites, you need to follow these steps:</p>
-<ol>
-<li>Visit the website on your browser.</li>
-<li>Search for MGR songs or browse the MGR category on the website.</li>
-<li>Select the song that you want to download and click on the download link.</li>
-<li>Choose the quality and format of the song that you want to download.</li>
-<li>Wait for the download to complete and enjoy your MGR song offline.</li>
-</ol>
-<p>However, we advise you to use these websites at your own risk and discretion. We do not endorse or recommend them in any way. We also suggest you to check the legality and safety of these websites before using them.</p> <h2>Tips and Tricks to Download MGR Songs Online</h2>
-<p>Downloading MGR songs online can be a fun and rewarding experience. However, it can also be a challenging and frustrating one if you do not follow some tips and tricks. Here are some of the tips and tricks that we recommend for downloading MGR songs online:</p>
-<h3>Check the Quality and Format of the Songs</h3>
-<p>The quality and format of the songs that you download online can affect your listening experience. You want to download songs that have high-quality sound and are compatible with your device and preference. Therefore, you need to check and choose the best quality and format for your MGR songs online.</p>
-<p>Some of the factors that you need to consider when checking and choosing the quality and format of the songs are:</p>
-<ul>
-<li>The bitrate: This is the amount of data that is encoded in a song per second. It is measured in kilobits per second (kbps). The higher the bitrate, the better the sound quality. However, the higher the bitrate, the larger the file size. You need to balance between quality and size when choosing the bitrate.</li>
-<li>The sample rate: This is the number of times that a song is sampled per second. It is measured in hertz (Hz). The higher the sample rate, the more accurate the sound reproduction. However, the higher the sample rate, the larger the file size. You need to balance between accuracy and size when choosing the sample rate.</li>
-<li>The file format: This is the type of file that a song is stored in. There are many file formats available for songs, such as MP3, AAC, FLAC, OGG, or WAV. Each file format has its own advantages and disadvantages. You need to choose a file format that is compatible with your device and preference.</li>
-</ul>
-<p>Generally, we suggest you to choose a bitrate of at least 128 kbps, a sample rate of at least 44.1 kHz, and a file format of MP3 or AAC for your MGR songs online. These are the most common and widely supported options that offer good quality and reasonable size.</p>
-<h3>Use a Reliable and Secure Internet Connection</h3>
-<p>The internet connection that you use to download MGR songs online can affect your downloading experience. You want to use a reliable and secure internet connection that can ensure fast and smooth downloads without interruptions or risks. Therefore, you need to use a reliable and secure internet connection for your MGR songs online.</p>
-<p>Some of the factors that you need to consider when using an internet connection are:</p>
-<ul>
-<li>The speed: This is the rate at which data is transferred over the internet. It is measured in megabits per second (Mbps). The higher the speed, the faster the downloads. However, the higher the speed, the more expensive the internet plan. You need to balance between speed and cost when choosing an internet plan.</li>
-<li>The stability: This is the consistency and reliability of an internet connection. It is affected by factors such as network congestion, signal strength, weather conditions, or hardware issues. The more stable an internet connection, the less interruptions or errors in downloads. You need to choose an internet provider that offers a stable service.</li>
-<li>The security: This is the protection and privacy of an internet connection. It is affected by factors such as encryption, authentication, firewall, or antivirus software. The more secure an internet connection, the less risks of malware, viruses, hacking, or identity theft in downloads. You need to use an internet connection that has adequate security measures.</li>
-</ul>
-<p>Generally, we suggest you to use a broadband or fiber-optic internet connection with a speed of at least 10 Mbps, a stability of at least 99%, and a security of at least WPA2 for your MGR songs online. These are the most common and widely available options that offer fast, smooth, and safe downloads.</p> <h3>Use a Download Manager or a Browser Extension</h3>
-<p>The download manager or browser extension that you use to download MGR songs online can affect your downloading experience. You want to use a download manager or browser extension that can enhance and optimize your downloads without complications or drawbacks. Therefore, you need to use a download manager or browser extension for your MGR songs online.</p>
-<p>Some of the factors that you need to consider when using a download manager or browser extension are:</p>
-<ul>
-<li>The functionality: This is the ability and performance of a download manager or browser extension. It is affected by factors such as features, compatibility, speed, resume, pause, or schedule options. The more functional a download manager or browser extension, the more efficient and convenient the downloads. You need to choose a download manager or browser extension that has the features and options that you need.</li>
-<li>The usability: This is the ease and simplicity of using a download manager or browser extension. It is affected by factors such as interface, design, navigation, or customization options. The more usable a download manager or browser extension, the more enjoyable and satisfying the downloads. You need to choose a download manager or browser extension that has a user-friendly and attractive interface.</li>
-<li>The reliability: This is the trustworthiness and safety of a download manager or browser extension. It is affected by factors such as reputation, reviews, ratings, or updates. The more reliable a download manager or browser extension, the more secure and risk-free the downloads. You need to choose a download manager or browser extension that has a good reputation and positive feedback.</li>
-</ul>
-<p>Generally, we suggest you to use a download manager such as IDM, FDM, or JDownloader or a browser extension such as Video DownloadHelper, SaveFrom.net Helper, or Flash Video Downloader for your MGR songs online. These are some of the most popular and widely used options that offer functional, usable, and reliable downloads.</p>
-<h2>Conclusion</h2>
-<p>Downloading MGR songs online is a great way to enjoy his melodious voice and inspiring lyrics. However, it can also be a challenging and frustrating one if you do not follow some tips and tricks. In this article, we have guided you through these tips and tricks and helped you download MGR songs online with ease.</p>
-<p>We have shown you some of the best websites to download MGR songs online, such as JioSaavn and Internet Archive. We have also shown you some of the tips and tricks to download MGR songs online, such as checking the quality and format of the songs, using a reliable and secure internet connection, and using a download manager or browser extension.</p>
-<p>We hope that this article has been helpful and informative for you. Now that you know how to download MGR songs online, why not give it a try? You can start by downloading some of his most famous songs such as Ulagam Sutrum Valiban, Adimai Penn, Enga Veetu Pillai, Anbe Vaa, or Ayirathil Oruvan. You will surely enjoy listening to them offline.</p>
-<p>Thank you for reading this article. If you have any questions or feedback, please feel free to leave them in the comments section below. We would love to hear from you.</p>
-<h2>FAQs</h2>
-<ol>
-<li><b>Who is MGR?</b></li>
-<li>MGR is an acronym for Marudhur Gopalan Ramachandran, a legendary actor, politician, and singer from Tamil Nadu, India.</li>
-<li><b>What are the genres of MGR songs?</b></li>
-<li>MGR songs cover various genres such as melody, duet, thathuva, devotional, patriotic, and folk.</li>
-<li><b>How many songs did MGR sing in his career?</b></li>
-<li>MGR sang over 500 songs in his career, mostly composed by MS Viswanathan, KV Mahadevan, Shankar Ganesh, and AR Rahman.</li>
-<li><b>How can I listen to MGR songs online without downloading them?</b></li>
-<li>You can listen to MGR songs online without downloading them by using streaming services such as YouTube, Spotify, Gaana, or Wynk.</li>
-<li><b>How can I download MGR songs offline without internet connection?</b></li>
-<li>You can download MGR songs offline without internet connection by using offline apps such as Vidmate, Snaptube, or Videoder.</li>
-</ol></p> 401be4b1e0<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/skf15963/summary/fengshen/models/longformer/configuration_longformer.py b/spaces/skf15963/summary/fengshen/models/longformer/configuration_longformer.py
deleted file mode 100644
index 14ad2b5557d4d0cd9d2397308b6a823c1789bb31..0000000000000000000000000000000000000000
--- a/spaces/skf15963/summary/fengshen/models/longformer/configuration_longformer.py
+++ /dev/null
@@ -1,16 +0,0 @@
-# coding=utf-8
-# Copyright 2021 The IDEA 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.
-
-from transformers import LongformerConfig
diff --git a/spaces/sklearn-docs/Gaussian-Classification-on-Iris/app.py b/spaces/sklearn-docs/Gaussian-Classification-on-Iris/app.py
deleted file mode 100644
index a569cab01ca33adae886dacc556342a62cb40e7f..0000000000000000000000000000000000000000
--- a/spaces/sklearn-docs/Gaussian-Classification-on-Iris/app.py
+++ /dev/null
@@ -1,46 +0,0 @@
-import numpy as np
-import matplotlib.pyplot as plt
-from sklearn import datasets
-from sklearn.gaussian_process import GaussianProcessClassifier
-from sklearn.gaussian_process.kernels import RBF
-import gradio as gr
-
-def plot_decision_boundary(kernel_type):
-    iris = datasets.load_iris()
-    X = iris.data[:, :2]  # we only take the first two features.
-    y = np.array(iris.target, dtype=int)
-
-    h = 0.02  # step size in the mesh
-
-    if kernel_type == "isotropic":
-        kernel = 1.0 * RBF([1.0])
-        clf = GaussianProcessClassifier(kernel=kernel).fit(X, y)
-    elif kernel_type == "anisotropic":
-        kernel = 1.0 * RBF([1.0, 1.0])
-        clf = GaussianProcessClassifier(kernel=kernel).fit(X, y)
-    else:
-        return None
-
-    x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
-    y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
-    xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
-
-    Z = clf.predict_proba(np.c_[xx.ravel(), yy.ravel()])
-    Z = Z.reshape((xx.shape[0], xx.shape[1], 3))
-
-    plt.figure(figsize=(7, 5))
-    plt.imshow(Z, extent=(x_min, x_max, y_min, y_max), origin="lower")
-    plt.scatter(X[:, 0], X[:, 1], c=np.array(["r", "g", "b"])[y], edgecolors=(0, 0, 0))
-    plt.xlabel("Sepal length")
-    plt.ylabel("Sepal width")
-    plt.xlim(xx.min(), xx.max())
-    plt.ylim(yy.min(), yy.max())
-    plt.xticks(())
-    plt.yticks(())
-    plt.title("%s, LML: %.3f" % (kernel_type.capitalize(), clf.log_marginal_likelihood(clf.kernel_.theta)))
-    plt.tight_layout()
-    return plt
-
-kernel_select = gr.inputs.Radio(["isotropic", "anisotropic"], label="Kernel Type")
-gr_interface = gr.Interface(fn=plot_decision_boundary, inputs=kernel_select, outputs="plot", title="Gaussian Process Classification on Iris Dataset", description="This example illustrates the predicted probability of GPC for an isotropic and anisotropic RBF kernel on a two-dimensional version for the iris-dataset. The anisotropic RBF kernel obtains slightly higher log-marginal-likelihood by assigning different length-scales to the two feature dimensions. See the original example at https://scikit-learn.org/stable/auto_examples/gaussian_process/plot_gpc_iris.html")
-gr_interface.launch()
diff --git a/spaces/sohojoe/soho-clip-embeddings-explorer/experimental/clip_api_app_client.py b/spaces/sohojoe/soho-clip-embeddings-explorer/experimental/clip_api_app_client.py
deleted file mode 100644
index f067aca7e890b8fb2076feb995256ae29d9fba65..0000000000000000000000000000000000000000
--- a/spaces/sohojoe/soho-clip-embeddings-explorer/experimental/clip_api_app_client.py
+++ /dev/null
@@ -1,55 +0,0 @@
-import ray
-from ray import serve
-import time
-import asyncio
-
-# Create a Semaphore object
-semaphore = asyncio.Semaphore(10)
-
-test_image_url = "https://static.wixstatic.com/media/4d6b49_42b9435ce1104008b1b5f7a3c9bfcd69~mv2.jpg/v1/fill/w_454,h_333,fp_0.50_0.50,q_90/4d6b49_42b9435ce1104008b1b5f7a3c9bfcd69~mv2.jpg"
-english_text = (
-    "It was the best of times, it was the worst of times, it was the age "
-    "of wisdom, it was the age of foolishness, it was the epoch of belief"
-)
-
-async def send_text_request(serve_client, number):
-    async with semaphore:
-        # async_handle = serve_client.get_handle("CLIPTransform", sync=False)
-        async_handle = serve.get_deployment("CLIPTransform").get_handle(sync=False)
-        # async_handle = serve.get_deployment("CLIPTransform").get_handle()
-        embeddings = ray.get(await async_handle.text_to_embeddings.remote(english_text))
-        # embeddings = await async_handle.text_to_embeddings.remote(english_text)
-        # embeddings = async_handle.text_to_embeddings.remote(english_text)
-        # embeddings = await ray.get(embeddings)
-        return number, embeddings
-
-# def process_text(server_client, numbers, max_workers=10):
-#     with ThreadPoolExecutor(max_workers=max_workers) as executor:
-#         futures = [executor.submit(send_text_request, server_client, number) for number in numbers]
-#         for future in as_completed(futures):
-#             n_result, result = future.result()
-#             print (f"{n_result} : {len(result[0])}")
-async def process_text(server_client, numbers):
-    tasks = [send_text_request(server_client, number) for number in numbers]
-    for future in asyncio.as_completed(tasks):
-        n_result, result = await future
-        print (f"{n_result} : {len(result[0])}")
-
-if __name__ == "__main__":
-    # n_calls = 100000
-    n_calls = 1
-    numbers = list(range(n_calls))
-    ray.init()
-    server_client = serve.start(detached=True) 
-    start_time = time.monotonic()
-
-    # Run the async function
-    asyncio.run(process_text(server_client, numbers))
-
-    end_time = time.monotonic()
-    total_time = end_time - start_time
-    avg_time_ms = total_time / n_calls * 1000
-    calls_per_sec = n_calls / total_time
-    print(f"Average time taken: {avg_time_ms:.2f} ms")
-    print(f"Number of calls per second: {calls_per_sec:.2f}")    
-    ray.shutdown()
diff --git a/spaces/sriramelango/Social_Classification_Public/fairseq/fairseq/data/shorten_dataset.py b/spaces/sriramelango/Social_Classification_Public/fairseq/fairseq/data/shorten_dataset.py
deleted file mode 100644
index 6ebb5d88feb3f29d1512a0873df304915d051209..0000000000000000000000000000000000000000
--- a/spaces/sriramelango/Social_Classification_Public/fairseq/fairseq/data/shorten_dataset.py
+++ /dev/null
@@ -1,78 +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 numpy as np
-from fairseq.data import data_utils
-
-from . import BaseWrapperDataset
-
-
-class TruncateDataset(BaseWrapperDataset):
-    """Truncate a sequence by returning the first truncation_length tokens"""
-
-    def __init__(self, dataset, truncation_length):
-        super().__init__(dataset)
-        assert truncation_length is not None
-        self.truncation_length = truncation_length
-        self.dataset = dataset
-
-    def __getitem__(self, index):
-        item = self.dataset[index]
-        item_len = item.size(0)
-        if item_len > self.truncation_length:
-            item = item[: self.truncation_length]
-        return item
-
-    @property
-    def sizes(self):
-        return np.minimum(self.dataset.sizes, self.truncation_length)
-
-    def __len__(self):
-        return len(self.dataset)
-
-
-class RandomCropDataset(TruncateDataset):
-    """Truncate a sequence by returning a random crop of truncation_length tokens"""
-
-    def __init__(self, dataset, truncation_length, seed=1):
-        super().__init__(dataset, truncation_length)
-        self.seed = seed
-        self.epoch = 0
-
-    @property
-    def can_reuse_epoch_itr_across_epochs(self):
-        return True  # only the crop changes, not item sizes
-
-    def set_epoch(self, epoch, **unused):
-        super().set_epoch(epoch)
-        self.epoch = epoch
-
-    def __getitem__(self, index):
-        with data_utils.numpy_seed(self.seed, self.epoch, index):
-            item = self.dataset[index]
-            item_len = item.size(0)
-            excess = item_len - self.truncation_length
-            if excess > 0:
-                start_idx = np.random.randint(0, excess)
-                item = item[start_idx : start_idx + self.truncation_length]
-            return item
-
-
-def maybe_shorten_dataset(
-    dataset,
-    split,
-    shorten_data_split_list,
-    shorten_method,
-    tokens_per_sample,
-    seed,
-):
-    truncate_split = (
-        split in shorten_data_split_list.split(",") or len(shorten_data_split_list) == 0
-    )
-    if shorten_method == "truncate" and truncate_split:
-        dataset = TruncateDataset(dataset, tokens_per_sample)
-    elif shorten_method == "random_crop" and truncate_split:
-        dataset = RandomCropDataset(dataset, tokens_per_sample, seed)
-    return dataset
diff --git a/spaces/srivarshan/argumentation-quality-analyzer/README.md b/spaces/srivarshan/argumentation-quality-analyzer/README.md
deleted file mode 100644
index c6a9388ff2029e0f08f876a11e4f8faf1834775f..0000000000000000000000000000000000000000
--- a/spaces/srivarshan/argumentation-quality-analyzer/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Arguementation Analyzer
-emoji: 😻
-colorFrom: green
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.13.0
-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/stogaja/xpathfinder/README.md b/spaces/stogaja/xpathfinder/README.md
deleted file mode 100644
index 609a2c1024283f0503e21d74f1eaf345e8a29717..0000000000000000000000000000000000000000
--- a/spaces/stogaja/xpathfinder/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Xpathfinder
-emoji: 🌖
-colorFrom: yellow
-colorTo: red
-sdk: streamlit
-sdk_version: 1.10.0
-app_file: app.py
-pinned: false
-license: afl-3.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/stomexserde/gpt4-ui/Examples/Amintiri Din Copilarie 1964 Download Torent LINK.md b/spaces/stomexserde/gpt4-ui/Examples/Amintiri Din Copilarie 1964 Download Torent LINK.md
deleted file mode 100644
index 262e243031f75a8e83457fcf20bbb5560a9499e5..0000000000000000000000000000000000000000
--- a/spaces/stomexserde/gpt4-ui/Examples/Amintiri Din Copilarie 1964 Download Torent LINK.md	
+++ /dev/null
@@ -1,22 +0,0 @@
-<br />
-<h1>Amintiri Din Copilarie 1964: A Classic Romanian Film Based on Ion Creangă's Autobiographical Book</h1>
-
-<p>Amintiri Din Copilarie (Memories of Childhood) is a Romanian film directed by Elisabeta Bostan in 1964. It is based on the literary work of the same name by Ion Creangă, one of the most famous Romanian writers, who offers a detailed account of his childhood in the 19th century rural Moldavia.</p>
-
-<p>The film follows the adventures and mischiefs of Nică, a young boy played by Ion Bocancea, who lives with his parents Ștefan and Smaranda (Emanoil Petruț and Corina Constantinescu), his grandfather David (Nicolae Veniaș), and his aunt Mărioara (Eliza Petrăchescu) in Humulești village. Nică is a curious, lively, and rebellious child who often gets into trouble with his family, his teachers, and his peers. He also has a vivid imagination and a love for storytelling, inspired by the folk tales and legends he hears from his grandfather and other villagers.</p>
-<h2>Amintiri Din Copilarie 1964 Download Torent</h2><br /><p><b><b>Download</b> &#10038; <a href="https://urlgoal.com/2uI9Iy">https://urlgoal.com/2uI9Iy</a></b></p><br /><br />
-
-<p>The film depicts various episodes from Nică's childhood, such as his first day at school, his encounter with a bear, his friendship with a gypsy boy, his fight with a priest's son, his visit to his uncle's house in Broșteni, his participation in a village festival, and his initiation into manhood. The film also portrays the customs, traditions, and values of the rural Romanian society at that time, as well as the humor, wisdom, and morality of Ion Creangă's writing style.</p>
-
-<p>Amintiri Din Copilarie is considered a classic of Romanian cinema and one of the best films for children ever made. It received several awards at national and international festivals, such as the Golden Lion at the Venice Film Festival in 1965. It was also very popular among audiences, being seen by over 5 million viewers in Romania. The film was followed by a sequel in 1965, Pupăza Din Tei (The Hoopoe from the Linden Tree), also directed by Elisabeta Bostan and starring Ion Bocancea as Nică.</p>
-
-<p>If you want to watch this film online or download it as a torrent file, you can search for it on various websites that offer free streaming or downloading services. However, be careful about the quality and legality of these sources, as they may not respect the rights of the filmmakers and distributors. Alternatively, you can buy or rent the DVD or Blu-ray version of the film from authorized sellers or online platforms.</p>
-
-<p>Amintiri Din Copilarie is not only a film, but also a book that you can read and enjoy. The book was written by Ion Creangă between 1875 and 1889, and it consists of four parts that cover different stages of his life, from his birth to his adolescence. The book is considered a masterpiece of Romanian literature and one of the best examples of autobiographical fiction in the world. It combines realistic elements with fantastic ones, creating a rich and colorful portrait of the author and his environment.</p>
-
-<p>If you want to read the book online or download it as a PDF or EPUB file, you can find it on various websites that offer free access to public domain works. However, be aware that some of these websites may not have the best quality or accuracy of the text, as they may contain errors or omissions. Alternatively, you can buy or borrow the printed version of the book from bookstores or libraries.</p>
-<p></p>
-
-<p>Whether you watch the film or read the book, Amintiri Din Copilarie will surely captivate you with its charm, humor, and nostalgia. It will also teach you valuable lessons about life, friendship, family, and culture. It is a timeless and universal story that can be enjoyed by people of all ages and backgrounds.</p> 81aa517590<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/stomexserde/gpt4-ui/Examples/ArcSoft ShowBiz 5.0.1.375 With Serial By KurdTM Keygen [VERIFIED].md b/spaces/stomexserde/gpt4-ui/Examples/ArcSoft ShowBiz 5.0.1.375 With Serial By KurdTM Keygen [VERIFIED].md
deleted file mode 100644
index 81cde3af30587bcc3b5b18a7f7fefdbe080ae8d6..0000000000000000000000000000000000000000
--- a/spaces/stomexserde/gpt4-ui/Examples/ArcSoft ShowBiz 5.0.1.375 With Serial By KurdTM Keygen [VERIFIED].md	
+++ /dev/null
@@ -1,23 +0,0 @@
-
-<h1>ArcSoft ShowBiz 5.0.1.375: A Powerful and Versatile Video Editor</h1>
-<p>ArcSoft ShowBiz 5.0.1.375 is a video editing software that allows you to create and edit videos like a pro. Whether you want to import files from your media library, external devices, or 3D cameras, ShowBiz 5 has you covered. You can enhance your videos with effects, transitions, titles, narration, and more. You can also switch between Storyboard and Timeline modes to suit your editing style and preferences.</p>
-<p>One of the features that makes ShowBiz 5 stand out is its support for 3D video creation and sharing. You can capture 3D images from various sources and edit them in ShowBiz 5 with ease. You can also upload your 3D videos to YouTube or export them as 3D files or discs.</p>
-<h2>ArcSoft ShowBiz 5.0.1.375 With Serial By KurdTM Keygen</h2><br /><p><b><b>Download File</b> &mdash; <a href="https://urlgoal.com/2uI96V">https://urlgoal.com/2uI96V</a></b></p><br /><br />
-<p>ShowBiz 5 also comes with a serial keygen by KurdTM that allows you to activate the software for free. KurdTM is a group of hackers that crack and distribute software for educational purposes only. They do not support piracy or illegal use of software.</p>
-<p>If you are looking for a powerful and versatile video editor that can handle both 2D and 3D videos, you should give ArcSoft ShowBiz 5.0.1.375 a try. You can download a trial version from <a href="https://archive.org/details/arcsoftshowbiz501375">here</a> or use the serial keygen by KurdTM to unlock the full version.</p>
-
-<p>Some of the integrated video editing tools that ShowBiz 5 offers are:</p>
-<ul>
-<li>Anti-Shaking: This tool helps you stabilize your shaky videos and make them smoother.</li>
-<li>Denoise: This tool reduces the noise level in your videos and improves their quality.</li>
-<li>Rotate & Flip: This tool lets you correct the orientation of your videos and flip them horizontally or vertically.</li>
-<li>Crop & Trim: This tool allows you to get rid of unwanted parts in your videos and adjust their duration.</li>
-<li>Color Adjustment: This tool enables you to change the hue, saturation, brightness, and contrast of your videos and enhance their appearance.</li>
-</ul>
-<p>With ShowBiz 5, you can also add various effects to your videos, such as Fuzzyapse, fades, blurs, smoke, and occlusion filters. You can also add texts, transitions, titles, and credits to your videos and customize their font, size, color, and position. Moreover, you can add your favorite music or a voice-over to your videos and adjust their volume and speed.</p>
-
-<p>Another feature that ShowBiz 5 boasts is its social media integration. You can share your videos directly on YouTube or Facebook without leaving the program. You can also upload your photos to Flickr and Twitter with a few clicks. ShowBiz 5 makes it easy for you to showcase your creativity and connect with your friends and fans online.</p>
-<p>ShowBiz 5 has received positive reviews from many users and critics. They praised its user-friendly interface, its support for various formats and devices, its 3D video editing capabilities, and its affordable price. Some of the drawbacks that they mentioned are its frequent crashes, its limited timeline controls, its lack of screen capturing feature, and its poor customer service.</p>
-<p>If you are interested in trying out ShowBiz 5 for yourself, you can download a free trial version from their official website. The trial version is valid for 15 days and has a watermark on the output videos. If you want to unlock the full version, you can use the serial keygen by KurdTM that is included in this article. However, we do not endorse or support piracy or illegal use of software. Please use ShowBiz 5 at your own risk and responsibility.</p> cec2833e83<br />
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\ No newline at end of file
diff --git a/spaces/stomexserde/gpt4-ui/Examples/Code Geass Mega 720p Mkv.md b/spaces/stomexserde/gpt4-ui/Examples/Code Geass Mega 720p Mkv.md
deleted file mode 100644
index 16f10fe5f210b68000240cf202db44cef0687d95..0000000000000000000000000000000000000000
--- a/spaces/stomexserde/gpt4-ui/Examples/Code Geass Mega 720p Mkv.md	
+++ /dev/null
@@ -1,25 +0,0 @@
-<br />
-Here is a possible title and article with html formatting for the keyword "Code Geass Mega 720p Mkv":
-
-<h1>How to Download Code Geass Complete 720p Dual-Audio with English Subtitles</h1>
-<p>Code Geass is a popular anime series that follows the story of Lelouch Lamperouge, a former prince of the Britannian Empire who gains a mysterious power called Geass and leads a rebellion against his father's tyranny. The series has two seasons, each with 25 episodes, and is available in both Japanese and English audio with English subtitles.</p>
-<h2>Code Geass Mega 720p Mkv</h2><br /><p><b><b>Download Zip</b> &#10038; <a href="https://urlgoal.com/2uI7En">https://urlgoal.com/2uI7En</a></b></p><br /><br />
-<p>If you are looking for a way to download Code Geass complete 720p dual-audio with English subtitles, you have come to the right place. In this article, we will show you how to use a torrent site called Nyaa to find and download the files you need. Nyaa is a website that hosts torrents for anime, manga, games, and other Japanese media. You can access it at <a href="https://nyaa.si">https://nyaa.si</a>.</p>
-<p>Before you proceed, you will need a torrent client software that can download and open torrent files. Some examples of torrent clients are uTorrent, BitTorrent, qBittorrent, and Transmission. You can download them from their official websites or from other sources. Make sure you have enough space on your device to store the downloaded files.</p>
-<p>Here are the steps to download Code Geass complete 720p dual-audio with English subtitles from Nyaa:</p>
-<ol>
-<li>Go to <a href="https://nyaa.si">https://nyaa.si</a> and type "Code Geass" in the search box. You can also use filters to narrow down your results by category, date, size, seeders, leechers, etc.</li>
-<li>Look for the torrent that matches your criteria. For example, if you want to download Code Geass complete 720p dual-audio with English subtitles, you can look for a torrent that has "Complete 720p [Dual-Audio] [English Subbed]" in its title. You can also check the file list and the comments to see what is included in the torrent.</li>
-<li>Click on the torrent title to open its page. You will see more information about the torrent, such as its description, file list, seeders, leechers, comments, etc. You will also see a button that says "Download Torrent" or "Magnet". Click on it to download the torrent file or copy the magnet link.</li>
-<li>Open your torrent client software and add the torrent file or the magnet link to start downloading. You can monitor the progress of your download and adjust the settings as you wish.</li>
-<li>Once your download is complete, you can open the folder where the files are stored and enjoy watching Code Geass complete 720p dual-audio with English subtitles.</li>
-</ol>
-<p>We hope this article was helpful for you. If you have any questions or feedback, feel free to leave a comment below.</p>Here are a few more paragraphs for the article:
-
-<p>Code Geass is not only a thrilling anime series that features action, drama, and romance, but also a thought-provoking one that explores themes such as morality, justice, loyalty, identity, and freedom. The series presents a complex and realistic world where different factions and ideologies clash and where the characters face difficult choices and consequences. The series also challenges the viewers to question their own beliefs and values and to empathize with different perspectives.</p>
-<p></p>
-<p>The main character of Code Geass is Lelouch Lamperouge, a brilliant and charismatic young man who is also a former prince of the Britannian Empire. He is driven by his hatred for his father, the Emperor of Britannia, who he blames for the death of his mother and the disability of his sister. He also despises the oppression and discrimination that Britannia inflicts on the conquered nations, especially Japan, where he lives under a false identity. He vows to destroy Britannia and create a peaceful world for his sister.</p>
-<p>Lelouch's life changes when he meets C.C., a mysterious girl who grants him the power of Geass, which allows him to command anyone to obey his orders. With this power, he becomes Zero, the leader of the Black Knights, a rebel group that fights against Britannia. He uses his intelligence, charisma, and strategy to outwit his enemies and gain allies. However, he also faces many challenges and obstacles, such as his best friend Suzaku Kururugi, who is a loyal soldier of Britannia; his half-siblings who are competing for the throne; and his own moral dilemmas and inner conflicts.</p>
-<p>Code Geass is a captivating anime series that will keep you on the edge of your seat with its twists and turns. It will also make you think and feel with its deep and complex characters and themes. If you are looking for an anime that combines action, drama, romance, and philosophy, Code Geass is a great choice.</p> 7196e7f11a<br />
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-<br />
\ No newline at end of file
diff --git a/spaces/stomexserde/gpt4-ui/Examples/Department Marathi Movie VERIFIED Download Kickass.md b/spaces/stomexserde/gpt4-ui/Examples/Department Marathi Movie VERIFIED Download Kickass.md
deleted file mode 100644
index 6fd94abb1a1a7e0f9dd6a765866d6e5a30a1006b..0000000000000000000000000000000000000000
--- a/spaces/stomexserde/gpt4-ui/Examples/Department Marathi Movie VERIFIED Download Kickass.md	
+++ /dev/null
@@ -1,26 +0,0 @@
-
-<h1>How to Download Department Marathi Movie from Kickass Torrents</h1>
-<p>If you are looking for a way to download Department Marathi Movie, a 2012 action thriller film directed by Ram Gopal Varma, you might want to try Kickass Torrents, one of the most popular torrent search tools among millions of torrent sites. In this article, we will show you a simple guide for Kickass movie download and how to convert downloaded movies to other formats for better compatibility.</p>
-<h2>Step 1: Visit the new and primary Kickass torrents site</h2>
-<p>The original KickassTorrents domain was taken offline and blocked by authorities in 2016, but it was brought back to life by former staff and moderators in December 2016. The new and primary Kickass torrents site is https://kickasstorrents.to/, where you can search for the resources you want without registration.</p>
-<h2>Department Marathi Movie Download Kickass</h2><br /><p><b><b>Download Zip</b> &#10145; <a href="https://urlgoal.com/2uI7hw">https://urlgoal.com/2uI7hw</a></b></p><br /><br />
-<h2>Step 2: Search for Department Marathi Movie</h2>
-<p>In the search box, type in "Department Marathi Movie" and hit enter. You will get a list of great resources with different file sizes, descriptions, screenshots, etc. You can also click on the tags below the search box to filter the results by category, such as movies, music, games, etc.</p>
-<h2>Step 3: Choose a torrent file and download it</h2>
-<p>Click on the torrent name that suits your preferences and needs. You will see more details about the torrent file, such as seeders, leechers, comments, ratings, etc. You can also read the comments to check if the torrent is safe and working. Below the title, you will see a magnet link and a kickass torrent free movie download button. You can either click on the magnet link to open it with your torrent client directly, or click on the download button to save the torrent file on your computer.</p>
-<h2>Step 4: Download and install a torrent client</h2>
-<p>If you don't have a torrent client installed on your computer, you will need one to download movies from KickassTorrents. A torrent client is a software that allows you to download files from other users who are sharing them. Some of the popular torrent clients are BitTorrent, uTorrent, BitComet, etc. You can download and install any of them from their official websites.</p>
-<h2>Step 5: Open the torrent file or magnet link with your torrent client</h2>
-<p>Once you have downloaded the torrent file or copied the magnet link, you can open it with your torrent client. You will see a window where you can choose the files you want to download and the location where you want to save them. You can also adjust some settings such as bandwidth limit, download speed, etc. Click on OK or Start to begin downloading Department Marathi Movie.</p>
-<h2>Step 6: Wait for the download to finish and enjoy watching Department Marathi Movie</h2>
-<p>The download time may vary depending on your internet speed and the number of seeders and leechers. You can check the progress of your download on your torrent client. Once the download is complete, you can open the movie file with your preferred media player and enjoy watching Department Marathi Movie.</p>
-<h3>Note:</h3>
-<p>If you are in a country where there is strict protection of copyright, the direct download of copyrighted resources from the site may bring you serious consequences. WonderFox doesn't encourage downloading copyrighted material without the owner's permission, and this article is for personal fair-use only. Kickass movies free download at your own risk.</p>
-<h3>Bonus Tip:</h3>
-<p>If you want to convert Department Marathi Movie to other formats for better compatibility with your devices or players, you can use WonderFox HD Video Converter Factory Pro, a powerful and easy-to-use video converter that supports over 500 formats and devices. You can also use it to edit videos, compress videos, download videos from online sites, record screen, make GIFs, etc. Here are the simple steps to convert Department Marathi Movie with WonderFox HD Video Converter Factory Pro:</p>
-<ol>
-<li>Download and install WonderFox HD Video Converter Factory Pro from https://www.videoconverterfactory.com/download/hd-video-converter-pro.exe</li>
-<li>Launch the program and click on "Converter" on the</p>
-<p></p> 81aa517590<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/stomexserde/gpt4-ui/Examples/FS2004-FSX - TURBINE SOUND STUDIOS - MD-500 SOUNDPACK No Survey No Password 2019.md b/spaces/stomexserde/gpt4-ui/Examples/FS2004-FSX - TURBINE SOUND STUDIOS - MD-500 SOUNDPACK No Survey No Password 2019.md
deleted file mode 100644
index aa5490531fdf9abaab52c2ea5e5710d175b2c322..0000000000000000000000000000000000000000
--- a/spaces/stomexserde/gpt4-ui/Examples/FS2004-FSX - TURBINE SOUND STUDIOS - MD-500 SOUNDPACK No Survey No Password 2019.md	
+++ /dev/null
@@ -1,64 +0,0 @@
-
-<h1>FS2004-FSX - Turbine Sound Studios - MD-500 Soundpack: A Review</h1>
-<p>If you are a fan of flight simulators, you probably know about <strong>FS2004-FSX</strong>, one of the most popular and realistic flight simulation software available. Whether you prefer flying commercial airliners, military jets, or helicopters, FS2004-FSX has something for everyone. But if you want to take your flight simulation experience to the next level, you might want to consider adding some custom sound packs to your game.</p>
-<h2>FS2004-FSX - TURBINE SOUND STUDIOS - MD-500 SOUNDPACK No Survey No Password 2019</h2><br /><p><b><b>DOWNLOAD</b> &#9745; <a href="https://urlgoal.com/2uI9Jo">https://urlgoal.com/2uI9Jo</a></b></p><br /><br />
-<p>One of the best sources of high-quality sound packs for FS2004-FSX is <strong>Turbine Sound Studios</strong>, a company that specializes in creating realistic and immersive audio for various aircraft models. Their products are designed to enhance the realism and immersion of flying by providing accurate and dynamic sounds that match the performance and behavior of the real aircraft.</p>
-<p>In this article, we will review one of their latest products, the <strong>MD-500 soundpack</strong>, which is compatible with both FS2004 and FSX. The MD-500 is a light utility helicopter that was developed from the Hughes 500, a civilian version of the US Army's OH-6A Cayuse/Loach. The MD-500 series includes several variants, such as the MD 500E, MD 520N, and MD 530F. The MD-500 is known for its distinctive egg-shaped fuselage, short-diameter main rotor system, and agile control response.</p>
-<p>The MD-500 soundpack by Turbine Sound Studios aims to provide a realistic and immersive audio experience for flying this helicopter in FS2004-FSX. It features custom sounds for both internal and external views, as well as cockpit environment sounds, wind sounds, APU sounds, and more. The sounds are recorded from real MD-500 helicopters and edited to match the engine pitch values and acoustic parameters of the game.</p>
-<p>In this review, we will cover the following aspects of the MD-500 soundpack:</p>
-<ul>
-<li>Installation and compatibility</li>
-<li>Sound quality and realism</li>
-<li>Performance and immersion</li>
-<li>Conclusion</li>
-<li>FAQs</li>
-</ul>
- <h2>Installation and Compatibility</h2>
-<p>The installation process of the MD-500 soundpack is fairly simple and straightforward. You can download the product from <a href="(^1^)">simMarket</a>, where it is sold for €9.16 (about $11). After purchasing, you will receive an email with a link to download a ZIP file that contains two folders: one for FS2004 (FS9) and one for FSX.</p>
-<p></p>
-<p>To install the sound pack for FS2004, you need to copy the contents of the FS9 folder into your main FS9 folder, usually located at C:\Program Files\Microsoft Games Flight Simulator 9. To install the sound pack for FSX, you need to copy the contents of the FSX folder into your main FSX folder, usually located at C:\Program Files\Microsoft Games\Microsoft Flight Simulator X. You can also use the installer.exe file provided in each folder to automate the installation process.</p>
-<p>The MD-500 soundpack is compatible with any MD-500 helicopter add-on for FS2004-FSX, such as the <a href="">Nemeth Designs MD 500 Defender</a> or the <a href="">Cera Sim MD 500E</a>. However, you might need to edit the sound.cfg file of your helicopter add-on to point to the correct sound folder of the MD-500 soundpack. You can do this by opening the sound.cfg file with a text editor and changing the sound path to "sound=MD500". For example, if you have the Nemeth Designs MD 500 Defender installed in your FSX, you need to open the file C:\Program Files\Microsoft Games\Microsoft Flight Simulator X\SimObjects\Rotorcraft\ND_MD500D\sound.cfg and change the line "sound=ND_MD500D" to "sound=MD500".</p>
-<p>After installing the sound pack, you can configure the sound settings and options in your FS2004-FSX game. You can adjust the volume levels of different sound categories, such as engine, cockpit, environment, and ATC. You can also enable or disable some sound effects, such as doppler shift, dynamic head latency, and reverse stereo. You can access these settings by clicking on Options > Settings > Sound in your game menu.</p>
- <h2>Sound Quality and Realism</h2>
-<p>The MD-500 soundpack by Turbine Sound Studios is a significant improvement over the default sounds of FS2004-FSX. The default sounds are generic and bland, and do not reflect the unique characteristics of the MD-500 helicopter. The MD-500 soundpack, on the other hand, provides custom sounds that are realistic and dynamic, and capture the essence of flying this helicopter.</p>
-<p>The MD-500 soundpack features sounds for both internal and external views, as well as cockpit environment sounds, wind sounds, APU sounds, and more. The sounds are recorded from real MD-500 helicopters and edited to match the engine pitch values and acoustic parameters of the game. The result is a rich and immersive audio experience that enhances the realism and enjoyment of flying.</p>
-<p>The internal sounds are crisp and clear, and convey the feeling of being inside the cockpit of the MD-500. You can hear the subtle nuances of the engine start-up and shut-down sequences, as well as the changes in engine RPM and torque as you manipulate the throttle and collective. You can also hear the cockpit environment sounds, such as switches, buttons, levers, gauges, alarms, radios, and more. These sounds add to the realism and immersion of flying.</p>
-<p>The external sounds are loud and powerful, and reflect the distinctive sound signature of the MD-500 helicopter. You can hear the roar of the turbine engine and the whine of the transmission as you fly by. You can also hear the thump of the main rotor blades and the whirr of the tail rotor as they cut through the air. The external sounds are affected by distance, direction, speed, altitude, weather, and terrain. These factors create a dynamic and realistic sound environment that varies with your flight situation.</p>
-<p>The MD-500 soundpack also features some special sound effects that add to the realism and immersion of flying. For example, you can hear a doppler effect when you fly past an object or another aircraft. You can also hear a dynamic head latency effect when you move your head inside or outside the cockpit. This effect simulates how sound travels differently through air or bone depending on where your ears are located. You can also hear a reverse stereo effect when you fly backwards or sideways. This effect simulates how sound sources switch sides depending on your flight direction.</p>
-<p>The MD-500 soundpack has some advantages and disadvantages compared to other sound packs or default sounds. Some of the advantages are:</p>
-<ul>
-<li>It provides custom sounds that are realistic and dynamic</li>
-<li>It captures the characteristics of the real MD-500 helicopter</li>
-<li>It enhances the realism and immersion of flying</li>
-<li>It is compatible with any MD-500 helicopter add-on for FS2004-FSX</li>
-<li>It is easy to install and configure</li>
-<li>It is affordable and worth its price</li>
-</ul>
-<p>Some of the disadvantages are:</p>
-<ul>
-<li>It might require some editing of sound.cfg files for some helicopter add-ons</li>
-<li>It might affect performance or frame rate for some users with low-end systems</li>
-<li <li>It might not suit everyone's taste or preference</li>
-</ul>
- <h2>Performance and Immersion</h2>
-<p>The MD-500 soundpack by Turbine Sound Studios does not have a significant impact on the performance or frame rate of FS2004-FSX. The sound files are optimized and compressed to reduce the load on the system. However, some users with low-end systems might experience some stuttering or lagging when using the sound pack. This might be due to other factors, such as graphics settings, scenery complexity, traffic density, weather effects, and so on. To improve the performance and frame rate, you can try lowering some of these settings or disabling some of the sound effects in the game menu.</p>
-<p>The MD-500 soundpack by Turbine Sound Studios greatly enhances the immersion and realism of flying the MD-500 helicopter in FS2004-FSX. The sound pack provides a realistic and dynamic audio experience that matches the performance and behavior of the real helicopter. The sound pack also creates a rich and immersive sound environment that varies with your flight situation. The sound pack makes you feel like you are really flying the MD-500 helicopter, not just playing a game.</p>
-<p>To get the most out of the MD-500 soundpack, you can try some tips and tricks that will improve your flight simulation experience. For example, you can use a good headset or speakers to enjoy the high-quality sounds. You can also use a joystick or a yoke and pedals to control the helicopter more accurately and smoothly. You can also use a TrackIR or a VR headset to move your head freely and look around the cockpit and outside. You can also use some add-ons or mods that will enhance the graphics, scenery, weather, traffic, and realism of FS2004-FSX.</p>
- <h2>Conclusion</h2>
-<p>The MD-500 soundpack by Turbine Sound Studios is one of the best sound packs for FS2004-FSX. It provides custom sounds that are realistic and dynamic, and capture the characteristics of the real MD-500 helicopter. It enhances the realism and immersion of flying, and creates a rich and immersive sound environment that varies with your flight situation. It is compatible with any MD-500 helicopter add-on for FS2004-FSX, and it is easy to install and configure. It does not have a significant impact on performance or frame rate, but it might require some editing of sound.cfg files for some helicopter add-ons. It is affordable and worth its price, but it might not suit everyone's taste or preference.</p>
-<p>In conclusion, I highly recommend the MD-500 soundpack by Turbine Sound Studios to anyone who loves flying helicopters in FS2004-FSX. It is one of the best products from Turbine Sound Studios, and it will make your flight simulation experience more realistic and enjoyable. If you are interested in buying or learning more about the MD-500 soundpack by Turbine Sound Studios, you can visit their <a href="">website</a> or their <a href="">Facebook page</a>. You can also watch some <a href="">videos</a> or read some <a href="">reviews</a> of the product online.</p>
- <h2>FAQs</h2>
-<p>Here are some common questions and answers about the MD-500 soundpack by Turbine Sound Studios:</p>
-<h3>Q: Does the MD-500 soundpack work with other helicopters or aircraft?</h3>
-<p>A: No, the MD-500 soundpack is designed specifically for the MD-500 helicopter series. It will not work with other helicopters or aircraft models.</p>
-<h3>Q: Does the MD-500 soundpack include any visual enhancements or liveries?</h3>
-<p>A: No, the MD-500 soundpack only includes audio enhancements. It does not include any visual enhancements or liveries for the MD-500 helicopter.</p>
-<h3>Q: How can I update or uninstall the MD-500 soundpack?</h3>
-<p>A: To update the MD-500 soundpack, you need to download the latest version from simMarket and install it over your existing installation. To uninstall the MD-500 soundpack, you need to delete the MD500 folder from your main FS2004-FSX folder.</p>
-<h3>Q: How can I contact Turbine Sound Studios for support or feedback?</h3>
-<p>A: You can contact Turbine Sound Studios by sending an email to <a href="mailto:turbinesoundstudios@gmail.com">turbinesoundstudios@gmail.com</a> or by filling out their <a href="">contact form</a>. You can also follow them on Facebook or YouTube for updates and news.</p>
-<h3>Q: Where can I find more products from Turbine Sound Studios?</h3>
-<p>A: A: You can find more products from Turbine Sound Studios on their <a href="">website</a> or on <a href="">simMarket</a>. They have sound packs for various aircraft models, such as Airbus, Boeing, Bombardier, Cessna, Embraer, Lockheed Martin, and more. They also have sound packs for helicopters, such as Bell, Eurocopter, Sikorsky, and more. You can also check out their <a href="">YouTube channel</a> for previews and demos of their products.</p>
- <p>I hope you enjoyed this article and found it useful. If you have any questions or comments, please feel free to leave them below. Thank you for reading and happy flying!</p> b2dd77e56b<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/sub314xxl/MetaGPT/metagpt/tools/search_engine_serper.py b/spaces/sub314xxl/MetaGPT/metagpt/tools/search_engine_serper.py
deleted file mode 100644
index 0eec2694bf1aee218ab0e6138664c8edf8d8f1e2..0000000000000000000000000000000000000000
--- a/spaces/sub314xxl/MetaGPT/metagpt/tools/search_engine_serper.py
+++ /dev/null
@@ -1,119 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-"""
-@Time    : 2023/5/23 18:27
-@Author  : alexanderwu
-@File    : search_engine_serpapi.py
-"""
-import json
-from typing import Any, Dict, Optional, Tuple
-
-import aiohttp
-from pydantic import BaseModel, Field, validator
-
-from metagpt.config import CONFIG
-
-
-class SerperWrapper(BaseModel):
-    search_engine: Any  #: :meta private:
-    payload: dict = Field(default={"page": 1, "num": 10})
-    serper_api_key: Optional[str] = None
-    aiosession: Optional[aiohttp.ClientSession] = None
-
-    class Config:
-        arbitrary_types_allowed = True
-
-    @validator("serper_api_key", always=True)
-    @classmethod
-    def check_serper_api_key(cls, val: str):
-        val = val or CONFIG.serper_api_key
-        if not val:
-            raise ValueError(
-                "To use, make sure you provide the serper_api_key when constructing an object. Alternatively, "
-                "ensure that the environment variable SERPER_API_KEY is set with your API key. You can obtain "
-                "an API key from https://serper.dev/."
-            )
-        return val
-
-    async def run(self, query: str, max_results: int = 8, as_string: bool = True, **kwargs: Any) -> str:
-        """Run query through Serper and parse result async."""
-        if isinstance(query, str):
-            return self._process_response((await self.results([query], max_results))[0], as_string=as_string)
-        else:
-            results = [self._process_response(res, as_string) for res in await self.results(query, max_results)]
-        return "\n".join(results) if as_string else results
-
-    async def results(self, queries: list[str], max_results: int = 8) -> dict:
-        """Use aiohttp to run query through Serper and return the results async."""
-
-        def construct_url_and_payload_and_headers() -> Tuple[str, Dict[str, str]]:
-            payloads = self.get_payloads(queries, max_results)
-            url = "https://google.serper.dev/search"
-            headers = self.get_headers()
-            return url, payloads, headers
-
-        url, payloads, headers = construct_url_and_payload_and_headers()
-        if not self.aiosession:
-            async with aiohttp.ClientSession() as session:
-                async with session.post(url, data=payloads, headers=headers) as response:
-                    res = await response.json()
-        else:
-            async with self.aiosession.get.post(url, data=payloads, headers=headers) as response:
-                res = await response.json()
-
-        return res
-
-    def get_payloads(self, queries: list[str], max_results: int) -> Dict[str, str]:
-        """Get payloads for Serper."""
-        payloads = []
-        for query in queries:
-            _payload = {
-                "q": query,
-                "num": max_results,
-            }
-            payloads.append({**self.payload, **_payload})
-        return json.dumps(payloads, sort_keys=True)
-
-    def get_headers(self) -> Dict[str, str]:
-        headers = {"X-API-KEY": self.serper_api_key, "Content-Type": "application/json"}
-        return headers
-
-    @staticmethod
-    def _process_response(res: dict, as_string: bool = False) -> str:
-        """Process response from SerpAPI."""
-        # logger.debug(res)
-        focus = ["title", "snippet", "link"]
-
-        def get_focused(x):
-            return {i: j for i, j in x.items() if i in focus}
-
-        if "error" in res.keys():
-            raise ValueError(f"Got error from SerpAPI: {res['error']}")
-        if "answer_box" in res.keys() and "answer" in res["answer_box"].keys():
-            toret = res["answer_box"]["answer"]
-        elif "answer_box" in res.keys() and "snippet" in res["answer_box"].keys():
-            toret = res["answer_box"]["snippet"]
-        elif "answer_box" in res.keys() and "snippet_highlighted_words" in res["answer_box"].keys():
-            toret = res["answer_box"]["snippet_highlighted_words"][0]
-        elif "sports_results" in res.keys() and "game_spotlight" in res["sports_results"].keys():
-            toret = res["sports_results"]["game_spotlight"]
-        elif "knowledge_graph" in res.keys() and "description" in res["knowledge_graph"].keys():
-            toret = res["knowledge_graph"]["description"]
-        elif "snippet" in res["organic"][0].keys():
-            toret = res["organic"][0]["snippet"]
-        else:
-            toret = "No good search result found"
-
-        toret_l = []
-        if "answer_box" in res.keys() and "snippet" in res["answer_box"].keys():
-            toret_l += [get_focused(res["answer_box"])]
-        if res.get("organic"):
-            toret_l += [get_focused(i) for i in res.get("organic")]
-
-        return str(toret) + "\n" + str(toret_l) if as_string else toret_l
-
-
-if __name__ == "__main__":
-    import fire
-
-    fire.Fire(SerperWrapper().run)
diff --git a/spaces/sub314xxl/MusicGen/audiocraft/quantization/core_vq.py b/spaces/sub314xxl/MusicGen/audiocraft/quantization/core_vq.py
deleted file mode 100644
index e1896bb1788a945a1f7be6369abb255ecf72c7a0..0000000000000000000000000000000000000000
--- a/spaces/sub314xxl/MusicGen/audiocraft/quantization/core_vq.py
+++ /dev/null
@@ -1,400 +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 typing as tp
-
-from einops import rearrange, repeat
-import flashy
-import torch
-from torch import nn, einsum
-import torch.nn.functional as F
-
-
-def exists(val: tp.Optional[tp.Any]) -> bool:
-    return val is not None
-
-
-def default(val: tp.Any, d: tp.Any) -> tp.Any:
-    return val if exists(val) else d
-
-
-def l2norm(t):
-    return F.normalize(t, p=2, dim=-1)
-
-
-def ema_inplace(moving_avg, new, decay: float):
-    moving_avg.data.mul_(decay).add_(new, alpha=(1 - decay))
-
-
-def laplace_smoothing(x, n_categories: int, epsilon: float = 1e-5):
-    return (x + epsilon) / (x.sum() + n_categories * epsilon)
-
-
-def uniform_init(*shape: int):
-    t = torch.empty(shape)
-    nn.init.kaiming_uniform_(t)
-    return t
-
-
-def sample_vectors(samples, num: int):
-    num_samples, device = samples.shape[0], samples.device
-
-    if num_samples >= num:
-        indices = torch.randperm(num_samples, device=device)[:num]
-    else:
-        indices = torch.randint(0, num_samples, (num,), device=device)
-
-    return samples[indices]
-
-
-def kmeans(samples, num_clusters: int, num_iters: int = 10):
-    dim, dtype = samples.shape[-1], samples.dtype
-
-    means = sample_vectors(samples, num_clusters)
-
-    for _ in range(num_iters):
-        diffs = rearrange(samples, "n d -> n () d") - rearrange(
-            means, "c d -> () c d"
-        )
-        dists = -(diffs ** 2).sum(dim=-1)
-
-        buckets = dists.max(dim=-1).indices
-        bins = torch.bincount(buckets, minlength=num_clusters)
-        zero_mask = bins == 0
-        bins_min_clamped = bins.masked_fill(zero_mask, 1)
-
-        new_means = buckets.new_zeros(num_clusters, dim, dtype=dtype)
-        new_means.scatter_add_(0, repeat(buckets, "n -> n d", d=dim), samples)
-        new_means = new_means / bins_min_clamped[..., None]
-
-        means = torch.where(zero_mask[..., None], means, new_means)
-
-    return means, bins
-
-
-def orthgonal_loss_fn(t):
-    # eq (2) from https://arxiv.org/abs/2112.00384
-    n = t.shape[0]
-    normed_codes = l2norm(t)
-    identity = torch.eye(n, device=t.device)
-    cosine_sim = einsum("i d, j d -> i j", normed_codes, normed_codes)
-    return ((cosine_sim - identity) ** 2).sum() / (n ** 2)
-
-
-class EuclideanCodebook(nn.Module):
-    """Codebook with Euclidean distance.
-
-    Args:
-        dim (int): Dimension.
-        codebook_size (int): Codebook size.
-        kmeans_init (bool): Whether to use k-means to initialize the codebooks.
-            If set to true, run the k-means algorithm on the first training batch and use
-            the learned centroids as initialization.
-        kmeans_iters (int): Number of iterations used for k-means algorithm at initialization.
-        decay (float): Decay for exponential moving average over the codebooks.
-        epsilon (float): Epsilon value for numerical stability.
-        threshold_ema_dead_code (int): Threshold for dead code expiration. Replace any codes
-            that have an exponential moving average cluster size less than the specified threshold with
-            randomly selected vector from the current batch.
-    """
-    def __init__(
-        self,
-        dim: int,
-        codebook_size: int,
-        kmeans_init: int = False,
-        kmeans_iters: int = 10,
-        decay: float = 0.8,
-        epsilon: float = 1e-5,
-        threshold_ema_dead_code: int = 2,
-    ):
-        super().__init__()
-        self.decay = decay
-        init_fn: tp.Union[tp.Callable[..., torch.Tensor], tp.Any] = uniform_init if not kmeans_init else torch.zeros
-        embed = init_fn(codebook_size, dim)
-
-        self.codebook_size = codebook_size
-
-        self.kmeans_iters = kmeans_iters
-        self.epsilon = epsilon
-        self.threshold_ema_dead_code = threshold_ema_dead_code
-
-        self.register_buffer("inited", torch.Tensor([not kmeans_init]))
-        self.register_buffer("cluster_size", torch.zeros(codebook_size))
-        self.register_buffer("embed", embed)
-        self.register_buffer("embed_avg", embed.clone())
-
-    @torch.jit.ignore
-    def init_embed_(self, data):
-        if self.inited:
-            return
-
-        embed, cluster_size = kmeans(data, self.codebook_size, self.kmeans_iters)
-        self.embed.data.copy_(embed)
-        self.embed_avg.data.copy_(embed.clone())
-        self.cluster_size.data.copy_(cluster_size)
-        self.inited.data.copy_(torch.Tensor([True]))
-        # Make sure all buffers across workers are in sync after initialization
-        flashy.distrib.broadcast_tensors(self.buffers())
-
-    def replace_(self, samples, mask):
-        modified_codebook = torch.where(
-            mask[..., None], sample_vectors(samples, self.codebook_size), self.embed
-        )
-        self.embed.data.copy_(modified_codebook)
-
-    def expire_codes_(self, batch_samples):
-        if self.threshold_ema_dead_code == 0:
-            return
-
-        expired_codes = self.cluster_size < self.threshold_ema_dead_code
-        if not torch.any(expired_codes):
-            return
-
-        batch_samples = rearrange(batch_samples, "... d -> (...) d")
-        self.replace_(batch_samples, mask=expired_codes)
-        flashy.distrib.broadcast_tensors(self.buffers())
-
-    def preprocess(self, x):
-        x = rearrange(x, "... d -> (...) d")
-        return x
-
-    def quantize(self, x):
-        embed = self.embed.t()
-        dist = -(
-            x.pow(2).sum(1, keepdim=True)
-            - 2 * x @ embed
-            + embed.pow(2).sum(0, keepdim=True)
-        )
-        embed_ind = dist.max(dim=-1).indices
-        return embed_ind
-
-    def postprocess_emb(self, embed_ind, shape):
-        return embed_ind.view(*shape[:-1])
-
-    def dequantize(self, embed_ind):
-        quantize = F.embedding(embed_ind, self.embed)
-        return quantize
-
-    def encode(self, x):
-        shape = x.shape
-        # pre-process
-        x = self.preprocess(x)
-        # quantize
-        embed_ind = self.quantize(x)
-        # post-process
-        embed_ind = self.postprocess_emb(embed_ind, shape)
-        return embed_ind
-
-    def decode(self, embed_ind):
-        quantize = self.dequantize(embed_ind)
-        return quantize
-
-    def forward(self, x):
-        shape, dtype = x.shape, x.dtype
-        x = self.preprocess(x)
-        self.init_embed_(x)
-
-        embed_ind = self.quantize(x)
-        embed_onehot = F.one_hot(embed_ind, self.codebook_size).type(dtype)
-        embed_ind = self.postprocess_emb(embed_ind, shape)
-        quantize = self.dequantize(embed_ind)
-
-        if self.training:
-            # We do the expiry of code at that point as buffers are in sync
-            # and all the workers will take the same decision.
-            self.expire_codes_(x)
-            ema_inplace(self.cluster_size, embed_onehot.sum(0), self.decay)
-            embed_sum = x.t() @ embed_onehot
-            ema_inplace(self.embed_avg, embed_sum.t(), self.decay)
-            cluster_size = (
-                laplace_smoothing(self.cluster_size, self.codebook_size, self.epsilon)
-                * self.cluster_size.sum()
-            )
-            embed_normalized = self.embed_avg / cluster_size.unsqueeze(1)
-            self.embed.data.copy_(embed_normalized)
-
-        return quantize, embed_ind
-
-
-class VectorQuantization(nn.Module):
-    """Vector quantization implementation.
-    Currently supports only euclidean distance.
-
-    Args:
-        dim (int): Dimension
-        codebook_size (int): Codebook size
-        codebook_dim (int): Codebook dimension. If not defined, uses the specified dimension in dim.
-        decay (float): Decay for exponential moving average over the codebooks.
-        epsilon (float): Epsilon value for numerical stability.
-        kmeans_init (bool): Whether to use kmeans to initialize the codebooks.
-        kmeans_iters (int): Number of iterations used for kmeans initialization.
-        threshold_ema_dead_code (int):
-        channels_last (bool): Channels are the last dimension in the input tensors.
-        commitment_weight (float): Weight for commitment loss.
-        orthogonal_reg_weight (float): Orthogonal regularization weights.
-        orthogonal_reg_active_codes_only (bool): Apply orthogonal regularization only on active codes.
-        orthogonal_reg_max_codes (optional int): Maximum number of codes to consider
-            for orthogonal regulariation.
-        threshold_ema_dead_code (int): Threshold for dead code expiration. Replace any codes
-            that have an exponential moving average cluster size less than the specified threshold with
-            randomly selected vector from the current batch.
-    """
-    def __init__(
-        self,
-        dim: int,
-        codebook_size: int,
-        codebook_dim: tp.Optional[int] = None,
-        decay: float = 0.8,
-        epsilon: float = 1e-5,
-        kmeans_init: bool = False,
-        kmeans_iters: int = 10,
-        threshold_ema_dead_code: int = 2,
-        channels_last: bool = False,
-        commitment_weight: float = 1.,
-        orthogonal_reg_weight: float = 0.0,
-        orthogonal_reg_active_codes_only: bool = False,
-        orthogonal_reg_max_codes: tp.Optional[int] = None,
-    ):
-        super().__init__()
-        _codebook_dim: int = default(codebook_dim, dim)
-
-        requires_projection = _codebook_dim != dim
-        self.project_in = (nn.Linear(dim, _codebook_dim) if requires_projection else nn.Identity())
-        self.project_out = (nn.Linear(_codebook_dim, dim) if requires_projection else nn.Identity())
-
-        self.epsilon = epsilon
-        self.commitment_weight = commitment_weight
-
-        self.orthogonal_reg_weight = orthogonal_reg_weight
-        self.orthogonal_reg_active_codes_only = orthogonal_reg_active_codes_only
-        self.orthogonal_reg_max_codes = orthogonal_reg_max_codes
-
-        self._codebook = EuclideanCodebook(dim=_codebook_dim, codebook_size=codebook_size,
-                                           kmeans_init=kmeans_init, kmeans_iters=kmeans_iters,
-                                           decay=decay, epsilon=epsilon,
-                                           threshold_ema_dead_code=threshold_ema_dead_code)
-        self.codebook_size = codebook_size
-
-        self.channels_last = channels_last
-
-    @property
-    def codebook(self):
-        return self._codebook.embed
-
-    @property
-    def inited(self):
-        return self._codebook.inited
-
-    def _preprocess(self, x):
-        if not self.channels_last:
-            x = rearrange(x, "b d n -> b n d")
-        return x
-
-    def _postprocess(self, quantize):
-        if not self.channels_last:
-            quantize = rearrange(quantize, "b n d -> b d n")
-        return quantize
-
-    def encode(self, x):
-        x = self._preprocess(x)
-        x = self.project_in(x)
-        embed_in = self._codebook.encode(x)
-        return embed_in
-
-    def decode(self, embed_ind):
-        quantize = self._codebook.decode(embed_ind)
-        quantize = self.project_out(quantize)
-        quantize = self._postprocess(quantize)
-        return quantize
-
-    def forward(self, x):
-        device = x.device
-        x = self._preprocess(x)
-
-        x = self.project_in(x)
-        quantize, embed_ind = self._codebook(x)
-
-        if self.training:
-            quantize = x + (quantize - x).detach()
-
-        loss = torch.tensor([0.0], device=device, requires_grad=self.training)
-
-        if self.training:
-            if self.commitment_weight > 0:
-                commit_loss = F.mse_loss(quantize.detach(), x)
-                loss = loss + commit_loss * self.commitment_weight
-
-            if self.orthogonal_reg_weight > 0:
-                codebook = self.codebook
-
-                if self.orthogonal_reg_active_codes_only:
-                    # only calculate orthogonal loss for the activated codes for this batch
-                    unique_code_ids = torch.unique(embed_ind)
-                    codebook = codebook[unique_code_ids]
-
-                num_codes = codebook.shape[0]
-                if exists(self.orthogonal_reg_max_codes) and num_codes > self.orthogonal_reg_max_codes:
-                    rand_ids = torch.randperm(num_codes, device=device)[:self.orthogonal_reg_max_codes]
-                    codebook = codebook[rand_ids]
-
-                orthogonal_reg_loss = orthgonal_loss_fn(codebook)
-                loss = loss + orthogonal_reg_loss * self.orthogonal_reg_weight
-
-        quantize = self.project_out(quantize)
-        quantize = self._postprocess(quantize)
-
-        return quantize, embed_ind, loss
-
-
-class ResidualVectorQuantization(nn.Module):
-    """Residual vector quantization implementation.
-
-    Follows Algorithm 1. in https://arxiv.org/pdf/2107.03312.pdf
-    """
-    def __init__(self, *, num_quantizers, **kwargs):
-        super().__init__()
-        self.layers = nn.ModuleList(
-            [VectorQuantization(**kwargs) for _ in range(num_quantizers)]
-        )
-
-    def forward(self, x, n_q: tp.Optional[int] = None):
-        quantized_out = 0.0
-        residual = x
-
-        all_losses = []
-        all_indices = []
-
-        n_q = n_q or len(self.layers)
-
-        for i, layer in enumerate(self.layers[:n_q]):
-            quantized, indices, loss = layer(residual)
-            residual = residual - quantized
-            quantized_out = quantized_out + quantized
-            all_indices.append(indices)
-            all_losses.append(loss)
-
-        out_losses, out_indices = map(torch.stack, (all_losses, all_indices))
-        return quantized_out, out_indices, out_losses
-
-    def encode(self, x: torch.Tensor, n_q: tp.Optional[int] = None) -> torch.Tensor:
-        residual = x
-        all_indices = []
-        n_q = n_q or len(self.layers)
-        for layer in self.layers[:n_q]:
-            indices = layer.encode(residual)
-            quantized = layer.decode(indices)
-            residual = residual - quantized
-            all_indices.append(indices)
-        out_indices = torch.stack(all_indices)
-        return out_indices
-
-    def decode(self, q_indices: torch.Tensor) -> torch.Tensor:
-        quantized_out = torch.tensor(0.0, device=q_indices.device)
-        for i, indices in enumerate(q_indices):
-            layer = self.layers[i]
-            quantized = layer.decode(indices)
-            quantized_out = quantized_out + quantized
-        return quantized_out
diff --git a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/A To Z Odia Film Download !EXCLUSIVE!.md b/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/A To Z Odia Film Download !EXCLUSIVE!.md
deleted file mode 100644
index 2ff5a3c8e0fc6a39c5458de7d1a465e5fc2fc7bf..0000000000000000000000000000000000000000
--- a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/A To Z Odia Film Download !EXCLUSIVE!.md	
+++ /dev/null
@@ -1,6 +0,0 @@
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diff --git a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Free [VERIFIED] Quickbooks Pro 2013 Validation Code Hit.md b/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Free [VERIFIED] Quickbooks Pro 2013 Validation Code Hit.md
deleted file mode 100644
index 22241127ebcf94b750223e9f497fb51184e08451..0000000000000000000000000000000000000000
--- a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Free [VERIFIED] Quickbooks Pro 2013 Validation Code Hit.md	
+++ /dev/null
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diff --git a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Gta Vice City Downgrade.md b/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Gta Vice City Downgrade.md
deleted file mode 100644
index 42902b215cfa3592ee15f0f9be66a328935a4081..0000000000000000000000000000000000000000
--- a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Gta Vice City Downgrade.md	
+++ /dev/null
@@ -1,12 +0,0 @@
-
-<p>There is no way to downgrade Vice City for PC. The fix is to downgrade San Andreas instead, then install Vice City. Then you install the latest patch. Vice City's patch was just 1.05. It is the same patch in the San Andreas version though.</p>
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diff --git a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Monede Si Bancnote Romanesti George Buzdugan Pdf 11.md b/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Monede Si Bancnote Romanesti George Buzdugan Pdf 11.md
deleted file mode 100644
index 9725fe6ebf20760a88d4d0f4b80c8b1226c27ff9..0000000000000000000000000000000000000000
--- a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Monede Si Bancnote Romanesti George Buzdugan Pdf 11.md	
+++ /dev/null
@@ -1,44 +0,0 @@
-
-<h1>Monede Si Bancnote Romanesti George Buzdugan Pdf 11: A Comprehensive Guide to Romanian Coins and Banknotes</h1>
-
-<p>If you are interested in the history and culture of Romania, you might want to learn more about its coins and banknotes. One of the best sources for this topic is the book "Monede Si Bancnote Romanesti" by George Buzdugan, a renowned numismatist and historian. This book is a free ebook that you can download as a PDF file from Scribd[^1^]. It contains 497 pages of detailed information and images of Romanian coins and banknotes from ancient times to modern days.</p>
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-<h2>What is "Monede Si Bancnote Romanesti"?</h2>
-
-<p>"Monede Si Bancnote Romanesti" (Romanian Coins and Banknotes) is a book written by George Buzdugan, a famous Romanian numismatist and historian. He was born in 1924 and died in 2010. He dedicated his life to the study and collection of Romanian coins and banknotes, as well as other related topics such as medals, orders, decorations, stamps, postcards, etc. He published many books and articles on these subjects, and was awarded several honors and distinctions for his contributions to Romanian culture and science.</p>
-
-<p>The book "Monede Si Bancnote Romanesti" is one of his most comprehensive and authoritative works. It was first published in 1977, in collaboration with Octavian Luchian and Constantin C. Oprescu, two other prominent numismatists. The book covers the history of Romanian money from the earliest times to the present day, with detailed descriptions and illustrations of each coin and banknote issued by various rulers, states, regions, or institutions. The book also includes information on the historical context, the minting techniques, the symbols, the legends, the values, the circulation, the rarity, and the collectors' market of each piece.</p>
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-<p>The book is divided into four main parts:</p>
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-<li>Part I: Ancient Coins (from the 6th century BC to the 3rd century AD)</li>
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-<li>Part III: Modern Coins (from the 19th century to the 20th century)</li>
-<li>Part IV: Banknotes (from the 18th century to the 20th century)</li>
-</ul>
-
-<p>Each part is further subdivided into chapters according to chronological periods or geographical areas. The book also has an introduction, a bibliography, an index, and several appendices.</p>
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deleted file mode 100644
index aa1fe469f03dafe6060988aa6c22629110989958..0000000000000000000000000000000000000000
--- a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/TheHunter  Call Of The Wild - Vurhonga Savanna 32 Bit Crack Extra Quality.md	
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diff --git a/spaces/surya12003/suryabot1/README.md b/spaces/surya12003/suryabot1/README.md
deleted file mode 100644
index b52015a4c0e80473a51e50878b42cb33408b31cf..0000000000000000000000000000000000000000
--- a/spaces/surya12003/suryabot1/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Suryabot1
-emoji: 🐢
-colorFrom: gray
-colorTo: yellow
-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/surya12003/suryabot1/app.py b/spaces/surya12003/suryabot1/app.py
deleted file mode 100644
index 2dbf3ae89c2e3fdab7134107dd346f984dca8eb1..0000000000000000000000000000000000000000
--- a/spaces/surya12003/suryabot1/app.py
+++ /dev/null
@@ -1,34 +0,0 @@
-import os
-import gradio as gr
-from langchain.chat_models import ChatOpenAI
-from langchain import LLMChain, PromptTemplate
-from langchain.memory import ConversationBufferMemory
-
-OPENAI_API_KEY=os.getenv('OPENAI_API_KEY')
-
-template = """Meet Riya, your youthful and witty personal assistant! At 21 years old, she's full of energy and always eager to help. Riya's goal is to assist you with any questions or problems you might have. Her enthusiasm shines through in every response, making interactions with her enjoyable and engaging.
-{chat_history}
-User: {user_message}
-Chatbot:"""
-
-prompt = PromptTemplate(
-    input_variables=["chat_history", "user_message"], template=template
-)
-
-memory = ConversationBufferMemory(memory_key="chat_history")
-
-llm_chain = LLMChain(
-    llm=ChatOpenAI(temperature='0.5', model_name="gpt-3.5-turbo"),
-    prompt=prompt,
-    verbose=True,
-    memory=memory,
-)
-
-def get_text_response(user_message,history):
-    response = llm_chain.predict(user_message = user_message)
-    return response
-
-demo = gr.ChatInterface(get_text_response)
-
-if __name__ == "__main__":
-    demo.launch() #To create a public link, set `share=True` in `launch()`. To enable errors and logs, set `debug=True` in `launch()`.
diff --git a/spaces/svjack/ControlNet-Pose-Chinese/annotator/uniformer/mmseg/models/losses/dice_loss.py b/spaces/svjack/ControlNet-Pose-Chinese/annotator/uniformer/mmseg/models/losses/dice_loss.py
deleted file mode 100644
index 27a77b962d7d8b3079c7d6cd9db52280c6fb4970..0000000000000000000000000000000000000000
--- a/spaces/svjack/ControlNet-Pose-Chinese/annotator/uniformer/mmseg/models/losses/dice_loss.py
+++ /dev/null
@@ -1,119 +0,0 @@
-"""Modified from https://github.com/LikeLy-Journey/SegmenTron/blob/master/
-segmentron/solver/loss.py (Apache-2.0 License)"""
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from ..builder import LOSSES
-from .utils import get_class_weight, weighted_loss
-
-
-@weighted_loss
-def dice_loss(pred,
-              target,
-              valid_mask,
-              smooth=1,
-              exponent=2,
-              class_weight=None,
-              ignore_index=255):
-    assert pred.shape[0] == target.shape[0]
-    total_loss = 0
-    num_classes = pred.shape[1]
-    for i in range(num_classes):
-        if i != ignore_index:
-            dice_loss = binary_dice_loss(
-                pred[:, i],
-                target[..., i],
-                valid_mask=valid_mask,
-                smooth=smooth,
-                exponent=exponent)
-            if class_weight is not None:
-                dice_loss *= class_weight[i]
-            total_loss += dice_loss
-    return total_loss / num_classes
-
-
-@weighted_loss
-def binary_dice_loss(pred, target, valid_mask, smooth=1, exponent=2, **kwards):
-    assert pred.shape[0] == target.shape[0]
-    pred = pred.reshape(pred.shape[0], -1)
-    target = target.reshape(target.shape[0], -1)
-    valid_mask = valid_mask.reshape(valid_mask.shape[0], -1)
-
-    num = torch.sum(torch.mul(pred, target) * valid_mask, dim=1) * 2 + smooth
-    den = torch.sum(pred.pow(exponent) + target.pow(exponent), dim=1) + smooth
-
-    return 1 - num / den
-
-
-@LOSSES.register_module()
-class DiceLoss(nn.Module):
-    """DiceLoss.
-
-    This loss is proposed in `V-Net: Fully Convolutional Neural Networks for
-    Volumetric Medical Image Segmentation <https://arxiv.org/abs/1606.04797>`_.
-
-    Args:
-        loss_type (str, optional): Binary or multi-class loss.
-            Default: 'multi_class'. Options are "binary" and "multi_class".
-        smooth (float): A float number to smooth loss, and avoid NaN error.
-            Default: 1
-        exponent (float): An float number to calculate denominator
-            value: \\sum{x^exponent} + \\sum{y^exponent}. Default: 2.
-        reduction (str, optional): The method used to reduce the loss. Options
-            are "none", "mean" and "sum". This parameter only works when
-            per_image is True. Default: 'mean'.
-        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. Default to 1.0.
-        ignore_index (int | None): The label index to be ignored. Default: 255.
-    """
-
-    def __init__(self,
-                 smooth=1,
-                 exponent=2,
-                 reduction='mean',
-                 class_weight=None,
-                 loss_weight=1.0,
-                 ignore_index=255,
-                 **kwards):
-        super(DiceLoss, self).__init__()
-        self.smooth = smooth
-        self.exponent = exponent
-        self.reduction = reduction
-        self.class_weight = get_class_weight(class_weight)
-        self.loss_weight = loss_weight
-        self.ignore_index = ignore_index
-
-    def forward(self,
-                pred,
-                target,
-                avg_factor=None,
-                reduction_override=None,
-                **kwards):
-        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 = pred.new_tensor(self.class_weight)
-        else:
-            class_weight = None
-
-        pred = F.softmax(pred, dim=1)
-        num_classes = pred.shape[1]
-        one_hot_target = F.one_hot(
-            torch.clamp(target.long(), 0, num_classes - 1),
-            num_classes=num_classes)
-        valid_mask = (target != self.ignore_index).long()
-
-        loss = self.loss_weight * dice_loss(
-            pred,
-            one_hot_target,
-            valid_mask=valid_mask,
-            reduction=reduction,
-            avg_factor=avg_factor,
-            smooth=self.smooth,
-            exponent=self.exponent,
-            class_weight=class_weight,
-            ignore_index=self.ignore_index)
-        return loss
diff --git a/spaces/taesiri/ConvolutionalHoughMatchingNetworks/data/download.py b/spaces/taesiri/ConvolutionalHoughMatchingNetworks/data/download.py
deleted file mode 100644
index 16da96a4ac0a6bfbeb176bcc04f7119db169a0d2..0000000000000000000000000000000000000000
--- a/spaces/taesiri/ConvolutionalHoughMatchingNetworks/data/download.py
+++ /dev/null
@@ -1,91 +0,0 @@
-r""" Functions to download semantic correspondence datasets """
-
-import tarfile
-import os
-
-import requests
-
-from . import pfpascal
-from . import pfwillow
-from . import spair
-
-
-def load_dataset(benchmark, datapath, thres, split='test'):
-    r""" Instantiate a correspondence dataset """
-    correspondence_benchmark = {
-        'spair': spair.SPairDataset,
-        'pfpascal': pfpascal.PFPascalDataset,
-        'pfwillow': pfwillow.PFWillowDataset
-    }
-
-    dataset = correspondence_benchmark.get(benchmark)
-    if dataset is None:
-        raise Exception('Invalid benchmark dataset %s.' % benchmark)
-
-    return dataset(benchmark, datapath, thres, split)
-
-
-def download_from_google(token_id, filename):
-    r""" Download desired filename from Google drive """
-
-    print('Downloading %s ...' % os.path.basename(filename))
-
-    url = 'https://docs.google.com/uc?export=download'
-    destination = filename + '.tar.gz'
-    session = requests.Session()
-
-    response = session.get(url, params={'id': token_id}, stream=True)
-    token = get_confirm_token(response)
-
-    if token:
-        params = {'id': token_id, 'confirm': token}
-        response = session.get(url, params=params, stream=True)
-    save_response_content(response, destination)
-    file = tarfile.open(destination, 'r:gz')
-
-    print("Extracting %s ..." % destination)
-    file.extractall(filename)
-    file.close()
-
-    os.remove(destination)
-    os.rename(filename, filename + '_tmp')
-    os.rename(os.path.join(filename + '_tmp', os.path.basename(filename)), filename)
-    os.rmdir(filename+'_tmp')
-
-
-def get_confirm_token(response):
-    r"""Retrieves confirm token"""
-    for key, value in response.cookies.items():
-        if key.startswith('download_warning'):
-            return value
-
-    return None
-
-
-def save_response_content(response, destination):
-    r"""Saves the response to the destination"""
-    chunk_size = 32768
-
-    with open(destination, "wb") as file:
-        for chunk in response.iter_content(chunk_size):
-            if chunk:
-                file.write(chunk)
-
-
-def download_dataset(datapath, benchmark):
-    r"""Downloads semantic correspondence benchmark dataset from Google drive"""
-    if not os.path.isdir(datapath):
-        os.mkdir(datapath)
-
-    file_data = {        
-        # 'spair': ('1s73NVEFPro260H1tXxCh1ain7oApR8of', 'SPair-71k') old version
-        'spair': ('1KSvB0k2zXA06ojWNvFjBv0Ake426Y76k', 'SPair-71k'),
-        'pfpascal': ('1OOwpGzJnTsFXYh-YffMQ9XKM_Kl_zdzg', 'PF-PASCAL'),
-        'pfwillow': ('1tDP0y8RO5s45L-vqnortRaieiWENQco_', 'PF-WILLOW')
-    }
-
-    file_id, filename = file_data[benchmark]
-    abs_filepath = os.path.join(datapath, filename)
-
-    if not os.path.isdir(abs_filepath):
-        download_from_google(file_id, abs_filepath)
diff --git a/spaces/taquynhnga/CNNs-interpretation-visualization/pages/2_SmoothGrad.py b/spaces/taquynhnga/CNNs-interpretation-visualization/pages/2_SmoothGrad.py
deleted file mode 100644
index 9b82e792c8c51b4047993515ebea73816da5b05b..0000000000000000000000000000000000000000
--- a/spaces/taquynhnga/CNNs-interpretation-visualization/pages/2_SmoothGrad.py
+++ /dev/null
@@ -1,124 +0,0 @@
-import streamlit as st
-import pandas as pd
-import numpy as np
-import random
-from backend.utils import make_grid, load_dataset, load_model, load_images
-
-from backend.smooth_grad import generate_smoothgrad_mask, ShowImage, fig2img
-from transformers import AutoFeatureExtractor, AutoModelForImageClassification
-import torch
-
-from matplotlib.backends.backend_agg import RendererAgg
-_lock = RendererAgg.lock
-
-st.set_page_config(layout='wide')
-BACKGROUND_COLOR = '#bcd0e7'
-
-
-st.title('Feature attribution visualization with SmoothGrad')
-st.write("""> **Which features are responsible for the current prediction of ConvNeXt?**
-
-In machine learning, it is helpful to identify the significant features of the input (e.g., pixels for images) that affect the model's prediction. 
-If the model makes an incorrect prediction, we might want to determine which features contributed to the mistake. 
-To do this, we can generate a feature importance mask, which is a grayscale image with the same size as the original image. 
-The brightness of each pixel in the mask represents the importance of that feature to the model's prediction.
-
-There are various methods to calculate an image sensitivity mask for a specific prediction. 
-One simple way is to use the gradient of a class prediction neuron concerning the input pixels, indicating how the prediction is affected by small pixel changes. 
-However, this method usually produces a noisy mask. 
-To reduce the noise, the SmoothGrad technique as described in [SmoothGrad: Removing noise by adding noise](https://arxiv.org/abs/1706.03825) by Daniel _et al_ is used, 
-which adds Gaussian noise to multiple copies of the image and averages the resulting gradients.
-""")
-
-instruction_text = """Users need to input the model(s), type of image set and image set setting to use this functionality.
-1. Choose model: Users can choose one or more models for comparison. 
-There are 3 models supported: [ConvNeXt](https://huggingface.co/facebook/convnext-tiny-224), 
-[ResNet](https://huggingface.co/microsoft/resnet-50) and [MobileNet](https://pytorch.org/hub/pytorch_vision_mobilenet_v2/). 
-These 3 models have similar number of parameters. 
-\n2. Choose type of Image set: There are 2 types of Image set. They are _User-defined set_ and _Random set_. 
-\n3. Image set setting: If users choose _User-defined set_ in Image set, 
-users need to enter a list of image IDs separated by commas (,). For example, `0,1,4,7` is a valid input.
-Check the page [ImageNet1k](/ImageNet1k) to see all the Image IDs.
-If users choose _Random set_ in Image set, users just need to choose the number of random images to display here.
-"""
-with st.expander("See more instruction", expanded=False):
-    st.write(instruction_text)
-    
-
-imagenet_df = pd.read_csv('./data/ImageNet_metadata.csv')
-
-# --------------------------- LOAD function -----------------------------
-
-
-images = []
-image_ids = []
-# INPUT ------------------------------
-st.header('Input')
-with st.form('smooth_grad_form'):
-    st.markdown('**Model and Input Setting**')
-    selected_models = st.multiselect('Model', options=['ConvNeXt', 'ResNet', 'MobileNet'])
-    selected_image_set = st.selectbox('Image set', ['User-defined set', 'Random set'])
-    
-    summit_button = st.form_submit_button('Set')
-    if summit_button:
-        setting_container = st.container()
-        # for id in image_ids:
-        #     images = load_images(image_ids)
-
-with st.form('2nd_form'):
-    st.markdown('**Image set setting**')
-    if selected_image_set == 'Random set':
-            no_images = st.slider('Number of images', 1, 50, value=10)
-            image_ids = random.sample(list(range(50_000)), k=no_images)
-    else:
-        text = st.text_area('Specific Image IDs', value='0')
-        image_ids = list(map(lambda x: int(x.strip()), text.split(',')))
-
-    run_button = st.form_submit_button('Display output')
-    if run_button:
-        for id in image_ids:
-            images = load_images(image_ids)
-    
-st.header('Output')
-
-models = {}
-feature_extractors = {}
-        
-for i, model_name in enumerate(selected_models):
-    models[model_name], feature_extractors[model_name] = load_model(model_name)
-
-
-# DISPLAY ----------------------------------
-if run_button:
-    header_cols = st.columns([1, 1] + [2]*len(selected_models))
-    header_cols[0].markdown(f'<div style="text-align: center;margin-bottom: 10px;background-color:{BACKGROUND_COLOR};"><b>Image ID</b></div>', unsafe_allow_html=True)
-    header_cols[1].markdown(f'<div style="text-align: center;margin-bottom: 10px;background-color:{BACKGROUND_COLOR};"><b>Original Image</b></div>', unsafe_allow_html=True)
-    for i, model_name in enumerate(selected_models):
-        header_cols[i + 2].markdown(f'<div style="text-align: center;margin-bottom: 10px;background-color:{BACKGROUND_COLOR};"><b>{model_name}</b></div>', unsafe_allow_html=True)
-    
-    grids = make_grid(cols=2+len(selected_models)*2, rows=len(image_ids)+1)
-
-
-@st.cache(allow_output_mutation=True)
-# @st.cache_data
-def generate_images(image_id, model_name):
-    j = image_ids.index(image_id)
-    image = images[j]['image']
-    return generate_smoothgrad_mask(
-        image, model_name,
-        models[model_name], feature_extractors[model_name], num_samples=10)
-
-with _lock:
-    for j, (image_id, image_dict) in enumerate(zip(image_ids, images)):
-        grids[j][0].write(f'{image_id}. {image_dict["label"]}')
-        image = image_dict['image']
-        ori_image = ShowImage(np.asarray(image))
-        grids[j][1].image(ori_image)
-
-        for i, model_name in enumerate(selected_models):
-            # ori_image, heatmap_image, masked_image = generate_smoothgrad_mask(image,
-            # model_name, models[model_name], feature_extractors[model_name], num_samples=10)
-            heatmap_image, masked_image = generate_images(image_id, model_name)
-            # grids[j][1].image(ori_image)
-            grids[j][i*2+2].image(heatmap_image)
-            grids[j][i*2+3].image(masked_image)
\ No newline at end of file
diff --git a/spaces/terfces0erbo/CollegeProjectV2/Gundam Scratch Build Manual 2 Download.md b/spaces/terfces0erbo/CollegeProjectV2/Gundam Scratch Build Manual 2 Download.md
deleted file mode 100644
index 1887ea91c95f5792845ea952111c7f497cefc00b..0000000000000000000000000000000000000000
--- a/spaces/terfces0erbo/CollegeProjectV2/Gundam Scratch Build Manual 2 Download.md	
+++ /dev/null
@@ -1,20 +0,0 @@
-<h2>gundam scratch build manual 2 download</h2><br /><p><b><b>Download</b> &#128505; <a href="https://bytlly.com/2uGlLA">https://bytlly.com/2uGlLA</a></b></p><br /><br />
-
-The manga is a continuation of the original Gundam and begins where the first Gundam ended. The first volume of the manga was published in Japan by Hobby Japan Publishing in December 2013, the manga is licensed in North America by Tokyopop.
-
-The sequel was released in Japan as part of the HJU Gundam Archives in 2017, the sequel begins where the first Gundam ended.
-
-The sequel to the anime series Gundam 00 sequel will be helmed by Gundam 00 animator and series director Hajime Yatate, who is best known for his work on the Gundam 00 OVAs, the sequel was announced during a trailer airing on July 2,2017, at the Gundam × Gojyo-san Gekijō: Shiro Kono Taiketsu no Ō-sama broadcast.
-
-The studio Sunrise decided to continue the project due to the public's high interest in the story and their favorable reception of the anime, despite the short production time. It is slated for release in Japan in 2020.
-
-The manga is set after the events of the anime film. Both the manga and the anime are set in the modern period of 20 years after the events of the second film. In an interview with Anime News Network, Yatate explained the story of the manga as "the conflict between the military and civilians", and that the manga is a re-imagining of the storyline of the anime.
-
-A sequel to the original Mobile Suit Gundam has been announced by Hajime Yatate, director and storywriter of the first Mobile Suit Gundam film. A manga called Gundam 00: A Re-imagining Of the original manga "Gundam" will be released in Japanese on March 1, 2020. The story will be a "re-imagining of the original manga" version of the events between the two films. The story will begin with the "identical principle" of the first film and after the events of the second film, will expand the time-line of the conflict between the military and civilians.
-
-The manga will be serialized in Shogakukan's Monthly Mobile Suit Gundam magazine starting on March 1, 2020.
-
-The sequel to the anime series Gundam 00 will be helmed by Gundam 00 animator and series director Hajime Yatate, who is best known for his work on the Gundam 00 OVAs, the sequel was announced during a trailer airing on July 2,2017, at the Gundam × Gojyo-san Gekij 4fefd39f24<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/thecherub/welovekaban/Dockerfile b/spaces/thecherub/welovekaban/Dockerfile
deleted file mode 100644
index eef259fa372a804549fb0af0913718a13344da34..0000000000000000000000000000000000000000
--- a/spaces/thecherub/welovekaban/Dockerfile
+++ /dev/null
@@ -1,11 +0,0 @@
-FROM node:18-bullseye-slim
-RUN apt-get update && \
-    apt-get install -y git
-RUN git clone https://gitgud.io/khanon/oai-reverse-proxy.git /app
-WORKDIR /app
-RUN npm install
-COPY Dockerfile greeting.md* .env* ./
-RUN npm run build
-EXPOSE 7860
-ENV NODE_ENV=production
-CMD [ "npm", "start" ]
diff --git a/spaces/tialenAdioni/chat-gpt-api/logs/Download Navisworks Freedom Today and Explore 3D Models in NWD Format.md b/spaces/tialenAdioni/chat-gpt-api/logs/Download Navisworks Freedom Today and Explore 3D Models in NWD Format.md
deleted file mode 100644
index 35fdd5c4aaeb51a766a14ad9484033ec8eeb961b..0000000000000000000000000000000000000000
--- a/spaces/tialenAdioni/chat-gpt-api/logs/Download Navisworks Freedom Today and Explore 3D Models in NWD Format.md	
+++ /dev/null
@@ -1,17 +0,0 @@
-
-<h1>Free Download Navisworks Freedom: A 3D Viewer for Navisworks</h1>
-<p>If you are looking for a free and easy way to view 3D models created with Navisworks, you should try Navisworks Freedom. Navisworks Freedom is a free viewer that allows you to open and explore 3D models in NWD format, which is a compressed and secure file format for Navisworks.</p>
-<p>Navisworks is a software that enables you to coordinate, collaborate, and review 3D design projects across different disciplines and platforms. With Navisworks, you can combine and analyze data from multiple sources, such as CAD, BIM, point cloud, and laser scanning. You can also create simulations, animations, and renderings of your 3D models.</p>
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-<h2>How to Download Navisworks Freedom</h2>
-<p>To download Navisworks Freedom, you can visit the Autodesk website  and choose the version that matches your operating system and language. You can also find the system requirements and installation instructions on the same page. The download is free and does not require any registration or login.</p>
-<p>Once you have downloaded and installed Navisworks Freedom, you can open any NWD file by double-clicking on it or by using the File menu in the software. You can then explore the 3D model using various tools and features, such as zooming, panning, rotating, measuring, sectioning, commenting, and more. You can also adjust the display settings, such as lighting, shading, textures, and colors.</p>
-<h2>Benefits of Using Navisworks Freedom</h2>
-<p>Navisworks Freedom is a practical solution for streaming large CAD models without any model preparation, third-party server hosting, setup time, or ongoing costs . It allows you to view 3D models in high quality and accuracy, as well as to interact with them in real time. You can also use Navisworks Freedom to review and comment on 3D models with your team members or clients.</p>
-<p>Navisworks Freedom is compatible with most of the popular CAD formats, such as DWG, DWF, DXF, RVT, IFC, NWC, and more. You can also view point cloud data and laser scans with Navisworks Freedom. However, if you want to edit or modify the 3D models, you will need to use Navisworks Manage or Navisworks Simulate, which are paid versions of the software.</p>
-<p></p>
-<h2>Conclusion</h2>
-<p>Navisworks Freedom is a free download that lets you view 3D models created with Navisworks in NWD format. It is a useful tool for anyone who wants to open and explore 3D models without having to install or pay for Navisworks. You can download Navisworks Freedom from the Autodesk website  and start viewing your 3D models today.</p> ddb901b051<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/tialenAdioni/chat-gpt-api/logs/How I Cracked Auto Keyboard 9.0 and Automated My Keyboard Tasks for Free.md b/spaces/tialenAdioni/chat-gpt-api/logs/How I Cracked Auto Keyboard 9.0 and Automated My Keyboard Tasks for Free.md
deleted file mode 100644
index b959456ee97d8bdb875fe32980aeb2d4bade9819..0000000000000000000000000000000000000000
--- a/spaces/tialenAdioni/chat-gpt-api/logs/How I Cracked Auto Keyboard 9.0 and Automated My Keyboard Tasks for Free.md	
+++ /dev/null
@@ -1,27 +0,0 @@
-<br />
-<h1>Auto Keyboard 9.0 Serial Key: How to Download and Activate for Free</h1>
-<p>Auto Keyboard 9.0 is a software that can simulate keyboard keystrokes and mouse actions automatically and repeatedly. It can save you a lot of time and effort if you have to perform a lot of repetitive tasks on your computer. However, Auto Keyboard 9.0 is not a free software and you need to pay $29.95 for a lifetime license. If you are looking for a way to download and activate Auto Keyboard 9.0 for free, this article will show you how.</p>
-<p>Disclaimer: This article is for educational purposes only. We do not condone piracy or illegal use of software. Please support the developers by purchasing the official version of Auto Keyboard 9.0.</p>
-<h2>auto keyboard 9.0 serial key</h2><br /><p><b><b>Download File</b> &#10145; <a href="https://urlcod.com/2uK3o1">https://urlcod.com/2uK3o1</a></b></p><br /><br />
-<h2>Step 1: Download Auto Keyboard 9.0 Trial Version</h2>
-<p>The first step is to download the trial version of Auto Keyboard 9.0 from its official website. The trial version allows you to use all the features of the software for 15 days, but it will add a watermark to your exported files. You can choose the Windows version according to your operating system.</p>
-<h2>Step 2: Download Auto Keyboard 9.0 Serial Key</h2>
-<p>The next step is to download the serial key for Auto Keyboard 9.0 from a reliable source. There are many websites that claim to provide the serial key, but some of them may contain viruses or malware that can harm your computer. Therefore, you should be careful and scan the file with an antivirus program before opening it. Here are some possible sources for the serial key:</p>
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-</ul>
-<h2>Step 3: Install Auto Keyboard 9.0 and Enter Serial Key</h2>
-<p>The final step is to install Auto Keyboard 9.0 and enter the serial key to activate it. Here are the detailed steps:</p>
-<ol>
-<li>Run the setup file of Auto Keyboard 9.0 and follow the instructions to install it on your computer.</li>
-<li>After installation, run Auto Keyboard 9.0 and click on Register button.</li>
-<li>Enter the registration name and key that you downloaded from one of the sources above.</li>
-<li>Click on OK button and enjoy using Auto Keyboard 9.0 without any watermark or limitation.</li>
-</ol>
-<h2>Conclusion</h2>
-<p>Auto Keyboard 9.0 is a useful software that can help you automate your keyboard and mouse tasks with ease. However, if you don't want to pay for it, you can try to download and activate it for free by following the steps above. However, we recommend that you purchase the official version of Auto Keyboard 9.0 to support the developers and get more updates and technical support.</p>
-<p></p> ddb901b051<br />
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diff --git a/spaces/tialenAdioni/chat-gpt-api/logs/How to Avoid the Risks of Using a Cracked Version of Vegas Pro and Get it Gratis Legally.md b/spaces/tialenAdioni/chat-gpt-api/logs/How to Avoid the Risks of Using a Cracked Version of Vegas Pro and Get it Gratis Legally.md
deleted file mode 100644
index ba10c29bfac028d0e32cbfb812c6f72bbc45024b..0000000000000000000000000000000000000000
--- a/spaces/tialenAdioni/chat-gpt-api/logs/How to Avoid the Risks of Using a Cracked Version of Vegas Pro and Get it Gratis Legally.md	
+++ /dev/null
@@ -1,27 +0,0 @@
-
-<h1>How to Get Vegas Pro Gratis for Video Editing</h1>
-<p>Vegas Pro is a professional video editing software that offers a range of features, such as multicam editing, color grading, audio mixing, motion tracking, and more. However, it is also a pricey software that costs hundreds of dollars for a license. If you are looking for a way to get Vegas Pro gratis, or for free, you might be tempted to download a cracked version from the internet. But beware, this can be risky and illegal.</p>
-<h2>vegas pro gratis</h2><br /><p><b><b>Download</b> &#8250;&#8250;&#8250;&#8250;&#8250; <a href="https://urlcod.com/2uK6x7">https://urlcod.com/2uK6x7</a></b></p><br /><br />
-<p>In this article, we will show you some of the dangers of using a cracked version of Vegas Pro, and some of the alternatives you can use instead.</p>
-<h2>The Dangers of Using a Cracked Version of Vegas Pro</h2>
-<p>A cracked version of Vegas Pro is a modified version that bypasses the activation process and allows you to use the software without paying for it. However, this comes with several drawbacks, such as:</p>
-<ul>
-<li><b>Malware:</b> Cracked versions of Vegas Pro can contain viruses, spyware, ransomware, or other malicious programs that can harm your computer or steal your personal information. You might end up losing your data, compromising your privacy, or even paying a ransom to unlock your files.</li>
-<li><b>Lack of updates:</b> Cracked versions of Vegas Pro do not receive any updates or patches from the official developer. This means that you will miss out on new features, bug fixes, security improvements, and compatibility with new formats and devices. You might also encounter errors, crashes, or glitches while using the software.</li>
-<li><b>Lack of support:</b> Cracked versions of Vegas Pro do not have any technical support or customer service from the official developer. If you encounter any problems or issues while using the software, you will have no one to help you or guide you. You will also have no access to online tutorials, forums, or communities that can offer tips and tricks.</li>
-<li><b>Legal issues:</b> Cracked versions of Vegas Pro are illegal and violate the terms and conditions of the software license. If you are caught using a cracked version of Vegas Pro, you might face legal consequences, such as fines, lawsuits, or even jail time. You might also damage your reputation or credibility as a video editor.</li>
-</ul>
-<p>As you can see, using a cracked version of Vegas Pro is not worth the risk. You might end up paying more than what you saved in the long run.</p>
-<p></p>
-<h2>The Alternatives to Using a Cracked Version of Vegas Pro</h2>
-<p>Fortunately, there are some alternatives to using a cracked version of Vegas Pro that are legal and safe. Here are some of them:</p>
-<ul>
-<li><b>Free trial:</b> The official website of Vegas Pro offers a free trial version that you can download and use for 30 days. This is a great way to test out the software and see if it suits your needs and preferences. You can access all the features and functions of the software without any limitations or watermarks. However, after 30 days, you will need to purchase a license to continue using the software.</li>
-<li><b>Discounts and deals:</b> The official website of Vegas Pro also offers discounts and deals from time to time that can help you save money on buying a license. For example, you can get up to 40% off on selected products during Black Friday or Cyber Monday sales. You can also get discounts if you are a student, teacher, or non-profit organization. You can check the website regularly or subscribe to their newsletter to get notified of any promotions or offers.</li>
-<li><b>Free alternatives:</b> There are also some free video editing software that you can use instead of Vegas Pro. These software may not have all the features and functions of Vegas Pro, but they can still help you create and edit videos with ease. Some of the popular free video editing software are DaVinci Resolve, HitFilm Express, Lightworks, Shotcut, and OpenShot. You can download these software from their official websites and use them without any restrictions or costs.</li>
-</ul>
-<p>These are some of the alternatives to using a cracked version of Vegas Pro that are legal and safe. You can choose the one that best suits your budget and needs.</p>
-<h2>Conclusion</h2>
-<p>Vegas Pro is a professional video editing software that offers a range of features and functions. However,</p> ddb901b051<br />
-<br />
-<br />
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diff --git a/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Dolphin Emulator APK Play GameCube and Wii Games on Android.md b/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Dolphin Emulator APK Play GameCube and Wii Games on Android.md
deleted file mode 100644
index 8eb25477de4a1def57dc592cd9194e7ed162006c..0000000000000000000000000000000000000000
--- a/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Dolphin Emulator APK Play GameCube and Wii Games on Android.md	
+++ /dev/null
@@ -1,102 +0,0 @@
-
-<h1>Dolphin Emulator 5.0-8715 APK: What You Need to Know</h1>
-<p>If you are a fan of Nintendo GameCube and Wii games, you might have heard of Dolphin Emulator. It is a software that allows you to play your favorite games on your Android device. But what is Dolphin Emulator 5.0-8715 APK and how can you get it? In this article, we will answer these questions and more.</p>
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- <h2>What is Dolphin Emulator?</h2>
-<p>Dolphin Emulator is an open-source project that aims to emulate the Nintendo GameCube and Wii consoles on various platforms, such as Windows, Linux, macOS, and Android. It was first released in 2003 and has since become one of the most popular emulators in the gaming community.</p>
- <h3>Features of Dolphin Emulator</h3>
-<p>Some of the features that make Dolphin Emulator stand out are:</p>
-<ul>
-<li>It supports high-definition graphics, up to 1080p, and enhances the original game quality.</li>
-<li>It allows you to customize the controls, using either touch screen, keyboard, mouse, or gamepad.</li>
-<li>It supports online multiplayer, using either Wi-Fi or Bluetooth.</li>
-<li>It has a save state feature, which lets you save and load your game progress at any point.</li>
-<li>It has a cheat code feature, which lets you modify the game parameters to your liking.</li>
-<li>It has a turbo mode feature, which lets you speed up or slow down the game speed.</li>
-</ul>
- <h3>Supported Platforms and Games</h3>
-<p>Dolphin Emulator supports a wide range of platforms, including Windows (7 or higher), Linux (Ubuntu 14.04 or higher), macOS (10.10 or higher), and Android (5.0 or higher). It also supports various architectures, such as x86, x64, ARM, and AArch64.</p>
-<p>Dolphin Emulator can run most of the GameCube and Wii games, such as Super Mario Sunshine, The Legend of Zelda: Twilight Princess, Metroid Prime, Resident Evil 4, Super Smash Bros. Melee, Mario Kart Wii, and many more. However, some games may not work properly or at all, due to compatibility issues or bugs.</p>
- <h2>What is Dolphin Emulator 5.0-8715 APK?</h2>
-<p>Dolphin Emulator 5.0-8715 APK is the latest version of the Dolphin Emulator app for Android devices. It was released on June 21st, 2023, and it contains several improvements and fixes over the previous versions.</p>
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- <h3>What's New in Dolphin Emulator 5.0-8715 APK?</h3>
-<p>Some of the new features and changes in Dolphin Emulator 5.0-8715 APK are:</p>
-<ul>
-<li>It adds support for Vulkan API, which improves the performance and stability of the app.</li>
-<li>It fixes several issues with audio, video, input, and network.</li>
-<li>It improves the compatibility with some games, such as Super Paper Mario, Sonic Colors, Kirby's Return to Dream Land, and others.</li>
-<li>It updates the user interface and adds new options for customization.</li>
-</ul>
- <h3>How to Download and Install Dolphin Emulator 5.0-8715 APK?</h3>
-<p>To download and install Dolphin Emulator 5.0-8715 APK on your Android device, you need to follow these steps:</p>
-<ol>
-<li>Go to the official website of Dolphin Emulator and download the APK file from the download section. Alternatively, you can use this link: [Dolphin Emulator 5.0-8715 APK].</li>
-<li>Enable the installation of apps from unknown sources on your device. To do this, go to Settings > Security > Unknown Sources and toggle it on.</li>
-<li>Locate the downloaded APK 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 finish.</li>
-<li>Launch the Dolphin Emulator app and enjoy playing your favorite games.</li>
-</ol>
- <h2>Pros and Cons of Dolphin Emulator 5.0-8715 APK</h2>
-<p>Dolphin Emulator 5.0-8715 APK has many advantages and disadvantages that you should consider before using it. Here are some of them:</p>
- <h3>Pros of Dolphin Emulator 5.0-8715 APK</h3>
-<ul>
-<li>It is free and open-source, which means you can use it without paying anything or worrying about legal issues.</li>
-<li>It is updated regularly, which means you can enjoy the latest features and fixes.</li>
-<li>It has a large community of users and developers, which means you can get support and feedback easily.</li>
-<li>It has a high compatibility rate, which means you can play most of the GameCube and Wii games on your Android device.</li>
-<li>It has a lot of customization options, which means you can adjust the settings to your preference and device specifications.</li>
-</ul>
- <h3>Cons of Dolphin Emulator 5.0-8715 APK</h3>
-<ul>
-<li>It requires a powerful device, which means you may experience lag, crashes, or glitches if your device is not capable enough.</li>
-<li>It consumes a lot of battery, which means you may need to charge your device frequently or use a power bank.</li>
-<li>It may not work with some games, which means you may encounter errors or bugs that prevent you from playing them.</li>
-<li>It may not support some features, such as motion controls or microphone input, which means you may miss out on some game functions.</li>
-<li>It may violate some terms of service, which means you may risk getting banned or sued by Nintendo or other parties.</li>
-</ul>
- <h2>Conclusion</h2>
-<p>Dolphin Emulator 5.0-8715 APK is a great app for playing Nintendo GameCube and Wii games on your Android device. It has many features, such as high-definition graphics, online multiplayer, save state, cheat code, turbo mode, and more. It also has a high compatibility rate, which means you can play most of the games without any problems. However, it also has some drawbacks, such as requiring a powerful device, consuming a lot of battery, not working with some games or features, and possibly violating some terms of service. Therefore, you should weigh the pros and cons before using it and do so at your own risk.</p>
- <h2>FAQs</h2>
-<p>Here are some frequently asked questions about Dolphin Emulator 5.0-8715 APK:</p>
- <h4>Q: Is Dolphin Emulator 5.0-8715 APK safe to use?</h4>
-<p>A: Dolphin Emulator 5.0-8715 APK is safe to use as long as you download it from the official website or a trusted source. However, you should be careful about the games you download and play, as they may contain viruses or malware that can harm your device or data.</p>
- <h4>Q: How can I get GameCube and Wii games for Dolphin Emulator 5.0-8715 APK?</h4>
-<p>A: You can get GameCube and Wii games for Dolphin Emulator 5.0-8715 APK by either ripping them from your own discs using a PC or a Wii console, or downloading them from the internet. However, you should only download games that you own legally and avoid pirated or illegal copies.</p>
- <h4>Q: How can I transfer my save files from Dolphin Emulator to Dolphin Emulator 5.0-8715 APK?</h4>
-<p>A: You can transfer your save files from Dolphin Emulator to Dolphin Emulator 5.0-8715 APK by copying them from the Dolphin Emu folder on your PC to the dolphin-emu folder on your Android device. You can use a USB cable or a cloud service to do this.</p>
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-<p>A: You can improve the performance of Dolphin Emulator 5.0-8715 APK by adjusting the settings to suit your device specifications and preferences. For example, you can lower the resolution, enable or disable anti-aliasing, change the backend, tweak the CPU and GPU settings, and more. You can also close other apps running in the background, clear the cache, and update your device software.</p>
- <h4>Q: How can I contact the developers of Dolphin Emulator 5.0-8715 APK?</h4>
-<p>A: You can contact the developers of Dolphin Emulator 5.0-8715 APK by visiting their official website, where you can find their email address, social media accounts, forums, blog, and wiki. You can also report bugs, request features, or give feedback through their GitHub page.</p> 197e85843d<br />
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diff --git a/spaces/timqian/like-history/static/js/main.5c83e060.js b/spaces/timqian/like-history/static/js/main.5c83e060.js
deleted file mode 100644
index e9d2ac7567ae39a6fbfdb351253faf744c55e694..0000000000000000000000000000000000000000
--- a/spaces/timqian/like-history/static/js/main.5c83e060.js
+++ /dev/null
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diff --git a/spaces/tioseFevbu/cartoon-converter/scripts/Hindi Movie Sirf Tum Full Video.md b/spaces/tioseFevbu/cartoon-converter/scripts/Hindi Movie Sirf Tum Full Video.md
deleted file mode 100644
index dec325f0c121a424e0d17e562719b0023d3295d3..0000000000000000000000000000000000000000
--- a/spaces/tioseFevbu/cartoon-converter/scripts/Hindi Movie Sirf Tum Full Video.md	
+++ /dev/null
@@ -1,18 +0,0 @@
-<br />
-<h1>How to Watch Sirf Tum, a Romantic Hindi Movie from 1999</h1>
-<p>Sirf Tum is a Hindi romantic movie that was released in 1999. The movie stars Sanjay Kapoor, Priya Gill and Sushmita Sen in the lead roles. The movie is about a long-distance relationship between two strangers who communicate through letters. The movie was a remake of the Tamil film Kadhal Kottai and was a box office success.</p>
-<h2>hindi movie sirf tum full video</h2><br /><p><b><b>Download</b> &raquo;&raquo;&raquo; <a href="https://urlcod.com/2uHxbj">https://urlcod.com/2uHxbj</a></b></p><br /><br />
-<p>If you want to watch Sirf Tum full video, you have a few options. You can either stream it online, download it or buy a DVD. Here are some of the ways you can watch Sirf Tum full video:</p>
-<ul>
-<li><b>Stream it online:</b> You can stream Sirf Tum full video on YouTube for free. The movie is uploaded by Moviestream, a channel that has 26.8K subscribers and 5.1M views for this movie[^1^]. You can also stream Sirf Tum full video on Internet Archive, a website that offers free access to digital content[^2^]. However, the quality of the video may not be very good.</li>
-<li><b>Download it:</b> You can download Sirf Tum full video from various websites that offer Hindi movies for download. However, you should be careful about the legality and safety of these websites. Some of them may contain viruses or malware that can harm your device. You should also respect the copyright of the movie and not distribute it without permission.</li>
-<li><b>Buy a DVD:</b> You can buy a DVD of Sirf Tum from online or offline stores that sell Hindi movies. This way, you can enjoy the movie in high quality and with subtitles. You can also keep the DVD as a collection or gift it to someone who likes romantic movies.</li>
-</ul>
-<p>Sirf Tum is a movie that will touch your heart with its simple and sweet story. It is a movie that celebrates love and hope. If you are looking for a romantic Hindi movie to watch, you should give Sirf Tum a try.</p>
-
-<p>One of the highlights of Sirf Tum is its music. The movie has a melodious and memorable soundtrack composed by Nadeem-Shravan. The songs are sung by popular singers like Kumar Sanu, Alka Yagnik, Udit Narayan and Anuradha Paudwal. The title song "Sirf Tum" is a romantic duet that expresses the longing of the lovers. The song "Pehli Pehli Baar Mohabbat Ki Hai" is a catchy and upbeat song that shows the joy of falling in love. The song "Dilbar Dilbar" is a sensual and seductive song that features Sushmita Sen in a special appearance.</p>
-<p>Another highlight of Sirf Tum is its cast. The movie has a talented and charming cast that brings the characters to life. Sanjay Kapoor plays Deepak, a shy and honest man who works as a postman in Delhi. He falls in love with Aarti, a woman he has never met but only exchanged letters with. Priya Gill plays Aarti, a kind and beautiful woman who lives in Nainital with her aunt. She also falls in love with Deepak through his letters. Sushmita Sen plays Neha, a rich and modern woman who is attracted to Deepak and tries to woo him. She adds a twist to the story and creates some conflict.</p>
-<p>Sirf Tum is a movie that will make you believe in the power of love. It is a movie that will make you smile and cry. It is a movie that will make you appreciate the small things in life. If you are a fan of Hindi movies or romantic movies, you should watch Sirf Tum full video.</p>
-<p></p> e93f5a0c3f<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_vendor/pyparsing/unicode.py b/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_vendor/pyparsing/unicode.py
deleted file mode 100644
index 06526203911de55da3c2a8c5ae73f48024c3f018..0000000000000000000000000000000000000000
--- a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_vendor/pyparsing/unicode.py
+++ /dev/null
@@ -1,352 +0,0 @@
-# unicode.py
-
-import sys
-from itertools import filterfalse
-from typing import List, Tuple, Union
-
-
-class _lazyclassproperty:
-    def __init__(self, fn):
-        self.fn = fn
-        self.__doc__ = fn.__doc__
-        self.__name__ = fn.__name__
-
-    def __get__(self, obj, cls):
-        if cls is None:
-            cls = type(obj)
-        if not hasattr(cls, "_intern") or any(
-            cls._intern is getattr(superclass, "_intern", [])
-            for superclass in cls.__mro__[1:]
-        ):
-            cls._intern = {}
-        attrname = self.fn.__name__
-        if attrname not in cls._intern:
-            cls._intern[attrname] = self.fn(cls)
-        return cls._intern[attrname]
-
-
-UnicodeRangeList = List[Union[Tuple[int, int], Tuple[int]]]
-
-
-class unicode_set:
-    """
-    A set of Unicode characters, for language-specific strings for
-    ``alphas``, ``nums``, ``alphanums``, and ``printables``.
-    A unicode_set is defined by a list of ranges in the Unicode character
-    set, in a class attribute ``_ranges``. Ranges can be specified using
-    2-tuples or a 1-tuple, such as::
-
-        _ranges = [
-            (0x0020, 0x007e),
-            (0x00a0, 0x00ff),
-            (0x0100,),
-            ]
-
-    Ranges are left- and right-inclusive. A 1-tuple of (x,) is treated as (x, x).
-
-    A unicode set can also be defined using multiple inheritance of other unicode sets::
-
-        class CJK(Chinese, Japanese, Korean):
-            pass
-    """
-
-    _ranges: UnicodeRangeList = []
-
-    @_lazyclassproperty
-    def _chars_for_ranges(cls):
-        ret = []
-        for cc in cls.__mro__:
-            if cc is unicode_set:
-                break
-            for rr in getattr(cc, "_ranges", ()):
-                ret.extend(range(rr[0], rr[-1] + 1))
-        return [chr(c) for c in sorted(set(ret))]
-
-    @_lazyclassproperty
-    def printables(cls):
-        "all non-whitespace characters in this range"
-        return "".join(filterfalse(str.isspace, cls._chars_for_ranges))
-
-    @_lazyclassproperty
-    def alphas(cls):
-        "all alphabetic characters in this range"
-        return "".join(filter(str.isalpha, cls._chars_for_ranges))
-
-    @_lazyclassproperty
-    def nums(cls):
-        "all numeric digit characters in this range"
-        return "".join(filter(str.isdigit, cls._chars_for_ranges))
-
-    @_lazyclassproperty
-    def alphanums(cls):
-        "all alphanumeric characters in this range"
-        return cls.alphas + cls.nums
-
-    @_lazyclassproperty
-    def identchars(cls):
-        "all characters in this range that are valid identifier characters, plus underscore '_'"
-        return "".join(
-            sorted(
-                set(
-                    "".join(filter(str.isidentifier, cls._chars_for_ranges))
-                    + "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyzªµº"
-                    + "ÀÁÂÃÄÅÆÇÈÉÊËÌÍÎÏÐÑÒÓÔÕÖØÙÚÛÜÝÞßàáâãäåæçèéêëìíîïðñòóôõöøùúûüýþÿ"
-                    + "_"
-                )
-            )
-        )
-
-    @_lazyclassproperty
-    def identbodychars(cls):
-        """
-        all characters in this range that are valid identifier body characters,
-        plus the digits 0-9
-        """
-        return "".join(
-            sorted(
-                set(
-                    cls.identchars
-                    + "0123456789"
-                    + "".join(
-                        [c for c in cls._chars_for_ranges if ("_" + c).isidentifier()]
-                    )
-                )
-            )
-        )
-
-
-class pyparsing_unicode(unicode_set):
-    """
-    A namespace class for defining common language unicode_sets.
-    """
-
-    # fmt: off
-
-    # define ranges in language character sets
-    _ranges: UnicodeRangeList = [
-        (0x0020, sys.maxunicode),
-    ]
-
-    class BasicMultilingualPlane(unicode_set):
-        "Unicode set for the Basic Multilingual Plane"
-        _ranges: UnicodeRangeList = [
-            (0x0020, 0xFFFF),
-        ]
-
-    class Latin1(unicode_set):
-        "Unicode set for Latin-1 Unicode Character Range"
-        _ranges: UnicodeRangeList = [
-            (0x0020, 0x007E),
-            (0x00A0, 0x00FF),
-        ]
-
-    class LatinA(unicode_set):
-        "Unicode set for Latin-A Unicode Character Range"
-        _ranges: UnicodeRangeList = [
-            (0x0100, 0x017F),
-        ]
-
-    class LatinB(unicode_set):
-        "Unicode set for Latin-B Unicode Character Range"
-        _ranges: UnicodeRangeList = [
-            (0x0180, 0x024F),
-        ]
-
-    class Greek(unicode_set):
-        "Unicode set for Greek Unicode Character Ranges"
-        _ranges: UnicodeRangeList = [
-            (0x0342, 0x0345),
-            (0x0370, 0x0377),
-            (0x037A, 0x037F),
-            (0x0384, 0x038A),
-            (0x038C,),
-            (0x038E, 0x03A1),
-            (0x03A3, 0x03E1),
-            (0x03F0, 0x03FF),
-            (0x1D26, 0x1D2A),
-            (0x1D5E,),
-            (0x1D60,),
-            (0x1D66, 0x1D6A),
-            (0x1F00, 0x1F15),
-            (0x1F18, 0x1F1D),
-            (0x1F20, 0x1F45),
-            (0x1F48, 0x1F4D),
-            (0x1F50, 0x1F57),
-            (0x1F59,),
-            (0x1F5B,),
-            (0x1F5D,),
-            (0x1F5F, 0x1F7D),
-            (0x1F80, 0x1FB4),
-            (0x1FB6, 0x1FC4),
-            (0x1FC6, 0x1FD3),
-            (0x1FD6, 0x1FDB),
-            (0x1FDD, 0x1FEF),
-            (0x1FF2, 0x1FF4),
-            (0x1FF6, 0x1FFE),
-            (0x2129,),
-            (0x2719, 0x271A),
-            (0xAB65,),
-            (0x10140, 0x1018D),
-            (0x101A0,),
-            (0x1D200, 0x1D245),
-            (0x1F7A1, 0x1F7A7),
-        ]
-
-    class Cyrillic(unicode_set):
-        "Unicode set for Cyrillic Unicode Character Range"
-        _ranges: UnicodeRangeList = [
-            (0x0400, 0x052F),
-            (0x1C80, 0x1C88),
-            (0x1D2B,),
-            (0x1D78,),
-            (0x2DE0, 0x2DFF),
-            (0xA640, 0xA672),
-            (0xA674, 0xA69F),
-            (0xFE2E, 0xFE2F),
-        ]
-
-    class Chinese(unicode_set):
-        "Unicode set for Chinese Unicode Character Range"
-        _ranges: UnicodeRangeList = [
-            (0x2E80, 0x2E99),
-            (0x2E9B, 0x2EF3),
-            (0x31C0, 0x31E3),
-            (0x3400, 0x4DB5),
-            (0x4E00, 0x9FEF),
-            (0xA700, 0xA707),
-            (0xF900, 0xFA6D),
-            (0xFA70, 0xFAD9),
-            (0x16FE2, 0x16FE3),
-            (0x1F210, 0x1F212),
-            (0x1F214, 0x1F23B),
-            (0x1F240, 0x1F248),
-            (0x20000, 0x2A6D6),
-            (0x2A700, 0x2B734),
-            (0x2B740, 0x2B81D),
-            (0x2B820, 0x2CEA1),
-            (0x2CEB0, 0x2EBE0),
-            (0x2F800, 0x2FA1D),
-        ]
-
-    class Japanese(unicode_set):
-        "Unicode set for Japanese Unicode Character Range, combining Kanji, Hiragana, and Katakana ranges"
-        _ranges: UnicodeRangeList = []
-
-        class Kanji(unicode_set):
-            "Unicode set for Kanji Unicode Character Range"
-            _ranges: UnicodeRangeList = [
-                (0x4E00, 0x9FBF),
-                (0x3000, 0x303F),
-            ]
-
-        class Hiragana(unicode_set):
-            "Unicode set for Hiragana Unicode Character Range"
-            _ranges: UnicodeRangeList = [
-                (0x3041, 0x3096),
-                (0x3099, 0x30A0),
-                (0x30FC,),
-                (0xFF70,),
-                (0x1B001,),
-                (0x1B150, 0x1B152),
-                (0x1F200,),
-            ]
-
-        class Katakana(unicode_set):
-            "Unicode set for Katakana  Unicode Character Range"
-            _ranges: UnicodeRangeList = [
-                (0x3099, 0x309C),
-                (0x30A0, 0x30FF),
-                (0x31F0, 0x31FF),
-                (0x32D0, 0x32FE),
-                (0xFF65, 0xFF9F),
-                (0x1B000,),
-                (0x1B164, 0x1B167),
-                (0x1F201, 0x1F202),
-                (0x1F213,),
-            ]
-
-    class Hangul(unicode_set):
-        "Unicode set for Hangul (Korean) Unicode Character Range"
-        _ranges: UnicodeRangeList = [
-            (0x1100, 0x11FF),
-            (0x302E, 0x302F),
-            (0x3131, 0x318E),
-            (0x3200, 0x321C),
-            (0x3260, 0x327B),
-            (0x327E,),
-            (0xA960, 0xA97C),
-            (0xAC00, 0xD7A3),
-            (0xD7B0, 0xD7C6),
-            (0xD7CB, 0xD7FB),
-            (0xFFA0, 0xFFBE),
-            (0xFFC2, 0xFFC7),
-            (0xFFCA, 0xFFCF),
-            (0xFFD2, 0xFFD7),
-            (0xFFDA, 0xFFDC),
-        ]
-
-    Korean = Hangul
-
-    class CJK(Chinese, Japanese, Hangul):
-        "Unicode set for combined Chinese, Japanese, and Korean (CJK) Unicode Character Range"
-
-    class Thai(unicode_set):
-        "Unicode set for Thai Unicode Character Range"
-        _ranges: UnicodeRangeList = [
-            (0x0E01, 0x0E3A),
-            (0x0E3F, 0x0E5B)
-        ]
-
-    class Arabic(unicode_set):
-        "Unicode set for Arabic Unicode Character Range"
-        _ranges: UnicodeRangeList = [
-            (0x0600, 0x061B),
-            (0x061E, 0x06FF),
-            (0x0700, 0x077F),
-        ]
-
-    class Hebrew(unicode_set):
-        "Unicode set for Hebrew Unicode Character Range"
-        _ranges: UnicodeRangeList = [
-            (0x0591, 0x05C7),
-            (0x05D0, 0x05EA),
-            (0x05EF, 0x05F4),
-            (0xFB1D, 0xFB36),
-            (0xFB38, 0xFB3C),
-            (0xFB3E,),
-            (0xFB40, 0xFB41),
-            (0xFB43, 0xFB44),
-            (0xFB46, 0xFB4F),
-        ]
-
-    class Devanagari(unicode_set):
-        "Unicode set for Devanagari Unicode Character Range"
-        _ranges: UnicodeRangeList = [
-            (0x0900, 0x097F),
-            (0xA8E0, 0xA8FF)
-        ]
-
-    # fmt: on
-
-
-pyparsing_unicode.Japanese._ranges = (
-    pyparsing_unicode.Japanese.Kanji._ranges
-    + pyparsing_unicode.Japanese.Hiragana._ranges
-    + pyparsing_unicode.Japanese.Katakana._ranges
-)
-
-pyparsing_unicode.BMP = pyparsing_unicode.BasicMultilingualPlane
-
-# add language identifiers using language Unicode
-pyparsing_unicode.العربية = pyparsing_unicode.Arabic
-pyparsing_unicode.中文 = pyparsing_unicode.Chinese
-pyparsing_unicode.кириллица = pyparsing_unicode.Cyrillic
-pyparsing_unicode.Ελληνικά = pyparsing_unicode.Greek
-pyparsing_unicode.עִברִית = pyparsing_unicode.Hebrew
-pyparsing_unicode.日本語 = pyparsing_unicode.Japanese
-pyparsing_unicode.Japanese.漢字 = pyparsing_unicode.Japanese.Kanji
-pyparsing_unicode.Japanese.カタカナ = pyparsing_unicode.Japanese.Katakana
-pyparsing_unicode.Japanese.ひらがな = pyparsing_unicode.Japanese.Hiragana
-pyparsing_unicode.한국어 = pyparsing_unicode.Korean
-pyparsing_unicode.ไทย = pyparsing_unicode.Thai
-pyparsing_unicode.देवनागरी = pyparsing_unicode.Devanagari
diff --git a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pkg_resources/_vendor/pyparsing/common.py b/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pkg_resources/_vendor/pyparsing/common.py
deleted file mode 100644
index 1859fb79cc4e78850b69742fca56698041ce59f8..0000000000000000000000000000000000000000
--- a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pkg_resources/_vendor/pyparsing/common.py
+++ /dev/null
@@ -1,424 +0,0 @@
-# common.py
-from .core import *
-from .helpers import delimited_list, any_open_tag, any_close_tag
-from datetime import datetime
-
-
-# some other useful expressions - using lower-case class name since we are really using this as a namespace
-class pyparsing_common:
-    """Here are some common low-level expressions that may be useful in
-    jump-starting parser development:
-
-    - numeric forms (:class:`integers<integer>`, :class:`reals<real>`,
-      :class:`scientific notation<sci_real>`)
-    - common :class:`programming identifiers<identifier>`
-    - network addresses (:class:`MAC<mac_address>`,
-      :class:`IPv4<ipv4_address>`, :class:`IPv6<ipv6_address>`)
-    - ISO8601 :class:`dates<iso8601_date>` and
-      :class:`datetime<iso8601_datetime>`
-    - :class:`UUID<uuid>`
-    - :class:`comma-separated list<comma_separated_list>`
-    - :class:`url`
-
-    Parse actions:
-
-    - :class:`convertToInteger`
-    - :class:`convertToFloat`
-    - :class:`convertToDate`
-    - :class:`convertToDatetime`
-    - :class:`stripHTMLTags`
-    - :class:`upcaseTokens`
-    - :class:`downcaseTokens`
-
-    Example::
-
-        pyparsing_common.number.runTests('''
-            # any int or real number, returned as the appropriate type
-            100
-            -100
-            +100
-            3.14159
-            6.02e23
-            1e-12
-            ''')
-
-        pyparsing_common.fnumber.runTests('''
-            # any int or real number, returned as float
-            100
-            -100
-            +100
-            3.14159
-            6.02e23
-            1e-12
-            ''')
-
-        pyparsing_common.hex_integer.runTests('''
-            # hex numbers
-            100
-            FF
-            ''')
-
-        pyparsing_common.fraction.runTests('''
-            # fractions
-            1/2
-            -3/4
-            ''')
-
-        pyparsing_common.mixed_integer.runTests('''
-            # mixed fractions
-            1
-            1/2
-            -3/4
-            1-3/4
-            ''')
-
-        import uuid
-        pyparsing_common.uuid.setParseAction(tokenMap(uuid.UUID))
-        pyparsing_common.uuid.runTests('''
-            # uuid
-            12345678-1234-5678-1234-567812345678
-            ''')
-
-    prints::
-
-        # any int or real number, returned as the appropriate type
-        100
-        [100]
-
-        -100
-        [-100]
-
-        +100
-        [100]
-
-        3.14159
-        [3.14159]
-
-        6.02e23
-        [6.02e+23]
-
-        1e-12
-        [1e-12]
-
-        # any int or real number, returned as float
-        100
-        [100.0]
-
-        -100
-        [-100.0]
-
-        +100
-        [100.0]
-
-        3.14159
-        [3.14159]
-
-        6.02e23
-        [6.02e+23]
-
-        1e-12
-        [1e-12]
-
-        # hex numbers
-        100
-        [256]
-
-        FF
-        [255]
-
-        # fractions
-        1/2
-        [0.5]
-
-        -3/4
-        [-0.75]
-
-        # mixed fractions
-        1
-        [1]
-
-        1/2
-        [0.5]
-
-        -3/4
-        [-0.75]
-
-        1-3/4
-        [1.75]
-
-        # uuid
-        12345678-1234-5678-1234-567812345678
-        [UUID('12345678-1234-5678-1234-567812345678')]
-    """
-
-    convert_to_integer = token_map(int)
-    """
-    Parse action for converting parsed integers to Python int
-    """
-
-    convert_to_float = token_map(float)
-    """
-    Parse action for converting parsed numbers to Python float
-    """
-
-    integer = Word(nums).set_name("integer").set_parse_action(convert_to_integer)
-    """expression that parses an unsigned integer, returns an int"""
-
-    hex_integer = (
-        Word(hexnums).set_name("hex integer").set_parse_action(token_map(int, 16))
-    )
-    """expression that parses a hexadecimal integer, returns an int"""
-
-    signed_integer = (
-        Regex(r"[+-]?\d+")
-        .set_name("signed integer")
-        .set_parse_action(convert_to_integer)
-    )
-    """expression that parses an integer with optional leading sign, returns an int"""
-
-    fraction = (
-        signed_integer().set_parse_action(convert_to_float)
-        + "/"
-        + signed_integer().set_parse_action(convert_to_float)
-    ).set_name("fraction")
-    """fractional expression of an integer divided by an integer, returns a float"""
-    fraction.add_parse_action(lambda tt: tt[0] / tt[-1])
-
-    mixed_integer = (
-        fraction | signed_integer + Opt(Opt("-").suppress() + fraction)
-    ).set_name("fraction or mixed integer-fraction")
-    """mixed integer of the form 'integer - fraction', with optional leading integer, returns float"""
-    mixed_integer.add_parse_action(sum)
-
-    real = (
-        Regex(r"[+-]?(?:\d+\.\d*|\.\d+)")
-        .set_name("real number")
-        .set_parse_action(convert_to_float)
-    )
-    """expression that parses a floating point number and returns a float"""
-
-    sci_real = (
-        Regex(r"[+-]?(?:\d+(?:[eE][+-]?\d+)|(?:\d+\.\d*|\.\d+)(?:[eE][+-]?\d+)?)")
-        .set_name("real number with scientific notation")
-        .set_parse_action(convert_to_float)
-    )
-    """expression that parses a floating point number with optional
-    scientific notation and returns a float"""
-
-    # streamlining this expression makes the docs nicer-looking
-    number = (sci_real | real | signed_integer).setName("number").streamline()
-    """any numeric expression, returns the corresponding Python type"""
-
-    fnumber = (
-        Regex(r"[+-]?\d+\.?\d*([eE][+-]?\d+)?")
-        .set_name("fnumber")
-        .set_parse_action(convert_to_float)
-    )
-    """any int or real number, returned as float"""
-
-    identifier = Word(identchars, identbodychars).set_name("identifier")
-    """typical code identifier (leading alpha or '_', followed by 0 or more alphas, nums, or '_')"""
-
-    ipv4_address = Regex(
-        r"(25[0-5]|2[0-4][0-9]|1?[0-9]{1,2})(\.(25[0-5]|2[0-4][0-9]|1?[0-9]{1,2})){3}"
-    ).set_name("IPv4 address")
-    "IPv4 address (``0.0.0.0 - 255.255.255.255``)"
-
-    _ipv6_part = Regex(r"[0-9a-fA-F]{1,4}").set_name("hex_integer")
-    _full_ipv6_address = (_ipv6_part + (":" + _ipv6_part) * 7).set_name(
-        "full IPv6 address"
-    )
-    _short_ipv6_address = (
-        Opt(_ipv6_part + (":" + _ipv6_part) * (0, 6))
-        + "::"
-        + Opt(_ipv6_part + (":" + _ipv6_part) * (0, 6))
-    ).set_name("short IPv6 address")
-    _short_ipv6_address.add_condition(
-        lambda t: sum(1 for tt in t if pyparsing_common._ipv6_part.matches(tt)) < 8
-    )
-    _mixed_ipv6_address = ("::ffff:" + ipv4_address).set_name("mixed IPv6 address")
-    ipv6_address = Combine(
-        (_full_ipv6_address | _mixed_ipv6_address | _short_ipv6_address).set_name(
-            "IPv6 address"
-        )
-    ).set_name("IPv6 address")
-    "IPv6 address (long, short, or mixed form)"
-
-    mac_address = Regex(
-        r"[0-9a-fA-F]{2}([:.-])[0-9a-fA-F]{2}(?:\1[0-9a-fA-F]{2}){4}"
-    ).set_name("MAC address")
-    "MAC address xx:xx:xx:xx:xx (may also have '-' or '.' delimiters)"
-
-    @staticmethod
-    def convert_to_date(fmt: str = "%Y-%m-%d"):
-        """
-        Helper to create a parse action for converting parsed date string to Python datetime.date
-
-        Params -
-        - fmt - format to be passed to datetime.strptime (default= ``"%Y-%m-%d"``)
-
-        Example::
-
-            date_expr = pyparsing_common.iso8601_date.copy()
-            date_expr.setParseAction(pyparsing_common.convertToDate())
-            print(date_expr.parseString("1999-12-31"))
-
-        prints::
-
-            [datetime.date(1999, 12, 31)]
-        """
-
-        def cvt_fn(ss, ll, tt):
-            try:
-                return datetime.strptime(tt[0], fmt).date()
-            except ValueError as ve:
-                raise ParseException(ss, ll, str(ve))
-
-        return cvt_fn
-
-    @staticmethod
-    def convert_to_datetime(fmt: str = "%Y-%m-%dT%H:%M:%S.%f"):
-        """Helper to create a parse action for converting parsed
-        datetime string to Python datetime.datetime
-
-        Params -
-        - fmt - format to be passed to datetime.strptime (default= ``"%Y-%m-%dT%H:%M:%S.%f"``)
-
-        Example::
-
-            dt_expr = pyparsing_common.iso8601_datetime.copy()
-            dt_expr.setParseAction(pyparsing_common.convertToDatetime())
-            print(dt_expr.parseString("1999-12-31T23:59:59.999"))
-
-        prints::
-
-            [datetime.datetime(1999, 12, 31, 23, 59, 59, 999000)]
-        """
-
-        def cvt_fn(s, l, t):
-            try:
-                return datetime.strptime(t[0], fmt)
-            except ValueError as ve:
-                raise ParseException(s, l, str(ve))
-
-        return cvt_fn
-
-    iso8601_date = Regex(
-        r"(?P<year>\d{4})(?:-(?P<month>\d\d)(?:-(?P<day>\d\d))?)?"
-    ).set_name("ISO8601 date")
-    "ISO8601 date (``yyyy-mm-dd``)"
-
-    iso8601_datetime = Regex(
-        r"(?P<year>\d{4})-(?P<month>\d\d)-(?P<day>\d\d)[T ](?P<hour>\d\d):(?P<minute>\d\d)(:(?P<second>\d\d(\.\d*)?)?)?(?P<tz>Z|[+-]\d\d:?\d\d)?"
-    ).set_name("ISO8601 datetime")
-    "ISO8601 datetime (``yyyy-mm-ddThh:mm:ss.s(Z|+-00:00)``) - trailing seconds, milliseconds, and timezone optional; accepts separating ``'T'`` or ``' '``"
-
-    uuid = Regex(r"[0-9a-fA-F]{8}(-[0-9a-fA-F]{4}){3}-[0-9a-fA-F]{12}").set_name("UUID")
-    "UUID (``xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx``)"
-
-    _html_stripper = any_open_tag.suppress() | any_close_tag.suppress()
-
-    @staticmethod
-    def strip_html_tags(s: str, l: int, tokens: ParseResults):
-        """Parse action to remove HTML tags from web page HTML source
-
-        Example::
-
-            # strip HTML links from normal text
-            text = '<td>More info at the <a href="https://github.com/pyparsing/pyparsing/wiki">pyparsing</a> wiki page</td>'
-            td, td_end = makeHTMLTags("TD")
-            table_text = td + SkipTo(td_end).setParseAction(pyparsing_common.stripHTMLTags)("body") + td_end
-            print(table_text.parseString(text).body)
-
-        Prints::
-
-            More info at the pyparsing wiki page
-        """
-        return pyparsing_common._html_stripper.transform_string(tokens[0])
-
-    _commasepitem = (
-        Combine(
-            OneOrMore(
-                ~Literal(",")
-                + ~LineEnd()
-                + Word(printables, exclude_chars=",")
-                + Opt(White(" \t") + ~FollowedBy(LineEnd() | ","))
-            )
-        )
-        .streamline()
-        .set_name("commaItem")
-    )
-    comma_separated_list = delimited_list(
-        Opt(quoted_string.copy() | _commasepitem, default="")
-    ).set_name("comma separated list")
-    """Predefined expression of 1 or more printable words or quoted strings, separated by commas."""
-
-    upcase_tokens = staticmethod(token_map(lambda t: t.upper()))
-    """Parse action to convert tokens to upper case."""
-
-    downcase_tokens = staticmethod(token_map(lambda t: t.lower()))
-    """Parse action to convert tokens to lower case."""
-
-    # fmt: off
-    url = Regex(
-        # https://mathiasbynens.be/demo/url-regex
-        # https://gist.github.com/dperini/729294
-        r"^" +
-        # protocol identifier (optional)
-        # short syntax // still required
-        r"(?:(?:(?P<scheme>https?|ftp):)?\/\/)" +
-        # user:pass BasicAuth (optional)
-        r"(?:(?P<auth>\S+(?::\S*)?)@)?" +
-        r"(?P<host>" +
-        # IP address exclusion
-        # private & local networks
-        r"(?!(?:10|127)(?:\.\d{1,3}){3})" +
-        r"(?!(?:169\.254|192\.168)(?:\.\d{1,3}){2})" +
-        r"(?!172\.(?:1[6-9]|2\d|3[0-1])(?:\.\d{1,3}){2})" +
-        # IP address dotted notation octets
-        # excludes loopback network 0.0.0.0
-        # excludes reserved space >= 224.0.0.0
-        # excludes network & broadcast addresses
-        # (first & last IP address of each class)
-        r"(?:[1-9]\d?|1\d\d|2[01]\d|22[0-3])" +
-        r"(?:\.(?:1?\d{1,2}|2[0-4]\d|25[0-5])){2}" +
-        r"(?:\.(?:[1-9]\d?|1\d\d|2[0-4]\d|25[0-4]))" +
-        r"|" +
-        # host & domain names, may end with dot
-        # can be replaced by a shortest alternative
-        # (?![-_])(?:[-\w\u00a1-\uffff]{0,63}[^-_]\.)+
-        r"(?:" +
-        r"(?:" +
-        r"[a-z0-9\u00a1-\uffff]" +
-        r"[a-z0-9\u00a1-\uffff_-]{0,62}" +
-        r")?" +
-        r"[a-z0-9\u00a1-\uffff]\." +
-        r")+" +
-        # TLD identifier name, may end with dot
-        r"(?:[a-z\u00a1-\uffff]{2,}\.?)" +
-        r")" +
-        # port number (optional)
-        r"(:(?P<port>\d{2,5}))?" +
-        # resource path (optional)
-        r"(?P<path>\/[^?# ]*)?" +
-        # query string (optional)
-        r"(\?(?P<query>[^#]*))?" +
-        # fragment (optional)
-        r"(#(?P<fragment>\S*))?" +
-        r"$"
-    ).set_name("url")
-    # fmt: on
-
-    # pre-PEP8 compatibility names
-    convertToInteger = convert_to_integer
-    convertToFloat = convert_to_float
-    convertToDate = convert_to_date
-    convertToDatetime = convert_to_datetime
-    stripHTMLTags = strip_html_tags
-    upcaseTokens = upcase_tokens
-    downcaseTokens = downcase_tokens
-
-
-_builtin_exprs = [
-    v for v in vars(pyparsing_common).values() if isinstance(v, ParserElement)
-]
diff --git a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/setuptools/_vendor/jaraco/__init__.py b/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/setuptools/_vendor/jaraco/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/tomofi/MMOCR/configs/_base_/schedules/schedule_adadelta_18e.py b/spaces/tomofi/MMOCR/configs/_base_/schedules/schedule_adadelta_18e.py
deleted file mode 100644
index 396e807de057bda1017437ee6cef312bba5dc67c..0000000000000000000000000000000000000000
--- a/spaces/tomofi/MMOCR/configs/_base_/schedules/schedule_adadelta_18e.py
+++ /dev/null
@@ -1,6 +0,0 @@
-# optimizer
-optimizer = dict(type='Adadelta', lr=0.5)
-optimizer_config = dict(grad_clip=dict(max_norm=0.5))
-# learning policy
-lr_config = dict(policy='step', step=[8, 14, 16])
-total_epochs = 18
diff --git a/spaces/tomofi/MMOCR/mmocr/utils/ocr.py b/spaces/tomofi/MMOCR/mmocr/utils/ocr.py
deleted file mode 100644
index d99dbe69a2589b258aeda0da338be7e966d72d0d..0000000000000000000000000000000000000000
--- a/spaces/tomofi/MMOCR/mmocr/utils/ocr.py
+++ /dev/null
@@ -1,720 +0,0 @@
-#!/usr/bin/env python
-# Copyright (c) OpenMMLab. All rights reserved.
-import copy
-import os
-import warnings
-from argparse import ArgumentParser, Namespace
-from pathlib import Path
-
-import mmcv
-import numpy as np
-import torch
-from mmcv.image.misc import tensor2imgs
-from mmcv.runner import load_checkpoint
-from mmcv.utils.config import Config
-
-from mmocr.apis import init_detector
-from mmocr.apis.inference import model_inference
-from mmocr.core.visualize import det_recog_show_result
-from mmocr.datasets.kie_dataset import KIEDataset
-from mmocr.datasets.pipelines.crop import crop_img
-from mmocr.models import build_detector
-from mmocr.utils.box_util import stitch_boxes_into_lines
-from mmocr.utils.fileio import list_from_file
-from mmocr.utils.model import revert_sync_batchnorm
-
-
-# Parse CLI arguments
-def parse_args():
-    parser = ArgumentParser()
-    parser.add_argument(
-        'img', type=str, help='Input image file or folder path.')
-    parser.add_argument(
-        '--output',
-        type=str,
-        default='',
-        help='Output file/folder name for visualization')
-    parser.add_argument(
-        '--det',
-        type=str,
-        default='PANet_IC15',
-        help='Pretrained text detection algorithm')
-    parser.add_argument(
-        '--det-config',
-        type=str,
-        default='',
-        help='Path to the custom config file of the selected det model. It '
-        'overrides the settings in det')
-    parser.add_argument(
-        '--det-ckpt',
-        type=str,
-        default='',
-        help='Path to the custom checkpoint file of the selected det model. '
-        'It overrides the settings in det')
-    parser.add_argument(
-        '--recog',
-        type=str,
-        default='SEG',
-        help='Pretrained text recognition algorithm')
-    parser.add_argument(
-        '--recog-config',
-        type=str,
-        default='',
-        help='Path to the custom config file of the selected recog model. It'
-        'overrides the settings in recog')
-    parser.add_argument(
-        '--recog-ckpt',
-        type=str,
-        default='',
-        help='Path to the custom checkpoint file of the selected recog model. '
-        'It overrides the settings in recog')
-    parser.add_argument(
-        '--kie',
-        type=str,
-        default='',
-        help='Pretrained key information extraction algorithm')
-    parser.add_argument(
-        '--kie-config',
-        type=str,
-        default='',
-        help='Path to the custom config file of the selected kie model. It'
-        'overrides the settings in kie')
-    parser.add_argument(
-        '--kie-ckpt',
-        type=str,
-        default='',
-        help='Path to the custom checkpoint file of the selected kie model. '
-        'It overrides the settings in kie')
-    parser.add_argument(
-        '--config-dir',
-        type=str,
-        default=os.path.join(str(Path.cwd()), 'configs/'),
-        help='Path to the config directory where all the config files '
-        'are located. Defaults to "configs/"')
-    parser.add_argument(
-        '--batch-mode',
-        action='store_true',
-        help='Whether use batch mode for inference')
-    parser.add_argument(
-        '--recog-batch-size',
-        type=int,
-        default=0,
-        help='Batch size for text recognition')
-    parser.add_argument(
-        '--det-batch-size',
-        type=int,
-        default=0,
-        help='Batch size for text detection')
-    parser.add_argument(
-        '--single-batch-size',
-        type=int,
-        default=0,
-        help='Batch size for separate det/recog inference')
-    parser.add_argument(
-        '--device', default=None, help='Device used for inference.')
-    parser.add_argument(
-        '--export',
-        type=str,
-        default='',
-        help='Folder where the results of each image are exported')
-    parser.add_argument(
-        '--export-format',
-        type=str,
-        default='json',
-        help='Format of the exported result file(s)')
-    parser.add_argument(
-        '--details',
-        action='store_true',
-        help='Whether include the text boxes coordinates and confidence values'
-    )
-    parser.add_argument(
-        '--imshow',
-        action='store_true',
-        help='Whether show image with OpenCV.')
-    parser.add_argument(
-        '--print-result',
-        action='store_true',
-        help='Prints the recognised text')
-    parser.add_argument(
-        '--merge', action='store_true', help='Merge neighboring boxes')
-    parser.add_argument(
-        '--merge-xdist',
-        type=float,
-        default=20,
-        help='The maximum x-axis distance to merge boxes')
-    args = parser.parse_args()
-    if args.det == 'None':
-        args.det = None
-    if args.recog == 'None':
-        args.recog = None
-    # Warnings
-    if args.merge and not (args.det and args.recog):
-        warnings.warn(
-            'Box merging will not work if the script is not'
-            ' running in detection + recognition mode.', UserWarning)
-    if not os.path.samefile(args.config_dir, os.path.join(str(
-            Path.cwd()))) and (args.det_config != ''
-                               or args.recog_config != ''):
-        warnings.warn(
-            'config_dir will be overridden by det-config or recog-config.',
-            UserWarning)
-    return args
-
-
-class MMOCR:
-
-    def __init__(self,
-                 det='PANet_IC15',
-                 det_config='',
-                 det_ckpt='',
-                 recog='SEG',
-                 recog_config='',
-                 recog_ckpt='',
-                 kie='',
-                 kie_config='',
-                 kie_ckpt='',
-                 config_dir=os.path.join(str(Path.cwd()), 'configs/'),
-                 device=None,
-                 **kwargs):
-
-        textdet_models = {
-            'DB_r18': {
-                'config':
-                'dbnet/dbnet_r18_fpnc_1200e_icdar2015.py',
-                'ckpt':
-                'dbnet/'
-                'dbnet_r18_fpnc_sbn_1200e_icdar2015_20210329-ba3ab597.pth'
-            },
-            'DB_r50': {
-                'config':
-                'dbnet/dbnet_r50dcnv2_fpnc_1200e_icdar2015.py',
-                'ckpt':
-                'dbnet/'
-                'dbnet_r50dcnv2_fpnc_sbn_1200e_icdar2015_20211025-9fe3b590.pth'
-            },
-            'DRRG': {
-                'config':
-                'drrg/drrg_r50_fpn_unet_1200e_ctw1500.py',
-                'ckpt':
-                'drrg/drrg_r50_fpn_unet_1200e_ctw1500_20211022-fb30b001.pth'
-            },
-            'FCE_IC15': {
-                'config':
-                'fcenet/fcenet_r50_fpn_1500e_icdar2015.py',
-                'ckpt':
-                'fcenet/fcenet_r50_fpn_1500e_icdar2015_20211022-daefb6ed.pth'
-            },
-            'FCE_CTW_DCNv2': {
-                'config':
-                'fcenet/fcenet_r50dcnv2_fpn_1500e_ctw1500.py',
-                'ckpt':
-                'fcenet/' +
-                'fcenet_r50dcnv2_fpn_1500e_ctw1500_20211022-e326d7ec.pth'
-            },
-            'MaskRCNN_CTW': {
-                'config':
-                'maskrcnn/mask_rcnn_r50_fpn_160e_ctw1500.py',
-                'ckpt':
-                'maskrcnn/'
-                'mask_rcnn_r50_fpn_160e_ctw1500_20210219-96497a76.pth'
-            },
-            'MaskRCNN_IC15': {
-                'config':
-                'maskrcnn/mask_rcnn_r50_fpn_160e_icdar2015.py',
-                'ckpt':
-                'maskrcnn/'
-                'mask_rcnn_r50_fpn_160e_icdar2015_20210219-8eb340a3.pth'
-            },
-            'MaskRCNN_IC17': {
-                'config':
-                'maskrcnn/mask_rcnn_r50_fpn_160e_icdar2017.py',
-                'ckpt':
-                'maskrcnn/'
-                'mask_rcnn_r50_fpn_160e_icdar2017_20210218-c6ec3ebb.pth'
-            },
-            'PANet_CTW': {
-                'config':
-                'panet/panet_r18_fpem_ffm_600e_ctw1500.py',
-                'ckpt':
-                'panet/'
-                'panet_r18_fpem_ffm_sbn_600e_ctw1500_20210219-3b3a9aa3.pth'
-            },
-            'PANet_IC15': {
-                'config':
-                'panet/panet_r18_fpem_ffm_600e_icdar2015.py',
-                'ckpt':
-                'panet/'
-                'panet_r18_fpem_ffm_sbn_600e_icdar2015_20210219-42dbe46a.pth'
-            },
-            'PS_CTW': {
-                'config': 'psenet/psenet_r50_fpnf_600e_ctw1500.py',
-                'ckpt':
-                'psenet/psenet_r50_fpnf_600e_ctw1500_20210401-216fed50.pth'
-            },
-            'PS_IC15': {
-                'config':
-                'psenet/psenet_r50_fpnf_600e_icdar2015.py',
-                'ckpt':
-                'psenet/psenet_r50_fpnf_600e_icdar2015_pretrain-eefd8fe6.pth'
-            },
-            'TextSnake': {
-                'config':
-                'textsnake/textsnake_r50_fpn_unet_1200e_ctw1500.py',
-                'ckpt':
-                'textsnake/textsnake_r50_fpn_unet_1200e_ctw1500-27f65b64.pth'
-            }
-        }
-
-        textrecog_models = {
-            'CRNN': {
-                'config': 'crnn/crnn_academic_dataset.py',
-                'ckpt': 'crnn/crnn_academic-a723a1c5.pth'
-            },
-            'SAR': {
-                'config': 'sar/sar_r31_parallel_decoder_academic.py',
-                'ckpt': 'sar/sar_r31_parallel_decoder_academic-dba3a4a3.pth'
-            },
-            'SAR_CN': {
-                'config':
-                'sar/sar_r31_parallel_decoder_chinese.py',
-                'ckpt':
-                'sar/sar_r31_parallel_decoder_chineseocr_20210507-b4be8214.pth'
-            },
-            'NRTR_1/16-1/8': {
-                'config': 'nrtr/nrtr_r31_1by16_1by8_academic.py',
-                'ckpt':
-                'nrtr/nrtr_r31_1by16_1by8_academic_20211124-f60cebf4.pth'
-            },
-            'NRTR_1/8-1/4': {
-                'config': 'nrtr/nrtr_r31_1by8_1by4_academic.py',
-                'ckpt':
-                'nrtr/nrtr_r31_1by8_1by4_academic_20211123-e1fdb322.pth'
-            },
-            'RobustScanner': {
-                'config': 'robust_scanner/robustscanner_r31_academic.py',
-                'ckpt': 'robustscanner/robustscanner_r31_academic-5f05874f.pth'
-            },
-            'SATRN': {
-                'config': 'satrn/satrn_academic.py',
-                'ckpt': 'satrn/satrn_academic_20211009-cb8b1580.pth'
-            },
-            'SATRN_sm': {
-                'config': 'satrn/satrn_small.py',
-                'ckpt': 'satrn/satrn_small_20211009-2cf13355.pth'
-            },
-            'ABINet': {
-                'config': 'abinet/abinet_academic.py',
-                'ckpt': 'abinet/abinet_academic-f718abf6.pth'
-            },
-            'SEG': {
-                'config': 'seg/seg_r31_1by16_fpnocr_academic.py',
-                'ckpt': 'seg/seg_r31_1by16_fpnocr_academic-72235b11.pth'
-            },
-            'CRNN_TPS': {
-                'config': 'tps/crnn_tps_academic_dataset.py',
-                'ckpt': 'tps/crnn_tps_academic_dataset_20210510-d221a905.pth'
-            }
-        }
-
-        kie_models = {
-            'SDMGR': {
-                'config': 'sdmgr/sdmgr_unet16_60e_wildreceipt.py',
-                'ckpt':
-                'sdmgr/sdmgr_unet16_60e_wildreceipt_20210520-7489e6de.pth'
-            }
-        }
-
-        self.td = det
-        self.tr = recog
-        self.kie = kie
-        self.device = device
-        if self.device is None:
-            self.device = torch.device(
-                'cuda' if torch.cuda.is_available() else 'cpu')
-
-        # Check if the det/recog model choice is valid
-        if self.td and self.td not in textdet_models:
-            raise ValueError(self.td,
-                             'is not a supported text detection algorthm')
-        elif self.tr and self.tr not in textrecog_models:
-            raise ValueError(self.tr,
-                             'is not a supported text recognition algorithm')
-        elif self.kie:
-            if self.kie not in kie_models:
-                raise ValueError(
-                    self.kie, 'is not a supported key information extraction'
-                    ' algorithm')
-            elif not (self.td and self.tr):
-                raise NotImplementedError(
-                    self.kie, 'has to run together'
-                    ' with text detection and recognition algorithms.')
-
-        self.detect_model = None
-        if self.td:
-            # Build detection model
-            if not det_config:
-                det_config = os.path.join(config_dir, 'textdet/',
-                                          textdet_models[self.td]['config'])
-            if not det_ckpt:
-                det_ckpt = 'https://download.openmmlab.com/mmocr/textdet/' + \
-                    textdet_models[self.td]['ckpt']
-
-            self.detect_model = init_detector(
-                det_config, det_ckpt, device=self.device)
-            self.detect_model = revert_sync_batchnorm(self.detect_model)
-
-        self.recog_model = None
-        if self.tr:
-            # Build recognition model
-            if not recog_config:
-                recog_config = os.path.join(
-                    config_dir, 'textrecog/',
-                    textrecog_models[self.tr]['config'])
-            if not recog_ckpt:
-                recog_ckpt = 'https://download.openmmlab.com/mmocr/' + \
-                    'textrecog/' + textrecog_models[self.tr]['ckpt']
-
-            self.recog_model = init_detector(
-                recog_config, recog_ckpt, device=self.device)
-            self.recog_model = revert_sync_batchnorm(self.recog_model)
-
-        self.kie_model = None
-        if self.kie:
-            # Build key information extraction model
-            if not kie_config:
-                kie_config = os.path.join(config_dir, 'kie/',
-                                          kie_models[self.kie]['config'])
-            if not kie_ckpt:
-                kie_ckpt = 'https://download.openmmlab.com/mmocr/' + \
-                    'kie/' + kie_models[self.kie]['ckpt']
-
-            kie_cfg = Config.fromfile(kie_config)
-            self.kie_model = build_detector(
-                kie_cfg.model, test_cfg=kie_cfg.get('test_cfg'))
-            self.kie_model = revert_sync_batchnorm(self.kie_model)
-            self.kie_model.cfg = kie_cfg
-            load_checkpoint(self.kie_model, kie_ckpt, map_location=self.device)
-
-        # Attribute check
-        for model in list(filter(None, [self.recog_model, self.detect_model])):
-            if hasattr(model, 'module'):
-                model = model.module
-
-    def readtext(self,
-                 img,
-                 output=None,
-                 details=False,
-                 export=None,
-                 export_format='json',
-                 batch_mode=False,
-                 recog_batch_size=0,
-                 det_batch_size=0,
-                 single_batch_size=0,
-                 imshow=False,
-                 print_result=False,
-                 merge=False,
-                 merge_xdist=20,
-                 **kwargs):
-        args = locals().copy()
-        [args.pop(x, None) for x in ['kwargs', 'self']]
-        args = Namespace(**args)
-
-        # Input and output arguments processing
-        self._args_processing(args)
-        self.args = args
-
-        pp_result = None
-
-        # Send args and models to the MMOCR model inference API
-        # and call post-processing functions for the output
-        if self.detect_model and self.recog_model:
-            det_recog_result = self.det_recog_kie_inference(
-                self.detect_model, self.recog_model, kie_model=self.kie_model)
-            pp_result = self.det_recog_pp(det_recog_result)
-        else:
-            for model in list(
-                    filter(None, [self.recog_model, self.detect_model])):
-                result = self.single_inference(model, args.arrays,
-                                               args.batch_mode,
-                                               args.single_batch_size)
-                pp_result = self.single_pp(result, model)
-
-        return pp_result
-
-    # Post processing function for end2end ocr
-    def det_recog_pp(self, result):
-        final_results = []
-        args = self.args
-        for arr, output, export, det_recog_result in zip(
-                args.arrays, args.output, args.export, result):
-            if output or args.imshow:
-                if self.kie_model:
-                    res_img = det_recog_show_result(arr, det_recog_result)
-                else:
-                    res_img = det_recog_show_result(
-                        arr, det_recog_result, out_file=output)
-                if args.imshow and not self.kie_model:
-                    mmcv.imshow(res_img, 'inference results')
-            if not args.details:
-                simple_res = {}
-                simple_res['filename'] = det_recog_result['filename']
-                simple_res['text'] = [
-                    x['text'] for x in det_recog_result['result']
-                ]
-                final_result = simple_res
-            else:
-                final_result = det_recog_result
-            if export:
-                mmcv.dump(final_result, export, indent=4)
-            if args.print_result:
-                print(final_result, end='\n\n')
-            final_results.append(final_result)
-        return final_results
-
-    # Post processing function for separate det/recog inference
-    def single_pp(self, result, model):
-        for arr, output, export, res in zip(self.args.arrays, self.args.output,
-                                            self.args.export, result):
-            if export:
-                mmcv.dump(res, export, indent=4)
-            if output or self.args.imshow:
-                res_img = model.show_result(arr, res, out_file=output)
-                if self.args.imshow:
-                    mmcv.imshow(res_img, 'inference results')
-            if self.args.print_result:
-                print(res, end='\n\n')
-        return result
-
-    def generate_kie_labels(self, result, boxes, class_list):
-        idx_to_cls = {}
-        if class_list is not None:
-            for line in list_from_file(class_list):
-                class_idx, class_label = line.strip().split()
-                idx_to_cls[class_idx] = class_label
-
-        max_value, max_idx = torch.max(result['nodes'].detach().cpu(), -1)
-        node_pred_label = max_idx.numpy().tolist()
-        node_pred_score = max_value.numpy().tolist()
-        labels = []
-        for i in range(len(boxes)):
-            pred_label = str(node_pred_label[i])
-            if pred_label in idx_to_cls:
-                pred_label = idx_to_cls[pred_label]
-            pred_score = node_pred_score[i]
-            labels.append((pred_label, pred_score))
-        return labels
-
-    def visualize_kie_output(self,
-                             model,
-                             data,
-                             result,
-                             out_file=None,
-                             show=False):
-        """Visualizes KIE output."""
-        img_tensor = data['img'].data
-        img_meta = data['img_metas'].data
-        gt_bboxes = data['gt_bboxes'].data.numpy().tolist()
-        if img_tensor.dtype == torch.uint8:
-            # The img tensor is the raw input not being normalized
-            # (For SDMGR non-visual)
-            img = img_tensor.cpu().numpy().transpose(1, 2, 0)
-        else:
-            img = tensor2imgs(
-                img_tensor.unsqueeze(0), **img_meta.get('img_norm_cfg', {}))[0]
-        h, w, _ = img_meta.get('img_shape', img.shape)
-        img_show = img[:h, :w, :]
-        model.show_result(
-            img_show, result, gt_bboxes, show=show, out_file=out_file)
-
-    # End2end ocr inference pipeline
-    def det_recog_kie_inference(self, det_model, recog_model, kie_model=None):
-        end2end_res = []
-        # Find bounding boxes in the images (text detection)
-        det_result = self.single_inference(det_model, self.args.arrays,
-                                           self.args.batch_mode,
-                                           self.args.det_batch_size)
-        bboxes_list = [res['boundary_result'] for res in det_result]
-
-        if kie_model:
-            kie_dataset = KIEDataset(
-                dict_file=kie_model.cfg.data.test.dict_file)
-
-        # For each bounding box, the image is cropped and
-        # sent to the recognition model either one by one
-        # or all together depending on the batch_mode
-        for filename, arr, bboxes, out_file in zip(self.args.filenames,
-                                                   self.args.arrays,
-                                                   bboxes_list,
-                                                   self.args.output):
-            img_e2e_res = {}
-            img_e2e_res['filename'] = filename
-            img_e2e_res['result'] = []
-            box_imgs = []
-            for bbox in bboxes:
-                box_res = {}
-                box_res['box'] = [round(x) for x in bbox[:-1]]
-                box_res['box_score'] = float(bbox[-1])
-                box = bbox[:8]
-                if len(bbox) > 9:
-                    min_x = min(bbox[0:-1:2])
-                    min_y = min(bbox[1:-1:2])
-                    max_x = max(bbox[0:-1:2])
-                    max_y = max(bbox[1:-1:2])
-                    box = [
-                        min_x, min_y, max_x, min_y, max_x, max_y, min_x, max_y
-                    ]
-                box_img = crop_img(arr, box)
-                if self.args.batch_mode:
-                    box_imgs.append(box_img)
-                else:
-                    recog_result = model_inference(recog_model, box_img)
-                    text = recog_result['text']
-                    text_score = recog_result['score']
-                    if isinstance(text_score, list):
-                        text_score = sum(text_score) / max(1, len(text))
-                    box_res['text'] = text
-                    box_res['text_score'] = text_score
-                img_e2e_res['result'].append(box_res)
-
-            if self.args.batch_mode:
-                recog_results = self.single_inference(
-                    recog_model, box_imgs, True, self.args.recog_batch_size)
-                for i, recog_result in enumerate(recog_results):
-                    text = recog_result['text']
-                    text_score = recog_result['score']
-                    if isinstance(text_score, (list, tuple)):
-                        text_score = sum(text_score) / max(1, len(text))
-                    img_e2e_res['result'][i]['text'] = text
-                    img_e2e_res['result'][i]['text_score'] = text_score
-
-            if self.args.merge:
-                img_e2e_res['result'] = stitch_boxes_into_lines(
-                    img_e2e_res['result'], self.args.merge_xdist, 0.5)
-
-            if kie_model:
-                annotations = copy.deepcopy(img_e2e_res['result'])
-                # Customized for kie_dataset, which
-                # assumes that boxes are represented by only 4 points
-                for i, ann in enumerate(annotations):
-                    min_x = min(ann['box'][::2])
-                    min_y = min(ann['box'][1::2])
-                    max_x = max(ann['box'][::2])
-                    max_y = max(ann['box'][1::2])
-                    annotations[i]['box'] = [
-                        min_x, min_y, max_x, min_y, max_x, max_y, min_x, max_y
-                    ]
-                ann_info = kie_dataset._parse_anno_info(annotations)
-                ann_info['ori_bboxes'] = ann_info.get('ori_bboxes',
-                                                      ann_info['bboxes'])
-                ann_info['gt_bboxes'] = ann_info.get('gt_bboxes',
-                                                     ann_info['bboxes'])
-                kie_result, data = model_inference(
-                    kie_model,
-                    arr,
-                    ann=ann_info,
-                    return_data=True,
-                    batch_mode=self.args.batch_mode)
-                # visualize KIE results
-                self.visualize_kie_output(
-                    kie_model,
-                    data,
-                    kie_result,
-                    out_file=out_file,
-                    show=self.args.imshow)
-                gt_bboxes = data['gt_bboxes'].data.numpy().tolist()
-                labels = self.generate_kie_labels(kie_result, gt_bboxes,
-                                                  kie_model.class_list)
-                for i in range(len(gt_bboxes)):
-                    img_e2e_res['result'][i]['label'] = labels[i][0]
-                    img_e2e_res['result'][i]['label_score'] = labels[i][1]
-
-            end2end_res.append(img_e2e_res)
-        return end2end_res
-
-    # Separate det/recog inference pipeline
-    def single_inference(self, model, arrays, batch_mode, batch_size=0):
-        result = []
-        if batch_mode:
-            if batch_size == 0:
-                result = model_inference(model, arrays, batch_mode=True)
-            else:
-                n = batch_size
-                arr_chunks = [
-                    arrays[i:i + n] for i in range(0, len(arrays), n)
-                ]
-                for chunk in arr_chunks:
-                    result.extend(
-                        model_inference(model, chunk, batch_mode=True))
-        else:
-            for arr in arrays:
-                result.append(model_inference(model, arr, batch_mode=False))
-        return result
-
-    # Arguments pre-processing function
-    def _args_processing(self, args):
-        # Check if the input is a list/tuple that
-        # contains only np arrays or strings
-        if isinstance(args.img, (list, tuple)):
-            img_list = args.img
-            if not all([isinstance(x, (np.ndarray, str)) for x in args.img]):
-                raise AssertionError('Images must be strings or numpy arrays')
-
-        # Create a list of the images
-        if isinstance(args.img, str):
-            img_path = Path(args.img)
-            if img_path.is_dir():
-                img_list = [str(x) for x in img_path.glob('*')]
-            else:
-                img_list = [str(img_path)]
-        elif isinstance(args.img, np.ndarray):
-            img_list = [args.img]
-
-        # Read all image(s) in advance to reduce wasted time
-        # re-reading the images for visualization output
-        args.arrays = [mmcv.imread(x) for x in img_list]
-
-        # Create a list of filenames (used for output images and result files)
-        if isinstance(img_list[0], str):
-            args.filenames = [str(Path(x).stem) for x in img_list]
-        else:
-            args.filenames = [str(x) for x in range(len(img_list))]
-
-        # If given an output argument, create a list of output image filenames
-        num_res = len(img_list)
-        if args.output:
-            output_path = Path(args.output)
-            if output_path.is_dir():
-                args.output = [
-                    str(output_path / f'out_{x}.png') for x in args.filenames
-                ]
-            else:
-                args.output = [str(args.output)]
-                if args.batch_mode:
-                    raise AssertionError('Output of multiple images inference'
-                                         ' must be a directory')
-        else:
-            args.output = [None] * num_res
-
-        # If given an export argument, create a list of
-        # result filenames for each image
-        if args.export:
-            export_path = Path(args.export)
-            args.export = [
-                str(export_path / f'out_{x}.{args.export_format}')
-                for x in args.filenames
-            ]
-        else:
-            args.export = [None] * num_res
-
-        return args
-
-
-# Create an inference pipeline with parsed arguments
-def main():
-    args = parse_args()
-    ocr = MMOCR(**vars(args))
-    ocr.readtext(**vars(args))
-
-
-if __name__ == '__main__':
-    main()
diff --git a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/mmdet/core/export/model_wrappers.py b/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/mmdet/core/export/model_wrappers.py
deleted file mode 100644
index e9988ba4e1be9185066f7fc202262b245958de2a..0000000000000000000000000000000000000000
--- a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/mmdet/core/export/model_wrappers.py
+++ /dev/null
@@ -1,106 +0,0 @@
-import os.path as osp
-import warnings
-
-import numpy as np
-import onnxruntime as ort
-import torch
-
-from mmdet.core import bbox2result
-from mmdet.models import BaseDetector
-
-
-class ONNXRuntimeDetector(BaseDetector):
-    """Wrapper for detector's inference with ONNXRuntime."""
-
-    def __init__(self, onnx_file, class_names, device_id):
-        super(ONNXRuntimeDetector, self).__init__()
-        # get the custom op path
-        ort_custom_op_path = ''
-        try:
-            from mmcv.ops import get_onnxruntime_op_path
-            ort_custom_op_path = get_onnxruntime_op_path()
-        except (ImportError, ModuleNotFoundError):
-            warnings.warn('If input model has custom op from mmcv, \
-                you may have to build mmcv with ONNXRuntime from source.')
-        session_options = ort.SessionOptions()
-        # register custom op for onnxruntime
-        if osp.exists(ort_custom_op_path):
-            session_options.register_custom_ops_library(ort_custom_op_path)
-        sess = ort.InferenceSession(onnx_file, session_options)
-        providers = ['CPUExecutionProvider']
-        options = [{}]
-        is_cuda_available = ort.get_device() == 'GPU'
-        if is_cuda_available:
-            providers.insert(0, 'CUDAExecutionProvider')
-            options.insert(0, {'device_id': device_id})
-
-        sess.set_providers(providers, options)
-
-        self.sess = sess
-        self.CLASSES = class_names
-        self.device_id = device_id
-        self.io_binding = sess.io_binding()
-        self.output_names = [_.name for _ in sess.get_outputs()]
-        self.is_cuda_available = is_cuda_available
-
-    def simple_test(self, img, img_metas, **kwargs):
-        raise NotImplementedError('This method is not implemented.')
-
-    def aug_test(self, imgs, img_metas, **kwargs):
-        raise NotImplementedError('This method is not implemented.')
-
-    def extract_feat(self, imgs):
-        raise NotImplementedError('This method is not implemented.')
-
-    def forward_test(self, imgs, img_metas, **kwargs):
-        input_data = imgs[0]
-        img_metas = img_metas[0]
-        batch_size = input_data.shape[0]
-        # set io binding for inputs/outputs
-        device_type = 'cuda' if self.is_cuda_available else 'cpu'
-        if not self.is_cuda_available:
-            input_data = input_data.cpu()
-        self.io_binding.bind_input(
-            name='input',
-            device_type=device_type,
-            device_id=self.device_id,
-            element_type=np.float32,
-            shape=input_data.shape,
-            buffer_ptr=input_data.data_ptr())
-
-        for name in self.output_names:
-            self.io_binding.bind_output(name)
-        # run session to get outputs
-        self.sess.run_with_iobinding(self.io_binding)
-        ort_outputs = self.io_binding.copy_outputs_to_cpu()
-        batch_dets, batch_labels = ort_outputs[:2]
-        batch_masks = ort_outputs[2] if len(ort_outputs) == 3 else None
-
-        results = []
-        for i in range(batch_size):
-            scale_factor = img_metas[i]['scale_factor']
-            dets, labels = batch_dets[i], batch_labels[i]
-            dets[:, :4] /= scale_factor
-            dets_results = bbox2result(dets, labels, len(self.CLASSES))
-            if batch_masks is not None:
-                masks = batch_masks[i]
-                img_h, img_w = img_metas[i]['img_shape'][:2]
-                ori_h, ori_w = img_metas[i]['ori_shape'][:2]
-                masks = masks[:, :img_h, :img_w]
-                mask_dtype = masks.dtype
-                masks = masks.astype(np.float32)
-                masks = torch.from_numpy(masks)
-                masks = torch.nn.functional.interpolate(
-                    masks.unsqueeze(0), size=(ori_h, ori_w))
-                masks = masks.squeeze(0).detach().numpy()
-                # convert mask to range(0,1)
-                if mask_dtype != np.bool:
-                    masks /= 255
-                masks = masks >= 0.5
-                segms_results = [[] for _ in range(len(self.CLASSES))]
-                for j in range(len(dets)):
-                    segms_results[labels[j]].append(masks[j])
-                results.append((dets_results, segms_results))
-            else:
-                results.append(dets_results)
-        return results
diff --git a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/tools/deployment/pytorch2onnx.py b/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/tools/deployment/pytorch2onnx.py
deleted file mode 100644
index 343402b517d0d9f4af25d608e6399b122de44e95..0000000000000000000000000000000000000000
--- a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/tools/deployment/pytorch2onnx.py
+++ /dev/null
@@ -1,275 +0,0 @@
-import argparse
-import os.path as osp
-import warnings
-
-import numpy as np
-import onnx
-import onnxruntime as rt
-import torch
-from mmcv import DictAction
-
-from mmdet.core.export import (build_model_from_cfg,
-                               generate_inputs_and_wrap_model,
-                               preprocess_example_input)
-
-
-def pytorch2onnx(config_path,
-                 checkpoint_path,
-                 input_img,
-                 input_shape,
-                 opset_version=11,
-                 show=False,
-                 output_file='tmp.onnx',
-                 verify=False,
-                 normalize_cfg=None,
-                 dataset='coco',
-                 test_img=None,
-                 do_simplify=False,
-                 cfg_options=None,
-                 dynamic_export=None):
-
-    input_config = {
-        'input_shape': input_shape,
-        'input_path': input_img,
-        'normalize_cfg': normalize_cfg
-    }
-
-    # prepare original model and meta for verifying the onnx model
-    orig_model = build_model_from_cfg(
-        config_path, checkpoint_path, cfg_options=cfg_options)
-    one_img, one_meta = preprocess_example_input(input_config)
-    model, tensor_data = generate_inputs_and_wrap_model(
-        config_path, checkpoint_path, input_config, cfg_options=cfg_options)
-    output_names = ['dets', 'labels']
-    if model.with_mask:
-        output_names.append('masks')
-    dynamic_axes = None
-    if dynamic_export:
-        dynamic_axes = {
-            'input': {
-                0: 'batch',
-                2: 'width',
-                3: 'height'
-            },
-            'dets': {
-                0: 'batch',
-                1: 'num_dets',
-            },
-            'labels': {
-                0: 'batch',
-                1: 'num_dets',
-            },
-        }
-        if model.with_mask:
-            dynamic_axes['masks'] = {0: 'batch', 1: 'num_dets'}
-
-    torch.onnx.export(
-        model,
-        tensor_data,
-        output_file,
-        input_names=['input'],
-        output_names=output_names,
-        export_params=True,
-        keep_initializers_as_inputs=True,
-        do_constant_folding=True,
-        verbose=show,
-        opset_version=opset_version,
-        dynamic_axes=dynamic_axes)
-
-    model.forward = orig_model.forward
-
-    # get the custom op path
-    ort_custom_op_path = ''
-    try:
-        from mmcv.ops import get_onnxruntime_op_path
-        ort_custom_op_path = get_onnxruntime_op_path()
-    except (ImportError, ModuleNotFoundError):
-        warnings.warn('If input model has custom op from mmcv, \
-            you may have to build mmcv with ONNXRuntime from source.')
-
-    if do_simplify:
-        from mmdet import digit_version
-        import onnxsim
-
-        min_required_version = '0.3.0'
-        assert digit_version(onnxsim.__version__) >= digit_version(
-            min_required_version
-        ), f'Requires to install onnx-simplify>={min_required_version}'
-
-        input_dic = {'input': one_img.detach().cpu().numpy()}
-        onnxsim.simplify(
-            output_file, input_data=input_dic, custom_lib=ort_custom_op_path)
-    print(f'Successfully exported ONNX model: {output_file}')
-
-    if verify:
-        from mmdet.core import get_classes, bbox2result
-        from mmdet.apis import show_result_pyplot
-
-        model.CLASSES = get_classes(dataset)
-        num_classes = len(model.CLASSES)
-        # check by onnx
-        onnx_model = onnx.load(output_file)
-        onnx.checker.check_model(onnx_model)
-        if dynamic_export:
-            # scale up to test dynamic shape
-            h, w = [int((_ * 1.5) // 32 * 32) for _ in input_shape[2:]]
-            input_config['input_shape'] = (1, 3, h, w)
-        if test_img is not None:
-            input_config['input_path'] = test_img
-        one_img, one_meta = preprocess_example_input(input_config)
-        tensor_data = [one_img]
-
-        # get pytorch output
-        pytorch_results = model(tensor_data, [[one_meta]], return_loss=False)
-        pytorch_results = pytorch_results[0]
-        # get onnx output
-        input_all = [node.name for node in onnx_model.graph.input]
-        input_initializer = [
-            node.name for node in onnx_model.graph.initializer
-        ]
-        net_feed_input = list(set(input_all) - set(input_initializer))
-        assert (len(net_feed_input) == 1)
-        session_options = rt.SessionOptions()
-        # register custom op for ONNX Runtime
-        if osp.exists(ort_custom_op_path):
-            session_options.register_custom_ops_library(ort_custom_op_path)
-        feed_input_img = one_img.detach().numpy()
-        if dynamic_export:
-            # test batch with two input images
-            feed_input_img = np.vstack([feed_input_img, feed_input_img])
-        sess = rt.InferenceSession(output_file, session_options)
-        onnx_outputs = sess.run(None, {net_feed_input[0]: feed_input_img})
-        output_names = [_.name for _ in sess.get_outputs()]
-        output_shapes = [_.shape for _ in onnx_outputs]
-        print(f'ONNX Runtime output names: {output_names}, \
-            output shapes: {output_shapes}')
-        # get last image's outputs
-        onnx_outputs = [_[-1] for _ in onnx_outputs]
-        ort_dets, ort_labels = onnx_outputs[:2]
-        onnx_results = bbox2result(ort_dets, ort_labels, num_classes)
-        if model.with_mask:
-            segm_results = onnx_outputs[2]
-            cls_segms = [[] for _ in range(num_classes)]
-            for i in range(ort_dets.shape[0]):
-                cls_segms[ort_labels[i]].append(segm_results[i])
-            onnx_results = (onnx_results, cls_segms)
-        # visualize predictions
-        if show:
-            show_result_pyplot(
-                model, one_meta['show_img'], pytorch_results, title='Pytorch')
-            show_result_pyplot(
-                model, one_meta['show_img'], onnx_results, title='ONNXRuntime')
-
-        # compare a part of result
-        if model.with_mask:
-            compare_pairs = list(zip(onnx_results, pytorch_results))
-        else:
-            compare_pairs = [(onnx_results, pytorch_results)]
-        err_msg = 'The numerical values are different between Pytorch' + \
-                  ' and ONNX, but it does not necessarily mean the' + \
-                  ' exported ONNX model is problematic.'
-        # check the numerical value
-        for onnx_res, pytorch_res in compare_pairs:
-            for o_res, p_res in zip(onnx_res, pytorch_res):
-                np.testing.assert_allclose(
-                    o_res, p_res, rtol=1e-03, atol=1e-05, err_msg=err_msg)
-        print('The numerical values are the same between Pytorch and ONNX')
-
-
-def parse_args():
-    parser = argparse.ArgumentParser(
-        description='Convert MMDetection models to ONNX')
-    parser.add_argument('config', help='test config file path')
-    parser.add_argument('checkpoint', help='checkpoint file')
-    parser.add_argument('--input-img', type=str, help='Images for input')
-    parser.add_argument(
-        '--show',
-        action='store_true',
-        help='Show onnx graph and detection outputs')
-    parser.add_argument('--output-file', type=str, default='tmp.onnx')
-    parser.add_argument('--opset-version', type=int, default=11)
-    parser.add_argument(
-        '--test-img', type=str, default=None, help='Images for test')
-    parser.add_argument(
-        '--dataset', type=str, default='coco', help='Dataset name')
-    parser.add_argument(
-        '--verify',
-        action='store_true',
-        help='verify the onnx model output against pytorch output')
-    parser.add_argument(
-        '--simplify',
-        action='store_true',
-        help='Whether to simplify onnx model.')
-    parser.add_argument(
-        '--shape',
-        type=int,
-        nargs='+',
-        default=[800, 1216],
-        help='input image size')
-    parser.add_argument(
-        '--mean',
-        type=float,
-        nargs='+',
-        default=[123.675, 116.28, 103.53],
-        help='mean value used for preprocess input data')
-    parser.add_argument(
-        '--std',
-        type=float,
-        nargs='+',
-        default=[58.395, 57.12, 57.375],
-        help='variance value used for preprocess input data')
-    parser.add_argument(
-        '--cfg-options',
-        nargs='+',
-        action=DictAction,
-        help='Override some settings in the used config, the key-value pair '
-        'in xxx=yyy format will be merged into config file. If the value to '
-        'be overwritten is a list, it should be like key="[a,b]" or key=a,b '
-        'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" '
-        'Note that the quotation marks are necessary and that no white space '
-        'is allowed.')
-    parser.add_argument(
-        '--dynamic-export',
-        action='store_true',
-        help='Whether to export onnx with dynamic axis.')
-    args = parser.parse_args()
-    return args
-
-
-if __name__ == '__main__':
-    args = parse_args()
-
-    assert args.opset_version == 11, 'MMDet only support opset 11 now'
-
-    if not args.input_img:
-        args.input_img = osp.join(
-            osp.dirname(__file__), '../../tests/data/color.jpg')
-
-    if len(args.shape) == 1:
-        input_shape = (1, 3, args.shape[0], args.shape[0])
-    elif len(args.shape) == 2:
-        input_shape = (1, 3) + tuple(args.shape)
-    else:
-        raise ValueError('invalid input shape')
-
-    assert len(args.mean) == 3
-    assert len(args.std) == 3
-
-    normalize_cfg = {'mean': args.mean, 'std': args.std}
-
-    # convert model to onnx file
-    pytorch2onnx(
-        args.config,
-        args.checkpoint,
-        args.input_img,
-        input_shape,
-        opset_version=args.opset_version,
-        show=args.show,
-        output_file=args.output_file,
-        verify=args.verify,
-        normalize_cfg=normalize_cfg,
-        dataset=args.dataset,
-        test_img=args.test_img,
-        do_simplify=args.simplify,
-        cfg_options=args.cfg_options,
-        dynamic_export=args.dynamic_export)
diff --git a/spaces/trysem/image-matting-app/ppmatting/datasets/matting_dataset.py b/spaces/trysem/image-matting-app/ppmatting/datasets/matting_dataset.py
deleted file mode 100644
index d782d6c35acbb583f8fbdc61685e222ff0437996..0000000000000000000000000000000000000000
--- a/spaces/trysem/image-matting-app/ppmatting/datasets/matting_dataset.py
+++ /dev/null
@@ -1,251 +0,0 @@
-# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#    http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import os
-import math
-
-import cv2
-import numpy as np
-import random
-import paddle
-from paddleseg.cvlibs import manager
-
-import ppmatting.transforms as T
-
-
-@manager.DATASETS.add_component
-class MattingDataset(paddle.io.Dataset):
-    """
-    Pass in a dataset that conforms to the format.
-        matting_dataset/
-        |--bg/
-        |
-        |--train/
-        |  |--fg/
-        |  |--alpha/
-        |
-        |--val/
-        |  |--fg/
-        |  |--alpha/
-        |  |--trimap/ (if existing)
-        |
-        |--train.txt
-        |
-        |--val.txt
-    See README.md for more information of dataset.
-
-    Args:
-        dataset_root(str): The root path of dataset.
-        transforms(list):  Transforms for image.
-        mode (str, optional): which part of dataset to use. it is one of ('train', 'val', 'trainval'). Default: 'train'.
-        train_file (str|list, optional): File list is used to train. It should be `foreground_image.png background_image.png`
-            or `foreground_image.png`. It shold be provided if mode equal to 'train'. Default: None.
-        val_file (str|list, optional): File list is used to evaluation. It should be `foreground_image.png background_image.png`
-            or `foreground_image.png` or ``foreground_image.png background_image.png trimap_image.png`.
-            It shold be provided if mode equal to 'val'. Default: None.
-        get_trimap (bool, optional): Whether to get triamp. Default: True.
-        separator (str, optional): The separator of train_file or val_file. If file name contains ' ', '|' may be perfect. Default: ' '.
-        key_del (tuple|list, optional): The key which is not need will be delete to accellect data reader. Default: None.
-        if_rssn (bool, optional): Whether to use RSSN while Compositing image. Including denoise and blur. Default: False.
-    """
-
-    def __init__(self,
-                 dataset_root,
-                 transforms,
-                 mode='train',
-                 train_file=None,
-                 val_file=None,
-                 get_trimap=True,
-                 separator=' ',
-                 key_del=None,
-                 if_rssn=False):
-        super().__init__()
-        self.dataset_root = dataset_root
-        self.transforms = T.Compose(transforms)
-        self.mode = mode
-        self.get_trimap = get_trimap
-        self.separator = separator
-        self.key_del = key_del
-        self.if_rssn = if_rssn
-
-        # check file
-        if mode == 'train' or mode == 'trainval':
-            if train_file is None:
-                raise ValueError(
-                    "When `mode` is 'train' or 'trainval', `train_file must be provided!"
-                )
-            if isinstance(train_file, str):
-                train_file = [train_file]
-            file_list = train_file
-
-        if mode == 'val' or mode == 'trainval':
-            if val_file is None:
-                raise ValueError(
-                    "When `mode` is 'val' or 'trainval', `val_file must be provided!"
-                )
-            if isinstance(val_file, str):
-                val_file = [val_file]
-            file_list = val_file
-
-        if mode == 'trainval':
-            file_list = train_file + val_file
-
-        # read file
-        self.fg_bg_list = []
-        for file in file_list:
-            file = os.path.join(dataset_root, file)
-            with open(file, 'r') as f:
-                lines = f.readlines()
-                for line in lines:
-                    line = line.strip()
-                    self.fg_bg_list.append(line)
-        if mode != 'val':
-            random.shuffle(self.fg_bg_list)
-
-    def __getitem__(self, idx):
-        data = {}
-        fg_bg_file = self.fg_bg_list[idx]
-        fg_bg_file = fg_bg_file.split(self.separator)
-        data['img_name'] = fg_bg_file[0]  # using in save prediction results
-        fg_file = os.path.join(self.dataset_root, fg_bg_file[0])
-        alpha_file = fg_file.replace('/fg', '/alpha')
-        fg = cv2.imread(fg_file)
-        alpha = cv2.imread(alpha_file, 0)
-        data['alpha'] = alpha
-        data['gt_fields'] = []
-
-        # line is: fg [bg] [trimap]
-        if len(fg_bg_file) >= 2:
-            bg_file = os.path.join(self.dataset_root, fg_bg_file[1])
-            bg = cv2.imread(bg_file)
-            data['img'], data['fg'], data['bg'] = self.composite(fg, alpha, bg)
-            if self.mode in ['train', 'trainval']:
-                data['gt_fields'].append('fg')
-                data['gt_fields'].append('bg')
-                data['gt_fields'].append('alpha')
-            if len(fg_bg_file) == 3 and self.get_trimap:
-                if self.mode == 'val':
-                    trimap_path = os.path.join(self.dataset_root, fg_bg_file[2])
-                    if os.path.exists(trimap_path):
-                        data['trimap'] = trimap_path
-                        data['gt_fields'].append('trimap')
-                        data['ori_trimap'] = cv2.imread(trimap_path, 0)
-                    else:
-                        raise FileNotFoundError(
-                            'trimap is not Found: {}'.format(fg_bg_file[2]))
-        else:
-            data['img'] = fg
-            if self.mode in ['train', 'trainval']:
-                data['fg'] = fg.copy()
-                data['bg'] = fg.copy()
-                data['gt_fields'].append('fg')
-                data['gt_fields'].append('bg')
-                data['gt_fields'].append('alpha')
-
-        data['trans_info'] = []  # Record shape change information
-
-        # Generate trimap from alpha if no trimap file provided
-        if self.get_trimap:
-            if 'trimap' not in data:
-                data['trimap'] = self.gen_trimap(
-                    data['alpha'], mode=self.mode).astype('float32')
-                data['gt_fields'].append('trimap')
-                if self.mode == 'val':
-                    data['ori_trimap'] = data['trimap'].copy()
-
-        # Delete key which is not need
-        if self.key_del is not None:
-            for key in self.key_del:
-                if key in data.keys():
-                    data.pop(key)
-                if key in data['gt_fields']:
-                    data['gt_fields'].remove(key)
-        data = self.transforms(data)
-
-        # When evaluation, gt should not be transforms.
-        if self.mode == 'val':
-            data['gt_fields'].append('alpha')
-
-        data['img'] = data['img'].astype('float32')
-        for key in data.get('gt_fields', []):
-            data[key] = data[key].astype('float32')
-
-        if 'trimap' in data:
-            data['trimap'] = data['trimap'][np.newaxis, :, :]
-        if 'ori_trimap' in data:
-            data['ori_trimap'] = data['ori_trimap'][np.newaxis, :, :]
-
-        data['alpha'] = data['alpha'][np.newaxis, :, :] / 255.
-
-        return data
-
-    def __len__(self):
-        return len(self.fg_bg_list)
-
-    def composite(self, fg, alpha, ori_bg):
-        if self.if_rssn:
-            if np.random.rand() < 0.5:
-                fg = cv2.fastNlMeansDenoisingColored(fg, None, 3, 3, 7, 21)
-                ori_bg = cv2.fastNlMeansDenoisingColored(ori_bg, None, 3, 3, 7,
-                                                         21)
-            if np.random.rand() < 0.5:
-                radius = np.random.choice([19, 29, 39, 49, 59])
-                ori_bg = cv2.GaussianBlur(ori_bg, (radius, radius), 0, 0)
-        fg_h, fg_w = fg.shape[:2]
-        ori_bg_h, ori_bg_w = ori_bg.shape[:2]
-
-        wratio = fg_w / ori_bg_w
-        hratio = fg_h / ori_bg_h
-        ratio = wratio if wratio > hratio else hratio
-
-        # Resize ori_bg if it is smaller than fg.
-        if ratio > 1:
-            resize_h = math.ceil(ori_bg_h * ratio)
-            resize_w = math.ceil(ori_bg_w * ratio)
-            bg = cv2.resize(
-                ori_bg, (resize_w, resize_h), interpolation=cv2.INTER_LINEAR)
-        else:
-            bg = ori_bg
-
-        bg = bg[0:fg_h, 0:fg_w, :]
-        alpha = alpha / 255
-        alpha = np.expand_dims(alpha, axis=2)
-        image = alpha * fg + (1 - alpha) * bg
-        image = image.astype(np.uint8)
-        return image, fg, bg
-
-    @staticmethod
-    def gen_trimap(alpha, mode='train', eval_kernel=7):
-        if mode == 'train':
-            k_size = random.choice(range(2, 5))
-            iterations = np.random.randint(5, 15)
-            kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,
-                                               (k_size, k_size))
-            dilated = cv2.dilate(alpha, kernel, iterations=iterations)
-            eroded = cv2.erode(alpha, kernel, iterations=iterations)
-            trimap = np.zeros(alpha.shape)
-            trimap.fill(128)
-            trimap[eroded > 254.5] = 255
-            trimap[dilated < 0.5] = 0
-        else:
-            k_size = eval_kernel
-            kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,
-                                               (k_size, k_size))
-            dilated = cv2.dilate(alpha, kernel)
-            trimap = np.zeros(alpha.shape)
-            trimap.fill(128)
-            trimap[alpha >= 250] = 255
-            trimap[dilated <= 5] = 0
-
-        return trimap
diff --git a/spaces/ucalyptus/PTI/configs/global_config.py b/spaces/ucalyptus/PTI/configs/global_config.py
deleted file mode 100644
index f95c51c56d8179f45095fd6e47554bbfaaf2ee55..0000000000000000000000000000000000000000
--- a/spaces/ucalyptus/PTI/configs/global_config.py
+++ /dev/null
@@ -1,12 +0,0 @@
-## Device
-cuda_visible_devices = '0'
-device = 'cuda:0'
-
-## Logs
-training_step = 1
-image_rec_result_log_snapshot = 100
-pivotal_training_steps = 0
-model_snapshot_interval = 400
-
-## Run name to be updated during PTI
-run_name = ''
diff --git a/spaces/umichVision/virtex-redcaps/virtex/utils/timer.py b/spaces/umichVision/virtex-redcaps/virtex/utils/timer.py
deleted file mode 100644
index 02f7992b6e382e57c5ce3f85f4d08df53e3ebe1a..0000000000000000000000000000000000000000
--- a/spaces/umichVision/virtex-redcaps/virtex/utils/timer.py
+++ /dev/null
@@ -1,69 +0,0 @@
-import time
-from typing import Optional
-
-
-class Timer(object):
-    r"""
-    A simple timer to record time per iteration and ETA of training. ETA is
-    estimated by moving window average with fixed window size.
-
-    Parameters
-    ----------
-    start_from: int, optional (default = 1)
-        Iteration from which counting should be started/resumed.
-    total_iterations: int, optional (default = None)
-        Total number of iterations. ETA will not be tracked (will remain "N/A")
-        if this is not provided.
-    window_size: int, optional (default = 20)
-        Window size for calculating ETA based on average of past few iterations.
-    """
-
-    def __init__(
-        self,
-        start_from: int = 1,
-        total_iterations: Optional[int] = None,
-        window_size: int = 20,
-    ):
-        # We decrement by 1 because `current_iter` changes increment during
-        # an iteration (for example, will change from 0 -> 1 on iteration 1).
-        self.current_iter = start_from - 1
-        self.total_iters = total_iterations
-
-        self._start_time = time.time()
-        self._times = [0.0] * window_size
-
-    def tic(self) -> None:
-        r"""Start recording time: call at the beginning of iteration."""
-        self._start_time = time.time()
-
-    def toc(self) -> None:
-        r"""Stop recording time: call at the end of iteration."""
-        self._times.append(time.time() - self._start_time)
-        self._times = self._times[1:]
-        self.current_iter += 1
-
-    @property
-    def stats(self) -> str:
-        r"""Return a single string with current iteration, time and ETA."""
-        return (
-            f"Iter {self.current_iter} | Time: {self._times[-1]:.3f} sec | "
-            f"ETA: {self.eta_hhmm}"
-        )
-
-    @property
-    def eta_hhmm(self) -> str:
-        r"""Return ETA in the form of ``hh mm`` string."""
-        if self.total_iters:
-            eta_sec = int(self.eta_sec)
-            return f"{eta_sec // 3600}h {((eta_sec % 3600) // 60):02d}m"
-        else:
-            return "N/A"
-
-    @property
-    def eta_sec(self) -> float:
-        r"""Return ETA in the form of seconds."""
-        if self.total_iters:
-            avg_time = sum(self._times) / len(self._times)
-            return avg_time * (self.total_iters - self.current_iter)
-        else:
-            return 0.0
diff --git a/spaces/unity/ML-Agents-SoccerTwos/Build/SOCCERTWOSAG.loader.js b/spaces/unity/ML-Agents-SoccerTwos/Build/SOCCERTWOSAG.loader.js
deleted file mode 100644
index 11a00f4dce0ac090822dcf127458486806f0d022..0000000000000000000000000000000000000000
--- a/spaces/unity/ML-Agents-SoccerTwos/Build/SOCCERTWOSAG.loader.js
+++ /dev/null
@@ -1 +0,0 @@
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e&&e.state?((t=e.state).window&&(t.window=null),e.state=null,D):N},n.inflateGetHeader=function(e,t){return e&&e.state&&0!=(2&(e=e.state).wrap)?((e.head=t).done=!1,D):N},n.inflateSetDictionary=function(e,t){var n,r=t.length;return!e||!e.state||0!==(n=e.state).wrap&&11!==n.mode?N:11===n.mode&&O(1,t,r,0)!==n.check?-3:M(e,t,r,r)?(n.mode=31,-4):(n.havedict=1,D)},n.inflateInfo="pako inflate (from Nodeca project)"},"zlib/constants.js":function(e,t,n){"use strict";t.exports={Z_NO_FLUSH:0,Z_PARTIAL_FLUSH:1,Z_SYNC_FLUSH:2,Z_FULL_FLUSH:3,Z_FINISH:4,Z_BLOCK:5,Z_TREES:6,Z_OK:0,Z_STREAM_END:1,Z_NEED_DICT:2,Z_ERRNO:-1,Z_STREAM_ERROR:-2,Z_DATA_ERROR:-3,Z_BUF_ERROR:-5,Z_NO_COMPRESSION:0,Z_BEST_SPEED:1,Z_BEST_COMPRESSION:9,Z_DEFAULT_COMPRESSION:-1,Z_FILTERED:1,Z_HUFFMAN_ONLY:2,Z_RLE:3,Z_FIXED:4,Z_DEFAULT_STRATEGY:0,Z_BINARY:0,Z_TEXT:1,Z_UNKNOWN:2,Z_DEFLATED:8}},"zlib/messages.js":function(e,t,n){"use strict";t.exports={2:"need dictionary",1:"stream end",0:"","-1":"file error","-2":"stream error","-3":"data error","-4":"insufficient memory","-5":"buffer error","-6":"incompatible version"}},"zlib/zstream.js":function(e,t,n){"use strict";t.exports=function(){this.input=null,this.next_in=0,this.avail_in=0,this.total_in=0,this.output=null,this.next_out=0,this.avail_out=0,this.total_out=0,this.msg="",this.state=null,this.data_type=2,this.adler=0}},"zlib/gzheader.js":function(e,t,n){"use strict";t.exports=function(){this.text=0,this.time=0,this.xflags=0,this.os=0,this.extra=null,this.extra_len=0,this.name="",this.comment="",this.hcrc=0,this.done=!1}},"zlib/adler32.js":function(e,t,n){"use strict";t.exports=function(e,t,n,r){for(var o=65535&e|0,a=e>>>16&65535|0,i=0;0!==n;){for(n-=i=2e3<n?2e3:n;a=a+(o=o+t[r++]|0)|0,--i;);o%=65521,a%=65521}return o|a<<16|0}},"zlib/crc32.js":function(e,t,n){"use strict";var s=function(){for(var e=[],t=0;t<256;t++){for(var n=t,r=0;r<8;r++)n=1&n?3988292384^n>>>1:n>>>1;e[t]=n}return e}();t.exports=function(e,t,n,r){var o=s,a=r+n;e^=-1;for(var 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e}if(a=65535&n,x<(r&=15)&&(k+=u[d++]<<x,(x+=8)<r&&(k+=u[d++]<<x,x+=8)),g<(a+=k&(1<<r)-1)){e.msg="invalid distance too far back",l.mode=30;break e}if(k>>>=r,x-=r,(r=f-b)<a){if(w<(r=a-r)&&l.sane){e.msg="invalid distance too far back",l.mode=30;break e}if(s=y,(i=0)===v){if(i+=p-r,r<o){for(o-=r;h[f++]=y[i++],--r;);i=f-a,s=h}}else if(v<r){if(i+=p+v-r,(r-=v)<o){for(o-=r;h[f++]=y[i++],--r;);if(i=0,v<o){for(o-=r=v;h[f++]=y[i++],--r;);i=f-a,s=h}}}else if(i+=v-r,r<o){for(o-=r;h[f++]=y[i++],--r;);i=f-a,s=h}for(;2<o;)h[f++]=s[i++],h[f++]=s[i++],h[f++]=s[i++],o-=3;o&&(h[f++]=s[i++],1<o&&(h[f++]=s[i++]))}else{for(i=f-a;h[f++]=h[i++],h[f++]=h[i++],h[f++]=h[i++],2<(o-=3););o&&(h[f++]=h[i++],1<o&&(h[f++]=h[i++]))}break}}break}}while(d<c&&f<m);k&=(1<<(x-=(o=x>>3)<<3))-1,e.next_in=d-=o,e.next_out=f,e.avail_in=d<c?c-d+5:5-(d-c),e.avail_out=f<m?m-f+257:257-(f-m),l.hold=k,l.bits=x}},"zlib/inftrees.js":function(e,t,n){"use strict";var A=e("../utils/common"),P=[3,4,5,6,7,8,9,10,11,13,15,17,19,23,27,31,35,43,51,59,67,83,99,115,131,163,195,227,258,0,0],D=[16,16,16,16,16,16,16,16,17,17,17,17,18,18,18,18,19,19,19,19,20,20,20,20,21,21,21,21,16,72,78],N=[1,2,3,4,5,7,9,13,17,25,33,49,65,97,129,193,257,385,513,769,1025,1537,2049,3073,4097,6145,8193,12289,16385,24577,0,0],z=[16,16,16,16,17,17,18,18,19,19,20,20,21,21,22,22,23,23,24,24,25,25,26,26,27,27,28,28,29,29,64,64];t.exports=function(e,t,n,r,o,a,i,s){for(var l,d,u,c,f,h,b,m,g,p=s.bits,w=0,v=0,y=0,k=0,x=0,_=0,S=0,E=0,C=0,B=0,U=null,T=0,R=new A.Buf16(16),L=new 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diff --git a/spaces/user238921933/stable-diffusion-webui/modules/sd_models.py b/spaces/user238921933/stable-diffusion-webui/modules/sd_models.py
deleted file mode 100644
index e25a5495783c2768d50b63b35e105175c1b78bbf..0000000000000000000000000000000000000000
--- a/spaces/user238921933/stable-diffusion-webui/modules/sd_models.py
+++ /dev/null
@@ -1,495 +0,0 @@
-import collections
-import os.path
-import sys
-import gc
-import torch
-import re
-import safetensors.torch
-from omegaconf import OmegaConf
-from os import mkdir
-from urllib import request
-import ldm.modules.midas as midas
-
-from ldm.util import instantiate_from_config
-
-from modules import paths, shared, modelloader, devices, script_callbacks, sd_vae, sd_disable_initialization, errors, hashes, sd_models_config
-from modules.paths import models_path
-from modules.sd_hijack_inpainting import do_inpainting_hijack
-from modules.timer import Timer
-
-model_dir = "Stable-diffusion"
-model_path = os.path.abspath(os.path.join(paths.models_path, model_dir))
-
-checkpoints_list = {}
-checkpoint_alisases = {}
-checkpoints_loaded = collections.OrderedDict()
-
-
-class CheckpointInfo:
-    def __init__(self, filename):
-        self.filename = filename
-        abspath = os.path.abspath(filename)
-
-        if shared.cmd_opts.ckpt_dir is not None and abspath.startswith(shared.cmd_opts.ckpt_dir):
-            name = abspath.replace(shared.cmd_opts.ckpt_dir, '')
-        elif abspath.startswith(model_path):
-            name = abspath.replace(model_path, '')
-        else:
-            name = os.path.basename(filename)
-
-        if name.startswith("\\") or name.startswith("/"):
-            name = name[1:]
-
-        self.name = name
-        self.name_for_extra = os.path.splitext(os.path.basename(filename))[0]
-        self.model_name = os.path.splitext(name.replace("/", "_").replace("\\", "_"))[0]
-        self.hash = model_hash(filename)
-
-        self.sha256 = hashes.sha256_from_cache(self.filename, "checkpoint/" + name)
-        self.shorthash = self.sha256[0:10] if self.sha256 else None
-
-        self.title = name if self.shorthash is None else f'{name} [{self.shorthash}]'
-
-        self.ids = [self.hash, self.model_name, self.title, name, f'{name} [{self.hash}]'] + ([self.shorthash, self.sha256, f'{self.name} [{self.shorthash}]'] if self.shorthash else [])
-
-    def register(self):
-        checkpoints_list[self.title] = self
-        for id in self.ids:
-            checkpoint_alisases[id] = self
-
-    def calculate_shorthash(self):
-        self.sha256 = hashes.sha256(self.filename, "checkpoint/" + self.name)
-        if self.sha256 is None:
-            return
-
-        self.shorthash = self.sha256[0:10]
-
-        if self.shorthash not in self.ids:
-            self.ids += [self.shorthash, self.sha256, f'{self.name} [{self.shorthash}]']
-
-        checkpoints_list.pop(self.title)
-        self.title = f'{self.name} [{self.shorthash}]'
-        self.register()
-
-        return self.shorthash
-
-
-try:
-    # this silences the annoying "Some weights of the model checkpoint were not used when initializing..." message at start.
-
-    from transformers import logging, CLIPModel
-
-    logging.set_verbosity_error()
-except Exception:
-    pass
-
-
-def setup_model():
-    if not os.path.exists(model_path):
-        os.makedirs(model_path)
-
-    list_models()
-    enable_midas_autodownload()
-
-
-def checkpoint_tiles():
-    def convert(name):
-        return int(name) if name.isdigit() else name.lower()
-
-    def alphanumeric_key(key):
-        return [convert(c) for c in re.split('([0-9]+)', key)]
-
-    return sorted([x.title for x in checkpoints_list.values()], key=alphanumeric_key)
-
-
-def list_models():
-    checkpoints_list.clear()
-    checkpoint_alisases.clear()
-
-    cmd_ckpt = shared.cmd_opts.ckpt
-    if shared.cmd_opts.no_download_sd_model or cmd_ckpt != shared.sd_model_file or os.path.exists(cmd_ckpt):
-        model_url = None
-    else:
-        model_url = "https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.safetensors"
-
-    model_list = modelloader.load_models(model_path=model_path, model_url=model_url, command_path=shared.cmd_opts.ckpt_dir, ext_filter=[".ckpt", ".safetensors"], download_name="v1-5-pruned-emaonly.safetensors", ext_blacklist=[".vae.ckpt", ".vae.safetensors"])
-
-    if os.path.exists(cmd_ckpt):
-        checkpoint_info = CheckpointInfo(cmd_ckpt)
-        checkpoint_info.register()
-
-        shared.opts.data['sd_model_checkpoint'] = checkpoint_info.title
-    elif cmd_ckpt is not None and cmd_ckpt != shared.default_sd_model_file:
-        print(f"Checkpoint in --ckpt argument not found (Possible it was moved to {model_path}: {cmd_ckpt}", file=sys.stderr)
-
-    for filename in model_list:
-        checkpoint_info = CheckpointInfo(filename)
-        checkpoint_info.register()
-
-
-def get_closet_checkpoint_match(search_string):
-    checkpoint_info = checkpoint_alisases.get(search_string, None)
-    if checkpoint_info is not None:
-        return checkpoint_info
-
-    found = sorted([info for info in checkpoints_list.values() if search_string in info.title], key=lambda x: len(x.title))
-    if found:
-        return found[0]
-
-    return None
-
-
-def model_hash(filename):
-    """old hash that only looks at a small part of the file and is prone to collisions"""
-
-    try:
-        with open(filename, "rb") as file:
-            import hashlib
-            m = hashlib.sha256()
-
-            file.seek(0x100000)
-            m.update(file.read(0x10000))
-            return m.hexdigest()[0:8]
-    except FileNotFoundError:
-        return 'NOFILE'
-
-
-def select_checkpoint():
-    model_checkpoint = shared.opts.sd_model_checkpoint
-        
-    checkpoint_info = checkpoint_alisases.get(model_checkpoint, None)
-    if checkpoint_info is not None:
-        return checkpoint_info
-
-    if len(checkpoints_list) == 0:
-        print("No checkpoints found. When searching for checkpoints, looked at:", file=sys.stderr)
-        if shared.cmd_opts.ckpt is not None:
-            print(f" - file {os.path.abspath(shared.cmd_opts.ckpt)}", file=sys.stderr)
-        print(f" - directory {model_path}", file=sys.stderr)
-        if shared.cmd_opts.ckpt_dir is not None:
-            print(f" - directory {os.path.abspath(shared.cmd_opts.ckpt_dir)}", file=sys.stderr)
-        print("Can't run without a checkpoint. Find and place a .ckpt or .safetensors file into any of those locations. The program will exit.", file=sys.stderr)
-        exit(1)
-
-    checkpoint_info = next(iter(checkpoints_list.values()))
-    if model_checkpoint is not None:
-        print(f"Checkpoint {model_checkpoint} not found; loading fallback {checkpoint_info.title}", file=sys.stderr)
-
-    return checkpoint_info
-
-
-chckpoint_dict_replacements = {
-    'cond_stage_model.transformer.embeddings.': 'cond_stage_model.transformer.text_model.embeddings.',
-    'cond_stage_model.transformer.encoder.': 'cond_stage_model.transformer.text_model.encoder.',
-    'cond_stage_model.transformer.final_layer_norm.': 'cond_stage_model.transformer.text_model.final_layer_norm.',
-}
-
-
-def transform_checkpoint_dict_key(k):
-    for text, replacement in chckpoint_dict_replacements.items():
-        if k.startswith(text):
-            k = replacement + k[len(text):]
-
-    return k
-
-
-def get_state_dict_from_checkpoint(pl_sd):
-    pl_sd = pl_sd.pop("state_dict", pl_sd)
-    pl_sd.pop("state_dict", None)
-
-    sd = {}
-    for k, v in pl_sd.items():
-        new_key = transform_checkpoint_dict_key(k)
-
-        if new_key is not None:
-            sd[new_key] = v
-
-    pl_sd.clear()
-    pl_sd.update(sd)
-
-    return pl_sd
-
-
-def read_state_dict(checkpoint_file, print_global_state=False, map_location=None):
-    _, extension = os.path.splitext(checkpoint_file)
-    if extension.lower() == ".safetensors":
-        device = map_location or shared.weight_load_location or devices.get_optimal_device_name()
-        pl_sd = safetensors.torch.load_file(checkpoint_file, device=device)
-    else:
-        pl_sd = torch.load(checkpoint_file, map_location=map_location or shared.weight_load_location)
-
-    if print_global_state and "global_step" in pl_sd:
-        print(f"Global Step: {pl_sd['global_step']}")
-
-    sd = get_state_dict_from_checkpoint(pl_sd)
-    return sd
-
-
-def get_checkpoint_state_dict(checkpoint_info: CheckpointInfo, timer):
-    sd_model_hash = checkpoint_info.calculate_shorthash()
-    timer.record("calculate hash")
-
-    if checkpoint_info in checkpoints_loaded:
-        # use checkpoint cache
-        print(f"Loading weights [{sd_model_hash}] from cache")
-        return checkpoints_loaded[checkpoint_info]
-
-    print(f"Loading weights [{sd_model_hash}] from {checkpoint_info.filename}")
-    res = read_state_dict(checkpoint_info.filename)
-    timer.record("load weights from disk")
-
-    return res
-
-
-def load_model_weights(model, checkpoint_info: CheckpointInfo, state_dict, timer):
-    sd_model_hash = checkpoint_info.calculate_shorthash()
-    timer.record("calculate hash")
-
-    shared.opts.data["sd_model_checkpoint"] = checkpoint_info.title
-
-    if state_dict is None:
-        state_dict = get_checkpoint_state_dict(checkpoint_info, timer)
-
-    model.load_state_dict(state_dict, strict=False)
-    del state_dict
-    timer.record("apply weights to model")
-
-    if shared.opts.sd_checkpoint_cache > 0:
-        # cache newly loaded model
-        checkpoints_loaded[checkpoint_info] = model.state_dict().copy()
-
-    if shared.cmd_opts.opt_channelslast:
-        model.to(memory_format=torch.channels_last)
-        timer.record("apply channels_last")
-
-    if not shared.cmd_opts.no_half:
-        vae = model.first_stage_model
-        depth_model = getattr(model, 'depth_model', None)
-
-        # with --no-half-vae, remove VAE from model when doing half() to prevent its weights from being converted to float16
-        if shared.cmd_opts.no_half_vae:
-            model.first_stage_model = None
-        # with --upcast-sampling, don't convert the depth model weights to float16
-        if shared.cmd_opts.upcast_sampling and depth_model:
-            model.depth_model = None
-
-        model.half()
-        model.first_stage_model = vae
-        if depth_model:
-            model.depth_model = depth_model
-
-        timer.record("apply half()")
-
-    devices.dtype = torch.float32 if shared.cmd_opts.no_half else torch.float16
-    devices.dtype_vae = torch.float32 if shared.cmd_opts.no_half or shared.cmd_opts.no_half_vae else torch.float16
-    devices.dtype_unet = model.model.diffusion_model.dtype
-    devices.unet_needs_upcast = shared.cmd_opts.upcast_sampling and devices.dtype == torch.float16 and devices.dtype_unet == torch.float16
-
-    model.first_stage_model.to(devices.dtype_vae)
-    timer.record("apply dtype to VAE")
-
-    # clean up cache if limit is reached
-    while len(checkpoints_loaded) > shared.opts.sd_checkpoint_cache:
-        checkpoints_loaded.popitem(last=False)
-
-    model.sd_model_hash = sd_model_hash
-    model.sd_model_checkpoint = checkpoint_info.filename
-    model.sd_checkpoint_info = checkpoint_info
-    shared.opts.data["sd_checkpoint_hash"] = checkpoint_info.sha256
-
-    model.logvar = model.logvar.to(devices.device)  # fix for training
-
-    sd_vae.delete_base_vae()
-    sd_vae.clear_loaded_vae()
-    vae_file, vae_source = sd_vae.resolve_vae(checkpoint_info.filename)
-    sd_vae.load_vae(model, vae_file, vae_source)
-    timer.record("load VAE")
-
-
-def enable_midas_autodownload():
-    """
-    Gives the ldm.modules.midas.api.load_model function automatic downloading.
-
-    When the 512-depth-ema model, and other future models like it, is loaded,
-    it calls midas.api.load_model to load the associated midas depth model.
-    This function applies a wrapper to download the model to the correct
-    location automatically.
-    """
-
-    midas_path = os.path.join(paths.models_path, 'midas')
-
-    # stable-diffusion-stability-ai hard-codes the midas model path to
-    # a location that differs from where other scripts using this model look.
-    # HACK: Overriding the path here.
-    for k, v in midas.api.ISL_PATHS.items():
-        file_name = os.path.basename(v)
-        midas.api.ISL_PATHS[k] = os.path.join(midas_path, file_name)
-
-    midas_urls = {
-        "dpt_large": "https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt",
-        "dpt_hybrid": "https://github.com/intel-isl/DPT/releases/download/1_0/dpt_hybrid-midas-501f0c75.pt",
-        "midas_v21": "https://github.com/AlexeyAB/MiDaS/releases/download/midas_dpt/midas_v21-f6b98070.pt",
-        "midas_v21_small": "https://github.com/AlexeyAB/MiDaS/releases/download/midas_dpt/midas_v21_small-70d6b9c8.pt",
-    }
-
-    midas.api.load_model_inner = midas.api.load_model
-
-    def load_model_wrapper(model_type):
-        path = midas.api.ISL_PATHS[model_type]
-        if not os.path.exists(path):
-            if not os.path.exists(midas_path):
-                mkdir(midas_path)
-    
-            print(f"Downloading midas model weights for {model_type} to {path}")
-            request.urlretrieve(midas_urls[model_type], path)
-            print(f"{model_type} downloaded")
-
-        return midas.api.load_model_inner(model_type)
-
-    midas.api.load_model = load_model_wrapper
-
-
-def repair_config(sd_config):
-
-    if not hasattr(sd_config.model.params, "use_ema"):
-        sd_config.model.params.use_ema = False
-
-    if shared.cmd_opts.no_half:
-        sd_config.model.params.unet_config.params.use_fp16 = False
-    elif shared.cmd_opts.upcast_sampling:
-        sd_config.model.params.unet_config.params.use_fp16 = True
-
-
-sd1_clip_weight = 'cond_stage_model.transformer.text_model.embeddings.token_embedding.weight'
-sd2_clip_weight = 'cond_stage_model.model.transformer.resblocks.0.attn.in_proj_weight'
-
-def load_model(checkpoint_info=None, already_loaded_state_dict=None, time_taken_to_load_state_dict=None):
-    from modules import lowvram, sd_hijack
-    checkpoint_info = checkpoint_info or select_checkpoint()
-
-    if shared.sd_model:
-        sd_hijack.model_hijack.undo_hijack(shared.sd_model)
-        shared.sd_model = None
-        gc.collect()
-        devices.torch_gc()
-
-    do_inpainting_hijack()
-
-    timer = Timer()
-
-    if already_loaded_state_dict is not None:
-        state_dict = already_loaded_state_dict
-    else:
-        state_dict = get_checkpoint_state_dict(checkpoint_info, timer)
-
-    checkpoint_config = sd_models_config.find_checkpoint_config(state_dict, checkpoint_info)
-    clip_is_included_into_sd = sd1_clip_weight in state_dict or sd2_clip_weight in state_dict
-
-    timer.record("find config")
-
-    sd_config = OmegaConf.load(checkpoint_config)
-    repair_config(sd_config)
-
-    timer.record("load config")
-
-    print(f"Creating model from config: {checkpoint_config}")
-
-    sd_model = None
-    try:
-        with sd_disable_initialization.DisableInitialization(disable_clip=clip_is_included_into_sd):
-            sd_model = instantiate_from_config(sd_config.model)
-    except Exception as e:
-        pass
-
-    if sd_model is None:
-        print('Failed to create model quickly; will retry using slow method.', file=sys.stderr)
-        sd_model = instantiate_from_config(sd_config.model)
-
-    sd_model.used_config = checkpoint_config
-
-    timer.record("create model")
-
-    load_model_weights(sd_model, checkpoint_info, state_dict, timer)
-
-    if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
-        lowvram.setup_for_low_vram(sd_model, shared.cmd_opts.medvram)
-    else:
-        sd_model.to(shared.device)
-
-    timer.record("move model to device")
-
-    sd_hijack.model_hijack.hijack(sd_model)
-
-    timer.record("hijack")
-
-    sd_model.eval()
-    shared.sd_model = sd_model
-
-    sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings(force_reload=True)  # Reload embeddings after model load as they may or may not fit the model
-
-    timer.record("load textual inversion embeddings")
-
-    script_callbacks.model_loaded_callback(sd_model)
-
-    timer.record("scripts callbacks")
-
-    print(f"Model loaded in {timer.summary()}.")
-
-    return sd_model
-
-
-def reload_model_weights(sd_model=None, info=None):
-    from modules import lowvram, devices, sd_hijack
-    checkpoint_info = info or select_checkpoint()
-
-    if not sd_model:
-        sd_model = shared.sd_model
-
-    if sd_model is None:  # previous model load failed
-        current_checkpoint_info = None
-    else:
-        current_checkpoint_info = sd_model.sd_checkpoint_info
-        if sd_model.sd_model_checkpoint == checkpoint_info.filename:
-            return
-
-        if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
-            lowvram.send_everything_to_cpu()
-        else:
-            sd_model.to(devices.cpu)
-
-        sd_hijack.model_hijack.undo_hijack(sd_model)
-
-    timer = Timer()
-
-    state_dict = get_checkpoint_state_dict(checkpoint_info, timer)
-
-    checkpoint_config = sd_models_config.find_checkpoint_config(state_dict, checkpoint_info)
-
-    timer.record("find config")
-
-    if sd_model is None or checkpoint_config != sd_model.used_config:
-        del sd_model
-        checkpoints_loaded.clear()
-        load_model(checkpoint_info, already_loaded_state_dict=state_dict, time_taken_to_load_state_dict=timer.records["load weights from disk"])
-        return shared.sd_model
-
-    try:
-        load_model_weights(sd_model, checkpoint_info, state_dict, timer)
-    except Exception as e:
-        print("Failed to load checkpoint, restoring previous")
-        load_model_weights(sd_model, current_checkpoint_info, None, timer)
-        raise
-    finally:
-        sd_hijack.model_hijack.hijack(sd_model)
-        timer.record("hijack")
-
-        script_callbacks.model_loaded_callback(sd_model)
-        timer.record("script callbacks")
-
-        if not shared.cmd_opts.lowvram and not shared.cmd_opts.medvram:
-            sd_model.to(devices.device)
-            timer.record("move model to device")
-
-    print(f"Weights loaded in {timer.summary()}.")
-
-    return sd_model
diff --git a/spaces/user238921933/stable-diffusion-webui/modules/shared.py b/spaces/user238921933/stable-diffusion-webui/modules/shared.py
deleted file mode 100644
index 2a3037ac85a4ce46300b769c97b63d736315f848..0000000000000000000000000000000000000000
--- a/spaces/user238921933/stable-diffusion-webui/modules/shared.py
+++ /dev/null
@@ -1,720 +0,0 @@
-import argparse
-import datetime
-import json
-import os
-import sys
-import time
-
-from PIL import Image
-import gradio as gr
-import tqdm
-
-import modules.interrogate
-import modules.memmon
-import modules.styles
-import modules.devices as devices
-from modules import localization, extensions, script_loading, errors, ui_components, shared_items
-from modules.paths import models_path, script_path, data_path
-
-
-demo = None
-
-sd_configs_path = os.path.join(script_path, "configs")
-sd_default_config = os.path.join(sd_configs_path, "v1-inference.yaml")
-sd_model_file = os.path.join(script_path, 'model.ckpt')
-default_sd_model_file = sd_model_file
-
-parser = argparse.ArgumentParser()
-parser.add_argument("--data-dir", type=str, default=os.path.dirname(os.path.dirname(os.path.realpath(__file__))), help="base path where all user data is stored",)
-parser.add_argument("--config", type=str, default=sd_default_config, help="path to config which constructs model",)
-parser.add_argument("--ckpt", type=str, default=sd_model_file, help="path to checkpoint of stable diffusion model; if specified, this checkpoint will be added to the list of checkpoints and loaded",)
-parser.add_argument("--ckpt-dir", type=str, default=None, help="Path to directory with stable diffusion checkpoints")
-parser.add_argument("--vae-dir", type=str, default=None, help="Path to directory with VAE files")
-parser.add_argument("--gfpgan-dir", type=str, help="GFPGAN directory", default=('./src/gfpgan' if os.path.exists('./src/gfpgan') else './GFPGAN'))
-parser.add_argument("--gfpgan-model", type=str, help="GFPGAN model file name", default=None)
-parser.add_argument("--no-half", action='store_true', help="do not switch the model to 16-bit floats")
-parser.add_argument("--no-half-vae", action='store_true', help="do not switch the VAE model to 16-bit floats")
-parser.add_argument("--no-progressbar-hiding", action='store_true', help="do not hide progressbar in gradio UI (we hide it because it slows down ML if you have hardware acceleration in browser)")
-parser.add_argument("--max-batch-count", type=int, default=16, help="maximum batch count value for the UI")
-parser.add_argument("--embeddings-dir", type=str, default=os.path.join(data_path, 'embeddings'), help="embeddings directory for textual inversion (default: embeddings)")
-parser.add_argument("--textual-inversion-templates-dir", type=str, default=os.path.join(script_path, 'textual_inversion_templates'), help="directory with textual inversion templates")
-parser.add_argument("--hypernetwork-dir", type=str, default=os.path.join(models_path, 'hypernetworks'), help="hypernetwork directory")
-parser.add_argument("--localizations-dir", type=str, default=os.path.join(script_path, 'localizations'), help="localizations directory")
-parser.add_argument("--allow-code", action='store_true', help="allow custom script execution from webui")
-parser.add_argument("--medvram", action='store_true', help="enable stable diffusion model optimizations for sacrificing a little speed for low VRM usage")
-parser.add_argument("--lowvram", action='store_true', help="enable stable diffusion model optimizations for sacrificing a lot of speed for very low VRM usage")
-parser.add_argument("--lowram", action='store_true', help="load stable diffusion checkpoint weights to VRAM instead of RAM")
-parser.add_argument("--always-batch-cond-uncond", action='store_true', help="disables cond/uncond batching that is enabled to save memory with --medvram or --lowvram")
-parser.add_argument("--unload-gfpgan", action='store_true', help="does not do anything.")
-parser.add_argument("--precision", type=str, help="evaluate at this precision", choices=["full", "autocast"], default="autocast")
-parser.add_argument("--upcast-sampling", action='store_true', help="upcast sampling. No effect with --no-half. Usually produces similar results to --no-half with better performance while using less memory.")
-parser.add_argument("--share", action='store_true', help="use share=True for gradio and make the UI accessible through their site")
-parser.add_argument("--ngrok", type=str, help="ngrok authtoken, alternative to gradio --share", default=None)
-parser.add_argument("--ngrok-region", type=str, help="The region in which ngrok should start.", default="us")
-parser.add_argument("--enable-insecure-extension-access", action='store_true', help="enable extensions tab regardless of other options")
-parser.add_argument("--codeformer-models-path", type=str, help="Path to directory with codeformer model file(s).", default=os.path.join(models_path, 'Codeformer'))
-parser.add_argument("--gfpgan-models-path", type=str, help="Path to directory with GFPGAN model file(s).", default=os.path.join(models_path, 'GFPGAN'))
-parser.add_argument("--esrgan-models-path", type=str, help="Path to directory with ESRGAN model file(s).", default=os.path.join(models_path, 'ESRGAN'))
-parser.add_argument("--bsrgan-models-path", type=str, help="Path to directory with BSRGAN model file(s).", default=os.path.join(models_path, 'BSRGAN'))
-parser.add_argument("--realesrgan-models-path", type=str, help="Path to directory with RealESRGAN model file(s).", default=os.path.join(models_path, 'RealESRGAN'))
-parser.add_argument("--clip-models-path", type=str, help="Path to directory with CLIP model file(s).", default=None)
-parser.add_argument("--xformers", action='store_true', help="enable xformers for cross attention layers")
-parser.add_argument("--force-enable-xformers", action='store_true', help="enable xformers for cross attention layers regardless of whether the checking code thinks you can run it; do not make bug reports if this fails to work")
-parser.add_argument("--xformers-flash-attention", action='store_true', help="enable xformers with Flash Attention to improve reproducibility (supported for SD2.x or variant only)")
-parser.add_argument("--deepdanbooru", action='store_true', help="does not do anything")
-parser.add_argument("--opt-split-attention", action='store_true', help="force-enables Doggettx's cross-attention layer optimization. By default, it's on for torch cuda.")
-parser.add_argument("--opt-sub-quad-attention", action='store_true', help="enable memory efficient sub-quadratic cross-attention layer optimization")
-parser.add_argument("--sub-quad-q-chunk-size", type=int, help="query chunk size for the sub-quadratic cross-attention layer optimization to use", default=1024)
-parser.add_argument("--sub-quad-kv-chunk-size", type=int, help="kv chunk size for the sub-quadratic cross-attention layer optimization to use", default=None)
-parser.add_argument("--sub-quad-chunk-threshold", type=int, help="the percentage of VRAM threshold for the sub-quadratic cross-attention layer optimization to use chunking", default=None)
-parser.add_argument("--opt-split-attention-invokeai", action='store_true', help="force-enables InvokeAI's cross-attention layer optimization. By default, it's on when cuda is unavailable.")
-parser.add_argument("--opt-split-attention-v1", action='store_true', help="enable older version of split attention optimization that does not consume all the VRAM it can find")
-parser.add_argument("--disable-opt-split-attention", action='store_true', help="force-disables cross-attention layer optimization")
-parser.add_argument("--disable-nan-check", action='store_true', help="do not check if produced images/latent spaces have nans; useful for running without a checkpoint in CI")
-parser.add_argument("--use-cpu", nargs='+', help="use CPU as torch device for specified modules", default=[], type=str.lower)
-parser.add_argument("--listen", action='store_true', help="launch gradio with 0.0.0.0 as server name, allowing to respond to network requests")
-parser.add_argument("--port", type=int, help="launch gradio with given server port, you need root/admin rights for ports < 1024, defaults to 7860 if available", default=None)
-parser.add_argument("--show-negative-prompt", action='store_true', help="does not do anything", default=False)
-parser.add_argument("--ui-config-file", type=str, help="filename to use for ui configuration", default=os.path.join(data_path, 'ui-config.json'))
-parser.add_argument("--hide-ui-dir-config", action='store_true', help="hide directory configuration from webui", default=False)
-parser.add_argument("--freeze-settings", action='store_true', help="disable editing settings", default=False)
-parser.add_argument("--ui-settings-file", type=str, help="filename to use for ui settings", default=os.path.join(data_path, 'config.json'))
-parser.add_argument("--gradio-debug",  action='store_true', help="launch gradio with --debug option")
-parser.add_argument("--gradio-auth", type=str, help='set gradio authentication like "username:password"; or comma-delimit multiple like "u1:p1,u2:p2,u3:p3"', default=None)
-parser.add_argument("--gradio-auth-path", type=str, help='set gradio authentication file path ex. "/path/to/auth/file" same auth format as --gradio-auth', default=None)
-parser.add_argument("--gradio-img2img-tool", type=str, help='does not do anything')
-parser.add_argument("--gradio-inpaint-tool", type=str, help="does not do anything")
-parser.add_argument("--opt-channelslast", action='store_true', help="change memory type for stable diffusion to channels last")
-parser.add_argument("--styles-file", type=str, help="filename to use for styles", default=os.path.join(data_path, 'styles.csv'))
-parser.add_argument("--autolaunch", action='store_true', help="open the webui URL in the system's default browser upon launch", default=False)
-parser.add_argument("--theme", type=str, help="launches the UI with light or dark theme", default=None)
-parser.add_argument("--use-textbox-seed", action='store_true', help="use textbox for seeds in UI (no up/down, but possible to input long seeds)", default=False)
-parser.add_argument("--disable-console-progressbars", action='store_true', help="do not output progressbars to console", default=False)
-parser.add_argument("--enable-console-prompts", action='store_true', help="print prompts to console when generating with txt2img and img2img", default=False)
-parser.add_argument('--vae-path', type=str, help='Checkpoint to use as VAE; setting this argument disables all settings related to VAE', default=None)
-parser.add_argument("--disable-safe-unpickle", action='store_true', help="disable checking pytorch models for malicious code", default=False)
-parser.add_argument("--api", action='store_true', help="use api=True to launch the API together with the webui (use --nowebui instead for only the API)")
-parser.add_argument("--api-auth", type=str, help='Set authentication for API like "username:password"; or comma-delimit multiple like "u1:p1,u2:p2,u3:p3"', default=None)
-parser.add_argument("--api-log", action='store_true', help="use api-log=True to enable logging of all API requests")
-parser.add_argument("--nowebui", action='store_true', help="use api=True to launch the API instead of the webui")
-parser.add_argument("--ui-debug-mode", action='store_true', help="Don't load model to quickly launch UI")
-parser.add_argument("--device-id", type=str, help="Select the default CUDA device to use (export CUDA_VISIBLE_DEVICES=0,1,etc might be needed before)", default=None)
-parser.add_argument("--administrator", action='store_true', help="Administrator rights", default=False)
-parser.add_argument("--cors-allow-origins", type=str, help="Allowed CORS origin(s) in the form of a comma-separated list (no spaces)", default=None)
-parser.add_argument("--cors-allow-origins-regex", type=str, help="Allowed CORS origin(s) in the form of a single regular expression", default=None)
-parser.add_argument("--tls-keyfile", type=str, help="Partially enables TLS, requires --tls-certfile to fully function", default=None)
-parser.add_argument("--tls-certfile", type=str, help="Partially enables TLS, requires --tls-keyfile to fully function", default=None)
-parser.add_argument("--server-name", type=str, help="Sets hostname of server", default=None)
-parser.add_argument("--gradio-queue", action='store_true', help="Uses gradio queue; experimental option; breaks restart UI button")
-parser.add_argument("--skip-version-check", action='store_true', help="Do not check versions of torch and xformers")
-parser.add_argument("--no-hashing", action='store_true', help="disable sha256 hashing of checkpoints to help loading performance", default=False)
-parser.add_argument("--no-download-sd-model", action='store_true', help="don't download SD1.5 model even if no model is found in --ckpt-dir", default=False)
-
-
-script_loading.preload_extensions(extensions.extensions_dir, parser)
-script_loading.preload_extensions(extensions.extensions_builtin_dir, parser)
-
-cmd_opts = parser.parse_args()
-
-restricted_opts = {
-    "samples_filename_pattern",
-    "directories_filename_pattern",
-    "outdir_samples",
-    "outdir_txt2img_samples",
-    "outdir_img2img_samples",
-    "outdir_extras_samples",
-    "outdir_grids",
-    "outdir_txt2img_grids",
-    "outdir_save",
-}
-
-ui_reorder_categories = [
-    "inpaint",
-    "sampler",
-    "checkboxes",
-    "hires_fix",
-    "dimensions",
-    "cfg",
-    "seed",
-    "batch",
-    "override_settings",
-    "scripts",
-]
-
-cmd_opts.disable_extension_access = (cmd_opts.share or cmd_opts.listen or cmd_opts.server_name) and not cmd_opts.enable_insecure_extension_access
-
-devices.device, devices.device_interrogate, devices.device_gfpgan, devices.device_esrgan, devices.device_codeformer = \
-    (devices.cpu if any(y in cmd_opts.use_cpu for y in [x, 'all']) else devices.get_optimal_device() for x in ['sd', 'interrogate', 'gfpgan', 'esrgan', 'codeformer'])
-
-device = devices.device
-weight_load_location = None if cmd_opts.lowram else "cpu"
-
-batch_cond_uncond = cmd_opts.always_batch_cond_uncond or not (cmd_opts.lowvram or cmd_opts.medvram)
-parallel_processing_allowed = not cmd_opts.lowvram and not cmd_opts.medvram
-xformers_available = False
-config_filename = cmd_opts.ui_settings_file
-
-os.makedirs(cmd_opts.hypernetwork_dir, exist_ok=True)
-hypernetworks = {}
-loaded_hypernetworks = []
-
-
-def reload_hypernetworks():
-    from modules.hypernetworks import hypernetwork
-    global hypernetworks
-
-    hypernetworks = hypernetwork.list_hypernetworks(cmd_opts.hypernetwork_dir)
-
-
-class State:
-    skipped = False
-    interrupted = False
-    job = ""
-    job_no = 0
-    job_count = 0
-    processing_has_refined_job_count = False
-    job_timestamp = '0'
-    sampling_step = 0
-    sampling_steps = 0
-    current_latent = None
-    current_image = None
-    current_image_sampling_step = 0
-    id_live_preview = 0
-    textinfo = None
-    time_start = None
-    need_restart = False
-    server_start = None
-
-    def skip(self):
-        self.skipped = True
-
-    def interrupt(self):
-        self.interrupted = True
-
-    def nextjob(self):
-        if opts.live_previews_enable and opts.show_progress_every_n_steps == -1:
-            self.do_set_current_image()
-
-        self.job_no += 1
-        self.sampling_step = 0
-        self.current_image_sampling_step = 0
-
-    def dict(self):
-        obj = {
-            "skipped": self.skipped,
-            "interrupted": self.interrupted,
-            "job": self.job,
-            "job_count": self.job_count,
-            "job_timestamp": self.job_timestamp,
-            "job_no": self.job_no,
-            "sampling_step": self.sampling_step,
-            "sampling_steps": self.sampling_steps,
-        }
-
-        return obj
-
-    def begin(self):
-        self.sampling_step = 0
-        self.job_count = -1
-        self.processing_has_refined_job_count = False
-        self.job_no = 0
-        self.job_timestamp = datetime.datetime.now().strftime("%Y%m%d%H%M%S")
-        self.current_latent = None
-        self.current_image = None
-        self.current_image_sampling_step = 0
-        self.id_live_preview = 0
-        self.skipped = False
-        self.interrupted = False
-        self.textinfo = None
-        self.time_start = time.time()
-
-        devices.torch_gc()
-
-    def end(self):
-        self.job = ""
-        self.job_count = 0
-
-        devices.torch_gc()
-
-    def set_current_image(self):
-        """sets self.current_image from self.current_latent if enough sampling steps have been made after the last call to this"""
-        if not parallel_processing_allowed:
-            return
-
-        if self.sampling_step - self.current_image_sampling_step >= opts.show_progress_every_n_steps and opts.live_previews_enable and opts.show_progress_every_n_steps != -1:
-            self.do_set_current_image()
-
-    def do_set_current_image(self):
-        if self.current_latent is None:
-            return
-
-        import modules.sd_samplers
-        if opts.show_progress_grid:
-            self.assign_current_image(modules.sd_samplers.samples_to_image_grid(self.current_latent))
-        else:
-            self.assign_current_image(modules.sd_samplers.sample_to_image(self.current_latent))
-
-        self.current_image_sampling_step = self.sampling_step
-
-    def assign_current_image(self, image):
-        self.current_image = image
-        self.id_live_preview += 1
-
-
-state = State()
-state.server_start = time.time()
-
-styles_filename = cmd_opts.styles_file
-prompt_styles = modules.styles.StyleDatabase(styles_filename)
-
-interrogator = modules.interrogate.InterrogateModels("interrogate")
-
-face_restorers = []
-
-class OptionInfo:
-    def __init__(self, default=None, label="", component=None, component_args=None, onchange=None, section=None, refresh=None):
-        self.default = default
-        self.label = label
-        self.component = component
-        self.component_args = component_args
-        self.onchange = onchange
-        self.section = section
-        self.refresh = refresh
-
-
-def options_section(section_identifier, options_dict):
-    for k, v in options_dict.items():
-        v.section = section_identifier
-
-    return options_dict
-
-
-def list_checkpoint_tiles():
-    import modules.sd_models
-    return modules.sd_models.checkpoint_tiles()
-
-
-def refresh_checkpoints():
-    import modules.sd_models
-    return modules.sd_models.list_models()
-
-
-def list_samplers():
-    import modules.sd_samplers
-    return modules.sd_samplers.all_samplers
-
-
-hide_dirs = {"visible": not cmd_opts.hide_ui_dir_config}
-
-options_templates = {}
-
-options_templates.update(options_section(('saving-images', "Saving images/grids"), {
-    "samples_save": OptionInfo(True, "Always save all generated images"),
-    "samples_format": OptionInfo('png', 'File format for images'),
-    "samples_filename_pattern": OptionInfo("", "Images filename pattern", component_args=hide_dirs),
-    "save_images_add_number": OptionInfo(True, "Add number to filename when saving", component_args=hide_dirs),
-
-    "grid_save": OptionInfo(True, "Always save all generated image grids"),
-    "grid_format": OptionInfo('png', 'File format for grids'),
-    "grid_extended_filename": OptionInfo(False, "Add extended info (seed, prompt) to filename when saving grid"),
-    "grid_only_if_multiple": OptionInfo(True, "Do not save grids consisting of one picture"),
-    "grid_prevent_empty_spots": OptionInfo(False, "Prevent empty spots in grid (when set to autodetect)"),
-    "n_rows": OptionInfo(-1, "Grid row count; use -1 for autodetect and 0 for it to be same as batch size", gr.Slider, {"minimum": -1, "maximum": 16, "step": 1}),
-
-    "enable_pnginfo": OptionInfo(True, "Save text information about generation parameters as chunks to png files"),
-    "save_txt": OptionInfo(False, "Create a text file next to every image with generation parameters."),
-    "save_images_before_face_restoration": OptionInfo(False, "Save a copy of image before doing face restoration."),
-    "save_images_before_highres_fix": OptionInfo(False, "Save a copy of image before applying highres fix."),
-    "save_images_before_color_correction": OptionInfo(False, "Save a copy of image before applying color correction to img2img results"),
-    "jpeg_quality": OptionInfo(80, "Quality for saved jpeg images", gr.Slider, {"minimum": 1, "maximum": 100, "step": 1}),
-    "export_for_4chan": OptionInfo(True, "If the saved image file size is above the limit, or its either width or height are above the limit, save a downscaled copy as JPG"),
-    "img_downscale_threshold": OptionInfo(4.0, "File size limit for the above option, MB", gr.Number),
-    "target_side_length": OptionInfo(4000, "Width/height limit for the above option, in pixels", gr.Number),
-
-    "use_original_name_batch": OptionInfo(True, "Use original name for output filename during batch process in extras tab"),
-    "use_upscaler_name_as_suffix": OptionInfo(False, "Use upscaler name as filename suffix in the extras tab"),
-    "save_selected_only": OptionInfo(True, "When using 'Save' button, only save a single selected image"),
-    "do_not_add_watermark": OptionInfo(False, "Do not add watermark to images"),
-
-    "temp_dir":  OptionInfo("", "Directory for temporary images; leave empty for default"),
-    "clean_temp_dir_at_start": OptionInfo(False, "Cleanup non-default temporary directory when starting webui"),
-
-}))
-
-options_templates.update(options_section(('saving-paths', "Paths for saving"), {
-    "outdir_samples": OptionInfo("", "Output directory for images; if empty, defaults to three directories below", component_args=hide_dirs),
-    "outdir_txt2img_samples": OptionInfo("outputs/txt2img-images", 'Output directory for txt2img images', component_args=hide_dirs),
-    "outdir_img2img_samples": OptionInfo("outputs/img2img-images", 'Output directory for img2img images', component_args=hide_dirs),
-    "outdir_extras_samples": OptionInfo("outputs/extras-images", 'Output directory for images from extras tab', component_args=hide_dirs),
-    "outdir_grids": OptionInfo("", "Output directory for grids; if empty, defaults to two directories below", component_args=hide_dirs),
-    "outdir_txt2img_grids": OptionInfo("outputs/txt2img-grids", 'Output directory for txt2img grids', component_args=hide_dirs),
-    "outdir_img2img_grids": OptionInfo("outputs/img2img-grids", 'Output directory for img2img grids', component_args=hide_dirs),
-    "outdir_save": OptionInfo("log/images", "Directory for saving images using the Save button", component_args=hide_dirs),
-}))
-
-options_templates.update(options_section(('saving-to-dirs', "Saving to a directory"), {
-    "save_to_dirs": OptionInfo(True, "Save images to a subdirectory"),
-    "grid_save_to_dirs": OptionInfo(True, "Save grids to a subdirectory"),
-    "use_save_to_dirs_for_ui": OptionInfo(False, "When using \"Save\" button, save images to a subdirectory"),
-    "directories_filename_pattern": OptionInfo("[date]", "Directory name pattern", component_args=hide_dirs),
-    "directories_max_prompt_words": OptionInfo(8, "Max prompt words for [prompt_words] pattern", gr.Slider, {"minimum": 1, "maximum": 20, "step": 1, **hide_dirs}),
-}))
-
-options_templates.update(options_section(('upscaling', "Upscaling"), {
-    "ESRGAN_tile": OptionInfo(192, "Tile size for ESRGAN upscalers. 0 = no tiling.", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}),
-    "ESRGAN_tile_overlap": OptionInfo(8, "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.", gr.Slider, {"minimum": 0, "maximum": 48, "step": 1}),
-    "realesrgan_enabled_models": OptionInfo(["R-ESRGAN 4x+", "R-ESRGAN 4x+ Anime6B"], "Select which Real-ESRGAN models to show in the web UI. (Requires restart)", gr.CheckboxGroup, lambda: {"choices": shared_items.realesrgan_models_names()}),
-    "upscaler_for_img2img": OptionInfo(None, "Upscaler for img2img", gr.Dropdown, lambda: {"choices": [x.name for x in sd_upscalers]}),
-}))
-
-options_templates.update(options_section(('face-restoration', "Face restoration"), {
-    "face_restoration_model": OptionInfo("CodeFormer", "Face restoration model", gr.Radio, lambda: {"choices": [x.name() for x in face_restorers]}),
-    "code_former_weight": OptionInfo(0.5, "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}),
-    "face_restoration_unload": OptionInfo(False, "Move face restoration model from VRAM into RAM after processing"),
-}))
-
-options_templates.update(options_section(('system', "System"), {
-    "show_warnings": OptionInfo(False, "Show warnings in console."),
-    "memmon_poll_rate": OptionInfo(8, "VRAM usage polls per second during generation. Set to 0 to disable.", gr.Slider, {"minimum": 0, "maximum": 40, "step": 1}),
-    "samples_log_stdout": OptionInfo(False, "Always print all generation info to standard output"),
-    "multiple_tqdm": OptionInfo(True, "Add a second progress bar to the console that shows progress for an entire job."),
-    "print_hypernet_extra": OptionInfo(False, "Print extra hypernetwork information to console."),
-}))
-
-options_templates.update(options_section(('training', "Training"), {
-    "unload_models_when_training": OptionInfo(False, "Move VAE and CLIP to RAM when training if possible. Saves VRAM."),
-    "pin_memory": OptionInfo(False, "Turn on pin_memory for DataLoader. Makes training slightly faster but can increase memory usage."),
-    "save_optimizer_state": OptionInfo(False, "Saves Optimizer state as separate *.optim file. Training of embedding or HN can be resumed with the matching optim file."),
-    "save_training_settings_to_txt": OptionInfo(True, "Save textual inversion and hypernet settings to a text file whenever training starts."),
-    "dataset_filename_word_regex": OptionInfo("", "Filename word regex"),
-    "dataset_filename_join_string": OptionInfo(" ", "Filename join string"),
-    "training_image_repeats_per_epoch": OptionInfo(1, "Number of repeats for a single input image per epoch; used only for displaying epoch number", gr.Number, {"precision": 0}),
-    "training_write_csv_every": OptionInfo(500, "Save an csv containing the loss to log directory every N steps, 0 to disable"),
-    "training_xattention_optimizations": OptionInfo(False, "Use cross attention optimizations while training"),
-    "training_enable_tensorboard": OptionInfo(False, "Enable tensorboard logging."),
-    "training_tensorboard_save_images": OptionInfo(False, "Save generated images within tensorboard."),
-    "training_tensorboard_flush_every": OptionInfo(120, "How often, in seconds, to flush the pending tensorboard events and summaries to disk."),
-}))
-
-options_templates.update(options_section(('sd', "Stable Diffusion"), {
-    "sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": list_checkpoint_tiles()}, refresh=refresh_checkpoints),
-    "sd_checkpoint_cache": OptionInfo(0, "Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
-    "sd_vae_checkpoint_cache": OptionInfo(0, "VAE Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
-    "sd_vae": OptionInfo("Automatic", "SD VAE", gr.Dropdown, lambda: {"choices": shared_items.sd_vae_items()}, refresh=shared_items.refresh_vae_list),
-    "sd_vae_as_default": OptionInfo(True, "Ignore selected VAE for stable diffusion checkpoints that have their own .vae.pt next to them"),
-    "inpainting_mask_weight": OptionInfo(1.0, "Inpainting conditioning mask strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
-    "initial_noise_multiplier": OptionInfo(1.0, "Noise multiplier for img2img", gr.Slider, {"minimum": 0.5, "maximum": 1.5, "step": 0.01}),
-    "img2img_color_correction": OptionInfo(False, "Apply color correction to img2img results to match original colors."),
-    "img2img_fix_steps": OptionInfo(False, "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising)."),
-    "img2img_background_color": OptionInfo("#ffffff", "With img2img, fill image's transparent parts with this color.", ui_components.FormColorPicker, {}),
-    "enable_quantization": OptionInfo(False, "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply."),
-    "enable_emphasis": OptionInfo(True, "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention"),
-    "enable_batch_seeds": OptionInfo(True, "Make K-diffusion samplers produce same images in a batch as when making a single image"),
-    "comma_padding_backtrack": OptionInfo(20, "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens", gr.Slider, {"minimum": 0, "maximum": 74, "step": 1 }),
-    "CLIP_stop_at_last_layers": OptionInfo(1, "Clip skip", gr.Slider, {"minimum": 1, "maximum": 12, "step": 1}),
-    "upcast_attn": OptionInfo(False, "Upcast cross attention layer to float32"),
-}))
-
-options_templates.update(options_section(('compatibility', "Compatibility"), {
-    "use_old_emphasis_implementation": OptionInfo(False, "Use old emphasis implementation. Can be useful to reproduce old seeds."),
-    "use_old_karras_scheduler_sigmas": OptionInfo(False, "Use old karras scheduler sigmas (0.1 to 10)."),
-    "no_dpmpp_sde_batch_determinism": OptionInfo(False, "Do not make DPM++ SDE deterministic across different batch sizes."),
-    "use_old_hires_fix_width_height": OptionInfo(False, "For hires fix, use width/height sliders to set final resolution rather than first pass (disables Upscale by, Resize width/height to)."),
-}))
-
-options_templates.update(options_section(('interrogate', "Interrogate Options"), {
-    "interrogate_keep_models_in_memory": OptionInfo(False, "Interrogate: keep models in VRAM"),
-    "interrogate_return_ranks": OptionInfo(False, "Interrogate: include ranks of model tags matches in results (Has no effect on caption-based interrogators)."),
-    "interrogate_clip_num_beams": OptionInfo(1, "Interrogate: num_beams for BLIP", gr.Slider, {"minimum": 1, "maximum": 16, "step": 1}),
-    "interrogate_clip_min_length": OptionInfo(24, "Interrogate: minimum description length (excluding artists, etc..)", gr.Slider, {"minimum": 1, "maximum": 128, "step": 1}),
-    "interrogate_clip_max_length": OptionInfo(48, "Interrogate: maximum description length", gr.Slider, {"minimum": 1, "maximum": 256, "step": 1}),
-    "interrogate_clip_dict_limit": OptionInfo(1500, "CLIP: maximum number of lines in text file (0 = No limit)"),
-    "interrogate_clip_skip_categories": OptionInfo([], "CLIP: skip inquire categories", gr.CheckboxGroup, lambda: {"choices": modules.interrogate.category_types()}, refresh=modules.interrogate.category_types),
-    "interrogate_deepbooru_score_threshold": OptionInfo(0.5, "Interrogate: deepbooru score threshold", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}),
-    "deepbooru_sort_alpha": OptionInfo(True, "Interrogate: deepbooru sort alphabetically"),
-    "deepbooru_use_spaces": OptionInfo(False, "use spaces for tags in deepbooru"),
-    "deepbooru_escape": OptionInfo(True, "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)"),
-    "deepbooru_filter_tags": OptionInfo("", "filter out those tags from deepbooru output (separated by comma)"),
-}))
-
-options_templates.update(options_section(('extra_networks', "Extra Networks"), {
-    "extra_networks_default_view": OptionInfo("cards", "Default view for Extra Networks", gr.Dropdown, {"choices": ["cards", "thumbs"]}),
-    "extra_networks_default_multiplier": OptionInfo(1.0, "Multiplier for extra networks", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
-    "sd_hypernetwork": OptionInfo("None", "Add hypernetwork to prompt", gr.Dropdown, lambda: {"choices": [""] + [x for x in hypernetworks.keys()]}, refresh=reload_hypernetworks),
-}))
-
-options_templates.update(options_section(('ui', "User interface"), {
-    "return_grid": OptionInfo(True, "Show grid in results for web"),
-    "do_not_show_images": OptionInfo(False, "Do not show any images in results for web"),
-    "add_model_hash_to_info": OptionInfo(True, "Add model hash to generation information"),
-    "add_model_name_to_info": OptionInfo(True, "Add model name to generation information"),
-    "disable_weights_auto_swap": OptionInfo(True, "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint."),
-    "send_seed": OptionInfo(True, "Send seed when sending prompt or image to other interface"),
-    "send_size": OptionInfo(True, "Send size when sending prompt or image to another interface"),
-    "font": OptionInfo("", "Font for image grids that have text"),
-    "js_modal_lightbox": OptionInfo(True, "Enable full page image viewer"),
-    "js_modal_lightbox_initially_zoomed": OptionInfo(True, "Show images zoomed in by default in full page image viewer"),
-    "show_progress_in_title": OptionInfo(True, "Show generation progress in window title."),
-    "samplers_in_dropdown": OptionInfo(True, "Use dropdown for sampler selection instead of radio group"),
-    "dimensions_and_batch_together": OptionInfo(True, "Show Width/Height and Batch sliders in same row"),
-    "keyedit_precision_attention": OptionInfo(0.1, "Ctrl+up/down precision when editing (attention:1.1)", gr.Slider, {"minimum": 0.01, "maximum": 0.2, "step": 0.001}),
-    "keyedit_precision_extra": OptionInfo(0.05, "Ctrl+up/down precision when editing <extra networks:0.9>", gr.Slider, {"minimum": 0.01, "maximum": 0.2, "step": 0.001}),
-    "quicksettings": OptionInfo("sd_model_checkpoint", "Quicksettings list"),
-    "ui_reorder": OptionInfo(", ".join(ui_reorder_categories), "txt2img/img2img UI item order"),
-    "ui_extra_networks_tab_reorder": OptionInfo("", "Extra networks tab order"),
-    "localization": OptionInfo("None", "Localization (requires restart)", gr.Dropdown, lambda: {"choices": ["None"] + list(localization.localizations.keys())}, refresh=lambda: localization.list_localizations(cmd_opts.localizations_dir)),
-}))
-
-options_templates.update(options_section(('ui', "Live previews"), {
-    "show_progressbar": OptionInfo(True, "Show progressbar"),
-    "live_previews_enable": OptionInfo(True, "Show live previews of the created image"),
-    "show_progress_grid": OptionInfo(True, "Show previews of all images generated in a batch as a grid"),
-    "show_progress_every_n_steps": OptionInfo(10, "Show new live preview image every N sampling steps. Set to -1 to show after completion of batch.", gr.Slider, {"minimum": -1, "maximum": 32, "step": 1}),
-    "show_progress_type": OptionInfo("Approx NN", "Image creation progress preview mode", gr.Radio, {"choices": ["Full", "Approx NN", "Approx cheap"]}),
-    "live_preview_content": OptionInfo("Prompt", "Live preview subject", gr.Radio, {"choices": ["Combined", "Prompt", "Negative prompt"]}),
-    "live_preview_refresh_period": OptionInfo(1000, "Progressbar/preview update period, in milliseconds")
-}))
-
-options_templates.update(options_section(('sampler-params', "Sampler parameters"), {
-    "hide_samplers": OptionInfo([], "Hide samplers in user interface (requires restart)", gr.CheckboxGroup, lambda: {"choices": [x.name for x in list_samplers()]}),
-    "eta_ddim": OptionInfo(0.0, "eta (noise multiplier) for DDIM", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
-    "eta_ancestral": OptionInfo(1.0, "eta (noise multiplier) for ancestral samplers", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
-    "ddim_discretize": OptionInfo('uniform', "img2img DDIM discretize", gr.Radio, {"choices": ['uniform', 'quad']}),
-    's_churn': OptionInfo(0.0, "sigma churn", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
-    's_tmin':  OptionInfo(0.0, "sigma tmin",  gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
-    's_noise': OptionInfo(1.0, "sigma noise", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
-    'eta_noise_seed_delta': OptionInfo(0, "Eta noise seed delta", gr.Number, {"precision": 0}),
-    'always_discard_next_to_last_sigma': OptionInfo(False, "Always discard next-to-last sigma"),
-}))
-
-options_templates.update(options_section(('postprocessing', "Postprocessing"), {
-    'postprocessing_enable_in_main_ui': OptionInfo([], "Enable postprocessing operations in txt2img and img2img tabs", ui_components.DropdownMulti, lambda: {"choices": [x.name for x in shared_items.postprocessing_scripts()]}),
-    'postprocessing_operation_order': OptionInfo([], "Postprocessing operation order", ui_components.DropdownMulti, lambda: {"choices": [x.name for x in shared_items.postprocessing_scripts()]}),
-    'upscaling_max_images_in_cache': OptionInfo(5, "Maximum number of images in upscaling cache", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
-}))
-
-options_templates.update(options_section((None, "Hidden options"), {
-    "disabled_extensions": OptionInfo([], "Disable those extensions"),
-    "sd_checkpoint_hash": OptionInfo("", "SHA256 hash of the current checkpoint"),
-}))
-
-options_templates.update()
-
-
-class Options:
-    data = None
-    data_labels = options_templates
-    typemap = {int: float}
-
-    def __init__(self):
-        self.data = {k: v.default for k, v in self.data_labels.items()}
-
-    def __setattr__(self, key, value):
-        if self.data is not None:
-            if key in self.data or key in self.data_labels:
-                assert not cmd_opts.freeze_settings, "changing settings is disabled"
-
-                info = opts.data_labels.get(key, None)
-                comp_args = info.component_args if info else None
-                if isinstance(comp_args, dict) and comp_args.get('visible', True) is False:
-                    raise RuntimeError(f"not possible to set {key} because it is restricted")
-
-                if cmd_opts.hide_ui_dir_config and key in restricted_opts:
-                    raise RuntimeError(f"not possible to set {key} because it is restricted")
-
-                self.data[key] = value
-                return
-
-        return super(Options, self).__setattr__(key, value)
-
-    def __getattr__(self, item):
-        if self.data is not None:
-            if item in self.data:
-                return self.data[item]
-
-        if item in self.data_labels:
-            return self.data_labels[item].default
-
-        return super(Options, self).__getattribute__(item)
-
-    def set(self, key, value):
-        """sets an option and calls its onchange callback, returning True if the option changed and False otherwise"""
-
-        oldval = self.data.get(key, None)
-        if oldval == value:
-            return False
-
-        try:
-            setattr(self, key, value)
-        except RuntimeError:
-            return False
-
-        if self.data_labels[key].onchange is not None:
-            try:
-                self.data_labels[key].onchange()
-            except Exception as e:
-                errors.display(e, f"changing setting {key} to {value}")
-                setattr(self, key, oldval)
-                return False
-
-        return True
-
-    def save(self, filename):
-        assert not cmd_opts.freeze_settings, "saving settings is disabled"
-
-        with open(filename, "w", encoding="utf8") as file:
-            json.dump(self.data, file, indent=4)
-
-    def same_type(self, x, y):
-        if x is None or y is None:
-            return True
-
-        type_x = self.typemap.get(type(x), type(x))
-        type_y = self.typemap.get(type(y), type(y))
-
-        return type_x == type_y
-
-    def load(self, filename):
-        with open(filename, "r", encoding="utf8") as file:
-            self.data = json.load(file)
-
-        bad_settings = 0
-        for k, v in self.data.items():
-            info = self.data_labels.get(k, None)
-            if info is not None and not self.same_type(info.default, v):
-                print(f"Warning: bad setting value: {k}: {v} ({type(v).__name__}; expected {type(info.default).__name__})", file=sys.stderr)
-                bad_settings += 1
-
-        if bad_settings > 0:
-            print(f"The program is likely to not work with bad settings.\nSettings file: {filename}\nEither fix the file, or delete it and restart.", file=sys.stderr)
-
-    def onchange(self, key, func, call=True):
-        item = self.data_labels.get(key)
-        item.onchange = func
-
-        if call:
-            func()
-
-    def dumpjson(self):
-        d = {k: self.data.get(k, self.data_labels.get(k).default) for k in self.data_labels.keys()}
-        return json.dumps(d)
-
-    def add_option(self, key, info):
-        self.data_labels[key] = info
-
-    def reorder(self):
-        """reorder settings so that all items related to section always go together"""
-
-        section_ids = {}
-        settings_items = self.data_labels.items()
-        for k, item in settings_items:
-            if item.section not in section_ids:
-                section_ids[item.section] = len(section_ids)
-
-        self.data_labels = {k: v for k, v in sorted(settings_items, key=lambda x: section_ids[x[1].section])}
-
-    def cast_value(self, key, value):
-        """casts an arbitrary to the same type as this setting's value with key
-        Example: cast_value("eta_noise_seed_delta", "12") -> returns 12 (an int rather than str)
-        """
-
-        if value is None:
-            return None
-
-        default_value = self.data_labels[key].default
-        if default_value is None:
-            default_value = getattr(self, key, None)
-        if default_value is None:
-            return None
-
-        expected_type = type(default_value)
-        if expected_type == bool and value == "False":
-            value = False
-        else:
-            value = expected_type(value)
-
-        return value
-
-
-
-opts = Options()
-if os.path.exists(config_filename):
-    opts.load(config_filename)
-
-settings_components = None
-"""assinged from ui.py, a mapping on setting anmes to gradio components repsponsible for those settings"""
-
-latent_upscale_default_mode = "Latent"
-latent_upscale_modes = {
-    "Latent": {"mode": "bilinear", "antialias": False},
-    "Latent (antialiased)": {"mode": "bilinear", "antialias": True},
-    "Latent (bicubic)": {"mode": "bicubic", "antialias": False},
-    "Latent (bicubic antialiased)": {"mode": "bicubic", "antialias": True},
-    "Latent (nearest)": {"mode": "nearest", "antialias": False},
-    "Latent (nearest-exact)": {"mode": "nearest-exact", "antialias": False},
-}
-
-sd_upscalers = []
-
-sd_model = None
-
-clip_model = None
-
-progress_print_out = sys.stdout
-
-
-class TotalTQDM:
-    def __init__(self):
-        self._tqdm = None
-
-    def reset(self):
-        self._tqdm = tqdm.tqdm(
-            desc="Total progress",
-            total=state.job_count * state.sampling_steps,
-            position=1,
-            file=progress_print_out
-        )
-
-    def update(self):
-        if not opts.multiple_tqdm or cmd_opts.disable_console_progressbars:
-            return
-        if self._tqdm is None:
-            self.reset()
-        self._tqdm.update()
-
-    def updateTotal(self, new_total):
-        if not opts.multiple_tqdm or cmd_opts.disable_console_progressbars:
-            return
-        if self._tqdm is None:
-            self.reset()
-        self._tqdm.total = new_total
-
-    def clear(self):
-        if self._tqdm is not None:
-            self._tqdm.close()
-            self._tqdm = None
-
-
-total_tqdm = TotalTQDM()
-
-mem_mon = modules.memmon.MemUsageMonitor("MemMon", device, opts)
-mem_mon.start()
-
-
-def listfiles(dirname):
-    filenames = [os.path.join(dirname, x) for x in sorted(os.listdir(dirname)) if not x.startswith(".")]
-    return [file for file in filenames if os.path.isfile(file)]
-
-
-def html_path(filename):
-    return os.path.join(script_path, "html", filename)
-
-
-def html(filename):
-    path = html_path(filename)
-
-    if os.path.exists(path):
-        with open(path, encoding="utf8") as file:
-            return file.read()
-
-    return ""
diff --git a/spaces/vaibhavsharda/semantic_clustering/README.md b/spaces/vaibhavsharda/semantic_clustering/README.md
deleted file mode 100644
index f9f05b7a53ad12f446ce24ef7c671789c007783a..0000000000000000000000000000000000000000
--- a/spaces/vaibhavsharda/semantic_clustering/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: Semantic Clustering
-emoji: 🏃
-colorFrom: indigo
-colorTo: purple
-sdk: streamlit
-sdk_version: 1.10.0
-app_file: app.py
-pinned: false
-license: mit
-duplicated_from: taskswithcode/semantic_clustering
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/vaishanthr/Simultaneous-Segmented-Depth-Prediction/yolov8/docs/reference/yolo/engine/validator.md b/spaces/vaishanthr/Simultaneous-Segmented-Depth-Prediction/yolov8/docs/reference/yolo/engine/validator.md
deleted file mode 100644
index c9355ea18a08370eb1870f9ff69fa92b975e4ade..0000000000000000000000000000000000000000
--- a/spaces/vaishanthr/Simultaneous-Segmented-Depth-Prediction/yolov8/docs/reference/yolo/engine/validator.md
+++ /dev/null
@@ -1,9 +0,0 @@
----
-description: Ensure YOLOv5 models meet constraints and standards with the BaseValidator class. Learn how to use it here.
-keywords: Ultralytics, YOLO, BaseValidator, models, validation, object detection
----
-
-## BaseValidator
----
-### ::: ultralytics.yolo.engine.validator.BaseValidator
-<br><br>
diff --git a/spaces/venkat-natchi/yolov3_obj_detector/config.py b/spaces/venkat-natchi/yolov3_obj_detector/config.py
deleted file mode 100644
index aaa406dc70e0a2c824dd0df23725b801e78a4d14..0000000000000000000000000000000000000000
--- a/spaces/venkat-natchi/yolov3_obj_detector/config.py
+++ /dev/null
@@ -1,144 +0,0 @@
-import albumentations as A
-import cv2
-
-from albumentations.pytorch import ToTensorV2
-
-DATASET='PASCAL_VOC'
-DEVICE = "cpu"
-NUM_WORKERS = 0
-BATCH_SIZE = 16
-IMAGE_SIZE = 416
-NUM_CLASSES = 20
-CONF_THRESHOLD = 0.05
-MAP_IOU_THRESH = 0.5
-NMS_IOU_THRESH = 0.45
-S = [IMAGE_SIZE // 32, IMAGE_SIZE // 16, IMAGE_SIZE // 8]
-PIN_MEMORY = True
-LOAD_MODEL = False
-SAVE_MODEL = True
-
-ANCHORS = [
-    [(0.28, 0.22), (0.38, 0.48), (0.9, 0.78)],
-    [(0.07, 0.15), (0.15, 0.11), (0.14, 0.29)],
-    [(0.02, 0.03), (0.04, 0.07), (0.08, 0.06)],
-]  # Note these have been rescaled to be between [0, 1]
-
-means = [0.485, 0.456, 0.406]
-
-scale = 1.1
-test_transforms = A.Compose(
-    [
-        A.LongestMaxSize(max_size=IMAGE_SIZE),
-        A.PadIfNeeded(
-            min_height=IMAGE_SIZE, min_width=IMAGE_SIZE, border_mode=cv2.BORDER_CONSTANT
-        ),
-        A.Normalize(mean=[0, 0, 0], std=[1, 1, 1], max_pixel_value=255,),
-        ToTensorV2(),
-    ],
-)
-
-PASCAL_CLASSES = [
-    "aeroplane",
-    "bicycle",
-    "bird",
-    "boat",
-    "bottle",
-    "bus",
-    "car",
-    "cat",
-    "chair",
-    "cow",
-    "diningtable",
-    "dog",
-    "horse",
-    "motorbike",
-    "person",
-    "pottedplant",
-    "sheep",
-    "sofa",
-    "train",
-    "tvmonitor"
-]
-
-COCO_LABELS = ['person',
- 'bicycle',
- 'car',
- 'motorcycle',
- 'airplane',
- 'bus',
- 'train',
- 'truck',
- 'boat',
- 'traffic light',
- 'fire hydrant',
- 'stop sign',
- 'parking meter',
- 'bench',
- 'bird',
- 'cat',
- 'dog',
- 'horse',
- 'sheep',
- 'cow',
- 'elephant',
- 'bear',
- 'zebra',
- 'giraffe',
- 'backpack',
- 'umbrella',
- 'handbag',
- 'tie',
- 'suitcase',
- 'frisbee',
- 'skis',
- 'snowboard',
- 'sports ball',
- 'kite',
- 'baseball bat',
- 'baseball glove',
- 'skateboard',
- 'surfboard',
- 'tennis racket',
- 'bottle',
- 'wine glass',
- 'cup',
- 'fork',
- 'knife',
- 'spoon',
- 'bowl',
- 'banana',
- 'apple',
- 'sandwich',
- 'orange',
- 'broccoli',
- 'carrot',
- 'hot dog',
- 'pizza',
- 'donut',
- 'cake',
- 'chair',
- 'couch',
- 'potted plant',
- 'bed',
- 'dining table',
- 'toilet',
- 'tv',
- 'laptop',
- 'mouse',
- 'remote',
- 'keyboard',
- 'cell phone',
- 'microwave',
- 'oven',
- 'toaster',
- 'sink',
- 'refrigerator',
- 'book',
- 'clock',
- 'vase',
- 'scissors',
- 'teddy bear',
- 'hair drier',
- 'toothbrush'
-]
-
diff --git a/spaces/vonbarnekowa/stable-diffusion/ldm/modules/ema.py b/spaces/vonbarnekowa/stable-diffusion/ldm/modules/ema.py
deleted file mode 100644
index bded25019b9bcbcd0260f0b8185f8c7859ca58c4..0000000000000000000000000000000000000000
--- a/spaces/vonbarnekowa/stable-diffusion/ldm/modules/ema.py
+++ /dev/null
@@ -1,80 +0,0 @@
-import torch
-from torch import nn
-
-
-class LitEma(nn.Module):
-    def __init__(self, model, decay=0.9999, use_num_upates=True):
-        super().__init__()
-        if decay < 0.0 or decay > 1.0:
-            raise ValueError('Decay must be between 0 and 1')
-
-        self.m_name2s_name = {}
-        self.register_buffer('decay', torch.tensor(decay, dtype=torch.float32))
-        self.register_buffer('num_updates', torch.tensor(0, dtype=torch.int) if use_num_upates
-        else torch.tensor(-1, dtype=torch.int))
-
-        for name, p in model.named_parameters():
-            if p.requires_grad:
-                # remove as '.'-character is not allowed in buffers
-                s_name = name.replace('.', '')
-                self.m_name2s_name.update({name: s_name})
-                self.register_buffer(s_name, p.clone().detach().data)
-
-        self.collected_params = []
-
-    def reset_num_updates(self):
-        del self.num_updates
-        self.register_buffer('num_updates', torch.tensor(0, dtype=torch.int))
-
-    def forward(self, model):
-        decay = self.decay
-
-        if self.num_updates >= 0:
-            self.num_updates += 1
-            decay = min(self.decay, (1 + self.num_updates) / (10 + self.num_updates))
-
-        one_minus_decay = 1.0 - decay
-
-        with torch.no_grad():
-            m_param = dict(model.named_parameters())
-            shadow_params = dict(self.named_buffers())
-
-            for key in m_param:
-                if m_param[key].requires_grad:
-                    sname = self.m_name2s_name[key]
-                    shadow_params[sname] = shadow_params[sname].type_as(m_param[key])
-                    shadow_params[sname].sub_(one_minus_decay * (shadow_params[sname] - m_param[key]))
-                else:
-                    assert not key in self.m_name2s_name
-
-    def copy_to(self, model):
-        m_param = dict(model.named_parameters())
-        shadow_params = dict(self.named_buffers())
-        for key in m_param:
-            if m_param[key].requires_grad:
-                m_param[key].data.copy_(shadow_params[self.m_name2s_name[key]].data)
-            else:
-                assert not key in self.m_name2s_name
-
-    def store(self, parameters):
-        """
-        Save the current parameters for restoring later.
-        Args:
-          parameters: Iterable of `torch.nn.Parameter`; the parameters to be
-            temporarily stored.
-        """
-        self.collected_params = [param.clone() for param in parameters]
-
-    def restore(self, parameters):
-        """
-        Restore the parameters stored with the `store` method.
-        Useful to validate the model with EMA parameters without affecting the
-        original optimization process. Store the parameters before the
-        `copy_to` method. After validation (or model saving), use this to
-        restore the former parameters.
-        Args:
-          parameters: Iterable of `torch.nn.Parameter`; the parameters to be
-            updated with the stored parameters.
-        """
-        for c_param, param in zip(self.collected_params, parameters):
-            param.data.copy_(c_param.data)
diff --git a/spaces/wangfuchao/bingo-wangfuchao/README.md b/spaces/wangfuchao/bingo-wangfuchao/README.md
deleted file mode 100644
index 5d6936218874c647b5d22e13ad4be7edb8936f92..0000000000000000000000000000000000000000
--- a/spaces/wangfuchao/bingo-wangfuchao/README.md
+++ /dev/null
@@ -1,28 +0,0 @@
----
-title: bingo
-emoji: 😊
-colorFrom: red
-colorTo: red
-sdk: docker
-license: mit
-duplicated_from: hf4all/bingo
----
-
-<div align="center">
-
-# Bingo 
-
-Bingo，一个让你呼吸顺畅 New Bing。
-
-高度还原 New Bing 网页版的主要操作，国内可用，兼容绝大多数微软 Bing AI 的功能，可自行部署使用。
-
-![Github stars](https://badgen.net/github/stars/weaigc/bingo?icon=github&label=stars)
-![Gthub issues](https://img.shields.io/github/issues/weaigc/bingo)
-[![docker build](https://github.com/weaigc/bingo/actions/workflows/docker.yml/badge.svg)](https://hub.docker.com/repository/docker/weaigc/bingo/)
-[![docker hub](https://badgen.net/docker/size/weaigc/bingo?icon=docker&label=image%20size)](https://hub.docker.com/repository/docker/weaigc/bingo/)
-[![MIT License](https://img.shields.io/badge/license-MIT-97c50f)](https://github.com/weaigc/bingo/blob/main/license)
-
-问题反馈请前往 https://github.com/weaigc/bingo/issues
-</div>
-
-
diff --git a/spaces/wangrongsheng/ChatImprovement/crazy_functions/test_project/cpp/cppipc/queue.h b/spaces/wangrongsheng/ChatImprovement/crazy_functions/test_project/cpp/cppipc/queue.h
deleted file mode 100644
index a21f3446e06b5826af7b554c8a7d9c5d80848b62..0000000000000000000000000000000000000000
--- a/spaces/wangrongsheng/ChatImprovement/crazy_functions/test_project/cpp/cppipc/queue.h
+++ /dev/null
@@ -1,216 +0,0 @@
-#pragma once
-
-#include <type_traits>
-#include <new>
-#include <utility>  // [[since C++14]]: std::exchange
-#include <algorithm>
-#include <atomic>
-#include <tuple>
-#include <thread>
-#include <chrono>
-#include <string>
-#include <cassert>  // assert
-
-#include "libipc/def.h"
-#include "libipc/shm.h"
-#include "libipc/rw_lock.h"
-
-#include "libipc/utility/log.h"
-#include "libipc/platform/detail.h"
-#include "libipc/circ/elem_def.h"
-
-namespace ipc {
-namespace detail {
-
-class queue_conn {
-protected:
-    circ::cc_t connected_ = 0;
-    shm::handle elems_h_;
-
-    template <typename Elems>
-    Elems* open(char const * name) {
-        if (name == nullptr || name[0] == '\0') {
-            ipc::error("fail open waiter: name is empty!\n");
-            return nullptr;
-        }
-        if (!elems_h_.acquire(name, sizeof(Elems))) {
-            return nullptr;
-        }
-        auto elems = static_cast<Elems*>(elems_h_.get());
-        if (elems == nullptr) {
-            ipc::error("fail acquire elems: %s\n", name);
-            return nullptr;
-        }
-        elems->init();
-        return elems;
-    }
-
-    void close() {
-        elems_h_.release();
-    }
-
-public:
-    queue_conn() = default;
-    queue_conn(const queue_conn&) = delete;
-    queue_conn& operator=(const queue_conn&) = delete;
-
-    bool connected() const noexcept {
-        return connected_ != 0;
-    }
-
-    circ::cc_t connected_id() const noexcept {
-        return connected_;
-    }
-
-    template <typename Elems>
-    auto connect(Elems* elems) noexcept
-                         /*needs 'optional' here*/
-     -> std::tuple<bool, bool, decltype(std::declval<Elems>().cursor())> {
-        if (elems == nullptr) return {};
-        // if it's already connected, just return
-        if (connected()) return {connected(), false, 0};
-        connected_ = elems->connect_receiver();
-        return {connected(), true, elems->cursor()};
-    }
-
-    template <typename Elems>
-    bool disconnect(Elems* elems) noexcept {
-        if (elems == nullptr) return false;
-        // if it's already disconnected, just return false
-        if (!connected()) return false;
-        elems->disconnect_receiver(std::exchange(connected_, 0));
-        return true;
-    }
-};
-
-template <typename Elems>
-class queue_base : public queue_conn {
-    using base_t = queue_conn;
-
-public:
-    using elems_t  = Elems;
-    using policy_t = typename elems_t::policy_t;
-
-protected:
-    elems_t * elems_ = nullptr;
-    decltype(std::declval<elems_t>().cursor()) cursor_ = 0;
-    bool sender_flag_ = false;
-
-public:
-    using base_t::base_t;
-
-    queue_base() = default;
-
-    explicit queue_base(char const * name)
-        : queue_base{} {
-        elems_ = open<elems_t>(name);
-    }
-
-    explicit queue_base(elems_t * elems) noexcept
-        : queue_base{} {
-        assert(elems != nullptr);
-        elems_ = elems;
-    }
-
-    /* not virtual */ ~queue_base() {
-        base_t::close();
-    }
-
-    elems_t       * elems()       noexcept { return elems_; }
-    elems_t const * elems() const noexcept { return elems_; }
-
-    bool ready_sending() noexcept {
-        if (elems_ == nullptr) return false;
-        return sender_flag_ || (sender_flag_ = elems_->connect_sender());
-    }
-
-    void shut_sending() noexcept {
-        if (elems_ == nullptr) return;
-        if (!sender_flag_) return;
-        elems_->disconnect_sender();
-    }
-
-    bool connect() noexcept {
-        auto tp = base_t::connect(elems_);
-        if (std::get<0>(tp) && std::get<1>(tp)) {
-            cursor_ = std::get<2>(tp);
-            return true;
-        }
-        return std::get<0>(tp);
-    }
-
-    bool disconnect() noexcept {
-        return base_t::disconnect(elems_);
-    }
-
-    std::size_t conn_count() const noexcept {
-        return (elems_ == nullptr) ? static_cast<std::size_t>(invalid_value) : elems_->conn_count();
-    }
-
-    bool valid() const noexcept {
-        return elems_ != nullptr;
-    }
-
-    bool empty() const noexcept {
-        return !valid() || (cursor_ == elems_->cursor());
-    }
-
-    template <typename T, typename F, typename... P>
-    bool push(F&& prep, P&&... params) {
-        if (elems_ == nullptr) return false;
-        return elems_->push(this, [&](void* p) {
-            if (prep(p)) ::new (p) T(std::forward<P>(params)...);
-        });
-    }
-
-    template <typename T, typename F, typename... P>
-    bool force_push(F&& prep, P&&... params) {
-        if (elems_ == nullptr) return false;
-        return elems_->force_push(this, [&](void* p) {
-            if (prep(p)) ::new (p) T(std::forward<P>(params)...);
-        });
-    }
-
-    template <typename T, typename F>
-    bool pop(T& item, F&& out) {
-        if (elems_ == nullptr) {
-            return false;
-        }
-        return elems_->pop(this, &(this->cursor_), [&item](void* p) {
-            ::new (&item) T(std::move(*static_cast<T*>(p)));
-        }, std::forward<F>(out));
-    }
-};
-
-} // namespace detail
-
-template <typename T, typename Policy>
-class queue final : public detail::queue_base<typename Policy::template elems_t<sizeof(T), alignof(T)>> {
-    using base_t = detail::queue_base<typename Policy::template elems_t<sizeof(T), alignof(T)>>;
-
-public:
-    using value_t = T;
-
-    using base_t::base_t;
-
-    template <typename... P>
-    bool push(P&&... params) {
-        return base_t::template push<T>(std::forward<P>(params)...);
-    }
-
-    template <typename... P>
-    bool force_push(P&&... params) {
-        return base_t::template force_push<T>(std::forward<P>(params)...);
-    }
-
-    bool pop(T& item) {
-        return base_t::pop(item, [](bool) {});
-    }
-
-    template <typename F>
-    bool pop(T& item, F&& out) {
-        return base_t::pop(item, std::forward<F>(out));
-    }
-};
-
-} // namespace ipc
diff --git a/spaces/whitphx/gradio-static-test/dist/assets/BlockTitle-a953ec46.js b/spaces/whitphx/gradio-static-test/dist/assets/BlockTitle-a953ec46.js
deleted file mode 100644
index b6eaa13f423124fca9fe4321ae0c5c46df185e73..0000000000000000000000000000000000000000
--- a/spaces/whitphx/gradio-static-test/dist/assets/BlockTitle-a953ec46.js
+++ /dev/null
@@ -1,2 +0,0 @@
-import{S as d,i as h,s as k,W as $,H as g,I as w,f as I,D as q,N as _,h as m,Y as B,Z as N,$ as S,q as r,n as j,t as c,p as C,r as p,e as D,m as H,o as J,J as L,L as T}from"../lite.js";import{I as W}from"./Info-7e9477b8.js";import"./Button-0391b19a.js";function b(f){let e,l;return e=new W({props:{$$slots:{default:[Y]},$$scope:{ctx:f}}}),{c(){D(e.$$.fragment)},m(n,o){H(e,n,o),l=!0},p(n,o){const u={};o&10&&(u.$$scope={dirty:o,ctx:n}),e.$set(u)},i(n){l||(r(e.$$.fragment,n),l=!0)},o(n){c(e.$$.fragment,n),l=!1},d(n){J(e,n)}}}function Y(f){let e;return{c(){e=L(f[1])},m(l,n){m(l,e,n)},p(l,n){n&2&&T(e,l[1])},d(l){l&&p(e)}}}function Z(f){let e,l,n,o;const u=f[2].default,a=$(u,f,f[3],null);let s=f[1]&&b(f);return{c(){e=g("span"),a&&a.c(),l=w(),s&&s.c(),n=I(),q(e,"class","svelte-1gfkn6j"),_(e,"sr-only",!f[0]),_(e,"hide",!f[0]),_(e,"has-info",f[1]!=null)},m(t,i){m(t,e,i),a&&a.m(e,null),m(t,l,i),s&&s.m(t,i),m(t,n,i),o=!0},p(t,[i]){a&&a.p&&(!o||i&8)&&B(a,u,t,t[3],o?S(u,t[3],i,null):N(t[3]),null),(!o||i&1)&&_(e,"sr-only",!t[0]),(!o||i&1)&&_(e,"hide",!t[0]),(!o||i&2)&&_(e,"has-info",t[1]!=null),t[1]?s?(s.p(t,i),i&2&&r(s,1)):(s=b(t),s.c(),r(s,1),s.m(n.parentNode,n)):s&&(j(),c(s,1,1,()=>{s=null}),C())},i(t){o||(r(a,t),r(s),o=!0)},o(t){c(a,t),c(s),o=!1},d(t){t&&p(e),a&&a.d(t),t&&p(l),s&&s.d(t),t&&p(n)}}}function v(f,e,l){let{$$slots:n={},$$scope:o}=e,{show_label:u=!0}=e,{info:a=void 0}=e;return f.$$set=s=>{"show_label"in s&&l(0,u=s.show_label),"info"in s&&l(1,a=s.info),"$$scope"in s&&l(3,o=s.$$scope)},[u,a,n,o]}class F extends d{constructor(e){super(),h(this,e,v,Z,k,{show_label:0,info:1})}}export{F as B};
-//# sourceMappingURL=BlockTitle-a953ec46.js.map
diff --git a/spaces/williambr/SteamlitMapPractice2/README.md b/spaces/williambr/SteamlitMapPractice2/README.md
deleted file mode 100644
index 42b02755c42d07004c9fdf5f17e1b27dfa8445e7..0000000000000000000000000000000000000000
--- a/spaces/williambr/SteamlitMapPractice2/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: SteamlitMapPractice2
-emoji: 🚀
-colorFrom: red
-colorTo: gray
-sdk: streamlit
-sdk_version: 1.10.0
-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/xdecoder/Demo/app.py b/spaces/xdecoder/Demo/app.py
deleted file mode 100644
index 3898ffcc510a21894313126ec9cb2ba49b352232..0000000000000000000000000000000000000000
--- a/spaces/xdecoder/Demo/app.py
+++ /dev/null
@@ -1,124 +0,0 @@
-# --------------------------------------------------------
-# X-Decoder -- Generalized Decoding for Pixel, Image, and Language
-# Copyright (c) 2022 Microsoft
-# Licensed under The MIT License [see LICENSE for details]
-# Written by Xueyan Zou (xueyan@cs.wisc.edu), Jianwei Yang (jianwyan@microsoft.com)
-# --------------------------------------------------------
-
-import os
-os.system("python -m pip install git+https://github.com/MaureenZOU/detectron2-xyz.git")
-
-import gradio as gr
-import torch
-import argparse
-
-from xdecoder.BaseModel import BaseModel
-from xdecoder import build_model
-from utils.distributed import init_distributed
-from utils.arguments import load_opt_from_config_files
-
-from tasks import *
-
-def parse_option():
-    parser = argparse.ArgumentParser('X-Decoder All-in-One Demo', add_help=False)
-    parser.add_argument('--conf_files', default="configs/xdecoder/svlp_focalt_lang.yaml", metavar="FILE", help='path to config file', )
-    args = parser.parse_args()
-
-    return args
-
-'''
-build args
-'''
-args = parse_option()
-opt = load_opt_from_config_files(args.conf_files)
-opt = init_distributed(opt)
-
-# META DATA
-pretrained_pth_last = os.path.join("xdecoder_focalt_last.pt")
-pretrained_pth_novg = os.path.join("xdecoder_focalt_last_novg.pt")
-
-if not os.path.exists(pretrained_pth_last):
-    os.system("wget {}".format("https://projects4jw.blob.core.windows.net/x-decoder/release/xdecoder_focalt_last.pt"))
-
-if not os.path.exists(pretrained_pth_novg):
-    os.system("wget {}".format("https://projects4jw.blob.core.windows.net/x-decoder/release/xdecoder_focalt_last_novg.pt"))
-
-
-'''
-build model
-'''
-model_last = BaseModel(opt, build_model(opt)).from_pretrained(pretrained_pth_last).eval().cuda()
-model_cap = BaseModel(opt, build_model(opt)).from_pretrained(pretrained_pth_novg).eval().cuda()
-
-with torch.no_grad():
-    model_last.model.sem_seg_head.predictor.lang_encoder.get_text_embeddings(["background", "background"], is_eval=True)
-    model_cap.model.sem_seg_head.predictor.lang_encoder.get_text_embeddings(["background", "background"], is_eval=True)
-
-'''
-inference model
-'''
-
-@torch.no_grad()
-def inference(image, task, *args, **kwargs):
-    image = image.convert("RGB")
-    with torch.autocast(device_type='cuda', dtype=torch.float16):
-        if task == 'Referring Editing':
-            return referring_inpainting(model_last, image, *args, **kwargs)
-        elif task == 'Referring Segmentation':
-            return referring_segmentation(model_last, image, *args, **kwargs)
-        elif task == 'Open Vocabulary Semantic Segmentation':
-            return open_semseg(model_last, image, *args, **kwargs)
-        elif task == 'Open Vocabulary Panoptic Segmentation':
-            return open_panoseg(model_last, image, *args, **kwargs)
-        elif task == 'Open Vocabulary Instance Segmentation':
-            return open_instseg(model_last, image, *args, **kwargs)
-        elif task == 'Image Captioning':
-            return image_captioning(model_cap, image, *args, **kwargs)
-        elif task == 'Referring Captioning (Beta)':
-            return referring_captioning([model_last, model_cap], image, *args, **kwargs)
-        elif task == 'Text Retrieval':
-            return text_retrieval(model_cap, image, *args, **kwargs)
-        elif task == 'Image/Region Retrieval':
-            return region_retrieval([model_cap, model_last], image, *args, **kwargs)
-
-'''
-launch app
-'''
-title = "X-Decoder All-in-One Demo"
-description = """<p style='text-align: center'> <a href='https://x-decoder-vl.github.io/' target='_blank'>Project Page</a> | <a href='https://arxiv.org/pdf/2212.11270.pdf' target='_blank'>Paper</a> | <a href='https://github.com/microsoft/X-Decoder' target='_blank'>Github Repo</a> | <a href='https://youtu.be/wYp6vmyolqE' target='_blank'>Video</a> </p>
-<p>Skip the queue by duplicating this space and upgrading to GPU in settings</p>
-<a href="https://huggingface.co/spaces/xdecoder/Demo?duplicate=true"><img src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>
-"""
-
-article = "The Demo is Run on X-Decoder (Focal-T)."
-
-inputs = [gr.inputs.Image(type='pil'), gr.inputs.Radio(choices=["Referring Segmentation", "Referring Editing", 'Open Vocabulary Semantic Segmentation','Open Vocabulary Instance Segmentation', "Open Vocabulary Panoptic Segmentation", "Image Captioning", "Text Retrieval", "Image/Region Retrieval", "Referring Captioning (Beta)"], type="value", default="OpenVocab Semantic Segmentation", label="Task"), gr.Textbox(label="xdecoder_text"), gr.Textbox(label="inpainting_text"), gr.Textbox(label="task_description")]
-gr.Interface(
-    fn=inference,
-    inputs=inputs,
-    outputs=[
-        gr.outputs.Image(
-        type="pil",
-        label="segmentation results"),
-        gr.Textbox(label="text results"),
-        gr.outputs.Image(
-        type="pil",
-        label="editing results"),
-    ],
-    examples=[
-    ["./images/fruit.jpg", "Referring Segmentation", "The larger watermelon.,The front white flower.,White tea pot.,Flower bunch.,white vase.,The peach on the left.,The brown knife.,The handkerchief.", '', 'Format: s,s,s'],
-    ["./images/apples.jpg", "Referring Editing", "the green apple", 'a red apple', 'x-decoder + ldm (inference takes ~20s),  use inpainting_text "clean and empty scene" for image inpainting'],
-    ["./images/horse.png", "Referring Editing", "the sky", 'a mountain', 'x-decoder + ldm (inference takes ~20s),  use inpainting_text "clean and empty scene" for image inpainting'],
-    ["./images/animals.png", "Open Vocabulary Semantic Segmentation", "zebra,antelope,giraffe,ostrich,sky,water,grass,sand,tree", '', 'Format: x,x,x'],
-    ["./images/owls.jpeg", "Open Vocabulary Instance Segmentation", "owl", '', 'Format: y,y,y'],
-    ["./images/mountain.jpeg", "Image Captioning", "", '', ''],
-    ["./images/rose.webp", "Text Retrieval", "lily,rose,peoney,tulip", '', 'Format: s,s,s'],
-    ["./images/region_retrieval.png", "Image/Region Retrieval", "The tangerine on the plate.", '', 'Please describe the object in a detailed way (80 images in the pool).'],
-    ["./images/landscape.jpg", "Referring Captioning (Beta)", "cloud", '', 'Please fill in a noun/noun phrase. (may start with a/the)'],
-    ],
-    title=title,
-    description=description,
-    article=article,
-    allow_flagging='never',
-    cache_examples=True,
-).launch()
\ No newline at end of file
diff --git a/spaces/xiaorong/fork2-so-vits/attentions.py b/spaces/xiaorong/fork2-so-vits/attentions.py
deleted file mode 100644
index 86bc73b5fe98cc7b443e9078553920346c996707..0000000000000000000000000000000000000000
--- a/spaces/xiaorong/fork2-so-vits/attentions.py
+++ /dev/null
@@ -1,300 +0,0 @@
-import math
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-import commons
-from modules import LayerNorm
-   
-
-class Encoder(nn.Module):
-  def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., window_size=4, **kwargs):
-    super().__init__()
-    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.window_size = window_size
-
-    self.drop = nn.Dropout(p_dropout)
-    self.attn_layers = nn.ModuleList()
-    self.norm_layers_1 = nn.ModuleList()
-    self.ffn_layers = nn.ModuleList()
-    self.norm_layers_2 = nn.ModuleList()
-    for i in range(self.n_layers):
-      self.attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, window_size=window_size))
-      self.norm_layers_1.append(LayerNorm(hidden_channels))
-      self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout))
-      self.norm_layers_2.append(LayerNorm(hidden_channels))
-
-  def forward(self, x, x_mask):
-    attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
-    x = x * x_mask
-    for i in range(self.n_layers):
-      y = self.attn_layers[i](x, x, attn_mask)
-      y = self.drop(y)
-      x = self.norm_layers_1[i](x + y)
-
-      y = self.ffn_layers[i](x, x_mask)
-      y = self.drop(y)
-      x = self.norm_layers_2[i](x + y)
-    x = x * x_mask
-    return x
-
-
-class Decoder(nn.Module):
-  def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., proximal_bias=False, proximal_init=True, **kwargs):
-    super().__init__()
-    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.proximal_bias = proximal_bias
-    self.proximal_init = proximal_init
-
-    self.drop = nn.Dropout(p_dropout)
-    self.self_attn_layers = nn.ModuleList()
-    self.norm_layers_0 = nn.ModuleList()
-    self.encdec_attn_layers = nn.ModuleList()
-    self.norm_layers_1 = nn.ModuleList()
-    self.ffn_layers = nn.ModuleList()
-    self.norm_layers_2 = nn.ModuleList()
-    for i in range(self.n_layers):
-      self.self_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, proximal_bias=proximal_bias, proximal_init=proximal_init))
-      self.norm_layers_0.append(LayerNorm(hidden_channels))
-      self.encdec_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout))
-      self.norm_layers_1.append(LayerNorm(hidden_channels))
-      self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout, causal=True))
-      self.norm_layers_2.append(LayerNorm(hidden_channels))
-
-  def forward(self, x, x_mask, h, h_mask):
-    """
-    x: decoder input
-    h: encoder output
-    """
-    self_attn_mask = commons.subsequent_mask(x_mask.size(2)).to(device=x.device, dtype=x.dtype)
-    encdec_attn_mask = h_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
-    x = x * x_mask
-    for i in range(self.n_layers):
-      y = self.self_attn_layers[i](x, x, self_attn_mask)
-      y = self.drop(y)
-      x = self.norm_layers_0[i](x + y)
-
-      y = self.encdec_attn_layers[i](x, h, encdec_attn_mask)
-      y = self.drop(y)
-      x = self.norm_layers_1[i](x + y)
-      
-      y = self.ffn_layers[i](x, x_mask)
-      y = self.drop(y)
-      x = self.norm_layers_2[i](x + y)
-    x = x * x_mask
-    return x
-
-
-class MultiHeadAttention(nn.Module):
-  def __init__(self, channels, out_channels, n_heads, p_dropout=0., window_size=None, heads_share=True, block_length=None, proximal_bias=False, proximal_init=False):
-    super().__init__()
-    assert channels % n_heads == 0
-
-    self.channels = channels
-    self.out_channels = out_channels
-    self.n_heads = n_heads
-    self.p_dropout = p_dropout
-    self.window_size = window_size
-    self.heads_share = heads_share
-    self.block_length = block_length
-    self.proximal_bias = proximal_bias
-    self.proximal_init = proximal_init
-    self.attn = None
-
-    self.k_channels = channels // n_heads
-    self.conv_q = nn.Conv1d(channels, channels, 1)
-    self.conv_k = nn.Conv1d(channels, channels, 1)
-    self.conv_v = nn.Conv1d(channels, channels, 1)
-    self.conv_o = nn.Conv1d(channels, out_channels, 1)
-    self.drop = nn.Dropout(p_dropout)
-
-    if window_size is not None:
-      n_heads_rel = 1 if heads_share else n_heads
-      rel_stddev = self.k_channels**-0.5
-      self.emb_rel_k = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
-      self.emb_rel_v = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
-
-    nn.init.xavier_uniform_(self.conv_q.weight)
-    nn.init.xavier_uniform_(self.conv_k.weight)
-    nn.init.xavier_uniform_(self.conv_v.weight)
-    if proximal_init:
-      with torch.no_grad():
-        self.conv_k.weight.copy_(self.conv_q.weight)
-        self.conv_k.bias.copy_(self.conv_q.bias)
-      
-  def forward(self, x, c, attn_mask=None):
-    q = self.conv_q(x)
-    k = self.conv_k(c)
-    v = self.conv_v(c)
-    
-    x, self.attn = self.attention(q, k, v, mask=attn_mask)
-
-    x = self.conv_o(x)
-    return x
-
-  def attention(self, query, key, value, mask=None):
-    # reshape [b, d, t] -> [b, n_h, t, d_k]
-    b, d, t_s, t_t = (*key.size(), query.size(2))
-    query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3)
-    key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
-    value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
-
-    scores = torch.matmul(query / math.sqrt(self.k_channels), key.transpose(-2, -1))
-    if self.window_size is not None:
-      assert t_s == t_t, "Relative attention is only available for self-attention."
-      key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s)
-      rel_logits = self._matmul_with_relative_keys(query /math.sqrt(self.k_channels), key_relative_embeddings)
-      scores_local = self._relative_position_to_absolute_position(rel_logits)
-      scores = scores + scores_local
-    if self.proximal_bias:
-      assert t_s == t_t, "Proximal bias is only available for self-attention."
-      scores = scores + self._attention_bias_proximal(t_s).to(device=scores.device, dtype=scores.dtype)
-    if mask is not None:
-      scores = scores.masked_fill(mask == 0, -1e4)
-      if self.block_length is not None:
-        assert t_s == t_t, "Local attention is only available for self-attention."
-        block_mask = torch.ones_like(scores).triu(-self.block_length).tril(self.block_length)
-        scores = scores.masked_fill(block_mask == 0, -1e4)
-    p_attn = F.softmax(scores, dim=-1) # [b, n_h, t_t, t_s]
-    p_attn = self.drop(p_attn)
-    output = torch.matmul(p_attn, value)
-    if self.window_size is not None:
-      relative_weights = self._absolute_position_to_relative_position(p_attn)
-      value_relative_embeddings = self._get_relative_embeddings(self.emb_rel_v, t_s)
-      output = output + self._matmul_with_relative_values(relative_weights, value_relative_embeddings)
-    output = output.transpose(2, 3).contiguous().view(b, d, t_t) # [b, n_h, t_t, d_k] -> [b, d, t_t]
-    return output, p_attn
-
-  def _matmul_with_relative_values(self, x, y):
-    """
-    x: [b, h, l, m]
-    y: [h or 1, m, d]
-    ret: [b, h, l, d]
-    """
-    ret = torch.matmul(x, y.unsqueeze(0))
-    return ret
-
-  def _matmul_with_relative_keys(self, x, y):
-    """
-    x: [b, h, l, d]
-    y: [h or 1, m, d]
-    ret: [b, h, l, m]
-    """
-    ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1))
-    return ret
-
-  def _get_relative_embeddings(self, relative_embeddings, length):
-    max_relative_position = 2 * self.window_size + 1
-    # Pad first before slice to avoid using cond ops.
-    pad_length = max(length - (self.window_size + 1), 0)
-    slice_start_position = max((self.window_size + 1) - length, 0)
-    slice_end_position = slice_start_position + 2 * length - 1
-    if pad_length > 0:
-      padded_relative_embeddings = F.pad(
-          relative_embeddings,
-          commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]))
-    else:
-      padded_relative_embeddings = relative_embeddings
-    used_relative_embeddings = padded_relative_embeddings[:,slice_start_position:slice_end_position]
-    return used_relative_embeddings
-
-  def _relative_position_to_absolute_position(self, x):
-    """
-    x: [b, h, l, 2*l-1]
-    ret: [b, h, l, l]
-    """
-    batch, heads, length, _ = x.size()
-    # Concat columns of pad to shift from relative to absolute indexing.
-    x = F.pad(x, commons.convert_pad_shape([[0,0],[0,0],[0,0],[0,1]]))
-
-    # Concat extra elements so to add up to shape (len+1, 2*len-1).
-    x_flat = x.view([batch, heads, length * 2 * length])
-    x_flat = F.pad(x_flat, commons.convert_pad_shape([[0,0],[0,0],[0,length-1]]))
-
-    # Reshape and slice out the padded elements.
-    x_final = x_flat.view([batch, heads, length+1, 2*length-1])[:, :, :length, length-1:]
-    return x_final
-
-  def _absolute_position_to_relative_position(self, x):
-    """
-    x: [b, h, l, l]
-    ret: [b, h, l, 2*l-1]
-    """
-    batch, heads, length, _ = x.size()
-    # padd along column
-    x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length-1]]))
-    x_flat = x.view([batch, heads, length**2 + length*(length -1)])
-    # add 0's in the beginning that will skew the elements after reshape
-    x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [length, 0]]))
-    x_final = x_flat.view([batch, heads, length, 2*length])[:,:,:,1:]
-    return x_final
-
-  def _attention_bias_proximal(self, length):
-    """Bias for self-attention to encourage attention to close positions.
-    Args:
-      length: an integer scalar.
-    Returns:
-      a Tensor with shape [1, 1, length, length]
-    """
-    r = torch.arange(length, dtype=torch.float32)
-    diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1)
-    return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0)
-
-
-class FFN(nn.Module):
-  def __init__(self, in_channels, out_channels, filter_channels, kernel_size, p_dropout=0., activation=None, causal=False):
-    super().__init__()
-    self.in_channels = in_channels
-    self.out_channels = out_channels
-    self.filter_channels = filter_channels
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.activation = activation
-    self.causal = causal
-
-    if causal:
-      self.padding = self._causal_padding
-    else:
-      self.padding = self._same_padding
-
-    self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size)
-    self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size)
-    self.drop = nn.Dropout(p_dropout)
-
-  def forward(self, x, x_mask):
-    x = self.conv_1(self.padding(x * x_mask))
-    if self.activation == "gelu":
-      x = x * torch.sigmoid(1.702 * x)
-    else:
-      x = torch.relu(x)
-    x = self.drop(x)
-    x = self.conv_2(self.padding(x * x_mask))
-    return x * x_mask
-  
-  def _causal_padding(self, x):
-    if self.kernel_size == 1:
-      return x
-    pad_l = self.kernel_size - 1
-    pad_r = 0
-    padding = [[0, 0], [0, 0], [pad_l, pad_r]]
-    x = F.pad(x, commons.convert_pad_shape(padding))
-    return x
-
-  def _same_padding(self, x):
-    if self.kernel_size == 1:
-      return x
-    pad_l = (self.kernel_size - 1) // 2
-    pad_r = self.kernel_size // 2
-    padding = [[0, 0], [0, 0], [pad_l, pad_r]]
-    x = F.pad(x, commons.convert_pad_shape(padding))
-    return x
diff --git a/spaces/xiaoyinqu/dreambooth/convertosd.py b/spaces/xiaoyinqu/dreambooth/convertosd.py
deleted file mode 100644
index e4bec6cbe894dd74b24f633cc66346d687d3f802..0000000000000000000000000000000000000000
--- a/spaces/xiaoyinqu/dreambooth/convertosd.py
+++ /dev/null
@@ -1,226 +0,0 @@
-# Script for converting a HF Diffusers saved pipeline to a Stable Diffusion checkpoint.
-# *Only* converts the UNet, VAE, and Text Encoder.
-# Does not convert optimizer state or any other thing.
-# Written by jachiam
-
-import argparse
-import os.path as osp
-
-import torch
-import gc 
-
-# =================#
-# UNet Conversion #
-# =================#
-
-unet_conversion_map = [
-    # (stable-diffusion, HF Diffusers)
-    ("time_embed.0.weight", "time_embedding.linear_1.weight"),
-    ("time_embed.0.bias", "time_embedding.linear_1.bias"),
-    ("time_embed.2.weight", "time_embedding.linear_2.weight"),
-    ("time_embed.2.bias", "time_embedding.linear_2.bias"),
-    ("input_blocks.0.0.weight", "conv_in.weight"),
-    ("input_blocks.0.0.bias", "conv_in.bias"),
-    ("out.0.weight", "conv_norm_out.weight"),
-    ("out.0.bias", "conv_norm_out.bias"),
-    ("out.2.weight", "conv_out.weight"),
-    ("out.2.bias", "conv_out.bias"),
-]
-
-unet_conversion_map_resnet = [
-    # (stable-diffusion, HF Diffusers)
-    ("in_layers.0", "norm1"),
-    ("in_layers.2", "conv1"),
-    ("out_layers.0", "norm2"),
-    ("out_layers.3", "conv2"),
-    ("emb_layers.1", "time_emb_proj"),
-    ("skip_connection", "conv_shortcut"),
-]
-
-unet_conversion_map_layer = []
-# hardcoded number of downblocks and resnets/attentions...
-# would need smarter logic for other networks.
-for i in range(4):
-    # loop over downblocks/upblocks
-
-    for j in range(2):
-        # loop over resnets/attentions for downblocks
-        hf_down_res_prefix = f"down_blocks.{i}.resnets.{j}."
-        sd_down_res_prefix = f"input_blocks.{3*i + j + 1}.0."
-        unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix))
-
-        if i < 3:
-            # no attention layers in down_blocks.3
-            hf_down_atn_prefix = f"down_blocks.{i}.attentions.{j}."
-            sd_down_atn_prefix = f"input_blocks.{3*i + j + 1}.1."
-            unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix))
-
-    for j in range(3):
-        # loop over resnets/attentions for upblocks
-        hf_up_res_prefix = f"up_blocks.{i}.resnets.{j}."
-        sd_up_res_prefix = f"output_blocks.{3*i + j}.0."
-        unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix))
-
-        if i > 0:
-            # no attention layers in up_blocks.0
-            hf_up_atn_prefix = f"up_blocks.{i}.attentions.{j}."
-            sd_up_atn_prefix = f"output_blocks.{3*i + j}.1."
-            unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix))
-
-    if i < 3:
-        # no downsample in down_blocks.3
-        hf_downsample_prefix = f"down_blocks.{i}.downsamplers.0.conv."
-        sd_downsample_prefix = f"input_blocks.{3*(i+1)}.0.op."
-        unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix))
-
-        # no upsample in up_blocks.3
-        hf_upsample_prefix = f"up_blocks.{i}.upsamplers.0."
-        sd_upsample_prefix = f"output_blocks.{3*i + 2}.{1 if i == 0 else 2}."
-        unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))
-
-hf_mid_atn_prefix = "mid_block.attentions.0."
-sd_mid_atn_prefix = "middle_block.1."
-unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))
-
-for j in range(2):
-    hf_mid_res_prefix = f"mid_block.resnets.{j}."
-    sd_mid_res_prefix = f"middle_block.{2*j}."
-    unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))
-
-
-def convert_unet_state_dict(unet_state_dict):
-    # buyer beware: this is a *brittle* function,
-    # and correct output requires that all of these pieces interact in
-    # the exact order in which I have arranged them.
-    mapping = {k: k for k in unet_state_dict.keys()}
-    for sd_name, hf_name in unet_conversion_map:
-        mapping[hf_name] = sd_name
-    for k, v in mapping.items():
-        if "resnets" in k:
-            for sd_part, hf_part in unet_conversion_map_resnet:
-                v = v.replace(hf_part, sd_part)
-            mapping[k] = v
-    for k, v in mapping.items():
-        for sd_part, hf_part in unet_conversion_map_layer:
-            v = v.replace(hf_part, sd_part)
-        mapping[k] = v
-    new_state_dict = {v: unet_state_dict[k] for k, v in mapping.items()}
-    return new_state_dict
-
-
-# ================#
-# VAE Conversion #
-# ================#
-
-vae_conversion_map = [
-    # (stable-diffusion, HF Diffusers)
-    ("nin_shortcut", "conv_shortcut"),
-    ("norm_out", "conv_norm_out"),
-    ("mid.attn_1.", "mid_block.attentions.0."),
-]
-
-for i in range(4):
-    # down_blocks have two resnets
-    for j in range(2):
-        hf_down_prefix = f"encoder.down_blocks.{i}.resnets.{j}."
-        sd_down_prefix = f"encoder.down.{i}.block.{j}."
-        vae_conversion_map.append((sd_down_prefix, hf_down_prefix))
-
-    if i < 3:
-        hf_downsample_prefix = f"down_blocks.{i}.downsamplers.0."
-        sd_downsample_prefix = f"down.{i}.downsample."
-        vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))
-
-        hf_upsample_prefix = f"up_blocks.{i}.upsamplers.0."
-        sd_upsample_prefix = f"up.{3-i}.upsample."
-        vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix))
-
-    # up_blocks have three resnets
-    # also, up blocks in hf are numbered in reverse from sd
-    for j in range(3):
-        hf_up_prefix = f"decoder.up_blocks.{i}.resnets.{j}."
-        sd_up_prefix = f"decoder.up.{3-i}.block.{j}."
-        vae_conversion_map.append((sd_up_prefix, hf_up_prefix))
-
-# this part accounts for mid blocks in both the encoder and the decoder
-for i in range(2):
-    hf_mid_res_prefix = f"mid_block.resnets.{i}."
-    sd_mid_res_prefix = f"mid.block_{i+1}."
-    vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))
-
-
-vae_conversion_map_attn = [
-    # (stable-diffusion, HF Diffusers)
-    ("norm.", "group_norm."),
-    ("q.", "query."),
-    ("k.", "key."),
-    ("v.", "value."),
-    ("proj_out.", "proj_attn."),
-]
-
-
-def reshape_weight_for_sd(w):
-    # convert HF linear weights to SD conv2d weights
-    return w.reshape(*w.shape, 1, 1)
-
-
-def convert_vae_state_dict(vae_state_dict):
-    mapping = {k: k for k in vae_state_dict.keys()}
-    for k, v in mapping.items():
-        for sd_part, hf_part in vae_conversion_map:
-            v = v.replace(hf_part, sd_part)
-        mapping[k] = v
-    for k, v in mapping.items():
-        if "attentions" in k:
-            for sd_part, hf_part in vae_conversion_map_attn:
-                v = v.replace(hf_part, sd_part)
-            mapping[k] = v
-    new_state_dict = {v: vae_state_dict[k] for k, v in mapping.items()}
-    weights_to_convert = ["q", "k", "v", "proj_out"]
-    print("Converting to CKPT ...")    
-    for k, v in new_state_dict.items():
-        for weight_name in weights_to_convert:
-            if f"mid.attn_1.{weight_name}.weight" in k:
-                new_state_dict[k] = reshape_weight_for_sd(v)
-    return new_state_dict
-
-
-# =========================#
-# Text Encoder Conversion #
-# =========================#
-# pretty much a no-op
-
-
-def convert_text_enc_state_dict(text_enc_dict):
-    return text_enc_dict
-
-
-def convert(model_path, checkpoint_path):    
-    unet_path = osp.join(model_path, "unet", "diffusion_pytorch_model.bin")
-    vae_path = osp.join(model_path, "vae", "diffusion_pytorch_model.bin")
-    text_enc_path = osp.join(model_path, "text_encoder", "pytorch_model.bin")
-
-    # Convert the UNet model
-    unet_state_dict = torch.load(unet_path, map_location='cpu')
-    unet_state_dict = convert_unet_state_dict(unet_state_dict)
-    unet_state_dict = {"model.diffusion_model." + k: v for k, v in unet_state_dict.items()}
-
-    # Convert the VAE model
-    vae_state_dict = torch.load(vae_path, map_location='cpu')
-    vae_state_dict = convert_vae_state_dict(vae_state_dict)
-    vae_state_dict = {"first_stage_model." + k: v for k, v in vae_state_dict.items()}
-
-    # Convert the text encoder model
-    text_enc_dict = torch.load(text_enc_path, map_location='cpu')
-    text_enc_dict = convert_text_enc_state_dict(text_enc_dict)
-    text_enc_dict = {"cond_stage_model.transformer." + k: v for k, v in text_enc_dict.items()}
-
-    # Put together new checkpoint
-    state_dict = {**unet_state_dict, **vae_state_dict, **text_enc_dict}
-    
-    state_dict = {k:v.half() for k,v in state_dict.items()}
-    state_dict = {"state_dict": state_dict}
-    torch.save(state_dict, checkpoint_path)
-    del state_dict, text_enc_dict, vae_state_dict, unet_state_dict
-    torch.cuda.empty_cache()
-    gc.collect()
diff --git a/spaces/xiaoyun235/White-box-Cartoonization/wbc/guided_filter.py b/spaces/xiaoyun235/White-box-Cartoonization/wbc/guided_filter.py
deleted file mode 100644
index fd019d145efc7f308cd96de90f4e7b648f6820b4..0000000000000000000000000000000000000000
--- a/spaces/xiaoyun235/White-box-Cartoonization/wbc/guided_filter.py
+++ /dev/null
@@ -1,87 +0,0 @@
-import tensorflow as tf
-import numpy as np
-
-
-
-
-def tf_box_filter(x, r):
-    k_size = int(2*r+1)
-    ch = x.get_shape().as_list()[-1]
-    weight = 1/(k_size**2)
-    box_kernel = weight*np.ones((k_size, k_size, ch, 1))
-    box_kernel = np.array(box_kernel).astype(np.float32)
-    output = tf.nn.depthwise_conv2d(x, box_kernel, [1, 1, 1, 1], 'SAME')
-    return output
-
-
-
-def guided_filter(x, y, r, eps=1e-2):
-    
-    x_shape = tf.shape(x)
-    #y_shape = tf.shape(y)
-
-    N = tf_box_filter(tf.ones((1, x_shape[1], x_shape[2], 1), dtype=x.dtype), r)
-
-    mean_x = tf_box_filter(x, r) / N
-    mean_y = tf_box_filter(y, r) / N
-    cov_xy = tf_box_filter(x * y, r) / N - mean_x * mean_y
-    var_x  = tf_box_filter(x * x, r) / N - mean_x * mean_x
-
-    A = cov_xy / (var_x + eps)
-    b = mean_y - A * mean_x
-
-    mean_A = tf_box_filter(A, r) / N
-    mean_b = tf_box_filter(b, r) / N
-
-    output = mean_A * x + mean_b
-
-    return output
-
-
-
-def fast_guided_filter(lr_x, lr_y, hr_x, r=1, eps=1e-8):
-    
-    #assert lr_x.shape.ndims == 4 and lr_y.shape.ndims == 4 and hr_x.shape.ndims == 4
-   
-    lr_x_shape = tf.shape(lr_x)
-    #lr_y_shape = tf.shape(lr_y)
-    hr_x_shape = tf.shape(hr_x)
-    
-    N = tf_box_filter(tf.ones((1, lr_x_shape[1], lr_x_shape[2], 1), dtype=lr_x.dtype), r)
-
-    mean_x = tf_box_filter(lr_x, r) / N
-    mean_y = tf_box_filter(lr_y, r) / N
-    cov_xy = tf_box_filter(lr_x * lr_y, r) / N - mean_x * mean_y
-    var_x  = tf_box_filter(lr_x * lr_x, r) / N - mean_x * mean_x
-
-    A = cov_xy / (var_x + eps)
-    b = mean_y - A * mean_x
-
-    mean_A = tf.image.resize_images(A, hr_x_shape[1: 3])
-    mean_b = tf.image.resize_images(b, hr_x_shape[1: 3])
-
-    output = mean_A * hr_x + mean_b
-    
-    return output
-
-
-if __name__ == '__main__':
-    import cv2
-    from tqdm import tqdm
-
-    input_photo = tf.placeholder(tf.float32, [1, None, None, 3])
-    #input_superpixel = tf.placeholder(tf.float32, [16, 256, 256, 3])
-    output = guided_filter(input_photo, input_photo, 5, eps=1)
-    image = cv2.imread('output_figure1/cartoon2.jpg')
-    image = image/127.5 - 1
-    image = np.expand_dims(image, axis=0)
-
-    config = tf.ConfigProto()
-    config.gpu_options.allow_growth = True
-    sess = tf.Session(config=config)
-    sess.run(tf.global_variables_initializer())
-
-    out = sess.run(output, feed_dict={input_photo: image})
-    out = (np.squeeze(out)+1)*127.5
-    out = np.clip(out, 0, 255).astype(np.uint8)
-    cv2.imwrite('output_figure1/cartoon2_filter.jpg', out)
diff --git a/spaces/xl2533/MakeInstruction/self/data/__init__.py b/spaces/xl2533/MakeInstruction/self/data/__init__.py
deleted file mode 100644
index 1868e1b8af4eaafbc633df6daabe7d5b3ebcf710..0000000000000000000000000000000000000000
--- a/spaces/xl2533/MakeInstruction/self/data/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-# -*-coding:utf-8 -*-
\ No newline at end of file
diff --git a/spaces/xuetao/bingo3/src/app/layout.tsx b/spaces/xuetao/bingo3/src/app/layout.tsx
deleted file mode 100644
index 8b5122759987177b8dc4e4356d1d06cea25c15ea..0000000000000000000000000000000000000000
--- a/spaces/xuetao/bingo3/src/app/layout.tsx
+++ /dev/null
@@ -1,47 +0,0 @@
-import { Metadata } from 'next'
-import { Toaster } from 'react-hot-toast'
-import { TailwindIndicator } from '@/components/tailwind-indicator'
-import { Providers } from '@/components/providers'
-import { Header } from '@/components/header'
-
-import '@/app/globals.scss'
-
-
-export const metadata: Metadata = {
-  title: {
-    default: 'Bing AI Chatbot',
-    template: `%s - Bing AI Chatbot`
-  },
-  description: 'Bing AI Chatbot Web App.',
-  themeColor: [
-    { media: '(prefers-color-scheme: light)', color: 'white' },
-    { media: '(prefers-color-scheme: dark)', color: 'dark' }
-  ],
-  icons: {
-    icon: '/favicon.ico',
-    shortcut: '../assets/images/logo.svg',
-    apple: '../assets/images/logo.svg'
-  }
-}
-
-interface RootLayoutProps {
-  children: React.ReactNode
-}
-
-export default function RootLayout({ children }: RootLayoutProps) {
-  return (
-    <html lang="zh-CN" suppressHydrationWarning>
-      <body>
-        <Toaster />
-        <Providers attribute="class" defaultTheme="system" enableSystem>
-          <div className="flex flex-col min-h-screen">
-            {/* @ts-ignore */}
-            <Header />
-            <main className="flex flex-col flex-1">{children}</main>
-          </div>
-          <TailwindIndicator />
-        </Providers>
-      </body>
-    </html>
-  )
-}
diff --git a/spaces/xxccc/gpt-academic/docs/waifu_plugin/waifu-tips.js b/spaces/xxccc/gpt-academic/docs/waifu_plugin/waifu-tips.js
deleted file mode 100644
index 8f9533a19e7d4914bde888ee2a107e4430242968..0000000000000000000000000000000000000000
--- a/spaces/xxccc/gpt-academic/docs/waifu_plugin/waifu-tips.js
+++ /dev/null
@@ -1,405 +0,0 @@
-window.live2d_settings = Array(); /*
-
-    く__,.ヘヽ.　　　　/　,ー､ 〉
-    　　　　　＼ ', !-─‐-i　/　/´
-    　　　 　 ／｀ｰ'　　　 L/／｀ヽ､            Live2D 看板娘 参数设置
-    　　 　 /　 ／,　 /|　 ,　 ,　　　 ',                                           Version 1.4.2
-    　　　ｲ 　/ /-‐/　ｉ　L_ ﾊ ヽ!　 i                            Update 2018.11.12
-    　　　 ﾚ ﾍ 7ｲ｀ﾄ　 ﾚ'ｧ-ﾄ､!ハ|　 |  
-    　　　　 !,/7 '0'　　 ´0iソ| 　 |　　　
-    　　　　 |.从"　　_　　 ,,,, / |./ 　 |             网页添加 Live2D 看板娘
-    　　　　 ﾚ'| i＞.､,,__　_,.イ / 　.i 　|                    https://www.fghrsh.net/post/123.html
-    　　　　　 ﾚ'| | / k_７_/ﾚ'ヽ,　ﾊ.　|           
-    　　　　　　 | |/i 〈|/　 i　,.ﾍ |　i　|    Thanks
-    　　　　　　.|/ /　ｉ： 　 ﾍ!　　＼　|          journey-ad / https://github.com/journey-ad/live2d_src
-    　　　 　 　 kヽ>､ﾊ 　 _,.ﾍ､ 　 /､!            xiazeyu / https://github.com/xiazeyu/live2d-widget.js
-    　　　　　　 !'〈//｀Ｔ´', ＼ ｀'7'ｰr'          Live2d Cubism SDK WebGL 2.1 Projrct & All model authors.
-    　　　　　　 ﾚ'ヽL__|___i,___,ンﾚ|ノ
-    　　　　　 　　　ﾄ-,/　|___./
-    　　　　　 　　　'ｰ'　　!_,.:*********************************************************************************/
-
-
-// 后端接口
-live2d_settings['modelAPI']             = '//live2d.fghrsh.net/api/';   // 自建 API 修改这里
-live2d_settings['tipsMessage']          = 'waifu-tips.json';            // 同目录下可省略路径
-live2d_settings['hitokotoAPI']          = 'lwl12.com';                  // 一言 API，可选 'lwl12.com', 'hitokoto.cn', 'jinrishici.com'(古诗词)
-
-// 默认模型
-live2d_settings['modelId']              = 1;            // 默认模型 ID，可在 F12 控制台找到
-live2d_settings['modelTexturesId']      = 53;           // 默认材质 ID，可在 F12 控制台找到
-
-// 工具栏设置
-live2d_settings['showToolMenu']         = true;         // 显示 工具栏          ，可选 true(真), false(假)
-live2d_settings['canCloseLive2d']       = true;         // 显示 关闭看板娘  按钮，可选 true(真), false(假)
-live2d_settings['canSwitchModel']       = true;         // 显示 模型切换    按钮，可选 true(真), false(假)
-live2d_settings['canSwitchTextures']    = true;         // 显示 材质切换    按钮，可选 true(真), false(假)
-live2d_settings['canSwitchHitokoto']    = true;         // 显示 一言切换    按钮，可选 true(真), false(假)
-live2d_settings['canTakeScreenshot']    = true;         // 显示 看板娘截图  按钮，可选 true(真), false(假)
-live2d_settings['canTurnToHomePage']    = true;         // 显示 返回首页    按钮，可选 true(真), false(假)
-live2d_settings['canTurnToAboutPage']   = true;         // 显示 跳转关于页  按钮，可选 true(真), false(假)
-
-// 模型切换模式
-live2d_settings['modelStorage']         = true;         // 记录 ID (刷新后恢复)，可选 true(真), false(假)
-live2d_settings['modelRandMode']        = 'switch';     // 模型切换，可选 'rand'(随机), 'switch'(顺序)
-live2d_settings['modelTexturesRandMode']= 'rand';       // 材质切换，可选 'rand'(随机), 'switch'(顺序)
-
-// 提示消息选项
-live2d_settings['showHitokoto']         = true;         // 显示一言
-live2d_settings['showF12Status']        = true;         // 显示加载状态
-live2d_settings['showF12Message']       = false;        // 显示看板娘消息
-live2d_settings['showF12OpenMsg']       = true;         // 显示控制台打开提示
-live2d_settings['showCopyMessage']      = true;         // 显示 复制内容 提示
-live2d_settings['showWelcomeMessage']   = true;         // 显示进入面页欢迎词
-
-//看板娘样式设置
-live2d_settings['waifuSize']            = '280x250';    // 看板娘大小，例如 '280x250', '600x535'
-live2d_settings['waifuTipsSize']        = '250x70';     // 提示框大小，例如 '250x70', '570x150'
-live2d_settings['waifuFontSize']        = '12px';       // 提示框字体，例如 '12px', '30px'
-live2d_settings['waifuToolFont']        = '14px';       // 工具栏字体，例如 '14px', '36px'
-live2d_settings['waifuToolLine']        = '20px';       // 工具栏行高，例如 '20px', '36px'
-live2d_settings['waifuToolTop']         = '0px'         // 工具栏顶部边距，例如 '0px', '-60px'
-live2d_settings['waifuMinWidth']        = '768px';      // 面页小于 指定宽度 隐藏看板娘，例如 'disable'(禁用), '768px'
-live2d_settings['waifuEdgeSide']        = 'left:0';     // 看板娘贴边方向，例如 'left:0'(靠左 0px), 'right:30'(靠右 30px)
-live2d_settings['waifuDraggable']       = 'disable';    // 拖拽样式，例如 'disable'(禁用), 'axis-x'(只能水平拖拽), 'unlimited'(自由拖拽)
-live2d_settings['waifuDraggableRevert'] = true;         // 松开鼠标还原拖拽位置，可选 true(真), false(假)
-
-// 其他杂项设置
-live2d_settings['l2dVersion']           = '1.4.2';        // 当前版本
-live2d_settings['l2dVerDate']           = '2018.11.12'; // 版本更新日期
-live2d_settings['homePageUrl']          = 'auto';       // 主页地址，可选 'auto'(自动), '{URL 网址}'
-live2d_settings['aboutPageUrl']         = 'https://www.fghrsh.net/post/123.html';   // 关于页地址, '{URL 网址}'
-live2d_settings['screenshotCaptureName']= 'live2d.png'; // 看板娘截图文件名，例如 'live2d.png'
-
-/****************************************************************************************************/
-
-String.prototype.render = function(context) {
-    var tokenReg = /(\\)?\{([^\{\}\\]+)(\\)?\}/g;
-
-    return this.replace(tokenReg, function (word, slash1, token, slash2) {
-        if (slash1 || slash2) { return word.replace('\\', ''); }
-        
-        var variables = token.replace(/\s/g, '').split('.');
-        var currentObject = context;
-        var i, length, variable;
-        
-        for (i = 0, length = variables.length; i < length; ++i) {
-            variable = variables[i];
-            currentObject = currentObject[variable];
-            if (currentObject === undefined || currentObject === null) return '';
-        }
-        return currentObject;
-    });
-};
-
-var re = /x/;
-console.log(re);
-
-function empty(obj) {return typeof obj=="undefined"||obj==null||obj==""?true:false}
-function getRandText(text) {return Array.isArray(text) ? text[Math.floor(Math.random() * text.length + 1)-1] : text}
-
-function showMessage(text, timeout, flag) {
-    if(flag || sessionStorage.getItem('waifu-text') === '' || sessionStorage.getItem('waifu-text') === null){
-        if(Array.isArray(text)) text = text[Math.floor(Math.random() * text.length + 1)-1];
-        if (live2d_settings.showF12Message) console.log('[Message]', text.replace(/<[^<>]+>/g,''));
-        
-        if(flag) sessionStorage.setItem('waifu-text', text);
-        
-        $('.waifu-tips').stop();
-        $('.waifu-tips').html(text).fadeTo(200, 1);
-        if (timeout === undefined) timeout = 5000;
-        hideMessage(timeout);
-    }
-}
-
-function hideMessage(timeout) {
-    $('.waifu-tips').stop().css('opacity',1);
-    if (timeout === undefined) timeout = 5000;
-    window.setTimeout(function() {sessionStorage.removeItem('waifu-text')}, timeout);
-    $('.waifu-tips').delay(timeout).fadeTo(200, 0);
-}
-
-function initModel(waifuPath, type) {
-    /* console welcome message */
-    eval(function(p,a,c,k,e,r){e=function(c){return(c<a?'':e(parseInt(c/a)))+((c=c%a)>35?String.fromCharCode(c+29):c.toString(36))};if(!''.replace(/^/,String)){while(c--)r[e(c)]=k[c]||e(c);k=[function(e){return r[e]}];e=function(){return'\\w+'};c=1};while(c--)if(k[c])p=p.replace(new RegExp('\\b'+e(c)+'\\b','g'),k[c]);return p}('8.d(" ");8.d("\\U,.\\y\\5.\\1\\1\\1\\1/\\1,\\u\\2 \\H\\n\\1\\1\\1\\1\\1\\b \', !-\\r\\j-i\\1/\\1/\\g\\n\\1\\1\\1 \\1 \\a\\4\\f\'\\1\\1\\1 L/\\a\\4\\5\\2\\n\\1\\1 \\1 /\\1 \\a,\\1 /|\\1 ,\\1 ,\\1\\1\\1 \',\\n\\1\\1\\1\\q \\1/ /-\\j/\\1\\h\\E \\9 \\5!\\1 i\\n\\1\\1\\1 \\3 \\6 7\\q\\4\\c\\1 \\3\'\\s-\\c\\2!\\t|\\1 |\\n\\1\\1\\1\\1 !,/7 \'0\'\\1\\1 \\X\\w| \\1 |\\1\\1\\1\\n\\1\\1\\1\\1 |.\\x\\"\\1\\l\\1\\1 ,,,, / |./ \\1 |\\n\\1\\1\\1\\1 \\3\'| i\\z.\\2,,A\\l,.\\B / \\1.i \\1|\\n\\1\\1\\1\\1\\1 \\3\'| | / C\\D/\\3\'\\5,\\1\\9.\\1|\\n\\1\\1\\1\\1\\1\\1 | |/i \\m|/\\1 i\\1,.\\6 |\\F\\1|\\n\\1\\1\\1\\1\\1\\1.|/ /\\1\\h\\G \\1 \\6!\\1\\1\\b\\1|\\n\\1\\1\\1 \\1 \\1 k\\5>\\2\\9 \\1 o,.\\6\\2 \\1 /\\2!\\n\\1\\1\\1\\1\\1\\1 !\'\\m//\\4\\I\\g\', \\b \\4\'7\'\\J\'\\n\\1\\1\\1\\1\\1\\1 \\3\'\\K|M,p,\\O\\3|\\P\\n\\1\\1\\1\\1\\1 \\1\\1\\1\\c-,/\\1|p./\\n\\1\\1\\1\\1\\1 \\1\\1\\1\'\\f\'\\1\\1!o,.:\\Q \\R\\S\\T v"+e.V+" / W "+e.N);8.d(" ");',60,60,'|u3000|uff64|uff9a|uff40|u30fd|uff8d||console|uff8a|uff0f|uff3c|uff84|log|live2d_settings|uff70|u00b4|uff49||u2010||u3000_|u3008||_|___|uff72|u2500|uff67|u30cf|u30fc||u30bd|u4ece|u30d8|uff1e|__|u30a4|k_|uff17_|u3000L_|u3000i|uff1a|u3009|uff34|uff70r|u30fdL__||___i|l2dVerDate|u30f3|u30ce|nLive2D|u770b|u677f|u5a18|u304f__|l2dVersion|FGHRSH|u00b40i'.split('|'),0,{}));
-    
-    /* 判断 JQuery */
-    if (typeof($.ajax) != 'function') typeof(jQuery.ajax) == 'function' ? window.$ = jQuery : console.log('[Error] JQuery is not defined.');
-    
-    /* 加载看板娘样式 */
-    live2d_settings.waifuSize = live2d_settings.waifuSize.split('x');
-    live2d_settings.waifuTipsSize = live2d_settings.waifuTipsSize.split('x');
-    live2d_settings.waifuEdgeSide = live2d_settings.waifuEdgeSide.split(':');
-    
-    $("#live2d").attr("width",live2d_settings.waifuSize[0]);
-    $("#live2d").attr("height",live2d_settings.waifuSize[1]);
-    $(".waifu-tips").width(live2d_settings.waifuTipsSize[0]);
-    $(".waifu-tips").height(live2d_settings.waifuTipsSize[1]);
-    $(".waifu-tips").css("top",live2d_settings.waifuToolTop);
-    $(".waifu-tips").css("font-size",live2d_settings.waifuFontSize);
-    $(".waifu-tool").css("font-size",live2d_settings.waifuToolFont);
-    $(".waifu-tool span").css("line-height",live2d_settings.waifuToolLine);
-    
-    if (live2d_settings.waifuEdgeSide[0] == 'left') $(".waifu").css("left",live2d_settings.waifuEdgeSide[1]+'px');
-    else if (live2d_settings.waifuEdgeSide[0] == 'right') $(".waifu").css("right",live2d_settings.waifuEdgeSide[1]+'px');
-    
-    window.waifuResize = function() { $(window).width() <= Number(live2d_settings.waifuMinWidth.replace('px','')) ? $(".waifu").hide() : $(".waifu").show(); };
-    if (live2d_settings.waifuMinWidth != 'disable') { waifuResize(); $(window).resize(function() {waifuResize()}); }
-    
-    try {
-        if (live2d_settings.waifuDraggable == 'axis-x') $(".waifu").draggable({ axis: "x", revert: live2d_settings.waifuDraggableRevert });
-        else if (live2d_settings.waifuDraggable == 'unlimited') $(".waifu").draggable({ revert: live2d_settings.waifuDraggableRevert });
-        else $(".waifu").css("transition", 'all .3s ease-in-out');
-    } catch(err) { console.log('[Error] JQuery UI is not defined.') }
-    
-    live2d_settings.homePageUrl = live2d_settings.homePageUrl == 'auto' ? window.location.protocol+'//'+window.location.hostname+'/' : live2d_settings.homePageUrl;
-    if (window.location.protocol == 'file:' && live2d_settings.modelAPI.substr(0,2) == '//') live2d_settings.modelAPI = 'http:'+live2d_settings.modelAPI;
-    
-    $('.waifu-tool .fui-home').click(function (){
-        //window.location = 'https://www.fghrsh.net/';
-        window.location = live2d_settings.homePageUrl;
-    });
-    
-    $('.waifu-tool .fui-info-circle').click(function (){
-        //window.open('https://imjad.cn/archives/lab/add-dynamic-poster-girl-with-live2d-to-your-blog-02');
-        window.open(live2d_settings.aboutPageUrl);
-    });
-    
-    if (typeof(waifuPath) == "object") loadTipsMessage(waifuPath); else {
-        $.ajax({
-            cache: true,
-            url: waifuPath == '' ? live2d_settings.tipsMessage : (waifuPath.substr(waifuPath.length-15)=='waifu-tips.json'?waifuPath:waifuPath+'waifu-tips.json'),
-            dataType: "json",
-            success: function (result){ loadTipsMessage(result); }
-        });
-    }
-    
-    if (!live2d_settings.showToolMenu) $('.waifu-tool').hide();
-    if (!live2d_settings.canCloseLive2d) $('.waifu-tool .fui-cross').hide();
-    if (!live2d_settings.canSwitchModel) $('.waifu-tool .fui-eye').hide();
-    if (!live2d_settings.canSwitchTextures) $('.waifu-tool .fui-user').hide();
-    if (!live2d_settings.canSwitchHitokoto) $('.waifu-tool .fui-chat').hide();
-    if (!live2d_settings.canTakeScreenshot) $('.waifu-tool .fui-photo').hide();
-    if (!live2d_settings.canTurnToHomePage) $('.waifu-tool .fui-home').hide();
-    if (!live2d_settings.canTurnToAboutPage) $('.waifu-tool .fui-info-circle').hide();
-
-    if (waifuPath === undefined) waifuPath = '';
-    var modelId = localStorage.getItem('modelId');
-    var modelTexturesId = localStorage.getItem('modelTexturesId');
-    
-    if (!live2d_settings.modelStorage || modelId == null) {
-        var modelId = live2d_settings.modelId;
-        var modelTexturesId = live2d_settings.modelTexturesId;
-    } loadModel(modelId, modelTexturesId);
-}
-
-function loadModel(modelId, modelTexturesId=0) {
-    if (live2d_settings.modelStorage) {
-        localStorage.setItem('modelId', modelId);
-        localStorage.setItem('modelTexturesId', modelTexturesId);
-    } else {
-        sessionStorage.setItem('modelId', modelId);
-        sessionStorage.setItem('modelTexturesId', modelTexturesId);
-    } loadlive2d('live2d', live2d_settings.modelAPI+'get/?id='+modelId+'-'+modelTexturesId, (live2d_settings.showF12Status ? console.log('[Status]','live2d','模型',modelId+'-'+modelTexturesId,'加载完成'):null));
-}
-
-function loadTipsMessage(result) {
-    window.waifu_tips = result;
-    
-    $.each(result.mouseover, function (index, tips){
-        $(document).on("mouseover", tips.selector, function (){
-            var text = getRandText(tips.text);
-            text = text.render({text: $(this).text()});
-            showMessage(text, 3000);
-        });
-    });
-    $.each(result.click, function (index, tips){
-        $(document).on("click", tips.selector, function (){
-            var text = getRandText(tips.text);
-            text = text.render({text: $(this).text()});
-            showMessage(text, 3000, true);
-        });
-    });
-    $.each(result.seasons, function (index, tips){
-        var now = new Date();
-        var after = tips.date.split('-')[0];
-        var before = tips.date.split('-')[1] || after;
-        
-        if((after.split('/')[0] <= now.getMonth()+1 && now.getMonth()+1 <= before.split('/')[0]) && 
-           (after.split('/')[1] <= now.getDate() && now.getDate() <= before.split('/')[1])){
-            var text = getRandText(tips.text);
-            text = text.render({year: now.getFullYear()});
-            showMessage(text, 6000, true);
-        }
-    });
-    
-    if (live2d_settings.showF12OpenMsg) {
-        re.toString = function() {
-            showMessage(getRandText(result.waifu.console_open_msg), 5000, true);
-            return '';
-        };
-    }
-    
-    if (live2d_settings.showCopyMessage) {
-        $(document).on('copy', function() {
-            showMessage(getRandText(result.waifu.copy_message), 5000, true);
-        });
-    }
-    
-    $('.waifu-tool .fui-photo').click(function(){
-        showMessage(getRandText(result.waifu.screenshot_message), 5000, true);
-        window.Live2D.captureName = live2d_settings.screenshotCaptureName;
-        window.Live2D.captureFrame = true;
-    });
-    
-    $('.waifu-tool .fui-cross').click(function(){
-        sessionStorage.setItem('waifu-dsiplay', 'none');
-        showMessage(getRandText(result.waifu.hidden_message), 1300, true);
-        window.setTimeout(function() {$('.waifu').hide();}, 1300);
-    });
-    
-    window.showWelcomeMessage = function(result) {
-        var text;
-        if (window.location.href == live2d_settings.homePageUrl) {
-            var now = (new Date()).getHours();
-            if (now > 23 || now <= 5) text = getRandText(result.waifu.hour_tips['t23-5']);
-            else if (now > 5 && now <= 7) text = getRandText(result.waifu.hour_tips['t5-7']);
-            else if (now > 7 && now <= 11) text = getRandText(result.waifu.hour_tips['t7-11']);
-            else if (now > 11 && now <= 14) text = getRandText(result.waifu.hour_tips['t11-14']);
-            else if (now > 14 && now <= 17) text = getRandText(result.waifu.hour_tips['t14-17']);
-            else if (now > 17 && now <= 19) text = getRandText(result.waifu.hour_tips['t17-19']);
-            else if (now > 19 && now <= 21) text = getRandText(result.waifu.hour_tips['t19-21']);
-            else if (now > 21 && now <= 23) text = getRandText(result.waifu.hour_tips['t21-23']);
-            else text = getRandText(result.waifu.hour_tips.default);
-        } else {
-            var referrer_message = result.waifu.referrer_message;
-            if (document.referrer !== '') {
-                var referrer = document.createElement('a');
-                referrer.href = document.referrer;
-                var domain = referrer.hostname.split('.')[1];
-                if (window.location.hostname == referrer.hostname)
-                    text = referrer_message.localhost[0] + document.title.split(referrer_message.localhost[2])[0] + referrer_message.localhost[1];
-                else if (domain == 'baidu')
-                    text = referrer_message.baidu[0] + referrer.search.split('&wd=')[1].split('&')[0] + referrer_message.baidu[1];
-                else if (domain == 'so')
-                    text = referrer_message.so[0] + referrer.search.split('&q=')[1].split('&')[0] + referrer_message.so[1];
-                else if (domain == 'google')
-                    text = referrer_message.google[0] + document.title.split(referrer_message.google[2])[0] + referrer_message.google[1];
-                else {
-                    $.each(result.waifu.referrer_hostname, function(i,val) {if (i==referrer.hostname) referrer.hostname = getRandText(val)});
-                    text = referrer_message.default[0] + referrer.hostname + referrer_message.default[1];
-                }
-            } else text = referrer_message.none[0] + document.title.split(referrer_message.none[2])[0] + referrer_message.none[1];
-        }
-        showMessage(text, 6000);
-    }; if (live2d_settings.showWelcomeMessage) showWelcomeMessage(result);
-    
-    var waifu_tips = result.waifu;
-    
-    function loadOtherModel() {
-        var modelId = modelStorageGetItem('modelId');
-        var modelRandMode = live2d_settings.modelRandMode;
-        
-        $.ajax({
-            cache: modelRandMode == 'switch' ? true : false,
-            url: live2d_settings.modelAPI+modelRandMode+'/?id='+modelId,
-            dataType: "json",
-            success: function(result) {
-                loadModel(result.model['id']);
-                var message = result.model['message'];
-                $.each(waifu_tips.model_message, function(i,val) {if (i==result.model['id']) message = getRandText(val)});
-                showMessage(message, 3000, true);
-            }
-        });
-    }
-    
-    function loadRandTextures() {
-        var modelId = modelStorageGetItem('modelId');
-        var modelTexturesId = modelStorageGetItem('modelTexturesId');
-        var modelTexturesRandMode = live2d_settings.modelTexturesRandMode;
-        
-        $.ajax({
-            cache: modelTexturesRandMode == 'switch' ? true : false,
-            url: live2d_settings.modelAPI+modelTexturesRandMode+'_textures/?id='+modelId+'-'+modelTexturesId,
-            dataType: "json",
-            success: function(result) {
-                if (result.textures['id'] == 1 && (modelTexturesId == 1 || modelTexturesId == 0))
-                    showMessage(waifu_tips.load_rand_textures[0], 3000, true);
-                else showMessage(waifu_tips.load_rand_textures[1], 3000, true);
-                loadModel(modelId, result.textures['id']);
-            }
-        });
-    }
-    
-    function modelStorageGetItem(key) { return live2d_settings.modelStorage ? localStorage.getItem(key) : sessionStorage.getItem(key); }
-    
-    /* 检测用户活动状态，并在空闲时显示一言 */
-    if (live2d_settings.showHitokoto) {
-        window.getActed = false; window.hitokotoTimer = 0; window.hitokotoInterval = false;
-        $(document).mousemove(function(e){getActed = true;}).keydown(function(){getActed = true;});
-        setInterval(function(){ if (!getActed) ifActed(); else elseActed(); }, 1000);
-    }
-    
-    function ifActed() {
-        if (!hitokotoInterval) {
-            hitokotoInterval = true;
-            hitokotoTimer = window.setInterval(showHitokotoActed, 30000);
-        }
-    }
-    
-    function elseActed() {
-        getActed = hitokotoInterval = false;
-        window.clearInterval(hitokotoTimer);
-    }
-    
-    function showHitokotoActed() {
-        if ($(document)[0].visibilityState == 'visible') showHitokoto();
-    }
-    
-    function showHitokoto() {
-    	switch(live2d_settings.hitokotoAPI) {
-    	    case 'lwl12.com':
-    	        $.getJSON('https://api.lwl12.com/hitokoto/v1?encode=realjson',function(result){
-        	        if (!empty(result.source)) {
-                        var text = waifu_tips.hitokoto_api_message['lwl12.com'][0];
-                        if (!empty(result.author)) text += waifu_tips.hitokoto_api_message['lwl12.com'][1];
-                        text = text.render({source: result.source, creator: result.author});
-                        window.setTimeout(function() {showMessage(text+waifu_tips.hitokoto_api_message['lwl12.com'][2], 3000, true);}, 5000);
-                    } showMessage(result.text, 5000, true);
-                });break;
-    	    case 'fghrsh.net':
-    	        $.getJSON('https://api.fghrsh.net/hitokoto/rand/?encode=jsc&uid=3335',function(result){
-            	    if (!empty(result.source)) {
-                        var text = waifu_tips.hitokoto_api_message['fghrsh.net'][0];
-                        text = text.render({source: result.source, date: result.date});
-                        window.setTimeout(function() {showMessage(text, 3000, true);}, 5000);
-                        showMessage(result.hitokoto, 5000, true);
-            	    }
-                });break;
-            case 'jinrishici.com':
-                $.ajax({
-                    url: 'https://v2.jinrishici.com/one.json',
-                    xhrFields: {withCredentials: true},
-                    success: function (result, status) {
-                        if (!empty(result.data.origin.title)) {
-                            var text = waifu_tips.hitokoto_api_message['jinrishici.com'][0];
-                            text = text.render({title: result.data.origin.title, dynasty: result.data.origin.dynasty, author:result.data.origin.author});
-                            window.setTimeout(function() {showMessage(text, 3000, true);}, 5000);
-                        } showMessage(result.data.content, 5000, true);
-                    }
-                });break;
-    	    default:
-    	        $.getJSON('https://v1.hitokoto.cn',function(result){
-            	    if (!empty(result.from)) {
-                        var text = waifu_tips.hitokoto_api_message['hitokoto.cn'][0];
-                        text = text.render({source: result.from, creator: result.creator});
-                        window.setTimeout(function() {showMessage(text, 3000, true);}, 5000);
-            	    }
-                    showMessage(result.hitokoto, 5000, true);
-                });
-    	}
-    }
-    
-    $('.waifu-tool .fui-eye').click(function (){loadOtherModel()});
-    $('.waifu-tool .fui-user').click(function (){loadRandTextures()});
-    $('.waifu-tool .fui-chat').click(function (){showHitokoto()});
-}
diff --git a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/gpt2/convert_gpt2_original_tf_checkpoint_to_pytorch.py b/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/gpt2/convert_gpt2_original_tf_checkpoint_to_pytorch.py
deleted file mode 100644
index 066ba06503affdb8fa2ef1b2df1a4cf6539efc22..0000000000000000000000000000000000000000
--- a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/gpt2/convert_gpt2_original_tf_checkpoint_to_pytorch.py
+++ /dev/null
@@ -1,69 +0,0 @@
-# coding=utf-8
-# Copyright 2018 The HuggingFace Inc. team.
-#
-# 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.
-"""Convert OpenAI GPT checkpoint."""
-
-
-import argparse
-
-import torch
-
-from transformers import GPT2Config, GPT2Model, load_tf_weights_in_gpt2
-from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
-
-
-logging.set_verbosity_info()
-
-
-def convert_gpt2_checkpoint_to_pytorch(gpt2_checkpoint_path, gpt2_config_file, pytorch_dump_folder_path):
-    # Construct model
-    if gpt2_config_file == "":
-        config = GPT2Config()
-    else:
-        config = GPT2Config.from_json_file(gpt2_config_file)
-    model = GPT2Model(config)
-
-    # Load weights from numpy
-    load_tf_weights_in_gpt2(model, config, gpt2_checkpoint_path)
-
-    # Save pytorch-model
-    pytorch_weights_dump_path = pytorch_dump_folder_path + "/" + WEIGHTS_NAME
-    pytorch_config_dump_path = pytorch_dump_folder_path + "/" + CONFIG_NAME
-    print(f"Save PyTorch model to {pytorch_weights_dump_path}")
-    torch.save(model.state_dict(), pytorch_weights_dump_path)
-    print(f"Save configuration file to {pytorch_config_dump_path}")
-    with open(pytorch_config_dump_path, "w", encoding="utf-8") as f:
-        f.write(config.to_json_string())
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    # Required parameters
-    parser.add_argument(
-        "--gpt2_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
-    )
-    parser.add_argument(
-        "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
-    )
-    parser.add_argument(
-        "--gpt2_config_file",
-        default="",
-        type=str,
-        help=(
-            "An optional config json file corresponding to the pre-trained OpenAI model. \n"
-            "This specifies the model architecture."
-        ),
-    )
-    args = parser.parse_args()
-    convert_gpt2_checkpoint_to_pytorch(args.gpt2_checkpoint_path, args.gpt2_config_file, args.pytorch_dump_folder_path)
diff --git a/spaces/ykilcher/apes/metrics/precision_recall.py b/spaces/ykilcher/apes/metrics/precision_recall.py
deleted file mode 100644
index 8200b7ef51963ae218e3b871de270a826bf10459..0000000000000000000000000000000000000000
--- a/spaces/ykilcher/apes/metrics/precision_recall.py
+++ /dev/null
@@ -1,62 +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.
-
-"""Precision/Recall (PR) from the paper "Improved Precision and Recall
-Metric for Assessing Generative Models". Matches the original implementation
-by Kynkaanniemi et al. at
-https://github.com/kynkaat/improved-precision-and-recall-metric/blob/master/precision_recall.py"""
-
-import torch
-from . import metric_utils
-
-#----------------------------------------------------------------------------
-
-def compute_distances(row_features, col_features, num_gpus, rank, col_batch_size):
-    assert 0 <= rank < num_gpus
-    num_cols = col_features.shape[0]
-    num_batches = ((num_cols - 1) // col_batch_size // num_gpus + 1) * num_gpus
-    col_batches = torch.nn.functional.pad(col_features, [0, 0, 0, -num_cols % num_batches]).chunk(num_batches)
-    dist_batches = []
-    for col_batch in col_batches[rank :: num_gpus]:
-        dist_batch = torch.cdist(row_features.unsqueeze(0), col_batch.unsqueeze(0))[0]
-        for src in range(num_gpus):
-            dist_broadcast = dist_batch.clone()
-            if num_gpus > 1:
-                torch.distributed.broadcast(dist_broadcast, src=src)
-            dist_batches.append(dist_broadcast.cpu() if rank == 0 else None)
-    return torch.cat(dist_batches, dim=1)[:, :num_cols] if rank == 0 else None
-
-#----------------------------------------------------------------------------
-
-def compute_pr(opts, max_real, num_gen, nhood_size, row_batch_size, col_batch_size):
-    detector_url = 'https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/metrics/vgg16.pt'
-    detector_kwargs = dict(return_features=True)
-
-    real_features = metric_utils.compute_feature_stats_for_dataset(
-        opts=opts, detector_url=detector_url, detector_kwargs=detector_kwargs,
-        rel_lo=0, rel_hi=0, capture_all=True, max_items=max_real).get_all_torch().to(torch.float16).to(opts.device)
-
-    gen_features = metric_utils.compute_feature_stats_for_generator(
-        opts=opts, detector_url=detector_url, detector_kwargs=detector_kwargs,
-        rel_lo=0, rel_hi=1, capture_all=True, max_items=num_gen).get_all_torch().to(torch.float16).to(opts.device)
-
-    results = dict()
-    for name, manifold, probes in [('precision', real_features, gen_features), ('recall', gen_features, real_features)]:
-        kth = []
-        for manifold_batch in manifold.split(row_batch_size):
-            dist = compute_distances(row_features=manifold_batch, col_features=manifold, num_gpus=opts.num_gpus, rank=opts.rank, col_batch_size=col_batch_size)
-            kth.append(dist.to(torch.float32).kthvalue(nhood_size + 1).values.to(torch.float16) if opts.rank == 0 else None)
-        kth = torch.cat(kth) if opts.rank == 0 else None
-        pred = []
-        for probes_batch in probes.split(row_batch_size):
-            dist = compute_distances(row_features=probes_batch, col_features=manifold, num_gpus=opts.num_gpus, rank=opts.rank, col_batch_size=col_batch_size)
-            pred.append((dist <= kth).any(dim=1) if opts.rank == 0 else None)
-        results[name] = float(torch.cat(pred).to(torch.float32).mean() if opts.rank == 0 else 'nan')
-    return results['precision'], results['recall']
-
-#----------------------------------------------------------------------------
diff --git a/spaces/ykilcher/apes/torch_utils/custom_ops.py b/spaces/ykilcher/apes/torch_utils/custom_ops.py
deleted file mode 100644
index 4cc4e43fc6f6ce79f2bd68a44ba87990b9b8564e..0000000000000000000000000000000000000000
--- a/spaces/ykilcher/apes/torch_utils/custom_ops.py
+++ /dev/null
@@ -1,126 +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 os
-import glob
-import torch
-import torch.utils.cpp_extension
-import importlib
-import hashlib
-import shutil
-from pathlib import Path
-
-from torch.utils.file_baton import FileBaton
-
-#----------------------------------------------------------------------------
-# Global options.
-
-verbosity = 'brief' # Verbosity level: 'none', 'brief', 'full'
-
-#----------------------------------------------------------------------------
-# Internal helper funcs.
-
-def _find_compiler_bindir():
-    patterns = [
-        'C:/Program Files (x86)/Microsoft Visual Studio/*/Professional/VC/Tools/MSVC/*/bin/Hostx64/x64',
-        'C:/Program Files (x86)/Microsoft Visual Studio/*/BuildTools/VC/Tools/MSVC/*/bin/Hostx64/x64',
-        'C:/Program Files (x86)/Microsoft Visual Studio/*/Community/VC/Tools/MSVC/*/bin/Hostx64/x64',
-        'C:/Program Files (x86)/Microsoft Visual Studio */vc/bin',
-    ]
-    for pattern in patterns:
-        matches = sorted(glob.glob(pattern))
-        if len(matches):
-            return matches[-1]
-    return None
-
-#----------------------------------------------------------------------------
-# Main entry point for compiling and loading C++/CUDA plugins.
-
-_cached_plugins = dict()
-
-def get_plugin(module_name, sources, **build_kwargs):
-    assert verbosity in ['none', 'brief', 'full']
-
-    # Already cached?
-    if module_name in _cached_plugins:
-        return _cached_plugins[module_name]
-
-    # Print status.
-    if verbosity == 'full':
-        print(f'Setting up PyTorch plugin "{module_name}"...')
-    elif verbosity == 'brief':
-        print(f'Setting up PyTorch plugin "{module_name}"... ', end='', flush=True)
-
-    try: # pylint: disable=too-many-nested-blocks
-        # Make sure we can find the necessary compiler binaries.
-        if os.name == 'nt' and os.system("where cl.exe >nul 2>nul") != 0:
-            compiler_bindir = _find_compiler_bindir()
-            if compiler_bindir is None:
-                raise RuntimeError(f'Could not find MSVC/GCC/CLANG installation on this computer. Check _find_compiler_bindir() in "{__file__}".')
-            os.environ['PATH'] += ';' + compiler_bindir
-
-        # Compile and load.
-        verbose_build = (verbosity == 'full')
-
-        # Incremental build md5sum trickery.  Copies all the input source files
-        # into a cached build directory under a combined md5 digest of the input
-        # source files.  Copying is done only if the combined digest has changed.
-        # This keeps input file timestamps and filenames the same as in previous
-        # extension builds, allowing for fast incremental rebuilds.
-        #
-        # This optimization is done only in case all the source files reside in
-        # a single directory (just for simplicity) and if the TORCH_EXTENSIONS_DIR
-        # environment variable is set (we take this as a signal that the user
-        # actually cares about this.)
-        source_dirs_set = set(os.path.dirname(source) for source in sources)
-        if len(source_dirs_set) == 1 and ('TORCH_EXTENSIONS_DIR' in os.environ):
-            all_source_files = sorted(list(x for x in Path(list(source_dirs_set)[0]).iterdir() if x.is_file()))
-
-            # Compute a combined hash digest for all source files in the same
-            # custom op directory (usually .cu, .cpp, .py and .h files).
-            hash_md5 = hashlib.md5()
-            for src in all_source_files:
-                with open(src, 'rb') as f:
-                    hash_md5.update(f.read())
-            build_dir = torch.utils.cpp_extension._get_build_directory(module_name, verbose=verbose_build) # pylint: disable=protected-access
-            digest_build_dir = os.path.join(build_dir, hash_md5.hexdigest())
-
-            if not os.path.isdir(digest_build_dir):
-                os.makedirs(digest_build_dir, exist_ok=True)
-                baton = FileBaton(os.path.join(digest_build_dir, 'lock'))
-                if baton.try_acquire():
-                    try:
-                        for src in all_source_files:
-                            shutil.copyfile(src, os.path.join(digest_build_dir, os.path.basename(src)))
-                    finally:
-                        baton.release()
-                else:
-                    # Someone else is copying source files under the digest dir,
-                    # wait until done and continue.
-                    baton.wait()
-            digest_sources = [os.path.join(digest_build_dir, os.path.basename(x)) for x in sources]
-            torch.utils.cpp_extension.load(name=module_name, build_directory=build_dir,
-                verbose=verbose_build, sources=digest_sources, **build_kwargs)
-        else:
-            torch.utils.cpp_extension.load(name=module_name, verbose=verbose_build, sources=sources, **build_kwargs)
-        module = importlib.import_module(module_name)
-
-    except:
-        if verbosity == 'brief':
-            print('Failed!')
-        raise
-
-    # Print status and add to cache.
-    if verbosity == 'full':
-        print(f'Done setting up PyTorch plugin "{module_name}".')
-    elif verbosity == 'brief':
-        print('Done.')
-    _cached_plugins[module_name] = module
-    return module
-
-#----------------------------------------------------------------------------
diff --git a/spaces/yl12053/so-vits-4.1-Matikanefukukitaru/modules/F0Predictor/crepe.py b/spaces/yl12053/so-vits-4.1-Matikanefukukitaru/modules/F0Predictor/crepe.py
deleted file mode 100644
index c6fb45c79bcd306202a2c0282b3d73a8074ced5d..0000000000000000000000000000000000000000
--- a/spaces/yl12053/so-vits-4.1-Matikanefukukitaru/modules/F0Predictor/crepe.py
+++ /dev/null
@@ -1,340 +0,0 @@
-from typing import Optional,Union
-try:
-    from typing import Literal
-except Exception as e:
-    from typing_extensions import Literal
-import numpy as np
-import torch
-import torchcrepe
-from torch import nn
-from torch.nn import functional as F
-import scipy
-
-#from:https://github.com/fishaudio/fish-diffusion
-
-def repeat_expand(
-    content: Union[torch.Tensor, np.ndarray], target_len: int, mode: str = "nearest"
-):
-    """Repeat content to target length.
-    This is a wrapper of torch.nn.functional.interpolate.
-
-    Args:
-        content (torch.Tensor): tensor
-        target_len (int): target length
-        mode (str, optional): interpolation mode. Defaults to "nearest".
-
-    Returns:
-        torch.Tensor: tensor
-    """
-
-    ndim = content.ndim
-
-    if content.ndim == 1:
-        content = content[None, None]
-    elif content.ndim == 2:
-        content = content[None]
-
-    assert content.ndim == 3
-
-    is_np = isinstance(content, np.ndarray)
-    if is_np:
-        content = torch.from_numpy(content)
-
-    results = torch.nn.functional.interpolate(content, size=target_len, mode=mode)
-
-    if is_np:
-        results = results.numpy()
-
-    if ndim == 1:
-        return results[0, 0]
-    elif ndim == 2:
-        return results[0]
-
-
-class BasePitchExtractor:
-    def __init__(
-        self,
-        hop_length: int = 512,
-        f0_min: float = 50.0,
-        f0_max: float = 1100.0,
-        keep_zeros: bool = True,
-    ):
-        """Base pitch extractor.
-
-        Args:
-            hop_length (int, optional): Hop length. Defaults to 512.
-            f0_min (float, optional): Minimum f0. Defaults to 50.0.
-            f0_max (float, optional): Maximum f0. Defaults to 1100.0.
-            keep_zeros (bool, optional): Whether keep zeros in pitch. Defaults to True.
-        """
-
-        self.hop_length = hop_length
-        self.f0_min = f0_min
-        self.f0_max = f0_max
-        self.keep_zeros = keep_zeros
-
-    def __call__(self, x, sampling_rate=44100, pad_to=None):
-        raise NotImplementedError("BasePitchExtractor is not callable.")
-
-    def post_process(self, x, sampling_rate, f0, pad_to):
-        if isinstance(f0, np.ndarray):
-            f0 = torch.from_numpy(f0).float().to(x.device)
-
-        if pad_to is None:
-            return f0
-
-        f0 = repeat_expand(f0, pad_to)
-
-        if self.keep_zeros:
-            return f0
-        
-        vuv_vector = torch.zeros_like(f0)
-        vuv_vector[f0 > 0.0] = 1.0
-        vuv_vector[f0 <= 0.0] = 0.0
-        
-        # 去掉0频率, 并线性插值
-        nzindex = torch.nonzero(f0).squeeze()
-        f0 = torch.index_select(f0, dim=0, index=nzindex).cpu().numpy()
-        time_org = self.hop_length / sampling_rate * nzindex.cpu().numpy()
-        time_frame = np.arange(pad_to) * self.hop_length / sampling_rate
-
-        if f0.shape[0] <= 0:
-            return torch.zeros(pad_to, dtype=torch.float, device=x.device),torch.zeros(pad_to, dtype=torch.float, device=x.device)
-
-        if f0.shape[0] == 1:
-            return torch.ones(pad_to, dtype=torch.float, device=x.device) * f0[0],torch.ones(pad_to, dtype=torch.float, device=x.device)
-    
-        # 大概可以用 torch 重写?
-        f0 = np.interp(time_frame, time_org, f0, left=f0[0], right=f0[-1])
-        vuv_vector = vuv_vector.cpu().numpy()
-        vuv_vector = np.ceil(scipy.ndimage.zoom(vuv_vector,pad_to/len(vuv_vector),order = 0))
-        
-        return f0,vuv_vector
-
-
-class MaskedAvgPool1d(nn.Module):
-    def __init__(
-        self, kernel_size: int, stride: Optional[int] = None, padding: Optional[int] = 0
-    ):
-        """An implementation of mean pooling that supports masked values.
-
-        Args:
-            kernel_size (int): The size of the median pooling window.
-            stride (int, optional): The stride of the median pooling window. Defaults to None.
-            padding (int, optional): The padding of the median pooling window. Defaults to 0.
-        """
-
-        super(MaskedAvgPool1d, self).__init__()
-        self.kernel_size = kernel_size
-        self.stride = stride or kernel_size
-        self.padding = padding
-
-    def forward(self, x, mask=None):
-        ndim = x.dim()
-        if ndim == 2:
-            x = x.unsqueeze(1)
-
-        assert (
-            x.dim() == 3
-        ), "Input tensor must have 2 or 3 dimensions (batch_size, channels, width)"
-
-        # Apply the mask by setting masked elements to zero, or make NaNs zero
-        if mask is None:
-            mask = ~torch.isnan(x)
-
-        # Ensure mask has the same shape as the input tensor
-        assert x.shape == mask.shape, "Input tensor and mask must have the same shape"
-
-        masked_x = torch.where(mask, x, torch.zeros_like(x))
-        # Create a ones kernel with the same number of channels as the input tensor
-        ones_kernel = torch.ones(x.size(1), 1, self.kernel_size, device=x.device)
-
-        # Perform sum pooling
-        sum_pooled = nn.functional.conv1d(
-            masked_x,
-            ones_kernel,
-            stride=self.stride,
-            padding=self.padding,
-            groups=x.size(1),
-        )
-
-        # Count the non-masked (valid) elements in each pooling window
-        valid_count = nn.functional.conv1d(
-            mask.float(),
-            ones_kernel,
-            stride=self.stride,
-            padding=self.padding,
-            groups=x.size(1),
-        )
-        valid_count = valid_count.clamp(min=1)  # Avoid division by zero
-
-        # Perform masked average pooling
-        avg_pooled = sum_pooled / valid_count
-
-        # Fill zero values with NaNs
-        avg_pooled[avg_pooled == 0] = float("nan")
-
-        if ndim == 2:
-            return avg_pooled.squeeze(1)
-
-        return avg_pooled
-
-
-class MaskedMedianPool1d(nn.Module):
-    def __init__(
-        self, kernel_size: int, stride: Optional[int] = None, padding: Optional[int] = 0
-    ):
-        """An implementation of median pooling that supports masked values.
-
-        This implementation is inspired by the median pooling implementation in
-        https://gist.github.com/rwightman/f2d3849281624be7c0f11c85c87c1598
-
-        Args:
-            kernel_size (int): The size of the median pooling window.
-            stride (int, optional): The stride of the median pooling window. Defaults to None.
-            padding (int, optional): The padding of the median pooling window. Defaults to 0.
-        """
-
-        super(MaskedMedianPool1d, self).__init__()
-        self.kernel_size = kernel_size
-        self.stride = stride or kernel_size
-        self.padding = padding
-
-    def forward(self, x, mask=None):
-        ndim = x.dim()
-        if ndim == 2:
-            x = x.unsqueeze(1)
-
-        assert (
-            x.dim() == 3
-        ), "Input tensor must have 2 or 3 dimensions (batch_size, channels, width)"
-
-        if mask is None:
-            mask = ~torch.isnan(x)
-
-        assert x.shape == mask.shape, "Input tensor and mask must have the same shape"
-
-        masked_x = torch.where(mask, x, torch.zeros_like(x))
-
-        x = F.pad(masked_x, (self.padding, self.padding), mode="reflect")
-        mask = F.pad(
-            mask.float(), (self.padding, self.padding), mode="constant", value=0
-        )
-
-        x = x.unfold(2, self.kernel_size, self.stride)
-        mask = mask.unfold(2, self.kernel_size, self.stride)
-
-        x = x.contiguous().view(x.size()[:3] + (-1,))
-        mask = mask.contiguous().view(mask.size()[:3] + (-1,)).to(x.device)
-
-        # Combine the mask with the input tensor
-        #x_masked = torch.where(mask.bool(), x, torch.fill_(torch.zeros_like(x),float("inf")))
-        x_masked = torch.where(mask.bool(), x, torch.FloatTensor([float("inf")]).to(x.device))
-
-        # Sort the masked tensor along the last dimension
-        x_sorted, _ = torch.sort(x_masked, dim=-1)
-
-        # Compute the count of non-masked (valid) values
-        valid_count = mask.sum(dim=-1)
-
-        # Calculate the index of the median value for each pooling window
-        median_idx = (torch.div((valid_count - 1), 2, rounding_mode='trunc')).clamp(min=0)
-
-        # Gather the median values using the calculated indices
-        median_pooled = x_sorted.gather(-1, median_idx.unsqueeze(-1).long()).squeeze(-1)
-
-        # Fill infinite values with NaNs
-        median_pooled[torch.isinf(median_pooled)] = float("nan")
-        
-        if ndim == 2:
-            return median_pooled.squeeze(1)
-
-        return median_pooled
-
-
-class CrepePitchExtractor(BasePitchExtractor):
-    def __init__(
-        self,
-        hop_length: int = 512,
-        f0_min: float = 50.0,
-        f0_max: float = 1100.0,
-        threshold: float = 0.05,
-        keep_zeros: bool = False,
-        device = None,
-        model: Literal["full", "tiny"] = "full",
-        use_fast_filters: bool = True,
-        decoder="viterbi"
-    ):
-        super().__init__(hop_length, f0_min, f0_max, keep_zeros)
-        if decoder == "viterbi":
-            self.decoder = torchcrepe.decode.viterbi
-        elif decoder == "argmax":
-            self.decoder = torchcrepe.decode.argmax
-        elif decoder == "weighted_argmax":
-            self.decoder = torchcrepe.decode.weighted_argmax
-        else:
-            raise "Unknown decoder"
-        self.threshold = threshold
-        self.model = model
-        self.use_fast_filters = use_fast_filters
-        self.hop_length = hop_length
-        if device is None:
-            self.dev = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-        else:
-            self.dev = torch.device(device)
-        if self.use_fast_filters:
-            self.median_filter = MaskedMedianPool1d(3, 1, 1).to(device)
-            self.mean_filter = MaskedAvgPool1d(3, 1, 1).to(device)
-
-    def __call__(self, x, sampling_rate=44100, pad_to=None):
-        """Extract pitch using crepe.
-
-
-        Args:
-            x (torch.Tensor): Audio signal, shape (1, T).
-            sampling_rate (int, optional): Sampling rate. Defaults to 44100.
-            pad_to (int, optional): Pad to length. Defaults to None.
-
-        Returns:
-            torch.Tensor: Pitch, shape (T // hop_length,).
-        """
-
-        assert x.ndim == 2, f"Expected 2D tensor, got {x.ndim}D tensor."
-        assert x.shape[0] == 1, f"Expected 1 channel, got {x.shape[0]} channels."
-
-        x = x.to(self.dev)
-        f0, pd = torchcrepe.predict(
-            x,
-            sampling_rate,
-            self.hop_length,
-            self.f0_min,
-            self.f0_max,
-            pad=True,
-            model=self.model,
-            batch_size=1024,
-            device=x.device,
-            return_periodicity=True,
-            decoder=self.decoder
-        )
-
-        # Filter, remove silence, set uv threshold, refer to the original warehouse readme
-        if self.use_fast_filters:
-            pd = self.median_filter(pd)
-        else:
-            pd = torchcrepe.filter.median(pd, 3)
-
-        pd = torchcrepe.threshold.Silence(-60.0)(pd, x, sampling_rate, 512)
-        f0 = torchcrepe.threshold.At(self.threshold)(f0, pd)
-        
-        if self.use_fast_filters:
-            f0 = self.mean_filter(f0)
-        else:
-            f0 = torchcrepe.filter.mean(f0, 3)
-
-        f0 = torch.where(torch.isnan(f0), torch.full_like(f0, 0), f0)[0]
-
-        if torch.all(f0 == 0):
-            rtn = f0.cpu().numpy() if pad_to==None else np.zeros(pad_to)
-            return rtn,rtn
-        
-        return self.post_process(x, sampling_rate, f0, pad_to)
diff --git a/spaces/ynhe/AskAnything/models/grit_src/third_party/CenterNet2/tests/config/dir1/dir1_b.py b/spaces/ynhe/AskAnything/models/grit_src/third_party/CenterNet2/tests/config/dir1/dir1_b.py
deleted file mode 100644
index 2dcb54cb1054c5d80ccc823af21f13b9ebbcf1a3..0000000000000000000000000000000000000000
--- a/spaces/ynhe/AskAnything/models/grit_src/third_party/CenterNet2/tests/config/dir1/dir1_b.py
+++ /dev/null
@@ -1,11 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-from detectron2.config import LazyConfig
-
-# equivalent to relative import
-dir1a_str, dir1a_dict = LazyConfig.load_rel("dir1_a.py", ("dir1a_str", "dir1a_dict"))
-
-dir1b_str = dir1a_str + "_from_b"
-dir1b_dict = dir1a_dict
-
-# Every import is a reload: not modified by other config files
-assert dir1a_dict.a == 1
diff --git a/spaces/ysharma/LLaVA_v1/llava/eval/eval_science_qa_gpt4.py b/spaces/ysharma/LLaVA_v1/llava/eval/eval_science_qa_gpt4.py
deleted file mode 100644
index c2ff17c915481fb556aba6ec816a9e08f519c515..0000000000000000000000000000000000000000
--- a/spaces/ysharma/LLaVA_v1/llava/eval/eval_science_qa_gpt4.py
+++ /dev/null
@@ -1,104 +0,0 @@
-import argparse
-import json
-import os
-import re
-import random
-from collections import defaultdict
-
-
-def get_args():
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--base-dir', type=str)
-    parser.add_argument('--gpt4-result', type=str)
-    parser.add_argument('--our-result', type=str)
-    parser.add_argument('--split', type=str, default='test')
-    parser.add_argument('--options', type=list, default=["A", "B", "C", "D", "E"])
-    return parser.parse_args()
-
-
-def convert_caps(results):
-    fakecaps = []
-    for result in results:
-        image_id = result['question_id']
-        caption = result['text']
-        fakecaps.append({"image_id": int(image_id), "caption": caption})
-    return fakecaps
-
-
-def get_pred_idx(prediction, choices, options):
-    """
-    Get the index (e.g. 2) from the prediction (e.g. 'C')
-    """
-    if prediction in options[:len(choices)]:
-        return options.index(prediction)
-    else:
-        return random.choice(range(len(choices)))
-
-
-if __name__ == "__main__":
-    args = get_args()
-
-    base_dir = args.base_dir
-    split_indices = json.load(open(os.path.join(base_dir, "pid_splits.json")))[args.split]
-    problems = json.load(open(os.path.join(base_dir, "problems.json")))
-    our_predictions = [json.loads(line) for line in open(args.our_result)]
-    our_predictions = {pred['question_id']: pred for pred in our_predictions}
-    split_problems = {idx: problems[idx] for idx in split_indices}
-
-    gpt4_predictions = json.load(open(args.gpt4_result))['outputs']
-
-    results = defaultdict(lambda: 0)
-
-    for prob_id, prob in split_problems.items():
-        if prob_id not in our_predictions:
-            continue
-        if prob_id not in gpt4_predictions:
-            continue
-        our_pred = our_predictions[prob_id]['text']
-        gpt4_pred = gpt4_predictions[prob_id]
-
-        pattern = re.compile(r'The answer is ([A-Z]).')
-        our_res = pattern.findall(our_pred)
-        if len(our_res) == 1:
-            our_answer = our_res[0]  # 'A', 'B', ...
-        else:
-            our_answer = "FAILED"
-        gpt4_res = pattern.findall(gpt4_pred)
-        if len(gpt4_res) == 1:
-            gpt4_answer = gpt4_res[0]  # 'A', 'B', ...
-        else:
-            gpt4_answer = "FAILED"
-
-        our_pred_idx = get_pred_idx(our_answer, prob['choices'], args.options)
-        gpt4_pred_idx = get_pred_idx(gpt4_answer, prob['choices'], args.options)
-
-        if gpt4_answer == 'FAILED':
-            results['gpt4_failed'] += 1
-            # continue
-            gpt4_pred_idx = our_pred_idx
-            # if our_pred_idx != prob['answer']:
-            #     print(our_predictions[prob_id]['prompt'])
-            #     print('-----------------')
-            #     print(f'LECTURE: {prob["lecture"]}')
-            #     print(f'SOLUTION: {prob["solution"]}')
-            #     print('=====================')
-        else:
-            # continue
-            pass
-        # gpt4_pred_idx = our_pred_idx
-
-        if gpt4_pred_idx == prob['answer']:
-            results['correct'] += 1
-        else:
-            results['incorrect'] += 1
-
-
-        if gpt4_pred_idx == prob['answer'] or our_pred_idx == prob['answer']:
-            results['correct_upperbound'] += 1
-
-    correct = results['correct']
-    total = results['correct'] + results['incorrect']
-    print(f'Total: {total}, Correct: {correct}, Accuracy: {correct / total * 100:.2f}%')
-    print(f'Total: {total}, Correct (upper): {results["correct_upperbound"]}, Accuracy: {results["correct_upperbound"] / total * 100:.2f}%')
-    print(f'Total: {total}, GPT-4 NO-ANS (RANDOM): {results["gpt4_failed"]}, Percentage: {results["gpt4_failed"] / total * 100:.2f}%')
-
diff --git a/spaces/ysharma/LangchainBot-space-creator/template/app_og.py b/spaces/ysharma/LangchainBot-space-creator/template/app_og.py
deleted file mode 100644
index caa0b8c3d9afa226ae432fdd7c565248736f8895..0000000000000000000000000000000000000000
--- a/spaces/ysharma/LangchainBot-space-creator/template/app_og.py
+++ /dev/null
@@ -1,80 +0,0 @@
-from langchain.llms import OpenAI
-from langchain.chains.qa_with_sources import load_qa_with_sources_chain
-from langchain.docstore.document import Document
-import requests
-import pathlib
-import subprocess
-import tempfile
-import os
-import gradio as gr
-import pickle
-
-# using a vector space for our search
-from langchain.embeddings.openai import OpenAIEmbeddings
-from langchain.vectorstores.faiss import FAISS
-from langchain.text_splitter import CharacterTextSplitter
-                
-#loading FAISS search index from disk
-with open("search_index.pickle", "rb") as f:
-    search_index = pickle.load(f)
-
-#Get GPT3 response using Langchain
-def print_answer(question, openai):   #openai_embeddings
-    #search_index = get_search_index()
-    chain = load_qa_with_sources_chain(openai) #(OpenAI(temperature=0)) 
-    response = (
-        chain(
-            {
-                "input_documents": search_index.similarity_search(question, k=4),
-                "question": question,
-            },
-            return_only_outputs=True,
-        )["output_text"]
-    )
-    if len(response.split('\n')[-1].split())>2:
-        response = response.split('\n')[0] + ', '.join([' <a href="' + response.split('\n')[-1].split()[i] + '" target="_blank"><u>Click Link' + str(i) + '</u></a>' for i in range(1,len(response.split('\n')[-1].split()))])
-    else: 
-        response = response.split('\n')[0] + ' <a href="' + response.split('\n')[-1].split()[-1] + '" target="_blank"><u>Click Link</u></a>'
-    return response
-
-
-def chat(message, history, openai_api_key):
-    #openai_embeddings = OpenAIEmbeddings(openai_api_key=openai_api_key)
-    openai = OpenAI(temperature=0, openai_api_key=openai_api_key )
-    #os.environ["OPENAI_API_KEY"] = openai_api_key
-    history = history or []
-    message = message.lower()
-    response = print_answer(message, openai)   #openai_embeddings
-    history.append((message, response))
-    return history, history
-
-
-with gr.Blocks() as demo:
-  gr.HTML("""<div style="text-align: center; max-width: 700px; margin: 0 auto;">
-        <div
-        style="
-            display: inline-flex;
-            align-items: center;
-            gap: 0.8rem;
-            font-size: 1.75rem;
-        "
-        >
-        <h1 style="font-weight: 900; margin-bottom: 7px; margin-top: 5px;">
-            $RepoName QandA - LangChain Bot
-        </h1>
-        </div>
-        <p style="margin-bottom: 10px; font-size: 94%">
-        Hi, I'm a Q and A $RepoName expert bot, start by typing in your OpenAI API key, questions/issues you are facing in your $RepoName implementations and then press enter.<br>
-        <a href="https://huggingface.co/spaces/ysharma/InstructPix2Pix_Chatbot?duplicate=true"><img src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>Duplicate Space with GPU Upgrade for fast Inference & no queue<br> 
-        Built using <a href="https://langchain.readthedocs.io/en/latest/" target="_blank">LangChain</a> and <a href="https://github.com/gradio-app/gradio" target="_blank">Gradio</a> for the $RepoName Repo
-        </p>
-    </div>""")  
-  with gr.Row():
-    question = gr.Textbox(label = 'Type in your questions about $RepoName here and press Enter!', placeholder = 'What questions do you want to ask about the $RepoName library?')
-    openai_api_key = gr.Textbox(type='password', label="Enter your OpenAI API key here")
-  state = gr.State()
-  chatbot = gr.Chatbot()
-  question.submit(chat, [question, state, openai_api_key], [chatbot, state])
-
-if __name__ == "__main__":
-    demo.launch()
\ No newline at end of file
diff --git a/spaces/zej97/AI-Research-Assistant/agent/__init__.py b/spaces/zej97/AI-Research-Assistant/agent/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/zengwj/GPT2-chitchat-training-huggingface/README.md b/spaces/zengwj/GPT2-chitchat-training-huggingface/README.md
deleted file mode 100644
index 9eab5b86a50fead40686c1c6f39e46cad94443cf..0000000000000000000000000000000000000000
--- a/spaces/zengwj/GPT2-chitchat-training-huggingface/README.md
+++ /dev/null
@@ -1,28 +0,0 @@
----
-title: GPT2 Chitchat Training On Huggingface
-emoji: 🔥
-colorFrom: yellow
-colorTo: gray
-sdk: docker
-pinned: false
-duplicated_from: tianpanyu/GPT2-chitchat-training-huggingface
----
-使用Hugging Face的服务器资源训练自己的GPT2-chitchat模型！
-
-使用方法: 
-
-  1.Duplicate本Space 
-
-  2.将语料上传到Files 点进去拿到文件下载链接并填入train_path.txt
-
-  3.等待训练完成在App页面中下载模型(位置:model/epochxx)
-
-注: 免费服务72h内必须有一次访问否则会进入休眠
-
-语料不宜过大
-
-模型使用: 可以用Hugging face的另一个space GPT2-chitchat 或者在本地部署
-
-如果要增加epoch 请修改install.sh (默认为30)
-
-如果使用GPU 请删除install.sh中的no_cuda 并修改torch的下载地址
diff --git a/spaces/zhang-wei-jian/docker/node_modules/nodemon/node_modules/debug/README.md b/spaces/zhang-wei-jian/docker/node_modules/nodemon/node_modules/debug/README.md
deleted file mode 100644
index 0ee7634ddd0a4a6337fe7677e99ba78cd626fa07..0000000000000000000000000000000000000000
--- a/spaces/zhang-wei-jian/docker/node_modules/nodemon/node_modules/debug/README.md
+++ /dev/null
@@ -1,437 +0,0 @@
-# debug
-[![Build Status](https://travis-ci.org/visionmedia/debug.svg?branch=master)](https://travis-ci.org/visionmedia/debug)  [![Coverage Status](https://coveralls.io/repos/github/visionmedia/debug/badge.svg?branch=master)](https://coveralls.io/github/visionmedia/debug?branch=master)  [![Slack](https://visionmedia-community-slackin.now.sh/badge.svg)](https://visionmedia-community-slackin.now.sh/) [![OpenCollective](https://opencollective.com/debug/backers/badge.svg)](#backers)
-[![OpenCollective](https://opencollective.com/debug/sponsors/badge.svg)](#sponsors)
-
-<img width="647" src="https://user-images.githubusercontent.com/71256/29091486-fa38524c-7c37-11e7-895f-e7ec8e1039b6.png">
-
-A tiny JavaScript debugging utility modelled after Node.js core's debugging
-technique. Works in Node.js and web browsers.
-
-## Installation
-
-```bash
-$ npm install debug
-```
-
-## Usage
-
-`debug` exposes a function; simply pass this function the name of your module, and it will return a decorated version of `console.error` for you to pass debug statements to. This will allow you to toggle the debug output for different parts of your module as well as the module as a whole.
-
-Example [_app.js_](./examples/node/app.js):
-
-```js
-var debug = require('debug')('http')
-  , http = require('http')
-  , name = 'My App';
-
-// fake app
-
-debug('booting %o', name);
-
-http.createServer(function(req, res){
-  debug(req.method + ' ' + req.url);
-  res.end('hello\n');
-}).listen(3000, function(){
-  debug('listening');
-});
-
-// fake worker of some kind
-
-require('./worker');
-```
-
-Example [_worker.js_](./examples/node/worker.js):
-
-```js
-var a = require('debug')('worker:a')
-  , b = require('debug')('worker:b');
-
-function work() {
-  a('doing lots of uninteresting work');
-  setTimeout(work, Math.random() * 1000);
-}
-
-work();
-
-function workb() {
-  b('doing some work');
-  setTimeout(workb, Math.random() * 2000);
-}
-
-workb();
-```
-
-The `DEBUG` environment variable is then used to enable these based on space or
-comma-delimited names.
-
-Here are some examples:
-
-<img width="647" alt="screen shot 2017-08-08 at 12 53 04 pm" src="https://user-images.githubusercontent.com/71256/29091703-a6302cdc-7c38-11e7-8304-7c0b3bc600cd.png">
-<img width="647" alt="screen shot 2017-08-08 at 12 53 38 pm" src="https://user-images.githubusercontent.com/71256/29091700-a62a6888-7c38-11e7-800b-db911291ca2b.png">
-<img width="647" alt="screen shot 2017-08-08 at 12 53 25 pm" src="https://user-images.githubusercontent.com/71256/29091701-a62ea114-7c38-11e7-826a-2692bedca740.png">
-
-#### Windows command prompt notes
-
-##### CMD
-
-On Windows the environment variable is set using the `set` command.
-
-```cmd
-set DEBUG=*,-not_this
-```
-
-Example:
-
-```cmd
-set DEBUG=* & node app.js
-```
-
-##### PowerShell (VS Code default)
-
-PowerShell uses different syntax to set environment variables.
-
-```cmd
-$env:DEBUG = "*,-not_this"
-```
-
-Example:
-
-```cmd
-$env:DEBUG='app';node app.js
-```
-
-Then, run the program to be debugged as usual.
-
-npm script example:
-```js
-  "windowsDebug": "@powershell -Command $env:DEBUG='*';node app.js",
-```
-
-## Namespace Colors
-
-Every debug instance has a color generated for it based on its namespace name.
-This helps when visually parsing the debug output to identify which debug instance
-a debug line belongs to.
-
-#### Node.js
-
-In Node.js, colors are enabled when stderr is a TTY. You also _should_ install
-the [`supports-color`](https://npmjs.org/supports-color) module alongside debug,
-otherwise debug will only use a small handful of basic colors.
-
-<img width="521" src="https://user-images.githubusercontent.com/71256/29092181-47f6a9e6-7c3a-11e7-9a14-1928d8a711cd.png">
-
-#### Web Browser
-
-Colors are also enabled on "Web Inspectors" that understand the `%c` formatting
-option. These are WebKit web inspectors, Firefox ([since version
-31](https://hacks.mozilla.org/2014/05/editable-box-model-multiple-selection-sublime-text-keys-much-more-firefox-developer-tools-episode-31/))
-and the Firebug plugin for Firefox (any version).
-
-<img width="524" src="https://user-images.githubusercontent.com/71256/29092033-b65f9f2e-7c39-11e7-8e32-f6f0d8e865c1.png">
-
-
-## Millisecond diff
-
-When actively developing an application it can be useful to see when the time spent between one `debug()` call and the next. Suppose for example you invoke `debug()` before requesting a resource, and after as well, the "+NNNms" will show you how much time was spent between calls.
-
-<img width="647" src="https://user-images.githubusercontent.com/71256/29091486-fa38524c-7c37-11e7-895f-e7ec8e1039b6.png">
-
-When stdout is not a TTY, `Date#toISOString()` is used, making it more useful for logging the debug information as shown below:
-
-<img width="647" src="https://user-images.githubusercontent.com/71256/29091956-6bd78372-7c39-11e7-8c55-c948396d6edd.png">
-
-
-## Conventions
-
-If you're using this in one or more of your libraries, you _should_ use the name of your library so that developers may toggle debugging as desired without guessing names. If you have more than one debuggers you _should_ prefix them with your library name and use ":" to separate features. For example "bodyParser" from Connect would then be "connect:bodyParser".  If you append a "*" to the end of your name, it will always be enabled regardless of the setting of the DEBUG environment variable.  You can then use it for normal output as well as debug output.
-
-## Wildcards
-
-The `*` character may be used as a wildcard. Suppose for example your library has
-debuggers named "connect:bodyParser", "connect:compress", "connect:session",
-instead of listing all three with
-`DEBUG=connect:bodyParser,connect:compress,connect:session`, you may simply do
-`DEBUG=connect:*`, or to run everything using this module simply use `DEBUG=*`.
-
-You can also exclude specific debuggers by prefixing them with a "-" character.
-For example, `DEBUG=*,-connect:*` would include all debuggers except those
-starting with "connect:".
-
-## Environment Variables
-
-When running through Node.js, you can set a few environment variables that will
-change the behavior of the debug logging:
-
-| Name      | Purpose                                         |
-|-----------|-------------------------------------------------|
-| `DEBUG`   | Enables/disables specific debugging namespaces. |
-| `DEBUG_HIDE_DATE` | Hide date from debug output (non-TTY).  |
-| `DEBUG_COLORS`| Whether or not to use colors in the debug output. |
-| `DEBUG_DEPTH` | Object inspection depth.                    |
-| `DEBUG_SHOW_HIDDEN` | Shows hidden properties on inspected objects. |
-
-
-__Note:__ The environment variables beginning with `DEBUG_` end up being
-converted into an Options object that gets used with `%o`/`%O` formatters.
-See the Node.js documentation for
-[`util.inspect()`](https://nodejs.org/api/util.html#util_util_inspect_object_options)
-for the complete list.
-
-## Formatters
-
-Debug uses [printf-style](https://wikipedia.org/wiki/Printf_format_string) formatting.
-Below are the officially supported formatters:
-
-| Formatter | Representation |
-|-----------|----------------|
-| `%O`      | Pretty-print an Object on multiple lines. |
-| `%o`      | Pretty-print an Object all on a single line. |
-| `%s`      | String. |
-| `%d`      | Number (both integer and float). |
-| `%j`      | JSON. Replaced with the string '[Circular]' if the argument contains circular references. |
-| `%%`      | Single percent sign ('%'). This does not consume an argument. |
-
-
-### Custom formatters
-
-You can add custom formatters by extending the `debug.formatters` object.
-For example, if you wanted to add support for rendering a Buffer as hex with
-`%h`, you could do something like:
-
-```js
-const createDebug = require('debug')
-createDebug.formatters.h = (v) => {
-  return v.toString('hex')
-}
-
-// …elsewhere
-const debug = createDebug('foo')
-debug('this is hex: %h', new Buffer('hello world'))
-//   foo this is hex: 68656c6c6f20776f726c6421 +0ms
-```
-
-
-## Browser Support
-
-You can build a browser-ready script using [browserify](https://github.com/substack/node-browserify),
-or just use the [browserify-as-a-service](https://wzrd.in/) [build](https://wzrd.in/standalone/debug@latest),
-if you don't want to build it yourself.
-
-Debug's enable state is currently persisted by `localStorage`.
-Consider the situation shown below where you have `worker:a` and `worker:b`,
-and wish to debug both. You can enable this using `localStorage.debug`:
-
-```js
-localStorage.debug = 'worker:*'
-```
-
-And then refresh the page.
-
-```js
-a = debug('worker:a');
-b = debug('worker:b');
-
-setInterval(function(){
-  a('doing some work');
-}, 1000);
-
-setInterval(function(){
-  b('doing some work');
-}, 1200);
-```
-
-
-## Output streams
-
-  By default `debug` will log to stderr, however this can be configured per-namespace by overriding the `log` method:
-
-Example [_stdout.js_](./examples/node/stdout.js):
-
-```js
-var debug = require('debug');
-var error = debug('app:error');
-
-// by default stderr is used
-error('goes to stderr!');
-
-var log = debug('app:log');
-// set this namespace to log via console.log
-log.log = console.log.bind(console); // don't forget to bind to console!
-log('goes to stdout');
-error('still goes to stderr!');
-
-// set all output to go via console.info
-// overrides all per-namespace log settings
-debug.log = console.info.bind(console);
-error('now goes to stdout via console.info');
-log('still goes to stdout, but via console.info now');
-```
-
-## Extend
-You can simply extend debugger 
-```js
-const log = require('debug')('auth');
-
-//creates new debug instance with extended namespace
-const logSign = log.extend('sign');
-const logLogin = log.extend('login');
-
-log('hello'); // auth hello
-logSign('hello'); //auth:sign hello
-logLogin('hello'); //auth:login hello
-```
-
-## Set dynamically
-
-You can also enable debug dynamically by calling the `enable()` method :
-
-```js
-let debug = require('debug');
-
-console.log(1, debug.enabled('test'));
-
-debug.enable('test');
-console.log(2, debug.enabled('test'));
-
-debug.disable();
-console.log(3, debug.enabled('test'));
-
-```
-
-print :   
-```
-1 false
-2 true
-3 false
-```
-
-Usage :  
-`enable(namespaces)`  
-`namespaces` can include modes separated by a colon and wildcards.
-   
-Note that calling `enable()` completely overrides previously set DEBUG variable : 
-
-```
-$ DEBUG=foo node -e 'var dbg = require("debug"); dbg.enable("bar"); console.log(dbg.enabled("foo"))'
-=> false
-```
-
-## Checking whether a debug target is enabled
-
-After you've created a debug instance, you can determine whether or not it is
-enabled by checking the `enabled` property:
-
-```javascript
-const debug = require('debug')('http');
-
-if (debug.enabled) {
-  // do stuff...
-}
-```
-
-You can also manually toggle this property to force the debug instance to be
-enabled or disabled.
-
-
-## Authors
-
- - TJ Holowaychuk
- - Nathan Rajlich
- - Andrew Rhyne
-
-## Backers
-
-Support us with a monthly donation and help us continue our activities. [[Become a backer](https://opencollective.com/debug#backer)]
-
-<a href="https://opencollective.com/debug/backer/0/website" target="_blank"><img src="https://opencollective.com/debug/backer/0/avatar.svg"></a>
-<a href="https://opencollective.com/debug/backer/1/website" target="_blank"><img src="https://opencollective.com/debug/backer/1/avatar.svg"></a>
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-<a href="https://opencollective.com/debug/backer/29/website" target="_blank"><img src="https://opencollective.com/debug/backer/29/avatar.svg"></a>
-
-
-## Sponsors
-
-Become a sponsor and get your logo on our README on Github with a link to your site. [[Become a sponsor](https://opencollective.com/debug#sponsor)]
-
-<a href="https://opencollective.com/debug/sponsor/0/website" target="_blank"><img src="https://opencollective.com/debug/sponsor/0/avatar.svg"></a>
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-<a href="https://opencollective.com/debug/sponsor/29/website" target="_blank"><img src="https://opencollective.com/debug/sponsor/29/avatar.svg"></a>
-
-## License
-
-(The MIT License)
-
-Copyright (c) 2014-2017 TJ Holowaychuk &lt;tj@vision-media.ca&gt;
-
-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.
diff --git a/spaces/zhanghaohui/szu-gpt-academic/docs/README.md.Portuguese.md b/spaces/zhanghaohui/szu-gpt-academic/docs/README.md.Portuguese.md
deleted file mode 100644
index 98f6054303cb88d429aacc9edec1e39f4dd7af95..0000000000000000000000000000000000000000
--- a/spaces/zhanghaohui/szu-gpt-academic/docs/README.md.Portuguese.md
+++ /dev/null
@@ -1,324 +0,0 @@
-> **Nota**
->
-> Ao instalar as dependências, por favor, selecione rigorosamente as versões **especificadas** no arquivo requirements.txt.
->
-> `pip install -r requirements.txt`
->
-
-# <img src="logo.png" width="40" > Otimização acadêmica GPT (GPT Academic)
-
-**Se você gostou deste projeto, por favor dê um Star. Se você criou atalhos acadêmicos mais úteis ou plugins funcionais, sinta-se livre para abrir uma issue ou pull request. Nós também temos um README em [Inglês|](README_EN.md)[日本語|](README_JP.md)[한국어|](https://github.com/mldljyh/ko_gpt_academic)[Русский|](README_RS.md)[Français](README_FR.md) traduzidos por este próprio projeto.
-Para traduzir este projeto para qualquer idioma com o GPT, leia e execute [`multi_language.py`](multi_language.py) (experimental).
-
-> **Nota**
->
-> 1. Por favor, preste atenção que somente os plugins de funções (botões) com a cor **vermelha** podem ler arquivos. Alguns plugins estão localizados no **menu suspenso** na área de plugins. Além disso, nós damos as boas-vindas com a **maior prioridade** e gerenciamos quaisquer novos plugins PR!
->
-> 2. As funções de cada arquivo neste projeto são detalhadas em [`self_analysis.md`](https://github.com/binary-husky/chatgpt_academic/wiki/chatgpt-academic%E9%A1%B9%E7%9B%AE%E8%87%AA%E8%AF%91%E8%A7%A3%E6%8A%A5%E5%91%8A), auto-análises do projeto geradas pelo GPT também estão podem ser chamadas a qualquer momento ao clicar nos plugins relacionados. As perguntas frequentes estão resumidas no [`wiki`](https://github.com/binary-husky/chatgpt_academic/wiki/%E5%B8%B8%E8%A7%81%E9%97%AE%E9%A2%98). [Instruções de Instalação](#installation).
->
-> 3. Este projeto é compatível com e incentiva o uso de modelos de linguagem nacionais, como chatglm e RWKV, Pangolin, etc. Suporta a coexistência de várias chaves de API e pode ser preenchido no arquivo de configuração como `API_KEY="openai-key1,openai-key2,api2d-key3"`. Quando precisar alterar temporariamente o `API_KEY`, basta digitar o `API_KEY` temporário na área de entrada e pressionar Enter para que ele entre em vigor. 
-
-<div align="center">
-
-Funcionalidade | Descrição
---- | ---
-Um clique de polimento | Suporte a um clique polimento, um clique encontrar erros de gramática no artigo
-Tradução chinês-inglês de um clique | Tradução chinês-inglês de um clique
-Explicação de código de um único clique | Exibir código, explicar código, gerar código, adicionar comentários ao código
-[Teclas de atalho personalizadas](https://www.bilibili.com/video/BV14s4y1E7jN) | Suporte a atalhos personalizados
-Projeto modular | Suporte para poderosos plugins[de função personalizada](https://github.com/binary-husky/chatgpt_academic/tree/master/crazy_functions), os plugins suportam[hot-reload](https://github.com/binary-husky/chatgpt_academic/wiki/%E5%87%BD%E6%95%B0%E6%8F%92%E4%BB%B6%E6%8C%87%E5%8D%97)
-[Análise automática do programa](https://www.bilibili.com/video/BV1cj411A7VW) | [Plugin de função][um clique para entender](https://github.com/binary-husky/chatgpt_academic/wiki/chatgpt-academic%E9%A1%B9%E7%9B%AE%E8%87%AA%E8%AF%91%E8%A7%A3%E6%8A%A5%E5%91%8A) o código-fonte do projeto
-[Análise do programa](https://www.bilibili.com/video/BV1cj411A7VW) | [Plugin de função] Um clique pode analisar a árvore de projetos do Python/C/C++/Java/Lua/...
-Leitura de artigos, [tradução](https://www.bilibili.com/video/BV1KT411x7Wn) de artigos | [Plugin de função] um clique para interpretar o resumo de artigos LaTeX/PDF e gerar resumo
-Tradução completa LATEX, polimento|[Plugin de função] Uma clique para traduzir ou polir um artigo LATEX
-Geração em lote de comentários | [Plugin de função] Um clique gera comentários de função em lote
-[Tradução chinês-inglês](https://www.bilibili.com/video/BV1yo4y157jV/) markdown | [Plugin de função] Você viu o README em 5 linguagens acima?
-Relatório de análise de chat | [Plugin de função] Gera automaticamente um resumo após a execução
-[Funcionalidade de tradução de artigos completos em PDF](https://www.bilibili.com/video/BV1KT411x7Wn) | [Plugin de função] Extrai o título e o resumo do artigo PDF e traduz o artigo completo (multithread)
-Assistente arXiv | [Plugin de função] Insira o url do artigo arXiv para traduzir o resumo + baixar PDF
-Assistente de integração acadêmica do Google | [Plugin de função] Dê qualquer URL de página de pesquisa acadêmica do Google e deixe o GPT escrever[trabalhos relacionados](https://www.bilibili.com/video/BV1GP411U7Az/)
-Agregação de informações da Internet + GPT | [Plugin de função] Um clique para obter informações do GPT através da Internet e depois responde a perguntas para informações nunca ficarem desatualizadas
-Exibição de fórmulas/imagem/tabela | Pode exibir simultaneamente a forma de renderização e[TEX] das fórmulas, suporte a fórmulas e realce de código
-Suporte de plugins de várias linhas | Suporte a várias chamadas em linha do chatgpt, um clique para processamento[de massa de texto](https://www.bilibili.com/video/BV1FT411H7c5/) ou programa
-Tema gradio escuro | Adicione ``` /?__theme=dark``` ao final da url do navegador para ativar o tema escuro
-[Suporte para vários modelos LLM](https://www.bilibili.com/video/BV1wT411p7yf), suporte para a nova interface API2D | A sensação de ser atendido simultaneamente por GPT3.5, GPT4, [Chatglm THU](https://github.com/THUDM/ChatGLM-6B), [Moss Fudan](https://github.com/OpenLMLab/MOSS) deve ser ótima, certo?
-Mais modelos LLM incorporados, suporte para a implantação[huggingface](https://huggingface.co/spaces/qingxu98/gpt-academic) | Adicione interface Newbing (New Bing), suporte [JittorLLMs](https://github.com/Jittor/JittorLLMs) THU Introdução ao suporte do LLaMA, RWKV e Pan Gu Alpha
-Mais recursos novos mostrados (geração de imagens, etc.) ... | Consulte o final deste documento ...  
-
-</div>
-
-- Nova interface (Modifique a opção LAYOUT em `config.py` para alternar entre o layout esquerdo/direito e o layout superior/inferior)
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/230361456-61078362-a966-4eb5-b49e-3c62ef18b860.gif" width="700" >
-</div>- All buttons are dynamically generated by reading functional.py, and you can add custom functions at will, liberating the clipboard
-
-<div align="center">
-<img src = "https://user-images.githubusercontent.com/96192199/231975334-b4788e91-4887-412f-8b43-2b9c5f41d248.gif" width="700">
-</div>
-
-- Proofreading/errors correction
-
-
-<div align="center">
-<img src = "https://user-images.githubusercontent.com/96192199/231980294-f374bdcb-3309-4560-b424-38ef39f04ebd.gif" width="700">
-</div>
-
-- If the output contains formulas, it will be displayed in both tex and rendering format at the same time, which is convenient for copying and reading
-
-
-<div align="center">
-<img src = "https://user-images.githubusercontent.com/96192199/230598842-1d7fcddd-815d-40ee-af60-baf488a199df.png" width="700">
-</div>
-
-- Don't want to read the project code? Just show the whole project to chatgpt
-
-
-<div align="center">
-<img src = "https://user-images.githubusercontent.com/96192199/226935232-6b6a73ce-8900-4aee-93f9-733c7e6fef53.png" width="700">
-</div>
-
-- Mix the use of multiple large language models (ChatGLM + OpenAI-GPT3.5 + [API2D](https://api2d.com/)-GPT4)
-
-
-<div align="center">
-<img src = "https://user-images.githubusercontent.com/96192199/232537274-deca0563-7aa6-4b5d-94a2-b7c453c47794.png" width="700">
-</div>
-
----
-# Instalação
-## Installation-Method 1: Run directly (Windows, Linux or MacOS)
-
-1. Download the project
-
-```sh
-git clone https://github.com/binary-husky/chatgpt_academic.git
-cd chatgpt_academic
-```
-
-2. Configure the API KEY
-
-In `config.py`, configure API KEY and other settings, [Special Network Environment Settings] (https://github.com/binary-husky/gpt_academic/issues/1).
-
-(P.S. When the program runs, it will first check whether there is a private configuration file named `config_private.py`, and use the configuration in it to cover the configuration with the same name in `config.py`. Therefore, if you can understand our configuration reading logic, we strongly recommend that you create a new configuration file named `config_private.py` next to `config.py`, and transfer (copy) the configuration in `config.py` to `config_private.py`. `config_private.py` is not controlled by git and can make your privacy information more secure. P.S. The project also supports configuring most options through `environment variables`. The writing format of environment variables is referenced to the `docker-compose` file. Reading priority: `environment variable` > `config_private.py` > `config.py`)
-
-
-3. Install dependencies
-
-```sh
-# (Option I: for those familiar with python)(python version is 3.9 or above, the newer the better), note: use the official pip source or the Alibaba pip source. Temporary solution for changing source: python -m pip install -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple/
-python -m pip install -r requirements.txt
-
-# (Option II: for those who are unfamiliar with python) use anaconda, the steps are also similar (https://www.bilibili.com/video/BV1rc411W7Dr):
-conda create -n gptac_venv python=3.11    # create anaconda environment
-conda activate gptac_venv                 # activate anaconda environment
-python -m pip install -r requirements.txt # This step is the same as the pip installation step
-```
-
-<details><summary>If you need to support Tsinghua ChatGLM / Fudan MOSS as the backend, click to expand here</summary>
-<p>
-
-[Optional Step] If you need to support Tsinghua ChatGLM / Fudan MOSS as the backend, you need to install more dependencies (prerequisite: familiar with Python + used Pytorch + computer configuration is strong):
-```sh
-# 【Optional Step I】support Tsinghua ChatGLM。Tsinghua ChatGLM Note: If you encounter a "Call ChatGLM fails cannot load ChatGLM parameters normally" error, refer to the following: 1: The default installed is torch+cpu version, and using cuda requires uninstalling torch and reinstalling torch+cuda; 2: If the model cannot be loaded due to insufficient computer configuration, you can modify the model accuracy in request_llm/bridge_chatglm.py and change AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True) to AutoTokenizer.from_pretrained("THUDM/chatglm-6b-int4", trust_remote_code=True)
-python -m pip install -r request_llm/requirements_chatglm.txt
-
-# 【Optional Step II】support Fudan MOSS
-python -m pip install -r request_llm/requirements_moss.txt
-git clone https://github.com/OpenLMLab/MOSS.git request_llm/moss  # Note: When executing this line of code, you must be in the project root path
-
-# 【Optional Step III】Make sure that the AVAIL_LLM_MODELS in the config.py configuration file contains the expected model. Currently, all supported models are as follows (jittorllms series currently only supports docker solutions):
-AVAIL_LLM_MODELS = ["gpt-3.5-turbo", "api2d-gpt-3.5-turbo", "gpt-4", "api2d-gpt-4", "chatglm", "newbing", "moss"] # + ["jittorllms_rwkv", "jittorllms_pangualpha", "jittorllms_llama"]
-```
-
-</p>
-</details>
-
-
-4. Run
-
-```sh
-python main.py
-```5. Plugin de Função de Teste
-```
-- Função de modelo de plug-in de teste (exige que o GPT responda ao que aconteceu hoje na história), você pode usar esta função como modelo para implementar funções mais complexas
-    Clique em "[Função de plug-in de modelo de demonstração] O que aconteceu hoje na história?"
-```
-
-## Instalação - Método 2: Usando o Docker
-
-1. Apenas ChatGPT (recomendado para a maioria das pessoas)
-
-``` sh
-git clone https://github.com/binary-husky/chatgpt_academic.git  # Baixar o projeto
-cd chatgpt_academic                                 # Entrar no caminho
-nano config.py                                       # Editar config.py com qualquer editor de texto configurando "Proxy", "API_KEY" e "WEB_PORT" (por exemplo, 50923), etc.
-docker build -t gpt-academic .                      # Instale
-
-# (Ùltima etapa - escolha 1) Dentro do ambiente Linux, é mais fácil e rápido usar `--net=host`
-docker run --rm -it --net=host gpt-academic
-# (Última etapa - escolha 2) Em ambientes macOS/windows, você só pode usar a opção -p para expor a porta do contêiner (por exemplo, 50923) para a porta no host
-docker run --rm -it -e WEB_PORT=50923 -p 50923:50923 gpt-academic
-```
-
-2. ChatGPT + ChatGLM + MOSS (conhecimento de Docker necessário)
-
-``` sh
-# Edite o arquivo docker-compose.yml, remova as soluções 1 e 3, mantenha a solução 2, e siga as instruções nos comentários do arquivo
-docker-compose up
-```
-
-3. ChatGPT + LLAMA + Pangu + RWKV (conhecimento de Docker necessário)
-``` sh
-# Edite o arquivo docker-compose.yml, remova as soluções 1 e 2, mantenha a solução 3, e siga as instruções nos comentários do arquivo 
-docker-compose up
-```
-
-
-## Instalação - Método 3: Outros Métodos de Implantação
-
-1. Como usar URLs de proxy inverso/microsoft Azure API
-Basta configurar o API_URL_REDIRECT de acordo com as instruções em `config.py`.
-
-2. Implantação em servidores em nuvem remotos (requer conhecimento e experiência de servidores em nuvem)
-Acesse [Wiki de implementação remota do servidor em nuvem](https://github.com/binary-husky/chatgpt_academic/wiki/%E4%BA%91%E6%9C%8D%E5%8A%A1%E5%99%A8%E8%BF%9C%E7%A8%8B%E9%83%A8%E7%BD%B2%E6%8C%87%E5%8D%97)
-
-3. Usando a WSL2 (sub-sistema do Windows para Linux)
-Acesse [Wiki da implantação da WSL2](https://github.com/binary-husky/chatgpt_academic/wiki/%E4%BD%BF%E7%94%A8WSL2%EF%BC%88Windows-Subsystem-for-Linux-%E5%AD%90%E7%B3%BB%E7%BB%9F%EF%BC%89%E9%83%A8%E7%BD%B2)
-
-4. Como executar em um subdiretório (ex. `http://localhost/subpath`)
-Acesse [Instruções de execução FastAPI](docs/WithFastapi.md)
-
-5. Execute usando o docker-compose
-Leia o arquivo docker-compose.yml e siga as instruções.
-
-# Uso Avançado
-## Customize novos botões de acesso rápido / plug-ins de função personalizados
-
-1. Personalizar novos botões de acesso rápido (atalhos acadêmicos)
-Abra `core_functional.py` em qualquer editor de texto e adicione os seguintes itens e reinicie o programa (Se o botão já foi adicionado e pode ser visto, prefixos e sufixos são compatíveis com modificações em tempo real e não exigem reinício do programa para ter efeito.)
-Por exemplo,
-```
-"Super Eng:": {
-  # Prefixo, será adicionado antes da sua entrada. Por exemplo, para descrever sua solicitação, como tradução, explicação de código, polimento, etc.
-  "Prefix": "Por favor, traduza o seguinte conteúdo para chinês e use uma tabela em Markdown para explicar termos próprios no texto: \n \n",
-
-  # Sufixo, será adicionado após a sua entrada. Por exemplo, emparelhado com o prefixo, pode colocar sua entrada entre aspas.
-  "Suffix": "",
-},
-```
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/226899272-477c2134-ed71-4326-810c-29891fe4a508.png" width="500" >
-</div>
-
-2. Personalizar plug-ins de função
-
-Escreva plug-ins de função poderosos para executar tarefas que você deseja e não pensava possível.
-A dificuldade geral de escrever e depurar plug-ins neste projeto é baixa e, se você tem algum conhecimento básico de python, pode implementar suas próprias funções sobre o modelo que fornecemos.
-Para mais detalhes, consulte o [Guia do plug-in de função.](https://github.com/binary-husky/chatgpt_academic/wiki/%E5%87%BD%E6%95%B0%E6%8F%92%E4%BB%B6%E6%8C%87%E5%8D%97).
-
----
-# Última atualização
-## Novas funções dinâmicas.
-
-1. Função de salvamento de diálogo. Ao chamar o plug-in de função "Salvar diálogo atual", é possível salvar o diálogo atual em um arquivo html legível e reversível. Além disso, ao chamar o plug-in de função "Carregar arquivo de histórico de diálogo" no menu suspenso da área de plug-in, é possível restaurar uma conversa anterior. Dica: clicar em "Carregar arquivo de histórico de diálogo" sem especificar um arquivo permite visualizar o cache do arquivo html de histórico. Clicar em "Excluir todo o registro de histórico de diálogo local" permite excluir todo o cache de arquivo html. 
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/235222390-24a9acc0-680f-49f5-bc81-2f3161f1e049.png" width="500" >
-</div>
-
-
-2. Geração de relatório. A maioria dos plug-ins gera um relatório de trabalho após a conclusão da execução.
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/227503770-fe29ce2c-53fd-47b0-b0ff-93805f0c2ff4.png" height="300" >
-<img src="https://user-images.githubusercontent.com/96192199/227504617-7a497bb3-0a2a-4b50-9a8a-95ae60ea7afd.png" height="300" >
-<img src="https://user-images.githubusercontent.com/96192199/227504005-efeaefe0-b687-49d0-bf95-2d7b7e66c348.png" height="300" >
-</div>
-
-3. Design modular de funcionalidades, com interfaces simples, mas suporte a recursos poderosos
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/229288270-093643c1-0018-487a-81e6-1d7809b6e90f.png" height="400" >
-<img src="https://user-images.githubusercontent.com/96192199/227504931-19955f78-45cd-4d1c-adac-e71e50957915.png" height="400" >
-</div>
-
-4. Este é um projeto de código aberto que é capaz de "auto-traduzir-se".
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/226936850-c77d7183-0749-4c1c-9875-fd4891842d0c.png" width="500" >
-</div>
-
-5. A tradução de outros projetos de código aberto é simples.
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/226935232-6b6a73ce-8900-4aee-93f9-733c7e6fef53.png" width="500" >
-</div>
-
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/226969067-968a27c1-1b9c-486b-8b81-ab2de8d3f88a.png" width="500" >
-</div>
-
-6. Recursos decorativos para o [live2d](https://github.com/fghrsh/live2d_demo) (desativados por padrão, é necessário modificar o arquivo `config.py`)
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/236432361-67739153-73e8-43fe-8111-b61296edabd9.png" width="500" >
-</div>
-
-7. Suporte ao modelo de linguagem MOSS
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/236639178-92836f37-13af-4fdd-984d-b4450fe30336.png" width="500" >
-</div>
-
-8. Geração de imagens pelo OpenAI
-<div align="center">
-<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/bc7ab234-ad90-48a0-8d62-f703d9e74665" width="500" >
-</div>
-
-9. Análise e resumo de áudio pelo OpenAI
-<div align="center">
-<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/709ccf95-3aee-498a-934a-e1c22d3d5d5b" width="500" >
-</div>
-
-10. Revisão e correção de erros de texto em Latex.
-<div align="center">
-<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/651ccd98-02c9-4464-91e1-77a6b7d1b033" width="500" >
-</div>
-
-## Versão:
-- Versão 3.5(Todo): Usar linguagem natural para chamar todas as funções do projeto (prioridade alta)
-- Versão 3.4(Todo): Melhorar o suporte à multithread para o chatglm local
-- Versão 3.3: +Funções integradas de internet
-- Versão 3.2: Suporte a mais interfaces de parâmetros de plug-in (função de salvar diálogo, interpretação de códigos de várias linguagens, perguntas de combinações LLM arbitrárias ao mesmo tempo)
-- Versão 3.1: Suporte a perguntas a vários modelos de gpt simultaneamente! Suporte para api2d e balanceamento de carga para várias chaves api
-- Versão 3.0: Suporte ao chatglm e outros LLMs de pequeno porte
-- Versão 2.6: Refatoração da estrutura de plug-in, melhoria da interatividade e adição de mais plug-ins
-- Versão 2.5: Autoatualização, resolvendo problemas de token de texto excessivamente longo e estouro ao compilar grandes projetos
-- Versão 2.4: (1) Adição de funcionalidade de tradução de texto completo em PDF; (2) Adição de funcionalidade de mudança de posição da área de entrada; (3) Adição de opção de layout vertical; (4) Otimização de plug-ins de multithread.
-- Versão 2.3: Melhoria da interatividade de multithread
-- Versão 2.2: Suporte à recarga a quente de plug-ins
-- Versão 2.1: Layout dobrável
-- Versão 2.0: Introdução de plug-ins de função modular
-- Versão 1.0: Funcionalidades básicasgpt_academic desenvolvedores QQ grupo-2: 610599535
-
-- Problemas conhecidos
-    - Extensões de tradução de alguns navegadores podem interferir na execução do front-end deste software
-    - Uma versão muito alta ou muito baixa do Gradio pode causar vários erros
-
-## Referências e Aprendizado
-
-```
-Foi feita referência a muitos projetos excelentes em código, principalmente:
-
-# Projeto1: ChatGLM-6B da Tsinghua:
-https://github.com/THUDM/ChatGLM-6B
-
-# Projeto2: JittorLLMs da Tsinghua:
-https://github.com/Jittor/JittorLLMs
-
-# Projeto3: Edge-GPT:
-https://github.com/acheong08/EdgeGPT
-
-# Projeto4: ChuanhuChatGPT:
-https://github.com/GaiZhenbiao/ChuanhuChatGPT
-
-# Projeto5: ChatPaper:
-https://github.com/kaixindelele/ChatPaper
-
-# Mais:
-https://github.com/gradio-app/gradio
-https://github.com/fghrsh/live2d_demo
-```
diff --git a/spaces/zhanpj/ChatGPT/assets/custom.css b/spaces/zhanpj/ChatGPT/assets/custom.css
deleted file mode 100644
index 3cf5f946a240f595e19f02259969f01d4b088012..0000000000000000000000000000000000000000
--- a/spaces/zhanpj/ChatGPT/assets/custom.css
+++ /dev/null
@@ -1,239 +0,0 @@
-:root {
-    --chatbot-color-light: #F3F3F3;
-    --chatbot-color-dark: #121111;
-}
-
-/* 覆盖gradio的页脚信息QAQ */
-footer {
-    display: none !important;
-}
-#footer{
-    text-align: center;
-}
-#footer div{
-    display: inline-block;
-}
-#footer .versions{
-    font-size: 85%;
-    opacity: 0.85;
-}
-
-/* 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;
-}
-
-/* usage_display */
-#usage_display {
-    position: relative;
-    margin: 0;
-    box-shadow: var(--block-shadow);
-    border-width: var(--block-border-width);
-    border-color: var(--block-border-color);
-    border-radius: var(--block-radius);
-    background: var(--block-background-fill);
-    width: 100%;
-    line-height: var(--line-sm);
-    min-height: 2em;
-}
-#usage_display p, #usage_display span {
-    margin: 0;
-    padding: .5em 1em;
-    font-size: .85em;
-    color: var(--body-text-color-subdued);
-}
-.progress-bar {
-    background-color: var(--input-background-fill);;
-    margin: 0 1em;
-    height: 20px;
-    border-radius: 10px;
-    overflow: hidden;
-}
-.progress {
-    background-color: var(--block-title-background-fill);;
-    height: 100%;
-    border-radius: 10px;
-    text-align: right;
-    transition: width 0.5s ease-in-out;
-}
-.progress-text {
-    /* color: white; */
-    color: var(--color-accent) !important;
-    font-size: 1em !important;
-    font-weight: bold;
-    padding-right: 10px;
-    line-height: 20px;
-}
-/* list */
-ol:not(.options), ul:not(.options) {
-    padding-inline-start: 2em !important;
-}
-
-/* 亮色 */
-@media (prefers-color-scheme: light) {
-    #chuanhu_chatbot {
-        background-color: var(--chatbot-color-light) !important;
-        color: #000000 !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;
-        color: #FFFFFF !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;
-    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: 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: 1.4em 1.2em 0em 1.4em;
-    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/zhigangjiang/3D-Room-Layout-Estimation_LGT-Net/utils/time_watch.py b/spaces/zhigangjiang/3D-Room-Layout-Estimation_LGT-Net/utils/time_watch.py
deleted file mode 100644
index e710d8376fe1d670ed40e9986298d9713ad4bdb0..0000000000000000000000000000000000000000
--- a/spaces/zhigangjiang/3D-Room-Layout-Estimation_LGT-Net/utils/time_watch.py
+++ /dev/null
@@ -1,25 +0,0 @@
-""" 
-@Date: 2021/07/18
-@description:
-"""
-import time
-
-
-class TimeWatch:
-    def __init__(self, name="", logger=None):
-        self.name = name
-        self.start = time.time()
-        self.logger = logger
-
-    def __del__(self):
-        end = time.time()
-        output = f"{self.name} | time use {(end - self.start):.2f}s."
-        if self.logger:
-            self.logger.info(output)
-        else:
-            print(output)
-
-
-if __name__ == '__main__':
-    w = TimeWatch("__main__")
-    time.sleep(2)
\ No newline at end of file
diff --git a/spaces/zhoujiaxin/zhoujiaxinchatgpt/src/components/ui/input.tsx b/spaces/zhoujiaxin/zhoujiaxinchatgpt/src/components/ui/input.tsx
deleted file mode 100644
index 684a857f3d769b78818fb13de1abaebfb09ca79c..0000000000000000000000000000000000000000
--- a/spaces/zhoujiaxin/zhoujiaxinchatgpt/src/components/ui/input.tsx
+++ /dev/null
@@ -1,25 +0,0 @@
-import * as React from 'react'
-
-import { cn } from '@/lib/utils'
-
-export interface InputProps
-  extends React.InputHTMLAttributes<HTMLInputElement> {}
-
-const Input = React.forwardRef<HTMLInputElement, InputProps>(
-  ({ className, type, ...props }, ref) => {
-    return (
-      <input
-        type={type}
-        className={cn(
-          'flex h-9 w-full rounded-md border border-input bg-transparent px-3 py-2 text-sm shadow-sm ring-offset-background file:border-0 file:bg-transparent file:text-sm file:font-medium placeholder:text-muted-foreground focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-ring focus-visible:ring-offset-2 disabled:cursor-not-allowed disabled:opacity-50',
-          className
-        )}
-        ref={ref}
-        {...props}
-      />
-    )
-  }
-)
-Input.displayName = 'Input'
-
-export { Input }
diff --git a/spaces/zhoupin30/zhoupin30/src/components/tone-selector.tsx b/spaces/zhoupin30/zhoupin30/src/components/tone-selector.tsx
deleted file mode 100644
index 5c6e464c91f564b895acd121f0a4a79ed9c5c356..0000000000000000000000000000000000000000
--- a/spaces/zhoupin30/zhoupin30/src/components/tone-selector.tsx
+++ /dev/null
@@ -1,43 +0,0 @@
-import React from 'react'
-import { BingConversationStyle } from '@/lib/bots/bing/types'
-import { cn } from '@/lib/utils'
-
-type ToneItem = {
-  type: BingConversationStyle,
-  name: string
-}
-
-const ToneList: ToneItem[] = [
-  { name: '有创造力', type: BingConversationStyle.Creative },
-  { name: '更平衡', type: BingConversationStyle.Balanced },
-  { name: '更精确', type: BingConversationStyle.Precise }
-]
-
-interface ToneSelectorProps {
-  type: BingConversationStyle | ''
-  onChange?: (type: BingConversationStyle) => void
-}
-
-export function ToneSelector({ type, onChange }: ToneSelectorProps) {
-  return (
-    <div className="fieldset">
-      <div className="legend">
-        选择对话样式
-      </div>
-      <div className="options-list-container">
-        <ul id="tone-options" className="options">
-          {
-            ToneList.map(tone => (
-              <li className="option" key={tone.name} onClick={() => onChange?.(tone.type)}>
-                <button className={cn(`tone-${type.toLowerCase()}`, { selected: tone.type === type}) } aria-pressed="true" >
-                  <span className="caption-2-strong label-modifier">更</span>
-                  <span className="body-1-strong label">{tone.name}</span>
-                </button>
-              </li>
-            ))
-          }
-        </ul>
-      </div>
-    </div>
-  )
-}
diff --git a/spaces/zhuolisam/resume-ranker/main.py b/spaces/zhuolisam/resume-ranker/main.py
deleted file mode 100644
index 72b459d2988d08adf1dab6530d0cdccbf03351eb..0000000000000000000000000000000000000000
--- a/spaces/zhuolisam/resume-ranker/main.py
+++ /dev/null
@@ -1,6 +0,0 @@
-# from fastapi import FastAPI, File, UploadFile
-# app = FastAPI()
-
-# @app.post("/resume")
-# async def root(name:str, email:str, about:str, file:UploadFile = File(...)):
-#     return {"name":name, "email":email, "about":about, "file_name":file.filename}
\ No newline at end of file
diff --git a/spaces/zomehwh/vits-models/models.py b/spaces/zomehwh/vits-models/models.py
deleted file mode 100644
index 8353b867f441de7e4d05aef980e672899c3a8889..0000000000000000000000000000000000000000
--- a/spaces/zomehwh/vits-models/models.py
+++ /dev/null
@@ -1,533 +0,0 @@
-import math
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-import commons
-import modules
-import attentions
-import monotonic_align
-
-from torch.nn import Conv1d, ConvTranspose1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
-from commons import init_weights, get_padding
-
-
-class StochasticDurationPredictor(nn.Module):
-  def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, n_flows=4, gin_channels=0):
-    super().__init__()
-    filter_channels = in_channels # it needs to be removed from future version.
-    self.in_channels = in_channels
-    self.filter_channels = filter_channels
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.n_flows = n_flows
-    self.gin_channels = gin_channels
-
-    self.log_flow = modules.Log()
-    self.flows = nn.ModuleList()
-    self.flows.append(modules.ElementwiseAffine(2))
-    for i in range(n_flows):
-      self.flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3))
-      self.flows.append(modules.Flip())
-
-    self.post_pre = nn.Conv1d(1, filter_channels, 1)
-    self.post_proj = nn.Conv1d(filter_channels, filter_channels, 1)
-    self.post_convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout)
-    self.post_flows = nn.ModuleList()
-    self.post_flows.append(modules.ElementwiseAffine(2))
-    for i in range(4):
-      self.post_flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3))
-      self.post_flows.append(modules.Flip())
-
-    self.pre = nn.Conv1d(in_channels, filter_channels, 1)
-    self.proj = nn.Conv1d(filter_channels, filter_channels, 1)
-    self.convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout)
-    if gin_channels != 0:
-      self.cond = nn.Conv1d(gin_channels, filter_channels, 1)
-
-  def forward(self, x, x_mask, w=None, g=None, reverse=False, noise_scale=1.0):
-    x = torch.detach(x)
-    x = self.pre(x)
-    if g is not None:
-      g = torch.detach(g)
-      x = x + self.cond(g)
-    x = self.convs(x, x_mask)
-    x = self.proj(x) * x_mask
-
-    if not reverse:
-      flows = self.flows
-      assert w is not None
-
-      logdet_tot_q = 0 
-      h_w = self.post_pre(w)
-      h_w = self.post_convs(h_w, x_mask)
-      h_w = self.post_proj(h_w) * x_mask
-      e_q = torch.randn(w.size(0), 2, w.size(2)).to(device=x.device, dtype=x.dtype) * x_mask
-      z_q = e_q
-      for flow in self.post_flows:
-        z_q, logdet_q = flow(z_q, x_mask, g=(x + h_w))
-        logdet_tot_q += logdet_q
-      z_u, z1 = torch.split(z_q, [1, 1], 1) 
-      u = torch.sigmoid(z_u) * x_mask
-      z0 = (w - u) * x_mask
-      logdet_tot_q += torch.sum((F.logsigmoid(z_u) + F.logsigmoid(-z_u)) * x_mask, [1,2])
-      logq = torch.sum(-0.5 * (math.log(2*math.pi) + (e_q**2)) * x_mask, [1,2]) - logdet_tot_q
-
-      logdet_tot = 0
-      z0, logdet = self.log_flow(z0, x_mask)
-      logdet_tot += logdet
-      z = torch.cat([z0, z1], 1)
-      for flow in flows:
-        z, logdet = flow(z, x_mask, g=x, reverse=reverse)
-        logdet_tot = logdet_tot + logdet
-      nll = torch.sum(0.5 * (math.log(2*math.pi) + (z**2)) * x_mask, [1,2]) - logdet_tot
-      return nll + logq # [b]
-    else:
-      flows = list(reversed(self.flows))
-      flows = flows[:-2] + [flows[-1]] # remove a useless vflow
-      z = torch.randn(x.size(0), 2, x.size(2)).to(device=x.device, dtype=x.dtype) * noise_scale
-      for flow in flows:
-        z = flow(z, x_mask, g=x, reverse=reverse)
-      z0, z1 = torch.split(z, [1, 1], 1)
-      logw = z0
-      return logw
-
-
-class DurationPredictor(nn.Module):
-  def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0):
-    super().__init__()
-
-    self.in_channels = in_channels
-    self.filter_channels = filter_channels
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.gin_channels = gin_channels
-
-    self.drop = nn.Dropout(p_dropout)
-    self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size, padding=kernel_size//2)
-    self.norm_1 = modules.LayerNorm(filter_channels)
-    self.conv_2 = nn.Conv1d(filter_channels, filter_channels, kernel_size, padding=kernel_size//2)
-    self.norm_2 = modules.LayerNorm(filter_channels)
-    self.proj = nn.Conv1d(filter_channels, 1, 1)
-
-    if gin_channels != 0:
-      self.cond = nn.Conv1d(gin_channels, in_channels, 1)
-
-  def forward(self, x, x_mask, g=None):
-    x = torch.detach(x)
-    if g is not None:
-      g = torch.detach(g)
-      x = x + self.cond(g)
-    x = self.conv_1(x * x_mask)
-    x = torch.relu(x)
-    x = self.norm_1(x)
-    x = self.drop(x)
-    x = self.conv_2(x * x_mask)
-    x = torch.relu(x)
-    x = self.norm_2(x)
-    x = self.drop(x)
-    x = self.proj(x * x_mask)
-    return x * x_mask
-
-
-class TextEncoder(nn.Module):
-  def __init__(self,
-      n_vocab,
-      out_channels,
-      hidden_channels,
-      filter_channels,
-      n_heads,
-      n_layers,
-      kernel_size,
-      p_dropout):
-    super().__init__()
-    self.n_vocab = n_vocab
-    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 = nn.Embedding(n_vocab, hidden_channels)
-    nn.init.normal_(self.emb.weight, 0.0, hidden_channels**-0.5)
-
-    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, x, x_lengths):
-    x = self.emb(x) * math.sqrt(self.hidden_channels) # [b, t, h]
-    x = torch.transpose(x, 1, -1) # [b, h, t]
-    x_mask = torch.unsqueeze(commons.sequence_mask(x_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 x, 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
-
-
-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
-
-
-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):
-        print('Removing weight norm...')
-        for l in self.ups:
-            remove_weight_norm(l)
-        for l in self.resblocks:
-            l.remove_weight_norm()
-
-
-class DiscriminatorP(torch.nn.Module):
-    def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
-        super(DiscriminatorP, self).__init__()
-        self.period = period
-        self.use_spectral_norm = use_spectral_norm
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
-        self.convs = nn.ModuleList([
-            norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
-            norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
-            norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
-            norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
-            norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(get_padding(kernel_size, 1), 0))),
-        ])
-        self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
-
-    def forward(self, x):
-        fmap = []
-
-        # 1d to 2d
-        b, c, t = x.shape
-        if t % self.period != 0: # pad first
-            n_pad = self.period - (t % self.period)
-            x = F.pad(x, (0, n_pad), "reflect")
-            t = t + n_pad
-        x = x.view(b, c, t // self.period, self.period)
-
-        for l in self.convs:
-            x = l(x)
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        x = torch.flatten(x, 1, -1)
-
-        return x, fmap
-
-
-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 MultiPeriodDiscriminator(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(MultiPeriodDiscriminator, self).__init__()
-        periods = [2,3,5,7,11]
-
-        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)
-            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 SynthesizerTrn(nn.Module):
-  """
-  Synthesizer for Training
-  """
-
-  def __init__(self, 
-    n_vocab,
-    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,
-    n_speakers=0,
-    gin_channels=0,
-    use_sdp=True,
-    **kwargs):
-
-    super().__init__()
-    self.n_vocab = n_vocab
-    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.n_speakers = n_speakers
-    self.gin_channels = gin_channels
-
-    self.use_sdp = use_sdp
-
-    self.enc_p = TextEncoder(n_vocab,
-        inter_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout)
-    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, 4, gin_channels=gin_channels)
-
-    if use_sdp:
-      self.dp = StochasticDurationPredictor(hidden_channels, 192, 3, 0.5, 4, gin_channels=gin_channels)
-    else:
-      self.dp = DurationPredictor(hidden_channels, 256, 3, 0.5, gin_channels=gin_channels)
-
-    if n_speakers > 1:
-      self.emb_g = nn.Embedding(n_speakers, gin_channels)
-
-  def forward(self, x, x_lengths, y, y_lengths, sid=None):
-
-    x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths)
-    if self.n_speakers > 0:
-      g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1]
-    else:
-      g = None
-
-    z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
-    z_p = self.flow(z, y_mask, g=g)
-
-    with torch.no_grad():
-      # negative cross-entropy
-      s_p_sq_r = torch.exp(-2 * logs_p) # [b, d, t]
-      neg_cent1 = torch.sum(-0.5 * math.log(2 * math.pi) - logs_p, [1], keepdim=True) # [b, 1, t_s]
-      neg_cent2 = torch.matmul(-0.5 * (z_p ** 2).transpose(1, 2), s_p_sq_r) # [b, t_t, d] x [b, d, t_s] = [b, t_t, t_s]
-      neg_cent3 = torch.matmul(z_p.transpose(1, 2), (m_p * s_p_sq_r)) # [b, t_t, d] x [b, d, t_s] = [b, t_t, t_s]
-      neg_cent4 = torch.sum(-0.5 * (m_p ** 2) * s_p_sq_r, [1], keepdim=True) # [b, 1, t_s]
-      neg_cent = neg_cent1 + neg_cent2 + neg_cent3 + neg_cent4
-
-      attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
-      attn = monotonic_align.maximum_path(neg_cent, attn_mask.squeeze(1)).unsqueeze(1).detach()
-
-    w = attn.sum(2)
-    if self.use_sdp:
-      l_length = self.dp(x, x_mask, w, g=g)
-      l_length = l_length / torch.sum(x_mask)
-    else:
-      logw_ = torch.log(w + 1e-6) * x_mask
-      logw = self.dp(x, x_mask, g=g)
-      l_length = torch.sum((logw - logw_)**2, [1,2]) / torch.sum(x_mask) # for averaging 
-
-    # expand prior
-    m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2)
-    logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1, 2)
-
-    z_slice, ids_slice = commons.rand_slice_segments(z, y_lengths, self.segment_size)
-    o = self.dec(z_slice, g=g)
-    return o, l_length, attn, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
-
-  def infer(self, x, x_lengths, sid=None, noise_scale=1, length_scale=1, noise_scale_w=1., max_len=None):
-    x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths)
-    if self.n_speakers > 0:
-      g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1]
-    else:
-      g = None
-
-    if self.use_sdp:
-      logw = self.dp(x, x_mask, g=g, reverse=True, noise_scale=noise_scale_w)
-    else:
-      logw = self.dp(x, x_mask, g=g)
-    w = torch.exp(logw) * x_mask * length_scale
-    w_ceil = torch.ceil(w)
-    y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long()
-    y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, None), 1).to(x_mask.dtype)
-    attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
-    attn = commons.generate_path(w_ceil, attn_mask)
-
-    m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2) # [b, t', t], [b, t, d] -> [b, d, t']
-    logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1, 2) # [b, t', t], [b, t, d] -> [b, d, t']
-
-    z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale
-    z = self.flow(z_p, y_mask, g=g, reverse=True)
-    o = self.dec((z * y_mask)[:,:,:max_len], g=g)
-    return o, attn, y_mask, (z, z_p, m_p, logs_p)
-
-  def voice_conversion(self, y, y_lengths, sid_src, sid_tgt):
-    assert self.n_speakers > 0, "n_speakers have to be larger than 0."
-    g_src = self.emb_g(sid_src).unsqueeze(-1)
-    g_tgt = self.emb_g(sid_tgt).unsqueeze(-1)
-    z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g_src)
-    z_p = self.flow(z, y_mask, g=g_src)
-    z_hat = self.flow(z_p, y_mask, g=g_tgt, reverse=True)
-    o_hat = self.dec(z_hat * y_mask, g=g_tgt)
-    return o_hat, y_mask, (z, z_p, z_hat)
-