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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>
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-<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 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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-<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>
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-<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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-<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>
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-<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>
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-<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>
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- <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>
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-<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>
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-<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>
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-<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>
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-<p>Some of the advanced settings that you might want to adjust are:</p>
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-<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>
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- <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>
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-<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>
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-<p>Here are some of the most common questions that people ask about Ccie Torrent:</p>
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-<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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- <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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spaces/1phancelerku/anime-remove-background/English Movies Download 2017 The Most Popular and Critically Acclaimed Films of the Year.md

@@ -1,145 +0,0 @@
-
-<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>
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-<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>
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spaces/232labs/VToonify/vtoonify/model/raft/core/extractor.py

@@ -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

spaces/4H17Joycelyn/text_generater/app.py

@@ -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()

spaces/7hao/bingo/src/components/ui/codeblock.tsx

@@ -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 }

spaces/AI-Hobbyist/Hoyo-RVC/infer_pack/modules/F0Predictor/DioF0Predictor.py

@@ -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))

spaces/AchyuthGamer/OpenGPT-Chat-UI/src/lib/types/Message.ts

@@ -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;
-};

spaces/Adapter/T2I-Adapter/ldm/modules/encoders/adapter.py

@@ -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

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/bejeweled/methods/BoardMethods.js

@@ -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;
-    }
-}

spaces/AiMimicry/sovits-models/inference/slicer.py

@@ -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

spaces/AkitoP/umamusume_bert_vits2/README.md

@@ -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

spaces/Al-Chan/Vits_League_of_Legends_Yuumi_TTS/voice_upload.py

@@ -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))

spaces/Alex123aaa/1234/app.py

@@ -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()

spaces/Alichuan/VITS-Umamusume-voice-synthesizer/text/shanghainese.py

@@ -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

spaces/Amite5h/EuroSAT_/README.md

@@ -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

spaces/Amon1/ChatGPTForAcadamic/functional.py

@@ -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",
-        },
-    }

spaces/Andy1621/uniformer_image_detection/configs/free_anchor/retinanet_free_anchor_r50_fpn_1x_coco.py

@@ -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))

spaces/Andy1621/uniformer_image_detection/mmdet/core/bbox/samplers/sampling_result.py

@@ -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

spaces/Andy1621/uniformer_image_segmentation/configs/fcn/fcn_r101-d8_769x769_40k_cityscapes.py

@@ -1,2 +0,0 @@
-_base_ = './fcn_r50-d8_769x769_40k_cityscapes.py'
-model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))

spaces/AnishKumbhar/DogDiseasePredictor/app.py

@@ -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}
-
-    

spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/cnn/bricks/registry.py

@@ -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')

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

@@ -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

spaces/Awesimo/jojogan/e4e/training/ranger.py

@@ -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

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

@@ -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,),
-    )

spaces/BaiyuS/Real-CUGAN-YZ/README.md

@@ -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

spaces/Bart92/RVC_HF/lib/uvr5_pack/utils.py

@@ -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

spaces/Benson/text-generation/Examples/Adivina La Pelcula.md

@@ -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 />

spaces/Benson/text-generation/Examples/Bloons Td 6 Descarga Uptodown.md

@@ -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 [email protected] 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 />

spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/chardet/universaldetector.py

@@ -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

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

@@ -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

spaces/Big-Web/MMSD/env/Lib/site-packages/pkg_resources/_vendor/importlib_resources/_legacy.py

@@ -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))

spaces/Big-Web/MMSD/env/Lib/site-packages/pkg_resources/_vendor/packaging/_manylinux.py

@@ -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)

spaces/CVPR/v-doc_abstractive_mac/descrip.md

@@ -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>.

spaces/Catspindev/monadical-labs-minecraft-skin-generator/README.md

@@ -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

spaces/Chomkwoy/Nilkessye/app.py

@@ -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()

spaces/CikeyQI/meme-api/meme_generator/memes/interview/__init__.py

@@ -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"],
-)

spaces/CognitiveLabs/GPT-auto-webscraping/ExcecuteFunction.py

@@ -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

spaces/CognitiveLabs/Research-Assistant/test/test3.py

@@ -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

spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/PIL/SgiImagePlugin.py

@@ -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

spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/merge/unicode.py

@@ -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>
-    )

spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/frontend/assets/ColorPicker-5063dbc4.css

@@ -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 var(--checkbox-border-color);border-radius:var(--checkbox-border-radius);background-color:var(--checkbox-background-color);line-height:var(--line-sm)}input.svelte-1ojmf70.svelte-1ojmf70.svelte-1ojmf70:checked,input.svelte-1ojmf70.svelte-1ojmf70.svelte-1ojmf70:checked:hover,input.svelte-1ojmf70.svelte-1ojmf70.svelte-1ojmf70:checked:focus{border-color:var(--checkbox-border-color-selected);background-image:var(--checkbox-check);background-color:var(--checkbox-background-color-selected)}input.svelte-1ojmf70.svelte-1ojmf70.svelte-1ojmf70:hover{border-color:var(--checkbox-border-color-hover);background-color:var(--checkbox-background-color-hover)}input.svelte-1ojmf70.svelte-1ojmf70.svelte-1ojmf70:focus{border-color:var(--checkbox-border-color-focus);background-color:var(--checkbox-background-color-focus)}input[disabled].svelte-1ojmf70.svelte-1ojmf70.svelte-1ojmf70,.disabled.svelte-1ojmf70.svelte-1ojmf70.svelte-1ojmf70{cursor:not-allowed}.wrap.svelte-1qxcj04.svelte-1qxcj04.svelte-1qxcj04{display:flex;flex-wrap:wrap;gap:var(--checkbox-label-gap)}label.svelte-1qxcj04.svelte-1qxcj04.svelte-1qxcj04{display:flex;align-items:center;transition:var(--button-transition);cursor:pointer;box-shadow:var(--checkbox-label-shadow);border:var(--checkbox-label-border-width) 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spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/huggingface_hub/utils/_datetime.py

@@ -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

spaces/Datasculptor/3D-Room-Layout-Estimation_LGT-Net/utils/conversion.py

@@ -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)

spaces/Detomo/Chatgpt_with_awesome_prompt/app.py

@@ -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')