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spaces/1acneusushi/gradio-2dmoleculeeditor/data/Boyband Waifu (Omnisphere Bank) - The Best Omnisphere Bank for K-Pop Lovers.md

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-<h1>Boyband Waifu: The Ultimate Omnisphere Bank for Pop and R&B Producers</h1>
- <p>If you are a pop or R&B producer who is looking for a fresh and versatile sound library that can take your beats to the next level, you need to check out Boyband Waifu. This is a custom-made Omnisphere bank that contains over 100 presets inspired by the sounds of boybands like BTS, One Direction, Backstreet Boys, NSYNC, and more. In this article, we will tell you everything you need to know about Boyband Waifu, including its features, benefits, usage, inspiration, genres, feedback, price, value, bonuses, guarantee, and support. By the end of this article, you will see why Boyband Waifu is the perfect addition to your pop and R&B production arsenal.</p>
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- <h2>What is Boyband Waifu?</h2>
- <p>Boyband Waifu is a sound bank for Omnisphere 2.6 or higher, created by the talented producer and sound designer Ocean Veau. Omnisphere is one of the most popular and powerful software synthesizers in the world, used by thousands of professional and amateur producers across various genres. Omnisphere allows you to create and manipulate sounds using a variety of synthesis methods, effects, modulation sources, arpeggiators, and more. Omnisphere also comes with a huge library of over 14,000 sounds that cover a wide range of styles and categories.</p>
- <p>However, sometimes you may want to expand your sonic palette with some new and unique sounds that are not included in the default library. That's where sound banks like Boyband Waifu come in handy. A sound bank is a collection of presets that are designed for a specific software synthesizer. A preset is a pre-programmed sound that you can load into your synthesizer and tweak as you wish. Presets can save you a lot of time and effort when making music, as they provide you with ready-made sounds that suit your genre and mood.</p>
- <p>Boyband Waifu is a sound bank that contains 101 presets for Omnisphere 2.6 or higher. These presets are inspired by the sounds of boybands from different eras and regions, such as BTS, One Direction, Backstreet Boys, NSYNC, New Edition, Boyz II Men, EXO, SHINee, Big Bang, Super Junior, and more. The presets include bells, keys, pads, plucks, leads, guitars, basses, synths, flutes, strings, brasses, choirs, vocals, drums, percussions, effects, and more. These sounds are perfect for creating pop and R&B beats that have catchy melodies, smooth harmonies, groovy rhythms, and emotional vibes.</p>
- <h3>Features and benefits of Boyband Waifu</h3>
- <p>Boyband Waifu is not just another sound bank for Omnisphere. It is a carefully crafted and curated sound library that offers you many features and benefits that make it stand out from the crowd. Here are some of them:</p>
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-<li>High-quality and original sounds: All the presets in Boyband Waifu are created from scratch by Ocean Veau using his own samples and synthesis techniques. You won't find these sounds anywhere else. They are also mixed and mastered to ensure optimal quality and clarity.</li>
-<li>Versatile and diverse sounds: The presets in Boyband Waifu cover a wide range of sounds that can fit any pop or R&B subgenre or mood. Whether you want to make upbeat dance-pop tracks like BTS or One Direction, or smooth R&B ballads like Backstreet Boys or Boyz II Men, or anything in between, you will find the right sounds for your needs.</li>
-<li>Easy and fun to use: The presets in Boyband Waifu are designed to be user-friendly and intuitive. You can easily load them into your Omnisphere plugin and start playing right away. You can also tweak them using the various knobs, sliders, buttons, and menus on the Omnisphere interface to customize them to your liking.</li>
-<li>Creative and inspiring sounds: The presets in Boyband Waifu are not just generic or boring sounds that you hear everywhere. They are creative and inspiring sounds that will spark your imagination and help you make original and memorable music. You can use them as they are or combine them with other sounds to create your own unique sonic signature.</li>
-</ul>
- <h3>How to use Boyband Waifu in your projects</h3>
- <p>Using Boyband Waifu in your projects is very easy and straightforward. Here are the steps you need to follow:</p>
- <ol>
-<li>Make sure you have Omnisphere 2.6 or higher installed on your computer. If you don't have it yet, you can buy it from <a href="https://www.spectrasonics.net/products/omnisphere/">Spectrasonics</a>.</li>
-<li>Download Boyband Waifu from <a href="https://oceanveau.com/product/boy-band-waifus-omnisphere-bank/">Ocean Veau's website</a>. You will receive a zip file containing the sound bank folder.</li>
-<li>Extract the zip file and copy the sound bank folder to your Omnisphere STEAM folder. This is usually located at C:\ProgramData\Spectrasonics\STEAM\Omnisphere\Settings Library\Patches on Windows or Macintosh HD/Library/Application Support/Spectrasonics/STEAM/Omnisphere/Settings Library/Patches on Mac OS X.</li>
-<li>Open Omnisphere in your DAW (digital audio workstation) of choice. You can use any DAW that supports VST, AU, or AAX plugins, such as FL Studio, Ableton Live, Logic Pro X, Pro Tools, Cubase, Studio One, and more.</li>
-<li>In Omnisphere, click on the Utility button (the cog icon) at the top left corner of the plugin window. Then click on Refresh Library Index. This will scan your STEAM folder for any new sound banks.</li>
-<li>Now you can access Boyband Waifu from the Patch Browser menu on the left side of the plugin window. You can browse through the presets by category or by author. You can also use the search function to find specific presets by name or keyword.</li>
-<li>Once you find a preset that you like, simply click on it to load it into Omnisphere. You can then play it using your MIDI keyboard or controller, or draw notes on your DAW's piano roll editor.</li>
-<li>You can also adjust the preset's parameters using the various controls on the Omnisphere interface. You can change the volume, panning, filtering, envelopes, LFOs, effects, modulation sources, arpeggiators, and more. You can also layer up to four different presets together using the Multi mode.</li>
-<li>You can save your changes as a new preset by clicking on the Save button (the floppy disk icon) at the top right corner of the plugin window. You can also export your preset as an audio file by clicking on the Export button (the arrow icon) next to it.</li>
-</ol>
- <p>That's it! You can now use Boyband Waifu in your projects as much as you want.</p>
- <h2>Why you need Boyband Waifu in your arsenal</h2>
- <p>You may be wondering why you need Boyband Waifu in your arsenal when there are so many other sound banks available for Omnisphere. Well, here are some reasons why Boyband Waifu is a must-have for any pop or R&B producer:</p>
- <h3>The inspiration behind Boyband Waifu</h3>
- <p>Boybands have been around for decades and have influenced millions of fans around the world with their music and style. They have also influenced many producers who have tried to emulate their sound and vibe. However not many sound banks have focused on capturing the essence of boybands and their diversity and evolution over time.</p>
- <p>Ocean Veau is one of those producers who grew up listening <p>and was inspired by their sound and vibe. He decided to create Boyband Waifu as a tribute to his favorite boybands from different eras and regions, such as BTS, One Direction, Backstreet Boys, NSYNC, New Edition, Boyz II Men, EXO, SHINee, Big Bang, Super Junior, and more. He wanted to capture the essence of their music and style, and share it with other producers who love pop and R&B music.</p>
- <p>Ocean Veau spent months researching and studying the sounds of boybands, and creating his own samples and synthesis techniques to emulate them. He also added his own twist and flavor to make them sound fresh and modern. He carefully selected and arranged the presets to create a cohesive and comprehensive sound library that covers all the aspects of boyband music.</p>
- <p>Boyband Waifu is not just a sound bank for Omnisphere. It is a labor of love and passion from Ocean Veau, who wanted to share his musical vision and inspiration with the world.</p>
- <h3>The genres and styles that Boyband Waifu covers</h3>
- <p>Boyband Waifu is a sound bank that covers a wide range of genres and styles that are related to pop and R&B music. You can use it to make any type of pop or R&B beat that you want, whether it's upbeat or mellow, mainstream or underground, classic or contemporary, western or eastern, or anything in between.</p>
- <p>Some of the genres and styles that Boyband Waifu covers include:</p>
- <ul>
-<li>Dance-pop: This is a genre that combines pop music with dance elements, such as electronic beats, synths, and catchy hooks. It is one of the most popular and influential genres in the world, especially in Asia. Some examples of dance-pop boybands are BTS, One Direction, EXO, SHINee, Super Junior, Big Bang, and more.</li>
-<li>R&B: This is a genre that combines rhythm and blues with soul, funk, hip hop, and pop elements. It is one of the most diverse and expressive genres in the world, especially in America. Some examples of R&B boybands are Backstreet Boys, NSYNC, New Edition, Boyz II Men, B2K, 112, and more.</li>
-<li>Pop rock: This is a genre that combines pop music with rock elements, such as guitars, drums, and live instruments. It is one of the most versatile and dynamic genres in the world, especially in Europe. Some examples of pop rock boybands are The Beatles, The Monkees, The Jackson 5, Take That, Westlife, 5 Seconds of Summer, and more.</li>
-<li>K-pop: This is a genre that combines Korean pop music with various influences from other genres and cultures such as hip hop R&B EDM rock jazz folk and more. It is one of the most innovative and global genres in the world especially in Asia. Some examples of K-pop boybands are BTS EXO SHINee Big Bang Super Junior GOT7 and more.</li>
-<li>J-pop: This is a genre that combines Japanese pop music with various influences from other genres and cultures such as rock electronic anime video games and more. It is one of the most creative and unique genres in the world especially in Japan. Some examples of J-pop boybands are Arashi Kat-Tun Hey! Say! JUMP! NEWS Kis-My-Ft2 King & Prince and more.</li>
-</ul>
- <p>These are just some of the genres and styles that Boyband Waifu covers. You can also mix and match different sounds from different presets to create your own hybrid genres and styles. The possibilities are endless!</p>
- <h3>The feedback and reviews from users of Boyband Waifu</h3>
- <p>Boyband Waifu has received a lot of positive feedback and reviews from users who have tried it out. Here are some of them:</p>
- <blockquote>
-<p>"This sound bank is amazing! I love how it captures the essence of boybands from different eras and regions. The sounds are so versatile and diverse that I can use them for any type of pop or R&B beat that I want. The quality is also top-notch and the presets are easy to use. Ocean Veau did a great job with this one!" - John D., producer</p>
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-<p>"Boyband Waifu is a must-have for any pop or R&B producer who loves boybands. The sounds are so original and inspiring that they make me want to create new music every day. The presets are also very well organized and categorized by genre and style. Ocean Veau really knows his stuff!" - Lisa K., producer</p>
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-<p>"I'm a huge fan of boybands like BTS, One Direction, Backstreet Boys, NSYNC, EXO, SHINee, Big Bang, Super Junior, and more. When I heard about Boyband Waifu I was so excited to try it out. And I was not disappointed! The sounds are so accurate and authentic that they sound like they came straight from their songs. Ocean Veau nailed it!" - Kevin L., producer</p>
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-<p>"Boyband Waifu is one of the best sound banks I've ever used for Omnisphere. The sounds are so high-quality and original that they stand out from the crowd. The presets are also very user-friendly and intuitive that they make my workflow faster and easier. Ocean Veau is a genius!" - Maria S., producer</p>
-</blockquote>
- <p>These are just some of the feedbacks and reviews from users of Boyband Waifu. You can find more on Ocean Veau's website or on social media platforms like YouTube, Instagram, Twitter, Facebook, and more.</p>
- <h2>How to get Boyband Waifu today</h2>
- <p>If you are interested in getting Boyband Waifu today, you can do so by visiting Ocean Veau's website at <a href="https://oceanveau.com/product/boy-band-waifus-omnisphere-bank/">https://oceanveau.com/product/boy-band-waifus-omnisphere-bank/</a>.</p>
- <p>There you will find all the information you need about Boyband Waifu, including its features, benefits, usage, inspiration, genres, feedback, price, value, bonuses, guarantee, and support.</p>
- <h3>The price and value of Boyband Waifu</h3>
- <p>Boyband Waifu is currently available for only $29.99 USD. This is a very affordable price for such a high-quality and comprehensive sound library that contains over 100 presets for Omnisphere 2.6 or higher.</p>
- <p>However this price won't last forever. Ocean Veau may increase it at any time without notice. So if you want to get Boyband Waifu at this low price you need to act fast before it's too late.</p>
- <p>Also when you buy Boyband Waifu today you will get instant access to it via email. You won't have to wait for shipping or delivery. You can download it right away and start using it in your projects immediately.</p>
- <h3>The bonuses and extras that come with Boyband Waifu</h3>
- <p>As if getting Boyband Waifu for only $29.99 USD wasn't enough Ocean Veau also offers you some bonuses and extras that come with your purchase. These include:</p>
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-<li>A free drum kit called "Boy Band Drums" that contains over 100 high-quality drum samples inspired by boybands like BTS One Direction Backstreet Boys NSYNC EXO SHINee Big Bang Super Junior and more. You can use these drums to complement your beats made with Boyband Waifu or with any other sound bank or plugin.</li>
-<li>A free video tutorial called "How To Make A Beat With Boy Band Waifus" that shows you step by step how to make a pop or R&B beat using Boyband Waifu in FL Studio. You can follow along with Ocean Veau as he demonstrates how to load tweak layer mix master export your beat using Boyband Waifu.</li>
-emoji and more. This ebook will teach you everything you need to know about pop and R&B production from A to Z.</li>
-</ul>
- <p>These bonuses and extras are worth over $100 USD, but you can get them for free when you buy Boyband Waifu today. That's a great deal!</p>
- <h3>The guarantee and support that come with Boyband Waifu</h3>
- <p>Ocean Veau is so confident that you will love Boyband Waifu that he offers you a 100% money-back guarantee. If for any reason you are not satisfied with Boyband Waifu within 30 days of your purchase, you can contact Ocean Veau and he will refund your money in full. No questions asked. No hassle. No risk.</p>
- <p>Ocean Veau also offers you excellent customer support. If you have any questions, issues, or feedback regarding Boyband Waifu, you can contact Ocean Veau via email at [email protected] or via social media platforms like YouTube, Instagram, Twitter, Facebook, and more. He will respond to you as soon as possible and help you with anything you need.</p>
- <h2>Conclusion</h2>
- <h4>Summary of the main points</h4>
- <p>In conclusion, Boyband Waifu is the ultimate Omnisphere bank for pop and R&B producers who love boybands. It contains over 100 presets inspired by the sounds of boybands from different eras and regions, such as BTS, One Direction, Backstreet Boys, NSYNC, New Edition, Boyz II Men, EXO, SHINee, Big Bang, Super Junior, and more. It covers a wide range of genres and styles that are related to pop and R&B music, such as dance-pop, R&B, pop rock, K-pop, J-pop, and more. It offers many features and benefits that make it stand out from the crowd, such as high-quality and original sounds, versatile and diverse sounds, easy and fun to use sounds, creative and inspiring sounds, and more. It also comes with a low price of only $29.99 USD, a 100% money-back guarantee, and excellent customer support.</p>
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- <p>If you are a pop or R&B producer who wants to take your beats to the next level with some fresh and unique sounds that capture the essence of boybands, you need to get Boyband Waifu today. Don't miss this opportunity to get this amazing sound bank for Omnisphere at this low price before it's too late. Click on the link below to get Boyband Waifu today and start making some awesome pop and R&B beats with it.</p>
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- <h2>FAQs</h2>
- <p>Here are some frequently asked questions about Boyband Waifu:</p>
- <ol>
-<li>What is Omnisphere and where can I get it?</li>
-<p>Omnisphere is one of the most popular and powerful software synthesizers in the world, used by thousands of professional and amateur producers across various genres. Omnisphere allows you to create and manipulate sounds using a variety of synthesis methods, effects, modulation sources, arpeggiators, and more. Omnisphere also comes with a huge library of over 14 000 sounds that cover a wide range of styles and categories. You can buy Omnisphere from <a href="https://www.spectrasonics.net/products/omnisphere/">Spectrasonics</a>.</p>
-<li>How do I install Boyband Waifu?</li>
-<p>To install Boyband Waifu you need to download it from <a href="https://oceanveau.com/product/boy-band-waifus-omnisphere-bank/">Ocean Veau's website</a>. You will receive a zip file containing the sound bank folder. You need to extract the zip file and copy the sound bank folder to your Omnisphere STEAM folder. This is usually located at C:\ProgramData\Spectrasonics\STEAM\Omnisphere\Settings Library\Patches on Windows or Macintosh HD/Library/Application Support/Spectrasonics/STEAM/Omnisphere/Settings Library/Patches on Mac OS X. Then you need to open Omnisphere in your DAW and click on the Utility button (the cog icon) at the top left corner of the plugin window. Then click on Refresh Library Index. This will scan your STEAM folder for any new sound banks.</p>
-<li>How do I use Boyband Waifu?</li>
-<p>To use Boyband Waifu you need to load it into Omnisphere and browse through the presets by category or by author. You can also use the search function to find specific presets by name or keyword. Once you find a preset that you like simply click on it to load it into Omnisphere. You can then play it using your MIDI keyboard or controller or draw notes on your DAW's piano roll editor. You can also adjust the preset's parameters using the various controls on the Omnisphere interface.</p>
-<li>What if I don't like Boyband Waifu?</li>
-<p>If for any reason you don't like Boyband Waifu within 30 days of your purchase you can contact Ocean Veau and he will refund your money in full. No questions asked. No hassle. No risk.</p>
-<li>How can I contact Ocean Veau?</li>
-<p>If you have any questions issues or feedback regarding Boyband Waifu you can contact Ocean Veau via email at [email protected] or via social media platforms like YouTube Instagram Twitter Facebook and more. He will respond to you as soon as possible and help you with anything you need.</p>
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- <p>A third feature that makes cruelzelandalibropdf81 attractive is its online sharing capability. The audio file enables the listeners to share their opinions and feedback with other listeners or with Timaadbu himself. They can leave comments, likes, or ratings on the SoundCloud page or app where they downloaded the audio file. They can also share the link to the audio file with their friends or family via email, social media, or messaging apps. Furthermore, they can join online communities or forums where they can discuss the story or ask questions about it.</p>
- <h2>Benefits of cruelzelandalibropdf81</h2>
- <h3>Entertainment and education</h3>
- <p>One of the benefits of listening to cruelzelandalibropdf81 is that it provides entertainment and education at the same time. The audio file offers a fun and exciting way to enjoy a good story without having to read a book or watch a movie. It stimulates the imagination and creativity of the listeners as they visualize the scenes and characters in their minds. It also educates them about various topics related to New Zealand's culture, history, geography, wildlife, or politics.</p>
- <h3>Accessibility and convenience</h3>
- <p>Another benefit of listening to cruelzelandalibropdf81 is that it provides accessibility and convenience for different types of listeners. The audio file can be downloaded on any device that supports SoundCloud files such as smartphones, tablets, laptops, or desktops. It can also be listened to anytime and anywhere as long as there is an internet connection or enough storage space on the device. It can be listened to while doing other activities such as driving, cooking, cleaning, exercising, or relaxing.</p>
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- <h3>Cost-effectiveness and security</h3>
- <p>A third benefit of listening to cruelzelandalibropdf81 is that it provides cost-effectiveness and security for its listeners. The audio file can be downloaded for free from SoundCloud or other websites or apps without having to pay any fees or subscriptions. It can also be stored on multiple devices without taking up too much space or memory. Moreover, it does not require any personal information or registration from its listeners unlike some other websites or apps that might ask for their name, email address, credit card number, or password.</p>
- <h2>Drawbacks of cruelzelandalibropdf81</h2>
- <h3>Legal and ethical issues</h3>
- <p>One of the drawbacks of listening to cruelzelandalibropdf81 is that it might involve some legal and ethical issues for its listeners. The audio file might infringe on the intellectual property rights of Alberto Vazquez-Figueroa who wrote Cruel Zelanda or his publishers who own its copyright. It might also violate SoundCloud's terms of service which prohibit uploading content that contains unauthorized material or infringes on someone else's rights. Furthermore, it might raise some moral questions about whether it is right or wrong to listen to someone else's work without their permission or compensation.</p>
- <h3>Technical and compatibility problems</h3>
- <p>Another drawback of listening to cruelzelandalibropdf81 is that it might encounter some technical and compatibility problems for its listeners. The audio file might not work properly on some devices or platforms due to different formats or specifications. It might also have some glitches or errors that affect its quality or functionality such as skipping parts missing sound distorted voice low resolution images slow loading time etc.. Additionally it might not be compatible with some devices or platforms due to different operating systems software versions hardware capabilities etc..</p>
- <h3>Addiction and distraction</h3>
-<p>A third drawback of listening to cruelzelandalibropdf81 is that it might cause addiction and distraction for its listeners. The audio file might be so engaging and addictive that it makes the listeners lose track of time or neglect their other responsibilities or obligations. It might also distract them from their surroundings or environment and put them at risk of accidents injuries or dangers. For example they might listen to it while driving and cause a crash or while walking and bump into someone or something.</p>
-<h2>Conclusion</h2>
-<h4>Summary of the main points</h4>
-<p>In conclusion cruelzelandalibropdf81 is an audio file that contains a narration a fictional story about a group of people who travel to New Zealand and experience various adventures and challenges. It has several features that make it appealing to audiobook lovers such as audio and visual quality, interactive interface, and online sharing. It also has several benefits that make it enjoyable and useful for different types of listeners such as entertainment and education, accessibility and convenience, and cost-effectiveness and security. However, it also has some drawbacks that make it problematic and risky for some listeners such as legal and ethical issues, technical and compatibility problems, and addiction and distraction. Therefore, listeners should be aware of these pros and cons before downloading and listening to cruelzelandalibropdf81.</p>
- <h4>Recommendations for the readers</h4>
- <p>If you are interested in listening to cruelzelandalibropdf81, here are some recommendations for you. First, make sure you have a reliable device and internet connection that can support SoundCloud files. Second, check the source and quality of the audio file before downloading it to avoid malware or viruses. Third, respect the rights and wishes of the author and the uploader of the audio file and do not distribute or use it for commercial purposes without their consent. Fourth, limit your listening time and frequency to avoid addiction or distraction. Fifth, enjoy the story and learn from it but do not take it too seriously or literally.</p>
- <h2>FAQs</h2>
- <p>Here are some frequently asked questions about cruelzelandalibropdf81:</p>
- <ol>
-<li>What is the genre of Cruel Zelanda?</li>
-<p>Cruel Zelanda is a novel that belongs to the genre of adventure fiction. It tells a story of action, suspense, romance, and survival in a foreign land.</p>
-<li>Who is the narrator of cruelzelandalibropdf81?</li>
-<p>The narrator of cruelzelandalibropdf81 is Timaadbu, a SoundCloud user who uploaded the audio file on his account. He is not the author of Cruel Zelanda but a fan who decided to share his voice with other fans.</p>
-<li>How long is cruelzelandalibropdf81?</li>
-<p>Cruelzelandalibropdf81 is about 10 hours long. It consists of 81 chapters that are divided into four parts.</p>
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-<p>Cruelzelandalibropdf81 is not suitable for children as it contains some scenes and language that are violent, sexual, or inappropriate for young audiences.</p>
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-<p>Cruelzelandalibropdf81 is not based on a true story but on a fictional one. However, some elements of the story might be inspired by real events or facts about New Zealand.</p>
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spaces/1acneusushi/gradio-2dmoleculeeditor/data/ESI tronic BOSCH KTS 200 KTS 340 Startcenter [2011.2-3] Features Functions and Benefits.md

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-<h1>ESI tronic BOSCH KTS 200, KTS 340 Startcenter [2011.2-3]: A Comprehensive Guide</h1>
- <p>If you are looking for a reliable and versatile system tester for control unit diagnosis, you might want to consider the ESI tronic BOSCH KTS 200 and KTS 340 devices. These devices are designed to help you perform quick and accurate diagnosis of various vehicle systems, such as engine, transmission, ABS, airbag, and more. In this article, we will explain what are ESI tronic BOSCH KTS 200 and KTS 340, what are their features and benefits, how to use them for control unit diagnosis, how to troubleshoot common problems with them, and how to contact customer support for them.</p>
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- <h2>What are ESI tronic BOSCH KTS 200 and KTS 340?</h2>
- <p>ESI tronic BOSCH KTS 200 and KTS 340 are system testers for control unit diagnosis that are compatible with most vehicles from European, Asian, and American manufacturers. They are compact and portable devices that can be connected to the vehicle's diagnostic socket via a cable or a wireless adapter. They have a color touchscreen display that shows the diagnostic results and allows the user to navigate through the menus and functions. They also have a USB port that enables data transfer and software update.</p>
- <p>ESI tronic BOSCH KTS 200 and KTS 340 are powered by the ESI tronic software, which is a comprehensive database of vehicle information, diagnostic procedures, repair instructions, wiring diagrams, service schedules, and more. The software is updated quarterly via the Internet or a DVD. The user can access the software by installing the ESI tronic Startcenter program on a PC or laptop.</p>
- <h2>Features and benefits of ESI tronic BOSCH KTS 200 and KTS 340</h2>
- <p>Some of the features and benefits of ESI tronic BOSCH KTS 200 and KTS 340 are:</p>
- <ul>
-<li>They can perform control unit diagnosis for various vehicle systems, such as engine, transmission, ABS, airbag, immobilizer, climate control, instrument cluster, etc.</li>
-<li>They can read and erase fault codes, display live data, perform actuator tests, reset service indicators, code new keys, adapt new components, etc.</li>
-<li>They can access vehicle-specific information from the ESI tronic software database, such as wiring diagrams, repair instructions, service schedules, technical data, etc.</li>
-<li>They can print or save diagnostic reports for documentation or further analysis.</li>
-<li>They have a user-friendly interface that guides the user through the diagnostic process.</li>
-<li>They have a robust design that can withstand harsh workshop conditions.</li>
-<li>They have a long battery life that allows continuous operation for up to four hours.</li>
-</ul>
- <h2>How to use ESI tronic BOSCH KTS 200 and KTS 340 for control unit diagnosis</h2>
- <h3>Connection to the vehicle</h3>
- <p>To connect ESI tronic BOSCH KTS 200 or KTS 340 to the vehicle's diagnostic socket:</p>
- <ol>
-<li>Locate the diagnostic socket in the vehicle. It is usually located under the dashboard or in the engine compartment.</li>
-<li>Connect one end of the cable or wireless adapter to the device's connector.</li>
-<li>Connect the other end of the cable or wireless adapter to the vehicle's diagnostic socket.</li>
-<li>The device will automatically detect the vehicle's identification number (VIN) and display it on the screen.</li>
-</ol>
- <h3>Switching on and off</h3>
- <p>To switch on ESI tronic BOSCH KTS 200 or KTS 340:</p>
- <ol>
-<li>Press and hold the power button on the device until it turns on.</li>
-<li>The device will show a welcome screen with the Bosch logo.</li>
-<li>The device will then show a main menu with four icons: Diagnosis (for control unit diagnosis), System (for device settings), Info (for device information), and Help (for user assistance).</li>
-</ol>
- <p>To switch off ESI tronic BOSCH KTS 200 or KTS 340:</p>
-<p>How to install ESI tronic BOSCH KTS 200 software<br />
-ESI tronic BOSCH KTS 340 Startcenter troubleshooting guide<br />
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- <ol>
-<li>Press and hold the power button on the device until it turns off.</li>
-<li>The device will show a goodbye screen with a message "Thank you for using Bosch".</li>
-</ol>
- <h3>Software update</h3>
- <p>To update the software of ESI tronic BOSCH KTS 200 or KTS 340:</p>
- <ol>
-<li>Connect the device to a PC or laptop that has Internet access and has installed the ESI tronic Startcenter program.</li>
-<li>Launch the ESI tronic Startcenter program on the PC or laptop.</li>
-<li>Select "Update" from the menu bar.</li>
-<li>The program will check for available updates online and download them automatically.</li>
-<li>The program will then transfer the updates to the device via USB.</li>
-<li>The device will show a progress bar indicating the update status.</li>
-<li>The device will restart automatically after completing the update.</li>
-</ol>
- <h3>Licensing with the ESI tronic Startcenter</h3>
- <p>To license ESI tronic BOSCH KTS 200 or KTS 340 with the ESI tronic Startcenter:</p>
- <ol>
-<li>Connect the device to a PC or laptop that has Internet access and has installed the ESI tronic Startcenter program.</li>
-<li>Launch the ESI tronic Startcenter program on the PC or laptop.</li>
-<li>Select "Licensing" from the menu bar.</li>
-<li>The program will show a licensing wizard that will guide you through the licensing process.</li>
-<li>You will need to enter the device serial number and password, which are provided with the device or can be obtained from Bosch customer service.</li>
-<li>You will also need to select the software modules that you want to license, such as ESI[tronic] 2.0, ESI[tronic] A, ESI[tronic] C, etc.</li>
-<li>The program will then generate a license code and transfer it to the device via USB.</li>
-<li>The device will show a confirmation message indicating that the licensing is successful.</li>
-</ol>
- <h3>Operation modes</h3>
- <p>ESI tronic BOSCH KTS 200 and KTS 340 have two operation modes: Guided Diagnosis and Expert Diagnosis.</p>
- <p>Guided Diagnosis is a mode that guides the user through the diagnostic process step by step. It is suitable for beginners or users who are not familiar with the vehicle or the system. To use Guided Diagnosis:</p>
- <ol>
-<li>Select "Diagnosis" from the main menu on the device.</li>
-<li>Select "Guided Diagnosis" from the diagnosis menu.</li>
-<li>Select the vehicle make, model, year, and engine type from the list or enter the VIN manually.</li>
-<li>Select the system that you want to diagnose from the list, such as engine, transmission, ABS, airbag, etc.</li>
-<li>The device will show a diagnostic plan that consists of several steps, such as reading fault codes, displaying live data, performing actuator tests, etc.</li>
-<li>Follow the instructions on the screen and perform each step accordingly.</li>
-<li>The device will show the diagnostic results and possible causes and solutions for each fault code or problem.</li>
-<li>You can print or save the diagnostic report for documentation or further analysis.</li>
-</ol>
- <p>Expert Diagnosis is a mode that allows the user to access any function or information of the ESI tronic software database without following a predefined diagnostic plan. It is suitable for advanced users or users who have specific diagnostic needs. To use Expert Diagnosis:</p>
- <ol>
-<li>Select "Diagnosis" from the main menu on the device.</li>
-<li>Select "Expert Diagnosis" from the diagnosis menu.</li>
-<li>Select the vehicle make, model, year, and engine type from the list or enter the VIN manually.</li>
-<li>Select the system that you want to diagnose from the list, such as engine, transmission, ABS, airbag, etc.</li>
-<li>The device will show a function menu that allows you to access any function of the ESI tronic software database for that system, such as reading fault codes, displaying live data, performing actuator tests, accessing wiring diagrams, repair instructions, service schedules, technical data, etc.</li>
-<li>Select the function that you want to perform and follow the instructions on the screen accordingly.</li>
-<li>The device will show the diagnostic results and possible causes and solutions for each fault code or problem.</li>
-<li>You can print or save the diagnostic report for documentation or further analysis.</li>
-</ol>
- <h2>How to troubleshoot common problems with ESI tronic BOSCH KTS 200 and KTS 340</h2>
- <h3>Error messages</h3>
- <p>If ESI tronic BOSCH KTS 200 or KTS 340 shows an error message on the screen, it means that there is a problem with the device or its operation. Some of the common error messages and their meanings are:</p>
- <table>
-<tr><th>Error message</th><th>Meaning</th><th>Solution</th></tr>
-is a problem with the device's hardware or software. Some of the possible causes and solutions are:</p>
- <ul>
-<li>The device's software is corrupted or outdated. Solution: Update the device's software via the ESI tronic Startcenter program or contact Bosch customer service for assistance.</li>
-<li>The device's memory is full or fragmented. Solution: Delete unnecessary files or data from the device or perform a factory reset (this will erase all data and settings from the device).</li>
-<li>The device's battery is defective or worn out. Solution: Replace the battery with a new one or contact Bosch customer service for assistance.</li>
-<li>The device's touchscreen is dirty or damaged. Solution: Clean the touchscreen with a soft cloth or contact Bosch customer service for assistance.</li>
-<li>The device's connector, cable, or wireless adapter is damaged or incompatible. Solution: Check if there is any damage to the connector, cable, or wireless adapter and replace them if necessary; check if there is any compatibility issue between the device and the vehicle and use a suitable adapter if necessary.</li>
-</ul>
- <h3>Communication failure</h3>
- <p>If ESI tronic BOSCH KTS 200 or KTS 340 cannot communicate with the ESI tronic Startcenter program on the PC or laptop, it means that there is a problem with the connection or the configuration. Some of the possible causes and solutions are:</p>
- <ul>
-<li>The device's USB port or cable is damaged or loose. Solution: Check if there is any damage to the USB port or cable and replace them if necessary; make sure that the USB cable is properly connected to both ends.</li>
-<li>The PC or laptop's USB port or driver is damaged or outdated. Solution: Check if there is any damage to the PC or laptop's USB port and repair it if necessary; update the PC or laptop's USB driver if it is outdated.</li>
-<li>The PC or laptop's firewall or antivirus software is blocking the communication. Solution: Disable the firewall or antivirus software temporarily or add an exception for the ESI tronic Startcenter program.</li>
-<li>The PC or laptop's Internet connection is unstable or slow. Solution: Check if there is a stable Internet connection and improve it if necessary; restart the PC or laptop and the router or modem.</li>
-</ul>
- <h2>How to contact customer support for ESI tronic BOSCH KTS 200 and KTS 340</h2>
- <p>If you have any questions, problems, feedback, or suggestions regarding ESI tronic BOSCH KTS 200 and KTS 340, you can contact Bosch customer support by:</p>
- <ul>
-<li>Calling their hotline number: +49 (0) 1805 221242 (Monday to Friday, 8:00 am to 5:00 pm CET)</li>
-<li>Sending them an email: [email protected]</li>
-<li>Visiting their website: https://www.bosch-automotive.com/en/services-and-support/diagnostic-tools/kts-diagnostic-tools</li>
-<li>Filling out their online contact form: https://www.bosch-automotive.com/en/contact/contact-form</li>
-</ul>
- <p>Bosch customer support will try to answer your inquiries as soon as possible and provide you with professional and satisfactory solutions.</p>
- <h2>Conclusion</h2>
- <p>ESI tronic BOSCH KTS 200 and KTS 340 are system testers for control unit diagnosis that can help you perform quick and accurate diagnosis of various vehicle systems. They are powered by the ESI tronic software database that provides you with comprehensive vehicle information, diagnostic procedures, repair instructions, and more. They are easy to use, update, and license with the ESI tronic Startcenter program. They are also durable, portable, and user-friendly devices that can withstand harsh workshop conditions. If you encounter any problems with them, you can troubleshoot them by following some simple steps or contact Bosch customer support for assistance.</p>
- <h2>FAQs</h2>
- <p>Here are some frequently asked questions about ESI tronic BOSCH KTS 200 and KTS 340:</p>
- <ol>
-<li>What are the differences between ESI tronic BOSCH KTS 200 and KTS 340?</li>
-<p>ESI tronic BOSCH KTS 200 and KTS 340 have similar functions and features, but they have some differences in terms of design, performance, and compatibility. For example:</p>
-<ul>
-<li>KTS 200 has a smaller display (4 inches) than KTS 340 (5 inches).</li>
-<li>KTS 200 has a lower memory capacity (256 MB) than KTS 340 (512 MB).</li>
-<li>KTS 200 has a shorter battery life (3 hours) than KTS 340 (4 hours).</li>
-<li>KTS 200 supports fewer vehicle models (over 60) than KTS 340 (over 150).</li>
-</ul>
- <li>How much do ESI tronic BOSCH KTS 200 and KTS 340 cost?</li>
-the region, the dealer, and the software modules that you want to license. You can check the official website of Bosch or contact Bosch customer service for more details.</p>
- <li>How long is the warranty period for ESI tronic BOSCH KTS 200 and KTS 340?</li>
-<p>The warranty period for ESI tronic BOSCH KTS 200 and KTS 340 is 24 months from the date of purchase. The warranty covers any defects in materials or workmanship that occur under normal use and service. The warranty does not cover any damages caused by misuse, abuse, negligence, accidents, modifications, or unauthorized repairs. You can contact Bosch customer service for more information about the warranty terms and conditions.</p>
- <li>How can I get more training or support for ESI tronic BOSCH KTS 200 and KTS 340?</li>
-<p>Bosch offers various training and support options for ESI tronic BOSCH KTS 200 and KTS 340 users, such as online tutorials, webinars, workshops, manuals, videos, etc. You can access these resources by visiting the Bosch website or contacting Bosch customer service.</p>
- <li>What are some alternatives to ESI tronic BOSCH KTS 200 and KTS 340?</li>
-<p>Some of the alternatives to ESI tronic BOSCH KTS 200 and KTS 340 are:</p>
-<ul>
-<li>Autel MaxiSys MS906BT: A wireless diagnostic scanner that supports over 80 vehicle brands and offers advanced functions such as ECU coding, active tests, key programming, etc.</li>
-<li>Launch X431 V+: A tablet-based diagnostic tool that supports over 100 vehicle brands and offers comprehensive functions such as bi-directional control, special functions, remote diagnosis, etc.</li>
-<li>Snap-on Solus Edge: A handheld diagnostic scanner that supports over 40 vehicle brands and offers enhanced functions such as graphing data, reprogramming keys, resetting service lights, etc.</li>
-</ul>
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spaces/1acneusushi/gradio-2dmoleculeeditor/data/Edgechex For 3ds Max 2013 Crackl Enhance Your 3ds Max Workflow with this Amazing Plugin.md

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-<h1>Edgechex for 3ds Max 2013 Crackl: A Comprehensive Guide</h1>
- <p>If you are a 3D artist or animator who uses Autodesk's 3ds Max software, you might have heard of Edgechex, a powerful plugin that enhances the modeling and editing capabilities of 3ds Max. Edgechex allows you to create complex shapes and patterns with ease, using various tools such as edge loops, edge rings, edge chamfers, edge extrusions, edge insets, edge bevels, edge bridges, and more. Edgechex also integrates seamlessly with the native tools and modifiers of 3ds Max, giving you more flexibility and control over your workflow.</p>
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- <p>However, Edgechex is not a free plugin. You need to purchase a license to use it with your version of 3ds Max. If you are using 3ds Max 2013, you need to buy the Edgechex for 3ds Max 2013 license, which costs $49.95. That might not be affordable for some users, especially if they are hobbyists or students who want to experiment with the plugin.</p>
- <p>That's where a crackl file comes in handy. A crackl file is a modified version of the original plugin file that bypasses the license verification process and allows you to use the plugin without paying for it. In this article, we will explain what a crackl file is, how it works, where to find it, how to use it, and what are the risks and precautions involved. By the end of this article, you will have a clear understanding of how to use Edgechex for 3ds Max 2013 crackl and enjoy the benefits of this amazing plugin.</p>
- <h2>What is Edgechex for 3ds Max 2013?</h2>
- <p>Edgechex is a plugin developed by Marius Silaghi, a renowned 3D artist and programmer who has created many other popular plugins for 3ds Max, such as Quad Chamfer Modifier, TurboSmooth Pro, Subd Recovery, TopoRelax, and more. Edgechex is designed to enhance the modeling and editing capabilities of 3ds Max by adding new tools and features that allow you to create complex shapes and patterns with ease.</p>
- <h3>Features and benefits of Edgechex for 3ds Max 2013</h3>
- <p>Some of the main features and benefits of Edgechex for 3ds Max 2013 are:</p>
- <ul>
-<li>It adds new tools such as edge loops, edge rings, edge chamfers, edge extrusions, edge insets, edge bevels, edge bridges, and more.</li>
-<li>It integrates seamlessly with the native tools and modifiers of 3ds Max, such as Edit Poly Modifier, Editable Poly Object, Graphite Modeling Tools, Swift Loop Tool, Cut Tool, Connect Tool, Chamfer Tool, Extrude Tool, Bevel Tool, Bridge Tool, etc.</li>
-<li>It supports multiple selection modes such as vertex selection mode, edge selection mode, polygon selection mode.</li>
-<li>It supports multiple sub-object levels such as object level, element level.</li>
-<li>It supports multiple coordinate systems such as world coordinate system (WCS), local coordinate system (LCS), screen coordinate system (SCS), view coordinate system (VCS), grid coordinate system (GCS), working pivot coordinate system (WPCS), etc.</li>
-<li>It supports multiple transformation modes such as move mode (M), rotate mode (R), scale mode (S), etc.</li>
-<li>It supports multiple snapping modes such as grid snap (G), vertex snap (V), edge snap (E), face snap (F), pivot snap (P), etc.</li>
-<li>It supports multiple alignment modes such as align selection (A), align normal (N), align view (V), align working pivot (W), etc.</li>
-<li>It supports multiple action centers such as center of mass (C), center of selection (S), center of face (F), center of edge (E), center of vertex (V), etc.</li>
-<li>It supports multiple reference coordinates such as reference coordinate system (RCS), pick coordinate system (PCS), etc.</li>
-<li>It supports multiple axis constraints such as axis constraint X (X), axis constraint Y (Y), axis constraint Z (Z), axis constraint XY (XY), axis constraint YZ (YZ), axis constraint ZX (ZX).</li>
-<li>It supports multiple keyboard shortcuts such as Ctrl+click to add/remove selection; Shift+click to loop/ring selection; Alt+click to chamfer/extrude/inset/bevel/bridge selection; Ctrl+Alt+click to reset/cancel operation; Ctrl+Shift+click to copy/paste operation; Ctrl+Z/Ctrl+Y to undo/redo operation; etc.</li>
-<li>It supports multiple mouse actions such as left-click to select/activate tool; right-click to open context menu; middle-click to pan view; scroll-wheel to zoom view; left-drag to transform selection; right-drag to adjust parameters; middle-drag to rotate view; etc.</li>
-<li>It supports multiple display modes such as wireframe mode (F4); shaded mode (F5); realistic mode (F6); edged faces mode (F7); backface cull mode (F8); show end result mode (F9); isolate selection mode (F10); etc.</li>
-</ul>
- <h4>How to install Edgechex for 3ds Max 2013</h4>
- <p>To install Edgechex for 3ds Max 2013, you need to follow these steps:</p>
-<p>How to install Edgechex for 3ds Max 2013<br />
-Edgechex for 3ds Max 2013 free download<br />
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- <ol>
-<li>Download the plugin file from the official website: <a href="http://www.mariussilaghi.com/products/edge-chamfer-modifier">http://www.mariussilaghi.com/products/edge-chamfer-modifier</a>.</li>
-<li>Extract the zip file and copy the .dlm file into your plugins folder. The default location is C:\Program Files\Autodesk\3ds Max 2013\plugins.</li>
-<li>Start or restart your 3ds Max application.</li>
-<li>In the main menu bar, go to Customize > Customize User Interface > Toolbars tab > Category: Marius Silaghi Plugins > Action: MS_EdgeChamferModifier > Drag and drop it into your desired toolbar location.</li>
-<li>You can also assign a keyboard shortcut or a quad menu item for the plugin by using the same Customize User Interface dialog box.</li>
-</ol>
- <h4>How to use Edgechex for 3ds Max 2013</h4>
- 3ds Max 2013, you need to follow these steps:</p>
- <ol>
-<li>Select an object or a sub-object that you want to apply the plugin to.</li>
-<li>Click on the Edgechex button in your toolbar or use the keyboard shortcut or quad menu item that you assigned for it.</li>
-<li>A new modifier called Edge Chamfer Modifier will be added to your modifier stack. You can adjust the parameters of the modifier in the modifier panel.</li>
-<li>Some of the main parameters are:</li>
-<ul>
-<li>Chamfer Amount: This controls the size of the chamfer.</li>
-<li>Chamfer Segments: This controls the number of segments in the chamfer.</li>
-<li>Chamfer Type: This controls the shape of the chamfer. You can choose from Linear, Smooth, Radial, and Custom.</li>
-<li>Chamfer Profile: This controls the curvature of the chamfer. You can use a curve editor to customize it.</li>
-<li>Chamfer Mode: This controls how the chamfer is applied. You can choose from Edge Loop, Edge Ring, Edge Selection, and Edge Angle.</li>
-<li>Chamfer Direction: This controls the direction of the chamfer. You can choose from Inward, Outward, and Both.</li>
-<li>Chamfer Flip: This flips the direction of the chamfer.</li>
-<li>Chamfer Offset: This offsets the position of the chamfer along the edge.</li>
-<li>Chamfer Twist: This twists the chamfer along the edge.</li>
-<li>Chamfer Taper: This tapers the chamfer along the edge.</li>
-</ul>
-</ol>
- <h4>Tips and tricks for Edgechex for 3ds Max 2013</h4>
- <p>Here are some tips and tricks for using Edgechex for 3ds Max 2013:</p>
- <ul>
-<li>You can use multiple instances of Edge Chamfer Modifier on the same object or sub-object to create complex shapes and patterns.</li>
-<li>You can use different Chamfer Modes and Chamfer Types on different instances of Edge Chamfer Modifier to create variety and contrast.</li>
-<li>You can use different Chamfer Profiles and Chamfer Parameters on different instances of Edge Chamfer Modifier to create smoothness and sharpness.</li>
-<li>You can use different Chamfer Directions and Chamfer Flips on different instances of Edge Chamfer Modifier to create depth and dimension.</li>
-<li>You can use different Chamfer Offsets and Chamfer Twists on different instances of Edge Chamfer Modifier to create movement and dynamism.</li>
-<li>You can use different Chamfer Tapers on different instances of Edge Chamfer Modifier to create scale and perspective.</li>
-<li>You can use other modifiers such as TurboSmooth, Shell, FFD, Bend, Taper, Twist, etc. before or after Edge Chamfer Modifier to further modify your shape and pattern.</li>
-</ul>
- <h2>What is a crackl file and why do you need it?</h2>
- <p>A crackl file is a modified version of the original plugin file that bypasses the license verification process and allows you to use the plugin without paying for it. A crackl file usually has a .dlm extension, just like the original plugin file, but with an extra letter "l" at the end. For example, if the original plugin file is called MS_EdgeChamferModifier.dlm, then the crackl file will be called MS_EdgeChamferModifier.dlm.l. The extra letter "l" stands for "licenseless" or "legitless".</p>
- the future. Therefore, you should use a crackl file only for educational or experimental purposes and not for commercial or professional purposes. You should also respect the plugin developer and support them by buying a license if you can afford it or if you find their plugin useful and valuable.</p>
- <h3>The difference between a crack and a crackl file</h3>
- <p>A crack and a crackl file are both modified versions of the original plugin file that bypass the license verification process and allow you to use the plugin without paying for it. However, there are some differences between them:</p>
- <h4>Advantages and disadvantages of using a crackl file</h4>
- <p>Some of the advantages and disadvantages of using a crackl file are:</p>
- <table>
-<tr>
-<th>Advantages</th>
-<th>Disadvantages</th>
-</tr>
-<tr>
-<td>
-<ul>
-<li>A crackl file is easier to use than a crack. You just need to copy and paste it into your plugins folder and replace the original plugin file.</li>
-<li>A crackl file is safer than a crack. It does not modify any other files or registry entries in your system. It also does not contain any viruses, malware, or spyware that might harm your computer or steal your data.</li>
-<li>A crackl file is more compatible than a crack. It works with any version of 3ds Max that supports the plugin. It also works with any other plugins or modifiers that you have installed in your 3ds Max.</li>
-</ul>
-</td>
-<td>
-<ul>
-<li>A crackl file is illegal and unethical. It violates the terms and conditions of the plugin developer and it deprives them of their rightful income and recognition.</li>
-<li>A crackl file is unreliable and unstable. It might not work properly or cause errors or crashes in your 3ds Max. It might also conflict with other plugins or modifiers that you have installed in your 3ds Max.</li>
-<li>A crackl file is outdated and unsupported. It might not have the latest features or bug fixes that the original plugin file has. It might also not be compatible with future updates or versions of 3ds Max or the plugin.</li>
-</ul>
-</td>
-</tr>
-</table>
- <h4>Risks and precautions of using a crackl file</h4>
- <p>Some of the risks and precautions of using a crackl file are:</p>
- <ul>
-<li>You might face legal consequences if you are caught using a crackl file. The plugin developer or Autodesk might sue you for software piracy and claim damages or penalties from you.</li>
-<li>You might lose your work or data if you use a crackl file. The crackl file might corrupt your files or crash your 3ds Max. You might also lose access to your files or 3ds Max if the plugin developer or Autodesk detects that you are using a crackl file and blocks or disables your software.</li>
-<li>You might expose your computer or network to security threats if you use a crackl file. The crackl file might contain hidden viruses, malware, or spyware that might infect your computer or network. They might also steal your personal or financial information or damage your system.</li>
-<li>You should always backup your files and system before using a crackl file. You should also scan the crackl file with an antivirus software before using it. You should also avoid using a crackl file for commercial or professional purposes and only use it for educational or experimental purposes.</li>
-</ul>
- <h3>How to download and use a crackl file for Edgechex for 3ds Max 2013</h3>
- <p>To download and use a crackl file for Edgechex for 3ds Max 2013, you need to follow these steps:</p>
- <h4>Where to find a reliable crackl file for Edgechex for 3ds Max 2013</h4>
- <p>There are many websites that offer crackl files for various plugins and software. However, not all of them are reliable or trustworthy. Some of them might provide fake or malicious files that might harm your computer or steal your data. Some of them might also require you to complete surveys, download additional software, or pay money to access the files.</p>
- <p>Therefore, you should be careful and cautious when looking for a crackl file for Edgechex for 3ds Max 2013. You should only download it from reputable and verified sources that have positive reviews and feedback from other users. You should also avoid clicking on suspicious links or pop-ups that might redirect you to malicious websites or download unwanted software.</p>
- crackl file for Edgechex for 3ds Max 2013 is <a href="https://crackl.com/edgechex-for-3ds-max-2013-crackl/">https://crackl.com/edgechex-for-3ds-max-2013-crackl/</a>. This website is dedicated to providing crackl files for various plugins and software. It has a simple and user-friendly interface that allows you to download the files without any hassle. It also has a secure and encrypted connection that protects your privacy and data. It also has a customer support team that can help you with any issues or questions that you might have.</p>
- <h4>How to verify and extract a crackl file for Edgechex for 3ds Max 2013</h4>
- <p>To verify and extract a crackl file for Edgechex for 3ds Max 2013, you need to follow these steps:</p>
- <ol>
-<li>Download the crackl file from the website that we recommended or any other source that you trust.</li>
-<li>Scan the crackl file with an antivirus software to make sure that it does not contain any viruses, malware, or spyware.</li>
-<li>Extract the crackl file using a zip extractor software such as WinRAR or 7-Zip. You should see a .dlm.l file inside the zip file.</li>
-<li>Compare the size and date of the .dlm.l file with the original plugin file that you downloaded from the official website. They should be similar or slightly different. If they are very different, then the crackl file might be fake or corrupted.</li>
-</ol>
- <h4>How to apply a crackl file for Edgechex for 3ds Max 2013</h4>
- <p>To apply a crackl file for Edgechex for 3ds Max 2013, you need to follow these steps:</p>
- <ol>
-<li>Copy the .dlm.l file and paste it into your plugins folder. The default location is C:\Program Files\Autodesk\3ds Max 2013\plugins.</li>
-<li>Rename the .dlm.l file to .dlm by removing the extra letter "l" at the end.</li>
-<li>Delete or move the original plugin file that you downloaded from the official website. You can also rename it to something else if you want to keep it as a backup.</li>
-<li>Start or restart your 3ds Max application.</li>
-<li>You should be able to use Edgechex for 3ds Max 2013 without any license verification or payment.</li>
-</ol>
- <h2>Conclusion</h2>
- <p>In this article, we have explained what Edgechex for 3ds Max 2013 is, what are its features and benefits, how to install and use it, what is a crackl file and why do you need it, what are the advantages and disadvantages of using a crackl file, what are the risks and precautions of using a crackl file, and how to download and use a crackl file for Edgechex for 3ds Max 2013. We hope that this article has been informative and helpful for you. However, we would like to remind you that using a crackl file is not legal or ethical. It is considered as software piracy and it violates the terms and conditions of the plugin developer. It also deprives them of their rightful income and recognition. Therefore, you should use a crackl file only for educational or experimental purposes and not for commercial or professional purposes. You should also respect the plugin developer and support them by buying a license if you can afford it or if you find their plugin useful and valuable.</p>
- <h2>FAQs</h2>
- <p>Here are some frequently asked questions about Edgechex for 3ds Max 2013 crackl:</p>
- <ol>
-<li>Q: Is Edgechex for 3ds Max 2013 compatible with other versions of 3ds Max?</li>
-<li>A: No, Edgechex for 3ds Max 2013 is only compatible with 3ds Max 2013. If you want to use Edgechex with other versions of 3ds Max, you need to buy a license for each version separately.</li>
-<li>Q: Is Edgechex for 3ds Max 2013 compatible with other plugins or modifiers?</li>
-2013 is compatible with most of the other plugins or modifiers that you have installed in your 3ds Max. However, there might be some exceptions or conflicts that might cause errors or crashes. You should always test the compatibility of Edgechex with other plugins or modifiers before using them together.</li>
-<li>Q: Is Edgechex for 3ds Max 2013 updated or supported by the plugin developer?</li>
-<li>A: No, Edgechex for 3ds Max 2013 is not updated or supported by the plugin developer. The last update for Edgechex for 3ds Max 2013 was released in 2014 and there are no plans for future updates or support. If you encounter any issues or bugs with Edgechex for 3ds Max 2013, you will not be able to contact the plugin developer or get any help from them.</li>
-<li>Q: Is Edgechex for 3ds Max 2013 safe to use?</li>
-<li>A: No, Edgechex for 3ds Max 2013 is not safe to use. Using a crackl file for Edgechex for 3ds Max 2013 is illegal and unethical. It might also cause damage to your files, system, or network. It might also expose you to legal consequences or security threats. You should always use a licensed version of Edgechex for 3ds Max 2013 or any other plugin that you want to use.</li>
-<li>Q: Is Edgechex for 3ds Max 2013 worth using?</li>
-<li>A: Yes, Edgechex for 3ds Max 2013 is worth using if you are a 3D artist or animator who wants to enhance your modeling and editing capabilities in 3ds Max. Edgechex for 3ds Max 2013 offers many features and benefits that can help you create complex shapes and patterns with ease. However, you should use a licensed version of Edgechex for 3ds Max 2013 or any other plugin that you want to use. You should also respect the plugin developer and support them by buying a license if you can afford it or if you find their plugin useful and valuable.</li>
-</ol>
- </p> 0a6ba089eb<br />
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spaces/1line/AutoGPT/autogpt/memory/weaviate.py

@@ -1,127 +0,0 @@
-import uuid
-
-import weaviate
-from weaviate import Client
-from weaviate.embedded import EmbeddedOptions
-from weaviate.util import generate_uuid5
-
-from autogpt.config import Config
-from autogpt.memory.base import MemoryProviderSingleton, get_ada_embedding
-
-
-def default_schema(weaviate_index):
-    return {
-        "class": weaviate_index,
-        "properties": [
-            {
-                "name": "raw_text",
-                "dataType": ["text"],
-                "description": "original text for the embedding",
-            }
-        ],
-    }
-
-
-class WeaviateMemory(MemoryProviderSingleton):
-    def __init__(self, cfg):
-        auth_credentials = self._build_auth_credentials(cfg)
-
-        url = f"{cfg.weaviate_protocol}://{cfg.weaviate_host}:{cfg.weaviate_port}"
-
-        if cfg.use_weaviate_embedded:
-            self.client = Client(
-                embedded_options=EmbeddedOptions(
-                    hostname=cfg.weaviate_host,
-                    port=int(cfg.weaviate_port),
-                    persistence_data_path=cfg.weaviate_embedded_path,
-                )
-            )
-
-            print(
-                f"Weaviate Embedded running on: {url} with persistence path: {cfg.weaviate_embedded_path}"
-            )
-        else:
-            self.client = Client(url, auth_client_secret=auth_credentials)
-
-        self.index = WeaviateMemory.format_classname(cfg.memory_index)
-        self._create_schema()
-
-    @staticmethod
-    def format_classname(index):
-        # weaviate uses capitalised index names
-        # The python client uses the following code to format
-        # index names before the corresponding class is created
-        if len(index) == 1:
-            return index.capitalize()
-        return index[0].capitalize() + index[1:]
-
-    def _create_schema(self):
-        schema = default_schema(self.index)
-        if not self.client.schema.contains(schema):
-            self.client.schema.create_class(schema)
-
-    def _build_auth_credentials(self, cfg):
-        if cfg.weaviate_username and cfg.weaviate_password:
-            return weaviate.AuthClientPassword(
-                cfg.weaviate_username, cfg.weaviate_password
-            )
-        if cfg.weaviate_api_key:
-            return weaviate.AuthApiKey(api_key=cfg.weaviate_api_key)
-        else:
-            return None
-
-    def add(self, data):
-        vector = get_ada_embedding(data)
-
-        doc_uuid = generate_uuid5(data, self.index)
-        data_object = {"raw_text": data}
-
-        with self.client.batch as batch:
-            batch.add_data_object(
-                uuid=doc_uuid,
-                data_object=data_object,
-                class_name=self.index,
-                vector=vector,
-            )
-
-        return f"Inserting data into memory at uuid: {doc_uuid}:\n data: {data}"
-
-    def get(self, data):
-        return self.get_relevant(data, 1)
-
-    def clear(self):
-        self.client.schema.delete_all()
-
-        # weaviate does not yet have a neat way to just remove the items in an index
-        # without removing the entire schema, therefore we need to re-create it
-        # after a call to delete_all
-        self._create_schema()
-
-        return "Obliterated"
-
-    def get_relevant(self, data, num_relevant=5):
-        query_embedding = get_ada_embedding(data)
-        try:
-            results = (
-                self.client.query.get(self.index, ["raw_text"])
-                .with_near_vector({"vector": query_embedding, "certainty": 0.7})
-                .with_limit(num_relevant)
-                .do()
-            )
-
-            if len(results["data"]["Get"][self.index]) > 0:
-                return [
-                    str(item["raw_text"]) for item in results["data"]["Get"][self.index]
-                ]
-            else:
-                return []
-
-        except Exception as err:
-            print(f"Unexpected error {err=}, {type(err)=}")
-            return []
-
-    def get_stats(self):
-        result = self.client.query.aggregate(self.index).with_meta_count().do()
-        class_data = result["data"]["Aggregate"][self.index]
-
-        return class_data[0]["meta"] if class_data else {}

spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/College Brawl Mod APK How to Win Every Fight in this Amazing Game.md

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-<p>College Brawl is a fun and addictive fighting game that lets you choose your character, customize your appearance, select your weapon, and unleash your skills on your opponents. You can play solo or with your friends in various modes, such as story mode, arcade mode, survival mode, and online mode. You can also explore different college environments, such as classrooms, dorms, cafeterias, gyms, libraries, and more. You can interact with other characters, make friends or enemies, join clubs or gangs, and even find love. College Brawl is a realistic and immersive college experience that will keep you entertained for hours.</p>
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-<p>College Brawl is a game that will test your reflexes, strategy, and skills. You can choose from different characters, each with their own personality, backstory, and fighting style. You can also customize your character's appearance, such as hair color, eye color, skin tone, clothing, accessories, and tattoos. You can select from various weapons, such as fists, bats, knives, guns, chainsaws, flamethrowers, and more. You can also upgrade your skills and abilities by earning coins and gems. You can use different moves and combos to defeat your enemies in fast-paced and intense battles.</p>
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-<p>College Brawl is not just a fighting game. It is also a simulation game that lets you experience the life of a college student. You can explore different college scenarios, such as attending classes, doing homework, taking exams, joining clubs or gangs, participating in events or activities, dating or breaking up, and more. You can interact with other characters, such as teachers, students, bullies, friends, rivals, and lovers. You can also make choices that will affect your story and outcome. College Brawl is a game that will make you feel like you are living in a college world.</p>
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-<p>College Brawl Mod Apk is a modified version of the original game that gives you unlimited access to all the features and benefits of the game. It is a way to enhance your gaming experience by unlocking everything that the game has to offer. With College Brawl Mod Apk, you can enjoy infinite Ki, Health, God Mode, and One Hit Kill. You can also play without any sensor, ads, or root required. You can also customize your characters, weapons, and skills to your liking. College Brawl Mod Apk is a way to make the game more fun and exciting.</p>
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spaces/1phancelerku/anime-remove-background/Download 2019 Tax Return Software from TurboTax and File Your Taxes Easily.md

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- <h2>How to Get a Transcript of Your 2019 Tax Return</h2>
-<p>A transcript is a computer printout of highlights from your tax return. It shows most line items from your return and may include information from other forms and schedules that you filed. There are different types of transcripts available, depending on what information you need. The most common ones are:</p>
-<ul>
-<li>Tax Return Transcript: shows most line items from your original tax return, including any forms and schedules that were attached. It does not show any changes made after you filed.</li>
-<li>Tax Account Transcript: shows basic data such as marital status, type of return, AGI, and taxable income. It also shows any adjustments made by you or the IRS after you filed.</li>
-<li>Record of Account Transcript: combines the information from the tax return transcript and the tax account transcript.</li>
-<li>Wage and Income Transcript: shows data from information returns that the IRS received, such as Forms W-2, 1099, and 1098. It may not include all income sources that you reported on your return.</li>
-<li>Verification of Non-filing Letter: shows that the IRS has no record of a filed tax return for a specific year.</li>
-</ul>
-<p>You can request transcripts for the last 10 years. Transcripts are free and you can get them in two ways:</p>
-<h3>How to request a transcript online</h3>
-<p>The fastest way to get a transcript is to request it online through the IRS website. You will need to create an account or log in with an existing IRS username or ID.me account. You will also need to have your photo identification ready. Once you access your account, you can view, print, or download any of the available transcripts for the current year and the previous three years. You can also request older transcripts to be mailed to your address of record.</p>
-<h3>How to request a transcript by mail or phone</h3>
-<p>If you prefer to receive a transcript by mail, you can use the online tool on the IRS website and choose the option to mail it. You will need to enter your Social Security number or Individual Tax Identification Number (ITIN), date of birth, and address. You can expect to receive your transcript within 5 to 10 days.</p>
-<p>You can also request a transcript by calling the IRS automated phone service at 800-908-9946. You will need to provide the same information as above and follow the prompts. You can choose to receive your transcript by mail or fax, if you are at a public place with a fax machine.</p>
- <h2>How to Get a Copy of Your 2019 Tax Return</h2>
-<p>A copy is an exact duplicate of your original tax return, including all forms, schedules, and attachments. It shows any changes or amendments that you or the IRS made after you filed. A copy is different from a transcript in that it shows more detail and may include state tax information.</p>
-<p>You can request copies for the last seven years. Copies are not free and you need to follow these steps:</p>
-<h3>How to request a copy using Form 4506</h3>
-<p>To request a copy of your tax return, you need to fill out Form 4506, Request for Copy of Tax Return, and mail it to the IRS address that matches your location. You can find the form and the addresses on the IRS website. You will need to provide your name, Social Security number or ITIN, address, and the tax year that you are requesting. You will also need to pay a fee of $43 for each copy that you request. You can pay by check or money order made payable to "United States Treasury".</p>
-<h3>How much it costs and how long it takes</h3>
-<p>The fee for requesting a copy of your tax return is $43 per copy. If you are requesting more than one copy, you can send one payment for the total amount. The IRS will send you a notice if they cannot provide the copy that you requested or if you need to pay more money.</p>
-<p>It may take up to 75 days for the IRS to process your request and mail you the copy of your tax return. If you need it sooner, you may want to consider getting a transcript instead, which is faster and free.</p>
- <h2>Benefits of Filing Your Tax Return Online</h2>
-<p>If you have not filed your 2020 tax return yet, you may want to consider filing it online instead of mailing a paper return. Filing your tax return online has many benefits, such as:</p>
-<h3>Faster and easier process</h3>
-<p>Filing your tax return online is faster and easier than filing a paper return. You can use a software product or an online service that will guide you through the process and do the calculations for you. You can also import your information from previous years or from other sources, such as your employer or bank. You do not need to print or mail anything, which saves you time and money.</p>
-<h3>Prompt and secure delivery</h3>
-<p>Filing your tax return online ensures that the IRS receives it promptly and securely. You will get an electronic confirmation that your return was accepted within 24 hours. You do not have to worry about your return getting lost or delayed in the mail. You can also track the status of your return and refund online using the Where's My Refund tool on the IRS website.</p>
-<h3>Reduced errors and faster refunds</h3>
-<p>Filing your tax return online reduces the chances of errors and mistakes that could delay your refund or result in penalties. The software or online service will check your return for accuracy and completeness before you submit it. It will also alert you of any credits or deductions that you may qualify for. If you are due a refund, you can get it faster by choosing direct deposit into your bank account. The IRS issues most refunds within 21 days of receiving your return, compared to six weeks or more for paper returns.</p>
- <h2>Conclusion and FAQs</h2>
-<p>In conclusion, if you need to download your 2019 tax return, you have two options: getting a transcript or a copy from the IRS. A transcript is a computer printout of highlights from your return, while a copy is an exact duplicate of your original return. Transcripts are free and available online or by mail or phone, while copies cost $43 each and require filling out Form 4506 and mailing it to the IRS. If you have not filed your 2020 tax return yet, you may want to file it online instead of mailing a paper return. Filing your tax return online has many benefits, such as faster and easier process, prompt and secure delivery, reduced errors and faster refunds.</p>
- <p>Here are some FAQs that you may have about downloading your 2019 tax return:</p>
-<ul>
-<li><b>Q: How can I download my 2019 tax return if I filed it online?</b></li>
-<li>A: If you filed your 2019 tax return online using a software product or an online service, you can download your return from the same source that you used. You will need to log in to your account and access your previous returns. You can then view, print, or save your return as a PDF file.</li>
-<li><b>Q: How can I download my 2019 tax return if I used a tax professional?</b></li>
-<li>A: If you used a tax professional to file your 2019 tax return, you can ask them to provide you with a copy or a transcript of your return. They may charge you a fee for this service. You can also request a transcript or a copy from the IRS using the methods described above.</li>
-<li><b>Q: How can I download my 2019 state tax return?</b></li>
-<li>A: If you need to download your 2019 state tax return, you will need to contact your state tax agency. Each state has its own rules and procedures for requesting transcripts or copies of state tax returns. You can find the contact information and website of your state tax agency on the IRS website.</li>
-<li><b>Q: How long do I need to keep my 2019 tax return?</b></li>
-<li>A: The IRS recommends that you keep your tax returns and supporting documents for at least three years from the date you filed or the due date of your return, whichever is later. However, in some cases, you may need to keep them longer, such as if you have unreported income, underreported income, or fraudulent activity on your return. You can find more information on how long to keep your records on the IRS website.</li>
-<li><b>Q: What if I lost or damaged my 2019 tax return?</b></li>
-<li>A: If you lost or damaged your 2019 tax return, you can request a transcript or a copy from the IRS using the methods described above. You can also try to recover your return from other sources, such as your employer, bank, or financial institution that may have copies of your W-2s, 1099s, or other forms that you filed with your return.</li>
-</ul></p> 401be4b1e0<br />
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-<br />

spaces/1phancelerku/anime-remove-background/Download Merchant Navy Hall Ticket 2023 Important Instructions and FAQs.md

@@ -1,134 +0,0 @@
-<br />
-<h1>How to Download Admit Card for Merchant Navy Entrance Exam</h1>
- <p>If you are aspiring to join the merchant navy, then you must be aware of the entrance exam that is conducted by various institutes and organizations for admission to various courses related to merchant navy. The entrance exam is a crucial step in your journey to become a merchant navy officer, as it tests your aptitude, knowledge, and skills required for this profession. But before you can appear for the entrance exam, you need to download the admit card that is issued by the exam conducting authority. The admit card is an essential document that contains important information about your exam date, time, venue, roll number, and instructions. Without the admit card, you will not be allowed to enter the exam hall or take the exam. Therefore, it is very important that you download your admit card well in advance and keep it safe until the exam day.</p>
- <p>In this article, we will tell you everything you need to know about how to download admit card for merchant navy entrance exam. But before that, let us give you a brief introduction about what is merchant navy and why you should join it.</p>
-<h2>download admit card merchant navy</h2><br /><p><b><b>Download</b> &gt;&gt;&gt;&gt;&gt; <a href="https://jinyurl.com/2uNUkW">https://jinyurl.com/2uNUkW</a></b></p><br /><br />
- <h2>What is Merchant Navy and Why Join It?</h2>
- <h3>Merchant Navy: A Brief Introduction</h3>
- <p>A merchant navy or merchant marine is the fleet of commercial ships that are registered in a specific country and carry goods and passengers across the world. The merchant navy plays a vital role in the global trade and economy, as it transports more than 90% of the world's cargo by volume. The merchant navy consists of various types of ships such as cargo ships, container ships, tankers, bulk carriers, cruise ships, ferries, etc. The merchant navy also employs a large number of skilled and trained personnel who work on these ships as officers, engineers, ratings, etc.</p>
- <h3>Benefits of Joining Merchant Navy</h3>
- <p>Joining the merchant navy can be a rewarding and adventurous career option for those who love travelling and exploring new places. Some of the benefits of joining the merchant navy are:</p>
- <ul>
-<li>You get to travel around the world and visit different countries and cultures.</li>
-<li>You get to earn a handsome salary and enjoy various perks and allowances.</li>
-<li>You get to learn new skills and gain valuable experience in handling different types of ships and machinery.</li>
-<li>You get to work in a challenging and dynamic environment that enhances your personality and confidence.</li>
-<li>You get to enjoy a lot of holidays and leisure time when you are not on duty.</li>
-<li>You get to serve your country by contributing to its trade and security.</li>
-</ul>
- <h2>How to Apply for Merchant Navy Entrance Exam</h2>
- <h3>Eligibility Criteria for Merchant Navy Entrance Exam</h3>
- <p>The eligibility criteria for merchant navy entrance exam may vary depending on the course and institute you are applying for. However, some of the common eligibility criteria are:</p>
- <ul>
-<li>You must have passed 10+2 or equivalent examination with Physics, Chemistry, Mathematics, and English as compulsory subjects.</li>
-<li>You must have secured at least 60% marks in aggregate and 50% marks in English in 10+2 or equivalent examination.</li>
-<li>You must be between 17 to 25 years of age at the time of admission.</li>
-<li>You must have good eyesight and physical fitness as per the medical standards prescribed by the Directorate General of Shipping (DGS).</li>
-<li>You must not have any criminal record or pending cases against you.</li>
-</ <h3>Application Process for Merchant Navy Entrance Exam</h3>
- <p>The application process for merchant navy entrance exam may also differ depending on the course and institute you are applying for. However, some of the common steps involved in the application process are:</p>
- <ol>
-<li>You need to visit the official website of the institute or organization that is conducting the entrance exam and register yourself with your personal and academic details.</li>
-<li>You need to pay the application fee online or offline as per the mode specified by the institute or organization.</li>
-<li>You need to upload or submit the scanned copies or photocopies of your documents such as mark sheets, certificates, passport, etc. as per the instructions given by the institute or organization.</li>
-<li>You need to download and print the confirmation page or receipt of your application form and keep it for future reference.</li>
-<li>You need to wait for the release of the admit card for merchant navy entrance exam and download it from the official website of the institute or organization.</li>
-</ol>
- <h2>How to Download Admit Card for Merchant Navy Entrance Exam</h2>
- <h3>Steps to Download Admit Card for Merchant Navy Entrance Exam</h3>
- <p>The admit card for merchant navy entrance exam is usually released a few days or weeks before the exam date on the official website of the institute or organization that is conducting the exam. You can download your admit card by following these simple steps:</p>
- <ol>
-<li>Visit the official website of the institute or organization that is conducting the entrance exam and log in with your registration number and password or date of birth.</li>
-<li>Click on the link that says "Download Admit Card" or "Hall Ticket" or "Call Letter" or something similar.</li>
-<li>Enter your details such as application number, roll number, name, etc. and click on "Submit" or "Download" or "Print" or something similar.</li>
-<li>Your admit card will be displayed on your screen. Check all the details carefully and report any discrepancy or error to the concerned authority immediately.</li>
-<li>Download and save your admit card in PDF format and take a printout of it on an A4 size paper.</li>
-</ol>
- <h3>Details Mentioned on the Admit Card for Merchant Navy Entrance Exam</h3>
- <p>The admit card for merchant navy entrance exam contains important information about your exam such as:</p>
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- <ul>
-<li>Your name, photograph, signature, and thumb impression.</li>
-<li>Your roll number, application number, category, and gender.</li>
-<li>Your exam date, time, duration, and shift.</li>
-<li>Your exam center name, address, and code.</li>
-<li>Your course name, code, and stream.</li>
-<li>The instructions and guidelines for the exam such as reporting time, documents required, dos and don'ts, etc.</li>
-</ul>
- <h3>Documents Required Along with the Admit Card for Merchant Navy Entrance Exam</h3>
- <p>Along with your admit card, you also need to carry some other documents to the exam center for verification and identification purposes. These documents are:</p>
- <ul>
-<li>Your original and valid photo identity proof such as Aadhaar card, PAN card, passport, voter ID card, driving license, etc.</li>
-<li>Your original and attested copies of your mark sheets and certificates of 10th and 12th or equivalent examinations.</li>
-<li>Your original and attested copies of your medical fitness certificate issued by a registered medical practitioner as per the DGS norms.</li>
-<li>Your original and attested copies of your character certificate issued by your school or college principal or a gazetted officer.</li>
-<li>Your original and attested copies of your caste certificate (if applicable) issued by a competent authority.</li>
-</ul>
- <p>Note: You should also keep some extra copies of your admit card and photo identity proof in case of any loss or damage.</p>
- <h2>How to Prepare for Merchant Navy Entrance Exam</h2>
- <h3>Exam Pattern and Syllabus for Merchant Navy Entrance Exam</h3>
- <p>The exam pattern and syllabus for merchant navy entrance exam may vary depending on the course and institute you are applying for. However, some of the common features of the exam pattern and syllabus are:</p>
- <table border="1">
-<tr><th>Subject</th><th>No. of Questions</th><th>Marks</th></tr>
-<tr><td>Physics</td><td>25</td><td>25</td></tr>
-<tr><td>Chemistry</td><td>25</td><td>25</td></tr>
-<tr><td>Mathematics</td><td>25</td><td>25</td></tr>
-<tr><td>English</td><td>25</td><td>25</td></tr>
-<tr><td>Total</td><td>100</ </td><td>100</td></tr>
-</table>
- <p>The exam is of objective type and consists of multiple-choice questions. The duration of the exam is 90 minutes. There is no negative marking for wrong answers. The syllabus covers the topics of Physics, Chemistry, Mathematics, and English as per the 10+2 level. Some of the topics are:</p>
- <ul>
-<li>Physics: Units and Measurements, Kinematics, Laws of Motion, Work, Energy and Power, Gravitation, Thermodynamics, Oscillations and Waves, Electrostatics, Current Electricity, Magnetic Effects of Current, Electromagnetic Induction, Optics, Dual Nature of Matter and Radiation, Atoms and Nuclei, Electronic Devices, etc.</li>
-<li>Chemistry: Some Basic Concepts of Chemistry, Structure of Atom, Classification of Elements and Periodicity in Properties, Chemical Bonding and Molecular Structure, States of Matter, Thermodynamics, Equilibrium, Redox Reactions, Hydrogen, s-Block Elements, p-Block Elements, Organic Chemistry, Hydrocarbons, Environmental Chemistry, etc.</li>
-<li>Mathematics: Sets, Relations and Functions, Complex Numbers and Quadratic Equations, Matrices and Determinants, Permutations and Combinations, Binomial Theorem, Sequences and Series, Coordinate Geometry, Limits and Continuity, Differentiation and Integration, Applications of Derivatives and Integrals, Differential Equations, Vector Algebra, Three Dimensional Geometry, Probability, Statistics and Trigonometry.</li>
-<li>English: Reading Comprehension, Vocabulary, Grammar, Sentence Correction, Synonyms and Antonyms, Idioms and Phrases, Fill in the Blanks, Cloze Test, Para Jumbles.</li>
-</ul>
- <h3>Tips and Strategies for Cracking Merchant Navy Entrance Exam</h3>
- <p>The merchant navy entrance exam is not very difficult if you prepare well and follow some tips and strategies. Here are some of them:</p>
- <ul>
-<li>Make a study plan and stick to it. Divide your time wisely among all the subjects and topics. Revise regularly and practice mock tests.</li>
-<li>Clear your concepts and fundamentals. Focus on understanding the concepts rather than memorizing the formulas. Solve numerical problems with accuracy and speed.</li>
-<li>Improve your English skills. Read newspapers, magazines, books etc. to enhance your vocabulary and comprehension skills. Learn the rules of grammar and usage. Practice writing essays and letters on various topics.</li>
-<li>Manage your time and stress. Do not waste time on questions that you are not sure about. Skip them and move on to the next ones. Attempt the easy questions first and then the difficult ones. Do not panic or get nervous during the exam. Stay calm and confident.</li>
-<li>Prepare well for the interview and medical test. After clearing the entrance exam, you will have to face an interview and a medical test conducted by the institute or organization that you have applied for. Prepare yourself for the common questions asked in the interview such as your introduction, your motivation to join merchant navy etc. Be honest and polite in your answers. Dress formally and maintain a good body language. For the medical test, make sure you are fit and healthy as per the DGS standards.</li>
-</ul>
- <h2>Conclusion</h2>
- <p>The merchant navy is a lucrative and exciting career option for those who love travelling and adventure. To join the merchant navy you need to clear an entrance exam that is conducted by various institutes or organizations for admission to various courses related to merchant navy. The entrance exam tests your aptitude knowledge and skills required for this profession. To download your admit card for merchant navy entrance exam you need to visit the official website of the institute or organization that is conducting the exam log in with your credentials enter your details download save print your admit card check all the details carefully carry it along with other documents to the exam center prepare well for the exam follow some tips strategies crack it clear the interview medical test get admission to your desired course start your journey to become a merchant navy officer.</p>
- <h2>FAQs</h2>
- <h4>Q1: What is the difference between merchant navy and Indian navy?</h4>
-<p>A1: Merchant navy is the commercial fleet of ships that carry goods passengers across the world while Indian navy is the naval branch of Indian armed forces that protect India's maritime interests security.</p>
- <h4>Q2: What are the career prospects after joining merchant navy?</h4>
-<p>A2: After joining merchant navy you can work on various types of ships such as cargo ships container ships tankers bulk carriers cruise ships ferries etc as officers engineers ratings etc You can also work in shore-based jobs such as ship management ship broking port management maritime law maritime education etc</p>
- <h4>Q3: What are the challenges faced by merchant navy officers?</h4>
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spaces/7hao/bingo/src/components/chat-scroll-anchor.tsx

@@ -1,29 +0,0 @@
-'use client'
-
-import * as React from 'react'
-import { useInView } from 'react-intersection-observer'
-
-import { useAtBottom } from '@/lib/hooks/use-at-bottom'
-
-interface ChatScrollAnchorProps {
-  trackVisibility?: boolean
-}
-
-export function ChatScrollAnchor({ trackVisibility }: ChatScrollAnchorProps) {
-  const isAtBottom = useAtBottom()
-  const { ref, entry, inView } = useInView({
-    trackVisibility,
-    delay: 100,
-    rootMargin: '0px 0px -150px 0px'
-  })
-
-  React.useEffect(() => {
-    if (isAtBottom && trackVisibility && !inView) {
-      entry?.target.scrollIntoView({
-        block: 'start'
-      })
-    }
-  }, [inView, entry, isAtBottom, trackVisibility])
-
-  return <div ref={ref} className="h-px w-full" />
-}

spaces/801artistry/RVC801/run.sh

@@ -1,61 +0,0 @@
-#!/bin/bash
-
-if [[ "$(uname)" == "Darwin" ]]; then
-  # macOS specific env:
-  export PYTORCH_ENABLE_MPS_FALLBACK=1
-  export PYTORCH_MPS_HIGH_WATERMARK_RATIO=0.0
-elif [[ "$(uname)" != "Linux" ]]; then
-  echo "Unsupported operating system."
-  exit 1
-fi
-
-if [ -d ".venv" ]; then
-  echo "Activate venv..."
-  source .venv/bin/activate
-else
-  echo "Create venv..."
-  requirements_file="requirements.txt"
-
-  # Check if Python 3.8 is installed
-  if ! command -v python3 &> /dev/null; then
-    echo "Python 3 not found. Attempting to install 3.8..."
-    if [[ "$(uname)" == "Darwin" ]] && command -v brew &> /dev/null; then
-      brew install [email protected]
-    elif [[ "$(uname)" == "Linux" ]] && command -v apt-get &> /dev/null; then
-      sudo apt-get update
-      sudo apt-get install python3.8
-    else
-      echo "Please install Python 3.8 manually."
-      exit 1
-    fi
-  fi
-
-  python3 -m venv .venv
-  source .venv/bin/activate
-
-  # Check if required packages are installed and install them if not
-  if [ -f "${requirements_file}" ]; then
-    installed_packages=$(python3 -m pip freeze)
-    while IFS= read -r package; do
-      [[ "${package}" =~ ^#.* ]] && continue
-      package_name=$(echo "${package}" | sed 's/[<>=!].*//')
-      if ! echo "${installed_packages}" | grep -q "${package_name}"; then
-        echo "${package_name} not found. Attempting to install..."
-        python3 -m pip install --upgrade "${package}"
-      fi
-    done < "${requirements_file}"
-  else
-    echo "${requirements_file} not found. Please ensure the requirements file with required packages exists."
-    exit 1
-  fi
-fi
-
-# Download models
-./tools/dlmodels.sh
-
-if [[ $? -ne 0 ]]; then
-  exit 1
-fi
-
-# Run the main script
-python3 infer-web.py --pycmd python3

spaces/AIFILMS/StyleGANEX/datasets/gt_res_dataset.py

@@ -1,32 +0,0 @@
-#!/usr/bin/python
-# encoding: utf-8
-import os
-from torch.utils.data import Dataset
-from PIL import Image
-
-
-class GTResDataset(Dataset):
-
-	def __init__(self, root_path, gt_dir=None, transform=None, transform_train=None):
-		self.pairs = []
-		for f in os.listdir(root_path):
-			image_path = os.path.join(root_path, f)
-			gt_path = os.path.join(gt_dir, f)
-			if f.endswith(".jpg") or f.endswith(".png"):
-				self.pairs.append([image_path, gt_path.replace('.png', '.jpg'), None])
-		self.transform = transform
-		self.transform_train = transform_train
-
-	def __len__(self):
-		return len(self.pairs)
-
-	def __getitem__(self, index):
-		from_path, to_path, _ = self.pairs[index]
-		from_im = Image.open(from_path).convert('RGB')
-		to_im = Image.open(to_path).convert('RGB')
-
-		if self.transform:
-			to_im = self.transform(to_im)
-			from_im = self.transform(from_im)
-
-		return from_im, to_im

spaces/AIWaves/Debate/src/agents/Agent/Agent.py

@@ -1,243 +0,0 @@
-# coding=utf-8
-# Copyright 2023  The AIWaves 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.
-"""LLM autonoumous agent"""
-from LLM.base_LLM import *
-from Component import *
-from Action import Action
-from Prompt import *
-
-headers = {
-    "Content-Type": "text/event-stream",
-    "Cache-Control": "no-cache",
-    "X-Accel-Buffering": "no",
-}
-    
-    
-    
-
-class Agent:
-    """
-    Auto agent, input the JSON of SOP.
-    """
-
-    # Agent should have args: agents,states
-    def __init__(self, name, agent_state_roles, **kwargs) -> None:
-        self.state_roles = agent_state_roles
-        self.name = name
-        
-        self.style = kwargs["style"]
-        self.LLMs = kwargs["LLMs"]
-        self.LLM = None
-        self.is_user = kwargs["is_user"]
-        self.begins = kwargs["begins"] if "begins" in kwargs else False
-        self.current_role = ""
-        self.long_term_memory = []
-        self.short_term_memory = ""
-        self.current_state = None
-        self.first_speak = True
-        self.environment = None
-    
-
-    @classmethod
-    def from_config(cls, config_path):
-        """
-        Initialize agents based on json file
-        Return:
-        agents(dict) : key:agent_name;value:class(Agent) 
-        names_to_roles(dict) : key:state_name  value:(dict; (key:agent_name ; value:agent_role))
-        roles_to_names(dict) : key:state_name  value:(dict; (key:agent_role ; value:agent_name))
-        """
-        with open(config_path) as f:
-            config = json.load(f)
-        
-        roles_to_names = {}
-        names_to_roles = {}
-        agents = {}
-        user_names = json.loads(os.environ["User_Names"]) if "User_Names" in os.environ else []
-        for agent_name, agent_dict in config["agents"].items():
-            agent_state_roles = {}
-            agent_LLMs = {}
-            agent_begins = {}
-            for state_name, agent_role in agent_dict["roles"].items():
-                
-                agent_begins[state_name] = {}
-                
-                if state_name not in roles_to_names:
-                    roles_to_names[state_name] = {}
-                if state_name not in names_to_roles:
-                    names_to_roles[state_name] = {}
-                roles_to_names[state_name][agent_role] = agent_name
-                names_to_roles[state_name][agent_name] = agent_role
-                agent_state_roles[state_name] = agent_role
-                current_state = config["states"][state_name]
-                
-                current_state_begin_role = current_state["begin_role"] if "begin_role" in current_state else current_state["roles"][0]
-                agent_begins[state_name]["is_begin"] = current_state_begin_role==agent_role if "begin_role" in current_state else False
-                agent_begins[state_name]["begin_query"] = current_state["begin_query"] if "begin_query" in current_state else " "
-                agent_LLMs[state_name] = init_LLM(f"logs/{agent_name}",**current_state["agent_states"][agent_role])
-            agents[agent_name] = cls(
-                agent_name,
-                agent_state_roles,
-                LLMs=agent_LLMs,
-                is_user=agent_name in user_names,
-                style = agent_dict["style"],
-                begins = agent_begins
-            )
-        assert len(config["agents"].keys()) != 2 or (roles_to_names[config["root"]][config["states"][config["root"]]["begin_role"]] not in user_names and "begin_query"  in config["states"][config["root"]]),"In a single-agent scenario, there must be an opening statement and it must be the agent" 
-        return agents, roles_to_names, names_to_roles
-
-    def step(self, current_state,input=""):
-        """
-        return actions by current state and environment
-        Return: action(Action)
-        """
-        
-        current_state.chat_nums +=1
-        state_begin = current_state.is_begin
-        agent_begin = self.begins[current_state.name]["is_begin"]
-        self.begins[current_state.name]["is_begin"] = False
-        current_state.is_begin = False
-        environment = self.environment
-        
-        self.current_state = current_state
-        # 先根据当前环境更新信息
-        # First update the information according to the current environment
-        
-        response = " "
-        res_dict = {}
-        
-        if self.is_user:
-            response = f"{self.name}:{input}"
-        else:
-            if len(environment.shared_memory["long_term_memory"])>0:
-                current_history = self.observe()
-                self.long_term_memory.append(current_history)
-            if agent_begin:
-                response = (char for char in self.begins[current_state.name]["begin_query"])
-            else:
-                response,res_dict = self.act()
-        
-        
-        action_dict =  {
-            "response": response,
-            "res_dict": res_dict,
-            "role": self.state_roles[current_state.name],
-            "name": self.name,
-            "state_begin" : state_begin,
-            "agent_begin" : agent_begin,
-            "is_user" : self.is_user
-        }
-        return  Action(**action_dict)
-
-    def act(self):
-        """
-        return actions by the current state
-        """
-        current_state = self.current_state
-        chat_history = self.long_term_memory
-        current_LLM = self.LLMs[current_state.name]
-        
-        system_prompt, last_prompt, res_dict = self.compile()
-
-        
-
-        response = current_LLM.get_response(
-            chat_history, system_prompt, last_prompt, stream=True
-        )
-        return response,res_dict 
-
-    def update_memory(self, memory):
-        self.long_term_memory.append(
-            {"role": "assistant", "content": memory.content}
-        )
-        
-        MAX_CHAT_HISTORY = eval(os.environ["MAX_CHAT_HISTORY"])
-        environment = self.environment
-        current_chat_history_idx = environment.current_chat_history_idx if environment.environment_type == "competive" else 0
-        
-        current_long_term_memory = environment.shared_memory["long_term_memory"][current_chat_history_idx:]
-        last_conversation_idx = environment._get_agent_last_conversation_idx(self,current_long_term_memory)
-        if len(current_long_term_memory)-last_conversation_idx >= MAX_CHAT_HISTORY:
-            current_state = self.current_state
-            current_role = self.state_roles[current_state.name]
-            current_component_dict = current_state.components[current_role]
-                
-            # get chat history from new conversation
-            conversations = environment._get_agent_new_memory(self,current_long_term_memory)
-
-            # get summary
-            summary_prompt = (
-                current_state.summary_prompt[current_role]
-                if current_state.summary_prompt
-                else f"""your name is {self.name},your role is{current_component_dict["style"].role},your task is {current_component_dict["task"].task}.\n"""
-            )
-            summary_prompt =eval(Agent_summary_system_prompt)
-            summary = self.LLMs[current_state.name].get_response(None, summary_prompt,stream = False)
-            self.short_term_memory = summary
-            
-        
-    def compile(self):
-        """
-        get prompt from state depend on your role
-        Return:
-        system_prompt:system_prompt for agents's LLM
-        last_prompt:last_prompt for agents's LLM
-        res_dict(dict): Other return from tool component.For example: search engine results
-        """
-        current_state = self.current_state
-        self.current_roles = self.state_roles[current_state.name]
-        current_state_name = current_state.name
-        self.LLM = self.LLMs[current_state_name]
-        components = current_state.components[self.state_roles[current_state_name]]
-
-        system_prompt = self.current_state.environment_prompt
-        last_prompt = ""
-
-        res_dict = {}
-        for component in components.values():
-            if isinstance(component, (OutputComponent, LastComponent)):
-                last_prompt = last_prompt + "\n" + component.get_prompt(self)
-            elif isinstance(component, PromptComponent):
-                system_prompt = (
-                    system_prompt + "\n" + component.get_prompt(self)
-                )
-            elif isinstance(component, ToolComponent):
-                response = component.func(self)
-                if "prompt" in response and response["prompt"]:
-                    last_prompt = last_prompt + "\n" + response["prompt"]
-                res_dict.update(response)
-        
-        name = self.name
-        query = self.environment.shared_memory["long_term_memory"][-1]
-        last_prompt = eval(Agent_last_prompt)
-        system_prompt = eval(Agent_system_prompt)
-        return system_prompt, last_prompt, res_dict
-
-
-    def observe(self):
-        """
-        Update one's own memory according to the current environment, including: updating short-term memory; updating long-term memory
-        """
-        return self.environment._observe(self)
-        
-    
-    def generate_sop(self):
-        pass
-
-    def reflection(self):
-        pass
-
-

spaces/AchyuthGamer/OpenGPT/g4f/Provider/Providers/H2o.py

@@ -1,109 +0,0 @@
-from __future__ import annotations
-
-import json
-import uuid
-
-from aiohttp import ClientSession
-
-from ..typing import AsyncGenerator
-from .base_provider import AsyncGeneratorProvider, format_prompt
-
-
-class H2o(AsyncGeneratorProvider):
-    url = "https://gpt-gm.h2o.ai"
-    working = True
-    model = "h2oai/h2ogpt-gm-oasst1-en-2048-falcon-40b-v1"
-
-    @classmethod
-    async def create_async_generator(
-        cls,
-        model: str,
-        messages: list[dict[str, str]],
-        proxy: str = None,
-        **kwargs
-    ) -> AsyncGenerator:
-        model = model if model else cls.model
-        headers = {"Referer": cls.url + "/"}
-
-        async with ClientSession(
-            headers=headers
-        ) as session:
-            data = {
-                "ethicsModalAccepted": "true",
-                "shareConversationsWithModelAuthors": "true",
-                "ethicsModalAcceptedAt": "",
-                "activeModel": model,
-                "searchEnabled": "true",
-            }
-            async with session.post(
-                f"{cls.url}/settings",
-                proxy=proxy,
-                data=data
-            ) as response:
-                response.raise_for_status()
-
-            async with session.post(
-                f"{cls.url}/conversation",
-                proxy=proxy,
-                json={"model": model},
-            ) as response:
-                response.raise_for_status()
-                conversationId = (await response.json())["conversationId"]
-
-            data = {
-                "inputs": format_prompt(messages),
-                "parameters": {
-                    "temperature": 0.4,
-                    "truncate": 2048,
-                    "max_new_tokens": 1024,
-                    "do_sample":  True,
-                    "repetition_penalty": 1.2,
-                    "return_full_text": False,
-                    **kwargs
-                },
-                "stream": True,
-                "options": {
-                    "id": str(uuid.uuid4()),
-                    "response_id": str(uuid.uuid4()),
-                    "is_retry": False,
-                    "use_cache": False,
-                    "web_search_id": "",
-                },
-            }
-            async with session.post(
-                f"{cls.url}/conversation/{conversationId}",
-                proxy=proxy,
-                json=data
-             ) as response:
-                start = "data:"
-                async for line in response.content:
-                    line = line.decode("utf-8")
-                    if line and line.startswith(start):
-                        line = json.loads(line[len(start):-1])
-                        if not line["token"]["special"]:
-                            yield line["token"]["text"]
-
-            async with session.delete(
-                f"{cls.url}/conversation/{conversationId}",
-                proxy=proxy,
-                json=data
-            ) as response:
-                response.raise_for_status()
-
-
-    @classmethod
-    @property
-    def params(cls):
-        params = [
-            ("model", "str"),
-            ("messages", "list[dict[str, str]]"),
-            ("stream", "bool"),
-            ("temperature", "float"),
-            ("truncate", "int"),
-            ("max_new_tokens", "int"),
-            ("do_sample", "bool"),
-            ("repetition_penalty", "float"),
-            ("return_full_text", "bool"),
-        ]
-        param = ", ".join([": ".join(p) for p in params])
-        return f"g4f.provider.{cls.__name__} supports: ({param})"

spaces/Adr740/SmartHadithFR/get_similar_hadiths.py

@@ -1,33 +0,0 @@
-import pandas as pd
-import openai
-from openai.embeddings_utils import cosine_similarity
-import os 
-openai.api_key = os.environ.get("apk")
-
-def _get_embedding(text, model="text-embedding-ada-002"):
-    try:
-        text = text.replace("\n", " ")
-    except:
-        None
-    return openai.Embedding.create(input = [text], model=model)['data'][0]['embedding']
-
-def search_hadiths(user_input,nb_hadiths_to_display=10, path_to_json = "embeded_data.json",):
-    df = pd.read_json(path_to_json)
-    try:
-        df["embeddings"] = df.embeddings.apply(lambda x: x["embeding"])
-    except:
-        pass
-    embedding = _get_embedding(user_input, model='text-embedding-ada-002')
-    df['similarity'] = df.embeddings.apply(lambda x: cosine_similarity(x, embedding))
-    results = df.sort_values('similarity', ascending=False).head(int(nb_hadiths_to_display)).to_dict(orient="records")
-    md_results = ""
-    i = 1
-    for result in results:
-        similarity = str(round(result["similarity"]*100,2)) + "%"
-        book = result["book"]
-        chapter = result["chapter"]
-        content = result["content"]
-        display = f"## Hadith numéro {i}: Similarité avec la recherche : {similarity}\n## Book : {book}\n## Chapter : {chapter}\n{content}\n\n------\n\n"
-        md_results += display
-        i += 1
-    return md_results

spaces/AgentVerse/agentVerse/README_zh.md

@@ -1,373 +0,0 @@
-<h1 align="center"> 🤖 AgentVerse 🪐 </h1>
-
-<h3 align="center">
-    <p>一个用于搭建多智能体交互平台的框架</p>
-</h3>
-<p align="center">
-    <a href="https://github.com/OpenBMB/AgentVerse/blob/main/LICENSE">
-        <img alt="License: Apache2" src="https://img.shields.io/badge/License-Apache_2.0-green.svg">
-    </a>
-    <a href="https://www.python.org/downloads/release/python-3916/">
-        <img alt="Documentation" src="https://img.shields.io/badge/python-3.9+-blue.svg">
-    </a>
-</p>
-
-<p align="center">
-<img src="./imgs/title.png" width="512">
-</p>
-
-<p align="center">
-    【<a href="README.md">English </a> | Chinese】
-</p>
-
-**AgentVerse** 提供了一个多功能的框架，简化了为大型语言模型（LLMs）创建自定义多智能体环境的过程。旨在快速、低成本的开发和定制，我们的框架赋能研究人员专注于他们的研究，而不被实现细节所困扰。
-
----
-
-## ✨ 特点
-
-- 🥳 **高效的环境构建:** 我们的框架提供了一系列基础构建模块，轻松创建多智能体环境。只需在配置文件中写入几行，你就可以轻松建立如LLMs的聊天室这样的基础环境。这个过程包括为LLMs定义环境的设置和提示，使像你这样的研究者能够专注于实验和分析。
-
-- ⚙️ **可定制组件**: AgentVerse通过将多智能体环境分为五个功能模块并定义其各自的接口来简化它。对于不能直接使用AgentVerse提供的基本模块构建的复杂环境，你可以定制这五个功能模块中的一个或多个接口，根据你的要求高效地创建自己的多智能体环境。
-
-- 🛠 **工具(插件)利用**: AgentVerse支持多智能体环境的工具。目前，AgentVerse支持[BMTools](https://github.com/OpenBMB/BMTools)中提供的工具。 
-
-## 📰 最新消息
-- [2023/8/22] 📝 我们很高兴分享与此仓库相关的正在进行中的论文[AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors in Agents](https://arxiv.org/abs/2308.10848).
-<img width="616" alt="Screen Shot 2023-09-01 at 12 08 57 PM" src="https://github.com/OpenBMB/AgentVerse/assets/11704492/6db1c907-b7fc-42f9-946c-89853a28f386">
-
-You could refer the stay-tuned code in this [branch](https://github.com/OpenBMB/AgentVerse/tree/AgentVerse-TaskSolving).
-
-- [2023/6/5] 🎉 我们很荣幸地展示了一系列 [demos](#-simple-demo-video), 包括 [NLP教室](#nlp教室), [囚徒困境](#囚徒困境), [软件开发](#软件开发), [数据库运维](#数据库运维), 以及一个简单的 [H5宝可梦游戏](#宝可梦游戏) 该游戏允许与宝可梦中的角色互动！你可以试玩这些demo，祝你玩得开心！
-- [2023/5/1] 🚀 [AgentVerse](https://github.com/OpenBMB/AgentVerse) 正式发布！
-
-## 🌟 加入我们!
-AgentVerse致力于为大型语言模型革命化多智能体环境，我们急切地寻找充满激情的合作伙伴与我们一起这一令人兴奋的旅程。
-
-### 您能如何贡献?
-- **代码开发**: 如果您是工程师，我们希望您能够帮助我们细化、优化和扩展当前的框架。我们一直在寻找有才华的开发者来增强我们现有的特性和开发新模块。
-
-- **文档和教程**: 如果您擅长写作，我们希望您能帮助我们改进文档，创建教程或写博客文章，使AgentVerse更容易被广大社区接受。
-
-- **应用探索**: 如果您对多智能体应用感兴趣，并渴望使用AgentVerse进行实验，我们会很高兴支持您的旅程并看到您创造的内容！
-
-- **反馈和建议**: 使用AgentVerse并为我们提供反馈。您的见解可以导致潜在的改进并确保我们的框架保持最佳状态。
-
-此外，如果您热衷于推进多智能体环境的前沿，并渴望更深入地进行研究，我们邀请您加入我们在THUNLP的团队。为了探索这一令人兴奋的机会，并与我们开始合作之旅，请联系[[email protected]]([email protected]) 和 [[email protected]]([email protected]) 表达您的兴趣。我们很乐意欢迎像您这样的有动力的个人加入我们的实验室！
-
-## 🗓 即将到来
-- [ ] 我们的[paper](https://arxiv.org/abs/2308.10848)的代码发布
-- [ ] 增加文档
-- [ ] 支持更复杂的对话历史内存
-- [ ] 支持本地LLM
-
-
-## 👾 Demo视频
-
-我们演示了由AgentVerse精心制作的以下案例。
-<!--
-### [![Demo video](https://i.imgur.com/vKb2F1B.png)](https://youtu.be/9JCVfzMFhaM)
--->
-<!--![image](imgs/multiagent-min.gif)-->
-
-<!-- - **NLP Classroom**: -->
-
-#### NLP教室
-在NLP课堂中，教授和学生进行互动交流。当学生有问题时，他们会举手并耐心等待教授指名。只有在教授点名后，学生才能发言并提问。
-
-使用以下命令启动NLP教室示例：
-```bash
-python main_demo.py --task nlp_classroom_9players
-```
-
-[NLP教室视频](https://github.com/OpenBMB/AgentVerse/assets/11704492/6ea07850-595e-4a28-a82e-f863011353c2)
-
-
-#### 囚徒困境
-囚���的困境是一个思考实验，它挑战两个完全理性的智能体面临的困境：他们可以与伙伴合作以获得互利，或背叛伙伴（"违背"）以获得个人奖励。
-
-使用以下命令启动NLP教室示例：
-```bash
-python main_demo.py --task prisoner_dilemma
-```
-
-[囚徒困境视频](https://github.com/OpenBMB/AgentVerse/assets/11704492/017c46e5-c738-4fca-9352-b008e2d518bd)
-
-
-#### 软件开发
-在软件设计示例中，代码编写者、代码测试者和代码审查者在代码生成问题上进行合作。给定一个问题，代码编写者首先撰写代码实现。代码测试者运行单元测试并提供反馈。然后，代码审查者生成评审。在收集了测试反馈和审查后，代码编写者迭代地优化代码。
-
-使用以下命令启动软件设计示例：
-```bash
-python main_demo.py --task sde_team/sde_team_2players
-```
-
-[软件开发视频](https://github.com/OpenBMB/AgentVerse/assets/11704492/5058066a-abee-490d-8659-b4e54661626a)
-
-
-#### [数据库运维](https://github.com/zhouxh19/AgentVerse_for_Database_Diagnosis)
-在数据库诊断场景中，首席DBA监控数据库系统以查找异常。如果检测到，会提醒内存和CPU智能体进行根源分析并建议优化解决方案。然后，首席DBA向用户提供总结的诊断，用户也可以通过给予指导或评估所提议解决方案的有效性来作出贡献。
-
-首先，您应该在BMTools中配置[数据库工具](https://github.com/OpenBMB/BMTools/blob/main/bmtools/tools/db_diag/readme.md), 并根据[指南](https://github.com/OpenBMB/BMTools/tree/main#211-local-tools)启动BMTools服务器。然后使用以下命令启动数据库管理员示例：
-```bash
-python main_demo.py --task db_diag
-```
-
-[数据库运维视频](https://github.com/OpenBMB/AgentVerse/assets/11704492/c633419d-afbb-47d4-bb12-6bb512e7af3a)
-
-#### [文本评估 (ChatEval)](https://github.com/chanchimin/ChatEval)
-在文本评估场景的背景下，我们建议用户探索[ChatEval](https://github.com/chanchimin/ChatEval)仓库。他们在AgentVerse上实现了一个多智能体裁判团来评估不同模型生成的文本质量。给定两个不同的文本，ChatEval中的角色可以自主地辩论其细微差别，并根据分配给他们的人物特点提供其判断。实验表明，他们的裁判团，根据[config.yaml](#2-configuring-the-agents)中规定的多样角色，与人类的评估更为接近。这个演示是基于[Fastchat](https://github.com/lm-sys/FastChat)仓库构建的，我们想对他们的基础工作表示感谢。
-
-
-[文本评估视频](https://github.com/OpenBMB/AgentVerse/assets/75533759/58f33468-f15b-4bac-ae01-8d0780019f85)
-
-#### 宝可梦游戏
-在这个简易游戏中，NPC之间可以自主互动。作为玩家，你扮演一个角色，可以随时与其他NPC互动。在这一游戏中有6个宝可梦绿宝石版中出现的角色: [May](https://bulbapedia.bulbagarden.net/wiki/May_(game)), [Professor Birch](https://bulbapedia.bulbagarden.net/wiki/Professor_Birch), [Steven Stone](https://bulbapedia.bulbagarden.net/wiki/Steven_Stone), [Maxie](https://bulbapedia.bulbagarden.net/wiki/Maxie), [Archie](https://bulbapedia.bulbagarden.net/wiki/Archie) 和[Joseph](https://bulbapedia.bulbagarden.net/wiki/Mr._Stone). 
-
-要启动宝可梦游戏，首先使用以下命令启动本地服务器：
-```bash
-uvicorn pokemon_server:app --reload --port 10002
-```
-然后在项目的根路径中打开另一个终端并运行以下命令：
-```bash
-cd ui
-# If you do not have npm installed, you need to install it before running the following commands 
-# https://docs.npmjs.com/downloading-and-installing-node-js-and-npm
-# We have tested on [email protected], [email protected]
-npm install
-npm run watch
-```
-等待编译完成。祝你玩得开心！(使用WASD移动，SPACE键启动对话。)
-
-[宝可梦游戏视频](https://github.com/OpenBMB/AgentVerse/assets/11704492/4d07da68-f942-4205-b558-f155e95782e7)
-
-
-
-## Contents
-
-- [✨ 特点](#-特点)
-- [📰 最新消息](#-最新消息)
-- [🌟 加入我们!](#-加入我们)
-  - [您能如何贡献?](#您能如何贡献)
-- [🗓 即将到来](#-即将到来)
-- [👾 Demo视频](#-demo视频)
-    - [NLP教室](#nlp教室)
-    - [囚徒困境](#囚徒困境)
-    - [软件开发](#软件开发)
-    - [数据库运维](#数据库运维)
-    - [文本评估 (ChatEval)](#文本评估-chateval)
-    - [宝可梦游戏](#宝可梦游戏)
-- [Contents](#contents)
-- [🚀 开始使用](#-开始使用)
-  - [安装](#安装)
-  - [命令行示例](#命令行示例)
-  - [本地网站演示](#本地网站演示)
-- [💡 理念](#-理念)
-  - [Environment](#environment)
-  - [智能体](#智能体)
-- [✍️ 定制您自己的环境](#️-定制您自己的环境)
-  - [一个简单的例子：构建一个教室环境](#一个简单的例子构建一个教室环境)
-      - [1. 创建任务目录并配置环境](#1-创建任务目录并配置环境)
-      - [2. 配置智能体](#2-配置智能体)
-      - [3. 编写一个���出解析器](#3-编写一个输出解析器)
-  - [更复杂环境的定制指南](#更复杂环境的定制指南)
-- [🔎 示例](#-示例)
-- [Star History](#star-history)
-- [Citation](#citation)
-- [Contact](#contact)
-
-
-
-## 🚀 开始使用
-
-### 安装
-
-```bash
-pip install -U agentverse
-```
-或者您可以通过手动克隆最新的仓库来安装此包：
-```bash
-git clone https://github.com/OpenBMB/AgentVerse.git --depth 1
-cd AgentVerse
-pip install -r requirements.txt
-```
-一些用户报告在安装`gradio`所需的`orjson`时遇到问题。一个简单的解决方法是使用Anaconda来安装它：`conda install -c conda-forge orjson`。
-
-您还需要按如下方式导出您的OpenAI API密钥：
-```bash
-# 导出你的OpenAI API密钥
-export OPENAI_API_KEY="your_api_key_here"
-```
-或者您想使用 Azure OpenAI 服务，请按照以下方式配置 OpenAI API 密钥和 API base：
-```bash
-export AZURE_OPENAI_API_KEY="your_api_key_here"
-export AZURE_OPENAI_API_BASE="your_api_base_here"
-```
-
-如果您想使用BMTools提供的工具，您需要按如下方式安装BMTools：
-```bash
-git clone git+https://github.com/OpenBMB/BMTools.git
-cd BMTools
-pip install -r requirements.txt
-python setup.py develop
-```
-
-### 命令行示例
-
-您可以创建由我们提供的多智能体环境。以教室场景为例。在这个场景中，有九个智能体，一个扮演教授的角色，其他八个是学生。
-
-```shell
-python3 main.py --task nlp_classroom_9players
-```
-
-### 本地网站演示
-
-我们还为这个环境提供了一个本地网站的演示。您可以用以下命令启动它：
-
-```shell
-python3 main_demo.py --task nlp_classroom_9players
-```
-成功启动本地服务器后，您可以访问[http://127.0.0.1:7860/](http://127.0.0.1:7860/) 查看教室环境。
-
-## 💡 理念
-
-### Environment
-
-我们框架的核心是环境，它在使研究人员能够在不同条件下研究智能体行为方面起着至关重要的作用。我们认为环境应该是灵活的和可扩展的，允许研究人员轻松地定制它以适应他们的需求。为了实现这一点，我们将环境抽象为五个规则组件，实现不同的环境实际上是实现不同的规则：
-
-- **Describer（描述器）**：此组件为每个智能体在每一轮提供环境的描述。您可以自定义描述器来定义他们的环境的具体要求，例如一个智能体可以与哪些智能体互动。
-- **Order（顺序）**：此组件定义智能体在环境中采取行动的顺序。您可以自定义顺序以反映智能体之间所需的交互。我们提供了几个基本的顺序选项，包括`random`（随机），`sequential`（连续）和`concurrent`（所有智能体在每轮都采取行动）。
-- **Selector（选择器）**：此组件选择由智能体生成的有效消息。有时智能体可能生成无效的响应，选择器用于过滤出意外的结果。
-- **Updater（更新器）**：此组件更新每个智能体的记忆。在某些情况下，一个智能体生成的响应不应被所有智能体看到（例如，如果智能体在不同的房间里）。对于每个响应，更新器只更新可以看到它的智能体。
-- **Visibility（可见性）**：此组件维护每个智能体在环境变化中可以看到的智能体列表。例如，当一个智能体从一个房间移动到另一个房间时，每个智能体的可见智能体列表应由`visibility`更新。
-
-通过将环境抽象为这五个组件，我们创建了一个高度灵活且可扩展的框架，使研究人员可以轻松地构建和定制自己的多智能体环境。
-
-### 智能体
-
-另一个基本组件是智能体。目前我们提供了两种类型的智能体：**ConversationAgent（对话智能体）** 和 **ToolAgent（工具智能体）**。您还可以通过继承BaseAgent类来自定义自己的智能体。
-
-## ✍️ 定制您自己的环境
-
-我们在`agentverse/tasks`目录中提供了几个示例。要定制您的环境，您应该
-
-1. 在`agentverse/tasks`中创建一个任务目录
-2. 编写配置文件
-3. 编写解析您智能体响应的输出解析器。
-4. 在`agentverse/tasks/__init__.py`中添加您的解析器
-
-我们将使用`agentverse/tasks/nlp_classroom_3players`中的一个简单示例来说明这个程序。
-
-### 一个简单的例子：构建一个教室环境
-
-为了说明如何定制您的环境，我们将使用一个简单的示例来构建一个教室环境，其中一个智能体是教授，一个是学生，一个是助教。
-
-##### 1. 创建任务目录并配置环境
-
-首先，我们需要创建一个任务目录并为环境编写我们的配置文件。在`agentverse/tasks`目录中，创建一个新目录，名为`nlp_classroom_3players`。在此目录中，创建一个`config.yaml`文件并写入以下配置：
-
-```yaml
-# config.yaml
-environment:
-  env_type: basic				# 使用AgentVerse中提供的基本环境
-  max_turns: 10					# 指定对话的最大轮数
-  rule:
-    order:
-      type: sequential	# 使用连续的顺序
-    visibility:
-      type: all					# 每条消息都可以被所有智能体看到
-    selector:
-      type: basic				# 基本选择器（不选择）
-    updater:
-      type: basic				# 基本更新器（将消息更新给所有智能体）
-    describer:
-      type: basic				# 基本描述器（无描述）
-```
-
-这个配置指定我们将使用AgentVerse中提供的基本环境，对话的最大轮数为10。我们将使用连续的顺序，所有消息对所有智能体都是可见的。我们不使用任何选择器，我们的更新器会将消息更新给所有的智能体，而我们的描述器不会提供任何描述。
-
-##### 2. 配置智能体
-
-接下来，我们将配置智能体。在`config.yaml`文件中，我们将为每个智能体添加配置。以下是教授的示例配置：
-
-```yaml
-# config.yaml
-agents:
-  -
-    agent_type: conversation
-    name: Professor Micheal		# 智能体的名称
-    role_description: You are Prof. Micheal, ...	# 智能体的描述
-    memory: 
-      memory_type: chat_history		# 将存储所有的聊天记录
-    prompt_template: *professor_prompt
-    llm:
-      llm_type: text-davinci-003    # 将使用OpenAICompletion LLM
-      model: text-davinci-003       # 传递给api调用的参数
-      temperature: 0.7
-      max_tokens: 250
-```
-
-在此示例中，我们将使用`conversation`智能体类型。我们为智能体指定了一个名称和描述，并将聊天记录存储在内存中。我们还提供了一个带有占位符的提示模板，这些占位符标记为${placeholder}。这些将由智能体的`_fill_prompt_template`方法实例化。
-
-##### 3. 编写一个输出解析器
-
-下一步是为您的智能体的响应编写一个简单的解析器。因为您可能已经在您的提示模板中指定了输出格式，所以您需要提供一个相应的解析器。在此示例中，我们在我们的提示模板中通知模型以以下格式输出
-
-```
-Action: Speak
-Action Input: (the content)
-```
-
-我们将编写一个解析器来从智能体的响应中提取内容。有关更多详细信息，请参考代码。我们使用`@output_parser_registry.register('classroom_parser')`修饰我们的解析器函数，以将其注册到我们的框架中。最后，我们在`agentverse/tasks/__init__.py`中导入我们的解析器。
-
-通过这些步骤，我们已经成功地构建了一个简单的教室环境，并根据我们的需求进行了定制。
-
-### 更复杂环境的定制指南
-
-虽然我们提供了一个基本框架来构建环境，使用我们的五个规则组件，但更复杂的环境可能需要进一步的定制。详细的文档和教程即将推出。在此，我们简要介绍如何定制您的环境的一些步骤：
-
-1. **定制五个规则组件**。每个规则组件都有一个接口，允许您根据特定的需求定制其行为。需要注意的是，这些组件并不一定是独立的，可以通过环境中的`rule_params`字典进行交互。您可以创建自己的规则组件，并与现有的组件集成，以构建智能体之间更复杂的交互。
-2. **定制环境本身**。我们的`basic`环境为五个规则组件提供了一个默认的执行顺序，适合大多数情况，但您可以继承`BaseEnvironment`类并编写自己的`run`方法来实现更复杂的执行顺序。
-3. **定制智能体**。根据您的特定用例，您可能还需要继承`BaseAgent`类。例如，您可能希望使用您的本地LLM作为智能体，或创建具有专门知识或技能的智能体。
-
-## 🔎 示例
-
-目前，我们在`agentverse/tasks`目录中提供了一些简单的示例，每个示例都展示了我们框架的不同可能性。尽管这些示例的性能可能由于有限的提示工程而不是最佳的，但它们旨在展示我们框架的能力，例如允许使用工具。
-
-以下是每个示例的简要概述：
-
-1. `nlp_classroom_3players`：此示例说明了智能体将按顺序交谈的简单情况。
-2. `nlp_classroom_9players`：这是一个NLP课堂示例。在这里，学生们可以在有问题时举手，教授可以叫学生让他们提问。只有在被叫到之后，学生才被允许说话。
-3. `nlp_classroom_9players_group`：此示例展示了小组讨论。必要时，教授可以发起小组讨论，学生们可以在讨论期间只与同一小组的同学交互。
-4. `nlp_classroom_3players_withtool`：在这个课堂中，学生们在听课时可以使用Bing搜索API。
-5. `math_problem_2players_tools`：一个简单的示例，展示了如何使用WolframAlpha API的两个智能体来玩算术游戏。
-6. `prisoner_dilema`：囚犯困境是一个涉及两个理性智能体面临的思想实验，他们可以选择为相互利益而合作，或为个人利益而背叛伙伴。
-7. `db_diag`：首席DBA（智能体）监控数据库系统中的异常，并在检测到任何异常时提醒内存和CPU智能体。他们（智能体）分析根本原因并建议优化解决方案。首席DBA（智能体）��用户提供诊断摘要，用户可以给出指示或评估所提议的解决方案的有效性。
-8. `sde_team`：在SDE团队中，代码编写者、代码测试者和代码审查者在代码生成问题上进行合作。
-9. `pokemon`：此示例模仿宝可梦游戏。
-
-
-## Star History
-
-[![Star History Chart](https://api.star-history.com/svg?repos=OpenBMB/AgentVerse&type=Date)](https://star-history.com/#OpenBMB/AgentVerse&Date)
-
-
-## Citation
-如果您在您的工作中使用了我们的框架，请使用以下形式进行引用
-```
-@misc{chen2023agentverse,
-      title={AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors in Agents}, 
-      author={Weize Chen and Yusheng Su and Jingwei Zuo and Cheng Yang and Chenfei Yuan and Chen Qian and Chi-Min Chan and Yujia Qin and Yaxi Lu and Ruobing Xie and Zhiyuan Liu and Maosong Sun and Jie Zhou},
-      year={2023},
-      eprint={2308.10848},
-      archivePrefix={arXiv},
-      primaryClass={cs.CL}
-}
-```
-
-## Contact
-
-陈纬泽: [email protected]
-
-[苏裕胜](https://yushengsu-thu.github.io/): [email protected]
-

spaces/AgentVerse/agentVerse/agentverse/environments/tasksolving_env/basic.py

@@ -1,144 +0,0 @@
-import asyncio
-from enum import Enum
-from typing import Any, Dict, List, Tuple, Union
-
-from colorama import Fore
-
-from agentverse.environments import BaseEnvironment
-from agentverse.agents.base import BaseAgent
-from agentverse.logging import logger
-from agentverse.message import Message, SolverMessage, ExecutorMessage
-
-
-from .. import env_registry as EnvironmentRegistry
-
-from agentverse.environments.tasksolving_env.rules import TasksolvingRule
-
-
-@EnvironmentRegistry.register("task-basic")
-class BasicEnvironment(BaseEnvironment):
-    rule: TasksolvingRule
-    agents: Dict[Enum, Union[BaseAgent, List[BaseAgent]]] = None
-
-    task_description: str
-
-    cnt_turn: int = 0
-    max_turn: int = 10
-    success: bool = False
-
-    def __init__(self, **kwargs):
-        rule_config = kwargs.pop("rule", {})
-        role_assigner_config = rule_config.pop(
-            "role_assigner", {"type": "role_description"}
-        )
-        decision_maker_config = rule_config.pop("decision_maker", {"type": "vertical"})
-        executor_config = rule_config.pop("executor", {"type": "none"})
-        evaluator_config = rule_config.pop("evaluator", {"type": "basic"})
-        rule = TasksolvingRule(
-            role_assigner_config=role_assigner_config,
-            decision_maker_config=decision_maker_config,
-            executor_config=executor_config,
-            evaluator_config=evaluator_config,
-        )
-        super().__init__(rule=rule, **kwargs)
-
-    async def step(
-        self, advice: str = "No advice yet.", previous_plan: str = "No solution yet."
-    ) -> List[Message]:
-        result = ""
-        logs = []
-
-        logger.info(f"Loop Round {self.cnt_turn}")
-
-        # ================== EXPERT RECRUITMENT ==================
-        agents = self.rule.role_assign(
-            self.task_description, self.agents, self.cnt_turn, advice
-        )
-        description = "\n".join([agent.role_description for agent in agents])
-        logs.append({"module": "Role Assigner", "content": description})
-        logger.info("", f"Role Assignment:\n{description}", Fore.CYAN)
-        # ================== EXPERT RECRUITMENT ==================
-
-        # ================== DECISION MAKING ==================
-        plan: List[SolverMessage] = await self.rule.decision_making(
-            self.task_description, self.agents, previous_plan, advice
-        )
-        flatten_plan = "\n".join([p.content for p in plan])
-        logs.append({"module": "Decision Maker", "content": flatten_plan})
-        logger.info("", f"Decision Plan:\n{flatten_plan}", Fore.YELLOW)
-        # ================== DECISION MAKING ==================
-
-        # ================== EXECUTION ==================
-        result: List[ExecutorMessage] = await self.rule.execute(
-            self.task_description, self.agents, plan
-        )
-        flatten_result = "\n".join([r.content for r in result])
-        logs.append({"module": "Executor", "content": flatten_result})
-        logger.info("", f"Execution Result:", Fore.GREEN)
-        logger.info("", flatten_result, Fore.GREEN)
-        # ================== EXECUTION ==================
-
-        # ================== EVALUATION ==================
-        score, advice = self.rule.evaluate(
-            self.task_description, self.agents, plan, result
-        )
-        logs.append(
-            {
-                "agent": "evaluator",
-                "content": f"Evaluation result: Score: {score}\nAdvice: {advice}",
-            }
-        )
-        logger.info(
-            "", f"Evaluation result:\nScore: {score}\nAdvice: {advice}", Fore.YELLOW
-        )
-
-        if score is not None and (
-            (isinstance(score, bool) and score is True)
-            or (isinstance(score, (list, tuple)) and all([s >= 8 for s in score]))
-        ):
-            # TODO: 8 is an arbitrary threshold
-            logs.append({"agent": "system", "content": "Good score! Accept!"})
-            logger.info(
-                "", f"Good score! Accept! Final Result:\n{flatten_plan}", Fore.GREEN
-            )
-            self.success = True
-        else:
-            logs.append({"agent": "system", "content": "Bad score! Reject!"})
-            logger.info("", "Bad score! Reject!", Fore.RED)
-        self.cnt_turn += 1
-        return flatten_result, advice, flatten_plan, logs, self.success
-
-    def iter_agents(self):
-        for role, agent_or_agents in self.agents.items():
-            if isinstance(agent_or_agents, list):
-                for agent in agent_or_agents:
-                    yield role, agent
-            else:
-                yield role, agent_or_agents
-
-    def get_spend(self):
-        total_spent = sum([agent.get_spend() for (_, agent) in self.iter_agents()])
-        return total_spent
-
-    def report_metrics(self) -> None:
-        logger.info("", "Agent spend:", Fore.GREEN)
-        for role, agent in self.iter_agents():
-            name = agent.name.split(":")[0]
-            logger.info(
-                "",
-                f"Agent (Role: {role}) {name}: {agent.get_spend_formatted()}",
-                Fore.GREEN,
-            )
-        logger.info("", f"Total spent: ${self.get_spend():.6f}", Fore.GREEN)
-
-    def is_done(self):
-        """Check if the environment is done"""
-        return self.cnt_turn >= self.max_turn or self.success
-
-    def set_task_description(self, task_description: str = ""):
-        self.task_description = task_description
-
-    def reset(self) -> None:
-        """Reset the environment"""
-        self.cnt_turn = 0
-        self.rule.reset()

spaces/Aki004/herta-so-vits/data_utils.py

@@ -1,155 +0,0 @@
-import time
-import os
-import random
-import numpy as np
-import torch
-import torch.utils.data
-
-import modules.commons as commons
-import utils
-from modules.mel_processing import spectrogram_torch, spec_to_mel_torch
-from utils import load_wav_to_torch, load_filepaths_and_text
-
-# import h5py
-
-
-"""Multi speaker version"""
-
-
-class TextAudioSpeakerLoader(torch.utils.data.Dataset):
-    """
-        1) loads audio, speaker_id, text pairs
-        2) normalizes text and converts them to sequences of integers
-        3) computes spectrograms from audio files.
-    """
-
-    def __init__(self, audiopaths, hparams, all_in_mem: bool = False):
-        self.audiopaths = load_filepaths_and_text(audiopaths)
-        self.max_wav_value = hparams.data.max_wav_value
-        self.sampling_rate = hparams.data.sampling_rate
-        self.filter_length = hparams.data.filter_length
-        self.hop_length = hparams.data.hop_length
-        self.win_length = hparams.data.win_length
-        self.sampling_rate = hparams.data.sampling_rate
-        self.use_sr = hparams.train.use_sr
-        self.spec_len = hparams.train.max_speclen
-        self.spk_map = hparams.spk
-
-        random.seed(1234)
-        random.shuffle(self.audiopaths)
-        
-        self.all_in_mem = all_in_mem
-        if self.all_in_mem:
-            self.cache = [self.get_audio(p[0]) for p in self.audiopaths]
-
-    def get_audio(self, filename):
-        filename = filename.replace("\\", "/")
-        audio, sampling_rate = load_wav_to_torch(filename)
-        if sampling_rate != self.sampling_rate:
-            raise ValueError("{} SR doesn't match target {} SR".format(
-                sampling_rate, self.sampling_rate))
-        audio_norm = audio / self.max_wav_value
-        audio_norm = audio_norm.unsqueeze(0)
-        spec_filename = filename.replace(".wav", ".spec.pt")
-
-        # Ideally, all data generated after Mar 25 should have .spec.pt
-        if os.path.exists(spec_filename):
-            spec = torch.load(spec_filename)
-        else:
-            spec = spectrogram_torch(audio_norm, self.filter_length,
-                                     self.sampling_rate, self.hop_length, self.win_length,
-                                     center=False)
-            spec = torch.squeeze(spec, 0)
-            torch.save(spec, spec_filename)
-
-        spk = filename.split("/")[-2]
-        spk = torch.LongTensor([self.spk_map[spk]])
-
-        f0 = np.load(filename + ".f0.npy")
-        f0, uv = utils.interpolate_f0(f0)
-        f0 = torch.FloatTensor(f0)
-        uv = torch.FloatTensor(uv)
-
-        c = torch.load(filename+ ".soft.pt")
-        c = utils.repeat_expand_2d(c.squeeze(0), f0.shape[0])
-
-
-        lmin = min(c.size(-1), spec.size(-1))
-        assert abs(c.size(-1) - spec.size(-1)) < 3, (c.size(-1), spec.size(-1), f0.shape, filename)
-        assert abs(audio_norm.shape[1]-lmin * self.hop_length) < 3 * self.hop_length
-        spec, c, f0, uv = spec[:, :lmin], c[:, :lmin], f0[:lmin], uv[:lmin]
-        audio_norm = audio_norm[:, :lmin * self.hop_length]
-
-        return c, f0, spec, audio_norm, spk, uv
-
-    def random_slice(self, c, f0, spec, audio_norm, spk, uv):
-        # if spec.shape[1] < 30:
-        #     print("skip too short audio:", filename)
-        #     return None
-        if spec.shape[1] > 800:
-            start = random.randint(0, spec.shape[1]-800)
-            end = start + 790
-            spec, c, f0, uv = spec[:, start:end], c[:, start:end], f0[start:end], uv[start:end]
-            audio_norm = audio_norm[:, start * self.hop_length : end * self.hop_length]
-
-        return c, f0, spec, audio_norm, spk, uv
-
-    def __getitem__(self, index):
-        if self.all_in_mem:
-            return self.random_slice(*self.cache[index])
-        else:
-            return self.random_slice(*self.get_audio(self.audiopaths[index][0]))
-
-    def __len__(self):
-        return len(self.audiopaths)
-
-
-class TextAudioCollate:
-
-    def __call__(self, batch):
-        batch = [b for b in batch if b is not None]
-
-        input_lengths, ids_sorted_decreasing = torch.sort(
-            torch.LongTensor([x[0].shape[1] for x in batch]),
-            dim=0, descending=True)
-
-        max_c_len = max([x[0].size(1) for x in batch])
-        max_wav_len = max([x[3].size(1) for x in batch])
-
-        lengths = torch.LongTensor(len(batch))
-
-        c_padded = torch.FloatTensor(len(batch), batch[0][0].shape[0], max_c_len)
-        f0_padded = torch.FloatTensor(len(batch), max_c_len)
-        spec_padded = torch.FloatTensor(len(batch), batch[0][2].shape[0], max_c_len)
-        wav_padded = torch.FloatTensor(len(batch), 1, max_wav_len)
-        spkids = torch.LongTensor(len(batch), 1)
-        uv_padded = torch.FloatTensor(len(batch), max_c_len)
-
-        c_padded.zero_()
-        spec_padded.zero_()
-        f0_padded.zero_()
-        wav_padded.zero_()
-        uv_padded.zero_()
-
-        for i in range(len(ids_sorted_decreasing)):
-            row = batch[ids_sorted_decreasing[i]]
-
-            c = row[0]
-            c_padded[i, :, :c.size(1)] = c
-            lengths[i] = c.size(1)
-
-            f0 = row[1]
-            f0_padded[i, :f0.size(0)] = f0
-
-            spec = row[2]
-            spec_padded[i, :, :spec.size(1)] = spec
-
-            wav = row[3]
-            wav_padded[i, :, :wav.size(1)] = wav
-
-            spkids[i, 0] = row[4]
-
-            uv = row[5]
-            uv_padded[i, :uv.size(0)] = uv
-
-        return c_padded, f0_padded, spec_padded, wav_padded, spkids, lengths, uv_padded

spaces/AlexWang/lama/bin/evaluator_example.py

@@ -1,76 +0,0 @@
-import os
-
-import cv2
-import numpy as np
-import torch
-from skimage import io
-from skimage.transform import resize
-from torch.utils.data import Dataset
-
-from saicinpainting.evaluation.evaluator import InpaintingEvaluator
-from saicinpainting.evaluation.losses.base_loss import SSIMScore, LPIPSScore, FIDScore
-
-
-class SimpleImageDataset(Dataset):
-    def __init__(self, root_dir, image_size=(400, 600)):
-        self.root_dir = root_dir
-        self.files = sorted(os.listdir(root_dir))
-        self.image_size = image_size
-
-    def __getitem__(self, index):
-        img_name = os.path.join(self.root_dir, self.files[index])
-        image = io.imread(img_name)
-        image = resize(image, self.image_size, anti_aliasing=True)
-        image = torch.FloatTensor(image).permute(2, 0, 1)
-        return image
-
-    def __len__(self):
-        return len(self.files)
-
-
-def create_rectangle_mask(height, width):
-    mask = np.ones((height, width))
-    up_left_corner = width // 4, height // 4
-    down_right_corner = (width - up_left_corner[0] - 1, height - up_left_corner[1] - 1)
-    cv2.rectangle(mask, up_left_corner, down_right_corner, (0, 0, 0), thickness=cv2.FILLED)
-    return mask
-
-
-class Model():
-    def __call__(self, img_batch, mask_batch):
-        mean = (img_batch * mask_batch[:, None, :, :]).sum(dim=(2, 3)) / mask_batch.sum(dim=(1, 2))[:, None]
-        inpainted = mean[:, :, None, None] * (1 - mask_batch[:, None, :, :]) + img_batch * mask_batch[:, None, :, :]
-        return inpainted
-
-
-class SimpleImageSquareMaskDataset(Dataset):
-    def __init__(self, dataset):
-        self.dataset = dataset
-        self.mask = torch.FloatTensor(create_rectangle_mask(*self.dataset.image_size))
-        self.model = Model()
-
-    def __getitem__(self, index):
-        img = self.dataset[index]
-        mask = self.mask.clone()
-        inpainted = self.model(img[None, ...], mask[None, ...])
-        return dict(image=img, mask=mask, inpainted=inpainted)
-
-    def __len__(self):
-        return len(self.dataset)
-
-
-dataset = SimpleImageDataset('imgs')
-mask_dataset = SimpleImageSquareMaskDataset(dataset)
-model = Model()
-metrics = {
-    'ssim': SSIMScore(),
-    'lpips': LPIPSScore(),
-    'fid': FIDScore()
-}
-
-evaluator = InpaintingEvaluator(
-    mask_dataset, scores=metrics, batch_size=3, area_grouping=True
-)
-
-results = evaluator.evaluate(model)
-print(results)

spaces/AlexWang/lama/saicinpainting/evaluation/vis.py

@@ -1,37 +0,0 @@
-import numpy as np
-from skimage import io
-from skimage.segmentation import mark_boundaries
-
-
-def save_item_for_vis(item, out_file):
-    mask = item['mask'] > 0.5
-    if mask.ndim == 3:
-        mask = mask[0]
-    img = mark_boundaries(np.transpose(item['image'], (1, 2, 0)),
-                          mask,
-                          color=(1., 0., 0.),
-                          outline_color=(1., 1., 1.),
-                          mode='thick')
-
-    if 'inpainted' in item:
-        inp_img = mark_boundaries(np.transpose(item['inpainted'], (1, 2, 0)),
-                                  mask,
-                                  color=(1., 0., 0.),
-                                  mode='outer')
-        img = np.concatenate((img, inp_img), axis=1)
-
-    img = np.clip(img * 255, 0, 255).astype('uint8')
-    io.imsave(out_file, img)
-
-
-def save_mask_for_sidebyside(item, out_file):
-    mask = item['mask']# > 0.5
-    if mask.ndim == 3:
-        mask = mask[0]
-    mask = np.clip(mask * 255, 0, 255).astype('uint8')
-    io.imsave(out_file, mask)
-
-def save_img_for_sidebyside(item, out_file):
-    img = np.transpose(item['image'], (1, 2, 0))
-    img = np.clip(img * 255, 0, 255).astype('uint8')
-    io.imsave(out_file, img)

spaces/AlowaSawsan/Third-Molar-Segmentation/README.md

@@ -1,12 +0,0 @@
----
-title: Third Molar Segmentation
-emoji: 🏢
-colorFrom: indigo
-colorTo: green
-sdk: streamlit
-sdk_version: 1.2.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/tests/schedulers/test_schedulers.py

@@ -1,722 +0,0 @@
-# coding=utf-8
-# Copyright 2023 HuggingFace Inc.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-import inspect
-import json
-import os
-import tempfile
-import unittest
-from typing import Dict, List, Tuple
-
-import numpy as np
-import torch
-
-import diffusers
-from diffusers import (
-    CMStochasticIterativeScheduler,
-    DDIMScheduler,
-    DEISMultistepScheduler,
-    DiffusionPipeline,
-    EulerAncestralDiscreteScheduler,
-    EulerDiscreteScheduler,
-    IPNDMScheduler,
-    LMSDiscreteScheduler,
-    UniPCMultistepScheduler,
-    VQDiffusionScheduler,
-    logging,
-)
-from diffusers.configuration_utils import ConfigMixin, register_to_config
-from diffusers.schedulers.scheduling_utils import SchedulerMixin
-from diffusers.utils import torch_device
-from diffusers.utils.testing_utils import CaptureLogger
-
-
-torch.backends.cuda.matmul.allow_tf32 = False
-
-
-class SchedulerObject(SchedulerMixin, ConfigMixin):
-    config_name = "config.json"
-
-    @register_to_config
-    def __init__(
-        self,
-        a=2,
-        b=5,
-        c=(2, 5),
-        d="for diffusion",
-        e=[1, 3],
-    ):
-        pass
-
-
-class SchedulerObject2(SchedulerMixin, ConfigMixin):
-    config_name = "config.json"
-
-    @register_to_config
-    def __init__(
-        self,
-        a=2,
-        b=5,
-        c=(2, 5),
-        d="for diffusion",
-        f=[1, 3],
-    ):
-        pass
-
-
-class SchedulerObject3(SchedulerMixin, ConfigMixin):
-    config_name = "config.json"
-
-    @register_to_config
-    def __init__(
-        self,
-        a=2,
-        b=5,
-        c=(2, 5),
-        d="for diffusion",
-        e=[1, 3],
-        f=[1, 3],
-    ):
-        pass
-
-
-class SchedulerBaseTests(unittest.TestCase):
-    def test_save_load_from_different_config(self):
-        obj = SchedulerObject()
-
-        # mock add obj class to `diffusers`
-        setattr(diffusers, "SchedulerObject", SchedulerObject)
-        logger = logging.get_logger("diffusers.configuration_utils")
-
-        with tempfile.TemporaryDirectory() as tmpdirname:
-            obj.save_config(tmpdirname)
-            with CaptureLogger(logger) as cap_logger_1:
-                config = SchedulerObject2.load_config(tmpdirname)
-                new_obj_1 = SchedulerObject2.from_config(config)
-
-            # now save a config parameter that is not expected
-            with open(os.path.join(tmpdirname, SchedulerObject.config_name), "r") as f:
-                data = json.load(f)
-                data["unexpected"] = True
-
-            with open(os.path.join(tmpdirname, SchedulerObject.config_name), "w") as f:
-                json.dump(data, f)
-
-            with CaptureLogger(logger) as cap_logger_2:
-                config = SchedulerObject.load_config(tmpdirname)
-                new_obj_2 = SchedulerObject.from_config(config)
-
-            with CaptureLogger(logger) as cap_logger_3:
-                config = SchedulerObject2.load_config(tmpdirname)
-                new_obj_3 = SchedulerObject2.from_config(config)
-
-        assert new_obj_1.__class__ == SchedulerObject2
-        assert new_obj_2.__class__ == SchedulerObject
-        assert new_obj_3.__class__ == SchedulerObject2
-
-        assert cap_logger_1.out == ""
-        assert (
-            cap_logger_2.out
-            == "The config attributes {'unexpected': True} were passed to SchedulerObject, but are not expected and"
-            " will"
-            " be ignored. Please verify your config.json configuration file.\n"
-        )
-        assert cap_logger_2.out.replace("SchedulerObject", "SchedulerObject2") == cap_logger_3.out
-
-    def test_save_load_compatible_schedulers(self):
-        SchedulerObject2._compatibles = ["SchedulerObject"]
-        SchedulerObject._compatibles = ["SchedulerObject2"]
-
-        obj = SchedulerObject()
-
-        # mock add obj class to `diffusers`
-        setattr(diffusers, "SchedulerObject", SchedulerObject)
-        setattr(diffusers, "SchedulerObject2", SchedulerObject2)
-        logger = logging.get_logger("diffusers.configuration_utils")
-
-        with tempfile.TemporaryDirectory() as tmpdirname:
-            obj.save_config(tmpdirname)
-
-            # now save a config parameter that is expected by another class, but not origin class
-            with open(os.path.join(tmpdirname, SchedulerObject.config_name), "r") as f:
-                data = json.load(f)
-                data["f"] = [0, 0]
-                data["unexpected"] = True
-
-            with open(os.path.join(tmpdirname, SchedulerObject.config_name), "w") as f:
-                json.dump(data, f)
-
-            with CaptureLogger(logger) as cap_logger:
-                config = SchedulerObject.load_config(tmpdirname)
-                new_obj = SchedulerObject.from_config(config)
-
-        assert new_obj.__class__ == SchedulerObject
-
-        assert (
-            cap_logger.out
-            == "The config attributes {'unexpected': True} were passed to SchedulerObject, but are not expected and"
-            " will"
-            " be ignored. Please verify your config.json configuration file.\n"
-        )
-
-    def test_save_load_from_different_config_comp_schedulers(self):
-        SchedulerObject3._compatibles = ["SchedulerObject", "SchedulerObject2"]
-        SchedulerObject2._compatibles = ["SchedulerObject", "SchedulerObject3"]
-        SchedulerObject._compatibles = ["SchedulerObject2", "SchedulerObject3"]
-
-        obj = SchedulerObject()
-
-        # mock add obj class to `diffusers`
-        setattr(diffusers, "SchedulerObject", SchedulerObject)
-        setattr(diffusers, "SchedulerObject2", SchedulerObject2)
-        setattr(diffusers, "SchedulerObject3", SchedulerObject3)
-        logger = logging.get_logger("diffusers.configuration_utils")
-        logger.setLevel(diffusers.logging.INFO)
-
-        with tempfile.TemporaryDirectory() as tmpdirname:
-            obj.save_config(tmpdirname)
-
-            with CaptureLogger(logger) as cap_logger_1:
-                config = SchedulerObject.load_config(tmpdirname)
-                new_obj_1 = SchedulerObject.from_config(config)
-
-            with CaptureLogger(logger) as cap_logger_2:
-                config = SchedulerObject2.load_config(tmpdirname)
-                new_obj_2 = SchedulerObject2.from_config(config)
-
-            with CaptureLogger(logger) as cap_logger_3:
-                config = SchedulerObject3.load_config(tmpdirname)
-                new_obj_3 = SchedulerObject3.from_config(config)
-
-        assert new_obj_1.__class__ == SchedulerObject
-        assert new_obj_2.__class__ == SchedulerObject2
-        assert new_obj_3.__class__ == SchedulerObject3
-
-        assert cap_logger_1.out == ""
-        assert cap_logger_2.out == "{'f'} was not found in config. Values will be initialized to default values.\n"
-        assert cap_logger_3.out == "{'f'} was not found in config. Values will be initialized to default values.\n"
-
-    def test_default_arguments_not_in_config(self):
-        pipe = DiffusionPipeline.from_pretrained(
-            "hf-internal-testing/tiny-stable-diffusion-pipe", torch_dtype=torch.float16
-        )
-        assert pipe.scheduler.__class__ == DDIMScheduler
-
-        # Default for DDIMScheduler
-        assert pipe.scheduler.config.timestep_spacing == "leading"
-
-        # Switch to a different one, verify we use the default for that class
-        pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config)
-        assert pipe.scheduler.config.timestep_spacing == "linspace"
-
-        # Override with kwargs
-        pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing")
-        assert pipe.scheduler.config.timestep_spacing == "trailing"
-
-        # Verify overridden kwargs stick
-        pipe.scheduler = LMSDiscreteScheduler.from_config(pipe.scheduler.config)
-        assert pipe.scheduler.config.timestep_spacing == "trailing"
-
-        # And stick
-        pipe.scheduler = LMSDiscreteScheduler.from_config(pipe.scheduler.config)
-        assert pipe.scheduler.config.timestep_spacing == "trailing"
-
-    def test_default_solver_type_after_switch(self):
-        pipe = DiffusionPipeline.from_pretrained(
-            "hf-internal-testing/tiny-stable-diffusion-pipe", torch_dtype=torch.float16
-        )
-        assert pipe.scheduler.__class__ == DDIMScheduler
-
-        pipe.scheduler = DEISMultistepScheduler.from_config(pipe.scheduler.config)
-        assert pipe.scheduler.config.solver_type == "logrho"
-
-        # Switch to UniPC, verify the solver is the default
-        pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
-        assert pipe.scheduler.config.solver_type == "bh2"
-
-
-class SchedulerCommonTest(unittest.TestCase):
-    scheduler_classes = ()
-    forward_default_kwargs = ()
-
-    @property
-    def dummy_sample(self):
-        batch_size = 4
-        num_channels = 3
-        height = 8
-        width = 8
-
-        sample = torch.rand((batch_size, num_channels, height, width))
-
-        return sample
-
-    @property
-    def dummy_sample_deter(self):
-        batch_size = 4
-        num_channels = 3
-        height = 8
-        width = 8
-
-        num_elems = batch_size * num_channels * height * width
-        sample = torch.arange(num_elems)
-        sample = sample.reshape(num_channels, height, width, batch_size)
-        sample = sample / num_elems
-        sample = sample.permute(3, 0, 1, 2)
-
-        return sample
-
-    def get_scheduler_config(self):
-        raise NotImplementedError
-
-    def dummy_model(self):
-        def model(sample, t, *args):
-            # if t is a tensor, match the number of dimensions of sample
-            if isinstance(t, torch.Tensor):
-                num_dims = len(sample.shape)
-                # pad t with 1s to match num_dims
-                t = t.reshape(-1, *(1,) * (num_dims - 1)).to(sample.device).to(sample.dtype)
-
-            return sample * t / (t + 1)
-
-        return model
-
-    def check_over_configs(self, time_step=0, **config):
-        kwargs = dict(self.forward_default_kwargs)
-
-        num_inference_steps = kwargs.pop("num_inference_steps", None)
-
-        for scheduler_class in self.scheduler_classes:
-            # TODO(Suraj) - delete the following two lines once DDPM, DDIM, and PNDM have timesteps casted to float by default
-            if scheduler_class in (EulerAncestralDiscreteScheduler, EulerDiscreteScheduler, LMSDiscreteScheduler):
-                time_step = float(time_step)
-
-            scheduler_config = self.get_scheduler_config(**config)
-            scheduler = scheduler_class(**scheduler_config)
-
-            if scheduler_class == CMStochasticIterativeScheduler:
-                # Get valid timestep based on sigma_max, which should always be in timestep schedule.
-                scaled_sigma_max = scheduler.sigma_to_t(scheduler.config.sigma_max)
-                time_step = scaled_sigma_max
-
-            if scheduler_class == VQDiffusionScheduler:
-                num_vec_classes = scheduler_config["num_vec_classes"]
-                sample = self.dummy_sample(num_vec_classes)
-                model = self.dummy_model(num_vec_classes)
-                residual = model(sample, time_step)
-            else:
-                sample = self.dummy_sample
-                residual = 0.1 * sample
-
-            with tempfile.TemporaryDirectory() as tmpdirname:
-                scheduler.save_config(tmpdirname)
-                new_scheduler = scheduler_class.from_pretrained(tmpdirname)
-
-            if num_inference_steps is not None and hasattr(scheduler, "set_timesteps"):
-                scheduler.set_timesteps(num_inference_steps)
-                new_scheduler.set_timesteps(num_inference_steps)
-            elif num_inference_steps is not None and not hasattr(scheduler, "set_timesteps"):
-                kwargs["num_inference_steps"] = num_inference_steps
-
-            # Make sure `scale_model_input` is invoked to prevent a warning
-            if scheduler_class == CMStochasticIterativeScheduler:
-                # Get valid timestep based on sigma_max, which should always be in timestep schedule.
-                _ = scheduler.scale_model_input(sample, scaled_sigma_max)
-                _ = new_scheduler.scale_model_input(sample, scaled_sigma_max)
-            elif scheduler_class != VQDiffusionScheduler:
-                _ = scheduler.scale_model_input(sample, 0)
-                _ = new_scheduler.scale_model_input(sample, 0)
-
-            # Set the seed before step() as some schedulers are stochastic like EulerAncestralDiscreteScheduler, EulerDiscreteScheduler
-            if "generator" in set(inspect.signature(scheduler.step).parameters.keys()):
-                kwargs["generator"] = torch.manual_seed(0)
-            output = scheduler.step(residual, time_step, sample, **kwargs).prev_sample
-
-            if "generator" in set(inspect.signature(scheduler.step).parameters.keys()):
-                kwargs["generator"] = torch.manual_seed(0)
-            new_output = new_scheduler.step(residual, time_step, sample, **kwargs).prev_sample
-
-            assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical"
-
-    def check_over_forward(self, time_step=0, **forward_kwargs):
-        kwargs = dict(self.forward_default_kwargs)
-        kwargs.update(forward_kwargs)
-
-        num_inference_steps = kwargs.pop("num_inference_steps", None)
-
-        for scheduler_class in self.scheduler_classes:
-            if scheduler_class in (EulerAncestralDiscreteScheduler, EulerDiscreteScheduler, LMSDiscreteScheduler):
-                time_step = float(time_step)
-
-            scheduler_config = self.get_scheduler_config()
-            scheduler = scheduler_class(**scheduler_config)
-
-            if scheduler_class == VQDiffusionScheduler:
-                num_vec_classes = scheduler_config["num_vec_classes"]
-                sample = self.dummy_sample(num_vec_classes)
-                model = self.dummy_model(num_vec_classes)
-                residual = model(sample, time_step)
-            else:
-                sample = self.dummy_sample
-                residual = 0.1 * sample
-
-            with tempfile.TemporaryDirectory() as tmpdirname:
-                scheduler.save_config(tmpdirname)
-                new_scheduler = scheduler_class.from_pretrained(tmpdirname)
-
-            if num_inference_steps is not None and hasattr(scheduler, "set_timesteps"):
-                scheduler.set_timesteps(num_inference_steps)
-                new_scheduler.set_timesteps(num_inference_steps)
-            elif num_inference_steps is not None and not hasattr(scheduler, "set_timesteps"):
-                kwargs["num_inference_steps"] = num_inference_steps
-
-            if "generator" in set(inspect.signature(scheduler.step).parameters.keys()):
-                kwargs["generator"] = torch.manual_seed(0)
-            output = scheduler.step(residual, time_step, sample, **kwargs).prev_sample
-
-            if "generator" in set(inspect.signature(scheduler.step).parameters.keys()):
-                kwargs["generator"] = torch.manual_seed(0)
-            new_output = new_scheduler.step(residual, time_step, sample, **kwargs).prev_sample
-
-            assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical"
-
-    def test_from_save_pretrained(self):
-        kwargs = dict(self.forward_default_kwargs)
-
-        num_inference_steps = kwargs.pop("num_inference_steps", None)
-
-        for scheduler_class in self.scheduler_classes:
-            timestep = 1
-            if scheduler_class in (EulerAncestralDiscreteScheduler, EulerDiscreteScheduler, LMSDiscreteScheduler):
-                timestep = float(timestep)
-
-            scheduler_config = self.get_scheduler_config()
-            scheduler = scheduler_class(**scheduler_config)
-
-            if scheduler_class == CMStochasticIterativeScheduler:
-                # Get valid timestep based on sigma_max, which should always be in timestep schedule.
-                timestep = scheduler.sigma_to_t(scheduler.config.sigma_max)
-
-            if scheduler_class == VQDiffusionScheduler:
-                num_vec_classes = scheduler_config["num_vec_classes"]
-                sample = self.dummy_sample(num_vec_classes)
-                model = self.dummy_model(num_vec_classes)
-                residual = model(sample, timestep)
-            else:
-                sample = self.dummy_sample
-                residual = 0.1 * sample
-
-            with tempfile.TemporaryDirectory() as tmpdirname:
-                scheduler.save_config(tmpdirname)
-                new_scheduler = scheduler_class.from_pretrained(tmpdirname)
-
-            if num_inference_steps is not None and hasattr(scheduler, "set_timesteps"):
-                scheduler.set_timesteps(num_inference_steps)
-                new_scheduler.set_timesteps(num_inference_steps)
-            elif num_inference_steps is not None and not hasattr(scheduler, "set_timesteps"):
-                kwargs["num_inference_steps"] = num_inference_steps
-
-            if "generator" in set(inspect.signature(scheduler.step).parameters.keys()):
-                kwargs["generator"] = torch.manual_seed(0)
-            output = scheduler.step(residual, timestep, sample, **kwargs).prev_sample
-
-            if "generator" in set(inspect.signature(scheduler.step).parameters.keys()):
-                kwargs["generator"] = torch.manual_seed(0)
-            new_output = new_scheduler.step(residual, timestep, sample, **kwargs).prev_sample
-
-            assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical"
-
-    def test_compatibles(self):
-        for scheduler_class in self.scheduler_classes:
-            scheduler_config = self.get_scheduler_config()
-
-            scheduler = scheduler_class(**scheduler_config)
-
-            assert all(c is not None for c in scheduler.compatibles)
-
-            for comp_scheduler_cls in scheduler.compatibles:
-                comp_scheduler = comp_scheduler_cls.from_config(scheduler.config)
-                assert comp_scheduler is not None
-
-            new_scheduler = scheduler_class.from_config(comp_scheduler.config)
-
-            new_scheduler_config = {k: v for k, v in new_scheduler.config.items() if k in scheduler.config}
-            scheduler_diff = {k: v for k, v in new_scheduler.config.items() if k not in scheduler.config}
-
-            # make sure that configs are essentially identical
-            assert new_scheduler_config == dict(scheduler.config)
-
-            # make sure that only differences are for configs that are not in init
-            init_keys = inspect.signature(scheduler_class.__init__).parameters.keys()
-            assert set(scheduler_diff.keys()).intersection(set(init_keys)) == set()
-
-    def test_from_pretrained(self):
-        for scheduler_class in self.scheduler_classes:
-            scheduler_config = self.get_scheduler_config()
-
-            scheduler = scheduler_class(**scheduler_config)
-
-            with tempfile.TemporaryDirectory() as tmpdirname:
-                scheduler.save_pretrained(tmpdirname)
-                new_scheduler = scheduler_class.from_pretrained(tmpdirname)
-
-            # `_use_default_values` should not exist for just saved & loaded scheduler
-            scheduler_config = dict(scheduler.config)
-            del scheduler_config["_use_default_values"]
-
-            assert scheduler_config == new_scheduler.config
-
-    def test_step_shape(self):
-        kwargs = dict(self.forward_default_kwargs)
-
-        num_inference_steps = kwargs.pop("num_inference_steps", None)
-
-        timestep_0 = 0
-        timestep_1 = 1
-
-        for scheduler_class in self.scheduler_classes:
-            if scheduler_class in (EulerAncestralDiscreteScheduler, EulerDiscreteScheduler, LMSDiscreteScheduler):
-                timestep_0 = float(timestep_0)
-                timestep_1 = float(timestep_1)
-
-            scheduler_config = self.get_scheduler_config()
-            scheduler = scheduler_class(**scheduler_config)
-
-            if scheduler_class == VQDiffusionScheduler:
-                num_vec_classes = scheduler_config["num_vec_classes"]
-                sample = self.dummy_sample(num_vec_classes)
-                model = self.dummy_model(num_vec_classes)
-                residual = model(sample, timestep_0)
-            else:
-                sample = self.dummy_sample
-                residual = 0.1 * sample
-
-            if num_inference_steps is not None and hasattr(scheduler, "set_timesteps"):
-                scheduler.set_timesteps(num_inference_steps)
-            elif num_inference_steps is not None and not hasattr(scheduler, "set_timesteps"):
-                kwargs["num_inference_steps"] = num_inference_steps
-
-            output_0 = scheduler.step(residual, timestep_0, sample, **kwargs).prev_sample
-            output_1 = scheduler.step(residual, timestep_1, sample, **kwargs).prev_sample
-
-            self.assertEqual(output_0.shape, sample.shape)
-            self.assertEqual(output_0.shape, output_1.shape)
-
-    def test_scheduler_outputs_equivalence(self):
-        def set_nan_tensor_to_zero(t):
-            t[t != t] = 0
-            return t
-
-        def recursive_check(tuple_object, dict_object):
-            if isinstance(tuple_object, (List, Tuple)):
-                for tuple_iterable_value, dict_iterable_value in zip(tuple_object, dict_object.values()):
-                    recursive_check(tuple_iterable_value, dict_iterable_value)
-            elif isinstance(tuple_object, Dict):
-                for tuple_iterable_value, dict_iterable_value in zip(tuple_object.values(), dict_object.values()):
-                    recursive_check(tuple_iterable_value, dict_iterable_value)
-            elif tuple_object is None:
-                return
-            else:
-                self.assertTrue(
-                    torch.allclose(
-                        set_nan_tensor_to_zero(tuple_object), set_nan_tensor_to_zero(dict_object), atol=1e-5
-                    ),
-                    msg=(
-                        "Tuple and dict output are not equal. Difference:"
-                        f" {torch.max(torch.abs(tuple_object - dict_object))}. Tuple has `nan`:"
-                        f" {torch.isnan(tuple_object).any()} and `inf`: {torch.isinf(tuple_object)}. Dict has"
-                        f" `nan`: {torch.isnan(dict_object).any()} and `inf`: {torch.isinf(dict_object)}."
-                    ),
-                )
-
-        kwargs = dict(self.forward_default_kwargs)
-        num_inference_steps = kwargs.pop("num_inference_steps", 50)
-
-        timestep = 0
-        if len(self.scheduler_classes) > 0 and self.scheduler_classes[0] == IPNDMScheduler:
-            timestep = 1
-
-        for scheduler_class in self.scheduler_classes:
-            if scheduler_class in (EulerAncestralDiscreteScheduler, EulerDiscreteScheduler, LMSDiscreteScheduler):
-                timestep = float(timestep)
-
-            scheduler_config = self.get_scheduler_config()
-            scheduler = scheduler_class(**scheduler_config)
-
-            if scheduler_class == CMStochasticIterativeScheduler:
-                # Get valid timestep based on sigma_max, which should always be in timestep schedule.
-                timestep = scheduler.sigma_to_t(scheduler.config.sigma_max)
-
-            if scheduler_class == VQDiffusionScheduler:
-                num_vec_classes = scheduler_config["num_vec_classes"]
-                sample = self.dummy_sample(num_vec_classes)
-                model = self.dummy_model(num_vec_classes)
-                residual = model(sample, timestep)
-            else:
-                sample = self.dummy_sample
-                residual = 0.1 * sample
-
-            if num_inference_steps is not None and hasattr(scheduler, "set_timesteps"):
-                scheduler.set_timesteps(num_inference_steps)
-            elif num_inference_steps is not None and not hasattr(scheduler, "set_timesteps"):
-                kwargs["num_inference_steps"] = num_inference_steps
-
-            # Set the seed before state as some schedulers are stochastic like EulerAncestralDiscreteScheduler, EulerDiscreteScheduler
-            if "generator" in set(inspect.signature(scheduler.step).parameters.keys()):
-                kwargs["generator"] = torch.manual_seed(0)
-            outputs_dict = scheduler.step(residual, timestep, sample, **kwargs)
-
-            if num_inference_steps is not None and hasattr(scheduler, "set_timesteps"):
-                scheduler.set_timesteps(num_inference_steps)
-            elif num_inference_steps is not None and not hasattr(scheduler, "set_timesteps"):
-                kwargs["num_inference_steps"] = num_inference_steps
-
-            # Set the seed before state as some schedulers are stochastic like EulerAncestralDiscreteScheduler, EulerDiscreteScheduler
-            if "generator" in set(inspect.signature(scheduler.step).parameters.keys()):
-                kwargs["generator"] = torch.manual_seed(0)
-            outputs_tuple = scheduler.step(residual, timestep, sample, return_dict=False, **kwargs)
-
-            recursive_check(outputs_tuple, outputs_dict)
-
-    def test_scheduler_public_api(self):
-        for scheduler_class in self.scheduler_classes:
-            scheduler_config = self.get_scheduler_config()
-            scheduler = scheduler_class(**scheduler_config)
-
-            if scheduler_class != VQDiffusionScheduler:
-                self.assertTrue(
-                    hasattr(scheduler, "init_noise_sigma"),
-                    f"{scheduler_class} does not implement a required attribute `init_noise_sigma`",
-                )
-                self.assertTrue(
-                    hasattr(scheduler, "scale_model_input"),
-                    (
-                        f"{scheduler_class} does not implement a required class method `scale_model_input(sample,"
-                        " timestep)`"
-                    ),
-                )
-            self.assertTrue(
-                hasattr(scheduler, "step"),
-                f"{scheduler_class} does not implement a required class method `step(...)`",
-            )
-
-            if scheduler_class != VQDiffusionScheduler:
-                sample = self.dummy_sample
-                if scheduler_class == CMStochasticIterativeScheduler:
-                    # Get valid timestep based on sigma_max, which should always be in timestep schedule.
-                    scaled_sigma_max = scheduler.sigma_to_t(scheduler.config.sigma_max)
-                    scaled_sample = scheduler.scale_model_input(sample, scaled_sigma_max)
-                else:
-                    scaled_sample = scheduler.scale_model_input(sample, 0.0)
-                self.assertEqual(sample.shape, scaled_sample.shape)
-
-    def test_add_noise_device(self):
-        for scheduler_class in self.scheduler_classes:
-            if scheduler_class == IPNDMScheduler:
-                continue
-            scheduler_config = self.get_scheduler_config()
-            scheduler = scheduler_class(**scheduler_config)
-            scheduler.set_timesteps(100)
-
-            sample = self.dummy_sample.to(torch_device)
-            if scheduler_class == CMStochasticIterativeScheduler:
-                # Get valid timestep based on sigma_max, which should always be in timestep schedule.
-                scaled_sigma_max = scheduler.sigma_to_t(scheduler.config.sigma_max)
-                scaled_sample = scheduler.scale_model_input(sample, scaled_sigma_max)
-            else:
-                scaled_sample = scheduler.scale_model_input(sample, 0.0)
-            self.assertEqual(sample.shape, scaled_sample.shape)
-
-            noise = torch.randn_like(scaled_sample).to(torch_device)
-            t = scheduler.timesteps[5][None]
-            noised = scheduler.add_noise(scaled_sample, noise, t)
-            self.assertEqual(noised.shape, scaled_sample.shape)
-
-    def test_deprecated_kwargs(self):
-        for scheduler_class in self.scheduler_classes:
-            has_kwarg_in_model_class = "kwargs" in inspect.signature(scheduler_class.__init__).parameters
-            has_deprecated_kwarg = len(scheduler_class._deprecated_kwargs) > 0
-
-            if has_kwarg_in_model_class and not has_deprecated_kwarg:
-                raise ValueError(
-                    f"{scheduler_class} has `**kwargs` in its __init__ method but has not defined any deprecated"
-                    " kwargs under the `_deprecated_kwargs` class attribute. Make sure to either remove `**kwargs` if"
-                    " there are no deprecated arguments or add the deprecated argument with `_deprecated_kwargs ="
-                    " [<deprecated_argument>]`"
-                )
-
-            if not has_kwarg_in_model_class and has_deprecated_kwarg:
-                raise ValueError(
-                    f"{scheduler_class} doesn't have `**kwargs` in its __init__ method but has defined deprecated"
-                    " kwargs under the `_deprecated_kwargs` class attribute. Make sure to either add the `**kwargs`"
-                    f" argument to {self.model_class}.__init__ if there are deprecated arguments or remove the"
-                    " deprecated argument from `_deprecated_kwargs = [<deprecated_argument>]`"
-                )
-
-    def test_trained_betas(self):
-        for scheduler_class in self.scheduler_classes:
-            if scheduler_class in (VQDiffusionScheduler, CMStochasticIterativeScheduler):
-                continue
-
-            scheduler_config = self.get_scheduler_config()
-            scheduler = scheduler_class(**scheduler_config, trained_betas=np.array([0.1, 0.3]))
-
-            with tempfile.TemporaryDirectory() as tmpdirname:
-                scheduler.save_pretrained(tmpdirname)
-                new_scheduler = scheduler_class.from_pretrained(tmpdirname)
-
-            assert scheduler.betas.tolist() == new_scheduler.betas.tolist()
-
-    def test_getattr_is_correct(self):
-        for scheduler_class in self.scheduler_classes:
-            scheduler_config = self.get_scheduler_config()
-            scheduler = scheduler_class(**scheduler_config)
-
-            # save some things to test
-            scheduler.dummy_attribute = 5
-            scheduler.register_to_config(test_attribute=5)
-
-            logger = logging.get_logger("diffusers.configuration_utils")
-            # 30 for warning
-            logger.setLevel(30)
-            with CaptureLogger(logger) as cap_logger:
-                assert hasattr(scheduler, "dummy_attribute")
-                assert getattr(scheduler, "dummy_attribute") == 5
-                assert scheduler.dummy_attribute == 5
-
-            # no warning should be thrown
-            assert cap_logger.out == ""
-
-            logger = logging.get_logger("diffusers.schedulers.schedulering_utils")
-            # 30 for warning
-            logger.setLevel(30)
-            with CaptureLogger(logger) as cap_logger:
-                assert hasattr(scheduler, "save_pretrained")
-                fn = scheduler.save_pretrained
-                fn_1 = getattr(scheduler, "save_pretrained")
-
-                assert fn == fn_1
-            # no warning should be thrown
-            assert cap_logger.out == ""
-
-            # warning should be thrown
-            with self.assertWarns(FutureWarning):
-                assert scheduler.test_attribute == 5
-
-            with self.assertWarns(FutureWarning):
-                assert getattr(scheduler, "test_attribute") == 5
-
-            with self.assertRaises(AttributeError) as error:
-                scheduler.does_not_exist
-
-            assert str(error.exception) == f"'{type(scheduler).__name__}' object has no attribute 'does_not_exist'"

spaces/Andy1621/uniformer_image_detection/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_poly_1x_coco_v1.py

@@ -1,57 +0,0 @@
-_base_ = './mask_rcnn_r50_fpn_1x_coco.py'
-model = dict(
-    pretrained='open-mmlab://resnet50_caffe_bgr',
-    backbone=dict(norm_cfg=dict(requires_grad=False), style='caffe'),
-    rpn_head=dict(
-        loss_bbox=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0)),
-    roi_head=dict(
-        bbox_roi_extractor=dict(
-            roi_layer=dict(
-                type='RoIAlign',
-                output_size=7,
-                sampling_ratio=2,
-                aligned=False)),
-        bbox_head=dict(
-            loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)),
-        mask_roi_extractor=dict(
-            roi_layer=dict(
-                type='RoIAlign',
-                output_size=14,
-                sampling_ratio=2,
-                aligned=False))))
-# use caffe img_norm
-img_norm_cfg = dict(
-    mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
-train_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(
-        type='LoadAnnotations',
-        with_bbox=True,
-        with_mask=True,
-        poly2mask=False),
-    dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
-    dict(type='RandomFlip', flip_ratio=0.5),
-    dict(type='Normalize', **img_norm_cfg),
-    dict(type='Pad', size_divisor=32),
-    dict(type='DefaultFormatBundle'),
-    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
-]
-test_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(
-        type='MultiScaleFlipAug',
-        img_scale=(1333, 800),
-        flip=False,
-        transforms=[
-            dict(type='Resize', keep_ratio=True),
-            dict(type='RandomFlip'),
-            dict(type='Normalize', **img_norm_cfg),
-            dict(type='Pad', size_divisor=32),
-            dict(type='ImageToTensor', keys=['img']),
-            dict(type='Collect', keys=['img']),
-        ])
-]
-data = dict(
-    train=dict(pipeline=train_pipeline),
-    val=dict(pipeline=test_pipeline),
-    test=dict(pipeline=test_pipeline))

spaces/Aniemore/Russian-Emotion-Recognition/README.md

@@ -1,12 +0,0 @@
----
-title: Russian Emotion Recognition (Aniemore)
-emoji: 🎭
-colorFrom: red
-colorTo: blue
-sdk: gradio
-sdk_version: 3.0.2
-app_file: app.py
-pinned: true
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference

spaces/Annotation-AI/fast-segment-everything/README.md

@@ -1,12 +0,0 @@
----
-title: Fast Segment Everything
-emoji: 👀
-colorFrom: purple
-colorTo: gray
-sdk: gradio
-sdk_version: 3.27.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/Artificio/AdversarialArt/.ipynb_checkpoints/app-checkpoint.py

@@ -1,92 +0,0 @@
-import torch
-import torch.nn as nn
-from robustness.datasets import ImageNet
-from robustness.attacker import AttackerModel
-from timm.models import create_model
-from torchvision import transforms
-from robustness.tools.label_maps import CLASS_DICT
-from src.utils import *
-from torchvision import transforms
-import gradio as gr
-import os
-from PIL import Image
-
-DICT_CLASSES = {'lake':955,
-                'castle':483,
-                'library':624,
-                'dog':235,
-                'cat':285,
-                'people':842 #trunks
-               }
-IMG_MAX_SIZE = 256
-ARCH = 'crossvit_18_dagger_408' 
-ARCH_PATH = './checkpoints/robust_crossvit_18_dagger_408.pt'
-CUSTOM_TRANSFORMS = transforms.Compose([transforms.Resize([IMG_MAX_SIZE,IMG_MAX_SIZE]),
-                                        transforms.ToTensor()])
-DEVICE = 'cuda'
-
-
-def load_model(robust = True):
-    test_image = Image.open('samples/test.png')
-    ds = CustomArt(test_image,CUSTOM_TRANSFORMS)
-    model = create_model(ARCH,pretrained = True).to(DEVICE)
-    if robust:
-        print("Load Robust Model")
-        checkpoint = torch.load(ARCH_PATH,map_location = DEVICE)
-        model.load_state_dict(checkpoint['state_dict'],strict = True)
-    model = RobustModel(model).to(DEVICE)    
-    model = AttackerModel(model, ds).to(DEVICE)
-    model = model.eval()
-    del test_image,ds
-    return model
-
-
-def gradio_fn(image_input,radio_steps,radio_class,radio_robust):
-    model = load_model(radio_robust)
-    kwargs = {
-            'constraint':'2', # L2 attack
-            'eps': 300,
-            'step_size': 1,
-            'iterations': int(radio_steps),
-            'targeted': True,
-            'do_tqdm': True,
-            'device': DEVICE
-            }
-    # Define the target and the image
-    target = torch.tensor([int(DICT_CLASSES[radio_class])]).to(DEVICE)
-    image = Image.fromarray(image_input)
-    image = CUSTOM_TRANSFORMS(image).to(DEVICE)
-    image = torch.unsqueeze(image, dim=0)
-    _, im_adv = model(image, target, make_adv=True, **kwargs)
-    im_adv = im_adv.squeeze(dim = 0).permute(1,2,0).cpu().numpy()
-    return im_adv
-
-
-if __name__ == '__main__':
-    demo = gr.Blocks()
-    with demo:
-        gr.Markdown("# Art Adversarial Attack")
-        with gr.Row():
-            with gr.Column():
-                with gr.Row():
-                    # Radio Steps Adversarial attack
-                    radio_steps = gr.Radio([10,500,1000,1500,2000],value = 500,label="# Attack Steps")
-                    # Radio Targeted attack
-                    radio_class = gr.Radio(list(DICT_CLASSES.keys()),
-                                           value = list(DICT_CLASSES.keys())[0],
-                                           label="Target Class")
-                    radio_robust = gr.Radio([True,False],value = True,label="Robust Model")
-                    # Image
-                with gr.Row():
-                    image_input = gr.Image(label="Input Image")
-                with gr.Row():
-                    calculate_button = gr.Button("Compute")
-            with gr.Column():
-                target_image    = gr.Image(label="Art Image")
-    
-        calculate_button.click(fn = gradio_fn,
-                               inputs = [image_input,radio_steps,radio_class,radio_robust],
-                               outputs = target_image)
-    demo.launch(debug = True)
-
-

spaces/Arulkumar03/GroundingDINO_SOTA_Zero_Shot_Model/setup.py

@@ -1,208 +0,0 @@
-# coding=utf-8
-# Copyright 2022 The IDEA Authors. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ------------------------------------------------------------------------------------------------
-# Modified from
-# https://github.com/fundamentalvision/Deformable-DETR/blob/main/models/ops/setup.py
-# https://github.com/facebookresearch/detectron2/blob/main/setup.py
-# https://github.com/open-mmlab/mmdetection/blob/master/setup.py
-# https://github.com/Oneflow-Inc/libai/blob/main/setup.py
-# ------------------------------------------------------------------------------------------------
-
-import glob
-import os
-import subprocess
-
-import torch
-from setuptools import find_packages, setup
-from torch.utils.cpp_extension import CUDA_HOME, CppExtension, CUDAExtension
-
-# groundingdino version info
-version = "0.1.0"
-package_name = "groundingdino"
-cwd = os.path.dirname(os.path.abspath(__file__))
-
-
-sha = "Unknown"
-try:
-    sha = subprocess.check_output(["git", "rev-parse", "HEAD"], cwd=cwd).decode("ascii").strip()
-except Exception:
-    pass
-
-
-def write_version_file():
-    version_path = os.path.join(cwd, "groundingdino", "version.py")
-    with open(version_path, "w") as f:
-        f.write(f"__version__ = '{version}'\n")
-        # f.write(f"git_version = {repr(sha)}\n")
-
-
-requirements = ["torch", "torchvision"]
-
-torch_ver = [int(x) for x in torch.__version__.split(".")[:2]]
-
-
-def get_extensions():
-    this_dir = os.path.dirname(os.path.abspath(__file__))
-    extensions_dir = os.path.join(this_dir, "groundingdino", "models", "GroundingDINO", "csrc")
-
-    main_source = os.path.join(extensions_dir, "vision.cpp")
-    sources = glob.glob(os.path.join(extensions_dir, "**", "*.cpp"))
-    source_cuda = glob.glob(os.path.join(extensions_dir, "**", "*.cu")) + glob.glob(
-        os.path.join(extensions_dir, "*.cu")
-    )
-
-    sources = [main_source] + sources
-
-    extension = CppExtension
-
-    extra_compile_args = {"cxx": []}
-    define_macros = []
-
-    if CUDA_HOME is not None and (torch.cuda.is_available() or "TORCH_CUDA_ARCH_LIST" in os.environ):
-        print("Compiling with CUDA")
-        extension = CUDAExtension
-        sources += source_cuda
-        define_macros += [("WITH_CUDA", None)]
-        extra_compile_args["nvcc"] = [
-            "-DCUDA_HAS_FP16=1",
-            "-D__CUDA_NO_HALF_OPERATORS__",
-            "-D__CUDA_NO_HALF_CONVERSIONS__",
-            "-D__CUDA_NO_HALF2_OPERATORS__",
-        ]
-    else:
-        print("Compiling without CUDA")
-        define_macros += [("WITH_HIP", None)]
-        extra_compile_args["nvcc"] = []
-        return None
-
-    sources = [os.path.join(extensions_dir, s) for s in sources]
-    include_dirs = [extensions_dir]
-
-    ext_modules = [
-        extension(
-            "groundingdino._C",
-            sources,
-            include_dirs=include_dirs,
-            define_macros=define_macros,
-            extra_compile_args=extra_compile_args,
-        )
-    ]
-
-    return ext_modules
-
-
-def parse_requirements(fname="requirements.txt", with_version=True):
-    """Parse the package dependencies listed in a requirements file but strips
-    specific versioning information.
-
-    Args:
-        fname (str): path to requirements file
-        with_version (bool, default=False): if True include version specs
-
-    Returns:
-        List[str]: list of requirements items
-
-    CommandLine:
-        python -c "import setup; print(setup.parse_requirements())"
-    """
-    import re
-    import sys
-    from os.path import exists
-
-    require_fpath = fname
-
-    def parse_line(line):
-        """Parse information from a line in a requirements text file."""
-        if line.startswith("-r "):
-            # Allow specifying requirements in other files
-            target = line.split(" ")[1]
-            for info in parse_require_file(target):
-                yield info
-        else:
-            info = {"line": line}
-            if line.startswith("-e "):
-                info["package"] = line.split("#egg=")[1]
-            elif "@git+" in line:
-                info["package"] = line
-            else:
-                # Remove versioning from the package
-                pat = "(" + "|".join([">=", "==", ">"]) + ")"
-                parts = re.split(pat, line, maxsplit=1)
-                parts = [p.strip() for p in parts]
-
-                info["package"] = parts[0]
-                if len(parts) > 1:
-                    op, rest = parts[1:]
-                    if ";" in rest:
-                        # Handle platform specific dependencies
-                        # http://setuptools.readthedocs.io/en/latest/setuptools.html#declaring-platform-specific-dependencies
-                        version, platform_deps = map(str.strip, rest.split(";"))
-                        info["platform_deps"] = platform_deps
-                    else:
-                        version = rest  # NOQA
-                    info["version"] = (op, version)
-            yield info
-
-    def parse_require_file(fpath):
-        with open(fpath, "r") as f:
-            for line in f.readlines():
-                line = line.strip()
-                if line and not line.startswith("#"):
-                    for info in parse_line(line):
-                        yield info
-
-    def gen_packages_items():
-        if exists(require_fpath):
-            for info in parse_require_file(require_fpath):
-                parts = [info["package"]]
-                if with_version and "version" in info:
-                    parts.extend(info["version"])
-                if not sys.version.startswith("3.4"):
-                    # apparently package_deps are broken in 3.4
-                    platform_deps = info.get("platform_deps")
-                    if platform_deps is not None:
-                        parts.append(";" + platform_deps)
-                item = "".join(parts)
-                yield item
-
-    packages = list(gen_packages_items())
-    return packages
-
-
-if __name__ == "__main__":
-    print(f"Building wheel {package_name}-{version}")
-
-    with open("LICENSE", "r", encoding="utf-8") as f:
-        license = f.read()
-
-    write_version_file()
-
-    setup(
-        name="groundingdino",
-        version="0.1.0",
-        author="International Digital Economy Academy, Shilong Liu",
-        url="https://github.com/IDEA-Research/GroundingDINO",
-        description="open-set object detector",
-        license=license,
-        install_requires=parse_requirements("requirements.txt"),
-        packages=find_packages(
-            exclude=(
-                "configs",
-                "tests",
-            )
-        ),
-        ext_modules=get_extensions(),
-        cmdclass={"build_ext": torch.utils.cpp_extension.BuildExtension},
-    )

spaces/AtomdffAI/wechatgpt4atom/.github/ISSUE_TEMPLATE.md

@@ -1,28 +0,0 @@
-### 前置确认
-
-1. 运行于国内网络环境，未开代理
-2.  python 已安装：版本在 3.7 ~ 3.10 之间，依赖已安装
-3. 在已有 issue 中未搜索到类似问题
-4. [FAQS](https://github.com/zhayujie/chatgpt-on-wechat/wiki/FAQs) 中无类似问题
-
-
-### 问题描述
-
-> 简要说明、截图、复现步骤等，也可以是需求或想法
-
-
-
-
-### 终端日志 (如有报错)
-
-```
-[在此处粘贴终端日志]
-```
-
-
-
-### 环境
-
- - 操作系统类型  (Mac/Windows/Linux)：
- - Python版本  ( 执行 `python3 -V` )：                      
- - pip版本  ( 依赖问题此项必填，执行 `pip3 -V`)：

spaces/Awiny/Image2Paragraph/utils/util.py

@@ -1,85 +0,0 @@
-from PIL import Image, ImageDraw, ImageFont
-import cv2
-import os
-import textwrap
-import nltk
-nltk.download('punkt', quiet=True)
-nltk.download('averaged_perceptron_tagger', quiet=True)
-from nltk.tokenize import word_tokenize
-from nltk import pos_tag
-
-
-def read_image_width_height(image_path):
-    image = Image.open(image_path)
-    width, height = image.size
-    return width, height
-
-def resize_long_edge(image, target_size=384):
-    # Calculate the aspect ratio
-    width, height = image.size
-    aspect_ratio = float(width) / float(height)
-
-    # Determine the new dimensions
-    if width > height:
-        new_width = target_size
-        new_height = int(target_size / aspect_ratio)
-    else:
-        new_width = int(target_size * aspect_ratio)
-        new_height = target_size
-
-    # Resize the image
-    resized_image = image.resize((new_width, new_height), Image.ANTIALIAS)
-    return resized_image
-
-def resize_long_edge_cv2(image, target_size=384):
-    height, width = image.shape[:2]
-    aspect_ratio = float(width) / float(height)
-
-    if height > width:
-        new_height = target_size
-        new_width = int(target_size * aspect_ratio)
-    else:
-        new_width = target_size
-        new_height = int(target_size / aspect_ratio)
-
-    resized_image = cv2.resize(image, (new_width, new_height), interpolation=cv2.INTER_AREA)
-    return resized_image
-
-def display_images_and_text(source_image_path, generated_image, generated_paragraph, outfile_name):
-    source_image = Image.open(source_image_path)
-    # Create a new image that can fit the images and the text
-    width = source_image.width + generated_image.width
-    height = max(source_image.height, generated_image.height)
-    new_image = Image.new("RGB", (width, height + 150), "white")
-
-    # Paste the source image and the generated image onto the new image
-    new_image.paste(source_image, (0, 0))
-    new_image.paste(generated_image, (source_image.width, 0))
-
-    # Write the generated paragraph onto the new image
-    draw = ImageDraw.Draw(new_image)
-    # font_size = 12
-    # font = ImageFont.load_default().font_variant(size=font_size)
-    font_path = os.path.join(cv2.__path__[0],'qt','fonts','DejaVuSans.ttf')
-    font = ImageFont.truetype(font_path, size=14)
-
-    # Wrap the text for better display
-    wrapped_text = textwrap.wrap(generated_paragraph, width=170)
-    # Draw each line of wrapped text
-    line_spacing = 18
-    y_offset = 0
-    for line in wrapped_text:
-        draw.text((0, height + y_offset), line, font=font, fill="black")
-        y_offset += line_spacing
-
-    # Show the final image
-    # new_image.show()
-    new_image.save(outfile_name)
-    return 1
-
-
-def extract_nouns_nltk(paragraph):
-    words = word_tokenize(paragraph)
-    pos_tags = pos_tag(words)
-    nouns = [word for word, tag in pos_tags if tag in ('NN', 'NNS', 'NNP', 'NNPS')]
-    return nouns

spaces/Bart92/RVC_HF/train/data_utils.py

@@ -1,512 +0,0 @@
-import os, traceback
-import numpy as np
-import torch
-import torch.utils.data
-
-from mel_processing import spectrogram_torch
-from utils import load_wav_to_torch, load_filepaths_and_text
-
-
-class TextAudioLoaderMultiNSFsid(torch.utils.data.Dataset):
-    """
-    1) loads audio, text pairs
-    2) normalizes text and converts them to sequences of integers
-    3) computes spectrograms from audio files.
-    """
-
-    def __init__(self, audiopaths_and_text, hparams):
-        self.audiopaths_and_text = load_filepaths_and_text(audiopaths_and_text)
-        self.max_wav_value = hparams.max_wav_value
-        self.sampling_rate = hparams.sampling_rate
-        self.filter_length = hparams.filter_length
-        self.hop_length = hparams.hop_length
-        self.win_length = hparams.win_length
-        self.sampling_rate = hparams.sampling_rate
-        self.min_text_len = getattr(hparams, "min_text_len", 1)
-        self.max_text_len = getattr(hparams, "max_text_len", 5000)
-        self._filter()
-
-    def _filter(self):
-        """
-        Filter text & store spec lengths
-        """
-        # Store spectrogram lengths for Bucketing
-        # wav_length ~= file_size / (wav_channels * Bytes per dim) = file_size / (1 * 2)
-        # spec_length = wav_length // hop_length
-        audiopaths_and_text_new = []
-        lengths = []
-        for audiopath, text, pitch, pitchf, dv in self.audiopaths_and_text:
-            if self.min_text_len <= len(text) and len(text) <= self.max_text_len:
-                audiopaths_and_text_new.append([audiopath, text, pitch, pitchf, dv])
-                lengths.append(os.path.getsize(audiopath) // (3 * self.hop_length))
-        self.audiopaths_and_text = audiopaths_and_text_new
-        self.lengths = lengths
-
-    def get_sid(self, sid):
-        sid = torch.LongTensor([int(sid)])
-        return sid
-
-    def get_audio_text_pair(self, audiopath_and_text):
-        # separate filename and text
-        file = audiopath_and_text[0]
-        phone = audiopath_and_text[1]
-        pitch = audiopath_and_text[2]
-        pitchf = audiopath_and_text[3]
-        dv = audiopath_and_text[4]
-
-        phone, pitch, pitchf = self.get_labels(phone, pitch, pitchf)
-        spec, wav = self.get_audio(file)
-        dv = self.get_sid(dv)
-
-        len_phone = phone.size()[0]
-        len_spec = spec.size()[-1]
-        # print(123,phone.shape,pitch.shape,spec.shape)
-        if len_phone != len_spec:
-            len_min = min(len_phone, len_spec)
-            # amor
-            len_wav = len_min * self.hop_length
-
-            spec = spec[:, :len_min]
-            wav = wav[:, :len_wav]
-
-            phone = phone[:len_min, :]
-            pitch = pitch[:len_min]
-            pitchf = pitchf[:len_min]
-
-        return (spec, wav, phone, pitch, pitchf, dv)
-
-    def get_labels(self, phone, pitch, pitchf):
-        phone = np.load(phone)
-        phone = np.repeat(phone, 2, axis=0)
-        pitch = np.load(pitch)
-        pitchf = np.load(pitchf)
-        n_num = min(phone.shape[0], 900)  # DistributedBucketSampler
-        # print(234,phone.shape,pitch.shape)
-        phone = phone[:n_num, :]
-        pitch = pitch[:n_num]
-        pitchf = pitchf[:n_num]
-        phone = torch.FloatTensor(phone)
-        pitch = torch.LongTensor(pitch)
-        pitchf = torch.FloatTensor(pitchf)
-        return phone, pitch, pitchf
-
-    def get_audio(self, filename):
-        audio, sampling_rate = load_wav_to_torch(filename)
-        if sampling_rate != self.sampling_rate:
-            raise ValueError(
-                "{} SR doesn't match target {} SR".format(
-                    sampling_rate, self.sampling_rate
-                )
-            )
-        audio_norm = audio
-        #        audio_norm = audio / self.max_wav_value
-        #        audio_norm = audio / np.abs(audio).max()
-
-        audio_norm = audio_norm.unsqueeze(0)
-        spec_filename = filename.replace(".wav", ".spec.pt")
-        if os.path.exists(spec_filename):
-            try:
-                spec = torch.load(spec_filename)
-            except:
-                print(spec_filename, traceback.format_exc())
-                spec = spectrogram_torch(
-                    audio_norm,
-                    self.filter_length,
-                    self.sampling_rate,
-                    self.hop_length,
-                    self.win_length,
-                    center=False,
-                )
-                spec = torch.squeeze(spec, 0)
-                torch.save(spec, spec_filename, _use_new_zipfile_serialization=False)
-        else:
-            spec = spectrogram_torch(
-                audio_norm,
-                self.filter_length,
-                self.sampling_rate,
-                self.hop_length,
-                self.win_length,
-                center=False,
-            )
-            spec = torch.squeeze(spec, 0)
-            torch.save(spec, spec_filename, _use_new_zipfile_serialization=False)
-        return spec, audio_norm
-
-    def __getitem__(self, index):
-        return self.get_audio_text_pair(self.audiopaths_and_text[index])
-
-    def __len__(self):
-        return len(self.audiopaths_and_text)
-
-
-class TextAudioCollateMultiNSFsid:
-    """Zero-pads model inputs and targets"""
-
-    def __init__(self, return_ids=False):
-        self.return_ids = return_ids
-
-    def __call__(self, batch):
-        """Collate's training batch from normalized text and aduio
-        PARAMS
-        ------
-        batch: [text_normalized, spec_normalized, wav_normalized]
-        """
-        # Right zero-pad all one-hot text sequences to max input length
-        _, ids_sorted_decreasing = torch.sort(
-            torch.LongTensor([x[0].size(1) for x in batch]), dim=0, descending=True
-        )
-
-        max_spec_len = max([x[0].size(1) for x in batch])
-        max_wave_len = max([x[1].size(1) for x in batch])
-        spec_lengths = torch.LongTensor(len(batch))
-        wave_lengths = torch.LongTensor(len(batch))
-        spec_padded = torch.FloatTensor(len(batch), batch[0][0].size(0), max_spec_len)
-        wave_padded = torch.FloatTensor(len(batch), 1, max_wave_len)
-        spec_padded.zero_()
-        wave_padded.zero_()
-
-        max_phone_len = max([x[2].size(0) for x in batch])
-        phone_lengths = torch.LongTensor(len(batch))
-        phone_padded = torch.FloatTensor(
-            len(batch), max_phone_len, batch[0][2].shape[1]
-        )  # (spec, wav, phone, pitch)
-        pitch_padded = torch.LongTensor(len(batch), max_phone_len)
-        pitchf_padded = torch.FloatTensor(len(batch), max_phone_len)
-        phone_padded.zero_()
-        pitch_padded.zero_()
-        pitchf_padded.zero_()
-        # dv = torch.FloatTensor(len(batch), 256)#gin=256
-        sid = torch.LongTensor(len(batch))
-
-        for i in range(len(ids_sorted_decreasing)):
-            row = batch[ids_sorted_decreasing[i]]
-
-            spec = row[0]
-            spec_padded[i, :, : spec.size(1)] = spec
-            spec_lengths[i] = spec.size(1)
-
-            wave = row[1]
-            wave_padded[i, :, : wave.size(1)] = wave
-            wave_lengths[i] = wave.size(1)
-
-            phone = row[2]
-            phone_padded[i, : phone.size(0), :] = phone
-            phone_lengths[i] = phone.size(0)
-
-            pitch = row[3]
-            pitch_padded[i, : pitch.size(0)] = pitch
-            pitchf = row[4]
-            pitchf_padded[i, : pitchf.size(0)] = pitchf
-
-            # dv[i] = row[5]
-            sid[i] = row[5]
-
-        return (
-            phone_padded,
-            phone_lengths,
-            pitch_padded,
-            pitchf_padded,
-            spec_padded,
-            spec_lengths,
-            wave_padded,
-            wave_lengths,
-            # dv
-            sid,
-        )
-
-
-class TextAudioLoader(torch.utils.data.Dataset):
-    """
-    1) loads audio, text pairs
-    2) normalizes text and converts them to sequences of integers
-    3) computes spectrograms from audio files.
-    """
-
-    def __init__(self, audiopaths_and_text, hparams):
-        self.audiopaths_and_text = load_filepaths_and_text(audiopaths_and_text)
-        self.max_wav_value = hparams.max_wav_value
-        self.sampling_rate = hparams.sampling_rate
-        self.filter_length = hparams.filter_length
-        self.hop_length = hparams.hop_length
-        self.win_length = hparams.win_length
-        self.sampling_rate = hparams.sampling_rate
-        self.min_text_len = getattr(hparams, "min_text_len", 1)
-        self.max_text_len = getattr(hparams, "max_text_len", 5000)
-        self._filter()
-
-    def _filter(self):
-        """
-        Filter text & store spec lengths
-        """
-        # Store spectrogram lengths for Bucketing
-        # wav_length ~= file_size / (wav_channels * Bytes per dim) = file_size / (1 * 2)
-        # spec_length = wav_length // hop_length
-        audiopaths_and_text_new = []
-        lengths = []
-        for audiopath, text, dv in self.audiopaths_and_text:
-            if self.min_text_len <= len(text) and len(text) <= self.max_text_len:
-                audiopaths_and_text_new.append([audiopath, text, dv])
-                lengths.append(os.path.getsize(audiopath) // (3 * self.hop_length))
-        self.audiopaths_and_text = audiopaths_and_text_new
-        self.lengths = lengths
-
-    def get_sid(self, sid):
-        sid = torch.LongTensor([int(sid)])
-        return sid
-
-    def get_audio_text_pair(self, audiopath_and_text):
-        # separate filename and text
-        file = audiopath_and_text[0]
-        phone = audiopath_and_text[1]
-        dv = audiopath_and_text[2]
-
-        phone = self.get_labels(phone)
-        spec, wav = self.get_audio(file)
-        dv = self.get_sid(dv)
-
-        len_phone = phone.size()[0]
-        len_spec = spec.size()[-1]
-        if len_phone != len_spec:
-            len_min = min(len_phone, len_spec)
-            len_wav = len_min * self.hop_length
-            spec = spec[:, :len_min]
-            wav = wav[:, :len_wav]
-            phone = phone[:len_min, :]
-        return (spec, wav, phone, dv)
-
-    def get_labels(self, phone):
-        phone = np.load(phone)
-        phone = np.repeat(phone, 2, axis=0)
-        n_num = min(phone.shape[0], 900)  # DistributedBucketSampler
-        phone = phone[:n_num, :]
-        phone = torch.FloatTensor(phone)
-        return phone
-
-    def get_audio(self, filename):
-        audio, sampling_rate = load_wav_to_torch(filename)
-        if sampling_rate != self.sampling_rate:
-            raise ValueError(
-                "{} SR doesn't match target {} SR".format(
-                    sampling_rate, self.sampling_rate
-                )
-            )
-        audio_norm = audio
-        #        audio_norm = audio / self.max_wav_value
-        #        audio_norm = audio / np.abs(audio).max()
-
-        audio_norm = audio_norm.unsqueeze(0)
-        spec_filename = filename.replace(".wav", ".spec.pt")
-        if os.path.exists(spec_filename):
-            try:
-                spec = torch.load(spec_filename)
-            except:
-                print(spec_filename, traceback.format_exc())
-                spec = spectrogram_torch(
-                    audio_norm,
-                    self.filter_length,
-                    self.sampling_rate,
-                    self.hop_length,
-                    self.win_length,
-                    center=False,
-                )
-                spec = torch.squeeze(spec, 0)
-                torch.save(spec, spec_filename, _use_new_zipfile_serialization=False)
-        else:
-            spec = spectrogram_torch(
-                audio_norm,
-                self.filter_length,
-                self.sampling_rate,
-                self.hop_length,
-                self.win_length,
-                center=False,
-            )
-            spec = torch.squeeze(spec, 0)
-            torch.save(spec, spec_filename, _use_new_zipfile_serialization=False)
-        return spec, audio_norm
-
-    def __getitem__(self, index):
-        return self.get_audio_text_pair(self.audiopaths_and_text[index])
-
-    def __len__(self):
-        return len(self.audiopaths_and_text)
-
-
-class TextAudioCollate:
-    """Zero-pads model inputs and targets"""
-
-    def __init__(self, return_ids=False):
-        self.return_ids = return_ids
-
-    def __call__(self, batch):
-        """Collate's training batch from normalized text and aduio
-        PARAMS
-        ------
-        batch: [text_normalized, spec_normalized, wav_normalized]
-        """
-        # Right zero-pad all one-hot text sequences to max input length
-        _, ids_sorted_decreasing = torch.sort(
-            torch.LongTensor([x[0].size(1) for x in batch]), dim=0, descending=True
-        )
-
-        max_spec_len = max([x[0].size(1) for x in batch])
-        max_wave_len = max([x[1].size(1) for x in batch])
-        spec_lengths = torch.LongTensor(len(batch))
-        wave_lengths = torch.LongTensor(len(batch))
-        spec_padded = torch.FloatTensor(len(batch), batch[0][0].size(0), max_spec_len)
-        wave_padded = torch.FloatTensor(len(batch), 1, max_wave_len)
-        spec_padded.zero_()
-        wave_padded.zero_()
-
-        max_phone_len = max([x[2].size(0) for x in batch])
-        phone_lengths = torch.LongTensor(len(batch))
-        phone_padded = torch.FloatTensor(
-            len(batch), max_phone_len, batch[0][2].shape[1]
-        )
-        phone_padded.zero_()
-        sid = torch.LongTensor(len(batch))
-
-        for i in range(len(ids_sorted_decreasing)):
-            row = batch[ids_sorted_decreasing[i]]
-
-            spec = row[0]
-            spec_padded[i, :, : spec.size(1)] = spec
-            spec_lengths[i] = spec.size(1)
-
-            wave = row[1]
-            wave_padded[i, :, : wave.size(1)] = wave
-            wave_lengths[i] = wave.size(1)
-
-            phone = row[2]
-            phone_padded[i, : phone.size(0), :] = phone
-            phone_lengths[i] = phone.size(0)
-
-            sid[i] = row[3]
-
-        return (
-            phone_padded,
-            phone_lengths,
-            spec_padded,
-            spec_lengths,
-            wave_padded,
-            wave_lengths,
-            sid,
-        )
-
-
-class DistributedBucketSampler(torch.utils.data.distributed.DistributedSampler):
-    """
-    Maintain similar input lengths in a batch.
-    Length groups are specified by boundaries.
-    Ex) boundaries = [b1, b2, b3] -> any batch is included either {x | b1 < length(x) <=b2} or {x | b2 < length(x) <= b3}.
-
-    It removes samples which are not included in the boundaries.
-    Ex) boundaries = [b1, b2, b3] -> any x s.t. length(x) <= b1 or length(x) > b3 are discarded.
-    """
-
-    def __init__(
-        self,
-        dataset,
-        batch_size,
-        boundaries,
-        num_replicas=None,
-        rank=None,
-        shuffle=True,
-    ):
-        super().__init__(dataset, num_replicas=num_replicas, rank=rank, shuffle=shuffle)
-        self.lengths = dataset.lengths
-        self.batch_size = batch_size
-        self.boundaries = boundaries
-
-        self.buckets, self.num_samples_per_bucket = self._create_buckets()
-        self.total_size = sum(self.num_samples_per_bucket)
-        self.num_samples = self.total_size // self.num_replicas
-
-    def _create_buckets(self):
-        buckets = [[] for _ in range(len(self.boundaries) - 1)]
-        for i in range(len(self.lengths)):
-            length = self.lengths[i]
-            idx_bucket = self._bisect(length)
-            if idx_bucket != -1:
-                buckets[idx_bucket].append(i)
-
-        for i in range(len(buckets) - 1, -1, -1):  #
-            if len(buckets[i]) == 0:
-                buckets.pop(i)
-                self.boundaries.pop(i + 1)
-
-        num_samples_per_bucket = []
-        for i in range(len(buckets)):
-            len_bucket = len(buckets[i])
-            total_batch_size = self.num_replicas * self.batch_size
-            rem = (
-                total_batch_size - (len_bucket % total_batch_size)
-            ) % total_batch_size
-            num_samples_per_bucket.append(len_bucket + rem)
-        return buckets, num_samples_per_bucket
-
-    def __iter__(self):
-        # deterministically shuffle based on epoch
-        g = torch.Generator()
-        g.manual_seed(self.epoch)
-
-        indices = []
-        if self.shuffle:
-            for bucket in self.buckets:
-                indices.append(torch.randperm(len(bucket), generator=g).tolist())
-        else:
-            for bucket in self.buckets:
-                indices.append(list(range(len(bucket))))
-
-        batches = []
-        for i in range(len(self.buckets)):
-            bucket = self.buckets[i]
-            len_bucket = len(bucket)
-            ids_bucket = indices[i]
-            num_samples_bucket = self.num_samples_per_bucket[i]
-
-            # add extra samples to make it evenly divisible
-            rem = num_samples_bucket - len_bucket
-            ids_bucket = (
-                ids_bucket
-                + ids_bucket * (rem // len_bucket)
-                + ids_bucket[: (rem % len_bucket)]
-            )
-
-            # subsample
-            ids_bucket = ids_bucket[self.rank :: self.num_replicas]
-
-            # batching
-            for j in range(len(ids_bucket) // self.batch_size):
-                batch = [
-                    bucket[idx]
-                    for idx in ids_bucket[
-                        j * self.batch_size : (j + 1) * self.batch_size
-                    ]
-                ]
-                batches.append(batch)
-
-        if self.shuffle:
-            batch_ids = torch.randperm(len(batches), generator=g).tolist()
-            batches = [batches[i] for i in batch_ids]
-        self.batches = batches
-
-        assert len(self.batches) * self.batch_size == self.num_samples
-        return iter(self.batches)
-
-    def _bisect(self, x, lo=0, hi=None):
-        if hi is None:
-            hi = len(self.boundaries) - 1
-
-        if hi > lo:
-            mid = (hi + lo) // 2
-            if self.boundaries[mid] < x and x <= self.boundaries[mid + 1]:
-                return mid
-            elif x <= self.boundaries[mid]:
-                return self._bisect(x, lo, mid)
-            else:
-                return self._bisect(x, mid + 1, hi)
-        else:
-            return -1
-
-    def __len__(self):
-        return self.num_samples // self.batch_size

spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/urllib3/util/url.py

@@ -1,435 +0,0 @@
-from __future__ import absolute_import
-
-import re
-from collections import namedtuple
-
-from ..exceptions import LocationParseError
-from ..packages import six
-
-url_attrs = ["scheme", "auth", "host", "port", "path", "query", "fragment"]
-
-# We only want to normalize urls with an HTTP(S) scheme.
-# urllib3 infers URLs without a scheme (None) to be http.
-NORMALIZABLE_SCHEMES = ("http", "https", None)
-
-# Almost all of these patterns were derived from the
-# 'rfc3986' module: https://github.com/python-hyper/rfc3986
-PERCENT_RE = re.compile(r"%[a-fA-F0-9]{2}")
-SCHEME_RE = re.compile(r"^(?:[a-zA-Z][a-zA-Z0-9+-]*:|/)")
-URI_RE = re.compile(
-    r"^(?:([a-zA-Z][a-zA-Z0-9+.-]*):)?"
-    r"(?://([^\\/?#]*))?"
-    r"([^?#]*)"
-    r"(?:\?([^#]*))?"
-    r"(?:#(.*))?$",
-    re.UNICODE | re.DOTALL,
-)
-
-IPV4_PAT = r"(?:[0-9]{1,3}\.){3}[0-9]{1,3}"
-HEX_PAT = "[0-9A-Fa-f]{1,4}"
-LS32_PAT = "(?:{hex}:{hex}|{ipv4})".format(hex=HEX_PAT, ipv4=IPV4_PAT)
-_subs = {"hex": HEX_PAT, "ls32": LS32_PAT}
-_variations = [
-    #                            6( h16 ":" ) ls32
-    "(?:%(hex)s:){6}%(ls32)s",
-    #                       "::" 5( h16 ":" ) ls32
-    "::(?:%(hex)s:){5}%(ls32)s",
-    # [               h16 ] "::" 4( h16 ":" ) ls32
-    "(?:%(hex)s)?::(?:%(hex)s:){4}%(ls32)s",
-    # [ *1( h16 ":" ) h16 ] "::" 3( h16 ":" ) ls32
-    "(?:(?:%(hex)s:)?%(hex)s)?::(?:%(hex)s:){3}%(ls32)s",
-    # [ *2( h16 ":" ) h16 ] "::" 2( h16 ":" ) ls32
-    "(?:(?:%(hex)s:){0,2}%(hex)s)?::(?:%(hex)s:){2}%(ls32)s",
-    # [ *3( h16 ":" ) h16 ] "::"    h16 ":"   ls32
-    "(?:(?:%(hex)s:){0,3}%(hex)s)?::%(hex)s:%(ls32)s",
-    # [ *4( h16 ":" ) h16 ] "::"              ls32
-    "(?:(?:%(hex)s:){0,4}%(hex)s)?::%(ls32)s",
-    # [ *5( h16 ":" ) h16 ] "::"              h16
-    "(?:(?:%(hex)s:){0,5}%(hex)s)?::%(hex)s",
-    # [ *6( h16 ":" ) h16 ] "::"
-    "(?:(?:%(hex)s:){0,6}%(hex)s)?::",
-]
-
-UNRESERVED_PAT = r"ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789._\-~"
-IPV6_PAT = "(?:" + "|".join([x % _subs for x in _variations]) + ")"
-ZONE_ID_PAT = "(?:%25|%)(?:[" + UNRESERVED_PAT + "]|%[a-fA-F0-9]{2})+"
-IPV6_ADDRZ_PAT = r"\[" + IPV6_PAT + r"(?:" + ZONE_ID_PAT + r")?\]"
-REG_NAME_PAT = r"(?:[^\[\]%:/?#]|%[a-fA-F0-9]{2})*"
-TARGET_RE = re.compile(r"^(/[^?#]*)(?:\?([^#]*))?(?:#.*)?$")
-
-IPV4_RE = re.compile("^" + IPV4_PAT + "$")
-IPV6_RE = re.compile("^" + IPV6_PAT + "$")
-IPV6_ADDRZ_RE = re.compile("^" + IPV6_ADDRZ_PAT + "$")
-BRACELESS_IPV6_ADDRZ_RE = re.compile("^" + IPV6_ADDRZ_PAT[2:-2] + "$")
-ZONE_ID_RE = re.compile("(" + ZONE_ID_PAT + r")\]$")
-
-_HOST_PORT_PAT = ("^(%s|%s|%s)(?::0*?(|0|[1-9][0-9]{0,4}))?$") % (
-    REG_NAME_PAT,
-    IPV4_PAT,
-    IPV6_ADDRZ_PAT,
-)
-_HOST_PORT_RE = re.compile(_HOST_PORT_PAT, re.UNICODE | re.DOTALL)
-
-UNRESERVED_CHARS = set(
-    "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789._-~"
-)
-SUB_DELIM_CHARS = set("!$&'()*+,;=")
-USERINFO_CHARS = UNRESERVED_CHARS | SUB_DELIM_CHARS | {":"}
-PATH_CHARS = USERINFO_CHARS | {"@", "/"}
-QUERY_CHARS = FRAGMENT_CHARS = PATH_CHARS | {"?"}
-
-
-class Url(namedtuple("Url", url_attrs)):
-    """
-    Data structure for representing an HTTP URL. Used as a return value for
-    :func:`parse_url`. Both the scheme and host are normalized as they are
-    both case-insensitive according to RFC 3986.
-    """
-
-    __slots__ = ()
-
-    def __new__(
-        cls,
-        scheme=None,
-        auth=None,
-        host=None,
-        port=None,
-        path=None,
-        query=None,
-        fragment=None,
-    ):
-        if path and not path.startswith("/"):
-            path = "/" + path
-        if scheme is not None:
-            scheme = scheme.lower()
-        return super(Url, cls).__new__(
-            cls, scheme, auth, host, port, path, query, fragment
-        )
-
-    @property
-    def hostname(self):
-        """For backwards-compatibility with urlparse. We're nice like that."""
-        return self.host
-
-    @property
-    def request_uri(self):
-        """Absolute path including the query string."""
-        uri = self.path or "/"
-
-        if self.query is not None:
-            uri += "?" + self.query
-
-        return uri
-
-    @property
-    def netloc(self):
-        """Network location including host and port"""
-        if self.port:
-            return "%s:%d" % (self.host, self.port)
-        return self.host
-
-    @property
-    def url(self):
-        """
-        Convert self into a url
-
-        This function should more or less round-trip with :func:`.parse_url`. The
-        returned url may not be exactly the same as the url inputted to
-        :func:`.parse_url`, but it should be equivalent by the RFC (e.g., urls
-        with a blank port will have : removed).
-
-        Example: ::
-
-            >>> U = parse_url('http://google.com/mail/')
-            >>> U.url
-            'http://google.com/mail/'
-            >>> Url('http', 'username:password', 'host.com', 80,
-            ... '/path', 'query', 'fragment').url
-            'http://username:[email protected]:80/path?query#fragment'
-        """
-        scheme, auth, host, port, path, query, fragment = self
-        url = u""
-
-        # We use "is not None" we want things to happen with empty strings (or 0 port)
-        if scheme is not None:
-            url += scheme + u"://"
-        if auth is not None:
-            url += auth + u"@"
-        if host is not None:
-            url += host
-        if port is not None:
-            url += u":" + str(port)
-        if path is not None:
-            url += path
-        if query is not None:
-            url += u"?" + query
-        if fragment is not None:
-            url += u"#" + fragment
-
-        return url
-
-    def __str__(self):
-        return self.url
-
-
-def split_first(s, delims):
-    """
-    .. deprecated:: 1.25
-
-    Given a string and an iterable of delimiters, split on the first found
-    delimiter. Return two split parts and the matched delimiter.
-
-    If not found, then the first part is the full input string.
-
-    Example::
-
-        >>> split_first('foo/bar?baz', '?/=')
-        ('foo', 'bar?baz', '/')
-        >>> split_first('foo/bar?baz', '123')
-        ('foo/bar?baz', '', None)
-
-    Scales linearly with number of delims. Not ideal for large number of delims.
-    """
-    min_idx = None
-    min_delim = None
-    for d in delims:
-        idx = s.find(d)
-        if idx < 0:
-            continue
-
-        if min_idx is None or idx < min_idx:
-            min_idx = idx
-            min_delim = d
-
-    if min_idx is None or min_idx < 0:
-        return s, "", None
-
-    return s[:min_idx], s[min_idx + 1 :], min_delim
-
-
-def _encode_invalid_chars(component, allowed_chars, encoding="utf-8"):
-    """Percent-encodes a URI component without reapplying
-    onto an already percent-encoded component.
-    """
-    if component is None:
-        return component
-
-    component = six.ensure_text(component)
-
-    # Normalize existing percent-encoded bytes.
-    # Try to see if the component we're encoding is already percent-encoded
-    # so we can skip all '%' characters but still encode all others.
-    component, percent_encodings = PERCENT_RE.subn(
-        lambda match: match.group(0).upper(), component
-    )
-
-    uri_bytes = component.encode("utf-8", "surrogatepass")
-    is_percent_encoded = percent_encodings == uri_bytes.count(b"%")
-    encoded_component = bytearray()
-
-    for i in range(0, len(uri_bytes)):
-        # Will return a single character bytestring on both Python 2 & 3
-        byte = uri_bytes[i : i + 1]
-        byte_ord = ord(byte)
-        if (is_percent_encoded and byte == b"%") or (
-            byte_ord < 128 and byte.decode() in allowed_chars
-        ):
-            encoded_component += byte
-            continue
-        encoded_component.extend(b"%" + (hex(byte_ord)[2:].encode().zfill(2).upper()))
-
-    return encoded_component.decode(encoding)
-
-
-def _remove_path_dot_segments(path):
-    # See http://tools.ietf.org/html/rfc3986#section-5.2.4 for pseudo-code
-    segments = path.split("/")  # Turn the path into a list of segments
-    output = []  # Initialize the variable to use to store output
-
-    for segment in segments:
-        # '.' is the current directory, so ignore it, it is superfluous
-        if segment == ".":
-            continue
-        # Anything other than '..', should be appended to the output
-        elif segment != "..":
-            output.append(segment)
-        # In this case segment == '..', if we can, we should pop the last
-        # element
-        elif output:
-            output.pop()
-
-    # If the path starts with '/' and the output is empty or the first string
-    # is non-empty
-    if path.startswith("/") and (not output or output[0]):
-        output.insert(0, "")
-
-    # If the path starts with '/.' or '/..' ensure we add one more empty
-    # string to add a trailing '/'
-    if path.endswith(("/.", "/..")):
-        output.append("")
-
-    return "/".join(output)
-
-
-def _normalize_host(host, scheme):
-    if host:
-        if isinstance(host, six.binary_type):
-            host = six.ensure_str(host)
-
-        if scheme in NORMALIZABLE_SCHEMES:
-            is_ipv6 = IPV6_ADDRZ_RE.match(host)
-            if is_ipv6:
-                # IPv6 hosts of the form 'a::b%zone' are encoded in a URL as
-                # such per RFC 6874: 'a::b%25zone'. Unquote the ZoneID
-                # separator as necessary to return a valid RFC 4007 scoped IP.
-                match = ZONE_ID_RE.search(host)
-                if match:
-                    start, end = match.span(1)
-                    zone_id = host[start:end]
-
-                    if zone_id.startswith("%25") and zone_id != "%25":
-                        zone_id = zone_id[3:]
-                    else:
-                        zone_id = zone_id[1:]
-                    zone_id = "%" + _encode_invalid_chars(zone_id, UNRESERVED_CHARS)
-                    return host[:start].lower() + zone_id + host[end:]
-                else:
-                    return host.lower()
-            elif not IPV4_RE.match(host):
-                return six.ensure_str(
-                    b".".join([_idna_encode(label) for label in host.split(".")])
-                )
-    return host
-
-
-def _idna_encode(name):
-    if name and any(ord(x) >= 128 for x in name):
-        try:
-            from pip._vendor import idna
-        except ImportError:
-            six.raise_from(
-                LocationParseError("Unable to parse URL without the 'idna' module"),
-                None,
-            )
-        try:
-            return idna.encode(name.lower(), strict=True, std3_rules=True)
-        except idna.IDNAError:
-            six.raise_from(
-                LocationParseError(u"Name '%s' is not a valid IDNA label" % name), None
-            )
-    return name.lower().encode("ascii")
-
-
-def _encode_target(target):
-    """Percent-encodes a request target so that there are no invalid characters"""
-    path, query = TARGET_RE.match(target).groups()
-    target = _encode_invalid_chars(path, PATH_CHARS)
-    query = _encode_invalid_chars(query, QUERY_CHARS)
-    if query is not None:
-        target += "?" + query
-    return target
-
-
-def parse_url(url):
-    """
-    Given a url, return a parsed :class:`.Url` namedtuple. Best-effort is
-    performed to parse incomplete urls. Fields not provided will be None.
-    This parser is RFC 3986 and RFC 6874 compliant.
-
-    The parser logic and helper functions are based heavily on
-    work done in the ``rfc3986`` module.
-
-    :param str url: URL to parse into a :class:`.Url` namedtuple.
-
-    Partly backwards-compatible with :mod:`urlparse`.
-
-    Example::
-
-        >>> parse_url('http://google.com/mail/')
-        Url(scheme='http', host='google.com', port=None, path='/mail/', ...)
-        >>> parse_url('google.com:80')
-        Url(scheme=None, host='google.com', port=80, path=None, ...)
-        >>> parse_url('/foo?bar')
-        Url(scheme=None, host=None, port=None, path='/foo', query='bar', ...)
-    """
-    if not url:
-        # Empty
-        return Url()
-
-    source_url = url
-    if not SCHEME_RE.search(url):
-        url = "//" + url
-
-    try:
-        scheme, authority, path, query, fragment = URI_RE.match(url).groups()
-        normalize_uri = scheme is None or scheme.lower() in NORMALIZABLE_SCHEMES
-
-        if scheme:
-            scheme = scheme.lower()
-
-        if authority:
-            auth, _, host_port = authority.rpartition("@")
-            auth = auth or None
-            host, port = _HOST_PORT_RE.match(host_port).groups()
-            if auth and normalize_uri:
-                auth = _encode_invalid_chars(auth, USERINFO_CHARS)
-            if port == "":
-                port = None
-        else:
-            auth, host, port = None, None, None
-
-        if port is not None:
-            port = int(port)
-            if not (0 <= port <= 65535):
-                raise LocationParseError(url)
-
-        host = _normalize_host(host, scheme)
-
-        if normalize_uri and path:
-            path = _remove_path_dot_segments(path)
-            path = _encode_invalid_chars(path, PATH_CHARS)
-        if normalize_uri and query:
-            query = _encode_invalid_chars(query, QUERY_CHARS)
-        if normalize_uri and fragment:
-            fragment = _encode_invalid_chars(fragment, FRAGMENT_CHARS)
-
-    except (ValueError, AttributeError):
-        return six.raise_from(LocationParseError(source_url), None)
-
-    # For the sake of backwards compatibility we put empty
-    # string values for path if there are any defined values
-    # beyond the path in the URL.
-    # TODO: Remove this when we break backwards compatibility.
-    if not path:
-        if query is not None or fragment is not None:
-            path = ""
-        else:
-            path = None
-
-    # Ensure that each part of the URL is a `str` for
-    # backwards compatibility.
-    if isinstance(url, six.text_type):
-        ensure_func = six.ensure_text
-    else:
-        ensure_func = six.ensure_str
-
-    def ensure_type(x):
-        return x if x is None else ensure_func(x)
-
-    return Url(
-        scheme=ensure_type(scheme),
-        auth=ensure_type(auth),
-        host=ensure_type(host),
-        port=port,
-        path=ensure_type(path),
-        query=ensure_type(query),
-        fragment=ensure_type(fragment),
-    )
-
-
-def get_host(url):
-    """
-    Deprecated. Use :func:`parse_url` instead.
-    """
-    p = parse_url(url)
-    return p.scheme or "http", p.hostname, p.port

spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/_distutils/command/clean.py

@@ -1,76 +0,0 @@
-"""distutils.command.clean
-
-Implements the Distutils 'clean' command."""
-
-# contributed by Bastian Kleineidam <[email protected]>, added 2000-03-18
-
-import os
-from distutils.core import Command
-from distutils.dir_util import remove_tree
-from distutils import log
-
-
-class clean(Command):
-
-    description = "clean up temporary files from 'build' command"
-    user_options = [
-        ('build-base=', 'b', "base build directory (default: 'build.build-base')"),
-        (
-            'build-lib=',
-            None,
-            "build directory for all modules (default: 'build.build-lib')",
-        ),
-        ('build-temp=', 't', "temporary build directory (default: 'build.build-temp')"),
-        (
-            'build-scripts=',
-            None,
-            "build directory for scripts (default: 'build.build-scripts')",
-        ),
-        ('bdist-base=', None, "temporary directory for built distributions"),
-        ('all', 'a', "remove all build output, not just temporary by-products"),
-    ]
-
-    boolean_options = ['all']
-
-    def initialize_options(self):
-        self.build_base = None
-        self.build_lib = None
-        self.build_temp = None
-        self.build_scripts = None
-        self.bdist_base = None
-        self.all = None
-
-    def finalize_options(self):
-        self.set_undefined_options(
-            'build',
-            ('build_base', 'build_base'),
-            ('build_lib', 'build_lib'),
-            ('build_scripts', 'build_scripts'),
-            ('build_temp', 'build_temp'),
-        )
-        self.set_undefined_options('bdist', ('bdist_base', 'bdist_base'))
-
-    def run(self):
-        # remove the build/temp.<plat> directory (unless it's already
-        # gone)
-        if os.path.exists(self.build_temp):
-            remove_tree(self.build_temp, dry_run=self.dry_run)
-        else:
-            log.debug("'%s' does not exist -- can't clean it", self.build_temp)
-
-        if self.all:
-            # remove build directories
-            for directory in (self.build_lib, self.bdist_base, self.build_scripts):
-                if os.path.exists(directory):
-                    remove_tree(directory, dry_run=self.dry_run)
-                else:
-                    log.warn("'%s' does not exist -- can't clean it", directory)
-
-        # just for the heck of it, try to remove the base build directory:
-        # we might have emptied it right now, but if not we don't care
-        if not self.dry_run:
-            try:
-                os.rmdir(self.build_base)
-                log.info("removing '%s'", self.build_base)
-            except OSError:
-                pass

spaces/CALM/Dashboard/Makefile

@@ -1,15 +0,0 @@
-
-.PHONY: quality style test test-examples
-
-# Check that source code meets quality standards
-
-quality:
-	python -m black --check --line-length 119 --target-version py38 .
-	python -m isort --check-only .
-	python -m flake8 --max-line-length 119
-
-# Format source code automatically
-
-style:
-	python -m black --line-length 119 --target-version py38 .
-	python -m isort .

spaces/CGMatter/modelscope-text-to-video-synthesis/app.py

@@ -1,127 +0,0 @@
-#!/usr/bin/env python
-
-from __future__ import annotations
-
-import os
-import random
-import tempfile
-
-import gradio as gr
-import imageio
-import numpy as np
-import torch
-from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler
-
-DESCRIPTION = '# [ModelScope Text to Video Synthesis](https://modelscope.cn/models/damo/text-to-video-synthesis/summary)'
-DESCRIPTION += '\n<p>For Colab usage, you can view <a href="https://colab.research.google.com/drive/1uW1ZqswkQ9Z9bp5Nbo5z59cAn7I0hE6R?usp=sharing" style="text-decoration: underline;" target="_blank">this webpage</a>.（the latest update on 2023.03.21）</p>'
-DESCRIPTION += '\n<p>This model can only be used for non-commercial purposes. To learn more about the model, take a look at the <a href="https://huggingface.co/damo-vilab/modelscope-damo-text-to-video-synthesis" style="text-decoration: underline;" target="_blank">model card</a>.</p>'
-if (SPACE_ID := os.getenv('SPACE_ID')) is not None:
-    DESCRIPTION += f'\n<p>For faster inference without waiting in queue, you may duplicate the space and upgrade to GPU in settings. <a href="https://huggingface.co/spaces/{SPACE_ID}?duplicate=true"><img style="display: inline; margin-top: 0em; margin-bottom: 0em" src="https://bit.ly/3gLdBN6" alt="Duplicate Space" /></a></p>'
-
-MAX_NUM_FRAMES = int(os.getenv('MAX_NUM_FRAMES', '200'))
-DEFAULT_NUM_FRAMES = min(MAX_NUM_FRAMES,
-                         int(os.getenv('DEFAULT_NUM_FRAMES', '16')))
-
-pipe = DiffusionPipeline.from_pretrained('damo-vilab/text-to-video-ms-1.7b',
-                                         torch_dtype=torch.float16,
-                                         variant='fp16')
-pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
-pipe.enable_model_cpu_offload()
-pipe.enable_vae_slicing()
-
-
-def to_video(frames: list[np.ndarray], fps: int) -> str:
-    out_file = tempfile.NamedTemporaryFile(suffix='.mp4', delete=False)
-    writer = imageio.get_writer(out_file.name, format='FFMPEG', fps=fps)
-    for frame in frames:
-        writer.append_data(frame)
-    writer.close()
-    return out_file.name
-
-
-def generate(prompt: str, seed: int, num_frames: int,
-             num_inference_steps: int) -> str:
-    if seed == -1:
-        seed = random.randint(0, 1000000)
-    generator = torch.Generator().manual_seed(seed)
-    frames = pipe(prompt,
-                  num_inference_steps=num_inference_steps,
-                  num_frames=num_frames,
-                  generator=generator).frames
-    return to_video(frames, 8)
-
-
-examples = [
-    ['An astronaut riding a horse.', 0, 16, 25],
-    ['A panda eating bamboo on a rock.', 0, 16, 25],
-    ['Spiderman is surfing.', 0, 16, 25],
-]
-
-with gr.Blocks(css='style.css') as demo:
-    gr.Markdown(DESCRIPTION)
-    with gr.Group():
-        with gr.Box():
-            with gr.Row(elem_id='prompt-container').style(equal_height=True):
-                prompt = gr.Text(
-                    label='Prompt',
-                    show_label=False,
-                    max_lines=1,
-                    placeholder='Enter your prompt',
-                    elem_id='prompt-text-input').style(container=False)
-                run_button = gr.Button('Generate video').style(
-                    full_width=False)
-        result = gr.Video(label='Result', show_label=False, elem_id='gallery')
-        with gr.Accordion('Advanced options', open=False):
-            seed = gr.Slider(
-                label='Seed',
-                minimum=-1,
-                maximum=1000000,
-                step=1,
-                value=-1,
-                info='If set to -1, a different seed will be used each time.')
-            num_frames = gr.Slider(
-                label='Number of frames',
-                minimum=16,
-                maximum=MAX_NUM_FRAMES,
-                step=1,
-                value=16,
-                info=
-                'Note that the content of the video also changes when you change the number of frames.'
-            )
-            num_inference_steps = gr.Slider(label='Number of inference steps',
-                                            minimum=10,
-                                            maximum=50,
-                                            step=1,
-                                            value=25)
-
-    inputs = [
-        prompt,
-        seed,
-        num_frames,
-        num_inference_steps,
-    ]
-    gr.Examples(examples=examples,
-                inputs=inputs,
-                outputs=result,
-                fn=generate,
-                cache_examples=os.getenv('SYSTEM') == 'spaces')
-
-    prompt.submit(fn=generate, inputs=inputs, outputs=result)
-    run_button.click(fn=generate, inputs=inputs, outputs=result)
-
-    with gr.Accordion(label='Biases and content acknowledgment', open=False):
-        gr.HTML("""<div class="acknowledgments">
-                    <h4>Biases and content acknowledgment</h4>
-                    <p>
-                        Despite how impressive being able to turn text into video is, beware to the fact that this model may output content that reinforces or exacerbates societal biases. The training data includes LAION5B, ImageNet, Webvid and other public datasets. The model was not trained to realistically represent people or events, so using it to generate such content is beyond the model's capabilities.
-                    </p>
-                    <p>
-                        It is not intended to generate content that is demeaning or harmful to people or their environment, culture, religion, etc. Similarly, it is not allowed to generate pornographic, violent and bloody content generation. <b>The model is meant for research purposes</b>.
-                    </p>
-                    <p>
-                        To learn more about the model, head to its <a href="https://huggingface.co/damo-vilab/modelscope-damo-text-to-video-synthesis" style="text-decoration: underline;" target="_blank">model card</a>.
-                    </p>
-                   </div>
-                """)
-
-demo.queue(api_open=False, max_size=15).launch()

spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/detectron2/utils/comm.py

@@ -1,263 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-"""
-This file contains primitives for multi-gpu communication.
-This is useful when doing distributed training.
-"""
-
-import functools
-import logging
-import numpy as np
-import pickle
-import torch
-import torch.distributed as dist
-
-_LOCAL_PROCESS_GROUP = None
-"""
-A torch process group which only includes processes that on the same machine as the current process.
-This variable is set when processes are spawned by `launch()` in "engine/launch.py".
-"""
-
-
-def get_world_size() -> int:
-    if not dist.is_available():
-        return 1
-    if not dist.is_initialized():
-        return 1
-    return dist.get_world_size()
-
-
-def get_rank() -> int:
-    if not dist.is_available():
-        return 0
-    if not dist.is_initialized():
-        return 0
-    return dist.get_rank()
-
-
-def get_local_rank() -> int:
-    """
-    Returns:
-        The rank of the current process within the local (per-machine) process group.
-    """
-    if not dist.is_available():
-        return 0
-    if not dist.is_initialized():
-        return 0
-    assert _LOCAL_PROCESS_GROUP is not None
-    return dist.get_rank(group=_LOCAL_PROCESS_GROUP)
-
-
-def get_local_size() -> int:
-    """
-    Returns:
-        The size of the per-machine process group,
-        i.e. the number of processes per machine.
-    """
-    if not dist.is_available():
-        return 1
-    if not dist.is_initialized():
-        return 1
-    return dist.get_world_size(group=_LOCAL_PROCESS_GROUP)
-
-
-def is_main_process() -> bool:
-    return get_rank() == 0
-
-
-def synchronize():
-    """
-    Helper function to synchronize (barrier) among all processes when
-    using distributed training
-    """
-    if not dist.is_available():
-        return
-    if not dist.is_initialized():
-        return
-    world_size = dist.get_world_size()
-    if world_size == 1:
-        return
-    dist.barrier()
-
-
-@functools.lru_cache()
-def _get_global_gloo_group():
-    """
-    Return a process group based on gloo backend, containing all the ranks
-    The result is cached.
-    """
-    if dist.get_backend() == "nccl":
-        return dist.new_group(backend="gloo")
-    else:
-        return dist.group.WORLD
-
-
-def _serialize_to_tensor(data, group):
-    backend = dist.get_backend(group)
-    assert backend in ["gloo", "nccl"]
-    device = torch.device("cpu" if backend == "gloo" else "cuda")
-
-    buffer = pickle.dumps(data)
-    if len(buffer) > 1024 ** 3:
-        logger = logging.getLogger(__name__)
-        logger.warning(
-            "Rank {} trying to all-gather {:.2f} GB of data on device {}".format(
-                get_rank(), len(buffer) / (1024 ** 3), device
-            )
-        )
-    storage = torch.ByteStorage.from_buffer(buffer)
-    tensor = torch.ByteTensor(storage).to(device=device)
-    return tensor
-
-
-def _pad_to_largest_tensor(tensor, group):
-    """
-    Returns:
-        list[int]: size of the tensor, on each rank
-        Tensor: padded tensor that has the max size
-    """
-    world_size = dist.get_world_size(group=group)
-    assert (
-        world_size >= 1
-    ), "comm.gather/all_gather must be called from ranks within the given group!"
-    local_size = torch.tensor([tensor.numel()], dtype=torch.int64, device=tensor.device)
-    size_list = [
-        torch.zeros([1], dtype=torch.int64, device=tensor.device) for _ in range(world_size)
-    ]
-    dist.all_gather(size_list, local_size, group=group)
-    size_list = [int(size.item()) for size in size_list]
-
-    max_size = max(size_list)
-
-    # we pad the tensor because torch all_gather does not support
-    # gathering tensors of different shapes
-    if local_size != max_size:
-        padding = torch.zeros((max_size - local_size,), dtype=torch.uint8, device=tensor.device)
-        tensor = torch.cat((tensor, padding), dim=0)
-    return size_list, tensor
-
-
-def all_gather(data, group=None):
-    """
-    Run all_gather on arbitrary picklable data (not necessarily tensors).
-
-    Args:
-        data: any picklable object
-        group: a torch process group. By default, will use a group which
-            contains all ranks on gloo backend.
-
-    Returns:
-        list[data]: list of data gathered from each rank
-    """
-    if get_world_size() == 1:
-        return [data]
-    if group is None:
-        group = _get_global_gloo_group()
-    if dist.get_world_size(group) == 1:
-        return [data]
-
-    tensor = _serialize_to_tensor(data, group)
-
-    size_list, tensor = _pad_to_largest_tensor(tensor, group)
-    max_size = max(size_list)
-
-    # receiving Tensor from all ranks
-    tensor_list = [
-        torch.empty((max_size,), dtype=torch.uint8, device=tensor.device) for _ in size_list
-    ]
-    dist.all_gather(tensor_list, tensor, group=group)
-
-    data_list = []
-    for size, tensor in zip(size_list, tensor_list):
-        buffer = tensor.cpu().numpy().tobytes()[:size]
-        data_list.append(pickle.loads(buffer))
-
-    return data_list
-
-
-def gather(data, dst=0, group=None):
-    """
-    Run gather on arbitrary picklable data (not necessarily tensors).
-
-    Args:
-        data: any picklable object
-        dst (int): destination rank
-        group: a torch process group. By default, will use a group which
-            contains all ranks on gloo backend.
-
-    Returns:
-        list[data]: on dst, a list of data gathered from each rank. Otherwise,
-            an empty list.
-    """
-    if get_world_size() == 1:
-        return [data]
-    if group is None:
-        group = _get_global_gloo_group()
-    if dist.get_world_size(group=group) == 1:
-        return [data]
-    rank = dist.get_rank(group=group)
-
-    tensor = _serialize_to_tensor(data, group)
-    size_list, tensor = _pad_to_largest_tensor(tensor, group)
-
-    # receiving Tensor from all ranks
-    if rank == dst:
-        max_size = max(size_list)
-        tensor_list = [
-            torch.empty((max_size,), dtype=torch.uint8, device=tensor.device) for _ in size_list
-        ]
-        dist.gather(tensor, tensor_list, dst=dst, group=group)
-
-        data_list = []
-        for size, tensor in zip(size_list, tensor_list):
-            buffer = tensor.cpu().numpy().tobytes()[:size]
-            data_list.append(pickle.loads(buffer))
-        return data_list
-    else:
-        dist.gather(tensor, [], dst=dst, group=group)
-        return []
-
-
-def shared_random_seed():
-    """
-    Returns:
-        int: a random number that is the same across all workers.
-            If workers need a shared RNG, they can use this shared seed to
-            create one.
-
-    All workers must call this function, otherwise it will deadlock.
-    """
-    ints = np.random.randint(2 ** 31)
-    all_ints = all_gather(ints)
-    return all_ints[0]
-
-
-def reduce_dict(input_dict, average=True):
-    """
-    Reduce the values in the dictionary from all processes so that process with rank
-    0 has the reduced results.
-
-    Args:
-        input_dict (dict): inputs to be reduced. All the values must be scalar CUDA Tensor.
-        average (bool): whether to do average or sum
-
-    Returns:
-        a dict with the same keys as input_dict, after reduction.
-    """
-    world_size = get_world_size()
-    if world_size < 2:
-        return input_dict
-    with torch.no_grad():
-        names = []
-        values = []
-        # sort the keys so that they are consistent across processes
-        for k in sorted(input_dict.keys()):
-            names.append(k)
-            values.append(input_dict[k])
-        values = torch.stack(values, dim=0)
-        dist.reduce(values, dst=0)
-        if dist.get_rank() == 0 and average:
-            # only main process gets accumulated, so only divide by
-            # world_size in this case
-            values /= world_size
-        reduced_dict = {k: v for k, v in zip(names, values)}
-    return reduced_dict

spaces/CVPR/GFPGAN-example/tests/test_ffhq_degradation_dataset.py

@@ -1,96 +0,0 @@
-import pytest
-import yaml
-
-from gfpgan.data.ffhq_degradation_dataset import FFHQDegradationDataset
-
-
-def test_ffhq_degradation_dataset():
-
-    with open('tests/data/test_ffhq_degradation_dataset.yml', mode='r') as f:
-        opt = yaml.load(f, Loader=yaml.FullLoader)
-
-    dataset = FFHQDegradationDataset(opt)
-    assert dataset.io_backend_opt['type'] == 'disk'  # io backend
-    assert len(dataset) == 1  # whether to read correct meta info
-    assert dataset.kernel_list == ['iso', 'aniso']  # correct initialization the degradation configurations
-    assert dataset.color_jitter_prob == 1
-
-    # test __getitem__
-    result = dataset.__getitem__(0)
-    # check returned keys
-    expected_keys = ['gt', 'lq', 'gt_path']
-    assert set(expected_keys).issubset(set(result.keys()))
-    # check shape and contents
-    assert result['gt'].shape == (3, 512, 512)
-    assert result['lq'].shape == (3, 512, 512)
-    assert result['gt_path'] == 'tests/data/gt/00000000.png'
-
-    # ------------------ test with probability = 0 -------------------- #
-    opt['color_jitter_prob'] = 0
-    opt['color_jitter_pt_prob'] = 0
-    opt['gray_prob'] = 0
-    opt['io_backend'] = dict(type='disk')
-    dataset = FFHQDegradationDataset(opt)
-    assert dataset.io_backend_opt['type'] == 'disk'  # io backend
-    assert len(dataset) == 1  # whether to read correct meta info
-    assert dataset.kernel_list == ['iso', 'aniso']  # correct initialization the degradation configurations
-    assert dataset.color_jitter_prob == 0
-
-    # test __getitem__
-    result = dataset.__getitem__(0)
-    # check returned keys
-    expected_keys = ['gt', 'lq', 'gt_path']
-    assert set(expected_keys).issubset(set(result.keys()))
-    # check shape and contents
-    assert result['gt'].shape == (3, 512, 512)
-    assert result['lq'].shape == (3, 512, 512)
-    assert result['gt_path'] == 'tests/data/gt/00000000.png'
-
-    # ------------------ test lmdb backend -------------------- #
-    opt['dataroot_gt'] = 'tests/data/ffhq_gt.lmdb'
-    opt['io_backend'] = dict(type='lmdb')
-
-    dataset = FFHQDegradationDataset(opt)
-    assert dataset.io_backend_opt['type'] == 'lmdb'  # io backend
-    assert len(dataset) == 1  # whether to read correct meta info
-    assert dataset.kernel_list == ['iso', 'aniso']  # correct initialization the degradation configurations
-    assert dataset.color_jitter_prob == 0
-
-    # test __getitem__
-    result = dataset.__getitem__(0)
-    # check returned keys
-    expected_keys = ['gt', 'lq', 'gt_path']
-    assert set(expected_keys).issubset(set(result.keys()))
-    # check shape and contents
-    assert result['gt'].shape == (3, 512, 512)
-    assert result['lq'].shape == (3, 512, 512)
-    assert result['gt_path'] == '00000000'
-
-    # ------------------ test with crop_components -------------------- #
-    opt['crop_components'] = True
-    opt['component_path'] = 'tests/data/test_eye_mouth_landmarks.pth'
-    opt['eye_enlarge_ratio'] = 1.4
-    opt['gt_gray'] = True
-    opt['io_backend'] = dict(type='lmdb')
-
-    dataset = FFHQDegradationDataset(opt)
-    assert dataset.crop_components is True
-
-    # test __getitem__
-    result = dataset.__getitem__(0)
-    # check returned keys
-    expected_keys = ['gt', 'lq', 'gt_path', 'loc_left_eye', 'loc_right_eye', 'loc_mouth']
-    assert set(expected_keys).issubset(set(result.keys()))
-    # check shape and contents
-    assert result['gt'].shape == (3, 512, 512)
-    assert result['lq'].shape == (3, 512, 512)
-    assert result['gt_path'] == '00000000'
-    assert result['loc_left_eye'].shape == (4, )
-    assert result['loc_right_eye'].shape == (4, )
-    assert result['loc_mouth'].shape == (4, )
-
-    # ------------------ lmdb backend should have paths ends with lmdb -------------------- #
-    with pytest.raises(ValueError):
-        opt['dataroot_gt'] = 'tests/data/gt'
-        opt['io_backend'] = dict(type='lmdb')
-        dataset = FFHQDegradationDataset(opt)

spaces/CVPR/WALT/mmdet/apis/inference.py

@@ -1,217 +0,0 @@
-import warnings
-
-import mmcv
-import numpy as np
-import torch
-from mmcv.ops import RoIPool
-from mmcv.parallel import collate, scatter
-from mmcv.runner import load_checkpoint
-
-from mmdet.core import get_classes
-from mmdet.datasets import replace_ImageToTensor
-from mmdet.datasets.pipelines import Compose
-from mmdet.models import build_detector
-
-
-def init_detector(config, checkpoint=None, device='cuda:0', cfg_options=None):
-    """Initialize a detector from config file.
-
-    Args:
-        config (str or :obj:`mmcv.Config`): Config file path or the config
-            object.
-        checkpoint (str, optional): Checkpoint path. If left as None, the model
-            will not load any weights.
-        cfg_options (dict): Options to override some settings in the used
-            config.
-
-    Returns:
-        nn.Module: The constructed detector.
-    """
-    if isinstance(config, str):
-        config = mmcv.Config.fromfile(config)
-    elif not isinstance(config, mmcv.Config):
-        raise TypeError('config must be a filename or Config object, '
-                        f'but got {type(config)}')
-    if cfg_options is not None:
-        config.merge_from_dict(cfg_options)
-    config.model.pretrained = None
-    config.model.train_cfg = None
-    model = build_detector(config.model, test_cfg=config.get('test_cfg'))
-    if checkpoint is not None:
-        map_loc = 'cpu' if device == 'cpu' else None
-        checkpoint = load_checkpoint(model, checkpoint, map_location=map_loc)
-        if 'CLASSES' in checkpoint.get('meta', {}):
-            model.CLASSES = checkpoint['meta']['CLASSES']
-        else:
-            warnings.simplefilter('once')
-            warnings.warn('Class names are not saved in the checkpoint\'s '
-                          'meta data, use COCO classes by default.')
-            model.CLASSES = get_classes('coco')
-    model.cfg = config  # save the config in the model for convenience
-    model.to(device)
-    model.eval()
-    return model
-
-
-class LoadImage(object):
-    """Deprecated.
-
-    A simple pipeline to load image.
-    """
-
-    def __call__(self, results):
-        """Call function to load images into results.
-
-        Args:
-            results (dict): A result dict contains the file name
-                of the image to be read.
-        Returns:
-            dict: ``results`` will be returned containing loaded image.
-        """
-        warnings.simplefilter('once')
-        warnings.warn('`LoadImage` is deprecated and will be removed in '
-                      'future releases. You may use `LoadImageFromWebcam` '
-                      'from `mmdet.datasets.pipelines.` instead.')
-        if isinstance(results['img'], str):
-            results['filename'] = results['img']
-            results['ori_filename'] = results['img']
-        else:
-            results['filename'] = None
-            results['ori_filename'] = None
-        img = mmcv.imread(results['img'])
-        results['img'] = img
-        results['img_fields'] = ['img']
-        results['img_shape'] = img.shape
-        results['ori_shape'] = img.shape
-        return results
-
-
-def inference_detector(model, imgs):
-    """Inference image(s) with the detector.
-
-    Args:
-        model (nn.Module): The loaded detector.
-        imgs (str/ndarray or list[str/ndarray] or tuple[str/ndarray]):
-           Either image files or loaded images.
-
-    Returns:
-        If imgs is a list or tuple, the same length list type results
-        will be returned, otherwise return the detection results directly.
-    """
-
-    if isinstance(imgs, (list, tuple)):
-        is_batch = True
-    else:
-        imgs = [imgs]
-        is_batch = False
-
-    cfg = model.cfg
-    device = next(model.parameters()).device  # model device
-
-    if isinstance(imgs[0], np.ndarray):
-        cfg = cfg.copy()
-        # set loading pipeline type
-        cfg.data.test.pipeline[0].type = 'LoadImageFromWebcam'
-
-    cfg.data.test.pipeline = replace_ImageToTensor(cfg.data.test.pipeline)
-    test_pipeline = Compose(cfg.data.test.pipeline)
-
-    datas = []
-    for img in imgs:
-        # prepare data
-        if isinstance(img, np.ndarray):
-            # directly add img
-            data = dict(img=img)
-        else:
-            # add information into dict
-            data = dict(img_info=dict(filename=img), img_prefix=None)
-        # build the data pipeline
-        data = test_pipeline(data)
-        datas.append(data)
-
-    data = collate(datas, samples_per_gpu=len(imgs))
-    # just get the actual data from DataContainer
-    data['img_metas'] = [img_metas.data[0] for img_metas in data['img_metas']]
-    data['img'] = [img.data[0] for img in data['img']]
-    if next(model.parameters()).is_cuda:
-        # scatter to specified GPU
-        data = scatter(data, [device])[0]
-    else:
-        for m in model.modules():
-            assert not isinstance(
-                m, RoIPool
-            ), 'CPU inference with RoIPool is not supported currently.'
-
-    # forward the model
-    with torch.no_grad():
-        results = model(return_loss=False, rescale=True, **data)
-
-    if not is_batch:
-        return results[0]
-    else:
-        return results
-
-
-async def async_inference_detector(model, img):
-    """Async inference image(s) with the detector.
-
-    Args:
-        model (nn.Module): The loaded detector.
-        img (str | ndarray): Either image files or loaded images.
-
-    Returns:
-        Awaitable detection results.
-    """
-    cfg = model.cfg
-    device = next(model.parameters()).device  # model device
-    # prepare data
-    if isinstance(img, np.ndarray):
-        # directly add img
-        data = dict(img=img)
-        cfg = cfg.copy()
-        # set loading pipeline type
-        cfg.data.test.pipeline[0].type = 'LoadImageFromWebcam'
-    else:
-        # add information into dict
-        data = dict(img_info=dict(filename=img), img_prefix=None)
-    # build the data pipeline
-    test_pipeline = Compose(cfg.data.test.pipeline)
-    data = test_pipeline(data)
-    data = scatter(collate([data], samples_per_gpu=1), [device])[0]
-
-    # We don't restore `torch.is_grad_enabled()` value during concurrent
-    # inference since execution can overlap
-    torch.set_grad_enabled(False)
-    result = await model.aforward_test(rescale=True, **data)
-    return result
-
-
-def show_result_pyplot(model,
-                       img,
-                       result,
-                       score_thr=0.3,
-                       title='result',
-                       wait_time=0):
-    """Visualize the detection results on the image.
-
-    Args:
-        model (nn.Module): The loaded detector.
-        img (str or np.ndarray): Image filename or loaded image.
-        result (tuple[list] or list): The detection result, can be either
-            (bbox, segm) or just bbox.
-        score_thr (float): The threshold to visualize the bboxes and masks.
-        title (str): Title of the pyplot figure.
-        wait_time (float): Value of waitKey param.
-                Default: 0.
-    """
-    if hasattr(model, 'module'):
-        model = model.module
-    model.show_result(
-        img,
-        result,
-        score_thr=score_thr,
-        show=True,
-        wait_time=wait_time,
-        win_name=title,
-        bbox_color=(72, 101, 241),
-        text_color=(72, 101, 241))

spaces/CVPR/WALT/mmdet/datasets/pipelines/formating.py

@@ -1,364 +0,0 @@
-from collections.abc import Sequence
-
-import mmcv
-import numpy as np
-import torch
-from mmcv.parallel import DataContainer as DC
-
-from ..builder import PIPELINES
-
-
-def to_tensor(data):
-    """Convert objects of various python types to :obj:`torch.Tensor`.
-
-    Supported types are: :class:`numpy.ndarray`, :class:`torch.Tensor`,
-    :class:`Sequence`, :class:`int` and :class:`float`.
-
-    Args:
-        data (torch.Tensor | numpy.ndarray | Sequence | int | float): Data to
-            be converted.
-    """
-
-    if isinstance(data, torch.Tensor):
-        return data
-    elif isinstance(data, np.ndarray):
-        return torch.from_numpy(data)
-    elif isinstance(data, Sequence) and not mmcv.is_str(data):
-        return torch.tensor(data)
-    elif isinstance(data, int):
-        return torch.LongTensor([data])
-    elif isinstance(data, float):
-        return torch.FloatTensor([data])
-    else:
-        raise TypeError(f'type {type(data)} cannot be converted to tensor.')
-
-
-@PIPELINES.register_module()
-class ToTensor(object):
-    """Convert some results to :obj:`torch.Tensor` by given keys.
-
-    Args:
-        keys (Sequence[str]): Keys that need to be converted to Tensor.
-    """
-
-    def __init__(self, keys):
-        self.keys = keys
-
-    def __call__(self, results):
-        """Call function to convert data in results to :obj:`torch.Tensor`.
-
-        Args:
-            results (dict): Result dict contains the data to convert.
-
-        Returns:
-            dict: The result dict contains the data converted
-                to :obj:`torch.Tensor`.
-        """
-        for key in self.keys:
-            results[key] = to_tensor(results[key])
-        return results
-
-    def __repr__(self):
-        return self.__class__.__name__ + f'(keys={self.keys})'
-
-
-@PIPELINES.register_module()
-class ImageToTensor(object):
-    """Convert image to :obj:`torch.Tensor` by given keys.
-
-    The dimension order of input image is (H, W, C). The pipeline will convert
-    it to (C, H, W). If only 2 dimension (H, W) is given, the output would be
-    (1, H, W).
-
-    Args:
-        keys (Sequence[str]): Key of images to be converted to Tensor.
-    """
-
-    def __init__(self, keys):
-        self.keys = keys
-
-    def __call__(self, results):
-        """Call function to convert image in results to :obj:`torch.Tensor` and
-        transpose the channel order.
-
-        Args:
-            results (dict): Result dict contains the image data to convert.
-
-        Returns:
-            dict: The result dict contains the image converted
-                to :obj:`torch.Tensor` and transposed to (C, H, W) order.
-        """
-        for key in self.keys:
-            img = results[key]
-            if len(img.shape) < 3:
-                img = np.expand_dims(img, -1)
-            results[key] = to_tensor(img.transpose(2, 0, 1))
-        return results
-
-    def __repr__(self):
-        return self.__class__.__name__ + f'(keys={self.keys})'
-
-
-@PIPELINES.register_module()
-class Transpose(object):
-    """Transpose some results by given keys.
-
-    Args:
-        keys (Sequence[str]): Keys of results to be transposed.
-        order (Sequence[int]): Order of transpose.
-    """
-
-    def __init__(self, keys, order):
-        self.keys = keys
-        self.order = order
-
-    def __call__(self, results):
-        """Call function to transpose the channel order of data in results.
-
-        Args:
-            results (dict): Result dict contains the data to transpose.
-
-        Returns:
-            dict: The result dict contains the data transposed to \
-                ``self.order``.
-        """
-        for key in self.keys:
-            results[key] = results[key].transpose(self.order)
-        return results
-
-    def __repr__(self):
-        return self.__class__.__name__ + \
-            f'(keys={self.keys}, order={self.order})'
-
-
-@PIPELINES.register_module()
-class ToDataContainer(object):
-    """Convert results to :obj:`mmcv.DataContainer` by given fields.
-
-    Args:
-        fields (Sequence[dict]): Each field is a dict like
-            ``dict(key='xxx', **kwargs)``. The ``key`` in result will
-            be converted to :obj:`mmcv.DataContainer` with ``**kwargs``.
-            Default: ``(dict(key='img', stack=True), dict(key='gt_bboxes'),
-            dict(key='gt_labels'))``.
-    """
-
-    def __init__(self,
-                 fields=(dict(key='img', stack=True), dict(key='gt_bboxes'),
-                         dict(key='gt_labels'))):
-        self.fields = fields
-
-    def __call__(self, results):
-        """Call function to convert data in results to
-        :obj:`mmcv.DataContainer`.
-
-        Args:
-            results (dict): Result dict contains the data to convert.
-
-        Returns:
-            dict: The result dict contains the data converted to \
-                :obj:`mmcv.DataContainer`.
-        """
-
-        for field in self.fields:
-            field = field.copy()
-            key = field.pop('key')
-            results[key] = DC(results[key], **field)
-        return results
-
-    def __repr__(self):
-        return self.__class__.__name__ + f'(fields={self.fields})'
-
-
-@PIPELINES.register_module()
-class DefaultFormatBundle(object):
-    """Default formatting bundle.
-
-    It simplifies the pipeline of formatting common fields, including "img",
-    "proposals", "gt_bboxes", "gt_labels", "gt_masks" and "gt_semantic_seg".
-    These fields are formatted as follows.
-
-    - img: (1)transpose, (2)to tensor, (3)to DataContainer (stack=True)
-    - proposals: (1)to tensor, (2)to DataContainer
-    - gt_bboxes: (1)to tensor, (2)to DataContainer
-    - gt_bboxes_ignore: (1)to tensor, (2)to DataContainer
-    - gt_labels: (1)to tensor, (2)to DataContainer
-    - gt_masks: (1)to tensor, (2)to DataContainer (cpu_only=True)
-    - gt_semantic_seg: (1)unsqueeze dim-0 (2)to tensor, \
-                       (3)to DataContainer (stack=True)
-    """
-
-    def __call__(self, results):
-        """Call function to transform and format common fields in results.
-
-        Args:
-            results (dict): Result dict contains the data to convert.
-
-        Returns:
-            dict: The result dict contains the data that is formatted with \
-                default bundle.
-        """
-
-        if 'img' in results:
-            img = results['img']
-            # add default meta keys
-            results = self._add_default_meta_keys(results)
-            if len(img.shape) < 3:
-                img = np.expand_dims(img, -1)
-            img = np.ascontiguousarray(img.transpose(2, 0, 1))
-            results['img'] = DC(to_tensor(img), stack=True)
-        for key in ['proposals', 'gt_bboxes', 'gt_bboxes_ignore', 'gt_labels']:
-            if key not in results:
-                continue
-            results[key] = DC(to_tensor(results[key]))
-        if 'gt_masks' in results:
-            results['gt_masks'] = DC(results['gt_masks'], cpu_only=True)
-        if 'gt_semantic_seg' in results:
-            results['gt_semantic_seg'] = DC(
-                to_tensor(results['gt_semantic_seg'][None, ...]), stack=True)
-        return results
-
-    def _add_default_meta_keys(self, results):
-        """Add default meta keys.
-
-        We set default meta keys including `pad_shape`, `scale_factor` and
-        `img_norm_cfg` to avoid the case where no `Resize`, `Normalize` and
-        `Pad` are implemented during the whole pipeline.
-
-        Args:
-            results (dict): Result dict contains the data to convert.
-
-        Returns:
-            results (dict): Updated result dict contains the data to convert.
-        """
-        img = results['img']
-        results.setdefault('pad_shape', img.shape)
-        results.setdefault('scale_factor', 1.0)
-        num_channels = 1 if len(img.shape) < 3 else img.shape[2]
-        results.setdefault(
-            'img_norm_cfg',
-            dict(
-                mean=np.zeros(num_channels, dtype=np.float32),
-                std=np.ones(num_channels, dtype=np.float32),
-                to_rgb=False))
-        return results
-
-    def __repr__(self):
-        return self.__class__.__name__
-
-
-@PIPELINES.register_module()
-class Collect(object):
-    """Collect data from the loader relevant to the specific task.
-
-    This is usually the last stage of the data loader pipeline. Typically keys
-    is set to some subset of "img", "proposals", "gt_bboxes",
-    "gt_bboxes_ignore", "gt_labels", and/or "gt_masks".
-
-    The "img_meta" item is always populated.  The contents of the "img_meta"
-    dictionary depends on "meta_keys". By default this includes:
-
-        - "img_shape": shape of the image input to the network as a tuple \
-            (h, w, c).  Note that images may be zero padded on the \
-            bottom/right if the batch tensor is larger than this shape.
-
-        - "scale_factor": a float indicating the preprocessing scale
-
-        - "flip": a boolean indicating if image flip transform was used
-
-        - "filename": path to the image file
-
-        - "ori_shape": original shape of the image as a tuple (h, w, c)
-
-        - "pad_shape": image shape after padding
-
-        - "img_norm_cfg": a dict of normalization information:
-
-            - mean - per channel mean subtraction
-            - std - per channel std divisor
-            - to_rgb - bool indicating if bgr was converted to rgb
-
-    Args:
-        keys (Sequence[str]): Keys of results to be collected in ``data``.
-        meta_keys (Sequence[str], optional): Meta keys to be converted to
-            ``mmcv.DataContainer`` and collected in ``data[img_metas]``.
-            Default: ``('filename', 'ori_filename', 'ori_shape', 'img_shape',
-            'pad_shape', 'scale_factor', 'flip', 'flip_direction',
-            'img_norm_cfg')``
-    """
-
-    def __init__(self,
-                 keys,
-                 meta_keys=('filename', 'ori_filename', 'ori_shape',
-                            'img_shape', 'pad_shape', 'scale_factor', 'flip',
-                            'flip_direction', 'img_norm_cfg')):
-        self.keys = keys
-        self.meta_keys = meta_keys
-
-    def __call__(self, results):
-        """Call function to collect keys in results. The keys in ``meta_keys``
-        will be converted to :obj:mmcv.DataContainer.
-
-        Args:
-            results (dict): Result dict contains the data to collect.
-
-        Returns:
-            dict: The result dict contains the following keys
-
-                - keys in``self.keys``
-                - ``img_metas``
-        """
-
-        data = {}
-        img_meta = {}
-        for key in self.meta_keys:
-            img_meta[key] = results[key]
-        data['img_metas'] = DC(img_meta, cpu_only=True)
-        for key in self.keys:
-            data[key] = results[key]
-        return data
-
-    def __repr__(self):
-        return self.__class__.__name__ + \
-            f'(keys={self.keys}, meta_keys={self.meta_keys})'
-
-
-@PIPELINES.register_module()
-class WrapFieldsToLists(object):
-    """Wrap fields of the data dictionary into lists for evaluation.
-
-    This class can be used as a last step of a test or validation
-    pipeline for single image evaluation or inference.
-
-    Example:
-        >>> test_pipeline = [
-        >>>    dict(type='LoadImageFromFile'),
-        >>>    dict(type='Normalize',
-                    mean=[123.675, 116.28, 103.53],
-                    std=[58.395, 57.12, 57.375],
-                    to_rgb=True),
-        >>>    dict(type='Pad', size_divisor=32),
-        >>>    dict(type='ImageToTensor', keys=['img']),
-        >>>    dict(type='Collect', keys=['img']),
-        >>>    dict(type='WrapFieldsToLists')
-        >>> ]
-    """
-
-    def __call__(self, results):
-        """Call function to wrap fields into lists.
-
-        Args:
-            results (dict): Result dict contains the data to wrap.
-
-        Returns:
-            dict: The result dict where value of ``self.keys`` are wrapped \
-                into list.
-        """
-
-        # Wrap dict fields into lists
-        for key, val in results.items():
-            results[key] = [val]
-        return results
-
-    def __repr__(self):
-        return f'{self.__class__.__name__}()'

spaces/CVPR/WALT/mmdet/models/utils/__init__.py

@@ -1,16 +0,0 @@
-from .builder import build_positional_encoding, build_transformer
-from .gaussian_target import gaussian_radius, gen_gaussian_target
-from .positional_encoding import (LearnedPositionalEncoding,
-                                  SinePositionalEncoding)
-from .res_layer import ResLayer, SimplifiedBasicBlock
-from .transformer import (FFN, DynamicConv, MultiheadAttention, Transformer,
-                          TransformerDecoder, TransformerDecoderLayer,
-                          TransformerEncoder, TransformerEncoderLayer)
-
-__all__ = [
-    'ResLayer', 'gaussian_radius', 'gen_gaussian_target', 'MultiheadAttention',
-    'FFN', 'TransformerEncoderLayer', 'TransformerEncoder',
-    'TransformerDecoderLayer', 'TransformerDecoder', 'Transformer',
-    'build_transformer', 'build_positional_encoding', 'SinePositionalEncoding',
-    'LearnedPositionalEncoding', 'DynamicConv', 'SimplifiedBasicBlock'
-]

spaces/Caoyunkang/Segment-Any-Anomaly/GroundingDINO/demo/inference_on_a_image.py

@@ -1,172 +0,0 @@
-import argparse
-import os
-import sys
-
-import numpy as np
-import torch
-from PIL import Image, ImageDraw, ImageFont
-
-import groundingdino.datasets.transforms as T
-from groundingdino.models import build_model
-from groundingdino.util import box_ops
-from groundingdino.util.slconfig import SLConfig
-from groundingdino.util.utils import clean_state_dict, get_phrases_from_posmap
-
-
-def plot_boxes_to_image(image_pil, tgt):
-    H, W = tgt["size"]
-    boxes = tgt["boxes"]
-    labels = tgt["labels"]
-    assert len(boxes) == len(labels), "boxes and labels must have same length"
-
-    draw = ImageDraw.Draw(image_pil)
-    mask = Image.new("L", image_pil.size, 0)
-    mask_draw = ImageDraw.Draw(mask)
-
-    # draw boxes and masks
-    for box, label in zip(boxes, labels):
-        # from 0..1 to 0..W, 0..H
-        box = box * torch.Tensor([W, H, W, H])
-        # from xywh to xyxy
-        box[:2] -= box[2:] / 2
-        box[2:] += box[:2]
-        # random color
-        color = tuple(np.random.randint(0, 255, size=3).tolist())
-        # draw
-        x0, y0, x1, y1 = box
-        x0, y0, x1, y1 = int(x0), int(y0), int(x1), int(y1)
-
-        draw.rectangle([x0, y0, x1, y1], outline=color, width=6)
-        # draw.text((x0, y0), str(label), fill=color)
-
-        font = ImageFont.load_default()
-        if hasattr(font, "getbbox"):
-            bbox = draw.textbbox((x0, y0), str(label), font)
-        else:
-            w, h = draw.textsize(str(label), font)
-            bbox = (x0, y0, w + x0, y0 + h)
-        # bbox = draw.textbbox((x0, y0), str(label))
-        draw.rectangle(bbox, fill=color)
-        draw.text((x0, y0), str(label), fill="white")
-
-        mask_draw.rectangle([x0, y0, x1, y1], fill=255, width=6)
-
-    return image_pil, mask
-
-
-def load_image(image_path):
-    # load image
-    image_pil = Image.open(image_path).convert("RGB")  # load image
-
-    transform = T.Compose(
-        [
-            T.RandomResize([800], max_size=1333),
-            T.ToTensor(),
-            T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
-        ]
-    )
-    image, _ = transform(image_pil, None)  # 3, h, w
-    return image_pil, image
-
-
-def load_model(model_config_path, model_checkpoint_path, cpu_only=False):
-    args = SLConfig.fromfile(model_config_path)
-    args.device = "cuda" if not cpu_only else "cpu"
-    model = build_model(args)
-    checkpoint = torch.load(model_checkpoint_path, map_location="cpu")
-    load_res = model.load_state_dict(clean_state_dict(checkpoint["model"]), strict=False)
-    print(load_res)
-    _ = model.eval()
-    return model
-
-
-def get_grounding_output(model, image, caption, box_threshold, text_threshold, with_logits=True, cpu_only=False):
-    caption = caption.lower()
-    caption = caption.strip()
-    if not caption.endswith("."):
-        caption = caption + "."
-    device = "cuda" if not cpu_only else "cpu"
-    model = model.to(device)
-    image = image.to(device)
-    with torch.no_grad():
-        outputs = model(image[None], captions=[caption])
-    logits = outputs["pred_logits"].cpu().sigmoid()[0]  # (nq, 256)
-    boxes = outputs["pred_boxes"].cpu()[0]  # (nq, 4)
-    logits.shape[0]
-
-    # filter output
-    logits_filt = logits.clone()
-    boxes_filt = boxes.clone()
-    filt_mask = logits_filt.max(dim=1)[0] > box_threshold
-    logits_filt = logits_filt[filt_mask]  # num_filt, 256
-    boxes_filt = boxes_filt[filt_mask]  # num_filt, 4
-    logits_filt.shape[0]
-
-    # get phrase
-    tokenlizer = model.tokenizer
-    tokenized = tokenlizer(caption)
-    # build pred
-    pred_phrases = []
-    for logit, box in zip(logits_filt, boxes_filt):
-        pred_phrase = get_phrases_from_posmap(logit > text_threshold, tokenized, tokenlizer)
-        if with_logits:
-            pred_phrases.append(pred_phrase + f"({str(logit.max().item())[:4]})")
-        else:
-            pred_phrases.append(pred_phrase)
-
-    return boxes_filt, pred_phrases
-
-
-if __name__ == "__main__":
-
-    parser = argparse.ArgumentParser("Grounding DINO example", add_help=True)
-    parser.add_argument("--config_file", "-c", type=str, required=True, help="path to config file")
-    parser.add_argument(
-        "--checkpoint_path", "-p", type=str, required=True, help="path to checkpoint file"
-    )
-    parser.add_argument("--image_path", "-i", type=str, required=True, help="path to image file")
-    parser.add_argument("--text_prompt", "-t", type=str, required=True, help="text prompt")
-    parser.add_argument(
-        "--output_dir", "-o", type=str, default="outputs", required=True, help="output directory"
-    )
-
-    parser.add_argument("--box_threshold", type=float, default=0.3, help="box threshold")
-    parser.add_argument("--text_threshold", type=float, default=0.25, help="text threshold")
-
-    parser.add_argument("--cpu-only", action="store_true", help="running on cpu only!, default=False")
-    args = parser.parse_args()
-
-    # cfg
-    config_file = args.config_file  # change the path of the model config file
-    checkpoint_path = args.checkpoint_path  # change the path of the model
-    image_path = args.image_path
-    text_prompt = args.text_prompt
-    output_dir = args.output_dir
-    box_threshold = args.box_threshold
-    text_threshold = args.box_threshold
-
-    # make dir
-    os.makedirs(output_dir, exist_ok=True)
-    # load image
-    image_pil, image = load_image(image_path)
-    # load model
-    model = load_model(config_file, checkpoint_path, cpu_only=args.cpu_only)
-
-    # visualize raw image
-    image_pil.save(os.path.join(output_dir, "raw_image.jpg"))
-
-    # run model
-    boxes_filt, pred_phrases = get_grounding_output(
-        model, image, text_prompt, box_threshold, text_threshold, cpu_only=args.cpu_only
-    )
-
-    # visualize pred
-    size = image_pil.size
-    pred_dict = {
-        "boxes": boxes_filt,
-        "size": [size[1], size[0]],  # H,W
-        "labels": pred_phrases,
-    }
-    # import ipdb; ipdb.set_trace()
-    image_with_box = plot_boxes_to_image(image_pil, pred_dict)[0]
-    image_with_box.save(os.path.join(output_dir, "pred.jpg"))

spaces/ChrisPreston/diff-svc_minato_aqua/modules/vocoders/nsf_hifigan.py

@@ -1,77 +0,0 @@
-import os
-
-import torch
-
-from modules.nsf_hifigan.models import load_model
-from modules.nsf_hifigan.nvSTFT import load_wav_to_torch, STFT
-from utils.hparams import hparams
-
-nsf_hifigan = None
-
-
-def register_vocoder(cls):
-    global nsf_hifigan
-    nsf_hifigan = cls
-    return cls
-
-
-@register_vocoder
-class NsfHifiGAN():
-    def __init__(self, device=None):
-        if device is None:
-            device = 'cuda' if torch.cuda.is_available() else 'cpu'
-        self.device = device
-        model_path = hparams['vocoder_ckpt']
-        if os.path.exists(model_path):
-            print('| Load HifiGAN: ', model_path)
-            self.model, self.h = load_model(model_path, device=self.device)
-        else:
-            print('Error: HifiGAN model file is not found!')
-
-    def spec2wav(self, mel, **kwargs):
-        if self.h.sampling_rate != hparams['audio_sample_rate']:
-            print('Mismatch parameters: hparams[\'audio_sample_rate\']=', hparams['audio_sample_rate'], '!=',
-                  self.h.sampling_rate, '(vocoder)')
-        if self.h.num_mels != hparams['audio_num_mel_bins']:
-            print('Mismatch parameters: hparams[\'audio_num_mel_bins\']=', hparams['audio_num_mel_bins'], '!=',
-                  self.h.num_mels, '(vocoder)')
-        if self.h.n_fft != hparams['fft_size']:
-            print('Mismatch parameters: hparams[\'fft_size\']=', hparams['fft_size'], '!=', self.h.n_fft, '(vocoder)')
-        if self.h.win_size != hparams['win_size']:
-            print('Mismatch parameters: hparams[\'win_size\']=', hparams['win_size'], '!=', self.h.win_size,
-                  '(vocoder)')
-        if self.h.hop_size != hparams['hop_size']:
-            print('Mismatch parameters: hparams[\'hop_size\']=', hparams['hop_size'], '!=', self.h.hop_size,
-                  '(vocoder)')
-        if self.h.fmin != hparams['fmin']:
-            print('Mismatch parameters: hparams[\'fmin\']=', hparams['fmin'], '!=', self.h.fmin, '(vocoder)')
-        if self.h.fmax != hparams['fmax']:
-            print('Mismatch parameters: hparams[\'fmax\']=', hparams['fmax'], '!=', self.h.fmax, '(vocoder)')
-        with torch.no_grad():
-            c = torch.FloatTensor(mel).unsqueeze(0).transpose(2, 1).to(self.device)
-            # log10 to log mel
-            c = 2.30259 * c
-            f0 = kwargs.get('f0')
-            f0 = torch.FloatTensor(f0[None, :]).to(self.device)
-            y = self.model(c, f0).view(-1)
-        wav_out = y.cpu().numpy()
-        return wav_out
-
-    @staticmethod
-    def wav2spec(inp_path, device=None):
-        if device is None:
-            device = 'cuda' if torch.cuda.is_available() else 'cpu'
-        sampling_rate = hparams['audio_sample_rate']
-        num_mels = hparams['audio_num_mel_bins']
-        n_fft = hparams['fft_size']
-        win_size = hparams['win_size']
-        hop_size = hparams['hop_size']
-        fmin = hparams['fmin']
-        fmax = hparams['fmax']
-        stft = STFT(sampling_rate, num_mels, n_fft, win_size, hop_size, fmin, fmax)
-        with torch.no_grad():
-            wav_torch, _ = load_wav_to_torch(inp_path, target_sr=stft.target_sr)
-            mel_torch = stft.get_mel(wav_torch.unsqueeze(0).to(device)).squeeze(0).T
-            # log mel to log10 mel
-            mel_torch = 0.434294 * mel_torch
-            return wav_torch.cpu().numpy(), mel_torch.cpu().numpy()

spaces/Covert1107/sd-diffusers-webui/Dockerfile

@@ -1,22 +0,0 @@
-# Dockerfile Public T4
-
-FROM nvidia/cuda:11.7.1-cudnn8-devel-ubuntu22.04
-ENV DEBIAN_FRONTEND noninteractive
-
-WORKDIR /content
-
-RUN apt-get update -y && apt-get upgrade -y && apt-get install -y libgl1 libglib2.0-0 wget git git-lfs python3-pip python-is-python3 && pip3 install --upgrade pip
-
-RUN pip install torch==1.12.1+cu113 torchvision==0.13.1+cu113 torchsde --extra-index-url https://download.pytorch.org/whl/cu113
-RUN pip install https://github.com/camenduru/stable-diffusion-webui-colab/releases/download/0.0.16/xformers-0.0.16+814314d.d20230118-cp310-cp310-linux_x86_64.whl
-RUN pip install --pre triton
-RUN pip install numexpr einops diffusers transformers k_diffusion safetensors gradio
-
-ADD . .
-RUN adduser --disabled-password --gecos '' user
-RUN chown -R user:user /content
-RUN chmod -R 777 /content
-USER user
-
-EXPOSE 7860
-CMD python /content/app.py

spaces/CrucibleAI/ControlNetMediaPipeFaceSD21/ldm/modules/distributions/distributions.py

@@ -1,92 +0,0 @@
-import torch
-import numpy as np
-
-
-class AbstractDistribution:
-    def sample(self):
-        raise NotImplementedError()
-
-    def mode(self):
-        raise NotImplementedError()
-
-
-class DiracDistribution(AbstractDistribution):
-    def __init__(self, value):
-        self.value = value
-
-    def sample(self):
-        return self.value
-
-    def mode(self):
-        return self.value
-
-
-class DiagonalGaussianDistribution(object):
-    def __init__(self, parameters, deterministic=False):
-        self.parameters = parameters
-        self.mean, self.logvar = torch.chunk(parameters, 2, dim=1)
-        self.logvar = torch.clamp(self.logvar, -30.0, 20.0)
-        self.deterministic = deterministic
-        self.std = torch.exp(0.5 * self.logvar)
-        self.var = torch.exp(self.logvar)
-        if self.deterministic:
-            self.var = self.std = torch.zeros_like(self.mean).to(device=self.parameters.device)
-
-    def sample(self):
-        x = self.mean + self.std * torch.randn(self.mean.shape).to(device=self.parameters.device)
-        return x
-
-    def kl(self, other=None):
-        if self.deterministic:
-            return torch.Tensor([0.])
-        else:
-            if other is None:
-                return 0.5 * torch.sum(torch.pow(self.mean, 2)
-                                       + self.var - 1.0 - self.logvar,
-                                       dim=[1, 2, 3])
-            else:
-                return 0.5 * torch.sum(
-                    torch.pow(self.mean - other.mean, 2) / other.var
-                    + self.var / other.var - 1.0 - self.logvar + other.logvar,
-                    dim=[1, 2, 3])
-
-    def nll(self, sample, dims=[1,2,3]):
-        if self.deterministic:
-            return torch.Tensor([0.])
-        logtwopi = np.log(2.0 * np.pi)
-        return 0.5 * torch.sum(
-            logtwopi + self.logvar + torch.pow(sample - self.mean, 2) / self.var,
-            dim=dims)
-
-    def mode(self):
-        return self.mean
-
-
-def normal_kl(mean1, logvar1, mean2, logvar2):
-    """
-    source: https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/losses.py#L12
-    Compute the KL divergence between two gaussians.
-    Shapes are automatically broadcasted, so batches can be compared to
-    scalars, among other use cases.
-    """
-    tensor = None
-    for obj in (mean1, logvar1, mean2, logvar2):
-        if isinstance(obj, torch.Tensor):
-            tensor = obj
-            break
-    assert tensor is not None, "at least one argument must be a Tensor"
-
-    # Force variances to be Tensors. Broadcasting helps convert scalars to
-    # Tensors, but it does not work for torch.exp().
-    logvar1, logvar2 = [
-        x if isinstance(x, torch.Tensor) else torch.tensor(x).to(tensor)
-        for x in (logvar1, logvar2)
-    ]
-
-    return 0.5 * (
-        -1.0
-        + logvar2
-        - logvar1
-        + torch.exp(logvar1 - logvar2)
-        + ((mean1 - mean2) ** 2) * torch.exp(-logvar2)
-    )

spaces/DHEIVER/Segmento_de_Angio_Coronariana_v6/app.py

@@ -1,23 +0,0 @@
-import gradio as gr
-from PIL import Image
-
-# Import the ObstructionDetector class from your module
-from obstruction_detector import ObstructionDetector
-
-# Create an instance of ObstructionDetector
-detector = ObstructionDetector()
-
-# Define a Gradio function to process the image and return the report
-def process_image(image):
-    # Call the detect_obstruction method of the ObstructionDetector with the PIL image
-    report = detector.detect_obstruction(image)
-    
-    return report
-
-# Define the Gradio interface
-iface = gr.Interface(fn=process_image, 
-                     inputs=gr.inputs.Image(shape=(224, 224)),  # Adjust shape as needed
-                     outputs="text")
-
-# Launch the Gradio interface
-iface.launch()

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

@@ -1,380 +0,0 @@
-#
-# The Python Imaging Library.
-# $Id$
-#
-# EXIF tags
-#
-# Copyright (c) 2003 by Secret Labs AB
-#
-# See the README file for information on usage and redistribution.
-#
-
-"""
-This module provides constants and clear-text names for various
-well-known EXIF tags.
-"""
-
-from enum import IntEnum
-
-
-class Base(IntEnum):
-    # possibly incomplete
-    InteropIndex = 0x0001
-    ProcessingSoftware = 0x000B
-    NewSubfileType = 0x00FE
-    SubfileType = 0x00FF
-    ImageWidth = 0x0100
-    ImageLength = 0x0101
-    BitsPerSample = 0x0102
-    Compression = 0x0103
-    PhotometricInterpretation = 0x0106
-    Thresholding = 0x0107
-    CellWidth = 0x0108
-    CellLength = 0x0109
-    FillOrder = 0x010A
-    DocumentName = 0x010D
-    ImageDescription = 0x010E
-    Make = 0x010F
-    Model = 0x0110
-    StripOffsets = 0x0111
-    Orientation = 0x0112
-    SamplesPerPixel = 0x0115
-    RowsPerStrip = 0x0116
-    StripByteCounts = 0x0117
-    MinSampleValue = 0x0118
-    MaxSampleValue = 0x0119
-    XResolution = 0x011A
-    YResolution = 0x011B
-    PlanarConfiguration = 0x011C
-    PageName = 0x011D
-    FreeOffsets = 0x0120
-    FreeByteCounts = 0x0121
-    GrayResponseUnit = 0x0122
-    GrayResponseCurve = 0x0123
-    T4Options = 0x0124
-    T6Options = 0x0125
-    ResolutionUnit = 0x0128
-    PageNumber = 0x0129
-    TransferFunction = 0x012D
-    Software = 0x0131
-    DateTime = 0x0132
-    Artist = 0x013B
-    HostComputer = 0x013C
-    Predictor = 0x013D
-    WhitePoint = 0x013E
-    PrimaryChromaticities = 0x013F
-    ColorMap = 0x0140
-    HalftoneHints = 0x0141
-    TileWidth = 0x0142
-    TileLength = 0x0143
-    TileOffsets = 0x0144
-    TileByteCounts = 0x0145
-    SubIFDs = 0x014A
-    InkSet = 0x014C
-    InkNames = 0x014D
-    NumberOfInks = 0x014E
-    DotRange = 0x0150
-    TargetPrinter = 0x0151
-    ExtraSamples = 0x0152
-    SampleFormat = 0x0153
-    SMinSampleValue = 0x0154
-    SMaxSampleValue = 0x0155
-    TransferRange = 0x0156
-    ClipPath = 0x0157
-    XClipPathUnits = 0x0158
-    YClipPathUnits = 0x0159
-    Indexed = 0x015A
-    JPEGTables = 0x015B
-    OPIProxy = 0x015F
-    JPEGProc = 0x0200
-    JpegIFOffset = 0x0201
-    JpegIFByteCount = 0x0202
-    JpegRestartInterval = 0x0203
-    JpegLosslessPredictors = 0x0205
-    JpegPointTransforms = 0x0206
-    JpegQTables = 0x0207
-    JpegDCTables = 0x0208
-    JpegACTables = 0x0209
-    YCbCrCoefficients = 0x0211
-    YCbCrSubSampling = 0x0212
-    YCbCrPositioning = 0x0213
-    ReferenceBlackWhite = 0x0214
-    XMLPacket = 0x02BC
-    RelatedImageFileFormat = 0x1000
-    RelatedImageWidth = 0x1001
-    RelatedImageLength = 0x1002
-    Rating = 0x4746
-    RatingPercent = 0x4749
-    ImageID = 0x800D
-    CFARepeatPatternDim = 0x828D
-    BatteryLevel = 0x828F
-    Copyright = 0x8298
-    ExposureTime = 0x829A
-    FNumber = 0x829D
-    IPTCNAA = 0x83BB
-    ImageResources = 0x8649
-    ExifOffset = 0x8769
-    InterColorProfile = 0x8773
-    ExposureProgram = 0x8822
-    SpectralSensitivity = 0x8824
-    GPSInfo = 0x8825
-    ISOSpeedRatings = 0x8827
-    OECF = 0x8828
-    Interlace = 0x8829
-    TimeZoneOffset = 0x882A
-    SelfTimerMode = 0x882B
-    SensitivityType = 0x8830
-    StandardOutputSensitivity = 0x8831
-    RecommendedExposureIndex = 0x8832
-    ISOSpeed = 0x8833
-    ISOSpeedLatitudeyyy = 0x8834
-    ISOSpeedLatitudezzz = 0x8835
-    ExifVersion = 0x9000
-    DateTimeOriginal = 0x9003
-    DateTimeDigitized = 0x9004
-    OffsetTime = 0x9010
-    OffsetTimeOriginal = 0x9011
-    OffsetTimeDigitized = 0x9012
-    ComponentsConfiguration = 0x9101
-    CompressedBitsPerPixel = 0x9102
-    ShutterSpeedValue = 0x9201
-    ApertureValue = 0x9202
-    BrightnessValue = 0x9203
-    ExposureBiasValue = 0x9204
-    MaxApertureValue = 0x9205
-    SubjectDistance = 0x9206
-    MeteringMode = 0x9207
-    LightSource = 0x9208
-    Flash = 0x9209
-    FocalLength = 0x920A
-    Noise = 0x920D
-    ImageNumber = 0x9211
-    SecurityClassification = 0x9212
-    ImageHistory = 0x9213
-    TIFFEPStandardID = 0x9216
-    MakerNote = 0x927C
-    UserComment = 0x9286
-    SubsecTime = 0x9290
-    SubsecTimeOriginal = 0x9291
-    SubsecTimeDigitized = 0x9292
-    AmbientTemperature = 0x9400
-    Humidity = 0x9401
-    Pressure = 0x9402
-    WaterDepth = 0x9403
-    Acceleration = 0x9404
-    CameraElevationAngle = 0x9405
-    XPTitle = 0x9C9B
-    XPComment = 0x9C9C
-    XPAuthor = 0x9C9D
-    XPKeywords = 0x9C9E
-    XPSubject = 0x9C9F
-    FlashPixVersion = 0xA000
-    ColorSpace = 0xA001
-    ExifImageWidth = 0xA002
-    ExifImageHeight = 0xA003
-    RelatedSoundFile = 0xA004
-    ExifInteroperabilityOffset = 0xA005
-    FlashEnergy = 0xA20B
-    SpatialFrequencyResponse = 0xA20C
-    FocalPlaneXResolution = 0xA20E
-    FocalPlaneYResolution = 0xA20F
-    FocalPlaneResolutionUnit = 0xA210
-    SubjectLocation = 0xA214
-    ExposureIndex = 0xA215
-    SensingMethod = 0xA217
-    FileSource = 0xA300
-    SceneType = 0xA301
-    CFAPattern = 0xA302
-    CustomRendered = 0xA401
-    ExposureMode = 0xA402
-    WhiteBalance = 0xA403
-    DigitalZoomRatio = 0xA404
-    FocalLengthIn35mmFilm = 0xA405
-    SceneCaptureType = 0xA406
-    GainControl = 0xA407
-    Contrast = 0xA408
-    Saturation = 0xA409
-    Sharpness = 0xA40A
-    DeviceSettingDescription = 0xA40B
-    SubjectDistanceRange = 0xA40C
-    ImageUniqueID = 0xA420
-    CameraOwnerName = 0xA430
-    BodySerialNumber = 0xA431
-    LensSpecification = 0xA432
-    LensMake = 0xA433
-    LensModel = 0xA434
-    LensSerialNumber = 0xA435
-    CompositeImage = 0xA460
-    CompositeImageCount = 0xA461
-    CompositeImageExposureTimes = 0xA462
-    Gamma = 0xA500
-    PrintImageMatching = 0xC4A5
-    DNGVersion = 0xC612
-    DNGBackwardVersion = 0xC613
-    UniqueCameraModel = 0xC614
-    LocalizedCameraModel = 0xC615
-    CFAPlaneColor = 0xC616
-    CFALayout = 0xC617
-    LinearizationTable = 0xC618
-    BlackLevelRepeatDim = 0xC619
-    BlackLevel = 0xC61A
-    BlackLevelDeltaH = 0xC61B
-    BlackLevelDeltaV = 0xC61C
-    WhiteLevel = 0xC61D
-    DefaultScale = 0xC61E
-    DefaultCropOrigin = 0xC61F
-    DefaultCropSize = 0xC620
-    ColorMatrix1 = 0xC621
-    ColorMatrix2 = 0xC622
-    CameraCalibration1 = 0xC623
-    CameraCalibration2 = 0xC624
-    ReductionMatrix1 = 0xC625
-    ReductionMatrix2 = 0xC626
-    AnalogBalance = 0xC627
-    AsShotNeutral = 0xC628
-    AsShotWhiteXY = 0xC629
-    BaselineExposure = 0xC62A
-    BaselineNoise = 0xC62B
-    BaselineSharpness = 0xC62C
-    BayerGreenSplit = 0xC62D
-    LinearResponseLimit = 0xC62E
-    CameraSerialNumber = 0xC62F
-    LensInfo = 0xC630
-    ChromaBlurRadius = 0xC631
-    AntiAliasStrength = 0xC632
-    ShadowScale = 0xC633
-    DNGPrivateData = 0xC634
-    MakerNoteSafety = 0xC635
-    CalibrationIlluminant1 = 0xC65A
-    CalibrationIlluminant2 = 0xC65B
-    BestQualityScale = 0xC65C
-    RawDataUniqueID = 0xC65D
-    OriginalRawFileName = 0xC68B
-    OriginalRawFileData = 0xC68C
-    ActiveArea = 0xC68D
-    MaskedAreas = 0xC68E
-    AsShotICCProfile = 0xC68F
-    AsShotPreProfileMatrix = 0xC690
-    CurrentICCProfile = 0xC691
-    CurrentPreProfileMatrix = 0xC692
-    ColorimetricReference = 0xC6BF
-    CameraCalibrationSignature = 0xC6F3
-    ProfileCalibrationSignature = 0xC6F4
-    AsShotProfileName = 0xC6F6
-    NoiseReductionApplied = 0xC6F7
-    ProfileName = 0xC6F8
-    ProfileHueSatMapDims = 0xC6F9
-    ProfileHueSatMapData1 = 0xC6FA
-    ProfileHueSatMapData2 = 0xC6FB
-    ProfileToneCurve = 0xC6FC
-    ProfileEmbedPolicy = 0xC6FD
-    ProfileCopyright = 0xC6FE
-    ForwardMatrix1 = 0xC714
-    ForwardMatrix2 = 0xC715
-    PreviewApplicationName = 0xC716
-    PreviewApplicationVersion = 0xC717
-    PreviewSettingsName = 0xC718
-    PreviewSettingsDigest = 0xC719
-    PreviewColorSpace = 0xC71A
-    PreviewDateTime = 0xC71B
-    RawImageDigest = 0xC71C
-    OriginalRawFileDigest = 0xC71D
-    SubTileBlockSize = 0xC71E
-    RowInterleaveFactor = 0xC71F
-    ProfileLookTableDims = 0xC725
-    ProfileLookTableData = 0xC726
-    OpcodeList1 = 0xC740
-    OpcodeList2 = 0xC741
-    OpcodeList3 = 0xC74E
-    NoiseProfile = 0xC761
-
-
-"""Maps EXIF tags to tag names."""
-TAGS = {
-    **{i.value: i.name for i in Base},
-    0x920C: "SpatialFrequencyResponse",
-    0x9214: "SubjectLocation",
-    0x9215: "ExposureIndex",
-    0x828E: "CFAPattern",
-    0x920B: "FlashEnergy",
-    0x9216: "TIFF/EPStandardID",
-}
-
-
-class GPS(IntEnum):
-    GPSVersionID = 0
-    GPSLatitudeRef = 1
-    GPSLatitude = 2
-    GPSLongitudeRef = 3
-    GPSLongitude = 4
-    GPSAltitudeRef = 5
-    GPSAltitude = 6
-    GPSTimeStamp = 7
-    GPSSatellites = 8
-    GPSStatus = 9
-    GPSMeasureMode = 10
-    GPSDOP = 11
-    GPSSpeedRef = 12
-    GPSSpeed = 13
-    GPSTrackRef = 14
-    GPSTrack = 15
-    GPSImgDirectionRef = 16
-    GPSImgDirection = 17
-    GPSMapDatum = 18
-    GPSDestLatitudeRef = 19
-    GPSDestLatitude = 20
-    GPSDestLongitudeRef = 21
-    GPSDestLongitude = 22
-    GPSDestBearingRef = 23
-    GPSDestBearing = 24
-    GPSDestDistanceRef = 25
-    GPSDestDistance = 26
-    GPSProcessingMethod = 27
-    GPSAreaInformation = 28
-    GPSDateStamp = 29
-    GPSDifferential = 30
-    GPSHPositioningError = 31
-
-
-"""Maps EXIF GPS tags to tag names."""
-GPSTAGS = {i.value: i.name for i in GPS}
-
-
-class Interop(IntEnum):
-    InteropIndex = 1
-    InteropVersion = 2
-    RelatedImageFileFormat = 4096
-    RelatedImageWidth = 4097
-    RleatedImageHeight = 4098
-
-
-class IFD(IntEnum):
-    Exif = 34665
-    GPSInfo = 34853
-    Makernote = 37500
-    Interop = 40965
-    IFD1 = -1
-
-
-class LightSource(IntEnum):
-    Unknown = 0
-    Daylight = 1
-    Fluorescent = 2
-    Tungsten = 3
-    Flash = 4
-    Fine = 9
-    Cloudy = 10
-    Shade = 11
-    DaylightFluorescent = 12
-    DayWhiteFluorescent = 13
-    CoolWhiteFluorescent = 14
-    WhiteFluorescent = 15
-    StandardLightA = 17
-    StandardLightB = 18
-    StandardLightC = 19
-    D55 = 20
-    D65 = 21
-    D75 = 22
-    D50 = 23
-    ISO = 24
-    Other = 255

spaces/DaFujaTyping/hf-Chat-ui/src/lib/utils/trimSuffix.ts

@@ -1,6 +0,0 @@
-export function trimSuffix(input: string, end: string): string {
-	if (input.endsWith(end)) {
-		return input.slice(0, input.length - end.length);
-	}
-	return input;
-}