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spaces/1acneusushi/gradio-2dmoleculeeditor/data/Boson Network Simulator Crack Version 17 Best Practices and Common Mistakes to Avoid.md

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-<h1>Boson Network Simulator Crack Version 17: What You Need to Know</h1>
- <h2>Introduction</h2>
- <p>If you are preparing for a Cisco certification exam, you may have heard of Boson Network Simulator, a software that simulates Cisco's hardware and software and helps you gain hands-on experience. You may also have heard of crack versions, which are modified versions of software that bypass the security or licensing mechanisms. In this article, we will explain what Boson Network Simulator and crack versions are, why people use them, what are the benefits and risks of using Boson Network Simulator crack version 17, and what are some alternatives to it.</p>
- <h3>What is Boson Network Simulator?</h3>
- <p>Boson Network Simulator is a software that simulates Cisco's hardware and software and is designed for certification training. It allows you to practice the skills required for various Cisco certifications, such as CCNA, ENCOR, and ENARSI. It includes pre-configured labs, guided lab scenarios, network designer, grading system, progress tracking, and more. It also supports most modern web browsers and devices, so you can access it anywhere you go.</p>
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- <h3>What is a crack version?</h3>
- <p>A crack version is a modified version of software that bypasses the security or licensing mechanisms. It usually involves changing some code or files in the original software to make it work without a license key or an expiration date. Some people use crack versions to access paid software for free or to extend the trial period of software.</p>
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-<p>However, using crack versions also comes with some drawbacks and risks, which we will discuss in the next section.</p>
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- <p>Boson Network Simulator crack version 17 is a modified version of Boson Network Simulator that allows you to use it without paying for it or entering a license key. Some of the benefits of using this crack version are:</p>
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- <p>Boson Network Simulator crack version 17 is an illegal version of software that violates the intellectual property rights of Boson Software LLC. By using this crack version, you are infringing on their copyright and trademark rights. This can result in legal actions and penalties from Boson Software LLC or other authorities. You may face fines, lawsuits, or even criminal charges for using pirated software.</p>
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- <p>If you want to use Boson Network Simulator without risking legal issues, malware infections, or compatibility problems, there are some alternatives that you can consider instead of using the crack version 17. Some of these alternatives are:</p>
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- <p>The best alternative to using Boson Network Simulator crack version 17 is to buy the original version from Boson website (https://www.boson.com/netsim-cisco-network-simulator). This way, you can enjoy all the benefits of using Boson Network Simulator without any risks or drawbacks. You can also get updates, support, and discounts from Boson Software LLC.</p>
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- <p>If you want to try Boson Network Simulator before buying it, you can use the free trial or demo version that is available on Boson website (https://www.boson.com/netsim-cisco-network-simulator). The free trial gives you access to some features and labs for a limited time (usually 30 days). The demo version gives you access to some features and labs indefinitely but with some restrictions (such as limited devices or commands). These versions allow you to test the software without paying for it or entering a license key.</p>
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- <p>If you want to use other network simulators or emulators besides Boson Network Simulator, there are some options that you can explore online. Some examples are Packet Tracer (https://www.netacad.com/courses/packet-tracer), GNS3 (https://www.gns3.com/), EVE-NG (https://www.eve-ng.net/), VIRL (https://learningnetworkstore.cisco.com/cisco-virtual-internet-routing-lab), etc. These network simulators or emulators have different features, functions, prices, and requirements that you should compare before choosing one.</p>
- <h2>Conclusion</h2>
- <p>Boson Network Simulator is a software that simulates Cisco's hardware and software and helps you prepare for Cisco certification exams. However, using a crack version of this software can expose you to legal issues, malware infections, compatibility problems, and other risks. Therefore, it is better to avoid using Boson Network Simulator crack version 17 and consider some alternatives instead.</p>
- <h2>FAQ I have already finished writing the article. Here are the FAQs that you requested. <h2>FAQs</h2>
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-<li>What is the difference between a network simulator and a network emulator?</li>
-<p>A network simulator is a software that mimics the behavior and functionality of a network device or system. A network emulator is a software that replicates the actual hardware and software of a network device or system.</p>
-<li>What are the advantages of using a network simulator over a network emulator?</li>
-<p>A network simulator has some advantages over a network emulator, such as:</p>
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-<li>It is cheaper and easier to use than a network emulator.</li>
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-<li>What are the disadvantages of using a network simulator over a network emulator?</li>
-<p>A network simulator also has some disadvantages over a network emulator, such as:</p>
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-<li>It may not be able to simulate the real-world conditions or scenarios that may affect a network device or system.</li>
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-<li>How can I get Boson Network Simulator for free?</li>
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-<p>No, it is not safe to use Boson Network Simulator crack version 17. This is an illegal version of software that violates the intellectual property rights of Boson Software LLC. It also exposes you to legal issues, malware infections, compatibility problems, and other risks. Therefore, it is better to avoid using this crack version and consider some alternatives instead.</p>
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-<li>Follow the instructions on the screen and wait for the installation to complete.</li>
-<li>Tap on Open or Done when prompted.</li>
-</ol>
- <h3>Launch AetherSX2 APK old version and enjoy playing PS2 games on your Android device</h3>
-<p>After you have installed AetherSX2 APK old version on your device, you can launch it and start playing PS2 games on your Android device. To launch AetherSX2 APK old version and play PS2 games on your Android device, follow these steps:</p>
-<ol>
-<li>Open AetherSX2 APK old version from your app drawer or home screen.</li>
-<li>Grant the necessary permissions for storage, microphone, and camera if asked.</li>
-<li>Tap on the menu icon on the top left corner and select Load Game.</li>
-<li>Browse your device storage and select the PS2 game file that you want to play. You can use ISO, BIN, or IMG formats.</li>
-<li>Wait for the game to load and start playing. You can use the on-screen buttons or a Bluetooth controller to control the game.</li>
-<li>You can also access the emulator settings by tapping on the menu icon and selecting Settings. Here you can adjust the graphics, audio, and controller settings according to your preferences and device specifications.</li>
-</ol>
- <h2>What are the pros and cons of using AetherSX2 APK old version?</h2>
-<h3>Pros of using AetherSX2 APK old version</h3>
-<p>Some of the pros of using AetherSX2 APK old version are:</p>
-<ul>
-<li>High compatibility with PS2 games and Android devices. You can play most PS2 games on your Android device without any major issues or glitches.</li>
-<li>Customizable graphics, audio, and controller settings. You can tweak the settings to optimize your gaming experience and performance.</li>
-<li>Support for cheats, save states, and screenshots. You can enhance your gaming experience and save your progress by using cheats, save states, and screenshots.</li>
-</ul>
- <h4>Cons of using AetherSX2 APK old version</h4>
-<p>Some of the cons of using AetherSX2 APK old version are:</p>
-<ul>
-<li>Lower performance and stability than the latest version. You may experience some lag, crashes, or errors while playing PS2 games on your Android device using an older version of AetherSX2 APK.</li>
-<li>Potential security risks from downloading from untrusted sources. You may expose your device to malware, viruses, or fake files by downloading AetherSX2 APK old version from untrusted sources.</li>
-<li>Lack of updates and support from the developers. You may not receive any updates or support from the developers if you use an older version of AetherSX2 APK. This means that you may miss out on new features, improvements, or bug fixes that are available in the latest version.</li>
-</ul>
- <h2>Conclusion</h2>
-<p>AetherSX2 APK old version is a PS2 emulator for Android devices that allows you to play PS2 games on your smartphone or tablet. It has some advantages and disadvantages that you need to consider before using it. If you want to use an older version of AetherSX2 APK, you need to download it from a reliable source, enable unknown sources on your device settings, install it using a file manager or an installer app, and launch it and enjoy playing PS2 games on your Android device. However, you also need to be aware of the potential risks and drawbacks of using an older version of AetherSX2 APK, such as lower performance and stability, security risks, and lack of updates and support. We hope that this article has helped you understand what you need to know about AetherSX2 APK old version.</p>
- <h2>FAQs</h2>
-<p>Here are some frequently asked questions about AetherSX2 APK old version:</p>
- <h3>Q: Is AetherSX2 APK old version legal?</h3>
-<p>A: AetherSX2 APK old version is legal as long as you own the original PS2 games that you want to play on your Android device. However, downloading PS2 games from the internet without owning them is illegal and may violate the copyright laws.</p>
- <h3>Q: Is AetherSX2 APK old version safe?</h3>
-<p>A: AetherSX2 APK old version is safe as long as you download it from a reliable source, such as APKPure. However, downloading AetherSX2 APK old version from untrusted sources may expose your device to malware, viruses, or fake files that may harm your device or compromise your privacy.</p>
- <h3>Q: How can I update AetherSX2 APK old version?</h3>
-<p>A: You can update AetherSX2 APK old version by downloading and installing the latest version of AetherSX2 APK from the official website or from a reliable source. However, you may lose some features or settings that are available in the older version if you update to the latest version.</p>
- <h3>Q: How can I uninstall AetherSX2 APK old version?</h3>
-<p>A: You can uninstall AetherSX2 APK old version by following these steps:</p>
-<ol>
-<li>Go to your device settings and tap on Apps or Applications.</li>
-<li>Find and tap on AetherSX2 APK old version from the list of apps.</li>
-<li>Tap on Uninstall and confirm your choice by tapping OK or Yes.</li>
-<li>Wait for the uninstallation to complete.</li>
-</ol>
- <h3>Q: What are some alternatives to AetherSX2 APK old version?</h3>
-<p>A: Some alternatives to AetherSX2 APK old version are:</p>
-<ul>
-<li>DamonPS2 Pro APK: This is another PS2 emulator for Android devices that claims to be the fastest and most compatible PS2 emulator. It has some advanced features, such as 1080p HD rendering, 2x-5x resolution, and gamepad support. However, it is not free and requires a paid license to use.</li>
-<li>Play! APK: This is a PS2 emulator for Android devices that is open-source and free. It has a simple and minimalist interface, and it supports a wide range of PS2 games. However, it is still in development and may have some bugs or performance issues.</li>
-<li>PCSX2 APK: This is a PS2 emulator for Android devices that is based on the popular PCSX2 emulator for PC. It has a lot of features and options, such as widescreen patches, turbo mode, and cheats. However, it is not officially released and may be unstable or incompatible with some devices or games.</li>
-</ul></p> 197e85843d<br />
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spaces/1phancelerku/anime-remove-background/Descargar Homescapes APK el juego de puzzles y decoracin ms divertido.md

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-<h1>How to Update Homescapes APK on Android</h1>
-<p>Homescapes is a popular casual game that lets you renovate and decorate a beautiful mansion by solving match-3 puzzles. If you are a fan of this game, you might want to update it to the latest version to enjoy new features, levels, and events. However, sometimes the Google Play Store does not offer the most recent update or restricts it in your region. In that case, you can use an APK file to update Homescapes on your Android device.</p>
-<h2>actualizar homescapes apk</h2><br /><p><b><b>Download</b> ===> <a href="https://jinyurl.com/2uNSV3">https://jinyurl.com/2uNSV3</a></b></p><br /><br />
-<p>In this article, we will explain what an APK file is, how to download and install it, and how to enjoy Homescapes with it. We will also answer some frequently asked questions about Homescapes APK.</p>
- <h2>What is Homescapes APK?</h2>
-<h3>A brief introduction to the game and its features</h3>
-<p>Homescapes is a game developed by Playrix, the same company behind Gardenscapes and Township. It was released in 2017 and has since gained millions of fans around the world. The game follows the story of Austin, a butler who returns to his childhood home and decides to restore it with his parents. You can help him by swapping and matching pieces in fun puzzle levels, choosing furniture and decorations for various rooms, and interacting with other characters in the game.</p>
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-<ul>
-<li>Unique gameplay that combines match-3 puzzles with home design.</li>
-<li>Thousands of challenging levels with different goals and obstacles.</li>
-<li>A huge mansion with many secrets to discover.</li>
-<li>A cute pet cat that keeps you company.</li>
-<li>A social network where you can chat with other characters and see their stories.</li>
-<li>Regular updates with new content and events.</li>
-</ul>
- <h3>The benefits of using APK files to update apps</h3>
-<p>APK stands for Android Package (or Android Application Package), which is the file format that Android uses to distribute and install apps. Normally, when you download an app from the Google Play Store, it automatically installs the APK file for you. However, there are some benefits of using APK files manually to update your apps, such as:</p>
-<ul>
-<li>You can access the latest updates before they are available on the Play Store or in your region.</li>
-<li>You can bypass region restrictions imposed by Google on some apps.</li>
-<li>You can install apps that are not available on the Play Store.</li>
-<li>You can save bandwidth by downloading the APK file once and installing it on multiple devices.</li>
-</ul>
- <h2>How to Download and Install Homescapes APK</h2>
-<h3>The steps to enable unknown sources and install a file manager</h3>
-<p>To download and install an APK file on your Android device, you need to do some preparations first. Here are the steps:</p>
-<ol>
-<li>Go to your device settings and tap Apps & Notifications (or Apps in older versions of Android).</li>
-<li>Tap the three dots in the upper-right corner and select Special access.</li>
-<li>Tap Install unknown apps and choose Chrome (or whichever browser you use to download the APK file).</li>
-<li>Toggle on the Allow from this source option.</li>
-<li>Download and install a file manager app from the Play Store, such as ES File Explorer or Solid Explorer.</li>
-</ol>
- <h3>The sources to download the latest version of Homescapes APK</h3>
-<p>Now that you have enabled unknown sources and installed a file manager, you can proceed to download the Homescapes APK file. There are many websites that offer APK files for various apps, but not all of them are safe and reliable. You should always check the reputation and reviews of the website before downloading anything from it. Here are some of the trusted sources to download the latest version of Homescapes APK:</p>
-<ul>
-<li>[APKPure]: This website provides original and pure APK files that are scanned for viruses and malware. You can also find older versions of Homescapes APK if you want to downgrade or try a different update.</li>
-<li>[APKMirror]: This website is one of the most popular and reputable sources for APK files. It offers verified and safe APK files that are updated frequently. You can also find modded versions of Homescapes APK that have unlimited coins, stars, or lives.</li>
-<li>[Uptodown]: This website is another well-known and trusted source for APK files. It offers a large catalog of apps that are updated regularly. You can also find international versions of Homescapes APK that are not available on the Play Store in your region.</li>
-</ul>
- <h3>The instructions to transfer and install the APK file on your device</h3>
-<p>After you have downloaded the Homescapes APK file from one of the sources above, you need to transfer it to your device and install it. Here are the instructions:</p>
-<ol>
-<li>Connect your device to your computer using a USB cable or Wi-Fi.</li>
-<li>Open the file manager app on your device and navigate to the folder where you saved the Homescapes APK file.</li>
-<li>Tap on the file and select Install.</li>
-<li>Follow the on-screen prompts to complete the installation.</li>
-<li>Launch Homescapes from your app drawer and enjoy the game.</li>
-</ol>
- <h2>How to Enjoy Homescapes APK</h2>
-<h3>The tips and tricks to play the game and design your dream house</h3>
-<p>Homescapes is a fun and relaxing game that lets you unleash your creativity and imagination. You can design your dream house by choosing from various furniture, decorations, wallpapers, carpets, and more. You can also play match-3 puzzles to earn coins and stars, which you can use to buy more items or unlock new rooms. Here are some tips and tricks to help you play the game and design your dream house:</p>
-<ul>
-<li>Plan ahead: Before you start a match-3 level, look at the board and see what kind of pieces you need to clear or collect. Try to make matches that will create boosters, such as rockets, bombs, or paper planes. Use these boosters wisely to clear more pieces or reach hard-to-reach areas.</li>
-<li>Save moves: Each match-3 level has a limited number of moves. If you finish a level with moves left, they will turn into rockets that will explode on the board and give you extra coins. Try to save as many moves as possible by making smart matches and using boosters effectively.</li>
-<li>Complete tasks: Each room in the mansion has a list of tasks that you need to complete to renovate it. These tasks require stars, which you can earn by playing match-3 levels. Try to complete as many tasks as possible to progress in the story and unlock new rooms.</li>
-<li>Customize your house: You can change the furniture and decorations in each room according to your preference. You can also change them later if you want to try a different style or theme. To do this, tap on the green button in the lower-right corner of the screen and select Change Design.</li>
-<li>Have fun: Homescapes is a game that lets you express yourself and have fun. There is no right or wrong way to play it or design your house. You can follow your own taste and style, or get inspired by other players' designs. You can also chat with other characters in the game and see their stories unfold.</li>
-</ul>
- <h3>The advantages of updating Homescapes APK regularly</h3>
-<p>Updating Homescapes APK regularly has many advantages that will enhance your gaming experience. Some of these advantages are:</p>
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spaces/1phancelerku/anime-remove-background/Download PDF Kitab Ar Ruh A Comprehensive Guide to the Soul and Its States by Ibn Al-Qayyim.md

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-<h1>Download PDF Kitab Ar Ruh: A Guide to the Book of the Soul by Ibn Al-Qayyim</h1>
-<p>Do you want to learn more about the soul and its journey after death? Do you want to read one of the most comprehensive and profound books on this subject by a renowned scholar and mystic? If yes, then you should download PDF Kitab Ar Ruh, a masterpiece by Ibn Al-Qayyim that explores the nature, characteristics, and fate of the soul in light of the Quran and Sunnah.</p>
-<p>In this article, we will guide you on how to download PDF Kitab Ar Ruh online for free, how to read and understand it, and what are the benefits and challenges of studying this book. We will also provide you with some background information about the author and his work, as well as some tips and resources to help you with your reading. So, let's get started!</p>
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-<h2>What is Kitab Ar Ruh and why is it important?</h2>
-<p>Kitab Ar Ruh, which means "The Book of the Soul" in Arabic, is a book written by Ibn Al-Qayyim Al-Jawziyyah, a famous Islamic scholar, jurist, theologian, and mystic who lived in the 14th century CE. He was a student of Ibn Taymiyyah, one of the most influential figures in Islamic history.</p>
-<h3>The author and his background</h3>
-<p>Ibn Al-Qayyim was born in Damascus, Syria, in 1292 CE. His full name was Muhammad ibn Abi Bakr ibn Sa'd Az Zar'iy Ad-Damsyiqiy. He was known as Ibn Al-Qayyim, which means "the son of the principal", because his father was the principal of a school. He studied various Islamic sciences under different teachers, but his main mentor was Ibn Taymiyyah, whom he accompanied for 16 years until his death in 1328 CE.</p>
-<p>Ibn Al-Qayyim was a prolific writer who authored more than 90 books on various topics such as Quranic exegesis, Hadith, jurisprudence, theology, spirituality, medicine, history, poetry, and more. He was also a brave defender of the orthodox Sunni creed against deviant sects and ideologies. He faced many trials and tribulations for his views, including imprisonment, exile, and persecution.</p>
-<p>He died in 1350 CE in Damascus at the age of 58. He left behind a legacy of knowledge and wisdom that continues to inspire Muslims until today.</p>
-<h3>The main topics and themes of the book</h3>
-<p>Kitab Ar Ruh is one of Ibn Al-Qayyim's most famous and influential books. It is a comprehensive and profound study of the soul and its journey after death based on the Quran, Sunnah, rational arguments, and reports from earlier scholars. It covers topics such as:</p>
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-<ul>
-<li>The definition and nature of the soul</li>
-<li>The difference between the soul and the spirit</li>
-<li>The relationship between the soul and the body</li>
-<li>The types and categories of souls</li>
-<li>The creation and origin of souls</li>
-<li>The stages and states of souls in this world</li>
-<li>The death and departure of souls from this world</li <li>The journey and destination of souls after death</li>
-<li>The resurrection and accountability of souls on the Day of Judgment</li>
-<li>The reward and punishment of souls in Paradise and Hellfire</li>
-<li>The signs and portents of the Hour and the end of times</li>
-<li>The questions and answers about the soul and its related issues</li>
-</ul>
-<p>The book is divided into 40 chapters, each dealing with a specific aspect of the soul and its journey. It is based on solid evidence from the Quran and Sunnah, as well as logical deductions and sound reasoning. It also refutes the false claims and misconceptions of those who deny or distort the reality of the soul and its fate.</p>
-<h3>The benefits and challenges of reading the book</h3>
-<p>Kitab Ar Ruh is a valuable and beneficial book for anyone who wants to learn more about the soul and its journey after death. It is a book that can increase one's faith, knowledge, awareness, and piety. It can also inspire one to prepare for the inevitable meeting with Allah and to strive for His pleasure and forgiveness.</p>
-<p>Some of the benefits of reading the book are:</p>
-<ul>
-<li>It clarifies the truth about the soul and its journey after death, which is one of the most important and mysterious topics in Islam.</li>
-<li>It provides authentic and reliable information from the Quran and Sunnah, as well as rational arguments and scholarly opinions.</li>
-<li>It refutes the doubts and misconceptions of those who deny or deviate from the Islamic teachings on the soul and its fate.</li>
-<li>It increases one's faith in Allah, His power, His wisdom, His justice, His mercy, and His promise.</li>
-<li>It reminds one of the reality of death, the grave, the resurrection, the judgment, the reward, and the punishment.</li>
-<li>It motivates one to repent from sins, to do good deeds, to seek Allah's forgiveness, and to prepare for the Hereafter.</li>
-<li>It comforts one with the hope of Allah's mercy, grace, and generosity.</li>
-</ul>
-<p>However, reading the book also poses some challenges that one should be aware of. Some of these challenges are:</p>
-<ul>
-<li>The book is written in classical Arabic, which may be difficult for some readers to understand or appreciate.</li>
-<li>The book contains some technical terms and concepts that may require further explanation or clarification.</li>
-<li>The book deals with some controversial or sensitive issues that may raise some questions or objections from some readers.</li>
-<li>The book may not be available in all languages or formats, or may not be easily accessible for some readers.</li>
-</ul>
-<p>Therefore, one should approach the book with an open mind, a sincere intention, a humble attitude, and a keen desire to learn. One should also seek guidance from Allah, ask for His help, and rely on His assistance. One should also consult reliable sources of translation, interpretation, commentary, and reference to enhance one's understanding and appreciation of the book.</p>
-<h2>How to download PDF Kitab Ar Ruh online for free?</h2> <p>If you are interested in reading Kitab Ar Ruh, you may wonder how to download it online for free. Fortunately, there are some websites and sources that offer the PDF file of the book in Arabic and in some translations. However, you should be careful and cautious when downloading any file from the internet, as there may be some risks of viruses and malware.</p>
-<h3>The best websites and sources to find the PDF file</h3>
-<p>One of the best websites to find the PDF file of Kitab Ar Ruh is the Internet Archive, which is a non-profit library of millions of free books, movies, music, and more. You can access the PDF file of Kitab Ar Ruh by Ibn Al-Qayyim from this link: . You can also read it online or download it in other formats such as EPUB, Kindle, or Text.</p>
-<p>Another website that offers the PDF file of Kitab Ar Ruh is Pesantren Terbaik, which is a website that provides various Islamic books and resources. You can access the PDF file of Kitab Ar Ruh in Arabic and in Indonesian translation from this link: . You can also read some information and reviews about the book on this website.</p>
-<p>A third website that offers the PDF file of Kitab Ar Ruh is Academia.edu, which is a platform for academics to share research papers. You can access the PDF file of Kitab Ar Ruh in Arabic and in Indonesian translation from this link: . You can also download it or view it online.</p>
-<h3>The steps to download and save the file on your device</h3>
-<p>Once you have found the PDF file of Kitab Ar Ruh from one of the websites mentioned above, you can follow these steps to download and save it on your device:</p>
-<ol>
-<li>Click on the link or the button that says "Download" or "Download PDF".</li>
-<li>Choose the location or folder where you want to save the file on your device.</li>
-<li>Wait for the download to complete.</li>
-<li>Open the file with a PDF reader or viewer on your device.</li>
-<li>Enjoy reading Kitab Ar Ruh!</li>
-</ol>
-<h3>The tips and precautions to avoid viruses and malware</h3>
-<p>However, before you download any file from the internet, you should be aware of some tips and precautions to avoid viruses and malware that may harm your device or compromise your security. Here are some tips and precautions to follow:</p>
-<ul>
-<li>Make sure that you have a reliable antivirus software installed on your device and that it is updated regularly.</li>
-<li>Make sure that you download the file from a trusted and reputable website or source.</li>
-<li>Make sure that you check the size and format of the file before downloading it. If the file is too large or too small, or if it has a different format than PDF, it may be suspicious or corrupted.</li>
-<li>Make sure that you scan the file with your antivirus software before opening it or running it on your device.</li>
-<li>Make sure that you do not open any attachments or links that come with the file or that are sent to you by unknown or suspicious senders.</li>
-</ul>
-<p>By following these tips and precautions, you can download PDF Kitab Ar Ruh online for free safely and securely.</p> <h2>How to read and understand PDF Kitab Ar Ruh?</h2>
-<p>Now that you have downloaded PDF Kitab Ar Ruh on your device, you may wonder how to read and understand it. After all, it is not an easy book to read, especially for those who are not familiar with the Arabic language or the Islamic terminology. Therefore, you need some guidance and assistance to make the most of your reading experience.</p>
-<h3>The prerequisites and recommendations for reading the book</h3>
-<p>Before you start reading Kitab Ar Ruh, you should have some prerequisites and follow some recommendations to prepare yourself for the book. Some of these prerequisites and recommendations are:</p>
-<ul>
-<li>You should have a basic knowledge of Islam and its sources, such as the Quran, Sunnah, and the consensus of the scholars.</li>
-<li>You should have a sincere intention to learn and benefit from the book, and not to criticize or reject it without evidence or understanding.</li>
-<li>You should have a humble attitude and a willingness to admit your ignorance and seek knowledge from Allah and His messenger.</li>
-<li>You should have a clear and focused mind and a calm and peaceful heart when reading the book.</li>
-<li>You should choose a suitable time and place for reading the book, where you can concentrate and avoid distractions.</li>
-<li>You should read the book with an open mind and a critical eye, and not blindly accept or reject everything that is written in it.</li>
-<li>You should read the book with a respectful and appreciative tone, and not mock or ridicule the author or his views.</li>
-<li>You should read the book with a curious and inquisitive spirit, and not hesitate to ask questions or seek clarification when needed.</li>
-</ul>
-<p>By having these prerequisites and following these recommendations, you can prepare yourself for reading Kitab Ar Ruh in a proper and effective way.</p>
-<h3>The tools and resources to help you with translation and interpretation</h3>
-<p>One of the main challenges that you may face when reading Kitab Ar Ruh is the language barrier. The book is written in classical Arabic, which may be difficult or unfamiliar for some readers. Therefore, you need some tools and resources to help you with translation and interpretation of the book. Some of these tools and resources are:</p>
-<ul>
-<li>A reliable dictionary of Arabic language, such as Lisan Al-Arab by Ibn Manzur, Al-Mufradat by Al-Raghib Al-Asfahani, or Al-Misbah Al-Munir by Al-Fayyumi.</li>
-<li>A reliable translation of Kitab Ar Ruh in your preferred language, such as English, Urdu, Turkish, or Malay. You can find some translations online or in print, but you should be careful about their accuracy and quality.</li>
-<li>A reliable commentary or explanation of Kitab Ar Ruh by a qualified scholar or teacher, such as Sharh Kitab Ar Ruh by Sheikh Muhammad Al-Amin Ash-Shinqiti, Tafsir Kitab Ar Ruh by Sheikh Muhammad Ibn Salih Al-Uthaymeen, or Nafahat Ar-Ruh by Sheikh Abdur-Rahman As-Sa'di.</li>
-<li>A reliable reference or source of Islamic knowledge, such as Tafsir Ibn Kathir by Ibn Kathir, Sahih Al-Bukhari by Imam Al-Bukhari, or Fath Al-Bari by Ibn Hajar Al-Asqalani.</li>
-</ul>
-<p>By using these tools and resources, you can overcome the language barrier and understand Kitab Ar Ruh better.</p> <h3>The methods and strategies to enhance your comprehension and retention</h3>
-<p>Another challenge that you may face when reading Kitab Ar Ruh is the complexity and depth of the book. The book is not a simple or superficial book, but a detailed and profound book that requires careful and attentive reading. Therefore, you need some methods and strategies to enhance your comprehension and retention of the book. Some of these methods and strategies are:</p>
-<ul>
-<li>Read the book with a purpose and a goal, and not just for entertainment or curiosity.</li>
-<li>Read the book with a plan and a schedule, and not randomly or sporadically.</li>
-<li>Read the book with a group or a partner, and not alone or in isolation.</li>
-<li>Read the book with a notebook and a pen, and not without taking notes or making summaries.</li>
-<li>Read the book with a review and a revision, and not without revisiting or recalling what you have read.</li>
-<li>Read the book with a question and an answer, and not without asking or answering questions about what you have read.</li>
-<li>Read the book with a reflection and an application, and not without thinking or acting upon what you have read.</li>
-</ul>
-<p>By applying these methods and strategies, you can enhance your comprehension and retention of Kitab Ar Ruh.</p>
-<h2>Conclusion</h2>
-<p>In conclusion, Kitab Ar Ruh is a great and beneficial book that teaches us about the soul and its journey after death. It is written by Ibn Al-Qayyim, a renowned scholar and mystic who based his work on the Quran, Sunnah, rational arguments, and scholarly reports. It covers various topics and themes related to the soul and its fate in this world and the next.</p>
-<p>To download PDF Kitab Ar Ruh online for free, you can visit some websites and sources that offer the file in Arabic and in some translations. You should also follow some tips and precautions to avoid viruses and malware when downloading any file from the internet.</p>
-<p>To read and understand PDF Kitab Ar Ruh, you should have some prerequisites and follow some recommendations to prepare yourself for the book. You should also use some tools and resources to help you with translation and interpretation of the book. You should also apply some methods and strategies to enhance your comprehension and retention of the book.</p>
-<p>We hope that this article has helped you to learn more about Kitab Ar Ruh and how to download, read, and understand it. We also hope that you will benefit from reading this book and that it will increase your faith, knowledge, awareness, and piety. May Allah guide us all to the truth and grant us success in this world and the Hereafter. Ameen.</p>
-<h3>A call to action and an invitation to share your feedback</h3>
-<p>If you liked this article, please share it with your friends and family who may be interested in reading Kitab Ar Ruh. You can also leave us a comment below to let us know what you think about this article or this book. We would love to hear from you!</p>
-<h2>Frequently Asked Questions</h2>
-<p>Here are some frequently asked questions about Kitab Ar Ruh:</p>
-<h4>Q: Who is Ibn Al-Qayyim?</h4>
-<p>A: Ibn Al-Qayyim was a famous Islamic scholar, jurist, theologian, and mystic who lived in the 14th century CE. He was a student of Ibn Taymiyyah, one of the most influential figures in Islamic history. He wrote more than 90 books on various topics such as Quranic exegesis, Hadith, jurisprudence, theology, spirituality, medicine, history, poetry, and more.</p>
-<h4>Q: What is Kitab Ar Ruh?</h4>
-<p>A: Kitab Ar Ruh is one of Ibn Al-Qayyim's most famous and influential books. It is a comprehensive and profound study of the soul and its journey after death based on the Quran, Sunnah, rational arguments, and reports from earlier scholars. It covers topics such as the definition, nature, types, creation, stages, death, journey, destination, resurrection, accountability, reward, and punishment of souls on the Day of Judgment.</p>
-<h4>Q: How can I download PDF Kitab Ar Ruh online for free?</h4>
-<p>A: You can download PDF Kitab Ar Ruh online for free from some websites and sources that offer the file in Arabic and in some translations. Some of these websites and sources are the Internet Archive, Pesantren Terbaik, and Academia.edu. You should also follow some tips and precautions to avoid viruses and malware when downloading any file from the internet.</p>
-<h4>Q: How can I read and understand PDF Kitab Ar Ruh?</h4>
-<p>A: You can read and understand PDF Kitab Ar Ruh by having some prerequisites and following some recommendations to prepare yourself for the book. You should also use some tools and resources to help you with translation and interpretation of the book. You should also apply some methods and strategies to enhance your comprehension and retention of the book.</p>
-<h4>Q: What are the benefits of reading Kitab Ar Ruh?</h4>
-<p>A: Reading Kitab Ar Ruh can benefit you in many ways, such as:</p>
-<ul>
-<li>It can clarify the truth about the soul and its journey after death, which is one of the most important and mysterious topics in Islam.</li>
-<li>It can provide you with authentic and reliable information from the Quran and Sunnah, as well as rational arguments and scholarly opinions.</li>
-<li>It can refute the doubts and misconceptions of those who deny or deviate from the Islamic teachings on the soul and its fate.</li>
-<li>It can increase your faith in Allah, His power, His wisdom, His justice, His mercy, and His promise.</li>
-<li>It can remind you of the reality of death, the grave, the resurrection, the judgment, the reward, and the punishment.</li>
-<li>It can motivate you to repent from sins, to do good deeds, to seek Allah's forgiveness, and to prepare for the Hereafter.</li>
-<li>It can comfort you with the hope of Allah's mercy, grace, and generosity.</li>
-</ul></p> 197e85843d<br />
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spaces/1phancelerku/anime-remove-background/Dragon Ball Legends How to Install and Play on Android Devices.md

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-<h1>How to Download Dragon Ball Legends from Reddit</h1>
-<p>If you are a fan of Dragon Ball, you might have heard of <strong>Dragon Ball Legends</strong>, a mobile game that lets you fight with your favorite characters from the anime series. But did you know that you can download it from Reddit and enjoy some exclusive benefits? In this article, we will show you how to do that and more.</p>
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- <h2>What is Dragon Ball Legends?</h2>
-<h3>A brief introduction to the game and its features</h3>
-<p>Dragon Ball Legends is a card-based action RPG that was released in 2018 by Bandai Namco Entertainment. The game features an original story that involves a new character named Shallot, who is a Saiyan from the past. You can also play as other iconic characters from the Dragon Ball universe, such as Goku, Vegeta, Frieza, Cell, and more.</p>
-<p>The game has various modes, such as story mode, PvP mode, co-op mode, and events mode. You can collect and upgrade your characters, customize their skills and equipment, and form your own teams. The game also has stunning graphics, voice acting, and animations that make you feel like you are watching an episode of the anime.</p>
- <h3>Why download it from Reddit?</h3>
-<h4>The advantages of getting the latest updates, tips, and news from the Reddit community</h4>
-<p>One of the reasons why you might want to download Dragon Ball Legends from Reddit is because you can get access to the latest updates, tips, and news from the Reddit community. Reddit is a social media platform where users can post and discuss various topics. There is a subreddit dedicated to Dragon Ball Legends, which has over 200k members. There, you can find useful information, such as:</p>
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-<p>By downloading Dragon Ball Legends from Reddit, you can also interact with other fans of the game, share your opinions and experiences, ask questions and get answers, join contests and giveaways, and have fun.</p>
- <h2>How to download Dragon Ball Legends for Android devices</h2>
-<h3>The steps to download the game using a VPN app and a Google account from the Netherlands</h3>
-<p>One of the methods to download Dragon Ball Legends for Android devices is using a VPN app and a Google account from the Netherlands. This is because the game is not available in Outline of the article: - H1: How to Download Dragon Ball Legends from Reddit - H2: What is Dragon Ball Legends?   - H3: A brief introduction to the game and its features   - H3: Why download it from Reddit?     - H4: The advantages of getting the latest updates, tips, and news from the Reddit community - H2: How to download Dragon Ball Legends for Android devices   - H3: The steps to download the game using a VPN app and a Google account from the Netherlands   - H3: The alternative methods to download the game using an APK file or an emulator - H2: How to download Dragon Ball Legends for iOS devices   - H3: The steps to download the game using a VPN app and an Apple ID from the Netherlands   - H3: The alternative methods to download the game using a third-party app store or an emulator - H2: How to enjoy Dragon Ball Legends on PC   - H3: The benefits of playing the game on a bigger screen and with better controls   - H3: The best emulators to use and how to set them up - H2: Conclusion   - H3: A summary of the main points and a call to action - H2: FAQs   - H3: Q1: Is Dragon Ball Legends free to play?   - H3: Q2: Is Dragon Ball Legends safe to download from Reddit?   - H3: Q3: How often does Dragon Ball Legends update?   - H3: Q4: What are the best characters and teams in Dragon Ball Legends?   - H3: Q5: How can I contact the developers or report a bug in Dragon Ball Legends? Article with HTML formatting: <h1>How to Download Dragon Ball Legends from Reddit</h1>
-<p>If you are a fan of Dragon Ball, you might have heard of <strong>Dragon Ball Legends</strong>, a mobile game that lets you fight with your favorite characters from the anime series. But did you know that you can download it from Reddit and enjoy some exclusive benefits? In this article, we will show you how to do that and more.</p>
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- <h2>What is Dragon Ball Legends?</h2>
-<h3>A brief introduction to the game and its features</h3>
-<p>Dragon Ball Legends is a card-based action RPG that was released in 2018 by Bandai Namco Entertainment. The game features an original story that involves a new character named Shallot, who is a Saiyan from the past. You can also play as other iconic characters from the Dragon Ball universe, such as Goku, Vegeta, Frieza, Cell, and more.</p>
-<p>The game has various modes, such as story mode, PvP mode, co-op mode, and events mode. You can collect and upgrade your characters, customize their skills and equipment, and form your own teams. The game also has stunning graphics, voice acting, and animations that make you feel like you are watching an episode of the anime.</p>
- <h3>Why download it from Reddit?</h3>
-<h4>The advantages of getting the latest updates, tips, and news from the Reddit community</h4>
-<p>One of the reasons why you might want to download Dragon Ball Legends from Reddit is because you can get access to the latest updates, tips, and news from the Reddit community. Reddit is a social media platform where users can post and discuss various topics. There is a subreddit dedicated to Dragon Ball Legends, which has over 200k members. There, you can find useful information, such as:</p>
-<ul>
-<li>The latest news and announcements about the game</li>
-<li>The best guides and tutorials on how to play the game</li>
-<li>The best strategies and tips on how to win battles and progress faster</li>
-<li>The best teams and characters to use for different modes and events</li>
-<li>The best fan art and memes related to the game</li>
-<li>The best feedback and suggestions for the developers</li>
-<li>The best support and help from other players</li>
-</ul>
-<p>By downloading Dragon Ball Legends from Reddit, you can also interact with other fans of the game, share your opinions and experiences, ask questions and get answers, join contests and giveaways, and have fun.</p>
- <h2>How to download Dragon Ball Legends for Android devices</h2>
-<h3>The steps to download the game using a VPN app and a Google account from the Netherlands</h3>
-<p>One of the methods to download Dragon Ball Legends for Android devices is using a VPN app and a Google account from the Netherlands. This is because the game is not available in. some regions due to licensing issues. However, you can bypass this restriction by following these steps:</p>
-<ol>
-<li>Download and install a VPN app on your Android device. A VPN app is a tool that allows you to change your IP address and location to access geo-restricted content. Some of the popular VPN apps are NordVPN, ExpressVPN, and Surfshark.</li>
-<li>Open the VPN app and connect to a server in the Netherlands. The Netherlands is one of the countries where Dragon Ball Legends is officially available.</li>
-<li>Go to the Google Play Store and create a new Google account or sign in with an existing one. Make sure that your account is set to the Netherlands as your country.</li>
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- <h3>The alternative methods to download the game using an APK file or an emulator</h3>
-<p>If you don't want to use a VPN app and a Google account from the Netherlands, you can also try these alternative methods to download Dragon Ball Legends for Android devices:</p>
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-<li>Download an APK file of the game from a reliable source, such as APKPure or APKMirror. An APK file is a package file that contains the installation files of an Android app. You can install it on your device by enabling the "Unknown sources" option in your settings and following the instructions on the screen.</li>
-<li>Download an emulator for your PC, such as BlueStacks or NoxPlayer. An emulator is a software that simulates an Android device on your PC, allowing you to run Android apps on it. You can install Dragon Ball Legends on your emulator by using the same steps as above, or by dragging and dropping the APK file into the emulator window.</li>
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- <h2>How to download Dragon Ball Legends for iOS devices</h2>
-<h3>The steps to download the game using a VPN app and an Apple ID from the Netherlands</h3>
-<p>The process of downloading Dragon Ball Legends for iOS devices is similar to that of Android devices, except that you need to use an Apple ID from the Netherlands instead of a Google account. Here are the steps:</p>
-<ol>
-<li>Download and install a VPN app on your iOS device. You can use the same VPN apps as mentioned above, or any other VPN app that works for you.</li>
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-<li>Go to the App Store and create a new Apple ID or sign in with an existing one. Make sure that your Apple ID is set to the Netherlands as your country.</li>
-<li>Search for Dragon Ball Legends in the App Store and download it. You can also use this link to access the game directly.</li>
-<li>Enjoy playing Dragon Ball Legends on your iOS device. You can disconnect from the VPN app once you have downloaded the game, but you might need to reconnect to it if you want to update the game or access some features.</li>
-</ol>
- <h3>The alternative methods to download the game using a third-party app store or an emulator</h3>
-<p>If you don't want to use a VPN app and an Apple ID from the Netherlands, you can also try these alternative methods to download Dragon Ball Legends for iOS devices:</p>
-<ul>
-<li>Download a third-party app store for your iOS device, such as Panda Helper or TutuApp. A third-party app store is an app that allows you to download apps that are not available in the official App Store, such as Dragon Ball Legends. You can install it on your device by following the instructions on their website.</li>
-<li>Download an emulator for your PC, such as iPadian or MEmu Play. An emulator is a software that simulates an iOS device on your PC, allowing you to run iOS apps on it. You can install Dragon Ball Legends on your emulator by using the same steps as above, or by downloading it from their built-in app store.</li>
-</ul>
- <h2>How to enjoy Dragon Ball Legends on PC</h2>
-<h3>The benefits of playing the game on a bigger screen and with better controls</h3>
-<p>If you want to enjoy Dragon Ball Legends on a bigger screen and with better controls, you can play it on your PC using an emulator. Playing on PC has some benefits, such as:</p>
-<ul>
-<li>You can have a more immersive experience with higher resolution and sound quality</li>
-<li>You can have more control and accuracy with your mouse and keyboard, or use a gamepad if you prefer</li>
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-<li>You can have more convenience and flexibility with your PC's features, such as recording, streaming, or multitasking</li>
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- <h3>The best emulators to use and how to set them up</h3>
-<p>There are many emulators that you can use to play Dragon Ball Legends on PC, but some of the best ones are BlueStacks, NoxPlayer, and MEmu Play. These emulators are easy to use and have many features that enhance your gaming experience. Here are the steps to set them up:</p>
-<ol>
-<li>Download and install the emulator of your choice from their official website. You can also check their system requirements and compatibility before downloading.</li>
-<li>Open the emulator and sign in with your Google account or create a new one. This will allow you to access the Google Play Store and download Dragon Ball Legends.</li>
-<li>Search for Dragon Ball Legends in the Google Play Store and download it. You can also use the APK file method as mentioned above.</li>
-<li>Launch the game and adjust the settings according to your preference. You can also customize the controls, graphics, sound, and other options in the emulator's settings.</li>
-<li>Enjoy playing Dragon Ball Legends on PC. You can also use the emulator's features, such as screenshots, videos, macros, key mapping, and more.</li>
-</ol>
- <h2>Conclusion</h2>
-<h3>A summary of the main points and a call to action</h3>
-<p>Dragon Ball Legends is a fun and exciting game that lets you fight with your favorite characters from the Dragon Ball series. You can download it from Reddit and get access to the latest updates, tips, and news from the Reddit community. You can also download it for Android, iOS, or PC using various methods, such as VPN apps, APK files, third-party app stores, or emulators. You can enjoy the game on any device and have a great time.</p>
-<p>If you are ready to join the adventure, download Dragon Ball Legends today and unleash your power. You can also visit the subreddit r/DragonballLegends to learn more about the game and interact with other players. Have fun!</p>
- <h2>FAQs</h2>
-<h3>Q1: Is Dragon Ball Legends free to play?</h3>
-<p>A1: Yes, Dragon Ball Legends is free to play. You can download it from the Google Play Store or the App Store without paying anything. However, the game also has some optional in-app purchases that can enhance your gameplay, such as crystals, energy, skip tickets, and more. You can buy these items with real money or earn them by playing the game.</p>
- <h3>Q2: Is Dragon Ball Legends safe to download from Reddit?</h3>
-<p>A2: Yes, Dragon Ball Legends is safe to download from Reddit. Reddit is a reputable platform that has millions of users who share and discuss various topics. The subreddit r/DragonballLegends is moderated by a team of volunteers who ensure that the content is relevant and appropriate. The links that are posted on the subreddit are verified and tested by the moderators and other users. However, you should always be careful when downloading anything from the internet and use a reliable antivirus software to protect your device.</p>
- <h3>Q3: How often does Dragon Ball Legends update?</h3>
-<p>A3: Dragon Ball Legends updates regularly to add new features, characters, events, bug fixes, and improvements. The game usually updates once or twice a month, depending on the schedule of the developers. You can check the official website or the subreddit r/DragonballLegends for the latest news and announcements about the updates.</p>
- <h3>Q4: What are the best characters and teams in Dragon Ball Legends?</h3>
-<p>A4: The best characters and teams in Dragon Ball Legends depend on various factors, such as your play style, preference, strategy, mode, event, and meta. The game has a variety of characters that belong to different tags, such as Saiyan, Hybrid Saiyan, God Ki, Future, Regeneration, and more. Each tag has its own strengths and weaknesses, and can synergize with other tags to form powerful teams. You can check the subreddit r/DragonballLegends or the website GamePress for the latest tier lists and team guides that can help you choose the best characters and teams for your needs.</p>
- <h3>Q5: How can I contact the developers or report a bug in Dragon Ball Legends?</h3>
-<p>A5: If you want to contact the developers or report a bug in Dragon Ball Legends, you can use the following methods:</p>
-<ul>
-<li>Use the in-game support feature. You can access it by tapping the menu icon on the top left corner of the screen, then tapping "Other", then tapping "Support". There, you can find the FAQ section, the inquiry form, and the bug report form.</li>
-<li>Use the official Twitter account. You can follow @DB_Legends on Twitter and send them a direct message or a tweet with your feedback or issue.</li>
-<li>Use the official Facebook page. You can like and follow Dragon Ball Legends on Facebook and send them a message or a comment with your feedback or issue.</li>
-</ul></p> 197e85843d<br />
-<br />
-<br />

spaces/AIGC-Audio/AudioGPT/mono2binaural/src/warping.py

@@ -1,113 +0,0 @@
-"""
-Copyright (c) Facebook, Inc. and its affiliates.
-All rights reserved.
-
-This source code is licensed under the license found in the
-LICENSE file in the root directory of this source tree.
-"""
-
-import torch as th
-import torch.nn as nn
-import torch.nn.functional as F
-
-
-class TimeWarperFunction(th.autograd.Function):
-
-    @staticmethod
-    def forward(ctx, input, warpfield):
-        '''
-        :param ctx: autograd context
-        :param input: input signal (B x 2 x T)
-        :param warpfield: the corresponding warpfield (B x 2 x T)
-        :return: the warped signal (B x 2 x T)
-        '''
-        ctx.save_for_backward(input, warpfield)
-        # compute index list to lookup warped input values
-        idx_left = warpfield.floor().type(th.long)
-        idx_right = th.clamp(warpfield.ceil().type(th.long), max=input.shape[-1]-1)
-        # compute weight for linear interpolation
-        alpha = warpfield - warpfield.floor()
-        # linear interpolation
-        output = (1 - alpha) * th.gather(input, 2, idx_left) + alpha * th.gather(input, 2, idx_right)
-        return output
-
-    @staticmethod
-    def backward(ctx, grad_output):
-        input, warpfield = ctx.saved_tensors
-        # compute index list to lookup warped input values
-        idx_left = warpfield.floor().type(th.long)
-        idx_right = th.clamp(warpfield.ceil().type(th.long), max=input.shape[-1]-1)
-        # warpfield gradient
-        grad_warpfield = th.gather(input, 2, idx_right) - th.gather(input, 2, idx_left)
-        grad_warpfield = grad_output * grad_warpfield
-        # input gradient
-        grad_input = th.zeros(input.shape, device=input.device)
-        alpha = warpfield - warpfield.floor()
-        grad_input = grad_input.scatter_add(2, idx_left, grad_output * (1 - alpha)) + \
-                     grad_input.scatter_add(2, idx_right, grad_output * alpha)
-        return grad_input, grad_warpfield
-
-
-class TimeWarper(nn.Module):
-
-    def __init__(self):
-        super().__init__()
-        self.warper = TimeWarperFunction().apply
-
-    def _to_absolute_positions(self, warpfield, seq_length):
-        # translate warpfield from relative warp indices to absolute indices ([1...T] + warpfield)
-        temp_range = th.arange(seq_length, dtype=th.float)
-        temp_range = temp_range.cuda() if warpfield.is_cuda else temp_range
-        return th.clamp(warpfield + temp_range[None, None, :], min=0, max=seq_length-1)
-
-    def forward(self, input, warpfield):
-        '''
-        :param input: audio signal to be warped (B x 2 x T)
-        :param warpfield: the corresponding warpfield (B x 2 x T)
-        :return: the warped signal (B x 2 x T)
-        '''
-        warpfield = self._to_absolute_positions(warpfield, input.shape[-1])
-        warped = self.warper(input, warpfield)
-        return warped
-
-
-class MonotoneTimeWarper(TimeWarper):
-
-    def forward(self, input, warpfield):
-        '''
-        :param input: audio signal to be warped (B x 2 x T)
-        :param warpfield: the corresponding warpfield (B x 2 x T)
-        :return: the warped signal (B x 2 x T), ensured to be monotonous
-        '''
-        warpfield = self._to_absolute_positions(warpfield, input.shape[-1])
-        # ensure monotonicity: each warp must be at least as big as previous_warp-1
-        warpfield = th.cummax(warpfield, dim=-1)[0]
-        # print('warpfield ',warpfield.shape)
-        # warp
-        warped = self.warper(input, warpfield)
-        return warped
-
-
-class GeometricTimeWarper(TimeWarper):
-
-    def __init__(self, sampling_rate=48000):
-        super().__init__()
-        self.sampling_rate = sampling_rate
-
-    def displacements2warpfield(self, displacements, seq_length):
-        distance = th.sum(displacements**2, dim=2) ** 0.5
-        distance = F.interpolate(distance, size=seq_length)
-        warpfield = -distance / 343.0 * self.sampling_rate
-        return warpfield
-
-    def forward(self, input, displacements):
-        '''
-        :param input: audio signal to be warped (B x 2 x T)
-        :param displacements: sequence of 3D displacement vectors for geometric warping (B x 3 x T)
-        :return: the warped signal (B x 2 x T)
-        '''
-        warpfield = self.displacements2warpfield(displacements, input.shape[-1])
-        # print('Ge warpfield ', warpfield.shape)
-        # assert 1==2
-        warped = super().forward(input, warpfield)
-        return warped

spaces/AIGC-Audio/AudioGPT/text_to_audio/Make_An_Audio/ldm/models/diffusion/ddpm_audio_inpaint.py

@@ -1,1081 +0,0 @@
-"""
-wild mixture of
-https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
-https://github.com/openai/improved-diffusion/blob/e94489283bb876ac1477d5dd7709bbbd2d9902ce/improved_diffusion/gaussian_diffusion.py
-https://github.com/CompVis/taming-transformers
--- merci
-"""
-import os
-import torch
-import torch.nn as nn
-import numpy as np
-import pytorch_lightning as pl
-from torch.optim.lr_scheduler import LambdaLR
-from einops import rearrange, repeat
-from contextlib import contextmanager
-from functools import partial
-from tqdm import tqdm
-from torchvision.utils import make_grid
-from pytorch_lightning.utilities.distributed import rank_zero_only
-
-from ldm.util import log_txt_as_img, exists, default, ismap, isimage, mean_flat, count_params, instantiate_from_config
-from ldm.modules.ema import LitEma
-from ldm.modules.distributions.distributions import normal_kl, DiagonalGaussianDistribution
-from ldm.models.autoencoder import VQModelInterface, IdentityFirstStage, AutoencoderKL
-from ldm.modules.diffusionmodules.util import make_beta_schedule, extract_into_tensor, noise_like
-from ldm.models.diffusion.ddim import DDIMSampler
-from ldm.models.diffusion.ddpm import DDPM, disabled_train
-
-__conditioning_keys__ = {'concat': 'c_concat',
-                         'crossattn': 'c_crossattn',
-                         'adm': 'y'}
-
-# add mel_dim and mel_length params to ensure correct shape
-class LatentDiffusion_audioinpaint(DDPM):
-    """main class"""
-    def __init__(self,
-                 first_stage_config,
-                 cond_stage_config,
-                 num_timesteps_cond=None,
-                 mel_dim=80,
-                 mel_length=848,
-                 cond_stage_key="image",
-                 cond_stage_trainable=False,
-                 concat_mode=True,
-                 cond_stage_forward=None,
-                 conditioning_key=None,
-                 scale_factor=1.0,
-                 scale_by_std=False,
-                 test_repeat=1,
-                 test_numsteps = None,
-                 *args, **kwargs):
-        self.num_timesteps_cond = default(num_timesteps_cond, 1)
-        self.scale_by_std = scale_by_std
-        assert self.num_timesteps_cond <= kwargs['timesteps']
-        # for backwards compatibility after implementation of DiffusionWrapper
-        if conditioning_key is None:
-            conditioning_key = 'concat' if concat_mode else 'crossattn'
-        if cond_stage_config == '__is_unconditional__':
-            conditioning_key = None
-        ckpt_path = kwargs.pop("ckpt_path", None)
-        ignore_keys = kwargs.pop("ignore_keys", [])
-        super().__init__(conditioning_key=conditioning_key, *args, **kwargs)
-        self.test_repeat = test_repeat
-        if test_numsteps == None:
-            self.test_numsteps = self.num_timesteps
-        self.concat_mode = concat_mode
-        self.mel_dim = mel_dim
-        self.mel_length = mel_length
-        self.cond_stage_trainable = cond_stage_trainable
-        self.cond_stage_key = cond_stage_key
-        try:
-            self.num_downs = len(first_stage_config.params.ddconfig.ch_mult) - 1
-        except:
-            self.num_downs = 0
-        if not scale_by_std:
-            self.scale_factor = scale_factor
-        else:
-            self.register_buffer('scale_factor', torch.tensor(scale_factor))
-        self.instantiate_first_stage(first_stage_config)
-        self.instantiate_cond_stage(cond_stage_config)
-        self.cond_stage_forward = cond_stage_forward
-        self.clip_denoised = False
-        self.bbox_tokenizer = None  
-
-        self.restarted_from_ckpt = False
-        if ckpt_path is not None:
-            self.init_from_ckpt(ckpt_path, ignore_keys)
-            self.restarted_from_ckpt = True
-
-    def make_cond_schedule(self, ):
-        self.cond_ids = torch.full(size=(self.num_timesteps,), fill_value=self.num_timesteps - 1, dtype=torch.long)
-        ids = torch.round(torch.linspace(0, self.num_timesteps - 1, self.num_timesteps_cond)).long()
-        self.cond_ids[:self.num_timesteps_cond] = ids
-
-    @rank_zero_only
-    @torch.no_grad()
-    def on_train_batch_start(self, batch, batch_idx, dataloader_idx):
-        # only for very first batch
-        if self.scale_by_std and self.current_epoch == 0 and self.global_step == 0 and batch_idx == 0 and not self.restarted_from_ckpt:
-            assert self.scale_factor == 1., 'rather not use custom rescaling and std-rescaling simultaneously'
-            # set rescale weight to 1./std of encodings
-            print("### USING STD-RESCALING ###")
-            x = super().get_input(batch, self.first_stage_key)
-            x = x.to(self.device)
-            encoder_posterior = self.encode_first_stage(x)
-            z = self.get_first_stage_encoding(encoder_posterior).detach()
-            del self.scale_factor
-            self.register_buffer('scale_factor', 1. / z.flatten().std())
-            print(f"setting self.scale_factor to {self.scale_factor}")
-            print("### USING STD-RESCALING ###")
-
-    def register_schedule(self,
-                          given_betas=None, beta_schedule="linear", timesteps=1000,
-                          linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
-        super().register_schedule(given_betas, beta_schedule, timesteps, linear_start, linear_end, cosine_s)
-
-        self.shorten_cond_schedule = self.num_timesteps_cond > 1
-        if self.shorten_cond_schedule:
-            self.make_cond_schedule()
-
-    def instantiate_first_stage(self, config):
-        model = instantiate_from_config(config)
-        self.first_stage_model = model.eval()
-        self.first_stage_model.train = disabled_train
-        for param in self.first_stage_model.parameters():
-            param.requires_grad = False
-
-    def instantiate_cond_stage(self, config):
-        if not self.cond_stage_trainable:
-            if config == "__is_first_stage__":# for no_text inpainting task
-                print("Using first stage also as cond stage.")
-                self.cond_stage_model = self.first_stage_model
-            elif config == "__is_unconditional__":# for unconditional image generation such as human face、ImageNet
-                print(f"Training {self.__class__.__name__} as an unconditional model.")
-                self.cond_stage_model = None
-                # self.be_unconditional = True
-            else:
-                model = instantiate_from_config(config)
-                self.cond_stage_model = model.eval()
-                self.cond_stage_model.train = disabled_train
-                for param in self.cond_stage_model.parameters():
-                    param.requires_grad = False
-        else:
-            assert config != '__is_first_stage__'
-            assert config != '__is_unconditional__'
-            model = instantiate_from_config(config)
-            self.cond_stage_model = model
-
-    def _get_denoise_row_from_list(self, samples, desc='', force_no_decoder_quantization=False):
-        denoise_row = []
-        for zd in tqdm(samples, desc=desc):
-            denoise_row.append(self.decode_first_stage(zd.to(self.device),
-                                                            force_not_quantize=force_no_decoder_quantization))
-        n_imgs_per_row = len(denoise_row)
-        denoise_row = torch.stack(denoise_row)  # n_log_step, n_row, C, H, W
-        denoise_grid = rearrange(denoise_row, 'n b c h w -> b n c h w')
-        denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w')
-        denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row)
-        return denoise_grid
-
-    def get_first_stage_encoding(self, encoder_posterior):# encode_emb from autoencoder
-        if isinstance(encoder_posterior, DiagonalGaussianDistribution):
-            z = encoder_posterior.sample()
-        elif isinstance(encoder_posterior, torch.Tensor):
-            z = encoder_posterior
-        else:
-            raise NotImplementedError(f"encoder_posterior of type '{type(encoder_posterior)}' not yet implemented")
-        return self.scale_factor * z
-
-    def get_learned_conditioning(self, c):
-        if self.cond_stage_forward is None:
-            if hasattr(self.cond_stage_model, 'encode') and callable(self.cond_stage_model.encode):
-                c = self.cond_stage_model.encode(c)
-                if isinstance(c, DiagonalGaussianDistribution):
-                    c = c.mode()
-            else:
-                c = self.cond_stage_model(c)
-        else:
-            assert hasattr(self.cond_stage_model, self.cond_stage_forward)
-            c = getattr(self.cond_stage_model, self.cond_stage_forward)(c)
-        return c
-
-    def meshgrid(self, h, w):
-        y = torch.arange(0, h).view(h, 1, 1).repeat(1, w, 1)
-        x = torch.arange(0, w).view(1, w, 1).repeat(h, 1, 1)
-
-        arr = torch.cat([y, x], dim=-1)
-        return arr
-
-    def delta_border(self, h, w):
-        """
-        :param h: height
-        :param w: width
-        :return: normalized distance to image border,
-         wtith min distance = 0 at border and max dist = 0.5 at image center
-        """
-        lower_right_corner = torch.tensor([h - 1, w - 1]).view(1, 1, 2)
-        arr = self.meshgrid(h, w) / lower_right_corner
-        dist_left_up = torch.min(arr, dim=-1, keepdims=True)[0]
-        dist_right_down = torch.min(1 - arr, dim=-1, keepdims=True)[0]
-        edge_dist = torch.min(torch.cat([dist_left_up, dist_right_down], dim=-1), dim=-1)[0]
-        return edge_dist
-
-    def get_weighting(self, h, w, Ly, Lx, device):
-        weighting = self.delta_border(h, w)
-        weighting = torch.clip(weighting, self.split_input_params["clip_min_weight"],
-                               self.split_input_params["clip_max_weight"], )
-        weighting = weighting.view(1, h * w, 1).repeat(1, 1, Ly * Lx).to(device)
-
-        if self.split_input_params["tie_braker"]:
-            L_weighting = self.delta_border(Ly, Lx)
-            L_weighting = torch.clip(L_weighting,
-                                     self.split_input_params["clip_min_tie_weight"],
-                                     self.split_input_params["clip_max_tie_weight"])
-
-            L_weighting = L_weighting.view(1, 1, Ly * Lx).to(device)
-            weighting = weighting * L_weighting
-        return weighting
-
-    def get_fold_unfold(self, x, kernel_size, stride, uf=1, df=1):  # todo load once not every time, shorten code
-        """
-        :param x: img of size (bs, c, h, w)
-        :return: n img crops of size (n, bs, c, kernel_size[0], kernel_size[1])
-        """
-        bs, nc, h, w = x.shape
-
-        # number of crops in image
-        Ly = (h - kernel_size[0]) // stride[0] + 1
-        Lx = (w - kernel_size[1]) // stride[1] + 1
-
-        if uf == 1 and df == 1:
-            fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
-            unfold = torch.nn.Unfold(**fold_params)
-
-            fold = torch.nn.Fold(output_size=x.shape[2:], **fold_params)
-
-            weighting = self.get_weighting(kernel_size[0], kernel_size[1], Ly, Lx, x.device).to(x.dtype)
-            normalization = fold(weighting).view(1, 1, h, w)  # normalizes the overlap
-            weighting = weighting.view((1, 1, kernel_size[0], kernel_size[1], Ly * Lx))
-
-        elif uf > 1 and df == 1:
-            fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
-            unfold = torch.nn.Unfold(**fold_params)
-
-            fold_params2 = dict(kernel_size=(kernel_size[0] * uf, kernel_size[0] * uf),
-                                dilation=1, padding=0,
-                                stride=(stride[0] * uf, stride[1] * uf))
-            fold = torch.nn.Fold(output_size=(x.shape[2] * uf, x.shape[3] * uf), **fold_params2)
-
-            weighting = self.get_weighting(kernel_size[0] * uf, kernel_size[1] * uf, Ly, Lx, x.device).to(x.dtype)
-            normalization = fold(weighting).view(1, 1, h * uf, w * uf)  # normalizes the overlap
-            weighting = weighting.view((1, 1, kernel_size[0] * uf, kernel_size[1] * uf, Ly * Lx))
-
-        elif df > 1 and uf == 1:
-            fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
-            unfold = torch.nn.Unfold(**fold_params)
-
-            fold_params2 = dict(kernel_size=(kernel_size[0] // df, kernel_size[0] // df),
-                                dilation=1, padding=0,
-                                stride=(stride[0] // df, stride[1] // df))
-            fold = torch.nn.Fold(output_size=(x.shape[2] // df, x.shape[3] // df), **fold_params2)
-
-            weighting = self.get_weighting(kernel_size[0] // df, kernel_size[1] // df, Ly, Lx, x.device).to(x.dtype)
-            normalization = fold(weighting).view(1, 1, h // df, w // df)  # normalizes the overlap
-            weighting = weighting.view((1, 1, kernel_size[0] // df, kernel_size[1] // df, Ly * Lx))
-
-        else:
-            raise NotImplementedError
-
-        return fold, unfold, normalization, weighting
-
-    @torch.no_grad()
-    def get_input(self, batch, k, return_first_stage_outputs=False, force_c_encode=False,
-                  cond_key=None, return_original_cond=False, bs=None):
-        x = super().get_input(batch, k)
-        if bs is not None:
-            x = x[:bs]
-        x = x.to(self.device)
-        encoder_posterior = self.encode_first_stage(x)
-        z = self.get_first_stage_encoding(encoder_posterior).detach()
-
-        if self.model.conditioning_key is not None:# 'crossattn' for txt2image, 'hybird' for txt_inpaint
-            if cond_key is None:
-                cond_key = self.cond_stage_key # 'caption' for txt_inpaint
-            if self.model.conditioning_key == 'hybrid':
-                xc = {}
-                assert cond_key == 'caption' # only txt_inpaint is implemented now
-                assert 'masked_image' in batch.keys() 
-                assert 'mask' in batch.keys()
-                masked_image = super().get_input(batch,'masked_image')
-                mask = super().get_input(batch,'mask')
-                if bs is not None:
-                    masked_image,mask = masked_image[:bs],mask[:bs]
-                masked_image,mask = masked_image.to(self.device),mask.to(self.device)
-                masked_image = self.get_first_stage_encoding(self.encode_first_stage(masked_image)).detach()
-                resized_mask = torch.nn.functional.interpolate(mask,size=masked_image.shape[-2:])
-                xc['c_concat'] = torch.cat((masked_image,resized_mask),dim = 1)
-                xc[cond_key] = batch[cond_key]
-            else:
-                if cond_key != self.first_stage_key:
-                    if cond_key in ['caption', 'coordinates_bbox']:
-                        xc = batch[cond_key]
-                    elif cond_key == 'class_label':
-                        xc = batch
-                    else:
-                        xc = super().get_input(batch, cond_key).to(self.device)
-                else:# cond_key == 'image'
-                    xc = x
-            if not self.cond_stage_trainable or force_c_encode:# cond_stage_trainable is true for txt2img，force_c_encoder = True，when called in log_images
-                if isinstance(xc, list):
-                    # import pudb; pudb.set_trace()
-                    c = self.get_learned_conditioning(xc)# 因为log_images内接下来要调用sample_log,所以需要预先得到处理好的c
-                if isinstance(xc, dict):
-                    c = {}
-                    c['c_concat'] = xc['c_concat']
-                    c['c_crossattn'] = self.get_learned_conditioning(xc[cond_key])
-                else:
-                    c = self.get_learned_conditioning(xc.to(self.device))
-            else:
-                c = xc
-            if bs is not None:
-                if isinstance(c,dict):
-                    for k in c.keys():
-                        c[k] = c[k][:bs]
-                else:
-                    c = c[:bs]
-
-            if self.use_positional_encodings:
-                pos_x, pos_y = self.compute_latent_shifts(batch)
-                ckey = __conditioning_keys__[self.model.conditioning_key]
-                c = {ckey: c, 'pos_x': pos_x, 'pos_y': pos_y}
-
-        else:
-            c = None
-            xc = None
-            if self.use_positional_encodings:
-                pos_x, pos_y = self.compute_latent_shifts(batch)
-                c = {'pos_x': pos_x, 'pos_y': pos_y}
-        out = [z, c]
-        if return_first_stage_outputs:
-            xrec = self.decode_first_stage(z)
-            out.extend([x, xrec])
-        if return_original_cond:
-            out.append(xc)
-        return out
-
-    @torch.no_grad()
-    def decode_first_stage(self, z, predict_cids=False, force_not_quantize=False):
-        if predict_cids:
-            if z.dim() == 4:
-                z = torch.argmax(z.exp(), dim=1).long()
-            z = self.first_stage_model.quantize.get_codebook_entry(z, shape=None)
-            z = rearrange(z, 'b h w c -> b c h w').contiguous()
-
-        z = 1. / self.scale_factor * z
-
-        if hasattr(self, "split_input_params"):
-            if self.split_input_params["patch_distributed_vq"]:
-                ks = self.split_input_params["ks"]  # eg. (128, 128)
-                stride = self.split_input_params["stride"]  # eg. (64, 64)
-                uf = self.split_input_params["vqf"]
-                bs, nc, h, w = z.shape
-                if ks[0] > h or ks[1] > w:
-                    ks = (min(ks[0], h), min(ks[1], w))
-                    print("reducing Kernel")
-
-                if stride[0] > h or stride[1] > w:
-                    stride = (min(stride[0], h), min(stride[1], w))
-                    print("reducing stride")
-
-                fold, unfold, normalization, weighting = self.get_fold_unfold(z, ks, stride, uf=uf)
-
-                z = unfold(z)  # (bn, nc * prod(**ks), L)
-                # 1. Reshape to img shape
-                z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1]))  # (bn, nc, ks[0], ks[1], L )
-
-                # 2. apply model loop over last dim
-                if isinstance(self.first_stage_model, VQModelInterface):
-                    output_list = [self.first_stage_model.decode(z[:, :, :, :, i],
-                                                                 force_not_quantize=predict_cids or force_not_quantize)
-                                   for i in range(z.shape[-1])]
-                else:
-
-                    output_list = [self.first_stage_model.decode(z[:, :, :, :, i])
-                                   for i in range(z.shape[-1])]
-
-                o = torch.stack(output_list, axis=-1)  # # (bn, nc, ks[0], ks[1], L)
-                o = o * weighting
-                # Reverse 1. reshape to img shape
-                o = o.view((o.shape[0], -1, o.shape[-1]))  # (bn, nc * ks[0] * ks[1], L)
-                # stitch crops together
-                decoded = fold(o)
-                decoded = decoded / normalization  # norm is shape (1, 1, h, w)
-                return decoded
-            else:
-                if isinstance(self.first_stage_model, VQModelInterface):
-                    return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)
-                else:
-                    return self.first_stage_model.decode(z)
-
-        else:
-            if isinstance(self.first_stage_model, VQModelInterface):
-                return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)
-            else:
-                return self.first_stage_model.decode(z)
-
-    # same as above but without decorator
-    def differentiable_decode_first_stage(self, z, predict_cids=False, force_not_quantize=False):
-        if predict_cids:
-            if z.dim() == 4:
-                z = torch.argmax(z.exp(), dim=1).long()
-            z = self.first_stage_model.quantize.get_codebook_entry(z, shape=None)
-            z = rearrange(z, 'b h w c -> b c h w').contiguous()
-
-        z = 1. / self.scale_factor * z
-
-        if hasattr(self, "split_input_params"):
-            if self.split_input_params["patch_distributed_vq"]:
-                ks = self.split_input_params["ks"]  # eg. (128, 128)
-                stride = self.split_input_params["stride"]  # eg. (64, 64)
-                uf = self.split_input_params["vqf"]
-                bs, nc, h, w = z.shape
-                if ks[0] > h or ks[1] > w:
-                    ks = (min(ks[0], h), min(ks[1], w))
-                    print("reducing Kernel")
-
-                if stride[0] > h or stride[1] > w:
-                    stride = (min(stride[0], h), min(stride[1], w))
-                    print("reducing stride")
-
-                fold, unfold, normalization, weighting = self.get_fold_unfold(z, ks, stride, uf=uf)
-
-                z = unfold(z)  # (bn, nc * prod(**ks), L)
-                # 1. Reshape to img shape
-                z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1]))  # (bn, nc, ks[0], ks[1], L )
-
-                # 2. apply model loop over last dim
-                if isinstance(self.first_stage_model, VQModelInterface):  
-                    output_list = [self.first_stage_model.decode(z[:, :, :, :, i],
-                                                                 force_not_quantize=predict_cids or force_not_quantize)
-                                   for i in range(z.shape[-1])]
-                else:
-
-                    output_list = [self.first_stage_model.decode(z[:, :, :, :, i])
-                                   for i in range(z.shape[-1])]
-
-                o = torch.stack(output_list, axis=-1)  # # (bn, nc, ks[0], ks[1], L)
-                o = o * weighting
-                # Reverse 1. reshape to img shape
-                o = o.view((o.shape[0], -1, o.shape[-1]))  # (bn, nc * ks[0] * ks[1], L)
-                # stitch crops together
-                decoded = fold(o)
-                decoded = decoded / normalization  # norm is shape (1, 1, h, w)
-                return decoded
-            else:
-                if isinstance(self.first_stage_model, VQModelInterface):
-                    return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)
-                else:
-                    return self.first_stage_model.decode(z)
-
-        else:
-            if isinstance(self.first_stage_model, VQModelInterface):
-                return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)
-            else:
-                return self.first_stage_model.decode(z)
-
-    @torch.no_grad()
-    def encode_first_stage(self, x):
-        if hasattr(self, "split_input_params"):
-            if self.split_input_params["patch_distributed_vq"]:
-                ks = self.split_input_params["ks"]  # eg. (128, 128)
-                stride = self.split_input_params["stride"]  # eg. (64, 64)
-                df = self.split_input_params["vqf"]
-                self.split_input_params['original_image_size'] = x.shape[-2:]
-                bs, nc, h, w = x.shape
-                if ks[0] > h or ks[1] > w:
-                    ks = (min(ks[0], h), min(ks[1], w))
-                    print("reducing Kernel")
-
-                if stride[0] > h or stride[1] > w:
-                    stride = (min(stride[0], h), min(stride[1], w))
-                    print("reducing stride")
-
-                fold, unfold, normalization, weighting = self.get_fold_unfold(x, ks, stride, df=df)
-                z = unfold(x)  # (bn, nc * prod(**ks), L)
-                # Reshape to img shape
-                z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1]))  # (bn, nc, ks[0], ks[1], L )
-
-                output_list = [self.first_stage_model.encode(z[:, :, :, :, i])
-                               for i in range(z.shape[-1])]
-
-                o = torch.stack(output_list, axis=-1)
-                o = o * weighting
-
-                # Reverse reshape to img shape
-                o = o.view((o.shape[0], -1, o.shape[-1]))  # (bn, nc * ks[0] * ks[1], L)
-                # stitch crops together
-                decoded = fold(o)
-                decoded = decoded / normalization
-                return decoded
-
-            else:
-                return self.first_stage_model.encode(x)
-        else:
-            return self.first_stage_model.encode(x)
-
-    def shared_step(self, batch, **kwargs):
-        x, c = self.get_input(batch, self.first_stage_key)# get latent and condition
-        loss = self(x, c)
-        return loss
-
-    def test_step(self,batch,batch_idx):
-        # TODO make self.test_repeat work
-        cond = {}
-        cond[self.cond_stage_key] = batch[self.cond_stage_key]
-        cond[self.cond_stage_key] = self.get_learned_conditioning(cond[self.cond_stage_key]) # c: string -> [B, T, Context_dim]
-        cond['c_crossattn'] = cond.pop(self.cond_stage_key)
-        masked_image = super().get_input(batch,'masked_image')
-        mask = super().get_input(batch,'mask')
-        masked_image,mask = masked_image.to(self.device),mask.to(self.device)
-        masked_image = self.get_first_stage_encoding(self.encode_first_stage(masked_image)).detach()
-        resized_mask = torch.nn.functional.interpolate(mask,size=masked_image.shape[-2:])
-        cond['c_concat'] = torch.cat((masked_image,resized_mask),dim = 1)
-        batch_size = len(batch[self.cond_stage_key])
-        # shape = [batch_size,self.channels,self.mel_dim,self.mel_length]
-        enc_emb = self.sample(cond,batch_size,timesteps=self.test_numsteps)
-        xrec = self.decode_first_stage(enc_emb)
-        reconstructions = (xrec + 1)/2 # to mel scale
-        test_ckpt_path = os.path.basename(self.trainer.tested_ckpt_path)
-        savedir = os.path.join(self.trainer.log_dir,f'output_imgs_{test_ckpt_path}','fake_class')
-        if not os.path.exists(savedir):
-            os.makedirs(savedir)
-
-        file_names = batch['f_name']
-        nfiles = len(file_names)
-        reconstructions = reconstructions.cpu().numpy().squeeze(1) # squuze channel dim
-        for k in range(reconstructions.shape[0]):
-            b,repeat = k % nfiles, k // nfiles
-            vname_num_split_index = file_names[b].rfind('_')# file_names[b]:video_name+'_'+num
-            v_n,num = file_names[b][:vname_num_split_index],file_names[b][vname_num_split_index+1:]
-            save_img_path = os.path.join(savedir,f'{v_n}_sample_{num}_{repeat}.npy')# the num_th caption, the repeat_th repitition
-            np.save(save_img_path,reconstructions[b])
-        
-        return None
-
-    def forward(self, x, c, *args, **kwargs):
-        t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long()
-        if self.model.conditioning_key is not None:
-            assert c is not None
-            if self.cond_stage_trainable:
-                if isinstance(c,dict):
-                    c[self.cond_stage_key] = self.get_learned_conditioning(c[self.cond_stage_key])
-                    c['c_crossattn'] = c.pop(self.cond_stage_key)
-                else:
-                    c = self.get_learned_conditioning(c) # c: string -> [B, T, Context_dim]
-            if self.shorten_cond_schedule:  # TODO: drop this option
-                tc = self.cond_ids[t].to(self.device)
-                c = self.q_sample(x_start=c, t=tc, noise=torch.randn_like(c.float()))
-        return self.p_losses(x, c, t, *args, **kwargs)
-
-    def _rescale_annotations(self, bboxes, crop_coordinates):  # TODO: move to dataset
-        def rescale_bbox(bbox):
-            x0 = torch.clamp((bbox[0] - crop_coordinates[0]) / crop_coordinates[2])
-            y0 = torch.clamp((bbox[1] - crop_coordinates[1]) / crop_coordinates[3])
-            w = min(bbox[2] / crop_coordinates[2], 1 - x0)
-            h = min(bbox[3] / crop_coordinates[3], 1 - y0)
-            return x0, y0, w, h
-
-        return [rescale_bbox(b) for b in bboxes]
-
-    def apply_model(self, x_noisy, t, cond, return_ids=False):
-        # make values to list to enable concat operation in 
-        if isinstance(cond, dict):
-            # hybrid case, cond is exptected to be a dict. (txt2inpaint)
-            cond_tmp = {}# use cond_tmp to avoid inplace edit
-            for k,v in cond.items():
-                if not isinstance(v, list):
-                    cond_tmp[k] = [cond[k]]
-                else:
-                    cond_tmp[k] = cond[k]
-            cond = cond_tmp
-        else:
-            if not isinstance(cond, list):
-                cond = [cond]
-            key = 'c_concat' if self.model.conditioning_key == 'concat' else 'c_crossattn'
-            cond = {key: cond}
-
-        if hasattr(self, "split_input_params"):
-            assert len(cond) == 1  # todo can only deal with one conditioning atm
-            assert not return_ids  
-            ks = self.split_input_params["ks"]  # eg. (128, 128)
-            stride = self.split_input_params["stride"]  # eg. (64, 64)
-
-            h, w = x_noisy.shape[-2:]
-
-            fold, unfold, normalization, weighting = self.get_fold_unfold(x_noisy, ks, stride)
-
-            z = unfold(x_noisy)  # (bn, nc * prod(**ks), L)
-            # Reshape to img shape
-            z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1]))  # (bn, nc, ks[0], ks[1], L )
-            z_list = [z[:, :, :, :, i] for i in range(z.shape[-1])]
-
-            if self.cond_stage_key in ["image", "LR_image", "segmentation",
-                                       'bbox_img'] and self.model.conditioning_key:  # todo check for completeness
-                c_key = next(iter(cond.keys()))  # get key
-                c = next(iter(cond.values()))  # get value
-                assert (len(c) == 1)  # todo extend to list with more than one elem
-                c = c[0]  # get element
-
-                c = unfold(c)
-                c = c.view((c.shape[0], -1, ks[0], ks[1], c.shape[-1]))  # (bn, nc, ks[0], ks[1], L )
-
-                cond_list = [{c_key: [c[:, :, :, :, i]]} for i in range(c.shape[-1])]
-
-            elif self.cond_stage_key == 'coordinates_bbox':
-                assert 'original_image_size' in self.split_input_params, 'BoudingBoxRescaling is missing original_image_size'
-
-                # assuming padding of unfold is always 0 and its dilation is always 1
-                n_patches_per_row = int((w - ks[0]) / stride[0] + 1)
-                full_img_h, full_img_w = self.split_input_params['original_image_size']
-                # as we are operating on latents, we need the factor from the original image size to the
-                # spatial latent size to properly rescale the crops for regenerating the bbox annotations
-                num_downs = self.first_stage_model.encoder.num_resolutions - 1
-                rescale_latent = 2 ** (num_downs)
-
-                # get top left postions of patches as conforming for the bbbox tokenizer, therefore we
-                # need to rescale the tl patch coordinates to be in between (0,1)
-                tl_patch_coordinates = [(rescale_latent * stride[0] * (patch_nr % n_patches_per_row) / full_img_w,
-                                         rescale_latent * stride[1] * (patch_nr // n_patches_per_row) / full_img_h)
-                                        for patch_nr in range(z.shape[-1])]
-
-                # patch_limits are tl_coord, width and height coordinates as (x_tl, y_tl, h, w)
-                patch_limits = [(x_tl, y_tl,
-                                 rescale_latent * ks[0] / full_img_w,
-                                 rescale_latent * ks[1] / full_img_h) for x_tl, y_tl in tl_patch_coordinates]
-                # patch_values = [(np.arange(x_tl,min(x_tl+ks, 1.)),np.arange(y_tl,min(y_tl+ks, 1.))) for x_tl, y_tl in tl_patch_coordinates]
-
-                # tokenize crop coordinates for the bounding boxes of the respective patches
-                patch_limits_tknzd = [torch.LongTensor(self.bbox_tokenizer._crop_encoder(bbox))[None].to(self.device)
-                                      for bbox in patch_limits]  # list of length l with tensors of shape (1, 2)
-                print(patch_limits_tknzd[0].shape)
-                # cut tknzd crop position from conditioning
-                assert isinstance(cond, dict), 'cond must be dict to be fed into model'
-                cut_cond = cond['c_crossattn'][0][..., :-2].to(self.device)
-                print(cut_cond.shape)
-
-                adapted_cond = torch.stack([torch.cat([cut_cond, p], dim=1) for p in patch_limits_tknzd])
-                adapted_cond = rearrange(adapted_cond, 'l b n -> (l b) n')
-                print(adapted_cond.shape)
-                adapted_cond = self.get_learned_conditioning(adapted_cond)
-                print(adapted_cond.shape)
-                adapted_cond = rearrange(adapted_cond, '(l b) n d -> l b n d', l=z.shape[-1])
-                print(adapted_cond.shape)
-
-                cond_list = [{'c_crossattn': [e]} for e in adapted_cond]
-
-            else:
-                cond_list = [cond for i in range(z.shape[-1])]  # Todo make this more efficient
-
-            # apply model by loop over crops
-            output_list = [self.model(z_list[i], t, **cond_list[i]) for i in range(z.shape[-1])]
-            assert not isinstance(output_list[0],
-                                  tuple)  # todo cant deal with multiple model outputs check this never happens
-
-            o = torch.stack(output_list, axis=-1)
-            o = o * weighting
-            # Reverse reshape to img shape
-            o = o.view((o.shape[0], -1, o.shape[-1]))  # (bn, nc * ks[0] * ks[1], L)
-            # stitch crops together
-            x_recon = fold(o) / normalization
-
-        else:
-            # x_noisy is tensor with shape [b,c,mel_len,T]
-            # if condition is caption ,cond['c_crossattn'] is a list, each item shape is [1, 77, 1280]
-            x_recon = self.model(x_noisy, t, **cond)# tensor with shape [b,c,mel_len,T]
-
-        if isinstance(x_recon, tuple) and not return_ids:
-            return x_recon[0]
-        else:
-            return x_recon
-
-    def _predict_eps_from_xstart(self, x_t, t, pred_xstart):
-        return (extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t - pred_xstart) / \
-               extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape)
-
-    def _prior_bpd(self, x_start):
-        """
-        Get the prior KL term for the variational lower-bound, measured in
-        bits-per-dim.
-        This term can't be optimized, as it only depends on the encoder.
-        :param x_start: the [N x C x ...] tensor of inputs.
-        :return: a batch of [N] KL values (in bits), one per batch element.
-        """
-        batch_size = x_start.shape[0]
-        t = torch.tensor([self.num_timesteps - 1] * batch_size, device=x_start.device)
-        qt_mean, _, qt_log_variance = self.q_mean_variance(x_start, t)
-        kl_prior = normal_kl(mean1=qt_mean, logvar1=qt_log_variance, mean2=0.0, logvar2=0.0)
-        return mean_flat(kl_prior) / np.log(2.0)
-
-    def p_losses(self, x_start, cond, t, noise=None):
-        noise = default(noise, lambda: torch.randn_like(x_start))
-        x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
-        model_output = self.apply_model(x_noisy, t, cond)
-
-        loss_dict = {}
-        prefix = 'train' if self.training else 'val'
-
-        if self.parameterization == "x0":
-            target = x_start
-        elif self.parameterization == "eps":
-            target = noise
-        else:
-            raise NotImplementedError()
-
-        loss_simple = self.get_loss(model_output, target, mean=False).mean([1, 2, 3])
-        loss_dict.update({f'{prefix}/loss_simple': loss_simple.mean()})
-
-        logvar_t = self.logvar[t].to(self.device)
-        loss = loss_simple / torch.exp(logvar_t) + logvar_t
-        # loss = loss_simple / torch.exp(self.logvar) + self.logvar
-        if self.learn_logvar:
-            loss_dict.update({f'{prefix}/loss_gamma': loss.mean()})
-            loss_dict.update({'logvar': self.logvar.data.mean()})
-
-        loss = self.l_simple_weight * loss.mean()
-
-        loss_vlb = self.get_loss(model_output, target, mean=False).mean(dim=(1, 2, 3))
-        loss_vlb = (self.lvlb_weights[t] * loss_vlb).mean()
-        loss_dict.update({f'{prefix}/loss_vlb': loss_vlb})
-        loss += (self.original_elbo_weight * loss_vlb)
-        loss_dict.update({f'{prefix}/loss': loss})
-
-        return loss, loss_dict
-
-    def p_mean_variance(self, x, c, t, clip_denoised: bool, return_codebook_ids=False, quantize_denoised=False,
-                        return_x0=False, score_corrector=None, corrector_kwargs=None):
-        t_in = t
-        model_out = self.apply_model(x, t_in, c, return_ids=return_codebook_ids)
-
-        if score_corrector is not None:
-            assert self.parameterization == "eps"
-            model_out = score_corrector.modify_score(self, model_out, x, t, c, **corrector_kwargs)
-
-        if return_codebook_ids:
-            model_out, logits = model_out
-
-        if self.parameterization == "eps":
-            x_recon = self.predict_start_from_noise(x, t=t, noise=model_out)
-        elif self.parameterization == "x0":
-            x_recon = model_out
-        else:
-            raise NotImplementedError()
-
-        if clip_denoised:
-            x_recon.clamp_(-1., 1.)
-        if quantize_denoised:
-            x_recon, _, [_, _, indices] = self.first_stage_model.quantize(x_recon)
-        model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t)
-        if return_codebook_ids:
-            return model_mean, posterior_variance, posterior_log_variance, logits
-        elif return_x0:
-            return model_mean, posterior_variance, posterior_log_variance, x_recon
-        else:
-            return model_mean, posterior_variance, posterior_log_variance
-
-    @torch.no_grad()
-    def p_sample(self, x, c, t, clip_denoised=False, repeat_noise=False,
-                 return_codebook_ids=False, quantize_denoised=False, return_x0=False,
-                 temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None):
-        b, *_, device = *x.shape, x.device
-        outputs = self.p_mean_variance(x=x, c=c, t=t, clip_denoised=clip_denoised,
-                                       return_codebook_ids=return_codebook_ids,
-                                       quantize_denoised=quantize_denoised,
-                                       return_x0=return_x0,
-                                       score_corrector=score_corrector, corrector_kwargs=corrector_kwargs)
-        if return_codebook_ids:
-            raise DeprecationWarning("Support dropped.")
-            model_mean, _, model_log_variance, logits = outputs
-        elif return_x0:
-            model_mean, _, model_log_variance, x0 = outputs
-        else:
-            model_mean, _, model_log_variance = outputs
-
-        noise = noise_like(x.shape, device, repeat_noise) * temperature
-        if noise_dropout > 0.:
-            noise = torch.nn.functional.dropout(noise, p=noise_dropout)
-        # no noise when t == 0
-        nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
-
-        if return_codebook_ids:
-            return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, logits.argmax(dim=1)
-        if return_x0:
-            return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, x0
-        else:
-            return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
-
-    @torch.no_grad()
-    def progressive_denoising(self, cond, shape, verbose=True, callback=None, quantize_denoised=False,
-                              img_callback=None, mask=None, x0=None, temperature=1., noise_dropout=0.,
-                              score_corrector=None, corrector_kwargs=None, batch_size=None, x_T=None, start_T=None,
-                              log_every_t=None):
-        if not log_every_t:
-            log_every_t = self.log_every_t
-        timesteps = self.num_timesteps
-        if batch_size is not None:
-            b = batch_size if batch_size is not None else shape[0]
-            shape = [batch_size] + list(shape)
-        else:
-            b = batch_size = shape[0]
-        if x_T is None:
-            img = torch.randn(shape, device=self.device)
-        else:
-            img = x_T
-        intermediates = []
-        if cond is not None:
-            if isinstance(cond, dict):
-                cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else
-                list(map(lambda x: x[:batch_size], cond[key])) for key in cond}
-            else:
-                cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size]
-
-        if start_T is not None:
-            timesteps = min(timesteps, start_T)
-        iterator = tqdm(reversed(range(0, timesteps)), desc='Progressive Generation',
-                        total=timesteps) if verbose else reversed(
-            range(0, timesteps))
-        if type(temperature) == float:
-            temperature = [temperature] * timesteps
-
-        for i in iterator:
-            ts = torch.full((b,), i, device=self.device, dtype=torch.long)
-            if self.shorten_cond_schedule:
-                assert self.model.conditioning_key != 'hybrid'
-                tc = self.cond_ids[ts].to(cond.device)
-                cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond))
-
-            img, x0_partial = self.p_sample(img, cond, ts,
-                                            clip_denoised=self.clip_denoised,
-                                            quantize_denoised=quantize_denoised, return_x0=True,
-                                            temperature=temperature[i], noise_dropout=noise_dropout,
-                                            score_corrector=score_corrector, corrector_kwargs=corrector_kwargs)
-            if mask is not None:
-                assert x0 is not None
-                img_orig = self.q_sample(x0, ts)
-                img = img_orig * mask + (1. - mask) * img
-
-            if i % log_every_t == 0 or i == timesteps - 1:
-                intermediates.append(x0_partial)
-            if callback: callback(i)
-            if img_callback: img_callback(img, i)
-        return img, intermediates
-
-    @torch.no_grad()
-    def p_sample_loop(self, cond, shape, return_intermediates=False,
-                      x_T=None, verbose=True, callback=None, timesteps=None, quantize_denoised=False,
-                      mask=None, x0=None, img_callback=None, start_T=None,
-                      log_every_t=None):
-
-        if not log_every_t:
-            log_every_t = self.log_every_t
-        device = self.betas.device
-        b = shape[0]
-        if x_T is None:
-            img = torch.randn(shape, device=device)
-        else:
-            img = x_T
-
-        intermediates = [img]
-        if timesteps is None:
-            timesteps = self.num_timesteps
-
-        if start_T is not None:
-            timesteps = min(timesteps, start_T)
-        iterator = tqdm(reversed(range(0, timesteps)), desc='Sampling t', total=timesteps) if verbose else reversed(
-            range(0, timesteps))
-
-        if mask is not None:
-            assert x0 is not None
-            assert x0.shape[2:3] == mask.shape[2:3]  # spatial size has to match
-
-        for i in iterator:
-            ts = torch.full((b,), i, device=device, dtype=torch.long)
-            if self.shorten_cond_schedule:
-                assert self.model.conditioning_key != 'hybrid'
-                tc = self.cond_ids[ts].to(cond.device)
-                cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond))
-
-            img = self.p_sample(img, cond, ts,
-                                clip_denoised=self.clip_denoised,
-                                quantize_denoised=quantize_denoised)
-            if mask is not None:
-                img_orig = self.q_sample(x0, ts)
-                img = img_orig * mask + (1. - mask) * img
-
-            if i % log_every_t == 0 or i == timesteps - 1:
-                intermediates.append(img)
-            if callback: callback(i)
-            if img_callback: img_callback(img, i)
-
-        if return_intermediates:
-            return img, intermediates
-        return img
-
-    @torch.no_grad()
-    def sample(self, cond, batch_size=16, return_intermediates=False, x_T=None,
-               verbose=True, timesteps=None, quantize_denoised=False,
-               mask=None, x0=None, shape=None,**kwargs):
-        if shape is None:
-            shape = (batch_size, self.channels, self.mel_dim, self.mel_length)
-        if cond is not None:
-            if isinstance(cond, dict):
-                cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else
-                list(map(lambda x: x[:batch_size], cond[key])) for key in cond}
-            else:
-                cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size]
-        return self.p_sample_loop(cond,
-                                  shape,
-                                  return_intermediates=return_intermediates, x_T=x_T,
-                                  verbose=verbose, timesteps=timesteps, quantize_denoised=quantize_denoised,
-                                  mask=mask, x0=x0)
-
-    @torch.no_grad()
-    def sample_log(self,cond,batch_size,ddim, ddim_steps,**kwargs):
-        if ddim:
-            ddim_sampler = DDIMSampler(self)
-            shape = (self.channels, self.mel_dim, self.mel_length)
-            samples, intermediates =ddim_sampler.sample(ddim_steps,batch_size,
-                                                        shape,cond,verbose=False,**kwargs)
-
-        else:
-            samples, intermediates = self.sample(cond=cond, batch_size=batch_size,
-                                                 return_intermediates=True,**kwargs)
-
-        return samples, intermediates
-
-    @torch.no_grad()
-    def log_images(self, batch, N=8, n_row=4, sample=True, ddim_steps=200, ddim_eta=1., return_keys=None,
-                   quantize_denoised=True, inpaint=True, plot_denoise_rows=False, plot_progressive_rows=True,
-                   plot_diffusion_rows=True, **kwargs):
-
-        use_ddim = ddim_steps is not None
-
-        log = dict()
-        z, c, x, xrec, xc = self.get_input(batch, self.first_stage_key,
-                                           return_first_stage_outputs=True,
-                                           force_c_encode=True,
-                                           return_original_cond=True,
-                                           bs=N)
-
-        N = min(x.shape[0], N)
-        n_row = min(x.shape[0], n_row)
-        log["inputs"] = x # 原始输入图像
-        log["reconstruction"] = xrec # 重建得到的图像
-        if self.model.conditioning_key is not None:
-            if hasattr(self.cond_stage_model, "decode"):# when cond_stage is first_stage. (bert embedder doesnot have decode)
-                xc = self.cond_stage_model.decode(c)# decoded masked image
-                log["conditioning"] = xc # 重建后的图像
-            elif self.cond_stage_key in ["caption"]:
-                xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["caption"])
-                log["conditioning"] = xc # 含有文本的图像 
-                if self.model.conditioning_key == 'hybrid':
-                    log["decoded_maskedimg"] = self.first_stage_model.decode(c['c_concat'][:,:self.first_stage_model.embed_dim])# c_concat is the concat result of masked_img latent and resized mask. get latent here to decode
-            elif self.cond_stage_key == 'class_label':
-                xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"])
-                log['conditioning'] = xc # 文本为类标签的图像
-            elif isimage(xc):
-                log["conditioning"] = xc
-            if ismap(xc):
-                log["original_conditioning"] = self.to_rgb(xc)
-
-        if plot_diffusion_rows:# diffusion每一步的图像
-            # get diffusion row
-            diffusion_row = list()
-            z_start = z[:n_row]
-            for t in range(self.num_timesteps):
-                if t % self.log_every_t == 0 or t == self.num_timesteps - 1:
-                    t = repeat(torch.tensor([t]), '1 -> b', b=n_row)
-                    t = t.to(self.device).long()
-                    noise = torch.randn_like(z_start)
-                    z_noisy = self.q_sample(x_start=z_start, t=t, noise=noise)
-                    diffusion_row.append(self.decode_first_stage(z_noisy))
-
-            diffusion_row = torch.stack(diffusion_row)  # n_log_step, n_row, C, H, W
-            diffusion_grid = rearrange(diffusion_row, 'n b c h w -> b n c h w')
-            diffusion_grid = rearrange(diffusion_grid, 'b n c h w -> (b n) c h w')
-            diffusion_grid = make_grid(diffusion_grid, nrow=diffusion_row.shape[0])
-            log["diffusion_row"] = diffusion_grid
-
-        if sample:# 
-            # get denoise row
-            with self.ema_scope("Plotting"):
-                samples, z_denoise_row = self.sample_log(cond=c,batch_size=N,ddim=use_ddim,
-                                                         ddim_steps=ddim_steps,eta=ddim_eta)
-                # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True)
-            x_samples = self.decode_first_stage(samples)
-            log["samples"] = x_samples
-            if plot_denoise_rows:
-                denoise_grid = self._get_denoise_row_from_list(z_denoise_row)
-                log["denoise_row"] = denoise_grid
-
-            if quantize_denoised and not isinstance(self.first_stage_model, AutoencoderKL) and not isinstance(
-                    self.first_stage_model, IdentityFirstStage):
-                # also display when quantizing x0 while sampling
-                with self.ema_scope("Plotting Quantized Denoised"):
-                    samples, z_denoise_row = self.sample_log(cond=c,batch_size=N,ddim=use_ddim,
-                                                             ddim_steps=ddim_steps,eta=ddim_eta,
-                                                             quantize_denoised=True)
-                    # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True,
-                    #                                      quantize_denoised=True)
-                x_samples = self.decode_first_stage(samples.to(self.device))
-                log["samples_x0_quantized"] = x_samples
-
-            if inpaint:
-                # make a simple center square
-                b, h, w = z.shape[0], z.shape[2], z.shape[3]
-                mask = torch.ones(N, h, w).to(self.device)
-                # zeros will be filled in
-                mask[:, h // 4:3 * h // 4, w // 4:3 * w // 4] = 0.
-                mask = mask[:, None, ...]# N,1,H,W
-                with self.ema_scope("Plotting Inpaint"):
-                    samples, _ = self.sample_log(cond=c,batch_size=N,ddim=use_ddim, eta=ddim_eta,
-                                                ddim_steps=ddim_steps, x0=z[:N], mask=mask)
-                x_samples = self.decode_first_stage(samples.to(self.device))
-                log["samples_inpainting"] = x_samples
-                log["mask"] = mask
-
-                # outpaint
-                with self.ema_scope("Plotting Outpaint"):
-                    samples, _ = self.sample_log(cond=c, batch_size=N, ddim=use_ddim,eta=ddim_eta,
-                                                ddim_steps=ddim_steps, x0=z[:N], mask=mask)
-                x_samples = self.decode_first_stage(samples.to(self.device))
-                log["samples_outpainting"] = x_samples
-
-        if plot_progressive_rows:
-            with self.ema_scope("Plotting Progressives"):
-                img, progressives = self.progressive_denoising(c,
-                                                               shape=(self.channels, self.mel_dim, self.mel_length),
-                                                               batch_size=N)
-            prog_row = self._get_denoise_row_from_list(progressives, desc="Progressive Generation")
-            log["progressive_row"] = prog_row
-
-        if return_keys:
-            if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0:
-                return log
-            else:
-                return {key: log[key] for key in return_keys}
-        return log
-
-    def configure_optimizers(self):
-        lr = self.learning_rate
-        params = list(self.model.parameters())
-        if self.cond_stage_trainable:
-            print(f"{self.__class__.__name__}: Also optimizing conditioner params!")
-            params = params + list(self.cond_stage_model.parameters())
-        if self.learn_logvar:
-            print('Diffusion model optimizing logvar')
-            params.append(self.logvar)
-        opt = torch.optim.AdamW(params, lr=lr)
-        if self.use_scheduler:
-            assert 'target' in self.scheduler_config
-            scheduler = instantiate_from_config(self.scheduler_config)
-
-            print("Setting up LambdaLR scheduler...")
-            scheduler = [
-                {
-                    'scheduler': LambdaLR(opt, lr_lambda=scheduler.schedule),
-                    'interval': 'step',
-                    'frequency': 1
-                }]
-            return [opt], scheduler
-        return opt
-
-    @torch.no_grad()
-    def to_rgb(self, x):
-        x = x.float()
-        if not hasattr(self, "colorize"):
-            self.colorize = torch.randn(3, x.shape[1], 1, 1).to(x)
-        x = nn.functional.conv2d(x, weight=self.colorize)
-        x = 2. * (x - x.min()) / (x.max() - x.min()) - 1.
-        return x
-

spaces/AILab-CVC/SEED-Bench_Leaderboard/README.md

@@ -1,13 +0,0 @@
----
-title: SEED-Bench Leaderboard
-emoji: 🏆
-colorFrom: gray
-colorTo: blue
-sdk: gradio
-sdk_version: 3.40.1
-app_file: app.py
-pinned: false
-license: cc-by-4.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/Acapellas/Extract_Vocals_Instrumentals/app.py

@@ -1,25 +0,0 @@
-import os
-import gradio as gr
-from scipy.io.wavfile import write
-
-
-def inference(audio):
-  os.makedirs("out", exist_ok=True)
-  write('test.wav', audio[0], audio[1])
-  os.system("python3 -m demucs.separate -n htdemucs --two-stems=vocals -d cpu test.wav -o out")
-  return "./out/htdemucs/test/vocals.wav","./out/htdemucs/test/no_vocals.wav"
-    
-title = "Extract Acapellas & Instrumentals"
-description = ""
-article = "<p style='text-align: center'></p>"
-
-examples=[['test.mp3']]
-gr.Interface(
-    inference, 
-    gr.Audio(type="numpy", label="Input"),
-    [gr.Audio(type="filepath", label="Acapella"),gr.Audio(type="filepath", label="Instrumental")],
-    title=title,
-    description=description,
-    article=article,
-    examples=examples
-    ).launch(enable_queue=True)

spaces/AchyuthGamer/OpenGPT-Chat-UI/vite.config.ts

@@ -1,21 +0,0 @@
-import { sveltekit } from "@sveltejs/kit/vite";
-import { defineConfig, searchForWorkspaceRoot } from "vite";
-import Icons from "unplugin-icons/vite";
-
-export default defineConfig({
-	plugins: [
-		sveltekit(),
-		Icons({
-			compiler: "svelte",
-		}),
-	],
-	server: {
-		fs: {
-			allow: [
-				// your custom rules
-				"/models/Xenova/LaMini-Flan-T5-783M/onnx/decoder_model_merged_quantized.onnx",
-				"/models/Xenova/LaMini-Flan-T5-783M/onnx/encoder_model_quantized.onnx",
-			],
-		},
-	},
-});

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

@@ -1,54 +0,0 @@
-from __future__ import annotations
-
-from aiohttp import ClientSession
-
-from .base_provider import AsyncProvider, format_prompt
-
-
-class Aichat(AsyncProvider):
-    url                   = "https://chat-gpt.org/chat"
-    working               = True
-    supports_gpt_35_turbo = True
-
-    @staticmethod
-    async def create_async(
-        model: str,
-        messages: list[dict[str, str]],
-        proxy: str = None,
-        **kwargs
-    ) -> str:
-        headers = {
-            "authority": "chat-gpt.org",
-            "accept": "*/*",
-            "cache-control": "no-cache",
-            "content-type": "application/json",
-            "origin": "https://chat-gpt.org",
-            "pragma": "no-cache",
-            "referer": "https://chat-gpt.org/chat",
-            "sec-ch-ua-mobile": "?0",
-            "sec-ch-ua-platform": '"macOS"',
-            "sec-fetch-dest": "empty",
-            "sec-fetch-mode": "cors",
-            "sec-fetch-site": "same-origin",
-            "user-agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/113.0.0.0 Safari/537.36",
-        }
-        async with ClientSession(
-            headers=headers
-        ) as session:
-            json_data = {
-                "message": format_prompt(messages),
-                "temperature": kwargs.get('temperature', 0.5),
-                "presence_penalty": 0,
-                "top_p": kwargs.get('top_p', 1),
-                "frequency_penalty": 0,
-            }
-            async with session.post(
-                "https://chat-gpt.org/api/text",
-                proxy=proxy,
-                json=json_data
-            ) as response:
-                response.raise_for_status()
-                result = await response.json()
-                if not result['response']:
-                    raise Exception(f"Error Response: {result}")
-                return result["message"]

spaces/AkitoP/umamusume_bert_vits2/resample.py

@@ -1,48 +0,0 @@
-import os
-import argparse
-import librosa
-from multiprocessing import Pool, cpu_count
-
-import soundfile
-from tqdm import tqdm
-
-
-def process(item):
-    spkdir, wav_name, args = item
-    speaker = spkdir.replace("\\", "/").split("/")[-1]
-    wav_path = os.path.join(args.in_dir, speaker, wav_name)
-    if os.path.exists(wav_path) and ".wav" in wav_path:
-        os.makedirs(os.path.join(args.out_dir, speaker), exist_ok=True)
-        wav, sr = librosa.load(wav_path, sr=args.sr)
-        soundfile.write(os.path.join(args.out_dir, speaker, wav_name), wav, sr)
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--sr", type=int, default=44100, help="sampling rate")
-    parser.add_argument(
-        "--in_dir", type=str, default="./raw", help="path to source dir"
-    )
-    parser.add_argument(
-        "--out_dir", type=str, default="./dataset", help="path to target dir"
-    )
-    args = parser.parse_args()
-    # processes = 8
-    processes = cpu_count() - 2 if cpu_count() > 4 else 1
-    pool = Pool(processes=processes)
-
-    for speaker in os.listdir(args.in_dir):
-        spk_dir = os.path.join(args.in_dir, speaker)
-        if os.path.isdir(spk_dir):
-            print(spk_dir)
-            for _ in tqdm(
-                pool.imap_unordered(
-                    process,
-                    [
-                        (spk_dir, i, args)
-                        for i in os.listdir(spk_dir)
-                        if i.endswith("wav")
-                    ],
-                )
-            ):
-                pass

spaces/Akmyradov/TurkmenTTSweSTT/vits/monotonic_align/setup.py

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

spaces/Al-Chan/Vits_League_of_Legends_Yuumi_TTS/text/sanskrit.py

@@ -1,62 +0,0 @@
-import re
-from indic_transliteration import sanscript
-
-
-# List of (iast, ipa) pairs:
-_iast_to_ipa = [(re.compile('%s' % x[0]), x[1]) for x in [
-    ('a', 'ə'),
-    ('ā', 'aː'),
-    ('ī', 'iː'),
-    ('ū', 'uː'),
-    ('ṛ', 'ɹ`'),
-    ('ṝ', 'ɹ`ː'),
-    ('ḷ', 'l`'),
-    ('ḹ', 'l`ː'),
-    ('e', 'eː'),
-    ('o', 'oː'),
-    ('k', 'k⁼'),
-    ('k⁼h', 'kʰ'),
-    ('g', 'g⁼'),
-    ('g⁼h', 'gʰ'),
-    ('ṅ', 'ŋ'),
-    ('c', 'ʧ⁼'),
-    ('ʧ⁼h', 'ʧʰ'),
-    ('j', 'ʥ⁼'),
-    ('ʥ⁼h', 'ʥʰ'),
-    ('ñ', 'n^'),
-    ('ṭ', 't`⁼'),
-    ('t`⁼h', 't`ʰ'),
-    ('ḍ', 'd`⁼'),
-    ('d`⁼h', 'd`ʰ'),
-    ('ṇ', 'n`'),
-    ('t', 't⁼'),
-    ('t⁼h', 'tʰ'),
-    ('d', 'd⁼'),
-    ('d⁼h', 'dʰ'),
-    ('p', 'p⁼'),
-    ('p⁼h', 'pʰ'),
-    ('b', 'b⁼'),
-    ('b⁼h', 'bʰ'),
-    ('y', 'j'),
-    ('ś', 'ʃ'),
-    ('ṣ', 's`'),
-    ('r', 'ɾ'),
-    ('l̤', 'l`'),
-    ('h', 'ɦ'),
-    ("'", ''),
-    ('~', '^'),
-    ('ṃ', '^')
-]]
-
-
-def devanagari_to_ipa(text):
-    text = text.replace('ॐ', 'ओम्')
-    text = re.sub(r'\s*।\s*$', '.', text)
-    text = re.sub(r'\s*।\s*', ', ', text)
-    text = re.sub(r'\s*॥', '.', text)
-    text = sanscript.transliterate(text, sanscript.DEVANAGARI, sanscript.IAST)
-    for regex, replacement in _iast_to_ipa:
-        text = re.sub(regex, replacement, text)
-    text = re.sub('(.)[`ː]*ḥ', lambda x: x.group(0)
-                  [:-1]+'h'+x.group(1)+'*', text)
-    return text

spaces/Amrrs/DragGan-Inversion/stylegan_human/utils/ImagesDataset.py

@@ -1,27 +0,0 @@
-# Copyright (c) SenseTime Research. All rights reserved.
-
-
-import os
-from torch.utils.data import Dataset
-from PIL import Image
-
-from utils.data_utils import make_dataset
-
-
-class ImagesDataset(Dataset):
-
-    def __init__(self, source_root, source_transform=None):
-        self.source_paths = sorted(make_dataset(source_root))
-        self.source_transform = source_transform
-
-    def __len__(self):
-        return len(self.source_paths)
-
-    def __getitem__(self, index):
-        fname, from_path = self.source_paths[index]
-        from_im = Image.open(from_path).convert('RGB')
-
-        if self.source_transform:
-            from_im = self.source_transform(from_im)
-
-        return fname, from_im

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/commands/env.py

@@ -1,84 +0,0 @@
-# Copyright 2023 The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import platform
-from argparse import ArgumentParser
-
-import huggingface_hub
-
-from .. import __version__ as version
-from ..utils import is_accelerate_available, is_torch_available, is_transformers_available, is_xformers_available
-from . import BaseDiffusersCLICommand
-
-
-def info_command_factory(_):
-    return EnvironmentCommand()
-
-
-class EnvironmentCommand(BaseDiffusersCLICommand):
-    @staticmethod
-    def register_subcommand(parser: ArgumentParser):
-        download_parser = parser.add_parser("env")
-        download_parser.set_defaults(func=info_command_factory)
-
-    def run(self):
-        hub_version = huggingface_hub.__version__
-
-        pt_version = "not installed"
-        pt_cuda_available = "NA"
-        if is_torch_available():
-            import torch
-
-            pt_version = torch.__version__
-            pt_cuda_available = torch.cuda.is_available()
-
-        transformers_version = "not installed"
-        if is_transformers_available():
-            import transformers
-
-            transformers_version = transformers.__version__
-
-        accelerate_version = "not installed"
-        if is_accelerate_available():
-            import accelerate
-
-            accelerate_version = accelerate.__version__
-
-        xformers_version = "not installed"
-        if is_xformers_available():
-            import xformers
-
-            xformers_version = xformers.__version__
-
-        info = {
-            "`diffusers` version": version,
-            "Platform": platform.platform(),
-            "Python version": platform.python_version(),
-            "PyTorch version (GPU?)": f"{pt_version} ({pt_cuda_available})",
-            "Huggingface_hub version": hub_version,
-            "Transformers version": transformers_version,
-            "Accelerate version": accelerate_version,
-            "xFormers version": xformers_version,
-            "Using GPU in script?": "<fill in>",
-            "Using distributed or parallel set-up in script?": "<fill in>",
-        }
-
-        print("\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n")
-        print(self.format_dict(info))
-
-        return info
-
-    @staticmethod
-    def format_dict(d):
-        return "\n".join([f"- {prop}: {val}" for prop, val in d.items()]) + "\n"

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/tests/pipelines/kandinsky/test_kandinsky_img2img.py

@@ -1,396 +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 gc
-import random
-import unittest
-
-import numpy as np
-import torch
-from PIL import Image
-from transformers import XLMRobertaTokenizerFast
-
-from diffusers import (
-    DDIMScheduler,
-    DDPMScheduler,
-    KandinskyImg2ImgPipeline,
-    KandinskyPriorPipeline,
-    UNet2DConditionModel,
-    VQModel,
-)
-from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
-from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
-from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
-
-from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
-
-
-enable_full_determinism()
-
-
-class Dummies:
-    @property
-    def text_embedder_hidden_size(self):
-        return 32
-
-    @property
-    def time_input_dim(self):
-        return 32
-
-    @property
-    def block_out_channels_0(self):
-        return self.time_input_dim
-
-    @property
-    def time_embed_dim(self):
-        return self.time_input_dim * 4
-
-    @property
-    def cross_attention_dim(self):
-        return 32
-
-    @property
-    def dummy_tokenizer(self):
-        tokenizer = XLMRobertaTokenizerFast.from_pretrained("YiYiXu/tiny-random-mclip-base")
-        return tokenizer
-
-    @property
-    def dummy_text_encoder(self):
-        torch.manual_seed(0)
-        config = MCLIPConfig(
-            numDims=self.cross_attention_dim,
-            transformerDimensions=self.text_embedder_hidden_size,
-            hidden_size=self.text_embedder_hidden_size,
-            intermediate_size=37,
-            num_attention_heads=4,
-            num_hidden_layers=5,
-            vocab_size=1005,
-        )
-
-        text_encoder = MultilingualCLIP(config)
-        text_encoder = text_encoder.eval()
-
-        return text_encoder
-
-    @property
-    def dummy_unet(self):
-        torch.manual_seed(0)
-
-        model_kwargs = {
-            "in_channels": 4,
-            # Out channels is double in channels because predicts mean and variance
-            "out_channels": 8,
-            "addition_embed_type": "text_image",
-            "down_block_types": ("ResnetDownsampleBlock2D", "SimpleCrossAttnDownBlock2D"),
-            "up_block_types": ("SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"),
-            "mid_block_type": "UNetMidBlock2DSimpleCrossAttn",
-            "block_out_channels": (self.block_out_channels_0, self.block_out_channels_0 * 2),
-            "layers_per_block": 1,
-            "encoder_hid_dim": self.text_embedder_hidden_size,
-            "encoder_hid_dim_type": "text_image_proj",
-            "cross_attention_dim": self.cross_attention_dim,
-            "attention_head_dim": 4,
-            "resnet_time_scale_shift": "scale_shift",
-            "class_embed_type": None,
-        }
-
-        model = UNet2DConditionModel(**model_kwargs)
-        return model
-
-    @property
-    def dummy_movq_kwargs(self):
-        return {
-            "block_out_channels": [32, 64],
-            "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
-            "in_channels": 3,
-            "latent_channels": 4,
-            "layers_per_block": 1,
-            "norm_num_groups": 8,
-            "norm_type": "spatial",
-            "num_vq_embeddings": 12,
-            "out_channels": 3,
-            "up_block_types": [
-                "AttnUpDecoderBlock2D",
-                "UpDecoderBlock2D",
-            ],
-            "vq_embed_dim": 4,
-        }
-
-    @property
-    def dummy_movq(self):
-        torch.manual_seed(0)
-        model = VQModel(**self.dummy_movq_kwargs)
-        return model
-
-    def get_dummy_components(self):
-        text_encoder = self.dummy_text_encoder
-        tokenizer = self.dummy_tokenizer
-        unet = self.dummy_unet
-        movq = self.dummy_movq
-
-        ddim_config = {
-            "num_train_timesteps": 1000,
-            "beta_schedule": "linear",
-            "beta_start": 0.00085,
-            "beta_end": 0.012,
-            "clip_sample": False,
-            "set_alpha_to_one": False,
-            "steps_offset": 0,
-            "prediction_type": "epsilon",
-            "thresholding": False,
-        }
-
-        scheduler = DDIMScheduler(**ddim_config)
-
-        components = {
-            "text_encoder": text_encoder,
-            "tokenizer": tokenizer,
-            "unet": unet,
-            "scheduler": scheduler,
-            "movq": movq,
-        }
-
-        return components
-
-    def get_dummy_inputs(self, device, seed=0):
-        image_embeds = floats_tensor((1, self.cross_attention_dim), rng=random.Random(seed)).to(device)
-        negative_image_embeds = floats_tensor((1, self.cross_attention_dim), rng=random.Random(seed + 1)).to(device)
-        # create init_image
-        image = floats_tensor((1, 3, 64, 64), rng=random.Random(seed)).to(device)
-        image = image.cpu().permute(0, 2, 3, 1)[0]
-        init_image = Image.fromarray(np.uint8(image)).convert("RGB").resize((256, 256))
-
-        if str(device).startswith("mps"):
-            generator = torch.manual_seed(seed)
-        else:
-            generator = torch.Generator(device=device).manual_seed(seed)
-        inputs = {
-            "prompt": "horse",
-            "image": init_image,
-            "image_embeds": image_embeds,
-            "negative_image_embeds": negative_image_embeds,
-            "generator": generator,
-            "height": 64,
-            "width": 64,
-            "num_inference_steps": 10,
-            "guidance_scale": 7.0,
-            "strength": 0.2,
-            "output_type": "np",
-        }
-        return inputs
-
-
-class KandinskyImg2ImgPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
-    pipeline_class = KandinskyImg2ImgPipeline
-    params = ["prompt", "image_embeds", "negative_image_embeds", "image"]
-    batch_params = [
-        "prompt",
-        "negative_prompt",
-        "image_embeds",
-        "negative_image_embeds",
-        "image",
-    ]
-    required_optional_params = [
-        "generator",
-        "height",
-        "width",
-        "strength",
-        "guidance_scale",
-        "negative_prompt",
-        "num_inference_steps",
-        "return_dict",
-        "guidance_scale",
-        "num_images_per_prompt",
-        "output_type",
-        "return_dict",
-    ]
-    test_xformers_attention = False
-
-    def get_dummy_components(self):
-        dummies = Dummies()
-        return dummies.get_dummy_components()
-
-    def get_dummy_inputs(self, device, seed=0):
-        dummies = Dummies()
-        return dummies.get_dummy_inputs(device=device, seed=seed)
-
-    def test_kandinsky_img2img(self):
-        device = "cpu"
-
-        components = self.get_dummy_components()
-
-        pipe = self.pipeline_class(**components)
-        pipe = pipe.to(device)
-
-        pipe.set_progress_bar_config(disable=None)
-
-        output = pipe(**self.get_dummy_inputs(device))
-        image = output.images
-
-        image_from_tuple = pipe(
-            **self.get_dummy_inputs(device),
-            return_dict=False,
-        )[0]
-
-        image_slice = image[0, -3:, -3:, -1]
-        image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]
-
-        assert image.shape == (1, 64, 64, 3)
-
-        expected_slice = np.array([0.5816, 0.5872, 0.4634, 0.5982, 0.4767, 0.4710, 0.4669, 0.4717, 0.4966])
-        assert (
-            np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
-        ), f" expected_slice {expected_slice}, but got {image_slice.flatten()}"
-        assert (
-            np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2
-        ), f" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"
-
-    @require_torch_gpu
-    def test_offloads(self):
-        pipes = []
-        components = self.get_dummy_components()
-        sd_pipe = self.pipeline_class(**components).to(torch_device)
-        pipes.append(sd_pipe)
-
-        components = self.get_dummy_components()
-        sd_pipe = self.pipeline_class(**components)
-        sd_pipe.enable_model_cpu_offload()
-        pipes.append(sd_pipe)
-
-        components = self.get_dummy_components()
-        sd_pipe = self.pipeline_class(**components)
-        sd_pipe.enable_sequential_cpu_offload()
-        pipes.append(sd_pipe)
-
-        image_slices = []
-        for pipe in pipes:
-            inputs = self.get_dummy_inputs(torch_device)
-            image = pipe(**inputs).images
-
-            image_slices.append(image[0, -3:, -3:, -1].flatten())
-
-        assert np.abs(image_slices[0] - image_slices[1]).max() < 1e-3
-        assert np.abs(image_slices[0] - image_slices[2]).max() < 1e-3
-
-
-@slow
-@require_torch_gpu
-class KandinskyImg2ImgPipelineIntegrationTests(unittest.TestCase):
-    def tearDown(self):
-        # clean up the VRAM after each test
-        super().tearDown()
-        gc.collect()
-        torch.cuda.empty_cache()
-
-    def test_kandinsky_img2img(self):
-        expected_image = load_numpy(
-            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
-            "/kandinsky/kandinsky_img2img_frog.npy"
-        )
-
-        init_image = load_image(
-            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png"
-        )
-        prompt = "A red cartoon frog, 4k"
-
-        pipe_prior = KandinskyPriorPipeline.from_pretrained(
-            "kandinsky-community/kandinsky-2-1-prior", torch_dtype=torch.float16
-        )
-        pipe_prior.to(torch_device)
-
-        pipeline = KandinskyImg2ImgPipeline.from_pretrained(
-            "kandinsky-community/kandinsky-2-1", torch_dtype=torch.float16
-        )
-        pipeline = pipeline.to(torch_device)
-
-        pipeline.set_progress_bar_config(disable=None)
-
-        generator = torch.Generator(device="cpu").manual_seed(0)
-        image_emb, zero_image_emb = pipe_prior(
-            prompt,
-            generator=generator,
-            num_inference_steps=5,
-            negative_prompt="",
-        ).to_tuple()
-
-        output = pipeline(
-            prompt,
-            image=init_image,
-            image_embeds=image_emb,
-            negative_image_embeds=zero_image_emb,
-            generator=generator,
-            num_inference_steps=100,
-            height=768,
-            width=768,
-            strength=0.2,
-            output_type="np",
-        )
-
-        image = output.images[0]
-
-        assert image.shape == (768, 768, 3)
-
-        assert_mean_pixel_difference(image, expected_image)
-
-    def test_kandinsky_img2img_ddpm(self):
-        expected_image = load_numpy(
-            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
-            "/kandinsky/kandinsky_img2img_ddpm_frog.npy"
-        )
-
-        init_image = load_image(
-            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/frog.png"
-        )
-        prompt = "A red cartoon frog, 4k"
-
-        pipe_prior = KandinskyPriorPipeline.from_pretrained(
-            "kandinsky-community/kandinsky-2-1-prior", torch_dtype=torch.float16
-        )
-        pipe_prior.to(torch_device)
-
-        scheduler = DDPMScheduler.from_pretrained("kandinsky-community/kandinsky-2-1", subfolder="ddpm_scheduler")
-        pipeline = KandinskyImg2ImgPipeline.from_pretrained(
-            "kandinsky-community/kandinsky-2-1", scheduler=scheduler, torch_dtype=torch.float16
-        )
-        pipeline = pipeline.to(torch_device)
-
-        pipeline.set_progress_bar_config(disable=None)
-
-        generator = torch.Generator(device="cpu").manual_seed(0)
-        image_emb, zero_image_emb = pipe_prior(
-            prompt,
-            generator=generator,
-            num_inference_steps=5,
-            negative_prompt="",
-        ).to_tuple()
-
-        output = pipeline(
-            prompt,
-            image=init_image,
-            image_embeds=image_emb,
-            negative_image_embeds=zero_image_emb,
-            generator=generator,
-            num_inference_steps=100,
-            height=768,
-            width=768,
-            strength=0.2,
-            output_type="np",
-        )
-
-        image = output.images[0]
-
-        assert image.shape == (768, 768, 3)
-
-        assert_mean_pixel_difference(image, expected_image)

spaces/Andy1621/uniformer_image_detection/configs/reppoints/reppoints_moment_x101_fpn_dconv_c3-c5_gn-neck+head_2x_coco.py

@@ -1,15 +0,0 @@
-_base_ = './reppoints_moment_r50_fpn_gn-neck+head_2x_coco.py'
-model = dict(
-    pretrained='open-mmlab://resnext101_32x4d',
-    backbone=dict(
-        type='ResNeXt',
-        depth=101,
-        groups=32,
-        base_width=4,
-        num_stages=4,
-        out_indices=(0, 1, 2, 3),
-        frozen_stages=1,
-        norm_cfg=dict(type='BN', requires_grad=True),
-        style='pytorch',
-        dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False),
-        stage_with_dcn=(False, True, True, True)))

spaces/Andy1621/uniformer_image_segmentation/configs/deeplabv3plus/deeplabv3plus_r101-d8_769x769_40k_cityscapes.py

@@ -1,2 +0,0 @@
-_base_ = './deeplabv3plus_r50-d8_769x769_40k_cityscapes.py'
-model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))

spaces/Andy1621/uniformer_image_segmentation/configs/dmnet/dmnet_r101-d8_512x512_160k_ade20k.py

@@ -1,2 +0,0 @@
-_base_ = './dmnet_r50-d8_512x512_160k_ade20k.py'
-model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))

spaces/Anonymous-123/ImageNet-Editing/object_removal/TFill/model/base_model.py

@@ -1,195 +0,0 @@
-import os, ntpath
-import torch
-from collections import OrderedDict
-from util import util
-from . import base_function
-from abc import abstractmethod
-
-
-class BaseModel():
-    """This class is an abstract base class for models"""
-    def __init__(self, opt):
-        """Initialize the BaseModel class"""
-        self.opt = opt
-        self.gpu_ids = opt.gpu_ids
-        self.isTrain = opt.isTrain
-        self.device = torch.device('cuda') if self.gpu_ids else torch.device('cpu')
-        self.save_dir = os.path.join(opt.checkpoints_dir, opt.name)
-        self.loss_names = []
-        self.model_names = []
-        self.visual_names = []
-        self.value_names = []
-        self.image_paths = []
-        self.optimizers = []
-        self.schedulers = []
-        self.metric = 0  # used for learning rate policy 'plateau'
-
-    def name(self):
-        return 'BaseModel'
-
-    @staticmethod
-    def modify_options(parser, is_train):
-        """Add new options and rewrite default values for existing options"""
-        return parser
-
-    @abstractmethod
-    def set_input(self, input):
-        """Unpack input data from the dataloader and perform necessary pre-processing steps"""
-        pass
-
-    @abstractmethod
-    def forward(self):
-        """Run forward pass; called by both functions <optimize_parameters> and <test>."""
-        pass
-
-    @abstractmethod
-    def optimize_parameters(self):
-        """Calculate losses, gradients, and update network weights; called in every training iteration"""
-        pass
-
-    def setup(self, opt):
-        """Load networks, create schedulers"""
-        if self.isTrain:
-            self.schedulers = [base_function.get_scheduler(optimizer, opt) for optimizer in self.optimizers]
-        if not self.isTrain or opt.continue_train:
-            load_suffix = '%d' % opt.which_iter if opt.which_iter > 0 else opt.epoch
-            self.load_networks(load_suffix)
-
-        self.print_networks()
-
-    def parallelize(self):
-        for name in self.model_names:
-            if isinstance(name, str):
-                net = getattr(self, 'net' + name)
-                net.to(self.device)
-                if len(self.opt.gpu_ids) > 0:
-                    setattr(self, 'net' + name, torch.nn.parallel.DataParallel(net, self.opt.gpu_ids))
-
-    def eval(self):
-        """Make models eval mode during test time"""
-        for name in self.model_names:
-            if isinstance(name, str):
-                net = getattr(self, 'net' + name)
-                net.eval()
-
-    def log_imgs(self):
-        """visualize the image during the training"""
-        pass
-
-    def test(self):
-        """Forward function used in test time"""
-        with torch.no_grad():
-            self.forward()
-
-    def get_image_paths(self):
-        """ Return image paths that are used to load current data"""
-        return self.image_paths
-
-    def update_learning_rate(self):
-        """Update learning rates for all the networks; called at the end of every epoch"""
-        for scheduler in self.schedulers:
-            if self.opt.lr_policy == 'plateau':
-                scheduler.step(self.metric)
-            else:
-                scheduler.step()
-        lr = self.optimizers[0].param_groups[0]['lr']
-        print('learning rate = %.7f' % lr)
-
-    def get_current_losses(self):
-        """Return training loss"""
-        errors_ret = OrderedDict()
-        for name in self.loss_names:
-            if isinstance(name, str):
-                try:
-                    errors_ret[name] = float(getattr(self, 'loss_' + name))
-                except:
-                    pass
-        return errors_ret
-
-    def get_current_visuals(self):
-        """Return visualization examples"""
-        visual_ret = OrderedDict()
-        for name in self.visual_names:
-            if isinstance(name, str):
-                value = getattr(self, name)
-                if isinstance(value, list):
-                    visual_ret[name] = value[-1]
-                else:
-                    visual_ret[name] = value
-        return visual_ret
-
-    def save_networks(self, epoch, save_path=None):
-        """Save all the networks to the disk."""
-        save_path = save_path if save_path!= None else self.save_dir
-        for name in self.model_names:
-            if isinstance(name, str):
-                filename = '%s_net_%s.pth' % (epoch, name)
-                path = os.path.join(save_path, filename)
-                net = getattr(self, 'net' + name)
-                if len(self.gpu_ids) > 0 and torch.cuda.is_available():
-                    torch.save(net.module.cpu().state_dict(), path)
-                    net.cuda(self.gpu_ids[0])
-                else:
-                    torch.save(net.cpu().state_dict(), path)
-
-    def load_networks(self, epoch, save_path=None):
-        """Load all the networks from the disk"""
-        save_path = save_path if save_path != None else self.save_dir
-        for name in self.model_names:
-            if isinstance(name, str):
-                filename = '%s_net_%s.pth' % (epoch, name)
-                path = os.path.join(save_path, filename)
-                net = getattr(self, 'net' + name)
-                if isinstance(net, torch.nn.DataParallel):
-                    net = net.module
-                print('loading the model from %s' % path)
-                try:
-                    state_dict = torch.load(path, map_location=str(self.device))
-                    if hasattr(state_dict, '_metadata'):
-                        del state_dict._metadata
-
-                    net.load_state_dict(state_dict)
-                except:
-                    print('Pretrained network %s is unmatched' % name)
-
-                if len(self.gpu_ids) > 0 and torch.cuda.is_available():
-                    net.cuda()
-
-    def print_networks(self):
-        """Print the total number of parameters in the network and (if verbose) network architecture"""
-
-        print('---------- Networks initialized -------------')
-        for name in self.model_names:
-            if isinstance(name, str):
-                net = getattr(self, 'net' + name)
-                num_params = 0
-                for param in net.parameters():
-                    num_params += param.numel()
-                print(net)
-                print('[Network %s] Total number of parameters : %.3f M' % (name, num_params / 1e6))
-        print('-----------------------------------------------')
-
-    def set_requires_grad(self, nets, requires_grad=False):
-        """Set requies_grad=Fasle for all the networks to avoid unnecessary computations
-        Parameters:
-            nets (network list)   -- a list of networks
-            requires_grad (bool)  -- whether the networks require gradients or not
-        """
-        if not isinstance(nets, list):
-            nets = [nets]
-        for net in nets:
-            if net is not None:
-                for param in net.parameters():
-                    param.requires_grad = requires_grad
-
-    def save_results(self, save_data, path=None, data_name='none'):
-        """save the training or testing results to disk"""
-        img_paths = self.get_image_paths()
-        for i in range(save_data.size(0)):
-            short_path = ntpath.basename(img_paths[i])  # get image path
-            name = os.path.splitext(short_path)[0]
-            img_name = '%s_%s.png' % (name, data_name)
-            util.mkdir(path)
-            img_path = os.path.join(path, img_name)
-            img_numpy = util.tensor2im(save_data[i].unsqueeze(0))
-            util.save_image(img_numpy, img_path)

spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmseg/apis/__init__.py

@@ -1,9 +0,0 @@
-from .inference import inference_segmentor, init_segmentor, show_result_pyplot
-from .test import multi_gpu_test, single_gpu_test
-from .train import get_root_logger, set_random_seed, train_segmentor
-
-__all__ = [
-    'get_root_logger', 'set_random_seed', 'train_segmentor', 'init_segmentor',
-    'inference_segmentor', 'multi_gpu_test', 'single_gpu_test',
-    'show_result_pyplot'
-]

spaces/Arnx/MusicGenXvAKN/tests/modules/__init__.py

@@ -1,5 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.

spaces/ArtyomKhyan/Detection/models/yolo.py

@@ -1,238 +0,0 @@
-import argparse
-
-from models.experimental import *
-
-
-class Detect(nn.Module):
-    def __init__(self, nc=80, anchors=()):  # detection layer
-        super(Detect, self).__init__()
-        self.stride = None  # strides computed during build
-        self.nc = nc  # number of classes
-        self.no = nc + 5  # number of outputs per anchor
-        self.nl = len(anchors)  # number of detection layers
-        self.na = len(anchors[0]) // 2  # number of anchors
-        self.grid = [torch.zeros(1)] * self.nl  # init grid
-        a = torch.tensor(anchors).float().view(self.nl, -1, 2)
-        self.register_buffer('anchors', a)  # shape(nl,na,2)
-        self.register_buffer('anchor_grid', a.clone().view(self.nl, 1, -1, 1, 1, 2))  # shape(nl,1,na,1,1,2)
-        self.export = False  # onnx export
-
-    def forward(self, x):
-        # x = x.copy()  # for profiling
-        z = []  # inference output
-        self.training |= self.export
-        for i in range(self.nl):
-            bs, _, ny, nx = x[i].shape  # x(bs,255,20,20) to x(bs,3,20,20,85)
-            x[i] = x[i].view(bs, self.na, self.no, ny, nx).permute(0, 1, 3, 4, 2).contiguous()
-
-            if not self.training:  # inference
-                if self.grid[i].shape[2:4] != x[i].shape[2:4]:
-                    self.grid[i] = self._make_grid(nx, ny).to(x[i].device)
-
-                y = x[i].sigmoid()
-                y[..., 0:2] = (y[..., 0:2] * 2. - 0.5 + self.grid[i].to(x[i].device)) * self.stride[i]  # xy
-                y[..., 2:4] = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i]  # wh
-                z.append(y.view(bs, -1, self.no))
-
-        return x if self.training else (torch.cat(z, 1), x)
-
-    @staticmethod
-    def _make_grid(nx=20, ny=20):
-        yv, xv = torch.meshgrid([torch.arange(ny), torch.arange(nx)])
-        return torch.stack((xv, yv), 2).view((1, 1, ny, nx, 2)).float()
-
-
-class Model(nn.Module):
-    def __init__(self, model_cfg='yolov5s.yaml', ch=3, nc=None):  # model, input channels, number of classes
-        super(Model, self).__init__()
-        if type(model_cfg) is dict:
-            self.md = model_cfg  # model dict
-        else:  # is *.yaml
-            with open(model_cfg) as f:
-                self.md = yaml.load(f, Loader=yaml.FullLoader)  # model dict
-
-        # Define model
-        if nc and nc != self.md['nc']:
-            print('Overriding %s nc=%g with nc=%g' % (model_cfg, self.md['nc'], nc))
-            self.md['nc'] = nc  # override yaml value
-        self.model, self.save = parse_model(self.md, ch=[ch])  # model, savelist, ch_out
-        # print([x.shape for x in self.forward(torch.zeros(1, ch, 64, 64))])
-
-        # Build strides, anchors
-        m = self.model[-1]  # Detect()
-        if isinstance(m, Detect):
-            s = 128  # 2x min stride
-            m.stride = torch.tensor([s / x.shape[-2] for x in self.forward(torch.zeros(1, ch, s, s))])  # forward
-            m.anchors /= m.stride.view(-1, 1, 1)
-            check_anchor_order(m)
-            self.stride = m.stride
-            self._initialize_biases()  # only run once
-            # print('Strides: %s' % m.stride.tolist())
-
-        # Init weights, biases
-        torch_utils.initialize_weights(self)
-        self._initialize_biases()  # only run once
-        torch_utils.model_info(self)
-        print('')
-
-    def forward(self, x, augment=False, profile=False):
-        if augment:
-            img_size = x.shape[-2:]  # height, width
-            s = [0.83, 0.67]  # scales
-            y = []
-            for i, xi in enumerate((x,
-                                    torch_utils.scale_img(x.flip(3), s[0]),  # flip-lr and scale
-                                    torch_utils.scale_img(x, s[1]),  # scale
-                                    )):
-                # cv2.imwrite('img%g.jpg' % i, 255 * xi[0].numpy().transpose((1, 2, 0))[:, :, ::-1])
-                y.append(self.forward_once(xi)[0])
-
-            y[1][..., :4] /= s[0]  # scale
-            y[1][..., 0] = img_size[1] - y[1][..., 0]  # flip lr
-            y[2][..., :4] /= s[1]  # scale
-            return torch.cat(y, 1), None  # augmented inference, train
-        else:
-            return self.forward_once(x, profile)  # single-scale inference, train
-
-    def forward_once(self, x, profile=False):
-        y, dt = [], []  # outputs
-        for m in self.model:
-            if m.f != -1:  # if not from previous layer
-                x = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f]  # from earlier layers
-
-            if profile:
-                try:
-                    import thop
-                    o = thop.profile(m, inputs=(x,), verbose=False)[0] / 1E9 * 2  # FLOPS
-                except:
-                    o = 0
-                t = torch_utils.time_synchronized()
-                for _ in range(10):
-                    _ = m(x)
-                dt.append((torch_utils.time_synchronized() - t) * 100)
-                print('%10.1f%10.0f%10.1fms %-40s' % (o, m.np, dt[-1], m.type))
-
-            x = m(x)  # run
-            y.append(x if m.i in self.save else None)  # save output
-
-        if profile:
-            print('%.1fms total' % sum(dt))
-        return x
-
-    def _initialize_biases(self, cf=None):  # initialize biases into Detect(), cf is class frequency
-        # cf = torch.bincount(torch.tensor(np.concatenate(dataset.labels, 0)[:, 0]).long(), minlength=nc) + 1.
-        m = self.model[-1]  # Detect() module
-        for f, s in zip(m.f, m.stride):  #  from
-            mi = self.model[f % m.i]
-            b = mi.bias.view(m.na, -1)  # conv.bias(255) to (3,85)
-            b[:, 4] += math.log(8 / (640 / s) ** 2)  # obj (8 objects per 640 image)
-            b[:, 5:] += math.log(0.6 / (m.nc - 0.99)) if cf is None else torch.log(cf / cf.sum())  # cls
-            mi.bias = torch.nn.Parameter(b.view(-1), requires_grad=True)
-
-    def _print_biases(self):
-        m = self.model[-1]  # Detect() module
-        for f in sorted([x % m.i for x in m.f]):  #  from
-            b = self.model[f].bias.detach().view(m.na, -1).T  # conv.bias(255) to (3,85)
-            print(('%g Conv2d.bias:' + '%10.3g' * 6) % (f, *b[:5].mean(1).tolist(), b[5:].mean()))
-
-    # def _print_weights(self):
-    #     for m in self.model.modules():
-    #         if type(m) is Bottleneck:
-    #             print('%10.3g' % (m.w.detach().sigmoid() * 2))  # shortcut weights
-
-    def fuse(self):  # fuse model Conv2d() + BatchNorm2d() layers
-        print('Fusing layers...')
-        for m in self.model.modules():
-            if type(m) is Conv:
-                m.conv = torch_utils.fuse_conv_and_bn(m.conv, m.bn)  # update conv
-                m.bn = None  # remove batchnorm
-                m.forward = m.fuseforward  # update forward
-        torch_utils.model_info(self)
-
-
-def parse_model(md, ch):  # model_dict, input_channels(3)
-    print('\n%3s%18s%3s%10s  %-40s%-30s' % ('', 'from', 'n', 'params', 'module', 'arguments'))
-    anchors, nc, gd, gw = md['anchors'], md['nc'], md['depth_multiple'], md['width_multiple']
-    na = (len(anchors[0]) // 2)  # number of anchors
-    no = na * (nc + 5)  # number of outputs = anchors * (classes + 5)
-
-    layers, save, c2 = [], [], ch[-1]  # layers, savelist, ch out
-    for i, (f, n, m, args) in enumerate(md['backbone'] + md['head']):  # from, number, module, args
-        m = eval(m) if isinstance(m, str) else m  # eval strings
-        for j, a in enumerate(args):
-            try:
-                args[j] = eval(a) if isinstance(a, str) else a  # eval strings
-            except:
-                pass
-
-        n = max(round(n * gd), 1) if n > 1 else n  # depth gain
-        if m in [nn.Conv2d, Conv, Bottleneck, SPP, DWConv, MixConv2d, Focus, CrossConv, BottleneckCSP, C3]:
-            c1, c2 = ch[f], args[0]
-
-            # Normal
-            # if i > 0 and args[0] != no:  # channel expansion factor
-            #     ex = 1.75  # exponential (default 2.0)
-            #     e = math.log(c2 / ch[1]) / math.log(2)
-            #     c2 = int(ch[1] * ex ** e)
-            # if m != Focus:
-            c2 = make_divisible(c2 * gw, 8) if c2 != no else c2
-
-            # Experimental
-            # if i > 0 and args[0] != no:  # channel expansion factor
-            #     ex = 1 + gw  # exponential (default 2.0)
-            #     ch1 = 32  # ch[1]
-            #     e = math.log(c2 / ch1) / math.log(2)  # level 1-n
-            #     c2 = int(ch1 * ex ** e)
-            # if m != Focus:
-            #     c2 = make_divisible(c2, 8) if c2 != no else c2
-
-            args = [c1, c2, *args[1:]]
-            if m in [BottleneckCSP, C3]:
-                args.insert(2, n)
-                n = 1
-        elif m is nn.BatchNorm2d:
-            args = [ch[f]]
-        elif m is Concat:
-            c2 = sum([ch[-1 if x == -1 else x + 1] for x in f])
-        elif m is Detect:
-            f = f or list(reversed([(-1 if j == i else j - 1) for j, x in enumerate(ch) if x == no]))
-        else:
-            c2 = ch[f]
-
-        m_ = nn.Sequential(*[m(*args) for _ in range(n)]) if n > 1 else m(*args)  # module
-        t = str(m)[8:-2].replace('__main__.', '')  # module type
-        np = sum([x.numel() for x in m_.parameters()])  # number params
-        m_.i, m_.f, m_.type, m_.np = i, f, t, np  # attach index, 'from' index, type, number params
-        print('%3s%18s%3s%10.0f  %-40s%-30s' % (i, f, n, np, t, args))  # print
-        save.extend(x % i for x in ([f] if isinstance(f, int) else f) if x != -1)  # append to savelist
-        layers.append(m_)
-        ch.append(c2)
-    return nn.Sequential(*layers), sorted(save)
-
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--cfg', type=str, default='yolov5s.yaml', help='model.yaml')
-    parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
-    opt = parser.parse_args()
-    opt.cfg = check_file(opt.cfg)  # check file
-    device = torch_utils.select_device(opt.device)
-
-    # Create model
-    model = Model(opt.cfg).to(device)
-    model.train()
-
-    # Profile
-    # img = torch.rand(8 if torch.cuda.is_available() else 1, 3, 640, 640).to(device)
-    # y = model(img, profile=True)
-
-    # ONNX export
-    # model.model[-1].export = True
-    # torch.onnx.export(model, img, opt.cfg.replace('.yaml', '.onnx'), verbose=True, opset_version=11)
-
-    # Tensorboard
-    # from torch.utils.tensorboard import SummaryWriter
-    # tb_writer = SummaryWriter()
-    # print("Run 'tensorboard --logdir=models/runs' to view tensorboard at http://localhost:6006/")
-    # tb_writer.add_graph(model.model, img)  # add model to tensorboard
-    # tb_writer.add_image('test', img[0], dataformats='CWH')  # add model to tensorboard

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

@@ -1,32 +0,0 @@
-from typing import Callable, Match, Optional
-import re
-
-from ._emoji_codes import EMOJI
-
-
-_ReStringMatch = Match[str]  # regex match object
-_ReSubCallable = Callable[[_ReStringMatch], str]  # Callable invoked by re.sub
-_EmojiSubMethod = Callable[[_ReSubCallable, str], str]  # Sub method of a compiled re
-
-
-def _emoji_replace(
-    text: str,
-    default_variant: Optional[str] = None,
-    _emoji_sub: _EmojiSubMethod = re.compile(r"(:(\S*?)(?:(?:\-)(emoji|text))?:)").sub,
-) -> str:
-    """Replace emoji code in text."""
-    get_emoji = EMOJI.__getitem__
-    variants = {"text": "\uFE0E", "emoji": "\uFE0F"}
-    get_variant = variants.get
-    default_variant_code = variants.get(default_variant, "") if default_variant else ""
-
-    def do_replace(match: Match[str]) -> str:
-        emoji_code, emoji_name, variant = match.groups()
-        try:
-            return get_emoji(emoji_name.lower()) + get_variant(
-                variant, default_variant_code
-            )
-        except KeyError:
-            return emoji_code
-
-    return _emoji_sub(do_replace, text)

spaces/Bart92/RVC_HF/infer/lib/train/losses.py

@@ -1,58 +0,0 @@
-import torch
-
-
-def feature_loss(fmap_r, fmap_g):
-    loss = 0
-    for dr, dg in zip(fmap_r, fmap_g):
-        for rl, gl in zip(dr, dg):
-            rl = rl.float().detach()
-            gl = gl.float()
-            loss += torch.mean(torch.abs(rl - gl))
-
-    return loss * 2
-
-
-def discriminator_loss(disc_real_outputs, disc_generated_outputs):
-    loss = 0
-    r_losses = []
-    g_losses = []
-    for dr, dg in zip(disc_real_outputs, disc_generated_outputs):
-        dr = dr.float()
-        dg = dg.float()
-        r_loss = torch.mean((1 - dr) ** 2)
-        g_loss = torch.mean(dg**2)
-        loss += r_loss + g_loss
-        r_losses.append(r_loss.item())
-        g_losses.append(g_loss.item())
-
-    return loss, r_losses, g_losses
-
-
-def generator_loss(disc_outputs):
-    loss = 0
-    gen_losses = []
-    for dg in disc_outputs:
-        dg = dg.float()
-        l = torch.mean((1 - dg) ** 2)
-        gen_losses.append(l)
-        loss += l
-
-    return loss, gen_losses
-
-
-def kl_loss(z_p, logs_q, m_p, logs_p, z_mask):
-    """
-    z_p, logs_q: [b, h, t_t]
-    m_p, logs_p: [b, h, t_t]
-    """
-    z_p = z_p.float()
-    logs_q = logs_q.float()
-    m_p = m_p.float()
-    logs_p = logs_p.float()
-    z_mask = z_mask.float()
-
-    kl = logs_p - logs_q - 0.5
-    kl += 0.5 * ((z_p - m_p) ** 2) * torch.exp(-2.0 * logs_p)
-    kl = torch.sum(kl * z_mask)
-    l = kl / torch.sum(z_mask)
-    return l

spaces/Benson/text-generation/Examples/Beta Messenger Download.md

@@ -1,21 +0,0 @@
-<br />
-<h1>Cómo descargar Messenger Beta y disfrutar de sus nuevas características</h1>
- Messenger es una de las aplicaciones de comunicación más populares del mundo, con más de 5 mil millones de descargas en Google Play. Le permite enviar mensajes de texto, voz, video y mensajes de grupo, así como hacer llamadas de voz y video, con sus amigos y familiares. Pero, ¿sabías que hay una versión beta de Messenger que te permite probar nuevas funciones antes de que se publiquen? En este artículo, te mostraremos cómo descargar Messenger Beta y disfrutar de sus nuevas características. <h2>¿Qué es Messenger Beta? </h2>
- Messenger Beta es una versión de Messenger que se actualiza con más frecuencia que la versión regular. Le da acceso a nuevas características y mejoras que aún no están disponibles en la versión estable. Por ejemplo, algunas de las características recientes que se introdujeron en Messenger Beta incluyen: - Modo oscuro: Un tema oscuro que reduce la fatiga ocular y ahorra vida de la batería. - Mensajes de desbloqueo: Una característica que le permite eliminar los mensajes que envió por error dentro de 10 minutos. - Ver juntos: una función que te permite ver vídeos con tus amigos mientras chateas en una videollamada. - Reacciones personalizadas: Una función que te permite crear tus propias reacciones emoji para mensajes. <h3>La diferencia entre Messenger y Messenger Beta</h3>
- La principal diferencia entre Messenger y Messenger Beta es que la versión beta es menos estable y puede tener errores o errores. Esto significa que algunas características pueden no funcionar correctamente o pueden causar fallos o fallas. Por lo tanto, si desea utilizar Messenger Beta, usted debe estar preparado para encontrar algunos problemas y reportarlos a los desarrolladores. Otra diferencia es que Messenger Beta puede tener diferentes cambios de diseño o diseño que la versión normal. Por ejemplo, la versión beta puede tener diferentes iconos, colores o fuentes que la versión estable. Estos cambios están destinados a probar los comentarios de los usuarios y mejorar la experiencia del usuario. <h3>Los beneficios de usar Messenger Beta</h3>
-
- Si desea utilizar Messenger en su PC o Mac, puede descargar la aplicación de escritorio desde el sitio web oficial. Sin embargo, si desea utilizar la versión beta, debe seguir estos pasos: <h3>Los requisitos para instalar Messenger Beta</h3>
- Para instalar Messenger Beta en tu PC o Mac, necesitas tener: - Un sistema operativo Windows 10 o macOS 10.10 o superior. - Una cuenta de Facebook o un número de teléfono. - Una conexión a Internet. <h3>Los pasos para descargar e instalar Messenger Beta</h3>
- Para descargar e instalar Messenger Beta en su PC o Mac, siga estos pasos: 1. Vaya a [el sitio web beta]( 1 ) y haga clic en "Iniciar sesión". 2. Introduzca sus credenciales de Facebook o su número de teléfono y haga clic en "Continuar". 3. Haga clic en "Descargar" y espere a que se descargue el archivo de instalación. 4. Abra el archivo de instalación y siga las instrucciones para instalar la aplicación. 5. Inicie la aplicación e inicie sesión con su cuenta o número de teléfono de Facebook. 6. Disfruta usando Messenger Beta en tu PC o Mac. <h2>Cómo descargar Messenger Beta para Android</h2>
- Si quieres usar Messenger en tu dispositivo Android, puedes descargar la aplicación desde Google Play. Sin embargo, si desea utilizar la versión beta, debe seguir estos pasos <h3>Los requisitos para instalar Messenger Beta</h3>
- Para instalar Messenger Beta en tu dispositivo Android, necesitas tener: - Un dispositivo Android con Android 5.0 o superior. - Una cuenta de Facebook o un número de teléfono. - Una conexión a Internet. <h3>Los pasos para descargar e instalar Messenger Beta</h3>
-
- Messenger Beta tiene las mismas funciones básicas que la versión normal de Messenger, como enviar y recibir mensajes, hacer llamadas de voz y video, crear chats grupales y usar pegatinas y emojis. Sin embargo, también tiene algunas características nuevas que puede probar y dar comentarios sobre. Estas son algunas de las principales características de Messenger Beta y cómo usarlas: <h3>Las principales características de Messenger Beta</h3>
- - Modo oscuro: Esta función le permite cambiar a un tema oscuro que reduce la fatiga ocular y ahorra vida de la batería. Para habilitar el modo oscuro, toca en la imagen de perfil en la esquina superior izquierda de la aplicación, luego toca en "Modo oscuro" y cámbiala de encendido o apagado. - Desembragar mensajes: Esta función le permite eliminar los mensajes que envió por error en 10 minutos. Para desbloquear un mensaje, toque y mantenga presionado el mensaje, luego toque en "Quitar" y elija "Eliminar para todos". - Ver juntos: Esta función le permite ver vídeos con sus amigos mientras chatea en una videollamada. Para usar esta función, inicie una videollamada con uno o más amigos, luego toque en el icono de video en la esquina inferior derecha de la pantalla, luego toque en "Ver juntos" y elija un video de la lista sugerida o busque uno. - Reacciones personalizadas: Esta función te permite crear tus propias reacciones emoji para mensajes. Para usar esta función, toca y mantén presionado un mensaje, luego toca el icono "+" en la esquina inferior derecha de la pantalla, luego elige un emoji de la lista o busca uno. <h3>Los consejos y trucos para usar Messenger Beta</h3>
-
- Messenger Beta es una gran manera de disfrutar de nuevas características y mejoras antes de que se lancen al público. También le da la oportunidad de dar retroalimentación y ayudar a mejorar la aplicación. Sin embargo, debes tener en cuenta que Messenger Beta es menos estable y puede tener errores o errores. Siempre puede volver a la versión regular si no le gusta la versión beta o encuentra demasiados problemas. Esperamos que este artículo te haya ayudado a aprender cómo descargar Messenger Beta y disfrutar de sus nuevas características. Si tiene alguna pregunta o comentario, háganoslo saber en la sección de comentarios a continuación. <h2>FAQs</h2>
-
-Google Play Store] e inicie sesión si es necesario. 2. Busque "Messenger" y toque en el icono de la aplicación. 3. Toque en "Actualizar" para descargar e instalar la última versión beta de Messenger. P: ¿Cómo invito a mis amigos a unirse a Messenger Beta? R: Puedes invitar a tus amigos a unirse a Messenger Beta siguiendo estos pasos: 1. Toca la imagen de tu perfil en la esquina superior izquierda de la aplicación y luego toca "Invitar amigos". 2. Elige cómo quieres invitar a tus amigos, como por SMS, correo electrónico o redes sociales. 3. Escribe un mensaje invitando a tus amigos a unirse a Messenger Beta y compartir el enlace a [el sitio web beta]. 4. Pulsa en "Enviar" para enviar tu invitación. </p>
-<h2>beta messenger download</h2><br /><p><b><b>DOWNLOAD</b> &#10040; <a href="https://bltlly.com/2v6K7O">https://bltlly.com/2v6K7O</a></b></p><br /><br /> 64aa2da5cf<br />
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spaces/Benson/text-generation/Examples/Descargar Flores De Amor.md

@@ -1,88 +0,0 @@
-
-<h1>Descargar Flores de amor: Cómo expresar tus sentimientos con flores hermosas</h1>
-<p>Las flores son una de las formas más universales y atemporales para mostrar su amor y aprecio por alguien. Ya sea para el Día de San Valentín, el Día de la Madre, un aniversario o simplemente porque, enviar o recibir un ramo de flores puede alegrar el día de cualquiera. ¿Pero sabías que también puedes descargar imágenes de flores de amor de forma gratuita y usarlas para diversos fines? En este artículo, exploraremos qué son las flores de amor, qué significan, cómo elegirlas, cómo descargarlas y cómo enviarlas en línea o en persona. </p>
- <h2>¿Qué son las flores de amor y qué significan? </h2>
-<p>Las flores de amor son cualquier tipo de flores que transmiten un mensaje de amor, afecto, admiración o romance. Diferentes flores tienen diferentes significados y simbolismo, dependiendo de su color, forma y origen. Por ejemplo, las rosas rojas son las flores de amor más icónicas, ya que representan pasión, deseo y devoción. Sin embargo, hay muchos otros tipos de flores que también pueden expresar amor, como peonías, tulipanes, orquídeas, girasoles, lavanda y más. Aquí están algunas de las flores más populares que simbolizan el amor y sus significados:</p>
-<h2>descargar flores de amor</h2><br /><p><b><b>Download Zip</b> &rArr;&rArr;&rArr; <a href="https://bltlly.com/2v6MFB">https://bltlly.com/2v6MFB</a></b></p><br /><br />
- <h3>Las flores más populares que simbolizan el amor</h3>
-<ul>
-<li><strong>Rosas rojas:</strong> La elección clásica para ocasiones románticas, las rosas rojas significan emociones y deseos profundos. También están asociados con la belleza, el coraje y el respeto. </li>
-<li><strong>Rosas rosadas:</strong> Una alternativa más suave y delicada a las rosas rojas, las rosas rosadas transmiten felicidad, dulzura, gratitud y admiración. También son adecuados para la amistad y la simpatía. </li>
-<li><strong>Rosas blancas:</strong> La más pura e inocente de todas las rosas, las rosas blancas simbolizan pureza, inocencia, reverencia, nuevos comienzos y amor verdadero. A menudo se utilizan para bodas y funerales. </li>
-
-<li><strong>Rosas de lavanda:</strong> La más rara y encantadora de todas las rosas, las rosas de lavanda representan encantamiento, misterio, realeza y amor a primera vista. Son ideales para expresar admiración y fascinación. </li>
-<li><strong>Peonías:</strong> Estas flores exuberantes y fragantes son conocidas por su elegancia y sofisticación. Simbolizan alegría, riqueza, honor, romance y belleza. También se consideran un amuleto de buena suerte para el matrimonio. </li>
-<li><strong>Tulipanes:</strong> Estas flores simples pero encantadoras son una de las primeras en florecer en primavera. Representan el amor perfecto, el afecto incondicional, la renovación y la esperanza. Vienen en varios colores, cada uno con su propio significado. Por ejemplo, los tulipanes rojos significan amor verdadero; los tulipanes rosados significan felicidad; los tulipanes amarillos significan pensamientos alegres; los tulipanes blancos significan perdón; los tulipanes morados significan realeza; y los tulipanes abigarrados significan ojos hermosos. </li>
-<li><strong>Orquídeas:</strong> Estas flores exóticas y elegantes son un símbolo de lujo, belleza, fuerza y gracia. También representan fertilidad, amor y seducción. Vienen en muchas variedades, formas y colores, cada uno con su propio significado. Por ejemplo, orquídeas blancas significan pureza, inocencia, y elegancia; orquídeas rosadas significan feminidad, gracia, y alegría; orquídeas amarillas significan amistad, nuevos comienzos, y felicidad; orquídeas púrpuras significan admiración, respeto, y dignidad; y orquídeas rojas significan pasión, deseo y valor. </li>
-<li><strong>Girasoles:</strong> Estas flores brillantes y alegres son un símbolo de felicidad, lealtad y admiración. También representan el sol, el calor y la positividad. A menudo se les da para expresar aprecio, apoyo y amor platónico. También pueden significar orgullo, arrogancia y apariencia falsa en algunas culturas. </li>
-
-</ul>
- <h2>Cómo elegir las flores de amor adecuadas para diferentes ocasiones</h2>
-<p>Elegir las flores de amor adecuadas para diferentes ocasiones puede ser complicado, ya que desea transmitir el mensaje y la emoción correctos a su destinatario. Aquí hay algunos consejos para ayudarle a elegir las mejores flores de amor para diversas situaciones:</p>
- <h3>Para el día de San Valentín</h3>
-<p>San Valentín es el día más romántico del año, y la oportunidad perfecta para expresar su amor y afecto por su pareja. La opción más común para el Día de San Valentín son las rosas rojas, ya que son el símbolo definitivo de la pasión y el deseo. Sin embargo, también puedes optar por otros tipos de flores de amor que se adapten a la personalidad y preferencias de tu pareja. Por ejemplo, usted puede elegir rosas rosadas para un socio dulce y apacible; rosas blancas para un socio puro e inocente; rosas lavandas para un socio misterioso y encantador; peonías para un socio sofisticado y elegante; tulipanes para un socio simple y encantador; orquídeas para una pareja exótica y elegante; girasoles para una pareja alegre y optimista; o lavanda para una pareja serena y tranquila. </p>
- <h3>Para el Día de la Madre</h3>
-<p>El Día de la Madre es el día para celebrar y apreciar la figura de tu madre o madre en tu vida. La mejor opción para el Día de la Madre son las rosas, ya que representan gratitud, admiración y felicidad. También puedes elegir otros tipos de flores de amor que expresen tu respeto y afecto por tu madre. Por ejemplo, puedes elegir rosas blancas para una madre pura y reverente; rosas amarillas para una madre alegre y cuidadosa; rosas lavandas para una madre fascinante y única; peonías para una madre rica y honorable; tulipanes para una madre perfecta e incondicional; orquídeas para una madre fuerte y hermosa; girasoles para una madre soleada y leal; o lavanda para una madre devota y pacífica. </p>
- <h3>Para un aniversario</h3>
-
- <h2>Cómo descargar imágenes de flores de amor gratis</h2>
-<p>Si quieres descargar imágenes de flores de amor gratis, tienes muchas opciones para elegir. Hay muchos sitios web que ofrecen fotos de flores de amor de alta calidad que puede descargar sin ningún costo o registro. También puede utilizar estas imágenes para diversos fines, tales como fondos de pantalla, fondos, salvapantallas, tarjetas, invitaciones, carteles, folletos, etc. Estos son algunos de los mejores sitios web para encontrar imágenes de flores de amor gratis:</p>
- <h3>Los mejores sitios web para encontrar fotos de flores de amor de alta calidad</h3>
-<ul>
-<li><strong>Pexels:</strong> Este Web site ofrece millares de fotos comunes libres de varias categorías, incluyendo flores, naturaleza, amor, romance, y más. Puede navegar por color, tamaño, orientación y popularidad. También puede buscar por palabras clave o etiquetas. Puede descargar cualquier imagen en diferentes resoluciones, o usarla directamente en su sitio web o redes sociales con la atribución adecuada. </li>
-<li><strong>Unsplash:</strong> Este sitio web ofrece millones de fotos gratuitas de alta resolución de varios temas, incluyendo flores, naturaleza, amor, romance y más. Puede explorar por colecciones, temas o elección de editores. También puede buscar por palabras clave o etiquetas. Puede descargar cualquier imagen en diferentes tamaños, o usarla directamente en su sitio web o redes sociales con la atribución adecuada. </li>
-<li><strong>Pixabay:</strong> Este sitio web ofrece más de 1,8 millones de imágenes gratuitas, videos y música de varias categorías, incluyendo flores, naturaleza, amor, romance y más. Puede navegar por imágenes, vídeos o música. También puede buscar por palabras clave o etiquetas. Puede descargar cualquier imagen en diferentes resoluciones, o usarla directamente en su sitio web o redes sociales sin atribución. </li>
-
-<li><strong>Canva:</strong> Este sitio web ofrece una herramienta de diseño en línea gratuita que le permite crear gráficos impresionantes, logotipos, folletos, carteles, tarjetas, invitaciones y más. Puede elegir entre miles de plantillas o comenzar desde cero. También puede acceder a millones de imágenes, iconos, fuentes y colores gratuitos. Puede descargar su diseño en diferentes formatos, o compartirlo en línea con un enlace o código. </li>
-</ul>
- <h3>Cómo usar imágenes de flores de amor para varios propósitos</h3>
-<p>Una vez que haya descargado sus imágenes de flores de amor, puede usarlas para diversos fines. Aquí hay algunos ejemplos de cómo puedes usar imágenes de flores de amor:</p>
-<ul>
-<li><strong>Fondos de pantalla:</strong> Puede configurar sus imágenes de flores de amor como su escritorio o fondo de pantalla móvil para iluminar su pantalla y estado de ánimo. También puede cambiar su fondo de pantalla según la temporada u ocasión. </li>
-<li><strong>Fondos:</strong> Puedes usar tus imágenes de flores de amor como fondos para tu sitio web, blog, perfil de redes sociales, presentación o documento. También puede agregar texto, formas, filtros o efectos para mejorar su fondo. </li>
-<li><strong>Protectores de pantalla:</strong> Puedes usar tus imágenes de flores de amor como protectores de pantalla para tu computadora o TV para evitar que se queme la pantalla y ahorrar energía. También puede personalizar la configuración del salvapantallas, como la duración, la transición y la velocidad. </li>
-<li><strong>Tarjetas:</strong> Puedes usar tus imágenes de flores de amor para crear hermosas tarjetas para tus seres queridos. Puede añadir texto, pegatinas, marcos o bordes para personalizar su tarjeta. También puede imprimir su tarjeta o enviarla en línea. </li>
-<li><strong>Invitaciones:</strong> Puedes usar tus imágenes de flores de amor para crear invitaciones elegantes para tus eventos. Puede añadir texto, iconos, logotipos o códigos QR para proporcionar los detalles de su evento. También puede imprimir su invitación o enviarla en línea. </li>
-
-<li><strong>Flyers:</strong> Puedes usar tus imágenes de flores de amor para crear folletos atractivos para tus productos, servicios o eventos. Puede agregar texto, iconos, viñetas o llamar a los botones de acción para proporcionar la información de su oferta. También puede imprimir su folleto o distribuirlo en línea. </li>
-</ul>
- <h2>Cómo enviar flores de amor en línea o en persona</h2>
-<p>Si quieres enviar flores de amor a alguien, tienes dos opciones: en línea o en persona. Ambas opciones tienen sus ventajas y desventajas, dependiendo de su presupuesto, tiempo y preferencia. Estos son algunos de los beneficios y consejos de enviar flores de amor en línea o en persona:</p>
- <h3>Los beneficios de ordenar flores de amor en línea</h3>
-<ul>
-<li><strong>Conveniencia:</strong> Puedes pedir flores de amor en línea desde cualquier lugar y en cualquier momento, siempre y cuando tengas una conexión a Internet y un dispositivo. Usted no tiene que ir a una floristería, esperar en la cola, o llevar las flores usted mismo. </li>
-<li><strong>Variedad:</strong> Puedes elegir entre una amplia gama de flores de amor en línea, ya que hay muchos sitios web que ofrecen diferentes tipos, colores, estilos y arreglos de flores. También puede personalizar su pedido según su preferencia y ocasión. </li>
-<li><strong>Entrega:</strong> Usted puede tener sus flores del amor entregadas a la dirección de su receptor, si es su hogar, oficina, o cualquier otra localización. También puede elegir la fecha y hora de entrega, y realizar un seguimiento del estado de su pedido en línea. </li>
-<li><strong>Sorpresa:</strong> Puedes sorprender a tu destinatario con flores de amor en línea, ya que no sabrán quién las envió hasta que abran la tarjeta o el mensaje. También puedes agregar una nota personal, una foto o un video para hacer tu regalo más especial. </li>
-</ul>
- <h3>Los consejos para entregar flores de amor en persona</h3>
-<ul>
-
-<li><strong>Presentación:</strong> Usted debe entregar sus flores del amor en persona con una presentación agradable, dependiendo del tipo y del tamaño de las flores. Por ejemplo, puedes envolverlos en un papel o celofán; ponerlos en un jarrón o una cesta; atarlos con una cinta o un lazo; o unirlos con un globo o un osito de peluche. </li>
-<li><strong>Expresión:</strong> Debes entregar tus flores de amor en persona con una expresión sincera, dependiendo del mensaje y la emoción que quieras transmitir. Por ejemplo, puedes sonreír y abrazarlos si quieres mostrar felicidad y afecto; besarlos y felicitarlos si quieres mostrar pasión y admiración; disculparte y explicarlos si quieres mostrar arrepentimiento y perdón; o agradécelos y agradécelos si quieres mostrar gratitud y respeto. </li>
-</ul>
- <h2>Conclusión</h2>
-<p>En conclusión, las flores de amor son una forma maravillosa de expresar tus sentimientos con flores hermosas. Tienen diferentes significados y simbolismo, dependiendo de su tipo y color. También se pueden usar para varias ocasiones y propósitos, como el Día de San Valentín, el Día de la Madre, un aniversario o simplemente porque. También puede descargar imágenes de flores de amor de forma gratuita y usarlas para fondos de pantalla, fondos, salvapantallas, tarjetas, invitaciones, carteles, folletos y más. También puede enviar flores de amor en línea o en persona, dependiendo de su conveniencia, presupuesto y preferencia. Sea lo que sea que elijas, las flores de amor son una gran manera de mostrar tu amor y hacer feliz a alguien. </p>
-<p></p>
- <h3>Resumen de los puntos principales</h3>
-<ul>
-<li>Las flores de amor son cualquier tipo de flores que transmiten un mensaje de amor, afecto, admiración o romance. </li>
-<li> Diferentes flores tienen diferentes significados y simbolismo, dependiendo de su color, forma y origen. </li>
-<li>Puedes elegir las flores de amor adecuadas para diferentes ocasiones, como el Día de San Valentín, el Día de la Madre, un aniversario o simplemente porque. </li>
-
-<li>Puedes usar imágenes de flores de amor para varios propósitos, como fondos de pantalla, fondos, salvapantallas, tarjetas, invitaciones, carteles, folletos y más. </li>
-<li> Puede enviar flores de amor en línea o en persona, dependiendo de su conveniencia, presupuesto y preferencia. </li>
-</ul>
- <h3>Llamada a la acción</h3>
-<p>Si usted está buscando una manera de expresar sus sentimientos con flores hermosas, descargar flores de amor hoy y sorprender a sus seres queridos con un regalo reflexivo y sincero. También puede pedir flores de amor en línea y hacer que se entreguen a la dirección de su destinatario con un mensaje personal. O puede entregarlos en persona y ver su reacción de primera mano. Sea lo que sea que elijas, las flores de amor son una manera segura de hacer que alguien sonría y se sienta amado. </p>
- <h2>Preguntas frecuentes</h2>
-<ul>
-<li><strong>Q: ¿Cuáles son los mejores sitios web para descargar imágenes de flores de amor gratis? </strong></li>
-<li>A: Algunos de los mejores sitios web para descargar imágenes de flores de amor gratis son Pexels, Unsplash, Pixabay, Freepik y Canva. Ofrecen miles de fotos de alta calidad de varios tipos de flores que puedes usar para diferentes propósitos. </li>
-<li><strong>Q: ¿Cuáles son los mejores tipos de flores para enviar para el Día de San Valentín? </strong></li>
-<li>A: El mejor tipo de flor para enviar para el día de San Valentín son las rosas rojas, ya que representan la pasión y el deseo. Sin embargo, también puedes elegir otros tipos de flores que se adapten a la personalidad y preferencias de tu pareja. Por ejemplo, usted puede elegir rosas rosadas para un socio dulce y apacible; rosas blancas para un socio puro e inocente; rosas lavandas para un socio misterioso y encantador; peonías para un socio sofisticado y elegante; tulipanes para un socio simple y encantador; orquídeas para una pareja exótica y elegante; girasoles para una pareja alegre y optimista; o lavanda para una pareja serena y tranquila. </li>
-<li><strong>Q: ¿Cómo puedo hacer mi entrega de flores de amor más especial? </strong></li>
-
-</ul></p> 64aa2da5cf<br />
-<br />
-<br />

spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_internal/resolution/base.py

@@ -1,20 +0,0 @@
-from typing import Callable, List, Optional
-
-from pip._internal.req.req_install import InstallRequirement
-from pip._internal.req.req_set import RequirementSet
-
-InstallRequirementProvider = Callable[
-    [str, Optional[InstallRequirement]], InstallRequirement
-]
-
-
-class BaseResolver:
-    def resolve(
-        self, root_reqs: List[InstallRequirement], check_supported_wheels: bool
-    ) -> RequirementSet:
-        raise NotImplementedError()
-
-    def get_installation_order(
-        self, req_set: RequirementSet
-    ) -> List[InstallRequirement]:
-        raise NotImplementedError()

spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/resolvelib/compat/collections_abc.py

@@ -1,6 +0,0 @@
-__all__ = ["Mapping", "Sequence"]
-
-try:
-    from collections.abc import Mapping, Sequence
-except ImportError:
-    from collections import Mapping, Sequence

spaces/Big-Web/MMSD/env/Lib/site-packages/urllib3/connection.py

@@ -1,572 +0,0 @@
-from __future__ import absolute_import
-
-import datetime
-import logging
-import os
-import re
-import socket
-import warnings
-from socket import error as SocketError
-from socket import timeout as SocketTimeout
-
-from .packages import six
-from .packages.six.moves.http_client import HTTPConnection as _HTTPConnection
-from .packages.six.moves.http_client import HTTPException  # noqa: F401
-from .util.proxy import create_proxy_ssl_context
-
-try:  # Compiled with SSL?
-    import ssl
-
-    BaseSSLError = ssl.SSLError
-except (ImportError, AttributeError):  # Platform-specific: No SSL.
-    ssl = None
-
-    class BaseSSLError(BaseException):
-        pass
-
-
-try:
-    # Python 3: not a no-op, we're adding this to the namespace so it can be imported.
-    ConnectionError = ConnectionError
-except NameError:
-    # Python 2
-    class ConnectionError(Exception):
-        pass
-
-
-try:  # Python 3:
-    # Not a no-op, we're adding this to the namespace so it can be imported.
-    BrokenPipeError = BrokenPipeError
-except NameError:  # Python 2:
-
-    class BrokenPipeError(Exception):
-        pass
-
-
-from ._collections import HTTPHeaderDict  # noqa (historical, removed in v2)
-from ._version import __version__
-from .exceptions import (
-    ConnectTimeoutError,
-    NewConnectionError,
-    SubjectAltNameWarning,
-    SystemTimeWarning,
-)
-from .util import SKIP_HEADER, SKIPPABLE_HEADERS, connection
-from .util.ssl_ import (
-    assert_fingerprint,
-    create_urllib3_context,
-    is_ipaddress,
-    resolve_cert_reqs,
-    resolve_ssl_version,
-    ssl_wrap_socket,
-)
-from .util.ssl_match_hostname import CertificateError, match_hostname
-
-log = logging.getLogger(__name__)
-
-port_by_scheme = {"http": 80, "https": 443}
-
-# When it comes time to update this value as a part of regular maintenance
-# (ie test_recent_date is failing) update it to ~6 months before the current date.
-RECENT_DATE = datetime.date(2022, 1, 1)
-
-_CONTAINS_CONTROL_CHAR_RE = re.compile(r"[^-!#$%&'*+.^_`|~0-9a-zA-Z]")
-
-
-class HTTPConnection(_HTTPConnection, object):
-    """
-    Based on :class:`http.client.HTTPConnection` but provides an extra constructor
-    backwards-compatibility layer between older and newer Pythons.
-
-    Additional keyword parameters are used to configure attributes of the connection.
-    Accepted parameters include:
-
-    - ``strict``: See the documentation on :class:`urllib3.connectionpool.HTTPConnectionPool`
-    - ``source_address``: Set the source address for the current connection.
-    - ``socket_options``: Set specific options on the underlying socket. If not specified, then
-      defaults are loaded from ``HTTPConnection.default_socket_options`` which includes disabling
-      Nagle's algorithm (sets TCP_NODELAY to 1) unless the connection is behind a proxy.
-
-      For example, if you wish to enable TCP Keep Alive in addition to the defaults,
-      you might pass:
-
-      .. code-block:: python
-
-         HTTPConnection.default_socket_options + [
-             (socket.SOL_SOCKET, socket.SO_KEEPALIVE, 1),
-         ]
-
-      Or you may want to disable the defaults by passing an empty list (e.g., ``[]``).
-    """
-
-    default_port = port_by_scheme["http"]
-
-    #: Disable Nagle's algorithm by default.
-    #: ``[(socket.IPPROTO_TCP, socket.TCP_NODELAY, 1)]``
-    default_socket_options = [(socket.IPPROTO_TCP, socket.TCP_NODELAY, 1)]
-
-    #: Whether this connection verifies the host's certificate.
-    is_verified = False
-
-    #: Whether this proxy connection (if used) verifies the proxy host's
-    #: certificate.
-    proxy_is_verified = None
-
-    def __init__(self, *args, **kw):
-        if not six.PY2:
-            kw.pop("strict", None)
-
-        # Pre-set source_address.
-        self.source_address = kw.get("source_address")
-
-        #: The socket options provided by the user. If no options are
-        #: provided, we use the default options.
-        self.socket_options = kw.pop("socket_options", self.default_socket_options)
-
-        # Proxy options provided by the user.
-        self.proxy = kw.pop("proxy", None)
-        self.proxy_config = kw.pop("proxy_config", None)
-
-        _HTTPConnection.__init__(self, *args, **kw)
-
-    @property
-    def host(self):
-        """
-        Getter method to remove any trailing dots that indicate the hostname is an FQDN.
-
-        In general, SSL certificates don't include the trailing dot indicating a
-        fully-qualified domain name, and thus, they don't validate properly when
-        checked against a domain name that includes the dot. In addition, some
-        servers may not expect to receive the trailing dot when provided.
-
-        However, the hostname with trailing dot is critical to DNS resolution; doing a
-        lookup with the trailing dot will properly only resolve the appropriate FQDN,
-        whereas a lookup without a trailing dot will search the system's search domain
-        list. Thus, it's important to keep the original host around for use only in
-        those cases where it's appropriate (i.e., when doing DNS lookup to establish the
-        actual TCP connection across which we're going to send HTTP requests).
-        """
-        return self._dns_host.rstrip(".")
-
-    @host.setter
-    def host(self, value):
-        """
-        Setter for the `host` property.
-
-        We assume that only urllib3 uses the _dns_host attribute; httplib itself
-        only uses `host`, and it seems reasonable that other libraries follow suit.
-        """
-        self._dns_host = value
-
-    def _new_conn(self):
-        """Establish a socket connection and set nodelay settings on it.
-
-        :return: New socket connection.
-        """
-        extra_kw = {}
-        if self.source_address:
-            extra_kw["source_address"] = self.source_address
-
-        if self.socket_options:
-            extra_kw["socket_options"] = self.socket_options
-
-        try:
-            conn = connection.create_connection(
-                (self._dns_host, self.port), self.timeout, **extra_kw
-            )
-
-        except SocketTimeout:
-            raise ConnectTimeoutError(
-                self,
-                "Connection to %s timed out. (connect timeout=%s)"
-                % (self.host, self.timeout),
-            )
-
-        except SocketError as e:
-            raise NewConnectionError(
-                self, "Failed to establish a new connection: %s" % e
-            )
-
-        return conn
-
-    def _is_using_tunnel(self):
-        # Google App Engine's httplib does not define _tunnel_host
-        return getattr(self, "_tunnel_host", None)
-
-    def _prepare_conn(self, conn):
-        self.sock = conn
-        if self._is_using_tunnel():
-            # TODO: Fix tunnel so it doesn't depend on self.sock state.
-            self._tunnel()
-            # Mark this connection as not reusable
-            self.auto_open = 0
-
-    def connect(self):
-        conn = self._new_conn()
-        self._prepare_conn(conn)
-
-    def putrequest(self, method, url, *args, **kwargs):
-        """ """
-        # Empty docstring because the indentation of CPython's implementation
-        # is broken but we don't want this method in our documentation.
-        match = _CONTAINS_CONTROL_CHAR_RE.search(method)
-        if match:
-            raise ValueError(
-                "Method cannot contain non-token characters %r (found at least %r)"
-                % (method, match.group())
-            )
-
-        return _HTTPConnection.putrequest(self, method, url, *args, **kwargs)
-
-    def putheader(self, header, *values):
-        """ """
-        if not any(isinstance(v, str) and v == SKIP_HEADER for v in values):
-            _HTTPConnection.putheader(self, header, *values)
-        elif six.ensure_str(header.lower()) not in SKIPPABLE_HEADERS:
-            raise ValueError(
-                "urllib3.util.SKIP_HEADER only supports '%s'"
-                % ("', '".join(map(str.title, sorted(SKIPPABLE_HEADERS))),)
-            )
-
-    def request(self, method, url, body=None, headers=None):
-        # Update the inner socket's timeout value to send the request.
-        # This only triggers if the connection is re-used.
-        if getattr(self, "sock", None) is not None:
-            self.sock.settimeout(self.timeout)
-
-        if headers is None:
-            headers = {}
-        else:
-            # Avoid modifying the headers passed into .request()
-            headers = headers.copy()
-        if "user-agent" not in (six.ensure_str(k.lower()) for k in headers):
-            headers["User-Agent"] = _get_default_user_agent()
-        super(HTTPConnection, self).request(method, url, body=body, headers=headers)
-
-    def request_chunked(self, method, url, body=None, headers=None):
-        """
-        Alternative to the common request method, which sends the
-        body with chunked encoding and not as one block
-        """
-        headers = headers or {}
-        header_keys = set([six.ensure_str(k.lower()) for k in headers])
-        skip_accept_encoding = "accept-encoding" in header_keys
-        skip_host = "host" in header_keys
-        self.putrequest(
-            method, url, skip_accept_encoding=skip_accept_encoding, skip_host=skip_host
-        )
-        if "user-agent" not in header_keys:
-            self.putheader("User-Agent", _get_default_user_agent())
-        for header, value in headers.items():
-            self.putheader(header, value)
-        if "transfer-encoding" not in header_keys:
-            self.putheader("Transfer-Encoding", "chunked")
-        self.endheaders()
-
-        if body is not None:
-            stringish_types = six.string_types + (bytes,)
-            if isinstance(body, stringish_types):
-                body = (body,)
-            for chunk in body:
-                if not chunk:
-                    continue
-                if not isinstance(chunk, bytes):
-                    chunk = chunk.encode("utf8")
-                len_str = hex(len(chunk))[2:]
-                to_send = bytearray(len_str.encode())
-                to_send += b"\r\n"
-                to_send += chunk
-                to_send += b"\r\n"
-                self.send(to_send)
-
-        # After the if clause, to always have a closed body
-        self.send(b"0\r\n\r\n")
-
-
-class HTTPSConnection(HTTPConnection):
-    """
-    Many of the parameters to this constructor are passed to the underlying SSL
-    socket by means of :py:func:`urllib3.util.ssl_wrap_socket`.
-    """
-
-    default_port = port_by_scheme["https"]
-
-    cert_reqs = None
-    ca_certs = None
-    ca_cert_dir = None
-    ca_cert_data = None
-    ssl_version = None
-    assert_fingerprint = None
-    tls_in_tls_required = False
-
-    def __init__(
-        self,
-        host,
-        port=None,
-        key_file=None,
-        cert_file=None,
-        key_password=None,
-        strict=None,
-        timeout=socket._GLOBAL_DEFAULT_TIMEOUT,
-        ssl_context=None,
-        server_hostname=None,
-        **kw
-    ):
-
-        HTTPConnection.__init__(self, host, port, strict=strict, timeout=timeout, **kw)
-
-        self.key_file = key_file
-        self.cert_file = cert_file
-        self.key_password = key_password
-        self.ssl_context = ssl_context
-        self.server_hostname = server_hostname
-
-        # Required property for Google AppEngine 1.9.0 which otherwise causes
-        # HTTPS requests to go out as HTTP. (See Issue #356)
-        self._protocol = "https"
-
-    def set_cert(
-        self,
-        key_file=None,
-        cert_file=None,
-        cert_reqs=None,
-        key_password=None,
-        ca_certs=None,
-        assert_hostname=None,
-        assert_fingerprint=None,
-        ca_cert_dir=None,
-        ca_cert_data=None,
-    ):
-        """
-        This method should only be called once, before the connection is used.
-        """
-        # If cert_reqs is not provided we'll assume CERT_REQUIRED unless we also
-        # have an SSLContext object in which case we'll use its verify_mode.
-        if cert_reqs is None:
-            if self.ssl_context is not None:
-                cert_reqs = self.ssl_context.verify_mode
-            else:
-                cert_reqs = resolve_cert_reqs(None)
-
-        self.key_file = key_file
-        self.cert_file = cert_file
-        self.cert_reqs = cert_reqs
-        self.key_password = key_password
-        self.assert_hostname = assert_hostname
-        self.assert_fingerprint = assert_fingerprint
-        self.ca_certs = ca_certs and os.path.expanduser(ca_certs)
-        self.ca_cert_dir = ca_cert_dir and os.path.expanduser(ca_cert_dir)
-        self.ca_cert_data = ca_cert_data
-
-    def connect(self):
-        # Add certificate verification
-        self.sock = conn = self._new_conn()
-        hostname = self.host
-        tls_in_tls = False
-
-        if self._is_using_tunnel():
-            if self.tls_in_tls_required:
-                self.sock = conn = self._connect_tls_proxy(hostname, conn)
-                tls_in_tls = True
-
-            # Calls self._set_hostport(), so self.host is
-            # self._tunnel_host below.
-            self._tunnel()
-            # Mark this connection as not reusable
-            self.auto_open = 0
-
-            # Override the host with the one we're requesting data from.
-            hostname = self._tunnel_host
-
-        server_hostname = hostname
-        if self.server_hostname is not None:
-            server_hostname = self.server_hostname
-
-        is_time_off = datetime.date.today() < RECENT_DATE
-        if is_time_off:
-            warnings.warn(
-                (
-                    "System time is way off (before {0}). This will probably "
-                    "lead to SSL verification errors"
-                ).format(RECENT_DATE),
-                SystemTimeWarning,
-            )
-
-        # Wrap socket using verification with the root certs in
-        # trusted_root_certs
-        default_ssl_context = False
-        if self.ssl_context is None:
-            default_ssl_context = True
-            self.ssl_context = create_urllib3_context(
-                ssl_version=resolve_ssl_version(self.ssl_version),
-                cert_reqs=resolve_cert_reqs(self.cert_reqs),
-            )
-
-        context = self.ssl_context
-        context.verify_mode = resolve_cert_reqs(self.cert_reqs)
-
-        # Try to load OS default certs if none are given.
-        # Works well on Windows (requires Python3.4+)
-        if (
-            not self.ca_certs
-            and not self.ca_cert_dir
-            and not self.ca_cert_data
-            and default_ssl_context
-            and hasattr(context, "load_default_certs")
-        ):
-            context.load_default_certs()
-
-        self.sock = ssl_wrap_socket(
-            sock=conn,
-            keyfile=self.key_file,
-            certfile=self.cert_file,
-            key_password=self.key_password,
-            ca_certs=self.ca_certs,
-            ca_cert_dir=self.ca_cert_dir,
-            ca_cert_data=self.ca_cert_data,
-            server_hostname=server_hostname,
-            ssl_context=context,
-            tls_in_tls=tls_in_tls,
-        )
-
-        # If we're using all defaults and the connection
-        # is TLSv1 or TLSv1.1 we throw a DeprecationWarning
-        # for the host.
-        if (
-            default_ssl_context
-            and self.ssl_version is None
-            and hasattr(self.sock, "version")
-            and self.sock.version() in {"TLSv1", "TLSv1.1"}
-        ):
-            warnings.warn(
-                "Negotiating TLSv1/TLSv1.1 by default is deprecated "
-                "and will be disabled in urllib3 v2.0.0. Connecting to "
-                "'%s' with '%s' can be enabled by explicitly opting-in "
-                "with 'ssl_version'" % (self.host, self.sock.version()),
-                DeprecationWarning,
-            )
-
-        if self.assert_fingerprint:
-            assert_fingerprint(
-                self.sock.getpeercert(binary_form=True), self.assert_fingerprint
-            )
-        elif (
-            context.verify_mode != ssl.CERT_NONE
-            and not getattr(context, "check_hostname", False)
-            and self.assert_hostname is not False
-        ):
-            # While urllib3 attempts to always turn off hostname matching from
-            # the TLS library, this cannot always be done. So we check whether
-            # the TLS Library still thinks it's matching hostnames.
-            cert = self.sock.getpeercert()
-            if not cert.get("subjectAltName", ()):
-                warnings.warn(
-                    (
-                        "Certificate for {0} has no `subjectAltName`, falling back to check for a "
-                        "`commonName` for now. This feature is being removed by major browsers and "
-                        "deprecated by RFC 2818. (See https://github.com/urllib3/urllib3/issues/497 "
-                        "for details.)".format(hostname)
-                    ),
-                    SubjectAltNameWarning,
-                )
-            _match_hostname(cert, self.assert_hostname or server_hostname)
-
-        self.is_verified = (
-            context.verify_mode == ssl.CERT_REQUIRED
-            or self.assert_fingerprint is not None
-        )
-
-    def _connect_tls_proxy(self, hostname, conn):
-        """
-        Establish a TLS connection to the proxy using the provided SSL context.
-        """
-        proxy_config = self.proxy_config
-        ssl_context = proxy_config.ssl_context
-        if ssl_context:
-            # If the user provided a proxy context, we assume CA and client
-            # certificates have already been set
-            return ssl_wrap_socket(
-                sock=conn,
-                server_hostname=hostname,
-                ssl_context=ssl_context,
-            )
-
-        ssl_context = create_proxy_ssl_context(
-            self.ssl_version,
-            self.cert_reqs,
-            self.ca_certs,
-            self.ca_cert_dir,
-            self.ca_cert_data,
-        )
-
-        # If no cert was provided, use only the default options for server
-        # certificate validation
-        socket = ssl_wrap_socket(
-            sock=conn,
-            ca_certs=self.ca_certs,
-            ca_cert_dir=self.ca_cert_dir,
-            ca_cert_data=self.ca_cert_data,
-            server_hostname=hostname,
-            ssl_context=ssl_context,
-        )
-
-        if ssl_context.verify_mode != ssl.CERT_NONE and not getattr(
-            ssl_context, "check_hostname", False
-        ):
-            # While urllib3 attempts to always turn off hostname matching from
-            # the TLS library, this cannot always be done. So we check whether
-            # the TLS Library still thinks it's matching hostnames.
-            cert = socket.getpeercert()
-            if not cert.get("subjectAltName", ()):
-                warnings.warn(
-                    (
-                        "Certificate for {0} has no `subjectAltName`, falling back to check for a "
-                        "`commonName` for now. This feature is being removed by major browsers and "
-                        "deprecated by RFC 2818. (See https://github.com/urllib3/urllib3/issues/497 "
-                        "for details.)".format(hostname)
-                    ),
-                    SubjectAltNameWarning,
-                )
-            _match_hostname(cert, hostname)
-
-        self.proxy_is_verified = ssl_context.verify_mode == ssl.CERT_REQUIRED
-        return socket
-
-
-def _match_hostname(cert, asserted_hostname):
-    # Our upstream implementation of ssl.match_hostname()
-    # only applies this normalization to IP addresses so it doesn't
-    # match DNS SANs so we do the same thing!
-    stripped_hostname = asserted_hostname.strip("u[]")
-    if is_ipaddress(stripped_hostname):
-        asserted_hostname = stripped_hostname
-
-    try:
-        match_hostname(cert, asserted_hostname)
-    except CertificateError as e:
-        log.warning(
-            "Certificate did not match expected hostname: %s. Certificate: %s",
-            asserted_hostname,
-            cert,
-        )
-        # Add cert to exception and reraise so client code can inspect
-        # the cert when catching the exception, if they want to
-        e._peer_cert = cert
-        raise
-
-
-def _get_default_user_agent():
-    return "python-urllib3/%s" % __version__
-
-
-class DummyConnection(object):
-    """Used to detect a failed ConnectionCls import."""
-
-    pass
-
-
-if not ssl:
-    HTTPSConnection = DummyConnection  # noqa: F811
-
-
-VerifiedHTTPSConnection = HTTPSConnection

spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/projects/DensePose/doc/TOOL_APPLY_NET.md

@@ -1,130 +0,0 @@
-# Apply Net
-
-`apply_net` is a tool to print or visualize DensePose results on a set of images.
-It has two modes: `dump` to save DensePose model results to a pickle file
-and `show` to visualize them on images.
-
-## Dump Mode
-
-The general command form is:
-```bash
-python apply_net.py dump [-h] [-v] [--output <dump_file>] <config> <model> <input>
-```
-
-There are three mandatory arguments:
- - `<config>`, configuration file for a given model;
- - `<model>`, model file with trained parameters
- - `<input>`, input image file name, pattern or folder
-
-One can additionally provide `--output` argument to define the output file name,
-which defaults to `output.pkl`.
-
-
-Examples:
-
-1. Dump results of a DensePose model with ResNet-50 FPN backbone for images
-   in a folder `images` to file `dump.pkl`:
-```bash
-python apply_net.py dump configs/densepose_rcnn_R_50_FPN_s1x.yaml DensePose_ResNet50_FPN_s1x-e2e.pkl images --output dump.pkl -v
-```
-
-2. Dump results of a DensePose model with ResNet-50 FPN backbone for images
-   with file name matching a pattern `image*.jpg` to file `results.pkl`:
-```bash
-python apply_net.py dump configs/densepose_rcnn_R_50_FPN_s1x.yaml DensePose_ResNet50_FPN_s1x-e2e.pkl "image*.jpg" --output results.pkl -v
-```
-
-If you want to load the pickle file generated by the above command:
-```
-# make sure DensePose is in your PYTHONPATH, or use the following line to add it:
-sys.path.append("/your_detectron2_path/detectron2_repo/projects/DensePose/")
-
-f = open('/your_result_path/results.pkl', 'rb')
-data = pickle.load(f)
-```
-
-The file `results.pkl` contains the list of results per image, for each image the result is a dictionary:
-```
-data: [{'file_name': '/your_path/image1.jpg',
-        'scores': tensor([0.9884]),
-        'pred_boxes_XYXY': tensor([[ 69.6114,   0.0000, 706.9797, 706.0000]]),
-        'pred_densepose': <densepose.structures.DensePoseResult object at 0x7f791b312470>},
-       {'file_name': '/your_path/image2.jpg',
-        'scores': tensor([0.9999, 0.5373, 0.3991]),
-        'pred_boxes_XYXY': tensor([[ 59.5734,   7.7535, 579.9311, 932.3619],
-                                   [612.9418, 686.1254, 612.9999, 704.6053],
-                                   [164.5081, 407.4034, 598.3944, 920.4266]]),
-        'pred_densepose': <densepose.structures.DensePoseResult object at 0x7f7071229be0>}]
-```
-
-We can use the following code, to parse the outputs of the first
-detected instance on the first image.
-```
-img_id, instance_id = 0, 0  # Look at the first image and the first detected instance
-bbox_xyxy = data[img_id]['pred_boxes_XYXY'][instance_id]
-result_encoded = data[img_id]['pred_densepose'].results[instance_id]
-iuv_arr = DensePoseResult.decode_png_data(*result_encoded)
-```
-The array `bbox_xyxy` contains (x0, y0, x1, y1) of the bounding box.
-
-The shape of `iuv_arr` is `[3, H, W]`, where (H, W) is the shape of the bounding box.
-- `iuv_arr[0,:,:]`: The patch index of image points, indicating which of the 24 surface patches the point is on.
-- `iuv_arr[1,:,:]`: The U-coordinate value of image points.
-- `iuv_arr[2,:,:]`: The V-coordinate value of image points.
-
-
-## Visualization Mode
-
-The general command form is:
-```bash
-python apply_net.py show [-h] [-v] [--min_score <score>] [--nms_thresh <threshold>] [--output <image_file>] <config> <model> <input> <visualizations>
-```
-
-There are four mandatory arguments:
- - `<config>`, configuration file for a given model;
- - `<model>`, model file with trained parameters
- - `<input>`, input image file name, pattern or folder
- - `<visualizations>`, visualizations specifier; currently available visualizations are:
-   * `bbox` - bounding boxes of detected persons;
-   * `dp_segm` - segmentation masks for detected persons;
-   * `dp_u` - each body part is colored according to the estimated values of the
-     U coordinate in part parameterization;
-   * `dp_v` - each body part is colored according to the estimated values of the
-     V coordinate in part parameterization;
-   * `dp_contour` - plots contours with color-coded U and V coordinates
-
-
-One can additionally provide the following optional arguments:
- - `--min_score` to only show detections with sufficient scores that are not lower than provided value
- - `--nms_thresh` to additionally apply non-maximum suppression to detections at a given threshold
- - `--output` to define visualization file name template, which defaults to `output.png`.
-   To distinguish output file names for different images, the tool appends 1-based entry index,
-   e.g. output.0001.png, output.0002.png, etc...
-
-
-The following examples show how to output results of a DensePose model
-with ResNet-50 FPN backbone using different visualizations for image `image.jpg`:
-
-1. Show bounding box and segmentation:
-```bash
-python apply_net.py show configs/densepose_rcnn_R_50_FPN_s1x.yaml DensePose_ResNet50_FPN_s1x-e2e.pkl image.jpg bbox,dp_segm -v
-```
-![Bounding Box + Segmentation Visualization](images/res_bbox_dp_segm.jpg)
-
-2. Show bounding box and estimated U coordinates for body parts:
-```bash
-python apply_net.py show configs/densepose_rcnn_R_50_FPN_s1x.yaml DensePose_ResNet50_FPN_s1x-e2e.pkl image.jpg bbox,dp_u -v
-```
-![Bounding Box + U Coordinate Visualization](images/res_bbox_dp_u.jpg)
-
-3. Show bounding box and estimated V coordinates for body parts:
-```bash
-python apply_net.py show configs/densepose_rcnn_R_50_FPN_s1x.yaml DensePose_ResNet50_FPN_s1x-e2e.pkl image.jpg bbox,dp_v -v
-```
-![Bounding Box + V Coordinate Visualization](images/res_bbox_dp_v.jpg)
-
-4. Show bounding box and estimated U and V coordinates via contour plots:
-```bash
-python apply_net.py show configs/densepose_rcnn_R_50_FPN_s1x.yaml DensePose_ResNet50_FPN_s1x-e2e.pkl image.jpg dp_contour,bbox -v
-```
-![Bounding Box + Contour Visualization](images/res_bbox_dp_contour.jpg)

spaces/CVPR/LIVE/thrust/thrust/system/detail/adl/swap_ranges.h

@@ -1,44 +0,0 @@
-/*
- *  Copyright 2008-2013 NVIDIA Corporation
- *
- *  Licensed under the Apache License, Version 2.0 (the "License");
- *  you may not use this file except in compliance with the License.
- *  You may obtain a fill of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- *  Unless required by applicable law or agreed to in writing, software
- *  distributed under the License is distributed on an "AS IS" BASIS,
- *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- *  See the License for the specific language governing permissions and
- *  limitations under the License.
- */
-
-#pragma once
-
-#include <thrust/detail/config.h>
-
-// the purpose of this header is to #include the swap_ranges.h header
-// of the sequential, host, and device systems. It should be #included in any
-// code which uses adl to dispatch swap_ranges
-
-#include <thrust/system/detail/sequential/swap_ranges.h>
-
-// SCons can't see through the #defines below to figure out what this header
-// includes, so we fake it out by specifying all possible files we might end up
-// including inside an #if 0.
-#if 0
-#include <thrust/system/cpp/detail/swap_ranges.h>
-#include <thrust/system/cuda/detail/swap_ranges.h>
-#include <thrust/system/omp/detail/swap_ranges.h>
-#include <thrust/system/tbb/detail/swap_ranges.h>
-#endif
-
-#define __THRUST_HOST_SYSTEM_SWAP_RANGES_HEADER <__THRUST_HOST_SYSTEM_ROOT/detail/swap_ranges.h>
-#include __THRUST_HOST_SYSTEM_SWAP_RANGES_HEADER
-#undef __THRUST_HOST_SYSTEM_SWAP_RANGES_HEADER
-
-#define __THRUST_DEVICE_SYSTEM_SWAP_RANGES_HEADER <__THRUST_DEVICE_SYSTEM_ROOT/detail/swap_ranges.h>
-#include __THRUST_DEVICE_SYSTEM_SWAP_RANGES_HEADER
-#undef __THRUST_DEVICE_SYSTEM_SWAP_RANGES_HEADER
-

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

@@ -1,56 +0,0 @@
-/*
- *  Copyright 2008-2013 NVIDIA Corporation
- *
- *  Licensed under the Apache License, Version 2.0 (the "License");
- *  you may not use this file except in compliance with the License.
- *  You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- *  Unless required by applicable law or agreed to in writing, software
- *  distributed under the License is distributed on an "AS IS" BASIS,
- *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- *  See the License for the specific language governing permissions and
- *  limitations under the License.
- */
-
-#pragma once
-
-#include <thrust/detail/config.h>
-#include <thrust/system/detail/sequential/execution_policy.h>
-
-namespace thrust
-{
-namespace system
-{
-namespace detail
-{
-namespace sequential
-{
-
-
-template<typename DerivedPolicy,
-         typename RandomAccessIterator>
-__host__ __device__
-void stable_primitive_sort(sequential::execution_policy<DerivedPolicy> &exec,
-                           RandomAccessIterator first,
-                           RandomAccessIterator last);
-
-
-template<typename DerivedPolicy,
-         typename RandomAccessIterator1,
-         typename RandomAccessIterator2>
-__host__ __device__
-void stable_primitive_sort_by_key(sequential::execution_policy<DerivedPolicy> &exec,
-                                  RandomAccessIterator1 keys_first,
-                                  RandomAccessIterator1 keys_last,
-                                  RandomAccessIterator2 values_first);
-
-
-} // end namespace sequential
-} // end namespace detail
-} // end namespace system
-} // end namespace thrust
-
-#include <thrust/system/detail/sequential/stable_primitive_sort.inl>
-

spaces/CVPR/LIVE/thrust/thrust/system/tbb/vector.h

@@ -1,65 +0,0 @@
-/*
- *  Copyright 2008-2013 NVIDIA Corporation
- *
- *  Licensed under the Apache License, Version 2.0 (the "License");
- *  you may not use this file except in compliance with the License.
- *  You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- *  Unless required by applicable law or agreed to in writing, software
- *  distributed under the License is distributed on an "AS IS" BASIS,
- *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- *  See the License for the specific language governing permissions and
- *  limitations under the License.
- */
-
-/*! \file thrust/system/tbb/vector.h
- *  \brief A dynamically-sizable array of elements which reside in memory available to
- *         Thrust's TBB system.
- */
-
-#pragma once
-
-#include <thrust/detail/config.h>
-#include <thrust/system/tbb/memory.h>
-#include <thrust/detail/vector_base.h>
-#include <vector>
-
-namespace thrust
-{
-namespace system
-{
-namespace tbb
-{
-
-/*! \p tbb::vector is a container that supports random access to elements,
- *  constant time removal of elements at the end, and linear time insertion
- *  and removal of elements at the beginning or in the middle. The number of
- *  elements in a \p tbb::vector may vary dynamically; memory management is
- *  automatic. The elements contained in a \p tbb::vector reside in memory
- *  available to the \p tbb system.
- *
- *  \tparam T The element type of the \p tbb::vector.
- *  \tparam Allocator The allocator type of the \p tbb::vector. Defaults to \p tbb::allocator.
- *
- *  \see http://www.sgi.com/tech/stl/Vector.html
- *  \see host_vector For the documentation of the complete interface which is
- *                   shared by \p tbb::vector
- *  \see device_vector
- */
-template<typename T, typename Allocator = allocator<T> >
-using vector = thrust::detail::vector_base<T, Allocator>;
-
-} // end tbb
-} // end system
-
-// alias system::tbb names at top-level
-namespace tbb
-{
-
-using thrust::system::tbb::vector;
-
-} // end tbb
-
-} // end thrust

spaces/CVPR/SPOTER_Sign_Language_Recognition/spoter_mod/augmentations/__init__.py

@@ -1,231 +0,0 @@
-
-import math
-import logging
-import cv2
-import random
-
-import numpy as np
-
-from normalization.body_normalization import BODY_IDENTIFIERS
-from normalization.hand_normalization import HAND_IDENTIFIERS
-
-
-HAND_IDENTIFIERS = [id + "_0" for id in HAND_IDENTIFIERS] + [id + "_1" for id in HAND_IDENTIFIERS]
-ARM_IDENTIFIERS_ORDER = ["neck", "$side$Shoulder", "$side$Elbow", "$side$Wrist"]
-
-
-def __random_pass(prob):
-    return random.random() < prob
-
-
-def __numpy_to_dictionary(data_array: np.ndarray) -> dict:
-    """
-    Supplementary method converting a NumPy array of body landmark data into dictionaries. The array data must match the
-    order of the BODY_IDENTIFIERS list.
-    """
-
-    output = {}
-
-    for landmark_index, identifier in enumerate(BODY_IDENTIFIERS):
-        output[identifier] = data_array[:, landmark_index].tolist()
-
-    return output
-
-
-def __dictionary_to_numpy(landmarks_dict: dict) -> np.ndarray:
-    """
-    Supplementary method converting dictionaries of body landmark data into respective NumPy arrays. The resulting array
-    will match the order of the BODY_IDENTIFIERS list.
-    """
-
-    output = np.empty(shape=(len(landmarks_dict["leftEar"]), len(BODY_IDENTIFIERS), 2))
-
-    for landmark_index, identifier in enumerate(BODY_IDENTIFIERS):
-        output[:, landmark_index, 0] = np.array(landmarks_dict[identifier])[:, 0]
-        output[:, landmark_index, 1] = np.array(landmarks_dict[identifier])[:, 1]
-
-    return output
-
-
-def __rotate(origin: tuple, point: tuple, angle: float):
-    """
-    Rotates a point counterclockwise by a given angle around a given origin.
-
-    :param origin: Landmark in the (X, Y) format of the origin from which to count angle of rotation
-    :param point: Landmark in the (X, Y) format to be rotated
-    :param angle: Angle under which the point shall be rotated
-    :return: New landmarks (coordinates)
-    """
-
-    ox, oy = origin
-    px, py = point
-
-    qx = ox + math.cos(angle) * (px - ox) - math.sin(angle) * (py - oy)
-    qy = oy + math.sin(angle) * (px - ox) + math.cos(angle) * (py - oy)
-
-    return qx, qy
-
-
-def __preprocess_row_sign(sign: dict) -> (dict, dict):
-    """
-    Supplementary method splitting the single-dictionary skeletal data into two dictionaries of body and hand landmarks
-    respectively.
-    """
-
-    sign_eval = sign
-
-    if "nose_X" in sign_eval:
-        body_landmarks = {identifier: [(x, y) for x, y in zip(sign_eval[identifier + "_X"], sign_eval[identifier + "_Y"])]
-                          for identifier in BODY_IDENTIFIERS}
-        hand_landmarks = {identifier: [(x, y) for x, y in zip(sign_eval[identifier + "_X"], sign_eval[identifier + "_Y"])]
-                          for identifier in HAND_IDENTIFIERS}
-
-    else:
-        body_landmarks = {identifier: sign_eval[identifier] for identifier in BODY_IDENTIFIERS}
-        hand_landmarks = {identifier: sign_eval[identifier] for identifier in HAND_IDENTIFIERS}
-
-    return body_landmarks, hand_landmarks
-
-
-def __wrap_sign_into_row(body_identifiers: dict, hand_identifiers: dict) -> dict:
-    """
-    Supplementary method for merging body and hand data into a single dictionary.
-    """
-
-    return {**body_identifiers, **hand_identifiers}
-
-
-def augment_rotate(sign: dict, angle_range: tuple) -> dict:
-    """
-    AUGMENTATION TECHNIQUE. All the joint coordinates in each frame are rotated by a random angle up to 13 degrees with
-    the center of rotation lying in the center of the frame, which is equal to [0.5; 0.5].
-
-    :param sign: Dictionary with sequential skeletal data of the signing person
-    :param angle_range: Tuple containing the angle range (minimal and maximal angle in degrees) to randomly choose the
-                        angle by which the landmarks will be rotated from
-
-    :return: Dictionary with augmented (by rotation) sequential skeletal data of the signing person
-    """
-
-    body_landmarks, hand_landmarks = __preprocess_row_sign(sign)
-    angle = math.radians(random.uniform(*angle_range))
-
-    body_landmarks = {key: [__rotate((0.5, 0.5), frame, angle) for frame in value] for key, value in
-                      body_landmarks.items()}
-    hand_landmarks = {key: [__rotate((0.5, 0.5), frame, angle) for frame in value] for key, value in
-                      hand_landmarks.items()}
-
-    return __wrap_sign_into_row(body_landmarks, hand_landmarks)
-
-
-def augment_shear(sign: dict, type: str, squeeze_ratio: tuple) -> dict:
-    """
-    AUGMENTATION TECHNIQUE.
-
-        - Squeeze. All the frames are squeezed from both horizontal sides. Two different random proportions up to 15% of
-        the original frame's width for both left and right side are cut.
-
-        - Perspective transformation. The joint coordinates are projected onto a new plane with a spatially defined
-        center of projection, which simulates recording the sign video with a slight tilt. Each time, the right or left
-        side, as well as the proportion by which both the width and height will be reduced, are chosen randomly. This
-        proportion is selected from a uniform distribution on the [0; 1) interval. Subsequently, the new plane is
-        delineated by reducing the width at the desired side and the respective vertical edge (height) at both of its
-        adjacent corners.
-
-    :param sign: Dictionary with sequential skeletal data of the signing person
-    :param type: Type of shear augmentation to perform (either 'squeeze' or 'perspective')
-    :param squeeze_ratio: Tuple containing the relative range from what the proportion of the original width will be
-                          randomly chosen. These proportions will either be cut from both sides or used to construct the
-                          new projection
-
-    :return: Dictionary with augmented (by squeezing or perspective transformation) sequential skeletal data of the
-             signing person
-    """
-
-    body_landmarks, hand_landmarks = __preprocess_row_sign(sign)
-
-    if type == "squeeze":
-        move_left = random.uniform(*squeeze_ratio)
-        move_right = random.uniform(*squeeze_ratio)
-
-        src = np.array(((0, 1), (1, 1), (0, 0), (1, 0)), dtype=np.float32)
-        dest = np.array(((0 + move_left, 1), (1 - move_right, 1), (0 + move_left, 0), (1 - move_right, 0)),
-                        dtype=np.float32)
-        mtx = cv2.getPerspectiveTransform(src, dest)
-
-    elif type == "perspective":
-
-        move_ratio = random.uniform(*squeeze_ratio)
-        src = np.array(((0, 1), (1, 1), (0, 0), (1, 0)), dtype=np.float32)
-
-        if __random_pass(0.5):
-            dest = np.array(((0 + move_ratio, 1 - move_ratio), (1, 1), (0 + move_ratio, 0 + move_ratio), (1, 0)),
-                            dtype=np.float32)
-        else:
-            dest = np.array(((0, 1), (1 - move_ratio, 1 - move_ratio), (0, 0), (1 - move_ratio, 0 + move_ratio)),
-                            dtype=np.float32)
-
-        mtx = cv2.getPerspectiveTransform(src, dest)
-
-    else:
-
-        logging.error("Unsupported shear type provided.")
-        return {}
-
-    landmarks_array = __dictionary_to_numpy(body_landmarks)
-    augmented_landmarks = cv2.perspectiveTransform(np.array(landmarks_array, dtype=np.float32), mtx)
-
-    augmented_zero_landmark = cv2.perspectiveTransform(np.array([[[0, 0]]], dtype=np.float32), mtx)[0][0]
-    augmented_landmarks = np.stack([np.where(sub == augmented_zero_landmark, [0, 0], sub) for sub in augmented_landmarks])
-
-    body_landmarks = __numpy_to_dictionary(augmented_landmarks)
-
-    return __wrap_sign_into_row(body_landmarks, hand_landmarks)
-
-
-def augment_arm_joint_rotate(sign: dict, probability: float, angle_range: tuple) -> dict:
-    """
-    AUGMENTATION TECHNIQUE. The joint coordinates of both arms are passed successively, and the impending landmark is
-    slightly rotated with respect to the current one. The chance of each joint to be rotated is 3:10 and the angle of
-    alternation is a uniform random angle up to +-4 degrees. This simulates slight, negligible variances in each
-    execution of a sign, which do not change its semantic meaning.
-
-    :param sign: Dictionary with sequential skeletal data of the signing person
-    :param probability: Probability of each joint to be rotated (float from the range [0, 1])
-    :param angle_range: Tuple containing the angle range (minimal and maximal angle in degrees) to randomly choose the
-                        angle by which the landmarks will be rotated from
-
-    :return: Dictionary with augmented (by arm joint rotation) sequential skeletal data of the signing person
-    """
-
-    body_landmarks, hand_landmarks = __preprocess_row_sign(sign)
-
-    # Iterate over both directions (both hands)
-    for side in ["left", "right"]:
-        # Iterate gradually over the landmarks on arm
-        for landmark_index, landmark_origin in enumerate(ARM_IDENTIFIERS_ORDER):
-            landmark_origin = landmark_origin.replace("$side$", side)
-
-            # End the process on the current hand if the landmark is not present
-            if landmark_origin not in body_landmarks:
-                break
-
-            # Perform rotation by provided probability
-            if __random_pass(probability):
-                angle = math.radians(random.uniform(*angle_range))
-
-                for to_be_rotated in ARM_IDENTIFIERS_ORDER[landmark_index + 1:]:
-                    to_be_rotated = to_be_rotated.replace("$side$", side)
-
-                    # Skip if the landmark is not present
-                    if to_be_rotated not in body_landmarks:
-                        continue
-
-                    body_landmarks[to_be_rotated] = [__rotate(body_landmarks[landmark_origin][frame_index], frame,
-                        angle) for frame_index, frame in enumerate(body_landmarks[to_be_rotated])]
-
-    return __wrap_sign_into_row(body_landmarks, hand_landmarks)
-
-
-if __name__ == "__main__":
-    pass

spaces/ChrisCaviar/ControlNet-v1-1/app_ip2p.py

@@ -1,92 +0,0 @@
-#!/usr/bin/env python
-
-import gradio as gr
-
-from utils import randomize_seed_fn
-
-
-def create_demo(process, max_images=12, default_num_images=3):
-    with gr.Blocks() as demo:
-        with gr.Row():
-            with gr.Column():
-                image = gr.Image()
-                prompt = gr.Textbox(label='Prompt')
-                run_button = gr.Button('Run')
-                with gr.Accordion('Advanced options', open=False):
-                    num_samples = gr.Slider(label='Number of images',
-                                            minimum=1,
-                                            maximum=max_images,
-                                            value=default_num_images,
-                                            step=1)
-                    image_resolution = gr.Slider(label='Image resolution',
-                                                 minimum=256,
-                                                 maximum=512,
-                                                 value=512,
-                                                 step=256)
-                    num_steps = gr.Slider(label='Number of steps',
-                                          minimum=1,
-                                          maximum=100,
-                                          value=20,
-                                          step=1)
-                    guidance_scale = gr.Slider(label='Guidance scale',
-                                               minimum=0.1,
-                                               maximum=30.0,
-                                               value=9.0,
-                                               step=0.1)
-                    seed = gr.Slider(label='Seed',
-                                     minimum=0,
-                                     maximum=1000000,
-                                     step=1,
-                                     value=0,
-                                     randomize=True)
-                    randomize_seed = gr.Checkbox(label='Randomize seed',
-                                                 value=True)
-                    a_prompt = gr.Textbox(
-                        label='Additional prompt',
-                        value='best quality, extremely detailed')
-                    n_prompt = gr.Textbox(
-                        label='Negative prompt',
-                        value=
-                        'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
-                    )
-            with gr.Column():
-                result = gr.Gallery(label='Output', show_label=False).style(
-                    columns=2, object_fit='scale-down')
-        inputs = [
-            image,
-            prompt,
-            a_prompt,
-            n_prompt,
-            num_samples,
-            image_resolution,
-            num_steps,
-            guidance_scale,
-            seed,
-        ]
-        prompt.submit(
-            fn=randomize_seed_fn,
-            inputs=[seed, randomize_seed],
-            outputs=seed,
-        ).then(
-            fn=process,
-            inputs=inputs,
-            outputs=result,
-        )
-        run_button.click(
-            fn=randomize_seed_fn,
-            inputs=[seed, randomize_seed],
-            outputs=seed,
-        ).then(
-            fn=process,
-            inputs=inputs,
-            outputs=result,
-            api_name='scribble',
-        )
-    return demo
-
-
-if __name__ == '__main__':
-    from model import Model
-    model = Model(task_name='ip2p')
-    demo = create_demo(model.process_ip2p)
-    demo.queue().launch()

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

@@ -1,5 +0,0 @@
-from duckduckgo_search import DDGS
-
-with DDGS() as ddgs:
-    for r in ddgs.answers("sun"):
-        print(r)

spaces/CrucibleAI/ControlNetMediaPipeFaceSD21/ldm/modules/midas/midas/base_model.py

@@ -1,16 +0,0 @@
-import torch
-
-
-class BaseModel(torch.nn.Module):
-    def load(self, path):
-        """Load model from file.
-
-        Args:
-            path (str): file path
-        """
-        parameters = torch.load(path, map_location=torch.device('cpu'))
-
-        if "optimizer" in parameters:
-            parameters = parameters["model"]
-
-        self.load_state_dict(parameters)

spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/aiofiles/__init__.py

@@ -1,22 +0,0 @@
-"""Utilities for asyncio-friendly file handling."""
-from .threadpool import (
-    open,
-    stdin,
-    stdout,
-    stderr,
-    stdin_bytes,
-    stdout_bytes,
-    stderr_bytes,
-)
-from . import tempfile
-
-__all__ = [
-    "open",
-    "tempfile",
-    "stdin",
-    "stdout",
-    "stderr",
-    "stdin_bytes",
-    "stdout_bytes",
-    "stderr_bytes",
-]

spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/aiohttp/__init__.py

@@ -1,216 +0,0 @@
-__version__ = "3.8.4"
-
-from typing import Tuple
-
-from . import hdrs as hdrs
-from .client import (
-    BaseConnector as BaseConnector,
-    ClientConnectionError as ClientConnectionError,
-    ClientConnectorCertificateError as ClientConnectorCertificateError,
-    ClientConnectorError as ClientConnectorError,
-    ClientConnectorSSLError as ClientConnectorSSLError,
-    ClientError as ClientError,
-    ClientHttpProxyError as ClientHttpProxyError,
-    ClientOSError as ClientOSError,
-    ClientPayloadError as ClientPayloadError,
-    ClientProxyConnectionError as ClientProxyConnectionError,
-    ClientRequest as ClientRequest,
-    ClientResponse as ClientResponse,
-    ClientResponseError as ClientResponseError,
-    ClientSession as ClientSession,
-    ClientSSLError as ClientSSLError,
-    ClientTimeout as ClientTimeout,
-    ClientWebSocketResponse as ClientWebSocketResponse,
-    ContentTypeError as ContentTypeError,
-    Fingerprint as Fingerprint,
-    InvalidURL as InvalidURL,
-    NamedPipeConnector as NamedPipeConnector,
-    RequestInfo as RequestInfo,
-    ServerConnectionError as ServerConnectionError,
-    ServerDisconnectedError as ServerDisconnectedError,
-    ServerFingerprintMismatch as ServerFingerprintMismatch,
-    ServerTimeoutError as ServerTimeoutError,
-    TCPConnector as TCPConnector,
-    TooManyRedirects as TooManyRedirects,
-    UnixConnector as UnixConnector,
-    WSServerHandshakeError as WSServerHandshakeError,
-    request as request,
-)
-from .cookiejar import CookieJar as CookieJar, DummyCookieJar as DummyCookieJar
-from .formdata import FormData as FormData
-from .helpers import BasicAuth, ChainMapProxy, ETag
-from .http import (
-    HttpVersion as HttpVersion,
-    HttpVersion10 as HttpVersion10,
-    HttpVersion11 as HttpVersion11,
-    WebSocketError as WebSocketError,
-    WSCloseCode as WSCloseCode,
-    WSMessage as WSMessage,
-    WSMsgType as WSMsgType,
-)
-from .multipart import (
-    BadContentDispositionHeader as BadContentDispositionHeader,
-    BadContentDispositionParam as BadContentDispositionParam,
-    BodyPartReader as BodyPartReader,
-    MultipartReader as MultipartReader,
-    MultipartWriter as MultipartWriter,
-    content_disposition_filename as content_disposition_filename,
-    parse_content_disposition as parse_content_disposition,
-)
-from .payload import (
-    PAYLOAD_REGISTRY as PAYLOAD_REGISTRY,
-    AsyncIterablePayload as AsyncIterablePayload,
-    BufferedReaderPayload as BufferedReaderPayload,
-    BytesIOPayload as BytesIOPayload,
-    BytesPayload as BytesPayload,
-    IOBasePayload as IOBasePayload,
-    JsonPayload as JsonPayload,
-    Payload as Payload,
-    StringIOPayload as StringIOPayload,
-    StringPayload as StringPayload,
-    TextIOPayload as TextIOPayload,
-    get_payload as get_payload,
-    payload_type as payload_type,
-)
-from .payload_streamer import streamer as streamer
-from .resolver import (
-    AsyncResolver as AsyncResolver,
-    DefaultResolver as DefaultResolver,
-    ThreadedResolver as ThreadedResolver,
-)
-from .streams import (
-    EMPTY_PAYLOAD as EMPTY_PAYLOAD,
-    DataQueue as DataQueue,
-    EofStream as EofStream,
-    FlowControlDataQueue as FlowControlDataQueue,
-    StreamReader as StreamReader,
-)
-from .tracing import (
-    TraceConfig as TraceConfig,
-    TraceConnectionCreateEndParams as TraceConnectionCreateEndParams,
-    TraceConnectionCreateStartParams as TraceConnectionCreateStartParams,
-    TraceConnectionQueuedEndParams as TraceConnectionQueuedEndParams,
-    TraceConnectionQueuedStartParams as TraceConnectionQueuedStartParams,
-    TraceConnectionReuseconnParams as TraceConnectionReuseconnParams,
-    TraceDnsCacheHitParams as TraceDnsCacheHitParams,
-    TraceDnsCacheMissParams as TraceDnsCacheMissParams,
-    TraceDnsResolveHostEndParams as TraceDnsResolveHostEndParams,
-    TraceDnsResolveHostStartParams as TraceDnsResolveHostStartParams,
-    TraceRequestChunkSentParams as TraceRequestChunkSentParams,
-    TraceRequestEndParams as TraceRequestEndParams,
-    TraceRequestExceptionParams as TraceRequestExceptionParams,
-    TraceRequestRedirectParams as TraceRequestRedirectParams,
-    TraceRequestStartParams as TraceRequestStartParams,
-    TraceResponseChunkReceivedParams as TraceResponseChunkReceivedParams,
-)
-
-__all__: Tuple[str, ...] = (
-    "hdrs",
-    # client
-    "BaseConnector",
-    "ClientConnectionError",
-    "ClientConnectorCertificateError",
-    "ClientConnectorError",
-    "ClientConnectorSSLError",
-    "ClientError",
-    "ClientHttpProxyError",
-    "ClientOSError",
-    "ClientPayloadError",
-    "ClientProxyConnectionError",
-    "ClientResponse",
-    "ClientRequest",
-    "ClientResponseError",
-    "ClientSSLError",
-    "ClientSession",
-    "ClientTimeout",
-    "ClientWebSocketResponse",
-    "ContentTypeError",
-    "Fingerprint",
-    "InvalidURL",
-    "RequestInfo",
-    "ServerConnectionError",
-    "ServerDisconnectedError",
-    "ServerFingerprintMismatch",
-    "ServerTimeoutError",
-    "TCPConnector",
-    "TooManyRedirects",
-    "UnixConnector",
-    "NamedPipeConnector",
-    "WSServerHandshakeError",
-    "request",
-    # cookiejar
-    "CookieJar",
-    "DummyCookieJar",
-    # formdata
-    "FormData",
-    # helpers
-    "BasicAuth",
-    "ChainMapProxy",
-    "ETag",
-    # http
-    "HttpVersion",
-    "HttpVersion10",
-    "HttpVersion11",
-    "WSMsgType",
-    "WSCloseCode",
-    "WSMessage",
-    "WebSocketError",
-    # multipart
-    "BadContentDispositionHeader",
-    "BadContentDispositionParam",
-    "BodyPartReader",
-    "MultipartReader",
-    "MultipartWriter",
-    "content_disposition_filename",
-    "parse_content_disposition",
-    # payload
-    "AsyncIterablePayload",
-    "BufferedReaderPayload",
-    "BytesIOPayload",
-    "BytesPayload",
-    "IOBasePayload",
-    "JsonPayload",
-    "PAYLOAD_REGISTRY",
-    "Payload",
-    "StringIOPayload",
-    "StringPayload",
-    "TextIOPayload",
-    "get_payload",
-    "payload_type",
-    # payload_streamer
-    "streamer",
-    # resolver
-    "AsyncResolver",
-    "DefaultResolver",
-    "ThreadedResolver",
-    # streams
-    "DataQueue",
-    "EMPTY_PAYLOAD",
-    "EofStream",
-    "FlowControlDataQueue",
-    "StreamReader",
-    # tracing
-    "TraceConfig",
-    "TraceConnectionCreateEndParams",
-    "TraceConnectionCreateStartParams",
-    "TraceConnectionQueuedEndParams",
-    "TraceConnectionQueuedStartParams",
-    "TraceConnectionReuseconnParams",
-    "TraceDnsCacheHitParams",
-    "TraceDnsCacheMissParams",
-    "TraceDnsResolveHostEndParams",
-    "TraceDnsResolveHostStartParams",
-    "TraceRequestChunkSentParams",
-    "TraceRequestEndParams",
-    "TraceRequestExceptionParams",
-    "TraceRequestRedirectParams",
-    "TraceRequestStartParams",
-    "TraceResponseChunkReceivedParams",
-)
-
-try:
-    from .worker import GunicornUVLoopWebWorker, GunicornWebWorker
-
-    __all__ += ("GunicornWebWorker", "GunicornUVLoopWebWorker")
-except ImportError:  # pragma: no cover
-    pass

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

@@ -1,65 +0,0 @@
-# SPDX-License-Identifier: MIT
-
-from attr import (
-    NOTHING,
-    Attribute,
-    AttrsInstance,
-    Factory,
-    _make_getattr,
-    assoc,
-    cmp_using,
-    define,
-    evolve,
-    field,
-    fields,
-    fields_dict,
-    frozen,
-    has,
-    make_class,
-    mutable,
-    resolve_types,
-    validate,
-)
-from attr._next_gen import asdict, astuple
-
-from . import converters, exceptions, filters, setters, validators
-
-
-__all__ = [
-    "__author__",
-    "__copyright__",
-    "__description__",
-    "__doc__",
-    "__email__",
-    "__license__",
-    "__title__",
-    "__url__",
-    "__version__",
-    "__version_info__",
-    "asdict",
-    "assoc",
-    "astuple",
-    "Attribute",
-    "AttrsInstance",
-    "cmp_using",
-    "converters",
-    "define",
-    "evolve",
-    "exceptions",
-    "Factory",
-    "field",
-    "fields_dict",
-    "fields",
-    "filters",
-    "frozen",
-    "has",
-    "make_class",
-    "mutable",
-    "NOTHING",
-    "resolve_types",
-    "setters",
-    "validate",
-    "validators",
-]
-
-__getattr__ = _make_getattr(__name__)

spaces/DarthVaderAI/Diffusion-Art/app.py

@@ -1,47 +0,0 @@
-import gradio as gr
-import torch
-
-from PIL import Image
-
-from PIL import Image
-from  Diffusion import diffusionandclipimagegenereation1
-
-device="cpu"
-
-source_img = gr.Image(source="upload", type="filepath", label="init_img | 256*256px")
-gallery = gr.Gallery(label="Generated images", show_label=False, elem_id="gallery").style(grid=[2], height="auto")
-
-ef resize(value,img):
-  #baseheight = value
-  img = Image.open(img)
-  #hpercent = (baseheight/float(img.size[1]))
-  #wsize = int((float(img.size[0])*float(hpercent)))
-  #img = img.resize((wsize,baseheight), Image.Resampling.LANCZOS)
-  img = img.resize((value,value), Image.Resampling.LANCZOS)
-  return img
-
-
-def infer(source_img, prompt, guide, steps, seed, strength): 
-    generator = torch.Generator('cpu').manual_seed(seed)
-    
-    source_image = resize(512, source_img)
-    source_image.save('source.png')
-    
-    images_list = img_pipe([prompt] * 2, init_image=source_image, strength=strength, guidance_scale=guide, num_inference_steps=steps)
-    images = []
-    safe_image = Image.open(r"unsafe.png")
-    
-    for i, image in enumerate(images_list["sample"]):
-        if(images_list["nsfw_content_detected"][i]):
-            images.append(safe_image)
-        else:
-            images.append(image)    
-    return images
-
-gr.Interface(fn=infer, inputs=[source_img,
-    "text",
-    gr.Slider(2, 15, value = 7, label = 'Guidence Scale'),
-    gr.Slider(10, 50, value = 25, step = 1, label = 'Number of Iterations'),
-    gr.Slider(label = "Seed", minimum = 0, maximum = 2147483647, step = 1, randomize = True),
-    gr.Slider(label='Strength', minimum = 0, maximum = 1, step = .05, value = .75)],
-    outputs=gallery,title=title,description=description, allow_flagging="manual", flagging_dir="flagged").queue(max_size=100).launch(enable_queue=True)

spaces/Datasculptor/3D-Room-Layout-Estimation_LGT-Net/Post-Porcessing.md

@@ -1,35 +0,0 @@
-# Post-Processing 
-## Step
-
-1. Simplify polygon by [DP algorithm](https://en.wikipedia.org/wiki/Ramer%E2%80%93Douglas%E2%80%93Peucker_algorithm)
-
-![img.png](src/fig/post_processing/img_0.png)
-   
-2. Detect occlusion, calculating box fill with 1 
-
-![img.png](src/fig/post_processing/img_1.png)
-
-3. Fill in reasonable sampling section
-
-![img.png](src/fig/post_processing/img_2.png)
-   
-4. Output processed polygon
-
-![img.png](src/fig/post_processing/img_3.png)
-
-## performance
-It works, and a performance comparison on the MatterportLayout dataset:
-
-| Method | 2D IoU(%)  | 3D IoU(%) | RMSE | $\mathbf{\delta_{1}}$ |
-|--|--|--|--|--|
-without post-proc    | 83.52 | 81.11 | 0.204 | 0.951 |
-original post-proc |83.12 | 80.71 | 0.230 | 0.936|\
-optimized  post-proc | 83.48 | 81.08| 0.214 | 0.940 |
-
-original:
-
-![img.png](src/fig/post_processing/original.png)
-
-optimized:
-
-![img.png](src/fig/post_processing/optimized.png)

spaces/Datasculptor/3D-Room-Layout-Estimation_LGT-Net/preprocessing/pano_lsd_align.py

@@ -1,911 +0,0 @@
-'''
-This script is helper function for preprocessing.
-Most of the code are converted from LayoutNet official's matlab code.
-All functions, naming rule and data flow follow official for easier
-converting and comparing.
-Code is not optimized for python or numpy yet.
-'''
-
-import sys
-import numpy as np
-from scipy.ndimage import map_coordinates
-import cv2
-from pylsd import lsd
-
-
-def computeUVN(n, in_, planeID):
-    '''
-    compute v given u and normal.
-    '''
-    if planeID == 2:
-        n = np.array([n[1], n[2], n[0]])
-    elif planeID == 3:
-        n = np.array([n[2], n[0], n[1]])
-    bc = n[0] * np.sin(in_) + n[1] * np.cos(in_)
-    bs = n[2]
-    out = np.arctan(-bc / (bs + 1e-9))
-    return out
-
-
-def computeUVN_vec(n, in_, planeID):
-    '''
-    vectorization version of computeUVN
-    @n         N x 3
-    @in_      MN x 1
-    @planeID   N
-    '''
-    n = n.copy()
-    if (planeID == 2).sum():
-        n[planeID == 2] = np.roll(n[planeID == 2], 2, axis=1)
-    if (planeID == 3).sum():
-        n[planeID == 3] = np.roll(n[planeID == 3], 1, axis=1)
-    n = np.repeat(n, in_.shape[0] // n.shape[0], axis=0)
-    assert n.shape[0] == in_.shape[0]
-    bc = n[:, [0]] * np.sin(in_) + n[:, [1]] * np.cos(in_)
-    bs = n[:, [2]]
-    out = np.arctan(-bc / (bs + 1e-9))
-    return out
-
-
-def xyz2uvN(xyz, planeID=1):
-    ID1 = (int(planeID) - 1 + 0) % 3
-    ID2 = (int(planeID) - 1 + 1) % 3
-    ID3 = (int(planeID) - 1 + 2) % 3
-    normXY = np.sqrt(xyz[:, [ID1]] ** 2 + xyz[:, [ID2]] ** 2)
-    normXY[normXY < 0.000001] = 0.000001
-    normXYZ = np.sqrt(xyz[:, [ID1]] ** 2 + xyz[:, [ID2]] ** 2 + xyz[:, [ID3]] ** 2)
-    v = np.arcsin(xyz[:, [ID3]] / normXYZ)
-    u = np.arcsin(xyz[:, [ID1]] / normXY)
-    valid = (xyz[:, [ID2]] < 0) & (u >= 0)
-    u[valid] = np.pi - u[valid]
-    valid = (xyz[:, [ID2]] < 0) & (u <= 0)
-    u[valid] = -np.pi - u[valid]
-    uv = np.hstack([u, v])
-    uv[np.isnan(uv[:, 0]), 0] = 0
-    return uv
-
-
-def uv2xyzN(uv, planeID=1):
-    ID1 = (int(planeID) - 1 + 0) % 3
-    ID2 = (int(planeID) - 1 + 1) % 3
-    ID3 = (int(planeID) - 1 + 2) % 3
-    xyz = np.zeros((uv.shape[0], 3))
-    xyz[:, ID1] = np.cos(uv[:, 1]) * np.sin(uv[:, 0])
-    xyz[:, ID2] = np.cos(uv[:, 1]) * np.cos(uv[:, 0])
-    xyz[:, ID3] = np.sin(uv[:, 1])
-    return xyz
-
-
-def uv2xyzN_vec(uv, planeID):
-    '''
-    vectorization version of uv2xyzN
-    @uv       N x 2
-    @planeID  N
-    '''
-    assert (planeID.astype(int) != planeID).sum() == 0
-    planeID = planeID.astype(int)
-    ID1 = (planeID - 1 + 0) % 3
-    ID2 = (planeID - 1 + 1) % 3
-    ID3 = (planeID - 1 + 2) % 3
-    ID = np.arange(len(uv))
-    xyz = np.zeros((len(uv), 3))
-    xyz[ID, ID1] = np.cos(uv[:, 1]) * np.sin(uv[:, 0])
-    xyz[ID, ID2] = np.cos(uv[:, 1]) * np.cos(uv[:, 0])
-    xyz[ID, ID3] = np.sin(uv[:, 1])
-    return xyz
-
-
-def warpImageFast(im, XXdense, YYdense):
-    minX = max(1., np.floor(XXdense.min()) - 1)
-    minY = max(1., np.floor(YYdense.min()) - 1)
-
-    maxX = min(im.shape[1], np.ceil(XXdense.max()) + 1)
-    maxY = min(im.shape[0], np.ceil(YYdense.max()) + 1)
-
-    im = im[int(round(minY-1)):int(round(maxY)),
-            int(round(minX-1)):int(round(maxX))]
-
-    assert XXdense.shape == YYdense.shape
-    out_shape = XXdense.shape
-    coordinates = [
-        (YYdense - minY).reshape(-1),
-        (XXdense - minX).reshape(-1),
-    ]
-    im_warp = np.stack([
-        map_coordinates(im[..., c], coordinates, order=1).reshape(out_shape)
-        for c in range(im.shape[-1])],
-        axis=-1)
-
-    return im_warp
-
-
-def rotatePanorama(img, vp=None, R=None):
-    '''
-    Rotate panorama
-        if R is given, vp (vanishing point) will be overlooked
-        otherwise R is computed from vp
-    '''
-    sphereH, sphereW, C = img.shape
-
-    # new uv coordinates
-    TX, TY = np.meshgrid(range(1, sphereW + 1), range(1, sphereH + 1))
-    TX = TX.reshape(-1, 1, order='F')
-    TY = TY.reshape(-1, 1, order='F')
-    ANGx = (TX - sphereW/2 - 0.5) / sphereW * np.pi * 2
-    ANGy = -(TY - sphereH/2 - 0.5) / sphereH * np.pi
-    uvNew = np.hstack([ANGx, ANGy])
-    xyzNew = uv2xyzN(uvNew, 1)
-
-    # rotation matrix
-    if R is None:
-        R = np.linalg.inv(vp.T)
-
-    xyzOld = np.linalg.solve(R, xyzNew.T).T
-    uvOld = xyz2uvN(xyzOld, 1)
-
-    Px = (uvOld[:, 0] + np.pi) / (2*np.pi) * sphereW + 0.5
-    Py = (-uvOld[:, 1] + np.pi/2) / np.pi * sphereH + 0.5
-
-    Px = Px.reshape(sphereH, sphereW, order='F')
-    Py = Py.reshape(sphereH, sphereW, order='F')
-
-    # boundary
-    imgNew = np.zeros((sphereH+2, sphereW+2, C), np.float64)
-    imgNew[1:-1, 1:-1, :] = img
-    imgNew[1:-1, 0, :] = img[:, -1, :]
-    imgNew[1:-1, -1, :] = img[:, 0, :]
-    imgNew[0, 1:sphereW//2+1, :] = img[0, sphereW-1:sphereW//2-1:-1, :]
-    imgNew[0, sphereW//2+1:-1, :] = img[0, sphereW//2-1::-1, :]
-    imgNew[-1, 1:sphereW//2+1, :] = img[-1, sphereW-1:sphereW//2-1:-1, :]
-    imgNew[-1, sphereW//2+1:-1, :] = img[0, sphereW//2-1::-1, :]
-    imgNew[0, 0, :] = img[0, 0, :]
-    imgNew[-1, -1, :] = img[-1, -1, :]
-    imgNew[0, -1, :] = img[0, -1, :]
-    imgNew[-1, 0, :] = img[-1, 0, :]
-
-    rotImg = warpImageFast(imgNew, Px+1, Py+1)
-
-    return rotImg
-
-
-def imgLookAt(im, CENTERx, CENTERy, new_imgH, fov):
-    sphereH = im.shape[0]
-    sphereW = im.shape[1]
-    warped_im = np.zeros((new_imgH, new_imgH, 3))
-    TX, TY = np.meshgrid(range(1, new_imgH + 1), range(1, new_imgH + 1))
-    TX = TX.reshape(-1, 1, order='F')
-    TY = TY.reshape(-1, 1, order='F')
-    TX = TX - 0.5 - new_imgH/2
-    TY = TY - 0.5 - new_imgH/2
-    r = new_imgH / 2 / np.tan(fov/2)
-
-    # convert to 3D
-    R = np.sqrt(TY ** 2 + r ** 2)
-    ANGy = np.arctan(- TY / r)
-    ANGy = ANGy + CENTERy
-
-    X = np.sin(ANGy) * R
-    Y = -np.cos(ANGy) * R
-    Z = TX
-
-    INDn = np.nonzero(np.abs(ANGy) > np.pi/2)
-
-    # project back to sphere
-    ANGx = np.arctan(Z / -Y)
-    RZY = np.sqrt(Z ** 2 + Y ** 2)
-    ANGy = np.arctan(X / RZY)
-
-    ANGx[INDn] = ANGx[INDn] + np.pi
-    ANGx = ANGx + CENTERx
-
-    INDy = np.nonzero(ANGy < -np.pi/2)
-    ANGy[INDy] = -np.pi - ANGy[INDy]
-    ANGx[INDy] = ANGx[INDy] + np.pi
-
-    INDx = np.nonzero(ANGx <= -np.pi);   ANGx[INDx] = ANGx[INDx] + 2 * np.pi
-    INDx = np.nonzero(ANGx >   np.pi);   ANGx[INDx] = ANGx[INDx] - 2 * np.pi
-    INDx = np.nonzero(ANGx >   np.pi);   ANGx[INDx] = ANGx[INDx] - 2 * np.pi
-    INDx = np.nonzero(ANGx >   np.pi);   ANGx[INDx] = ANGx[INDx] - 2 * np.pi
-
-    Px = (ANGx + np.pi) / (2*np.pi) * sphereW + 0.5
-    Py = ((-ANGy) + np.pi/2) / np.pi * sphereH + 0.5
-
-    INDxx = np.nonzero(Px < 1)
-    Px[INDxx] = Px[INDxx] + sphereW
-    im = np.concatenate([im, im[:, :2]], 1)
-
-    Px = Px.reshape(new_imgH, new_imgH, order='F')
-    Py = Py.reshape(new_imgH, new_imgH, order='F')
-
-    warped_im = warpImageFast(im, Px, Py)
-
-    return warped_im
-
-
-def separatePano(panoImg, fov, x, y, imgSize=320):
-    '''cut a panorama image into several separate views'''
-    assert x.shape == y.shape
-    if not isinstance(fov, np.ndarray):
-        fov = fov * np.ones_like(x)
-
-    sepScene = [
-        {
-            'img': imgLookAt(panoImg.copy(), xi, yi, imgSize, fovi),
-            'vx': xi,
-            'vy': yi,
-            'fov': fovi,
-            'sz': imgSize,
-        }
-        for xi, yi, fovi in zip(x, y, fov)
-    ]
-
-    return sepScene
-
-
-def lsdWrap(img):
-    '''
-    Opencv implementation of
-    Rafael Grompone von Gioi, Jérémie Jakubowicz, Jean-Michel Morel, and Gregory Randall,
-    LSD: a Line Segment Detector, Image Processing On Line, vol. 2012.
-    [Rafael12] http://www.ipol.im/pub/art/2012/gjmr-lsd/?utm_source=doi
-    @img
-        input image
-    '''
-    if len(img.shape) == 3:
-        img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
-
-    lines = lsd(img, quant=0.7)
-    if lines is None:
-        return np.zeros_like(img), np.array([])
-    edgeMap = np.zeros_like(img)
-    for i in range(lines.shape[0]):
-        pt1 = (int(lines[i, 0]), int(lines[i, 1]))
-        pt2 = (int(lines[i, 2]), int(lines[i, 3]))
-        width = lines[i, 4]
-        cv2.line(edgeMap, pt1, pt2, 255, int(np.ceil(width / 2)))
-    edgeList = np.concatenate([lines, np.ones_like(lines[:, :2])], 1)
-    return edgeMap, edgeList
-
-
-def edgeFromImg2Pano(edge):
-    edgeList = edge['edgeLst']
-    if len(edgeList) == 0:
-        return np.array([])
-
-    vx = edge['vx']
-    vy = edge['vy']
-    fov = edge['fov']
-    imH, imW = edge['img'].shape
-
-    R = (imW/2) / np.tan(fov/2)
-
-    # im is the tangent plane, contacting with ball at [x0 y0 z0]
-    x0 = R * np.cos(vy) * np.sin(vx)
-    y0 = R * np.cos(vy) * np.cos(vx)
-    z0 = R * np.sin(vy)
-    vecposX = np.array([np.cos(vx), -np.sin(vx), 0])
-    vecposY = np.cross(np.array([x0, y0, z0]), vecposX)
-    vecposY = vecposY / np.sqrt(vecposY @ vecposY.T)
-    vecposX = vecposX.reshape(1, -1)
-    vecposY = vecposY.reshape(1, -1)
-    Xc = (0 + imW-1) / 2
-    Yc = (0 + imH-1) / 2
-
-    vecx1 = edgeList[:, [0]] - Xc
-    vecy1 = edgeList[:, [1]] - Yc
-    vecx2 = edgeList[:, [2]] - Xc
-    vecy2 = edgeList[:, [3]] - Yc
-
-    vec1 = np.tile(vecx1, [1, 3]) * vecposX + np.tile(vecy1, [1, 3]) * vecposY
-    vec2 = np.tile(vecx2, [1, 3]) * vecposX + np.tile(vecy2, [1, 3]) * vecposY
-    coord1 = [[x0, y0, z0]] + vec1
-    coord2 = [[x0, y0, z0]] + vec2
-
-    normal = np.cross(coord1, coord2, axis=1)
-    normal = normal / np.linalg.norm(normal, axis=1, keepdims=True)
-
-    panoList = np.hstack([normal, coord1, coord2, edgeList[:, [-1]]])
-
-    return panoList
-
-
-def _intersection(range1, range2):
-    if range1[1] < range1[0]:
-        range11 = [range1[0], 1]
-        range12 = [0, range1[1]]
-    else:
-        range11 = range1
-        range12 = [0, 0]
-
-    if range2[1] < range2[0]:
-        range21 = [range2[0], 1]
-        range22 = [0, range2[1]]
-    else:
-        range21 = range2
-        range22 = [0, 0]
-
-    b = max(range11[0], range21[0]) < min(range11[1], range21[1])
-    if b:
-        return b
-    b2 = max(range12[0], range22[0]) < min(range12[1], range22[1])
-    b = b or b2
-    return b
-
-
-def _insideRange(pt, range):
-    if range[1] > range[0]:
-        b = pt >= range[0] and pt <= range[1]
-    else:
-        b1 = pt >= range[0] and pt <= 1
-        b2 = pt >= 0 and pt <= range[1]
-        b = b1 or b2
-    return b
-
-
-def combineEdgesN(edges):
-    '''
-    Combine some small line segments, should be very conservative
-    OUTPUT
-        lines: combined line segments
-        ori_lines: original line segments
-        line format [nx ny nz projectPlaneID umin umax LSfov score]
-    '''
-    arcList = []
-    for edge in edges:
-        panoLst = edge['panoLst']
-        if len(panoLst) == 0:
-            continue
-        arcList.append(panoLst)
-    arcList = np.vstack(arcList)
-
-    # ori lines
-    numLine = len(arcList)
-    ori_lines = np.zeros((numLine, 8))
-    areaXY = np.abs(arcList[:, 2])
-    areaYZ = np.abs(arcList[:, 0])
-    areaZX = np.abs(arcList[:, 1])
-    planeIDs = np.argmax(np.stack([areaXY, areaYZ, areaZX], -1), 1) + 1  # XY YZ ZX
-
-    for i in range(numLine):
-        ori_lines[i, :3] = arcList[i, :3]
-        ori_lines[i, 3] = planeIDs[i]
-        coord1 = arcList[i, 3:6]
-        coord2 = arcList[i, 6:9]
-        uv = xyz2uvN(np.stack([coord1, coord2]), planeIDs[i])
-        umax = uv[:, 0].max() + np.pi
-        umin = uv[:, 0].min() + np.pi
-        if umax - umin > np.pi:
-            ori_lines[i, 4:6] = np.array([umax, umin]) / 2 / np.pi
-        else:
-            ori_lines[i, 4:6] = np.array([umin, umax]) / 2 / np.pi
-        ori_lines[i, 6] = np.arccos((
-            np.dot(coord1, coord2) / (np.linalg.norm(coord1) * np.linalg.norm(coord2))
-            ).clip(-1, 1))
-        ori_lines[i, 7] = arcList[i, 9]
-
-    # additive combination
-    lines = ori_lines.copy()
-    for _ in range(3):
-        numLine = len(lines)
-        valid_line = np.ones(numLine, bool)
-        for i in range(numLine):
-            if not valid_line[i]:
-                continue
-            dotProd = (lines[:, :3] * lines[[i], :3]).sum(1)
-            valid_curr = np.logical_and((np.abs(dotProd) > np.cos(np.pi / 180)), valid_line)
-            valid_curr[i] = False
-            for j in np.nonzero(valid_curr)[0]:
-                range1 = lines[i, 4:6]
-                range2 = lines[j, 4:6]
-                valid_rag = _intersection(range1, range2)
-                if not valid_rag:
-                    continue
-
-                # combine
-                I = np.argmax(np.abs(lines[i, :3]))
-                if lines[i, I] * lines[j, I] > 0:
-                    nc = lines[i, :3] * lines[i, 6] + lines[j, :3] * lines[j, 6]
-                else:
-                    nc = lines[i, :3] * lines[i, 6] - lines[j, :3] * lines[j, 6]
-                nc = nc / np.linalg.norm(nc)
-
-                if _insideRange(range1[0], range2):
-                    nrmin = range2[0]
-                else:
-                    nrmin = range1[0]
-
-                if _insideRange(range1[1], range2):
-                    nrmax = range2[1]
-                else:
-                    nrmax = range1[1]
-
-                u = np.array([[nrmin], [nrmax]]) * 2 * np.pi - np.pi
-                v = computeUVN(nc, u, lines[i, 3])
-                xyz = uv2xyzN(np.hstack([u, v]), lines[i, 3])
-                l = np.arccos(np.dot(xyz[0, :], xyz[1, :]).clip(-1, 1))
-                scr = (lines[i,6]*lines[i,7] + lines[j,6]*lines[j,7]) / (lines[i,6]+lines[j,6])
-
-                lines[i] = [*nc, lines[i, 3], nrmin, nrmax, l, scr]
-                valid_line[j] = False
-
-        lines = lines[valid_line]
-
-    return lines, ori_lines
-
-
-def icosahedron2sphere(level):
-    # this function use a icosahedron to sample uniformly on a sphere
-    a = 2 / (1 + np.sqrt(5))
-    M = np.array([
-        0, a, -1, a, 1, 0, -a, 1, 0,
-        0, a, 1, -a, 1, 0, a, 1, 0,
-        0, a, 1, 0, -a, 1, -1, 0, a,
-        0, a, 1, 1, 0, a, 0, -a, 1,
-        0, a, -1, 0, -a, -1, 1, 0, -a,
-        0, a, -1, -1, 0, -a, 0, -a, -1,
-        0, -a, 1, a, -1, 0, -a, -1, 0,
-        0, -a, -1, -a, -1, 0, a, -1, 0,
-        -a, 1, 0, -1, 0, a, -1, 0, -a,
-        -a, -1, 0, -1, 0, -a, -1, 0, a,
-        a, 1, 0, 1, 0, -a, 1, 0, a,
-        a, -1, 0, 1, 0, a, 1, 0, -a,
-        0, a, 1, -1, 0, a, -a, 1, 0,
-        0, a, 1, a, 1, 0, 1, 0, a,
-        0, a, -1, -a, 1, 0, -1, 0, -a,
-        0, a, -1, 1, 0, -a, a, 1, 0,
-        0, -a, -1, -1, 0, -a, -a, -1, 0,
-        0, -a, -1, a, -1, 0, 1, 0, -a,
-        0, -a, 1, -a, -1, 0, -1, 0, a,
-        0, -a, 1, 1, 0, a, a, -1, 0])
-
-    coor = M.T.reshape(3, 60, order='F').T
-    coor, idx = np.unique(coor, return_inverse=True, axis=0)
-    tri = idx.reshape(3, 20, order='F').T
-
-    # extrude
-    coor = list(coor / np.tile(np.linalg.norm(coor, axis=1, keepdims=True), (1, 3)))
-
-    for _ in range(level):
-        triN = []
-        for t in range(len(tri)):
-            n = len(coor)
-            coor.append((coor[tri[t, 0]] + coor[tri[t, 1]]) / 2)
-            coor.append((coor[tri[t, 1]] + coor[tri[t, 2]]) / 2)
-            coor.append((coor[tri[t, 2]] + coor[tri[t, 0]]) / 2)
-
-            triN.append([n, tri[t, 0], n+2])
-            triN.append([n, tri[t, 1], n+1])
-            triN.append([n+1, tri[t, 2], n+2])
-            triN.append([n, n+1, n+2])
-        tri = np.array(triN)
-
-        # uniquefy
-        coor, idx = np.unique(coor, return_inverse=True, axis=0)
-        tri = idx[tri]
-
-        # extrude
-        coor = list(coor / np.tile(np.sqrt(np.sum(coor * coor, 1, keepdims=True)), (1, 3)))
-
-    return np.array(coor), np.array(tri)
-
-
-def curveFitting(inputXYZ, weight):
-    '''
-    @inputXYZ: N x 3
-    @weight  : N x 1
-    '''
-    l = np.linalg.norm(inputXYZ, axis=1, keepdims=True)
-    inputXYZ = inputXYZ / l
-    weightXYZ = inputXYZ * weight
-    XX = np.sum(weightXYZ[:, 0] ** 2)
-    YY = np.sum(weightXYZ[:, 1] ** 2)
-    ZZ = np.sum(weightXYZ[:, 2] ** 2)
-    XY = np.sum(weightXYZ[:, 0] * weightXYZ[:, 1])
-    YZ = np.sum(weightXYZ[:, 1] * weightXYZ[:, 2])
-    ZX = np.sum(weightXYZ[:, 2] * weightXYZ[:, 0])
-
-    A = np.array([
-        [XX, XY, ZX],
-        [XY, YY, YZ],
-        [ZX, YZ, ZZ]])
-    U, S, Vh = np.linalg.svd(A)
-    outputNM = Vh[-1, :]
-    outputNM = outputNM / np.linalg.norm(outputNM)
-
-    return outputNM
-
-
-def sphereHoughVote(segNormal, segLength, segScores, binRadius, orthTolerance, candiSet, force_unempty=True):
-    # initial guess
-    numLinesg = len(segNormal)
-
-    voteBinPoints = candiSet.copy()
-    voteBinPoints = voteBinPoints[~(voteBinPoints[:,2] < 0)]
-    reversValid = (segNormal[:, 2] < 0).reshape(-1)
-    segNormal[reversValid] = -segNormal[reversValid]
-
-    voteBinUV = xyz2uvN(voteBinPoints)
-    numVoteBin = len(voteBinPoints)
-    voteBinValues = np.zeros(numVoteBin)
-    for i in range(numLinesg):
-        tempNorm = segNormal[[i]]
-        tempDots = (voteBinPoints * tempNorm).sum(1)
-
-        valid = np.abs(tempDots) < np.cos((90 - binRadius) * np.pi / 180)
-
-        voteBinValues[valid] = voteBinValues[valid] + segScores[i] * segLength[i]
-
-    checkIDs1 = np.nonzero(voteBinUV[:, [1]] > np.pi / 3)[0]
-    voteMax = 0
-    checkID1Max = 0
-    checkID2Max = 0
-    checkID3Max = 0
-
-    for j in range(len(checkIDs1)):
-        checkID1 = checkIDs1[j]
-        vote1 = voteBinValues[checkID1]
-        if voteBinValues[checkID1] == 0 and force_unempty:
-            continue
-        checkNormal = voteBinPoints[[checkID1]]
-        dotProduct = (voteBinPoints * checkNormal).sum(1)
-        checkIDs2 = np.nonzero(np.abs(dotProduct) < np.cos((90 - orthTolerance) * np.pi / 180))[0]
-
-        for i in range(len(checkIDs2)):
-            checkID2 = checkIDs2[i]
-            if voteBinValues[checkID2] == 0 and force_unempty:
-                continue
-            vote2 = vote1 + voteBinValues[checkID2]
-            cpv = np.cross(voteBinPoints[checkID1], voteBinPoints[checkID2]).reshape(1, 3)
-            cpn = np.linalg.norm(cpv)
-            dotProduct = (voteBinPoints * cpv).sum(1) / cpn
-            checkIDs3 = np.nonzero(np.abs(dotProduct) > np.cos(orthTolerance * np.pi / 180))[0]
-
-            for k in range(len(checkIDs3)):
-                checkID3 = checkIDs3[k]
-                if voteBinValues[checkID3] == 0 and force_unempty:
-                    continue
-                vote3 = vote2 + voteBinValues[checkID3]
-                if vote3 > voteMax:
-                    lastStepCost = vote3 - voteMax
-                    if voteMax != 0:
-                        tmp = (voteBinPoints[[checkID1Max, checkID2Max, checkID3Max]] * \
-                               voteBinPoints[[checkID1, checkID2, checkID3]]).sum(1)
-                        lastStepAngle = np.arccos(tmp.clip(-1, 1))
-                    else:
-                        lastStepAngle = np.zeros(3)
-
-                    checkID1Max = checkID1
-                    checkID2Max = checkID2
-                    checkID3Max = checkID3
-
-                    voteMax = vote3
-
-    if checkID1Max == 0:
-        print('[WARN] sphereHoughVote: no orthogonal voting exist', file=sys.stderr)
-        return None, 0, 0
-    initXYZ = voteBinPoints[[checkID1Max, checkID2Max, checkID3Max]]
-
-    # refine
-    refiXYZ = np.zeros((3, 3))
-    dotprod = (segNormal * initXYZ[[0]]).sum(1)
-    valid = np.abs(dotprod) < np.cos((90 - binRadius) * np.pi / 180)
-    validNm = segNormal[valid]
-    validWt = segLength[valid] * segScores[valid]
-    validWt = validWt / validWt.max()
-    refiNM = curveFitting(validNm, validWt)
-    refiXYZ[0] = refiNM.copy()
-
-    dotprod = (segNormal * initXYZ[[1]]).sum(1)
-    valid = np.abs(dotprod) < np.cos((90 - binRadius) * np.pi / 180)
-    validNm = segNormal[valid]
-    validWt = segLength[valid] * segScores[valid]
-    validWt = validWt / validWt.max()
-    validNm = np.vstack([validNm, refiXYZ[[0]]])
-    validWt = np.vstack([validWt, validWt.sum(0, keepdims=1) * 0.1])
-    refiNM = curveFitting(validNm, validWt)
-    refiXYZ[1] = refiNM.copy()
-
-    refiNM = np.cross(refiXYZ[0], refiXYZ[1])
-    refiXYZ[2] = refiNM / np.linalg.norm(refiNM)
-
-    return refiXYZ, lastStepCost, lastStepAngle
-
-
-def findMainDirectionEMA(lines):
-    '''compute vp from set of lines'''
-
-    # initial guess
-    segNormal = lines[:, :3]
-    segLength = lines[:, [6]]
-    segScores = np.ones((len(lines), 1))
-
-    shortSegValid = (segLength < 5 * np.pi / 180).reshape(-1)
-    segNormal = segNormal[~shortSegValid, :]
-    segLength = segLength[~shortSegValid]
-    segScores = segScores[~shortSegValid]
-
-    numLinesg = len(segNormal)
-    candiSet, tri = icosahedron2sphere(3)
-    ang = np.arccos((candiSet[tri[0,0]] * candiSet[tri[0,1]]).sum().clip(-1, 1)) / np.pi * 180
-    binRadius = ang / 2
-    initXYZ, score, angle = sphereHoughVote(segNormal, segLength, segScores, 2*binRadius, 2, candiSet)
-
-    if initXYZ is None:
-        print('[WARN] findMainDirectionEMA: initial failed', file=sys.stderr)
-        return None, score, angle
-
-    # iterative refine
-    iter_max = 3
-    candiSet, tri = icosahedron2sphere(5)
-    numCandi = len(candiSet)
-    angD = np.arccos((candiSet[tri[0, 0]] * candiSet[tri[0, 1]]).sum().clip(-1, 1)) / np.pi * 180
-    binRadiusD = angD / 2
-    curXYZ = initXYZ.copy()
-    tol = np.linspace(4*binRadius, 4*binRadiusD, iter_max)  # shrink down ls and candi
-    for it in range(iter_max):
-        dot1 = np.abs((segNormal * curXYZ[[0]]).sum(1))
-        dot2 = np.abs((segNormal * curXYZ[[1]]).sum(1))
-        dot3 = np.abs((segNormal * curXYZ[[2]]).sum(1))
-        valid1 = dot1 < np.cos((90 - tol[it]) * np.pi / 180)
-        valid2 = dot2 < np.cos((90 - tol[it]) * np.pi / 180)
-        valid3 = dot3 < np.cos((90 - tol[it]) * np.pi / 180)
-        valid = valid1 | valid2 | valid3
-
-        if np.sum(valid) == 0:
-            print('[WARN] findMainDirectionEMA: zero line segments for voting', file=sys.stderr)
-            break
-
-        subSegNormal = segNormal[valid]
-        subSegLength = segLength[valid]
-        subSegScores = segScores[valid]
-
-        dot1 = np.abs((candiSet * curXYZ[[0]]).sum(1))
-        dot2 = np.abs((candiSet * curXYZ[[1]]).sum(1))
-        dot3 = np.abs((candiSet * curXYZ[[2]]).sum(1))
-        valid1 = dot1 > np.cos(tol[it] * np.pi / 180)
-        valid2 = dot2 > np.cos(tol[it] * np.pi / 180)
-        valid3 = dot3 > np.cos(tol[it] * np.pi / 180)
-        valid = valid1 | valid2 | valid3
-
-        if np.sum(valid) == 0:
-            print('[WARN] findMainDirectionEMA: zero line segments for voting', file=sys.stderr)
-            break
-
-        subCandiSet = candiSet[valid]
-
-        tcurXYZ, _, _ = sphereHoughVote(subSegNormal, subSegLength, subSegScores, 2*binRadiusD, 2, subCandiSet)
-
-        if tcurXYZ is None:
-            print('[WARN] findMainDirectionEMA: no answer found', file=sys.stderr)
-            break
-        curXYZ = tcurXYZ.copy()
-
-    mainDirect = curXYZ.copy()
-    mainDirect[0] = mainDirect[0] * np.sign(mainDirect[0,2])
-    mainDirect[1] = mainDirect[1] * np.sign(mainDirect[1,2])
-    mainDirect[2] = mainDirect[2] * np.sign(mainDirect[2,2])
-
-    uv = xyz2uvN(mainDirect)
-    I1 = np.argmax(uv[:,1])
-    J = np.setdiff1d(np.arange(3), I1)
-    I2 = np.argmin(np.abs(np.sin(uv[J,0])))
-    I2 = J[I2]
-    I3 = np.setdiff1d(np.arange(3), np.hstack([I1, I2]))
-    mainDirect = np.vstack([mainDirect[I1], mainDirect[I2], mainDirect[I3]])
-
-    mainDirect[0] = mainDirect[0] * np.sign(mainDirect[0,2])
-    mainDirect[1] = mainDirect[1] * np.sign(mainDirect[1,1])
-    mainDirect[2] = mainDirect[2] * np.sign(mainDirect[2,0])
-
-    mainDirect = np.vstack([mainDirect, -mainDirect])
-
-    return mainDirect, score, angle
-
-
-def multi_linspace(start, stop, num):
-    div = (num - 1)
-    y = np.arange(0, num, dtype=np.float64)
-    steps = (stop - start) / div
-    return steps.reshape(-1, 1) * y + start.reshape(-1, 1)
-
-
-def assignVanishingType(lines, vp, tol, area=10):
-    numLine = len(lines)
-    numVP = len(vp)
-    typeCost = np.zeros((numLine, numVP))
-    # perpendicular
-    for vid in range(numVP):
-        cosint = (lines[:, :3] * vp[[vid]]).sum(1)
-        typeCost[:, vid] = np.arcsin(np.abs(cosint).clip(-1, 1))
-
-    # infinity
-    u = np.stack([lines[:, 4], lines[:, 5]], -1)
-    u = u.reshape(-1, 1) * 2 * np.pi - np.pi
-    v = computeUVN_vec(lines[:, :3], u, lines[:, 3])
-    xyz = uv2xyzN_vec(np.hstack([u, v]), np.repeat(lines[:, 3], 2))
-    xyz = multi_linspace(xyz[0::2].reshape(-1), xyz[1::2].reshape(-1), 100)
-    xyz = np.vstack([blk.T for blk in np.split(xyz, numLine)])
-    xyz = xyz / np.linalg.norm(xyz, axis=1, keepdims=True)
-    for vid in range(numVP):
-        ang = np.arccos(np.abs((xyz * vp[[vid]]).sum(1)).clip(-1, 1))
-        notok = (ang < area * np.pi / 180).reshape(numLine, 100).sum(1) != 0
-        typeCost[notok, vid] = 100
-
-    I = typeCost.min(1)
-    tp = typeCost.argmin(1)
-    tp[I > tol] = numVP + 1
-
-    return tp, typeCost
-
-
-def refitLineSegmentB(lines, vp, vpweight=0.1):
-    '''
-    Refit direction of line segments
-    INPUT:
-        lines: original line segments
-        vp: vannishing point
-        vpweight: if set to 0, lines will not change; if set to inf, lines will
-                  be forced to pass vp
-    '''
-    numSample = 100
-    numLine = len(lines)
-    xyz = np.zeros((numSample+1, 3))
-    wei = np.ones((numSample+1, 1))
-    wei[numSample] = vpweight * numSample
-    lines_ali = lines.copy()
-    for i in range(numLine):
-        n = lines[i, :3]
-        sid = lines[i, 4] * 2 * np.pi
-        eid = lines[i, 5] * 2 * np.pi
-        if eid < sid:
-            x = np.linspace(sid, eid + 2 * np.pi, numSample) % (2 * np.pi)
-        else:
-            x = np.linspace(sid, eid, numSample)
-        u = -np.pi + x.reshape(-1, 1)
-        v = computeUVN(n, u, lines[i, 3])
-        xyz[:numSample] = uv2xyzN(np.hstack([u, v]), lines[i, 3])
-        xyz[numSample] = vp
-        outputNM = curveFitting(xyz, wei)
-        lines_ali[i, :3] = outputNM
-
-    return lines_ali
-
-
-def paintParameterLine(parameterLine, width, height):
-    lines = parameterLine.copy()
-    panoEdgeC = np.zeros((height, width))
-
-    num_sample = max(height, width)
-    for i in range(len(lines)):
-        n = lines[i, :3]
-        sid = lines[i, 4] * 2 * np.pi
-        eid = lines[i, 5] * 2 * np.pi
-        if eid < sid:
-            x = np.linspace(sid, eid + 2 * np.pi, num_sample)
-            x = x % (2 * np.pi)
-        else:
-            x = np.linspace(sid, eid, num_sample)
-        u = -np.pi + x.reshape(-1, 1)
-        v = computeUVN(n, u, lines[i, 3])
-        xyz = uv2xyzN(np.hstack([u, v]), lines[i, 3])
-        uv = xyz2uvN(xyz, 1)
-        m = np.minimum(np.floor((uv[:,0] + np.pi) / (2 * np.pi) * width) + 1,
-            width).astype(np.int32)
-        n = np.minimum(np.floor(((np.pi / 2) - uv[:, 1]) / np.pi * height) + 1,
-            height).astype(np.int32)
-        panoEdgeC[n-1, m-1] = i
-
-    return panoEdgeC
-
-
-def panoEdgeDetection(img, viewSize=320, qError=0.7, refineIter=3):
-    '''
-    line detection on panorama
-       INPUT:
-           img: image waiting for detection, double type, range 0~1
-           viewSize: image size of croped views
-           qError: set smaller if more line segment wanted
-       OUTPUT:
-           oLines: detected line segments
-           vp: vanishing point
-           views: separate views of panorama
-           edges: original detection of line segments in separate views
-           panoEdge: image for visualize line segments
-    '''
-    cutSize = viewSize
-    fov = np.pi / 3
-    xh = np.arange(-np.pi, np.pi*5/6, np.pi/6)
-    yh = np.zeros(xh.shape[0])
-    xp = np.array([-3/3, -2/3, -1/3, 0/3,  1/3, 2/3, -3/3, -2/3, -1/3,  0/3,  1/3,  2/3]) * np.pi
-    yp = np.array([ 1/4,  1/4,  1/4, 1/4,  1/4, 1/4, -1/4, -1/4, -1/4, -1/4, -1/4, -1/4]) * np.pi
-    x = np.concatenate([xh, xp, [0, 0]])
-    y = np.concatenate([yh, yp, [np.pi/2., -np.pi/2]])
-
-    sepScene = separatePano(img.copy(), fov, x, y, cutSize)
-    edge = []
-    for i, scene in enumerate(sepScene):
-        edgeMap, edgeList = lsdWrap(scene['img'])
-        edge.append({
-            'img': edgeMap,
-            'edgeLst': edgeList,
-            'vx': scene['vx'],
-            'vy': scene['vy'],
-            'fov': scene['fov'],
-        })
-        edge[-1]['panoLst'] = edgeFromImg2Pano(edge[-1])
-    lines, olines = combineEdgesN(edge)
-
-    clines = lines.copy()
-    for _ in range(refineIter):
-        mainDirect, score, angle = findMainDirectionEMA(clines)
-
-        tp, typeCost = assignVanishingType(lines, mainDirect[:3], 0.1, 10)
-        lines1 = lines[tp==0]
-        lines2 = lines[tp==1]
-        lines3 = lines[tp==2]
-
-        lines1rB = refitLineSegmentB(lines1, mainDirect[0], 0)
-        lines2rB = refitLineSegmentB(lines2, mainDirect[1], 0)
-        lines3rB = refitLineSegmentB(lines3, mainDirect[2], 0)
-
-        clines = np.vstack([lines1rB, lines2rB, lines3rB])
-
-    panoEdge1r = paintParameterLine(lines1rB, img.shape[1], img.shape[0])
-    panoEdge2r = paintParameterLine(lines2rB, img.shape[1], img.shape[0])
-    panoEdge3r = paintParameterLine(lines3rB, img.shape[1], img.shape[0])
-    panoEdger = np.stack([panoEdge1r, panoEdge2r, panoEdge3r], -1)
-
-    # output
-    olines = clines
-    vp = mainDirect
-    views = sepScene
-    edges = edge
-    panoEdge = panoEdger
-
-    return olines, vp, views, edges, panoEdge, score, angle
-
-
-if __name__ == '__main__':
-
-    # disable OpenCV3's non thread safe OpenCL option
-    cv2.ocl.setUseOpenCL(False)
-
-    import os
-    import argparse
-    import PIL
-    from PIL import Image
-    import time
-
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--i', required=True)
-    parser.add_argument('--o_prefix', required=True)
-    parser.add_argument('--qError', default=0.7, type=float)
-    parser.add_argument('--refineIter', default=3, type=int)
-    args = parser.parse_args()
-
-    # Read image
-    img_ori = np.array(Image.open(args.i).resize((1024, 512)))
-
-    # Vanishing point estimation & Line segments detection
-    s_time = time.time()
-    olines, vp, views, edges, panoEdge, score, angle = panoEdgeDetection(img_ori,
-                                                                         qError=args.qError,
-                                                                         refineIter=args.refineIter)
-    print('Elapsed time: %.2f' % (time.time() - s_time))
-    panoEdge = (panoEdge > 0)
-
-    print('Vanishing point:')
-    for v in vp[2::-1]:
-        print('%.6f %.6f %.6f' % tuple(v))
-
-    # Visualization
-    edg = rotatePanorama(panoEdge.astype(np.float64), vp[2::-1])
-    img = rotatePanorama(img_ori / 255.0, vp[2::-1])
-    one = img.copy() * 0.5
-    one[(edg > 0.5).sum(-1) > 0] = 0
-    one[edg[..., 0] > 0.5, 0] = 1
-    one[edg[..., 1] > 0.5, 1] = 1
-    one[edg[..., 2] > 0.5, 2] = 1
-    Image.fromarray((edg * 255).astype(np.uint8)).save('%s_edg.png' % args.o_prefix)
-    Image.fromarray((img * 255).astype(np.uint8)).save('%s_img.png' % args.o_prefix)
-    Image.fromarray((one * 255).astype(np.uint8)).save('%s_one.png' % args.o_prefix)

spaces/Datasculptor/DescriptionGPT/detic/__init__.py

@@ -1,19 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-from .modeling.meta_arch import custom_rcnn
-from .modeling.roi_heads import detic_roi_heads
-from .modeling.roi_heads import res5_roi_heads
-from .modeling.backbone import swintransformer
-from .modeling.backbone import timm
-
-
-from .data.datasets import lvis_v1
-from .data.datasets import imagenet
-from .data.datasets import cc
-from .data.datasets import objects365
-from .data.datasets import oid
-from .data.datasets import coco_zeroshot
-
-try:
-    from .modeling.meta_arch import d2_deformable_detr
-except:
-    pass