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spaces/1acneusushi/gradio-2dmoleculeeditor/data/El Inolvidable Simon Birch [DVDRIP][.Spanish.].por.GammaRay.avi.md

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-<h1>El Inolvidable Simon Birch: A Heartwarming Story of Faith and Friendship</h1>
-<p>Have you ever watched a movie that made you laugh, cry, and think at the same time? A movie that touched your heart and inspired your soul? A movie that showed you the beauty of life and the power of faith? If not, then you should definitely watch El Inolvidable Simon Birch, a 1998 American comedy-drama film based on the novel A Prayer for Owen Meany by John Irving. In this article, I will tell you what this movie is about, who are the main characters, what are the themes and messages, and why you should watch it.</p>
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-<h2>Introduction</h2>
-<h3>What is the movie about?</h3>
-<p>El Inolvidable Simon Birch is a movie about a boy named Simon Birch who was born with a rare condition that made him very small and weak. Despite his physical limitations, he has a strong spirit and a firm belief that God has a special plan for him. He lives in a small town in New Hampshire in the 1960s with his parents who don't care much about him. His only friend is Joe Wenteworth, a boy who was born out of wedlock and doesn't know who his father is. Together, they go through many adventures and challenges as they try to find their purpose in life.</p>
-<h3>Who are the main characters?</h3>
-<p>The main characters of the movie are:</p>
-<ul>
-<li>Simon Birch (played by Ian Michael Smith): The protagonist of the movie. He is a 12-year-old boy who suffers from Morquio syndrome, a rare genetic disorder that affects his growth and development. He is very smart, witty, and courageous. He believes that he is God's instrument and that he has a destiny to fulfill.</li>
-<li>Joe Wenteworth (played by Joseph Mazzello): The narrator and deuteragonist of the movie. He is Simon's best friend and confidant. He is an illegitimate child who lives with his single mother Rebecca. He is loyal, kind, and protective of Simon. He is also curious about his father's identity.</li>
-<li>Rebecca Wenteworth (played by Ashley Judd): Joe's mother and Simon's surrogate mother. She is a beautiful, loving, and independent woman who works as a librarian. She loves her son unconditionally and supports his friendship with Simon. She also has a secret affair with Ben Goodrich, the town's baseball coach.</li>
-<li>Ben Goodrich (played by Oliver Platt): Rebecca's lover and Joe's potential father. He is a friendly, funny, and caring man who works as a baseball coach at the local school. He has a good relationship with Joe and Simon and treats them like his own sons.</li>
-<li>Reverend Russell (played by David Strathairn): The town's minister and antagonist of the movie. He is a strict, stern, and hypocritical man who dislikes Simon for his unconventional views on religion. He tries to prevent Simon from participating in the church activities and often clashes with him.</li>
-</ul>
-<h3>Why is it called El Inolvidable Simon Birch?</h3>
-<p>The movie is called El Inolvidable Simon Birch because it is the Spanish title of the film. The original title was Simon Birch, but it was changed to El Inolvidable Simon Birch for the Spanish-speaking markets. The word "inolvidable" means "unforgettable" in Spanish, which reflects how Simon left a lasting impression on everyone who knew him.</p>
-<h2>Plot Summary</h2>
-<h3>Simon's birth and childhood</h3>
-<p>The movie begins with a flashback of Simon's birth in 1952. He was born prematurely and weighed less than two pounds. The doctors told his parents that he would not survive long, but he miraculously did. However, they also said that he would never grow beyond three feet tall and that he would have many health problems throughout his life.</p>
-<p>Simon grew up feeling different from everyone else. He was often bullied by other kids for his size and appearance. He also had trouble breathing and had to use an oxygen tank sometimes. His parents were ashamed of him and neglected him. They never celebrated his birthday or gave him any presents.</p>
-<p>The only person who cared for him was Rebecca Wenteworth, Joe's mother. She treated him like her own son and gave him love and attention. She also encouraged him to join the church choir and the Christmas pageant, where he met Joe.</p>
-<h3>Simon's friendship with Joe</h3>
-<p>Simon and Joe became best friends since they were both outsiders in their own way. They shared everything with each other and supported each other through thick and thin. They also had fun together by playing baseball, watching movies, reading comics, and exploring the town.</p>
-<p>One day, they decided to sneak into Rebecca's bedroom to look for clues about Joe's father. They found a locket with a picture of Rebecca and a man they didn't recognize. They also found a baseball signed by Mickey Mantle, which they assumed belonged to Joe's father.</p>
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-<p>They took the baseball with them to play catch at the lake. However, when Simon threw the ball to Joe, he missed it and hit Rebecca instead, who was on a boat with Ben Goodrich. The ball caused Rebecca to fall into the water and drown.</p>
-<p>Simon felt guilty for killing Rebecca and wondered if it was part of God's plan for him. Joe was devastated by losing his mother and blamed Simon for her death. He also learned that Ben Goodrich was his father after finding out that he had the same locket as Rebecca.</p>
-<h3>Simon's quest to find his destiny</h3>
-<p>After Rebecca's funeral, Joe moved in with Ben Goodrich while Simon stayed with his parents. They drifted apart for a while until Ben invited Simon to join them on a camping trip. There, they reconciled their friendship and decided to run away together to find Joe's real father.</p>
-<p>They boarded a bus that took them to another town where they met Miss Leavey (played by Jan Hooks), an old friend of Rebecca who ran an orphanage. She recognized Joe from Rebecca's pictures and offered to help them find Joe's father.</p>
-<p>She took them to a diner where she introduced them to Mr. Baines (played by Jim Carrey), an adult version of Joe who narrated the story from the beginning. He told them that he never found out who his father was but that he didn't care anymore because he had Ben as his father figure.</p>
-<p>He also told them that he became a successful writer because of Simon's influence on him. He said that Simon taught him how to see the world differently and how to appreciate life more.</p>
-<h3>Simon's heroic act and death</h3>
-<p>The next day, they went back to their hometown on another bus that was carrying some children from Miss Leavey's orphanage. On their way, they encountered an accident where a truck hit their bus and caused it to plunge into a frozen lake.</p>
-<p>Simon managed to escape from the bus through a window but saw that many children were still trapped inside. He decided to go back into the water to rescue them one by one using his oxygen tank as an air supply.</p>
-<p>He saved all the children except one girl named Marjorie (played by Sam Morton), who was too scared to leave her seatbelt. Simon tried to calm her down but ran out of air before he could free her.</p>
-<p>Joe saw what happened from outside and dived into the water to help them. He reached them just in time before they drowned but couldn't pull them out because they were too heavy.</p>
-<p>Luckily, Ben arrived at the scene with some firefighters who cut open the bus roof using chainsaws. They pulled out Joe, Simon, Marjorie out of the water along with other survivors.</p>
-<p>However, it was too late for Simon who died from hypothermia in Joe's arms. Before he died, he told Joe that he finally found his destiny: saving those children from drowning.</p>
- <h2>Themes and Messages</h2>
-<h3>The power of faith and belief</h3>
-<p>One of the main themes of the movie is the power of faith and belief. Simon is a character who has a strong faith in God and believes that he has a special mission in life. He doesn't let his physical condition or the negative opinions of others stop him from pursuing his dreams. He also inspires others to have faith and hope in themselves and in a higher purpose.</p>
-<p>For example, he convinces Joe to believe that his father is someone important and that he can find him someday. He also helps Marjorie overcome her fear of water by telling her that God loves her and that he will protect her. He also shows Reverend Russell that he is wrong about judging him and that he is a true believer.</p>
-<h3>The value of friendship and loyalty</h3>
-<p>Another theme of the movie is the value of friendship and loyalty. Simon and Joe are best friends who share a bond that transcends their differences and circumstances. They are always there for each other and support each other through good times and bad times. They also have fun together and enjoy each other's company.</p>
-<p>For example, they play baseball together even though Simon is not good at it. They also watch movies together and laugh at the funny scenes. They also run away together to find Joe's father and have an adventure. They also risk their lives for each other when they face danger.</p>
-<h3>The meaning of life and death</h3>
-<p>A third theme of the movie is the meaning of life and death. Simon is a character who has a different perspective on life and death than most people. He doesn't fear death because he believes that it is part of God's plan for him. He also thinks that life is a gift that should be cherished and lived fully.</p>
-<p>For example, he celebrates his birthday every day because he doesn't know when he will die. He also makes a list of things he wants to do before he dies, such as kissing a girl, seeing the ocean, and being a hero. He also sacrifices his life to save others because he thinks that it is his destiny.</p>
-<h2>Conclusion</h2>
-<h3>Why you should watch this movie</h3>
-<p>El Inolvidable Simon Birch is a movie that will make you laugh, cry, and think. It is a movie that will touch your heart and inspire your soul. It is a movie that will show you the beauty of life and the power of faith.</p>
-<p>You should watch this movie because it will teach you some valuable lessons about friendship, loyalty, courage, belief, purpose, and destiny. You should watch this movie because it will make you appreciate what you have and what you can do. You should watch this movie because it will make you remember Simon Birch, an unforgettable boy who changed the lives of many people.</p>
-<h3>FAQs</h3>
-<ol>
-<li>Q: Is El Inolvidable Simon Birch based on a true story?<br>A: No, El Inolvidable Simon Birch is not based on a true story. It is based on a novel called A Prayer for Owen Meany by John Irving. However, some aspects of the movie are inspired by real events or people, such as the bus accident or the actor who played Simon.</li>
-<li>Q: Who played Simon Birch?<br>A: Simon Birch was played by Ian Michael Smith, a boy who was born with Morquio syndrome, the same condition as Simon's character. He was discovered by the director Mark Steven Johnson after seeing his picture in an article about children with rare diseases. He was 11 years old when he made his debut in the movie.</li>
-<li>Q: What happened to Ian Michael Smith after the movie?<br>A: Ian Michael Smith continued his acting career after the movie. He appeared in several TV shows and movies, such as The Secret Agent Club (1996), The Final Season (2007), and The Lurking Man (2017). He also graduated from MIT with a degree in computer science and became a software engineer.</li>
-<li>Q: Why did John Irving dislike the movie?<br>A: John Irving, the author of the novel A Prayer for Owen Meany, disliked the movie adaptation because he felt that it changed too many things from his original story. He didn't like how the characters' names were changed, how the setting was moved from New England to New Hampshire, how some scenes were added or deleted, and how some themes were altered or omitted. He also didn't like how the movie used his title without his permission.</li>
-<li>Q: Where can I watch El Inolvidable Simon Birch?<br>A: You can watch El Inolvidable Simon Birch on various streaming platforms, such as Amazon Prime Video, YouTube, Google Play Movies & TV, iTunes, Vudu, or Hulu. You can also buy or rent it on DVD or Blu-ray.</li>
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-"""Unit tests for the commands module"""
-from unittest.mock import MagicMock, patch
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-import pytest
-
-import autogpt.agent.agent_manager as agent_manager
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-APK5-30 axial fan impeller material<br />
-APK5-30 axial fan casing material</p>
-<ul>
-<li>It uses an aluminum impeller instead of a steel or plastic one, which makes it lighter and more resistant to corrosion.</li>
-<li>It uses a belt drive system instead of a direct drive system, which allows it to adjust the speed and torque more easily.</li>
-<li>It uses an internal support leg instead of an external one, which reduces the air resistance and increases the efficiency.</li>
-</ul>
- <h3>How APK5-30 performs better than other axial fans</h3>
-<p>APK5-30 performs better than other axial fans in several ways, such as:</p>
-<ul>
-<li>It has a higher airflow rate and pressure than other axial fans of the same size and power.</li>
-<li>It has a lower noise level and vibration level than other axial fans of the same size and power.</li>
-<li>It has a longer service life and lower maintenance cost than other axial fans of the same size and power.</li>
-</ul>
- <h3>How APK5-30 saves energy and costs than other axial fans</h3>
-<p>APK5-30 saves energy and costs than other axial fans in several ways, such as:</p>
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-<li>It uses a top-runner efficiency motor (IE3 equivalent) that consumes less electricity and emits less carbon dioxide. (Except for 0.2 to 0.4kW models)</li>
-<li>It has a simple structure with few components, which reduces the initial purchase price and installation cost.</li>
-<li>It has a low operating cost due to its high efficiency and low maintenance requirements.</li>
-</ul>
- <h2>Conclusion</h2>
- <h3>Summary of the main points</h3>
-<p>In conclusion, APK5-30 is a type of axial fan that uses an aluminum impeller and a belt drive system to create a high-efficiency airflow. It has many features and benefits that make it a superior choice for cooling and ventilation purposes. It is easy to install, operate, and maintain, and it performs better than other axial fans in terms of airflow, pressure, noise, vibration, service life, and maintenance cost. It also saves energy and costs by using a top-runner efficiency motor (IE3 equivalent) that reduces electricity consumption and carbon emissions. (Except for 0.2 to 0.4kW models)</p>
- <h3>Call to action</h3>
-<p>If you are interested in purchasing APK5-30 or learning more about it, please visit our website or contact us today. We will be happy to assist you with any questions or inquiries you may have. Don't miss this opportunity to get your hands on this amazing product that will improve your cooling and ventilation needs.</p>
- <h2>Frequently Asked Questions</h2>
- <h4>What is the warranty period for APK5-30?</h4>
-<p>The warranty period for APK5-30 is one year from the date of purchase. If you encounter any problems with the product during this period, please contact us for repair or replacement.</p>
- <h4>What are the dimensions and weight of APK5-30?</h4>
-<p>The dimensions of APK5-30 are 300mm x 300mm x 300mm (L x W x H) and the weight is 9kg.</p>
- <h4>What are the applications of What are the applications of APK5-30?</h4>
-<p>APK5-30 can be used for various cooling and ventilation applications, such as:</p>
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-<li>Machinery and equipment cooling</li>
-<li>Exhaust and smoke removal</li>
-<li>Air conditioning and dehumidification</li>
-<li>Greenhouse and farm ventilation</li>
-<li>Warehouse and factory ventilation</li>
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- <h4>How can I order APK5-30 online?</h4>
-<p>You can order APK5-30 online by visiting our website and filling out the order form. You will need to provide your name, address, phone number, email, and payment method. We will confirm your order and ship the product to you as soon as possible.</p>
- <h4>What are the safety precautions for using APK5-30?</h4>
-<p>When using APK5-30, you should follow these safety precautions:</p>
-<ul>
-<li>Do not touch the fan or the impeller when it is running or hot.</li>
-<li>Do not insert any objects or fingers into the fan or the duct.</li>
-<li>Do not use the fan in wet, dusty, or flammable environments.</li>
-<li>Do not overload the fan or the power supply.</li>
-<li>Do not modify or repair the fan without authorization.</li>
-</ul></p> 197e85843d<br />
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spaces/1phancelerku/anime-remove-background/Bubble Shooter Enjoy the Original Bubble Pop Game on Your iOS Device.md

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-<h1>Bubble Shooter for iPhone Free Download: How to Play the Classic and Addictive Game on Your iOS Device</h1>
- <p>If you are looking for a fun and relaxing game to play on your iPhone, you might want to try Bubble Shooter. Bubble Shooter is a classic and addictive game that has been around for decades and is still popular among millions of players worldwide. In this article, we will tell you everything you need to know about Bubble Shooter, including what it is, how to download it for free, and how to play it on your iOS device. Let's get started!</p>
- <h2>What is Bubble Shooter?</h2>
- <p>Bubble Shooter is a puzzle game that involves shooting bubbles of the same color to make them pop and clear the board. The game is simple to learn but challenging to master, as you need to aim carefully and plan your moves ahead. The game has many variations and versions, but the basic concept remains the same: match 3 or more bubbles of the same color to burst them and score points.</p>
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- <h3>The history of Bubble Shooter</h3>
- <p>Bubble Shooter was originally developed by a company called Taito in 1994 as an arcade game called Puzzle Bobble. The game was a spin-off of the popular platformer game Bubble Bobble, which featured two cute dragons named Bub and Bob. Puzzle Bobble was later ported to various home consoles and computers, and became a huge hit worldwide. The game spawned several sequels and clones, and inspired many other bubble shooting games over the years.</p>
- <h3>The gameplay of Bubble Shooter</h3>
- <p>The gameplay of Bubble Shooter is very simple: you have a cannon at the bottom of the screen that shoots bubbles of different colors. You can aim the cannon by moving your finger or mouse cursor on the screen, and tap or click to fire a bubble. Your goal is to match 3 or more bubbles of the same color to make them pop and clear them from the board. If you clear all the bubbles, you win the level and move on to the next one. If the bubbles reach the bottom of the screen, you lose the game and have to start over.</p>
- <h3>The benefits of playing Bubble Shooter</h3>
- <p>Bubble Shooter is not only a fun and entertaining game, but also a beneficial one. Playing Bubble Shooter can help you improve your skills in various ways, such as:</p>
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-<li>Enhancing your concentration and focus</li>
-<li>Boosting your memory and cognitive abilities</li>
-<li>Developing your hand-eye coordination and reaction speed</li>
-<li>Reducing your stress and anxiety levels</li>
-<li>Increasing your creativity and problem-solving skills</li>
-</ul>
- <p>Besides, playing Bubble Shooter can also make you happy and relaxed, as popping bubbles can release endorphins in your brain that make you feel good.</p>
- <h2>How to download Bubble Shooter for iPhone for free?</h2>
- <p>If you want to play Bubble Shooter on your iPhone, you have plenty of options to choose from. There are many free apps that offer different versions and variations of Bubble Shooter on the App Store. Here are some of the best ones that we recommend:</p>
- <h3>The best Bubble Shooter apps on the App Store</h3>
- <h4>Bubble Shooter - Pop Bubbles</h4>
- <p>This app is one of the most popular and highly rated Bubble Shooter games on the App Store. It offers a classic and addictive gameplay with thousands of fun levels, amazing graphics and sounds, and various challenges and rewards. You can also play with your friends and family online and compete for the highest score. The app is free to download and play, but it contains ads and in-app purchases. You can download it from here: [Bubble Shooter - Pop Bubbles].</p>
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- <p>This app is another great option for Bubble Shooter fans. It features a smooth and easy gameplay with over 3000 exciting levels, stunning graphics and effects, and a relaxing soundtrack. You can also customize your bubble shooter with different skins and themes, and enjoy daily bonuses and gifts. The app is free to download and play, but it contains ads and in-app purchases. You can download it from here: [Bubble Shooter - Addictive!].</p>
- <h4>Bobble Shooter</h4>
- <p>This app is a unique and innovative take on the Bubble Shooter genre. It combines the classic bubble popping gameplay with a physics-based puzzle element. You have to shoot bobbles of different shapes and sizes to create clusters of the same color and make them explode. The game has hundreds of challenging levels, colorful graphics and animations, and a catchy music. The app is free to download and play, but it contains ads and in-app purchases. You can download it from here: [Bobble Shooter].</p>
- <h3>How to install and launch Bubble Shooter on your iPhone</h3>
- <p>Installing and launching Bubble Shooter on your iPhone is very easy. Just follow these simple steps:</p>
- <ol>
-<li>Open the App Store on your iPhone and search for the Bubble Shooter app that you want to download.</li>
-<li>Tap on the app icon and then tap on the Get button to start the download process.</li>
-<li>Wait for the app to finish downloading and then tap on the Open button to launch it.</li>
-<li>Alternatively, you can also find the app icon on your home screen and tap on it to launch it.</li>
-</ol>
- <h3>How to update and delete Bubble Shooter on your iPhone</h3>
- <p>Updating and deleting Bubble Shooter on your iPhone is also very simple. Just follow these simple steps:</p>
- <ol>
-<li>To update Bubble Shooter, open the App Store on your iPhone and tap on the Updates tab at the bottom.</li>
-<li>Find the Bubble Shooter app that you want to update and tap on the Update button next to it.</li>
-<li>Wait for the app to finish updating and then launch it as usual.</li>
-<li>To delete Bubble Shooter, press and hold the app icon on your home screen until it starts to wiggle.</li>
-<li>Tap on the X button on the top left corner of the app icon and then tap on Delete to confirm.</li>
-</ol>
- <h2>How to play Bubble Shooter on your iPhone?</h2>
- <p>Playing Bubble Shooter on your iPhone is very fun and easy. Here are some tips and tricks that will help you enjoy the game more:</p>
- <h3>The basic rules and tips of Bubble Shooter</h3>
- <p>The basic rules of Bubble Shooter are as follows:</p>
- <ul>
-<li>You have a limited number of bubbles to shoot in each level.</li>
-<li>You have to match 3 or more bubbles of the same color to pop them and clear them from the board.</li>
-<li>You can bounce the bubbles off the walls to reach difficult spots.</li>
-<li>You can see the next bubble that you are going to shoot at the bottom of the screen.</li>
-<li>You can swap the current bubble with the next one by tapping on it.</li>
-<li>You can use special bubbles that have different effects, such as bombs, rainbows, stars, etc.</li>
-<li>You can earn coins and gems by popping bubbles, completing levels, and achieving goals.</li>
-<li>You can use coins and gems to buy power-ups, boosters, lives, etc.</li>
-</ul>
- <p>Some tips that will help you improve your performance are:</p>
- <ul>
-<li>Aim carefully before you shoot a bubble.</li>
-<li>Try to pop as many bubbles as possible with one shot.</li>
-<li>Try to create chain reactions by popping bubbles that are connected to other bubbles of the same color.</li>
-<li>Try to clear the top rows of bubbles first, as they will drop all the bubbles below them when they pop.</li>
-<li>Try to avoid leaving isolated bubbles that are hard to reach or match.</li>
-<li>Use power-ups and boosters wisely, as they can help you clear difficult levels or get out of tricky situations.</li>
-</ul>
- <h3>The different game modes and levels of Bubble Shooter</h3>
- <p>Bubble Shooter offers a variety of game modes and levels that will keep you entertained for hours. Some of them are:</p>
- <ul>
-<li> <li>Classic mode: This is the original and most popular mode of Bubble Shooter. It has hundreds of levels that range from easy to hard, and each level has a different layout and goal. You can play this mode offline or online, and you can also choose the difficulty level and the bubble design.</li>
-<li>Arcade mode: This is a fast-paced and exciting mode of Bubble Shooter. It has endless levels that get harder and harder as you progress, and each level has a time limit and a score target. You have to pop as many bubbles as you can before the time runs out, and you can also use power-ups and boosters to speed up your progress.</li>
-<li>Puzzle mode: This is a challenging and brain-teasing mode of Bubble Shooter. It has hundreds of levels that require logic and strategy to solve, and each level has a unique puzzle and goal. You have to pop all the bubbles using the least number of shots, and you can also use hints and skips to help you out.</li>
-<li>Adventure mode: This is a fun and adventurous mode of Bubble Shooter. It has hundreds of levels that are based on different themes and stories, such as pirates, fairies, dinosaurs, etc. You have to pop bubbles and collect items to complete the levels, and you can also encounter obstacles and enemies along the way.</li>
-</ul>
- <h3>The features and settings of Bubble Shooter</h3>
- <p>Bubble Shooter also has many features and settings that will enhance your gaming experience. Some of them are:</p>
- <ul>
-<li>You can connect your Facebook account to Bubble Shooter and share your progress, achievements, and scores with your friends.</li>
-<li>You can play with other players from around the world in the multiplayer mode and compete for the highest score.</li>
-<li>You can join or create a team with other players and chat, cooperate, and exchange gifts with them.</li>
-<li>You can participate in various events, tournaments, and challenges that offer special rewards and prizes.</li>
-<li>You can customize your bubble shooter with different skins, themes, backgrounds, sounds, etc.</li>
-<li>You can adjust the game settings according to your preferences, such as the volume, the language, the notifications, etc.</li>
-</ul>
- <h2>Conclusion</h2>
- <p>Bubble Shooter is a classic and addictive game that you can play on your iPhone for free. It offers a simple but challenging gameplay with thousands of fun levels, amazing graphics and sounds, and various game modes and features. It also helps you improve your skills, reduce your stress, and have fun with your friends. If you are looking for a game that will keep you entertained for hours, download Bubble Shooter today and enjoy popping bubbles!</p>
- <h2>FAQs</h2>
- <p>Here are some frequently asked questions about Bubble Shooter:</p>
- <ol>
-<li>How do I get more coins and gems in Bubble Shooter?</li>
-<p>You can get more coins and gems in Bubble Shooter by popping bubbles, completing levels, achieving goals, watching ads, spinning the wheel, opening chests, collecting daily bonuses, joining events, buying them with real money, etc.</p>
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-<p>You can use power-ups and boosters in Bubble Shooter by tapping on them before or during the game. Power-ups are special bubbles that have different effects, such as bombs, rainbows, stars, etc. Boosters are items that help you in various ways, such as extra moves, fireballs, magnets, etc.</p>
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-<p>You can unlock new levels in Bubble Shooter by completing the previous levels or by paying coins or gems. You can also unlock new levels by joining events or teams that offer exclusive levels.</p>
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-<p>You can reset your progress in Bubble Shooter by deleting the app from your iPhone and reinstalling it. However, this will also erase all your coins, gems, power-ups, boosters, lives, etc. If you want to keep them, you can connect your Facebook account to Bubble Shooter and sync your progress across different devices.</p>
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-<p>You can contact the support team of Bubble Shooter by tapping on the settings icon on the main screen and then tapping on the help button. You can also email them at [email protected] or visit their website at www.bubbleshooter.com.</p>
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spaces/1phancelerku/anime-remove-background/Crafting and Building 1.18 APK A Free Game with Amazing Graphics and Multiplayer Mode.md

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-<td>Amy</td>
-<td>5 stars</td>
-<td>I love this game! It's so fun and creative. I can build anything I want and play with my friends online. It's like Minecraft but better.</td>
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-<td>This game is awesome, but it has some bugs and glitches. Sometimes the game crashes or freezes, and sometimes the blocks disappear or change color. Please fix these issues.</td>
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-<h1>MuksOS AI Launcher 2.0: A Smart and Interactive Android Launcher</h1>
-<p>If you are looking for a new and innovative way to interact with your phone, you might want to check out MuksOS AI Launcher 2.0. This is a unique android launcher that combines the features of an app launcher, a virtual assistant, and an AI tool for your DIY automation projects. In this article, we will tell you what MuksOS AI Launcher 2.0 is, what are its features, how to download it, and answer some frequently asked questions.</p>
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-<p>MuksOS AI Launcher 2.0 is an android app developed by Dr. Mukesh Bangar, a computer engineer and researcher in artificial intelligence. It is designed to make your phone smarter and more responsive by using voice, gestures, or text commands. You can use MuksOS AI Launcher 2.0 to open apps, make calls, search the web, set alarms, reminders, and more. You can also use it as a virtual assistant that can assist you anytime, anywhere with its cool and unique features like JARVIS has in Iron Man movie. And if you are into DIY automation projects, you can use MuksOS AI Launcher 2.0 as an easy AI tool to create amazing things using object recognition and smart connect features.</p>
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-<p>MuksOS AI Launcher 2.0 is a smart and interactive android launcher that offers you a new and innovative way to interact with your phone. It has many features that make it stand out from other android launchers, such as teachable, fast and smooth, multiple voice options, 100 % privacy, user friendly, power saver, esthetic theme, dark and light theme, works offline, favorite apps, hide apps, etc. It also has some premium features that you can unlock by downloading the mod apk version of the app, such as write on home screen, voice access, speech reminders and speech alarm, smart connect, vision ability etc. If you are looking for a smart and interactive android launcher that combines the features of an app launcher, a virtual assistant and an AI tool for your DIY automation projects then MuksOS AI Launcher 2.0 is the perfect choice for you.</p>
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-<h1>What is an online rp launcher and why you need one</h1>
-<p>If you are a fan of Grand Theft Auto (GTA) Online, you might have heard of online rp launchers. These are software applications that allow you to play GTA Online on customized dedicated servers, with different game modes, maps, vehicles, weapons, and more. Online rp launchers are also known as multiplayer modifications or frameworks, and they enable you to create or join your own GTA Online community.</p>
-<p>Online rp launchers work by modifying the game files of GTA V, but without affecting your original installation or your access to GTA Online. This means that you can switch between GTA Online and online rp launchers without getting banned by Rockstar. Online rp launchers also use Rockstar's network code with improvements, so you can enjoy the best synchronization and performance possible.</p>
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-<p>There are many online rp launchers available for GTA Online, but not all of them are created equal. Some online rp launchers may have more features, compatibility, or popularity than others. Here are some factors to consider when choosing the best online rp launcher for your needs:</p>
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-<p>The features of an online rp launcher determine what you can do with it. Some online rp launchers may have more options for customization, streaming, AI, scripting, or hosting than others. For example, some online rp launchers may allow you to use custom cars, maps, weapons, and more dynamically, while others may require you to download them manually. Some online rp launchers may also have more support for different programming languages or tools than others.</p>
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-<p>One of the most popular and well-known online rp launchers is FiveM. FiveM is a modification for GTA V that enables you to play multiplayer on customized dedicated servers powered by Cfx.re. FiveM has been around since 2014 and has over 178k players playing right now.</p>
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-<li>Scripting: FiveM allows servers to use different programming languages and frameworks, such as Lua, C#, JavaScript, and more. This means that you can create or join servers with different game modes, features, and mechanics.</li>
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-<h2>RAGE Multiplayer - fun, free and easy</h2>
-<p>Another popular and well-known online rp launcher is RAGE Multiplayer. RAGE Multiplayer is a modification for GTA V that enables you to play multiplayer on customized dedicated servers powered by RAGE Technology Group. RAGE Multiplayer has been around since 2017 and has over 15k players playing right now.</p>
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-<p>RAGE Multiplayer is compatible with Windows 7 or higher and the latest version of GTA V. RAGE Multiplayer also has a large and active community of players, servers, developers, and streamers. You can find more information about RAGE Multiplayer on their website or their Discord.</p>
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-<p>GTA RP servers are one of the most popular types of online rp launchers. GTA RP stands for Grand Theft Auto Roleplay, which is a game mode where you create a character and live a virtual life in the GTA world. You can interact with other players, follow the laws, get a job, join a gang, or do whatever you want.</p>
-<p>GTA RP servers are usually hosted by online rp launchers such as FiveM or RAGE Multiplayer. To play on GTA RP servers, you need to have GTA V installed on your PC and an online rp launcher of your choice. You also need to find a GTA RP server that suits your preferences and style. Some GTA RP servers may have different rules, themes, whitelists, applications, or requirements than others.</p>
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-<li>Launch your online rp launcher and select the server browser.</li>
-<li>Search for a GTA RP server that you like and click on it.</li>
-<li>Read the server's description, rules, website, Discord, or any other information provided by the server owner.</li>
-<li>If the server requires an application or a whitelist, follow the instructions given by the server owner to apply or register.</li>
-<li>If the server does not require an application or a whitelist, or if you have been accepted or whitelisted, click on connect to join the server.</li>
-<li>Create your character and start roleplaying.</li>
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-<p>GTA RP servers are fun and immersive ways to enjoy GTA Online with other players. You can make friends, enemies, allies, rivals, lovers, or anything else you can imagine. You can also explore different aspects of the GTA world that you may not have seen before. GTA RP servers are like living in your own GTA movie or TV show.</p> <h2>Tips and tricks for online rp launcher users</h2>
-<p>Online rp launchers are great ways to enhance your GTA Online experience, but they also come with some challenges and risks. Here are some tips and tricks for online rp launcher users to make the most out of their online rp launcher adventures:</p>
-<h3>Backup your game files</h3>
-<p>Before installing or using any online rp launcher, it is always a good idea to backup your game files. This way, you can restore your original GTA V installation in case something goes wrong or you want to play GTA Online again. You can backup your game files by copying the GTA V folder to another location on your PC or using a backup software.</p>
-<h3>Follow the server rules</h3>
-<p>When playing on any online rp launcher server, you should always follow the server rules and respect the other players. This is especially important for GTA RP servers, where you are expected to roleplay realistically and follow the server's theme and lore. Breaking the server rules or disrupting the roleplay can result in a kick, a ban, or a report from the server owner or the admins.</p>
-<h3>Update your online rp launcher regularly</h3>
-<p>Online rp launchers are constantly being updated and improved by their developers and communities. To enjoy the latest features, fixes, and enhancements, you should always update your online rp launcher regularly. You can check for updates on the online rp launcher's website, Discord, or launcher. You should also update your GTA V game whenever a new patch or update is released by Rockstar.</p>
-<h3>Use a VPN</h3>
-<p>Using a VPN (virtual private network) can help you protect your privacy and security when playing on online rp launcher servers. A VPN can hide your IP address and encrypt your data, making it harder for hackers, trackers, or malicious players to access your information or harm your PC. A VPN can also help you bypass geo-restrictions or firewalls that may prevent you from accessing certain online rp launcher servers.</p>
-<h3>Have fun</h3>
-<p>The most important tip for online rp launcher users is to have fun. Online rp launchers are meant to provide you with endless possibilities and opportunities to enjoy GTA Online in new and creative ways. You can explore different worlds, meet new people, create your own stories, or just have a blast. Online rp launchers are all about having fun.</p>
-<h1>Conclusion</h1>
-<p>Online rp launchers are software applications that allow you to play GTA Online on customized dedicated servers with different game modes, maps, vehicles, weapons, and more. Online rp launchers are also known as multiplayer modifications or frameworks, and they enable you to create or join your own GTA Online community.</p>
-<p>There are many online rp launchers available for GTA Online, but some of the most popular and well-known ones are FiveM and RAGE Multiplayer. These online rp launchers have many features, compatibility, and popularity that make them stand out from other online rp launchers.</p>
-<p>GTA RP servers are one of the most popular types of online rp launchers. GTA RP stands for Grand Theft Auto Roleplay, which is a game mode where you create a character and live a virtual life in the GTA world. You can interact with other players, follow the laws, get a job, join a gang, or do whatever you want.</p>
-<p>To play on GTA RP servers, you need to have GTA V installed on your PC and an online rp launcher of your choice. You also need to find a GTA RP server that suits your preferences and style. Some GTA RP servers may have different rules, themes, whitelists, applications, or requirements than others.</p>
-<p>To make the most out of your online rp launcher experience, you should follow some tips and tricks such as backing up your game files, following the server rules, updating your online rp launcher regularly, using a VPN, and having fun.</p>
-<p>If you are looking for a new way to enjoy GTA Online with more freedom, creativity, and fun, you should definitely try online rp launchers. They will change the way you play GTA Online forever.</p>
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-<li><strong>What is an online rp launcher?</strong></li>
-<li>An online rp launcher is a software application that allows you to play GTA Online on customized dedicated servers with different game modes, maps, vehicles, weapons, and more.</li>
-<li><strong>How do I install an online rp launcher?</strong></li>
-<li>To install an online rp launcher, you need to download the launcher from its website or Discord and select your GTA V folder. You also need to have GTA V installed on your PC.</li>
-<li><strong>Can I play GTA Online with an online rp launcher?</strong></li>
-<li>You can still play GTA Online with an online rp launcher, but you need to switch back to your original GTA V installation. Online rp launchers do not affect your GTA Online access or progress.</li>
-<li><strong>What are some of the best online rp launchers?</strong></li>
-<li>Some of the best online rp launchers are FiveM and RAGE Multiplayer. These online rp launchers have many features, compatibility, and popularity that make them stand out from other online rp launchers.</li>
-<li><strong>What are GTA RP servers?</strong></li>
-<li>GTA RP servers are online rp launcher servers that use a game mode called Grand Theft Auto Roleplay, where you create a character and live a virtual life in the GTA world. You can interact with other players, follow the laws, get a job, join a gang, or do whatever you want.</li>
-<li><strong>How do I join a GTA RP server?</strong></li>
-<li>To join a GTA RP server, you need to launch your online rp launcher and select the server browser. Then, you need to search for a GTA RP server that you like and click on it. You may also need to apply or register for some GTA RP servers that have whitelists or applications.</li>
-</ul></p> 401be4b1e0<br />
-<br />
-<br />

spaces/A00001/bingothoo/src/lib/utils.ts

@@ -1,158 +0,0 @@
-import { clsx, type ClassValue } from 'clsx'
-import { customAlphabet } from 'nanoid'
-import { twMerge } from 'tailwind-merge'
-
-export function cn(...inputs: ClassValue[]) {
-  return twMerge(clsx(inputs))
-}
-
-export const nanoid = customAlphabet(
-  '0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz',
-  7
-) // 7-character random string
-
-export function createChunkDecoder() {
-  const decoder = new TextDecoder()
-  return function (chunk: Uint8Array | undefined): string {
-    if (!chunk) return ''
-    return decoder.decode(chunk, { stream: true })
-  }
-}
-
-export function random (start: number, end: number) {
-  return start + Math.ceil(Math.random() * (end - start))
-}
-
-export function randomIP() {
-  return `11.${random(104, 107)}.${random(1, 255)}.${random(1, 255)}`
-}
-
-export const defaultUID = Math.random().toString(36).slice(2)
-
-export function parseHeadersFromCurl(content: string) {
-  const re = /-H '([^:]+):\s*([^']+)/mg
-  const headers: HeadersInit = {}
-  content = content.replaceAll('-H "', '-H \'').replaceAll('" ^', '\'\\').replaceAll('^\\^"', '"') // 将 cmd curl 转成 bash curl
-  content.replace(re, (_: string, key: string, value: string) => {
-    headers[key] = value
-    return ''
-  })
-
-  return headers
-}
-
-export const ChunkKeys = ['BING_HEADER', 'BING_HEADER1', 'BING_HEADER2']
-export function encodeHeadersToCookie(content: string) {
-  const base64Content = btoa(content)
-  const contentChunks = base64Content.match(/.{1,4000}/g) || []
-  return ChunkKeys.map((key, index) => `${key}=${contentChunks[index] ?? ''}`)
-}
-
-export function extraCurlFromCookie(cookies: Partial<{ [key: string]: string }>) {
-  let base64Content = ''
-  ChunkKeys.forEach((key) => {
-    base64Content += (cookies[key] || '')
-  })
-  try {
-    return atob(base64Content)
-  } catch(e) {
-    return ''
-  }
-}
-
-export function extraHeadersFromCookie(cookies: Partial<{ [key: string]: string }>) {
-  return parseHeadersFromCurl(extraCurlFromCookie(cookies))
-}
-
-export function formatDate(input: string | number | Date): string {
-  const date = new Date(input)
-  return date.toLocaleDateString('en-US', {
-    month: 'long',
-    day: 'numeric',
-    year: 'numeric'
-  })
-}
-
-export function parseCookie(cookie: string, cookieName: string) {
-  const targetCookie = new RegExp(`(?:[; ]|^)${cookieName}=([^;]*)`).test(cookie) ? RegExp.$1 : cookie
-  return targetCookie ? decodeURIComponent(targetCookie).trim() : cookie.indexOf('=') === -1 ? cookie.trim() : ''
-}
-
-export function setCookie(key: string, value: string) {
-  const maxAge = 86400 * 30
-  document.cookie = `${key}=${value || ''}; Path=/; Max-Age=${maxAge}; SameSite=None; Secure`
-}
-
-export function getCookie(cookieName: string) {
-  const re = new RegExp(`(?:[; ]|^)${cookieName}=([^;]*)`)
-  return re.test(document.cookie) ? RegExp.$1 : ''
-}
-
-export function parseCookies(cookie: string, cookieNames: string[]) {
-  const cookies: { [key: string]: string } = {}
-  cookieNames.forEach(cookieName => {
-    cookies[cookieName] = parseCookie(cookie, cookieName)
-  })
-  return cookies
-}
-
-export const DEFAULT_UA = 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/115.0.0.0 Safari/537.36 Edg/115.0.0.0'
-export const DEFAULT_IP = process.env.BING_IP || randomIP()
-
-export function parseUA(ua?: string, default_ua = DEFAULT_UA) {
-  return / EDGE?/i.test(decodeURIComponent(ua || '')) ? decodeURIComponent(ua!.trim()) : default_ua
-}
-
-export function createHeaders(cookies: Partial<{ [key: string]: string }>, defaultHeaders?: Partial<{ [key: string]: string }>, type?: string) {
-  let {
-    BING_COOKIE = process.env.BING_COOKIE,
-    BING_UA = process.env.BING_UA,
-    BING_IP = process.env.BING_IP,
-    BING_HEADER = process.env.BING_HEADER,
-    IMAGE_ONLY = process.env.IMAGE_ONLY ?? '1',
-  } = cookies
-
-  if (BING_HEADER) {
-    const headers = extraHeadersFromCookie({
-      BING_HEADER,
-      ...cookies,
-    }) || {}
-    if (/^(1|true|yes)$/.test(String(IMAGE_ONLY)) && type !== 'image') {
-      // 仅画图时设置 cookie
-      headers.cookie = `_U=${defaultUID}`
-    }
-    if (headers['user-agent']) {
-      return headers
-    }
-  }
-
-  const ua = parseUA(BING_UA)
-
-  if (!BING_COOKIE) {
-    BING_COOKIE = defaultHeaders?.IMAGE_BING_COOKIE || defaultUID // hf 暂时不用 Cookie 也可以正常使用
-  }
-
-  const parsedCookie = parseCookie(BING_COOKIE, '_U')
-  if (!parsedCookie) {
-    throw new Error('Invalid Cookie')
-  }
-  return {
-    'x-forwarded-for': BING_IP || DEFAULT_IP,
-    'Accept-Encoding': 'gzip, deflate, br',
-    'Accept-Language': 'zh-CN,zh;q=0.9,en;q=0.8,en-GB;q=0.7,en-US;q=0.6',
-    'User-Agent': ua!,
-    'x-ms-useragent': 'azsdk-js-api-client-factory/1.0.0-beta.1 core-rest-pipeline/1.10.0 OS/Win32',
-    cookie: `_U=${parsedCookie}` || '',
-  }
-}
-
-export class WatchDog {
-  private tid = 0
-  watch(fn: Function, timeout = 2000) {
-    clearTimeout(this.tid)
-    this.tid = setTimeout(fn, timeout + Math.random() * 1000)
-  }
-  reset() {
-    clearTimeout(this.tid)
-  }
-}

spaces/AIConsultant/MusicGen/audiocraft/utils/deadlock.py

@@ -1,58 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-import logging
-import os
-from queue import Queue, Empty
-import signal
-import sys
-import threading
-import traceback
-
-logger = logging.getLogger(__name__)
-
-
-class DeadlockDetect:
-    def __init__(self, use: bool = False, timeout: float = 120.):
-        self.use = use
-        self.timeout = timeout
-        self._queue: Queue = Queue()
-
-    def update(self, stage: str):
-        if self.use:
-            self._queue.put(stage)
-
-    def __enter__(self):
-        if self.use:
-            self._thread = threading.Thread(target=self._detector_thread)
-            self._thread.start()
-
-    def __exit__(self, exc_type, exc_val, exc_tb):
-        if self.use:
-            self._queue.put(None)
-            self._thread.join()
-
-    def _detector_thread(self):
-        logger.debug("Deadlock detector started")
-        last_stage = "init"
-        while True:
-            try:
-                stage = self._queue.get(timeout=self.timeout)
-            except Empty:
-                break
-            if stage is None:
-                logger.debug("Exiting deadlock detector thread")
-                return
-            else:
-                last_stage = stage
-        logger.error("Deadlock detector timed out, last stage was %s", last_stage)
-        for th in threading.enumerate():
-            print(th, file=sys.stderr)
-            traceback.print_stack(sys._current_frames()[th.ident])
-            print(file=sys.stderr)
-        sys.stdout.flush()
-        sys.stderr.flush()
-        os.kill(os.getpid(), signal.SIGKILL)

spaces/AIZero2Hero4Health/5-ImageToLineDrawing-GR/app.py

@@ -1,126 +0,0 @@
-import numpy as np
-import torch
-import torch.nn as nn
-import gradio as gr
-from PIL import Image
-import torchvision.transforms as transforms
-
-norm_layer = nn.InstanceNorm2d
-
-class ResidualBlock(nn.Module):
-    def __init__(self, in_features):
-        super(ResidualBlock, self).__init__()
-
-        conv_block = [  nn.ReflectionPad2d(1),
-                        nn.Conv2d(in_features, in_features, 3),
-                        norm_layer(in_features),
-                        nn.ReLU(inplace=True),
-                        nn.ReflectionPad2d(1),
-                        nn.Conv2d(in_features, in_features, 3),
-                        norm_layer(in_features)
-                        ]
-
-        self.conv_block = nn.Sequential(*conv_block)
-
-    def forward(self, x):
-        return x + self.conv_block(x)
-
-
-class Generator(nn.Module):
-    def __init__(self, input_nc, output_nc, n_residual_blocks=9, sigmoid=True):
-        super(Generator, self).__init__()
-
-        # Initial convolution block
-        model0 = [   nn.ReflectionPad2d(3),
-                    nn.Conv2d(input_nc, 64, 7),
-                    norm_layer(64),
-                    nn.ReLU(inplace=True) ]
-        self.model0 = nn.Sequential(*model0)
-
-        # Downsampling
-        model1 = []
-        in_features = 64
-        out_features = in_features*2
-        for _ in range(2):
-            model1 += [  nn.Conv2d(in_features, out_features, 3, stride=2, padding=1),
-                        norm_layer(out_features),
-                        nn.ReLU(inplace=True) ]
-            in_features = out_features
-            out_features = in_features*2
-        self.model1 = nn.Sequential(*model1)
-
-        model2 = []
-        # Residual blocks
-        for _ in range(n_residual_blocks):
-            model2 += [ResidualBlock(in_features)]
-        self.model2 = nn.Sequential(*model2)
-
-        # Upsampling
-        model3 = []
-        out_features = in_features//2
-        for _ in range(2):
-            model3 += [  nn.ConvTranspose2d(in_features, out_features, 3, stride=2, padding=1, output_padding=1),
-                        norm_layer(out_features),
-                        nn.ReLU(inplace=True) ]
-            in_features = out_features
-            out_features = in_features//2
-        self.model3 = nn.Sequential(*model3)
-
-        # Output layer
-        model4 = [  nn.ReflectionPad2d(3),
-                        nn.Conv2d(64, output_nc, 7)]
-        if sigmoid:
-            model4 += [nn.Sigmoid()]
-
-        self.model4 = nn.Sequential(*model4)
-
-    def forward(self, x, cond=None):
-        out = self.model0(x)
-        out = self.model1(out)
-        out = self.model2(out)
-        out = self.model3(out)
-        out = self.model4(out)
-
-        return out
-
-model1 = Generator(3, 1, 3)
-model1.load_state_dict(torch.load('model.pth', map_location=torch.device('cpu')))
-model1.eval()
-
-model2 = Generator(3, 1, 3)
-model2.load_state_dict(torch.load('model2.pth', map_location=torch.device('cpu')))
-model2.eval()
-
-def predict(input_img, ver):
-    input_img = Image.open(input_img)
-    transform = transforms.Compose([transforms.Resize(256, Image.BICUBIC), transforms.ToTensor()])
-    input_img = transform(input_img)
-    input_img = torch.unsqueeze(input_img, 0)
-
-    drawing = 0
-    with torch.no_grad():
-        if ver == 'Simple Lines':
-            drawing = model2(input_img)[0].detach()
-        else:
-            drawing = model1(input_img)[0].detach()
-    
-    drawing = transforms.ToPILImage()(drawing)
-    return drawing
-
-title="Image to Line Drawings - Complex and Simple Portraits and Landscapes"
-examples=[
-['01.jpeg', 'Simple Lines'], ['02.jpeg', 'Simple Lines'], ['03.jpeg', 'Simple Lines'],
-['07.jpeg', 'Complex Lines'], ['08.jpeg', 'Complex Lines'], ['09.jpeg', 'Complex Lines'],
-['10.jpeg', 'Simple Lines'], ['11.jpeg', 'Simple Lines'], ['12.jpeg', 'Simple Lines'],
-['01.jpeg', 'Complex Lines'], ['02.jpeg', 'Complex Lines'], ['03.jpeg', 'Complex Lines'],
-['04.jpeg', 'Simple Lines'], ['05.jpeg', 'Simple Lines'], ['06.jpeg', 'Simple Lines'],
-['07.jpeg', 'Simple Lines'], ['08.jpeg', 'Simple Lines'], ['09.jpeg', 'Simple Lines'],
-['04.jpeg', 'Complex Lines'], ['05.jpeg', 'Complex Lines'], ['06.jpeg', 'Complex Lines'],
-['10.jpeg', 'Complex Lines'], ['11.jpeg', 'Complex Lines'], ['12.jpeg', 'Complex Lines']
-]
-
-iface = gr.Interface(predict, [gr.inputs.Image(type='filepath'),
-    gr.inputs.Radio(['Complex Lines','Simple Lines'], type="value", default='Simple Lines', label='version')],
-    gr.outputs.Image(type="pil"), title=title,examples=examples)
-
-iface.launch()

spaces/Abubakari/Sales_Prediction/README.md

@@ -1,12 +0,0 @@
----
-title: Sales Prediction
-emoji: 💻
-colorFrom: blue
-colorTo: red
-sdk: streamlit
-sdk_version: 1.17.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/AchyuthGamer/OpenGPT/g4f/Provider/ChatgptX.py

@@ -1,97 +0,0 @@
-from __future__ import annotations
-
-import re
-import json
-
-from aiohttp import ClientSession
-from ..typing import AsyncResult, Messages
-from .base_provider import AsyncGeneratorProvider
-from .helper import format_prompt
-
-
-class ChatgptX(AsyncGeneratorProvider):
-    url = "https://chatgptx.de"
-    supports_gpt_35_turbo = True
-    working               = True
-
-    @classmethod
-    async def create_async_generator(
-        cls,
-        model: str,
-        messages: Messages,
-        **kwargs
-    ) -> AsyncResult:
-        headers = {
-            'accept-language': 'de-DE,de;q=0.9,en-DE;q=0.8,en;q=0.7,en-US',
-            'sec-ch-ua': '"Google Chrome";v="117", "Not;A=Brand";v="8", "Chromium";v="117"',
-            'sec-ch-ua-mobile': '?0',
-            'sec-ch-ua-platform': 'Linux',
-            'sec-fetch-dest': 'empty',
-            'sec-fetch-mode': 'cors',
-            'sec-fetch-site': 'same-origin',
-            'user-agent': 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/117.0.0.0 Safari/537.36',
-        }
-        async with ClientSession(headers=headers) as session:
-            async with session.get(f"{cls.url}/") as response:
-                response = await response.text()
-                result = re.search(r'<meta name="csrf-token" content="(.*?)"', response)
-                if result:
-                    csrf_token = result.group(1)
-                result = re.search(r"openconversions\('(.*?)'\)", response)
-                if result:
-                    chat_id = result.group(1)
-                result = re.search(r'<input type="hidden" id="user_id" value="(.*?)"', response)
-                if result:
-                    user_id = result.group(1)
-
-            if not csrf_token or not chat_id or not user_id:
-                raise RuntimeError("Missing csrf_token, chat_id or user_id")
-
-            data = {
-                '_token': csrf_token,
-                'user_id': user_id,
-                'chats_id': chat_id,
-                'prompt': format_prompt(messages),
-                'current_model': "gpt3"
-            }
-            headers = {
-                'authority': 'chatgptx.de',
-                'accept': 'application/json, text/javascript, */*; q=0.01',
-                'origin': cls.url,
-                'referer': f'{cls.url}/',
-                'x-csrf-token': csrf_token,
-                'x-requested-with': 'XMLHttpRequest'
-            }
-            async with session.post(cls.url + '/sendchat', data=data, headers=headers) as response:
-                response.raise_for_status()
-                chat = await response.json()
-                if "response" not in chat or not chat["response"]:
-                    raise RuntimeError(f'Response: {chat}')
-            headers = {
-                'authority': 'chatgptx.de',
-                'accept': 'text/event-stream',
-                'referer': f'{cls.url}/',
-                'x-csrf-token': csrf_token,
-                'x-requested-with': 'XMLHttpRequest'
-            }
-            data = {
-                "user_id": user_id,
-                "chats_id": chat_id,
-                "prompt": format_prompt(messages),
-                "current_model": "gpt3",
-                "conversions_id": chat["conversions_id"],
-                "ass_conversions_id": chat["ass_conversions_id"],
-            }
-            async with session.get(f'{cls.url}/chats_stream', params=data, headers=headers) as response:
-                response.raise_for_status()
-                async for line in response.content:
-                    if line.startswith(b"data: "):
-                        row = line[6:-1]
-                        if row == b"[DONE]":
-                            break
-                        try:
-                            content = json.loads(row)["choices"][0]["delta"].get("content")
-                        except:
-                            raise RuntimeError(f"Broken line: {line.decode()}")
-                        if content:
-                            yield content

spaces/AgentVerse/agentVerse/agentverse/environments/simulation_env/rules/visibility/oneself.py

@@ -1,18 +0,0 @@
-from __future__ import annotations
-
-from typing import TYPE_CHECKING, Any
-
-from . import visibility_registry as VisibilityRegistry
-from .base import BaseVisibility
-
-if TYPE_CHECKING:
-    from agentverse.environments import BaseEnvironment
-
-
-@VisibilityRegistry.register("oneself")
-class OneselfVisibility(BaseVisibility):
-    """Only the agent itself can see the message"""
-
-    def update_visible_agents(self, environment: BaseEnvironment):
-        for agent in environment.agents:
-            agent.set_receiver(set({agent.name}))

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/maker/Factory.d.ts

@@ -1,6 +0,0 @@
-import Maker from './Maker';
-
-export default function (
-    styles?: Object | string,
-    customBuilders?: Maker.BuildersType
-): Maker;

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/ninepatch/Factory.js

@@ -1,13 +0,0 @@
-import NinePatch from './NinePatch.js';
-import ObjectFactory from '../ObjectFactory.js';
-import SetValue from '../../../plugins/utils/object/SetValue.js';
-
-ObjectFactory.register('ninePatch', function (x, y, width, height, key, columns, rows, config) {
-    var gameObject = new NinePatch(this.scene, x, y, width, height, key, columns, rows, config);
-    this.scene.add.existing(gameObject);
-    return gameObject;
-});
-
-SetValue(window, 'RexPlugins.UI.NinePatch', NinePatch);
-
-export default NinePatch;

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/space/Factory.js

@@ -1,14 +0,0 @@
-import Space from './Space.js';
-import ObjectFactory from '../ObjectFactory.js';
-import SetValue from '../../../plugins/utils/object/SetValue.js';
-
-ObjectFactory.register('space', function () {
-    var gameObject = new Space(this.scene);
-    // Don't add Zone into scene
-    // this.scene.add.existing(gameObject);
-    return gameObject;
-});
-
-SetValue(window, 'RexPlugins.UI.Space', Space);
-
-export default Space;

spaces/Aloento/9Nine-PITS/text/cleaners.py

@@ -1,113 +0,0 @@
-import re
-
-from text import cleaned_text_to_sequence
-from text.english import english_to_ipa
-from text.japanese import japanese_to_ipa
-from text.mandarin import chinese_to_ipa, pinyin_to_ipa
-from text.symbols import symbols
-
-
-def str_replace(data):
-  zh_tab = [";", ":", "\"", "'"]
-  eng_tab = [".", ",", ' ', " "]
-
-  for index in range(len(zh_tab)):
-    if zh_tab[index] in data:
-      data = data.replace(zh_tab[index], eng_tab[index])
-
-  return data
-
-
-def clean_text(text):
-  cleaned_text, lang_seq = cje_cleaner(text)
-  cleaned_text = str_replace(cleaned_text)
-  cleaned_text, lang_seq = remove_invalid_text(cleaned_text, lang_seq)
-
-  return cleaned_text, lang_seq
-
-
-def text_to_sequence(text):
-  cleaned_text, lang_seq = clean_text(text)
-  return cleaned_text_to_sequence(cleaned_text), lang_seq
-
-
-lang_map = {
-  "ZH": 0,
-  "JA": 1,
-  "EN": 3,
-  "P": 0,
-  "other": 5
-}
-
-
-def cje_cleaner(text: str):
-  text = str_replace(text).replace("\"", '')
-
-  # find all text blocks enclosed in [JA], [ZH], [EN], [P]
-  original_text = text
-  blocks = re.finditer(r'\[(JA|ZH|EN|P)\](.*?)\[\1\]', text)
-
-  cleaned_text = ""
-  lang_seq = []
-  last_end = 0
-
-  for block in blocks:
-    start, end = block.span()
-
-    # insert text not enclosed in any blocks
-    ipa = original_text[last_end:start]
-    lang_seq += [lang_map["other"] for i in ipa]
-
-    cleaned_text += ipa
-    last_end = end
-    language = block.group(1)
-    text = block.group(2)
-
-    if language == 'P':
-      ipa = pinyin_to_ipa(text)
-      lang_seq += [lang_map[language] for i in ipa]
-      cleaned_text += ipa
-
-    if language == 'JA':
-      ipa = japanese_to_ipa(text)
-      lang_seq += [lang_map[language] for i in ipa]
-      cleaned_text += ipa
-
-    elif language == 'ZH':
-      ipa = chinese_to_ipa(text)
-      lang_seq += [lang_map[language] for i in ipa]
-      cleaned_text += ipa
-
-    elif language == 'EN':
-      ipa = english_to_ipa(text)
-      lang_seq += [lang_map[language] for i in ipa]
-      cleaned_text += ipa
-
-  ipa = original_text[last_end:]
-
-  lang_seq += [lang_map["other"] for i in ipa]
-  cleaned_text += ipa
-
-  assert len(cleaned_text) == len(lang_seq)
-  return cleaned_text, lang_seq
-
-
-def remove_invalid_text(cleaned_text, lang_seq):
-  new_cleaned_text = ''
-  new_lang_seq = []
-
-  for symbol, la in zip(cleaned_text, lang_seq):
-    if symbol not in symbols:
-      print("Invalid Symbol:", symbol)
-      print("In: ", cleaned_text)
-      continue
-
-    if la == lang_map["other"]:
-      print("Invalid Lang:", symbol)
-      print("In: ", cleaned_text)
-      continue
-
-    new_cleaned_text += symbol
-    new_lang_seq.append(la)
-
-  return new_cleaned_text, new_lang_seq

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/models/README.md

@@ -1,3 +0,0 @@
-# Models
-
-For more detail on the models, please refer to the [docs](https://huggingface.co/docs/diffusers/api/models/overview).

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/schedulers/scheduling_ddim_inverse.py

@@ -1,349 +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.
-
-# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
-# and https://github.com/hojonathanho/diffusion
-import math
-from dataclasses import dataclass
-from typing import List, Optional, Tuple, Union
-
-import numpy as np
-import torch
-
-from diffusers.configuration_utils import ConfigMixin, register_to_config
-from diffusers.schedulers.scheduling_utils import SchedulerMixin
-from diffusers.utils import BaseOutput, deprecate
-
-
-@dataclass
-# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->DDIM
-class DDIMSchedulerOutput(BaseOutput):
-    """
-    Output class for the scheduler's step function output.
-
-    Args:
-        prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
-            Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the
-            denoising loop.
-        pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
-            The predicted denoised sample (x_{0}) based on the model output from the current timestep.
-            `pred_original_sample` can be used to preview progress or for guidance.
-    """
-
-    prev_sample: torch.FloatTensor
-    pred_original_sample: Optional[torch.FloatTensor] = None
-
-
-# Copied from diffusers.schedulers.scheduling_ddpm.betas_for_alpha_bar
-def betas_for_alpha_bar(
-    num_diffusion_timesteps,
-    max_beta=0.999,
-    alpha_transform_type="cosine",
-):
-    """
-    Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of
-    (1-beta) over time from t = [0,1].
-
-    Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up
-    to that part of the diffusion process.
-
-
-    Args:
-        num_diffusion_timesteps (`int`): the number of betas to produce.
-        max_beta (`float`): the maximum beta to use; use values lower than 1 to
-                     prevent singularities.
-        alpha_transform_type (`str`, *optional*, default to `cosine`): the type of noise schedule for alpha_bar.
-                     Choose from `cosine` or `exp`
-
-    Returns:
-        betas (`np.ndarray`): the betas used by the scheduler to step the model outputs
-    """
-    if alpha_transform_type == "cosine":
-
-        def alpha_bar_fn(t):
-            return math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2
-
-    elif alpha_transform_type == "exp":
-
-        def alpha_bar_fn(t):
-            return math.exp(t * -12.0)
-
-    else:
-        raise ValueError(f"Unsupported alpha_tranform_type: {alpha_transform_type}")
-
-    betas = []
-    for i in range(num_diffusion_timesteps):
-        t1 = i / num_diffusion_timesteps
-        t2 = (i + 1) / num_diffusion_timesteps
-        betas.append(min(1 - alpha_bar_fn(t2) / alpha_bar_fn(t1), max_beta))
-    return torch.tensor(betas, dtype=torch.float32)
-
-
-# Copied from diffusers.schedulers.scheduling_ddim.rescale_zero_terminal_snr
-def rescale_zero_terminal_snr(betas):
-    """
-    Rescales betas to have zero terminal SNR Based on https://arxiv.org/pdf/2305.08891.pdf (Algorithm 1)
-
-
-    Args:
-        betas (`torch.FloatTensor`):
-            the betas that the scheduler is being initialized with.
-
-    Returns:
-        `torch.FloatTensor`: rescaled betas with zero terminal SNR
-    """
-    # Convert betas to alphas_bar_sqrt
-    alphas = 1.0 - betas
-    alphas_cumprod = torch.cumprod(alphas, dim=0)
-    alphas_bar_sqrt = alphas_cumprod.sqrt()
-
-    # Store old values.
-    alphas_bar_sqrt_0 = alphas_bar_sqrt[0].clone()
-    alphas_bar_sqrt_T = alphas_bar_sqrt[-1].clone()
-
-    # Shift so the last timestep is zero.
-    alphas_bar_sqrt -= alphas_bar_sqrt_T
-
-    # Scale so the first timestep is back to the old value.
-    alphas_bar_sqrt *= alphas_bar_sqrt_0 / (alphas_bar_sqrt_0 - alphas_bar_sqrt_T)
-
-    # Convert alphas_bar_sqrt to betas
-    alphas_bar = alphas_bar_sqrt**2  # Revert sqrt
-    alphas = alphas_bar[1:] / alphas_bar[:-1]  # Revert cumprod
-    alphas = torch.cat([alphas_bar[0:1], alphas])
-    betas = 1 - alphas
-
-    return betas
-
-
-class DDIMInverseScheduler(SchedulerMixin, ConfigMixin):
-    """
-    DDIMInverseScheduler is the reverse scheduler of [`DDIMScheduler`].
-
-    [`~ConfigMixin`] takes care of storing all config attributes that are passed in the scheduler's `__init__`
-    function, such as `num_train_timesteps`. They can be accessed via `scheduler.config.num_train_timesteps`.
-    [`SchedulerMixin`] provides general loading and saving functionality via the [`SchedulerMixin.save_pretrained`] and
-    [`~SchedulerMixin.from_pretrained`] functions.
-
-    For more details, see the original paper: https://arxiv.org/abs/2010.02502
-
-    Args:
-        num_train_timesteps (`int`): number of diffusion steps used to train the model.
-        beta_start (`float`): the starting `beta` value of inference.
-        beta_end (`float`): the final `beta` value.
-        beta_schedule (`str`):
-            the beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from
-            `linear`, `scaled_linear`, or `squaredcos_cap_v2`.
-        trained_betas (`np.ndarray`, optional):
-            option to pass an array of betas directly to the constructor to bypass `beta_start`, `beta_end` etc.
-        clip_sample (`bool`, default `True`):
-            option to clip predicted sample for numerical stability.
-        clip_sample_range (`float`, default `1.0`):
-            the maximum magnitude for sample clipping. Valid only when `clip_sample=True`.
-        set_alpha_to_zero (`bool`, default `True`):
-            each diffusion step uses the value of alphas product at that step and at the previous one. For the final
-            step there is no previous alpha. When this option is `True` the previous alpha product is fixed to `0`,
-            otherwise it uses the value of alpha at step `num_train_timesteps - 1`.
-        steps_offset (`int`, default `0`):
-            an offset added to the inference steps. You can use a combination of `offset=1` and
-            `set_alpha_to_zero=False`, to make the last step use step `num_train_timesteps - 1` for the previous alpha
-            product.
-        prediction_type (`str`, default `epsilon`, optional):
-            prediction type of the scheduler function, one of `epsilon` (predicting the noise of the diffusion
-            process), `sample` (directly predicting the noisy sample`) or `v_prediction` (see section 2.4
-            https://imagen.research.google/video/paper.pdf)
-        timestep_spacing (`str`, default `"leading"`):
-            The way the timesteps should be scaled. Refer to Table 2. of [Common Diffusion Noise Schedules and Sample
-            Steps are Flawed](https://arxiv.org/abs/2305.08891) for more information.
-        rescale_betas_zero_snr (`bool`, default `False`):
-            whether to rescale the betas to have zero terminal SNR (proposed by https://arxiv.org/pdf/2305.08891.pdf).
-            This can enable the model to generate very bright and dark samples instead of limiting it to samples with
-            medium brightness. Loosely related to
-            [`--offset_noise`](https://github.com/huggingface/diffusers/blob/74fd735eb073eb1d774b1ab4154a0876eb82f055/examples/dreambooth/train_dreambooth.py#L506).
-    """
-
-    order = 1
-    ignore_for_config = ["kwargs"]
-    _deprecated_kwargs = ["set_alpha_to_zero"]
-
-    @register_to_config
-    def __init__(
-        self,
-        num_train_timesteps: int = 1000,
-        beta_start: float = 0.0001,
-        beta_end: float = 0.02,
-        beta_schedule: str = "linear",
-        trained_betas: Optional[Union[np.ndarray, List[float]]] = None,
-        clip_sample: bool = True,
-        set_alpha_to_one: bool = True,
-        steps_offset: int = 0,
-        prediction_type: str = "epsilon",
-        clip_sample_range: float = 1.0,
-        timestep_spacing: str = "leading",
-        rescale_betas_zero_snr: bool = False,
-        **kwargs,
-    ):
-        if kwargs.get("set_alpha_to_zero", None) is not None:
-            deprecation_message = (
-                "The `set_alpha_to_zero` argument is deprecated. Please use `set_alpha_to_one` instead."
-            )
-            deprecate("set_alpha_to_zero", "1.0.0", deprecation_message, standard_warn=False)
-            set_alpha_to_one = kwargs["set_alpha_to_zero"]
-        if trained_betas is not None:
-            self.betas = torch.tensor(trained_betas, dtype=torch.float32)
-        elif beta_schedule == "linear":
-            self.betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32)
-        elif beta_schedule == "scaled_linear":
-            # this schedule is very specific to the latent diffusion model.
-            self.betas = (
-                torch.linspace(beta_start**0.5, beta_end**0.5, num_train_timesteps, dtype=torch.float32) ** 2
-            )
-        elif beta_schedule == "squaredcos_cap_v2":
-            # Glide cosine schedule
-            self.betas = betas_for_alpha_bar(num_train_timesteps)
-        else:
-            raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}")
-
-        # Rescale for zero SNR
-        if rescale_betas_zero_snr:
-            self.betas = rescale_zero_terminal_snr(self.betas)
-
-        self.alphas = 1.0 - self.betas
-        self.alphas_cumprod = torch.cumprod(self.alphas, dim=0)
-
-        # At every step in inverted ddim, we are looking into the next alphas_cumprod
-        # For the initial step, there is no current alphas_cumprod, and the index is out of bounds
-        # `set_alpha_to_one` decides whether we set this parameter simply to one
-        # in this case, self.step() just output the predicted noise
-        # or whether we use the initial alpha used in training the diffusion model.
-        self.initial_alpha_cumprod = torch.tensor(1.0) if set_alpha_to_one else self.alphas_cumprod[0]
-
-        # standard deviation of the initial noise distribution
-        self.init_noise_sigma = 1.0
-
-        # setable values
-        self.num_inference_steps = None
-        self.timesteps = torch.from_numpy(np.arange(0, num_train_timesteps).copy().astype(np.int64))
-
-    # Copied from diffusers.schedulers.scheduling_ddim.DDIMScheduler.scale_model_input
-    def scale_model_input(self, sample: torch.FloatTensor, timestep: Optional[int] = None) -> torch.FloatTensor:
-        """
-        Ensures interchangeability with schedulers that need to scale the denoising model input depending on the
-        current timestep.
-
-        Args:
-            sample (`torch.FloatTensor`): input sample
-            timestep (`int`, optional): current timestep
-
-        Returns:
-            `torch.FloatTensor`: scaled input sample
-        """
-        return sample
-
-    def set_timesteps(self, num_inference_steps: int, device: Union[str, torch.device] = None):
-        """
-        Sets the discrete timesteps used for the diffusion chain. Supporting function to be run before inference.
-
-        Args:
-            num_inference_steps (`int`):
-                the number of diffusion steps used when generating samples with a pre-trained model.
-        """
-
-        if num_inference_steps > self.config.num_train_timesteps:
-            raise ValueError(
-                f"`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:"
-                f" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle"
-                f" maximal {self.config.num_train_timesteps} timesteps."
-            )
-
-        self.num_inference_steps = num_inference_steps
-
-        # "leading" and "trailing" corresponds to annotation of Table 1. of https://arxiv.org/abs/2305.08891
-        if self.config.timestep_spacing == "leading":
-            step_ratio = self.config.num_train_timesteps // self.num_inference_steps
-            # creates integer timesteps by multiplying by ratio
-            # casting to int to avoid issues when num_inference_step is power of 3
-            timesteps = (np.arange(0, num_inference_steps) * step_ratio).round().copy().astype(np.int64)
-            timesteps += self.config.steps_offset
-        elif self.config.timestep_spacing == "trailing":
-            step_ratio = self.config.num_train_timesteps / self.num_inference_steps
-            # creates integer timesteps by multiplying by ratio
-            # casting to int to avoid issues when num_inference_step is power of 3
-            timesteps = np.round(np.arange(self.config.num_train_timesteps, 0, -step_ratio)[::-1]).astype(np.int64)
-            timesteps -= 1
-        else:
-            raise ValueError(
-                f"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'leading' or 'trailing'."
-            )
-
-        # Roll timesteps array by one to reflect reversed origin and destination semantics for each step
-        timesteps = np.roll(timesteps, 1)
-        timesteps[0] = int(timesteps[1] - step_ratio)
-        self.timesteps = torch.from_numpy(timesteps).to(device)
-
-    def step(
-        self,
-        model_output: torch.FloatTensor,
-        timestep: int,
-        sample: torch.FloatTensor,
-        eta: float = 0.0,
-        use_clipped_model_output: bool = False,
-        variance_noise: Optional[torch.FloatTensor] = None,
-        return_dict: bool = True,
-    ) -> Union[DDIMSchedulerOutput, Tuple]:
-        # 1. get previous step value (=t+1)
-        prev_timestep = timestep + self.config.num_train_timesteps // self.num_inference_steps
-
-        # 2. compute alphas, betas
-        # change original implementation to exactly match noise levels for analogous forward process
-        alpha_prod_t = self.alphas_cumprod[timestep] if timestep >= 0 else self.initial_alpha_cumprod
-        alpha_prod_t_prev = self.alphas_cumprod[prev_timestep]
-
-        beta_prod_t = 1 - alpha_prod_t
-
-        # 3. compute predicted original sample from predicted noise also called
-        # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
-        if self.config.prediction_type == "epsilon":
-            pred_original_sample = (sample - beta_prod_t ** (0.5) * model_output) / alpha_prod_t ** (0.5)
-            pred_epsilon = model_output
-        elif self.config.prediction_type == "sample":
-            pred_original_sample = model_output
-            pred_epsilon = (sample - alpha_prod_t ** (0.5) * pred_original_sample) / beta_prod_t ** (0.5)
-        elif self.config.prediction_type == "v_prediction":
-            pred_original_sample = (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
-            pred_epsilon = (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
-        else:
-            raise ValueError(
-                f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"
-                " `v_prediction`"
-            )
-
-        # 4. Clip or threshold "predicted x_0"
-        if self.config.clip_sample:
-            pred_original_sample = pred_original_sample.clamp(
-                -self.config.clip_sample_range, self.config.clip_sample_range
-            )
-
-        # 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
-        pred_sample_direction = (1 - alpha_prod_t_prev) ** (0.5) * pred_epsilon
-
-        # 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
-        prev_sample = alpha_prod_t_prev ** (0.5) * pred_original_sample + pred_sample_direction
-
-        if not return_dict:
-            return (prev_sample, pred_original_sample)
-        return DDIMSchedulerOutput(prev_sample=prev_sample, pred_original_sample=pred_original_sample)
-
-    def __len__(self):
-        return self.config.num_train_timesteps

spaces/Andy1621/uniformer_image_detection/configs/instaboost/README.md

@@ -1,44 +0,0 @@
-# InstaBoost for MMDetection
-
-[ALGORITHM]
-
-Configs in this directory is the implementation for ICCV2019 paper "InstaBoost: Boosting Instance Segmentation Via Probability Map Guided Copy-Pasting" and provided by the authors of the paper. InstaBoost is a data augmentation method for object detection and instance segmentation. The paper has been released on [`arXiv`](https://arxiv.org/abs/1908.07801).
-
-```latex
-@inproceedings{fang2019instaboost,
-  title={Instaboost: Boosting instance segmentation via probability map guided copy-pasting},
-  author={Fang, Hao-Shu and Sun, Jianhua and Wang, Runzhong and Gou, Minghao and Li, Yong-Lu and Lu, Cewu},
-  booktitle={Proceedings of the IEEE International Conference on Computer Vision},
-  pages={682--691},
-  year={2019}
-}
-```
-
-## Usage
-
-### Requirements
-
-You need to install `instaboostfast` before using it.
-
-```shell
-pip install instaboostfast
-```
-
-The code and more details can be found [here](https://github.com/GothicAi/Instaboost).
-
-### Integration with MMDetection
-
-InstaBoost have been already integrated in the data pipeline, thus all you need is to add or change **InstaBoost** configurations after **LoadImageFromFile**. We have provided examples like [this](mask_rcnn_r50_fpn_instaboost_4x#L121). You can refer to [`InstaBoostConfig`](https://github.com/GothicAi/InstaBoost-pypi#instaboostconfig) for more details.
-
-## Results and Models
-
-- All models were trained on `coco_2017_train` and tested on `coco_2017_val` for conveinience of evaluation and comparison. In the paper, the results are obtained from `test-dev`.
-- To balance accuracy and training time when using InstaBoost, models released in this page are all trained for 48 Epochs. Other training and testing configs strictly follow the original framework.
-- For results and models in MMDetection V1.x, please refer to [Instaboost](https://github.com/GothicAi/Instaboost).
-
-|     Network     |       Backbone       | Lr schd | Mem (GB) | Inf time (fps) | box AP  | mask AP | Config |     Download       |
-| :-------------: |      :--------:      | :-----: | :------: | :------------: | :------:| :-----: | :------: | :-----------------: |
-|    Mask R-CNN   |       R-50-FPN       |   4x    | 4.4      | 17.5           | 40.6    | 36.6    | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/instaboost/mask_rcnn_r50_fpn_instaboost_4x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/instaboost/mask_rcnn_r50_fpn_instaboost_4x_coco/mask_rcnn_r50_fpn_instaboost_4x_coco_20200307-d025f83a.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/instaboost/mask_rcnn_r50_fpn_instaboost_4x_coco/mask_rcnn_r50_fpn_instaboost_4x_coco_20200307_223635.log.json) |
-|    Mask R-CNN   |      R-101-FPN       |   4x    | 6.4       |                | 42.5    | 38.0    | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/instaboost/mask_rcnn_r101_fpn_instaboost_4x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/instaboost/mask_rcnn_r101_fpn_instaboost_4x_coco/mask_rcnn_r101_fpn_instaboost_4x_coco_20200703_235738-f23f3a5f.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/instaboost/mask_rcnn_r101_fpn_instaboost_4x_coco/mask_rcnn_r101_fpn_instaboost_4x_coco_20200703_235738.log.json) |
-|    Mask R-CNN   |   X-101-64x4d-FPN    |   4x    | 10.7     |                | 44.7    | 39.7    | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/instaboost/mask_rcnn_x101_64x4d_fpn_instaboost_4x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/instaboost/mask_rcnn_x101_64x4d_fpn_instaboost_4x_coco/mask_rcnn_x101_64x4d_fpn_instaboost_4x_coco_20200515_080947-8ed58c1b.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/instaboost/mask_rcnn_x101_64x4d_fpn_instaboost_4x_coco/mask_rcnn_x101_64x4d_fpn_instaboost_4x_coco_20200515_080947.log.json) |
-|  Cascade R-CNN  |       R-101-FPN      |   4x    | 6.0      | 12.0            | 43.7    | 38.0    | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/instaboost/cascade_mask_rcnn_r50_fpn_instaboost_4x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/instaboost/cascade_mask_rcnn_r50_fpn_instaboost_4x_coco/cascade_mask_rcnn_r50_fpn_instaboost_4x_coco_20200307-c19d98d9.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/instaboost/cascade_mask_rcnn_r50_fpn_instaboost_4x_coco/cascade_mask_rcnn_r50_fpn_instaboost_4x_coco_20200307_223646.log.json) |

spaces/Andy1621/uniformer_image_segmentation/configs/ann/ann_r101-d8_512x512_160k_ade20k.py

@@ -1,2 +0,0 @@
-_base_ = './ann_r50-d8_512x512_160k_ade20k.py'
-model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))

spaces/AnishKumbhar/ChatBot/text-generation-webui-main/update_windows.bat

@@ -1,37 +0,0 @@
-@echo off
-
-cd /D "%~dp0"
-
-set PATH=%PATH%;%SystemRoot%\system32
-
-echo "%CD%"| findstr /C:" " >nul && echo This script relies on Miniconda which can not be silently installed under a path with spaces. && goto end
-
-@rem fix failed install when installing to a separate drive
-set TMP=%cd%\installer_files
-set TEMP=%cd%\installer_files
-
-@rem deactivate existing conda envs as needed to avoid conflicts
-(call conda deactivate && call conda deactivate && call conda deactivate) 2>nul
-
-@rem config
-set CONDA_ROOT_PREFIX=%cd%\installer_files\conda
-set INSTALL_ENV_DIR=%cd%\installer_files\env
-
-@rem environment isolation
-set PYTHONNOUSERSITE=1
-set PYTHONPATH=
-set PYTHONHOME=
-set "CUDA_PATH=%INSTALL_ENV_DIR%"
-set "CUDA_HOME=%CUDA_PATH%"
-
-@rem activate installer env
-call "%CONDA_ROOT_PREFIX%\condabin\conda.bat" activate "%INSTALL_ENV_DIR%" || ( echo. && echo Miniconda hook not found. && goto end )
-
-@rem update installer env
-call python one_click.py --update && (
-    echo.
-    echo Done!
-)
-
-:end
-pause

spaces/Apex-X/ROOPOK/roop/core.py

@@ -1,215 +0,0 @@
-import os
-import sys
-# single thread doubles cuda performance - needs to be set before torch import
-if any(arg.startswith('--execution-provider') for arg in sys.argv):
-    os.environ['OMP_NUM_THREADS'] = '1'
-# reduce tensorflow log level
-os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
-import warnings
-from typing import List
-import platform
-import signal
-import shutil
-import argparse
-import torch
-import onnxruntime
-import tensorflow
-
-import roop.globals
-import roop.metadata
-import roop.ui as ui
-from roop.predicter import predict_image, predict_video
-from roop.processors.frame.core import get_frame_processors_modules
-from roop.utilities import has_image_extension, is_image, is_video, detect_fps, create_video, extract_frames, get_temp_frame_paths, restore_audio, create_temp, move_temp, clean_temp, normalize_output_path
-
-if 'ROCMExecutionProvider' in roop.globals.execution_providers:
-    del torch
-
-warnings.filterwarnings('ignore', category=FutureWarning, module='insightface')
-warnings.filterwarnings('ignore', category=UserWarning, module='torchvision')
-
-
-def parse_args() -> None:
-    signal.signal(signal.SIGINT, lambda signal_number, frame: destroy())
-    program = argparse.ArgumentParser(formatter_class=lambda prog: argparse.HelpFormatter(prog, max_help_position=100))
-    program.add_argument('-s', '--source', help='select an source image', dest='source_path')
-    program.add_argument('-t', '--target', help='select an target image or video', dest='target_path')
-    program.add_argument('-o', '--output', help='select output file or directory', dest='output_path')
-    program.add_argument('--frame-processor', help='frame processors (choices: face_swapper, face_enhancer, ...)', dest='frame_processor', default=['face_swapper'], nargs='+')
-    program.add_argument('--keep-fps', help='keep original fps', dest='keep_fps', action='store_true', default=False)
-    program.add_argument('--keep-audio', help='keep original audio', dest='keep_audio', action='store_true', default=True)
-    program.add_argument('--keep-frames', help='keep temporary frames', dest='keep_frames', action='store_true', default=False)
-    program.add_argument('--many-faces', help='process every face', dest='many_faces', action='store_true', default=False)
-    program.add_argument('--video-encoder', help='adjust output video encoder', dest='video_encoder', default='libx264', choices=['libx264', 'libx265', 'libvpx-vp9'])
-    program.add_argument('--video-quality', help='adjust output video quality', dest='video_quality', type=int, default=18, choices=range(52), metavar='[0-51]')
-    program.add_argument('--max-memory', help='maximum amount of RAM in GB', dest='max_memory', type=int, default=suggest_max_memory())
-    program.add_argument('--execution-provider', help='available execution provider (choices: cpu, ...)', dest='execution_provider', default=['cpu'], choices=suggest_execution_providers(), nargs='+')
-    program.add_argument('--execution-threads', help='number of execution threads', dest='execution_threads', type=int, default=suggest_execution_threads())
-    program.add_argument('-v', '--version', action='version', version=f'{roop.metadata.name} {roop.metadata.version}')
-
-    args = program.parse_args()
-
-    roop.globals.source_path = args.source_path
-    roop.globals.target_path = args.target_path
-    roop.globals.output_path = normalize_output_path(roop.globals.source_path, roop.globals.target_path, args.output_path)
-    roop.globals.frame_processors = args.frame_processor
-    roop.globals.headless = args.source_path or args.target_path or args.output_path
-    roop.globals.keep_fps = args.keep_fps
-    roop.globals.keep_audio = args.keep_audio
-    roop.globals.keep_frames = args.keep_frames
-    roop.globals.many_faces = args.many_faces
-    roop.globals.video_encoder = args.video_encoder
-    roop.globals.video_quality = args.video_quality
-    roop.globals.max_memory = args.max_memory
-    roop.globals.execution_providers = decode_execution_providers(args.execution_provider)
-    roop.globals.execution_threads = args.execution_threads
-
-
-def encode_execution_providers(execution_providers: List[str]) -> List[str]:
-    return [execution_provider.replace('ExecutionProvider', '').lower() for execution_provider in execution_providers]
-
-
-def decode_execution_providers(execution_providers: List[str]) -> List[str]:
-    return [provider for provider, encoded_execution_provider in zip(onnxruntime.get_available_providers(), encode_execution_providers(onnxruntime.get_available_providers()))
-            if any(execution_provider in encoded_execution_provider for execution_provider in execution_providers)]
-
-
-def suggest_max_memory() -> int:
-    if platform.system().lower() == 'darwin':
-        return 4
-    return 16
-
-
-def suggest_execution_providers() -> List[str]:
-    return encode_execution_providers(onnxruntime.get_available_providers())
-
-
-def suggest_execution_threads() -> int:
-    if 'DmlExecutionProvider' in roop.globals.execution_providers:
-        return 1
-    if 'ROCMExecutionProvider' in roop.globals.execution_providers:
-        return 1
-    return 8
-
-
-def limit_resources() -> None:
-    # prevent tensorflow memory leak
-    gpus = tensorflow.config.experimental.list_physical_devices('GPU')
-    for gpu in gpus:
-        tensorflow.config.experimental.set_virtual_device_configuration(gpu, [
-            tensorflow.config.experimental.VirtualDeviceConfiguration(memory_limit=1024)
-        ])
-    # limit memory usage
-    if roop.globals.max_memory:
-        memory = roop.globals.max_memory * 1024 ** 3
-        if platform.system().lower() == 'darwin':
-            memory = roop.globals.max_memory * 1024 ** 6
-        if platform.system().lower() == 'windows':
-            import ctypes
-            kernel32 = ctypes.windll.kernel32
-            kernel32.SetProcessWorkingSetSize(-1, ctypes.c_size_t(memory), ctypes.c_size_t(memory))
-        else:
-            import resource
-            resource.setrlimit(resource.RLIMIT_DATA, (memory, memory))
-
-
-def release_resources() -> None:
-    if 'CUDAExecutionProvider' in roop.globals.execution_providers:
-        torch.cuda.empty_cache()
-
-
-def pre_check() -> bool:
-    if sys.version_info < (3, 9):
-        update_status('Python version is not supported - please upgrade to 3.9 or higher.')
-        return False
-    if not shutil.which('ffmpeg'):
-        update_status('ffmpeg is not installed.')
-        return False
-    return True
-
-
-def update_status(message: str, scope: str = 'ROOP.CORE') -> None:
-    print(f'[{scope}] {message}')
-    if not roop.globals.headless:
-        ui.update_status(message)
-
-
-def start() -> None:
-    for frame_processor in get_frame_processors_modules(roop.globals.frame_processors):
-        if not frame_processor.pre_start():
-            return
-    # process image to image
-    if has_image_extension(roop.globals.target_path):
-        if predict_image(roop.globals.target_path):
-            destroy()
-        shutil.copy2(roop.globals.target_path, roop.globals.output_path)
-        for frame_processor in get_frame_processors_modules(roop.globals.frame_processors):
-            update_status('Progressing...', frame_processor.NAME)
-            frame_processor.process_image(roop.globals.source_path, roop.globals.output_path, roop.globals.output_path)
-            frame_processor.post_process()
-            release_resources()
-        if is_image(roop.globals.target_path):
-            update_status('Processing to image succeed!')
-        else:
-            update_status('Processing to image failed!')
-        return
-    # process image to videos
-    if predict_video(roop.globals.target_path):
-        destroy()
-    update_status('Creating temp resources...')
-    create_temp(roop.globals.target_path)
-    update_status('Extracting frames...')
-    extract_frames(roop.globals.target_path)
-    temp_frame_paths = get_temp_frame_paths(roop.globals.target_path)
-    for frame_processor in get_frame_processors_modules(roop.globals.frame_processors):
-        update_status('Progressing...', frame_processor.NAME)
-        frame_processor.process_video(roop.globals.source_path, temp_frame_paths)
-        frame_processor.post_process()
-        release_resources()
-    # handles fps
-    if roop.globals.keep_fps:
-        update_status('Detecting fps...')
-        fps = detect_fps(roop.globals.target_path)
-        update_status(f'Creating video with {fps} fps...')
-        create_video(roop.globals.target_path, fps)
-    else:
-        update_status('Creating video with 30.0 fps...')
-        create_video(roop.globals.target_path)
-    # handle audio
-    if roop.globals.keep_audio:
-        if roop.globals.keep_fps:
-            update_status('Restoring audio...')
-        else:
-            update_status('Restoring audio might cause issues as fps are not kept...')
-        restore_audio(roop.globals.target_path, roop.globals.output_path)
-    else:
-        move_temp(roop.globals.target_path, roop.globals.output_path)
-    # clean and validate
-    clean_temp(roop.globals.target_path)
-    if is_video(roop.globals.target_path):
-        update_status('Processing to video succeed!')
-    else:
-        update_status('Processing to video failed!')
-
-
-def destroy() -> None:
-    if roop.globals.target_path:
-        clean_temp(roop.globals.target_path)
-    quit()
-
-
-def run() -> None:
-    parse_args()
-    if not pre_check():
-        return
-    for frame_processor in get_frame_processors_modules(roop.globals.frame_processors):
-        if not frame_processor.pre_check():
-            return
-    limit_resources()
-    if roop.globals.headless:
-        start()
-    else:
-        window = ui.init(start, destroy)
-        window.mainloop()
-
-         

spaces/Aristo/trafficsign/README.md

@@ -1,13 +0,0 @@
----
-title: Trafficsign
-emoji: 🏃
-colorFrom: red
-colorTo: pink
-sdk: gradio
-sdk_version: 2.9.4
-app_file: app.py
-pinned: false
-license: afl-3.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference

spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/_distutils/dir_util.py

@@ -1,243 +0,0 @@
-"""distutils.dir_util
-
-Utility functions for manipulating directories and directory trees."""
-
-import os
-import errno
-from distutils.errors import DistutilsInternalError, DistutilsFileError
-from distutils import log
-
-# cache for by mkpath() -- in addition to cheapening redundant calls,
-# eliminates redundant "creating /foo/bar/baz" messages in dry-run mode
-_path_created = {}
-
-
-def mkpath(name, mode=0o777, verbose=1, dry_run=0):  # noqa: C901
-    """Create a directory and any missing ancestor directories.
-
-    If the directory already exists (or if 'name' is the empty string, which
-    means the current directory, which of course exists), then do nothing.
-    Raise DistutilsFileError if unable to create some directory along the way
-    (eg. some sub-path exists, but is a file rather than a directory).
-    If 'verbose' is true, print a one-line summary of each mkdir to stdout.
-    Return the list of directories actually created.
-
-    os.makedirs is not used because:
-
-    a) It's new to Python 1.5.2, and
-    b) it blows up if the directory already exists (in which case it should
-       silently succeed).
-    """
-
-    global _path_created
-
-    # Detect a common bug -- name is None
-    if not isinstance(name, str):
-        raise DistutilsInternalError(
-            "mkpath: 'name' must be a string (got {!r})".format(name)
-        )
-
-    # XXX what's the better way to handle verbosity? print as we create
-    # each directory in the path (the current behaviour), or only announce
-    # the creation of the whole path? (quite easy to do the latter since
-    # we're not using a recursive algorithm)
-
-    name = os.path.normpath(name)
-    created_dirs = []
-    if os.path.isdir(name) or name == '':
-        return created_dirs
-    if _path_created.get(os.path.abspath(name)):
-        return created_dirs
-
-    (head, tail) = os.path.split(name)
-    tails = [tail]  # stack of lone dirs to create
-
-    while head and tail and not os.path.isdir(head):
-        (head, tail) = os.path.split(head)
-        tails.insert(0, tail)  # push next higher dir onto stack
-
-    # now 'head' contains the deepest directory that already exists
-    # (that is, the child of 'head' in 'name' is the highest directory
-    # that does *not* exist)
-    for d in tails:
-        # print "head = %s, d = %s: " % (head, d),
-        head = os.path.join(head, d)
-        abs_head = os.path.abspath(head)
-
-        if _path_created.get(abs_head):
-            continue
-
-        if verbose >= 1:
-            log.info("creating %s", head)
-
-        if not dry_run:
-            try:
-                os.mkdir(head, mode)
-            except OSError as exc:
-                if not (exc.errno == errno.EEXIST and os.path.isdir(head)):
-                    raise DistutilsFileError(
-                        "could not create '{}': {}".format(head, exc.args[-1])
-                    )
-            created_dirs.append(head)
-
-        _path_created[abs_head] = 1
-    return created_dirs
-
-
-def create_tree(base_dir, files, mode=0o777, verbose=1, dry_run=0):
-    """Create all the empty directories under 'base_dir' needed to put 'files'
-    there.
-
-    'base_dir' is just the name of a directory which doesn't necessarily
-    exist yet; 'files' is a list of filenames to be interpreted relative to
-    'base_dir'.  'base_dir' + the directory portion of every file in 'files'
-    will be created if it doesn't already exist.  'mode', 'verbose' and
-    'dry_run' flags are as for 'mkpath()'.
-    """
-    # First get the list of directories to create
-    need_dir = set()
-    for file in files:
-        need_dir.add(os.path.join(base_dir, os.path.dirname(file)))
-
-    # Now create them
-    for dir in sorted(need_dir):
-        mkpath(dir, mode, verbose=verbose, dry_run=dry_run)
-
-
-def copy_tree(  # noqa: C901
-    src,
-    dst,
-    preserve_mode=1,
-    preserve_times=1,
-    preserve_symlinks=0,
-    update=0,
-    verbose=1,
-    dry_run=0,
-):
-    """Copy an entire directory tree 'src' to a new location 'dst'.
-
-    Both 'src' and 'dst' must be directory names.  If 'src' is not a
-    directory, raise DistutilsFileError.  If 'dst' does not exist, it is
-    created with 'mkpath()'.  The end result of the copy is that every
-    file in 'src' is copied to 'dst', and directories under 'src' are
-    recursively copied to 'dst'.  Return the list of files that were
-    copied or might have been copied, using their output name.  The
-    return value is unaffected by 'update' or 'dry_run': it is simply
-    the list of all files under 'src', with the names changed to be
-    under 'dst'.
-
-    'preserve_mode' and 'preserve_times' are the same as for
-    'copy_file'; note that they only apply to regular files, not to
-    directories.  If 'preserve_symlinks' is true, symlinks will be
-    copied as symlinks (on platforms that support them!); otherwise
-    (the default), the destination of the symlink will be copied.
-    'update' and 'verbose' are the same as for 'copy_file'.
-    """
-    from distutils.file_util import copy_file
-
-    if not dry_run and not os.path.isdir(src):
-        raise DistutilsFileError("cannot copy tree '%s': not a directory" % src)
-    try:
-        names = os.listdir(src)
-    except OSError as e:
-        if dry_run:
-            names = []
-        else:
-            raise DistutilsFileError(
-                "error listing files in '{}': {}".format(src, e.strerror)
-            )
-
-    if not dry_run:
-        mkpath(dst, verbose=verbose)
-
-    outputs = []
-
-    for n in names:
-        src_name = os.path.join(src, n)
-        dst_name = os.path.join(dst, n)
-
-        if n.startswith('.nfs'):
-            # skip NFS rename files
-            continue
-
-        if preserve_symlinks and os.path.islink(src_name):
-            link_dest = os.readlink(src_name)
-            if verbose >= 1:
-                log.info("linking %s -> %s", dst_name, link_dest)
-            if not dry_run:
-                os.symlink(link_dest, dst_name)
-            outputs.append(dst_name)
-
-        elif os.path.isdir(src_name):
-            outputs.extend(
-                copy_tree(
-                    src_name,
-                    dst_name,
-                    preserve_mode,
-                    preserve_times,
-                    preserve_symlinks,
-                    update,
-                    verbose=verbose,
-                    dry_run=dry_run,
-                )
-            )
-        else:
-            copy_file(
-                src_name,
-                dst_name,
-                preserve_mode,
-                preserve_times,
-                update,
-                verbose=verbose,
-                dry_run=dry_run,
-            )
-            outputs.append(dst_name)
-
-    return outputs
-
-
-def _build_cmdtuple(path, cmdtuples):
-    """Helper for remove_tree()."""
-    for f in os.listdir(path):
-        real_f = os.path.join(path, f)
-        if os.path.isdir(real_f) and not os.path.islink(real_f):
-            _build_cmdtuple(real_f, cmdtuples)
-        else:
-            cmdtuples.append((os.remove, real_f))
-    cmdtuples.append((os.rmdir, path))
-
-
-def remove_tree(directory, verbose=1, dry_run=0):
-    """Recursively remove an entire directory tree.
-
-    Any errors are ignored (apart from being reported to stdout if 'verbose'
-    is true).
-    """
-    global _path_created
-
-    if verbose >= 1:
-        log.info("removing '%s' (and everything under it)", directory)
-    if dry_run:
-        return
-    cmdtuples = []
-    _build_cmdtuple(directory, cmdtuples)
-    for cmd in cmdtuples:
-        try:
-            cmd[0](cmd[1])
-            # remove dir from cache if it's already there
-            abspath = os.path.abspath(cmd[1])
-            if abspath in _path_created:
-                del _path_created[abspath]
-        except OSError as exc:
-            log.warn("error removing %s: %s", directory, exc)
-
-
-def ensure_relative(path):
-    """Take the full path 'path', and make it a relative path.
-
-    This is useful to make 'path' the second argument to os.path.join().
-    """
-    drive, path = os.path.splitdrive(path)
-    if path[0:1] == os.sep:
-        path = drive + path[1:]
-    return path

spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/_path.py

@@ -1,29 +0,0 @@
-import os
-from typing import Union
-
-_Path = Union[str, os.PathLike]
-
-
-def ensure_directory(path):
-    """Ensure that the parent directory of `path` exists"""
-    dirname = os.path.dirname(path)
-    os.makedirs(dirname, exist_ok=True)
-
-
-def same_path(p1: _Path, p2: _Path) -> bool:
-    """Differs from os.path.samefile because it does not require paths to exist.
-    Purely string based (no comparison between i-nodes).
-    >>> same_path("a/b", "./a/b")
-    True
-    >>> same_path("a/b", "a/./b")
-    True
-    >>> same_path("a/b", "././a/b")
-    True
-    >>> same_path("a/b", "./a/b/c/..")
-    True
-    >>> same_path("a/b", "../a/b/c")
-    False
-    >>> same_path("a", "a/b")
-    False
-    """
-    return os.path.normpath(p1) == os.path.normpath(p2)

spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/_vendor/typing_extensions.py

@@ -1,2296 +0,0 @@
-import abc
-import collections
-import collections.abc
-import operator
-import sys
-import typing
-
-# After PEP 560, internal typing API was substantially reworked.
-# This is especially important for Protocol class which uses internal APIs
-# quite extensively.
-PEP_560 = sys.version_info[:3] >= (3, 7, 0)
-
-if PEP_560:
-    GenericMeta = type
-else:
-    # 3.6
-    from typing import GenericMeta, _type_vars  # noqa
-
-# The two functions below are copies of typing internal helpers.
-# They are needed by _ProtocolMeta
-
-
-def _no_slots_copy(dct):
-    dict_copy = dict(dct)
-    if '__slots__' in dict_copy:
-        for slot in dict_copy['__slots__']:
-            dict_copy.pop(slot, None)
-    return dict_copy
-
-
-def _check_generic(cls, parameters):
-    if not cls.__parameters__:
-        raise TypeError(f"{cls} is not a generic class")
-    alen = len(parameters)
-    elen = len(cls.__parameters__)
-    if alen != elen:
-        raise TypeError(f"Too {'many' if alen > elen else 'few'} arguments for {cls};"
-                        f" actual {alen}, expected {elen}")
-
-
-# Please keep __all__ alphabetized within each category.
-__all__ = [
-    # Super-special typing primitives.
-    'ClassVar',
-    'Concatenate',
-    'Final',
-    'ParamSpec',
-    'Self',
-    'Type',
-
-    # ABCs (from collections.abc).
-    'Awaitable',
-    'AsyncIterator',
-    'AsyncIterable',
-    'Coroutine',
-    'AsyncGenerator',
-    'AsyncContextManager',
-    'ChainMap',
-
-    # Concrete collection types.
-    'ContextManager',
-    'Counter',
-    'Deque',
-    'DefaultDict',
-    'OrderedDict',
-    'TypedDict',
-
-    # Structural checks, a.k.a. protocols.
-    'SupportsIndex',
-
-    # One-off things.
-    'Annotated',
-    'final',
-    'IntVar',
-    'Literal',
-    'NewType',
-    'overload',
-    'Protocol',
-    'runtime',
-    'runtime_checkable',
-    'Text',
-    'TypeAlias',
-    'TypeGuard',
-    'TYPE_CHECKING',
-]
-
-if PEP_560:
-    __all__.extend(["get_args", "get_origin", "get_type_hints"])
-
-# 3.6.2+
-if hasattr(typing, 'NoReturn'):
-    NoReturn = typing.NoReturn
-# 3.6.0-3.6.1
-else:
-    class _NoReturn(typing._FinalTypingBase, _root=True):
-        """Special type indicating functions that never return.
-        Example::
-
-          from typing import NoReturn
-
-          def stop() -> NoReturn:
-              raise Exception('no way')
-
-        This type is invalid in other positions, e.g., ``List[NoReturn]``
-        will fail in static type checkers.
-        """
-        __slots__ = ()
-
-        def __instancecheck__(self, obj):
-            raise TypeError("NoReturn cannot be used with isinstance().")
-
-        def __subclasscheck__(self, cls):
-            raise TypeError("NoReturn cannot be used with issubclass().")
-
-    NoReturn = _NoReturn(_root=True)
-
-# Some unconstrained type variables.  These are used by the container types.
-# (These are not for export.)
-T = typing.TypeVar('T')  # Any type.
-KT = typing.TypeVar('KT')  # Key type.
-VT = typing.TypeVar('VT')  # Value type.
-T_co = typing.TypeVar('T_co', covariant=True)  # Any type covariant containers.
-T_contra = typing.TypeVar('T_contra', contravariant=True)  # Ditto contravariant.
-
-ClassVar = typing.ClassVar
-
-# On older versions of typing there is an internal class named "Final".
-# 3.8+
-if hasattr(typing, 'Final') and sys.version_info[:2] >= (3, 7):
-    Final = typing.Final
-# 3.7
-elif sys.version_info[:2] >= (3, 7):
-    class _FinalForm(typing._SpecialForm, _root=True):
-
-        def __repr__(self):
-            return 'typing_extensions.' + self._name
-
-        def __getitem__(self, parameters):
-            item = typing._type_check(parameters,
-                                      f'{self._name} accepts only single type')
-            return typing._GenericAlias(self, (item,))
-
-    Final = _FinalForm('Final',
-                       doc="""A special typing construct to indicate that a name
-                       cannot be re-assigned or overridden in a subclass.
-                       For example:
-
-                           MAX_SIZE: Final = 9000
-                           MAX_SIZE += 1  # Error reported by type checker
-
-                           class Connection:
-                               TIMEOUT: Final[int] = 10
-                           class FastConnector(Connection):
-                               TIMEOUT = 1  # Error reported by type checker
-
-                       There is no runtime checking of these properties.""")
-# 3.6
-else:
-    class _Final(typing._FinalTypingBase, _root=True):
-        """A special typing construct to indicate that a name
-        cannot be re-assigned or overridden in a subclass.
-        For example:
-
-            MAX_SIZE: Final = 9000
-            MAX_SIZE += 1  # Error reported by type checker
-
-            class Connection:
-                TIMEOUT: Final[int] = 10
-            class FastConnector(Connection):
-                TIMEOUT = 1  # Error reported by type checker
-
-        There is no runtime checking of these properties.
-        """
-
-        __slots__ = ('__type__',)
-
-        def __init__(self, tp=None, **kwds):
-            self.__type__ = tp
-
-        def __getitem__(self, item):
-            cls = type(self)
-            if self.__type__ is None:
-                return cls(typing._type_check(item,
-                           f'{cls.__name__[1:]} accepts only single type.'),
-                           _root=True)
-            raise TypeError(f'{cls.__name__[1:]} cannot be further subscripted')
-
-        def _eval_type(self, globalns, localns):
-            new_tp = typing._eval_type(self.__type__, globalns, localns)
-            if new_tp == self.__type__:
-                return self
-            return type(self)(new_tp, _root=True)
-
-        def __repr__(self):
-            r = super().__repr__()
-            if self.__type__ is not None:
-                r += f'[{typing._type_repr(self.__type__)}]'
-            return r
-
-        def __hash__(self):
-            return hash((type(self).__name__, self.__type__))
-
-        def __eq__(self, other):
-            if not isinstance(other, _Final):
-                return NotImplemented
-            if self.__type__ is not None:
-                return self.__type__ == other.__type__
-            return self is other
-
-    Final = _Final(_root=True)
-
-
-# 3.8+
-if hasattr(typing, 'final'):
-    final = typing.final
-# 3.6-3.7
-else:
-    def final(f):
-        """This decorator can be used to indicate to type checkers that
-        the decorated method cannot be overridden, and decorated class
-        cannot be subclassed. For example:
-
-            class Base:
-                @final
-                def done(self) -> None:
-                    ...
-            class Sub(Base):
-                def done(self) -> None:  # Error reported by type checker
-                    ...
-            @final
-            class Leaf:
-                ...
-            class Other(Leaf):  # Error reported by type checker
-                ...
-
-        There is no runtime checking of these properties.
-        """
-        return f
-
-
-def IntVar(name):
-    return typing.TypeVar(name)
-
-
-# 3.8+:
-if hasattr(typing, 'Literal'):
-    Literal = typing.Literal
-# 3.7:
-elif sys.version_info[:2] >= (3, 7):
-    class _LiteralForm(typing._SpecialForm, _root=True):
-
-        def __repr__(self):
-            return 'typing_extensions.' + self._name
-
-        def __getitem__(self, parameters):
-            return typing._GenericAlias(self, parameters)
-
-    Literal = _LiteralForm('Literal',
-                           doc="""A type that can be used to indicate to type checkers
-                           that the corresponding value has a value literally equivalent
-                           to the provided parameter. For example:
-
-                               var: Literal[4] = 4
-
-                           The type checker understands that 'var' is literally equal to
-                           the value 4 and no other value.
-
-                           Literal[...] cannot be subclassed. There is no runtime
-                           checking verifying that the parameter is actually a value
-                           instead of a type.""")
-# 3.6:
-else:
-    class _Literal(typing._FinalTypingBase, _root=True):
-        """A type that can be used to indicate to type checkers that the
-        corresponding value has a value literally equivalent to the
-        provided parameter. For example:
-
-            var: Literal[4] = 4
-
-        The type checker understands that 'var' is literally equal to the
-        value 4 and no other value.
-
-        Literal[...] cannot be subclassed. There is no runtime checking
-        verifying that the parameter is actually a value instead of a type.
-        """
-
-        __slots__ = ('__values__',)
-
-        def __init__(self, values=None, **kwds):
-            self.__values__ = values
-
-        def __getitem__(self, values):
-            cls = type(self)
-            if self.__values__ is None:
-                if not isinstance(values, tuple):
-                    values = (values,)
-                return cls(values, _root=True)
-            raise TypeError(f'{cls.__name__[1:]} cannot be further subscripted')
-
-        def _eval_type(self, globalns, localns):
-            return self
-
-        def __repr__(self):
-            r = super().__repr__()
-            if self.__values__ is not None:
-                r += f'[{", ".join(map(typing._type_repr, self.__values__))}]'
-            return r
-
-        def __hash__(self):
-            return hash((type(self).__name__, self.__values__))
-
-        def __eq__(self, other):
-            if not isinstance(other, _Literal):
-                return NotImplemented
-            if self.__values__ is not None:
-                return self.__values__ == other.__values__
-            return self is other
-
-    Literal = _Literal(_root=True)
-
-
-_overload_dummy = typing._overload_dummy  # noqa
-overload = typing.overload
-
-
-# This is not a real generic class.  Don't use outside annotations.
-Type = typing.Type
-
-# Various ABCs mimicking those in collections.abc.
-# A few are simply re-exported for completeness.
-
-
-class _ExtensionsGenericMeta(GenericMeta):
-    def __subclasscheck__(self, subclass):
-        """This mimics a more modern GenericMeta.__subclasscheck__() logic
-        (that does not have problems with recursion) to work around interactions
-        between collections, typing, and typing_extensions on older
-        versions of Python, see https://github.com/python/typing/issues/501.
-        """
-        if self.__origin__ is not None:
-            if sys._getframe(1).f_globals['__name__'] not in ['abc', 'functools']:
-                raise TypeError("Parameterized generics cannot be used with class "
-                                "or instance checks")
-            return False
-        if not self.__extra__:
-            return super().__subclasscheck__(subclass)
-        res = self.__extra__.__subclasshook__(subclass)
-        if res is not NotImplemented:
-            return res
-        if self.__extra__ in subclass.__mro__:
-            return True
-        for scls in self.__extra__.__subclasses__():
-            if isinstance(scls, GenericMeta):
-                continue
-            if issubclass(subclass, scls):
-                return True
-        return False
-
-
-Awaitable = typing.Awaitable
-Coroutine = typing.Coroutine
-AsyncIterable = typing.AsyncIterable
-AsyncIterator = typing.AsyncIterator
-
-# 3.6.1+
-if hasattr(typing, 'Deque'):
-    Deque = typing.Deque
-# 3.6.0
-else:
-    class Deque(collections.deque, typing.MutableSequence[T],
-                metaclass=_ExtensionsGenericMeta,
-                extra=collections.deque):
-        __slots__ = ()
-
-        def __new__(cls, *args, **kwds):
-            if cls._gorg is Deque:
-                return collections.deque(*args, **kwds)
-            return typing._generic_new(collections.deque, cls, *args, **kwds)
-
-ContextManager = typing.ContextManager
-# 3.6.2+
-if hasattr(typing, 'AsyncContextManager'):
-    AsyncContextManager = typing.AsyncContextManager
-# 3.6.0-3.6.1
-else:
-    from _collections_abc import _check_methods as _check_methods_in_mro  # noqa
-
-    class AsyncContextManager(typing.Generic[T_co]):
-        __slots__ = ()
-
-        async def __aenter__(self):
-            return self
-
-        @abc.abstractmethod
-        async def __aexit__(self, exc_type, exc_value, traceback):
-            return None
-
-        @classmethod
-        def __subclasshook__(cls, C):
-            if cls is AsyncContextManager:
-                return _check_methods_in_mro(C, "__aenter__", "__aexit__")
-            return NotImplemented
-
-DefaultDict = typing.DefaultDict
-
-# 3.7.2+
-if hasattr(typing, 'OrderedDict'):
-    OrderedDict = typing.OrderedDict
-# 3.7.0-3.7.2
-elif (3, 7, 0) <= sys.version_info[:3] < (3, 7, 2):
-    OrderedDict = typing._alias(collections.OrderedDict, (KT, VT))
-# 3.6
-else:
-    class OrderedDict(collections.OrderedDict, typing.MutableMapping[KT, VT],
-                      metaclass=_ExtensionsGenericMeta,
-                      extra=collections.OrderedDict):
-
-        __slots__ = ()
-
-        def __new__(cls, *args, **kwds):
-            if cls._gorg is OrderedDict:
-                return collections.OrderedDict(*args, **kwds)
-            return typing._generic_new(collections.OrderedDict, cls, *args, **kwds)
-
-# 3.6.2+
-if hasattr(typing, 'Counter'):
-    Counter = typing.Counter
-# 3.6.0-3.6.1
-else:
-    class Counter(collections.Counter,
-                  typing.Dict[T, int],
-                  metaclass=_ExtensionsGenericMeta, extra=collections.Counter):
-
-        __slots__ = ()
-
-        def __new__(cls, *args, **kwds):
-            if cls._gorg is Counter:
-                return collections.Counter(*args, **kwds)
-            return typing._generic_new(collections.Counter, cls, *args, **kwds)
-
-# 3.6.1+
-if hasattr(typing, 'ChainMap'):
-    ChainMap = typing.ChainMap
-elif hasattr(collections, 'ChainMap'):
-    class ChainMap(collections.ChainMap, typing.MutableMapping[KT, VT],
-                   metaclass=_ExtensionsGenericMeta,
-                   extra=collections.ChainMap):
-
-        __slots__ = ()
-
-        def __new__(cls, *args, **kwds):
-            if cls._gorg is ChainMap:
-                return collections.ChainMap(*args, **kwds)
-            return typing._generic_new(collections.ChainMap, cls, *args, **kwds)
-
-# 3.6.1+
-if hasattr(typing, 'AsyncGenerator'):
-    AsyncGenerator = typing.AsyncGenerator
-# 3.6.0
-else:
-    class AsyncGenerator(AsyncIterator[T_co], typing.Generic[T_co, T_contra],
-                         metaclass=_ExtensionsGenericMeta,
-                         extra=collections.abc.AsyncGenerator):
-        __slots__ = ()
-
-NewType = typing.NewType
-Text = typing.Text
-TYPE_CHECKING = typing.TYPE_CHECKING
-
-
-def _gorg(cls):
-    """This function exists for compatibility with old typing versions."""
-    assert isinstance(cls, GenericMeta)
-    if hasattr(cls, '_gorg'):
-        return cls._gorg
-    while cls.__origin__ is not None:
-        cls = cls.__origin__
-    return cls
-
-
-_PROTO_WHITELIST = ['Callable', 'Awaitable',
-                    'Iterable', 'Iterator', 'AsyncIterable', 'AsyncIterator',
-                    'Hashable', 'Sized', 'Container', 'Collection', 'Reversible',
-                    'ContextManager', 'AsyncContextManager']
-
-
-def _get_protocol_attrs(cls):
-    attrs = set()
-    for base in cls.__mro__[:-1]:  # without object
-        if base.__name__ in ('Protocol', 'Generic'):
-            continue
-        annotations = getattr(base, '__annotations__', {})
-        for attr in list(base.__dict__.keys()) + list(annotations.keys()):
-            if (not attr.startswith('_abc_') and attr not in (
-                    '__abstractmethods__', '__annotations__', '__weakref__',
-                    '_is_protocol', '_is_runtime_protocol', '__dict__',
-                    '__args__', '__slots__',
-                    '__next_in_mro__', '__parameters__', '__origin__',
-                    '__orig_bases__', '__extra__', '__tree_hash__',
-                    '__doc__', '__subclasshook__', '__init__', '__new__',
-                    '__module__', '_MutableMapping__marker', '_gorg')):
-                attrs.add(attr)
-    return attrs
-
-
-def _is_callable_members_only(cls):
-    return all(callable(getattr(cls, attr, None)) for attr in _get_protocol_attrs(cls))
-
-
-# 3.8+
-if hasattr(typing, 'Protocol'):
-    Protocol = typing.Protocol
-# 3.7
-elif PEP_560:
-    from typing import _collect_type_vars  # noqa
-
-    def _no_init(self, *args, **kwargs):
-        if type(self)._is_protocol:
-            raise TypeError('Protocols cannot be instantiated')
-
-    class _ProtocolMeta(abc.ABCMeta):
-        # This metaclass is a bit unfortunate and exists only because of the lack
-        # of __instancehook__.
-        def __instancecheck__(cls, instance):
-            # We need this method for situations where attributes are
-            # assigned in __init__.
-            if ((not getattr(cls, '_is_protocol', False) or
-                 _is_callable_members_only(cls)) and
-                    issubclass(instance.__class__, cls)):
-                return True
-            if cls._is_protocol:
-                if all(hasattr(instance, attr) and
-                       (not callable(getattr(cls, attr, None)) or
-                        getattr(instance, attr) is not None)
-                       for attr in _get_protocol_attrs(cls)):
-                    return True
-            return super().__instancecheck__(instance)
-
-    class Protocol(metaclass=_ProtocolMeta):
-        # There is quite a lot of overlapping code with typing.Generic.
-        # Unfortunately it is hard to avoid this while these live in two different
-        # modules. The duplicated code will be removed when Protocol is moved to typing.
-        """Base class for protocol classes. Protocol classes are defined as::
-
-            class Proto(Protocol):
-                def meth(self) -> int:
-                    ...
-
-        Such classes are primarily used with static type checkers that recognize
-        structural subtyping (static duck-typing), for example::
-
-            class C:
-                def meth(self) -> int:
-                    return 0
-
-            def func(x: Proto) -> int:
-                return x.meth()
-
-            func(C())  # Passes static type check
-
-        See PEP 544 for details. Protocol classes decorated with
-        @typing_extensions.runtime act as simple-minded runtime protocol that checks
-        only the presence of given attributes, ignoring their type signatures.
-
-        Protocol classes can be generic, they are defined as::
-
-            class GenProto(Protocol[T]):
-                def meth(self) -> T:
-                    ...
-        """
-        __slots__ = ()
-        _is_protocol = True
-
-        def __new__(cls, *args, **kwds):
-            if cls is Protocol:
-                raise TypeError("Type Protocol cannot be instantiated; "
-                                "it can only be used as a base class")
-            return super().__new__(cls)
-
-        @typing._tp_cache
-        def __class_getitem__(cls, params):
-            if not isinstance(params, tuple):
-                params = (params,)
-            if not params and cls is not typing.Tuple:
-                raise TypeError(
-                    f"Parameter list to {cls.__qualname__}[...] cannot be empty")
-            msg = "Parameters to generic types must be types."
-            params = tuple(typing._type_check(p, msg) for p in params)  # noqa
-            if cls is Protocol:
-                # Generic can only be subscripted with unique type variables.
-                if not all(isinstance(p, typing.TypeVar) for p in params):
-                    i = 0
-                    while isinstance(params[i], typing.TypeVar):
-                        i += 1
-                    raise TypeError(
-                        "Parameters to Protocol[...] must all be type variables."
-                        f" Parameter {i + 1} is {params[i]}")
-                if len(set(params)) != len(params):
-                    raise TypeError(
-                        "Parameters to Protocol[...] must all be unique")
-            else:
-                # Subscripting a regular Generic subclass.
-                _check_generic(cls, params)
-            return typing._GenericAlias(cls, params)
-
-        def __init_subclass__(cls, *args, **kwargs):
-            tvars = []
-            if '__orig_bases__' in cls.__dict__:
-                error = typing.Generic in cls.__orig_bases__
-            else:
-                error = typing.Generic in cls.__bases__
-            if error:
-                raise TypeError("Cannot inherit from plain Generic")
-            if '__orig_bases__' in cls.__dict__:
-                tvars = _collect_type_vars(cls.__orig_bases__)
-                # Look for Generic[T1, ..., Tn] or Protocol[T1, ..., Tn].
-                # If found, tvars must be a subset of it.
-                # If not found, tvars is it.
-                # Also check for and reject plain Generic,
-                # and reject multiple Generic[...] and/or Protocol[...].
-                gvars = None
-                for base in cls.__orig_bases__:
-                    if (isinstance(base, typing._GenericAlias) and
-                            base.__origin__ in (typing.Generic, Protocol)):
-                        # for error messages
-                        the_base = base.__origin__.__name__
-                        if gvars is not None:
-                            raise TypeError(
-                                "Cannot inherit from Generic[...]"
-                                " and/or Protocol[...] multiple types.")
-                        gvars = base.__parameters__
-                if gvars is None:
-                    gvars = tvars
-                else:
-                    tvarset = set(tvars)
-                    gvarset = set(gvars)
-                    if not tvarset <= gvarset:
-                        s_vars = ', '.join(str(t) for t in tvars if t not in gvarset)
-                        s_args = ', '.join(str(g) for g in gvars)
-                        raise TypeError(f"Some type variables ({s_vars}) are"
-                                        f" not listed in {the_base}[{s_args}]")
-                    tvars = gvars
-            cls.__parameters__ = tuple(tvars)
-
-            # Determine if this is a protocol or a concrete subclass.
-            if not cls.__dict__.get('_is_protocol', None):
-                cls._is_protocol = any(b is Protocol for b in cls.__bases__)
-
-            # Set (or override) the protocol subclass hook.
-            def _proto_hook(other):
-                if not cls.__dict__.get('_is_protocol', None):
-                    return NotImplemented
-                if not getattr(cls, '_is_runtime_protocol', False):
-                    if sys._getframe(2).f_globals['__name__'] in ['abc', 'functools']:
-                        return NotImplemented
-                    raise TypeError("Instance and class checks can only be used with"
-                                    " @runtime protocols")
-                if not _is_callable_members_only(cls):
-                    if sys._getframe(2).f_globals['__name__'] in ['abc', 'functools']:
-                        return NotImplemented
-                    raise TypeError("Protocols with non-method members"
-                                    " don't support issubclass()")
-                if not isinstance(other, type):
-                    # Same error as for issubclass(1, int)
-                    raise TypeError('issubclass() arg 1 must be a class')
-                for attr in _get_protocol_attrs(cls):
-                    for base in other.__mro__:
-                        if attr in base.__dict__:
-                            if base.__dict__[attr] is None:
-                                return NotImplemented
-                            break
-                        annotations = getattr(base, '__annotations__', {})
-                        if (isinstance(annotations, typing.Mapping) and
-                                attr in annotations and
-                                isinstance(other, _ProtocolMeta) and
-                                other._is_protocol):
-                            break
-                    else:
-                        return NotImplemented
-                return True
-            if '__subclasshook__' not in cls.__dict__:
-                cls.__subclasshook__ = _proto_hook
-
-            # We have nothing more to do for non-protocols.
-            if not cls._is_protocol:
-                return
-
-            # Check consistency of bases.
-            for base in cls.__bases__:
-                if not (base in (object, typing.Generic) or
-                        base.__module__ == 'collections.abc' and
-                        base.__name__ in _PROTO_WHITELIST or
-                        isinstance(base, _ProtocolMeta) and base._is_protocol):
-                    raise TypeError('Protocols can only inherit from other'
-                                    f' protocols, got {repr(base)}')
-            cls.__init__ = _no_init
-# 3.6
-else:
-    from typing import _next_in_mro, _type_check  # noqa
-
-    def _no_init(self, *args, **kwargs):
-        if type(self)._is_protocol:
-            raise TypeError('Protocols cannot be instantiated')
-
-    class _ProtocolMeta(GenericMeta):
-        """Internal metaclass for Protocol.
-
-        This exists so Protocol classes can be generic without deriving
-        from Generic.
-        """
-        def __new__(cls, name, bases, namespace,
-                    tvars=None, args=None, origin=None, extra=None, orig_bases=None):
-            # This is just a version copied from GenericMeta.__new__ that
-            # includes "Protocol" special treatment. (Comments removed for brevity.)
-            assert extra is None  # Protocols should not have extra
-            if tvars is not None:
-                assert origin is not None
-                assert all(isinstance(t, typing.TypeVar) for t in tvars), tvars
-            else:
-                tvars = _type_vars(bases)
-                gvars = None
-                for base in bases:
-                    if base is typing.Generic:
-                        raise TypeError("Cannot inherit from plain Generic")
-                    if (isinstance(base, GenericMeta) and
-                            base.__origin__ in (typing.Generic, Protocol)):
-                        if gvars is not None:
-                            raise TypeError(
-                                "Cannot inherit from Generic[...] or"
-                                " Protocol[...] multiple times.")
-                        gvars = base.__parameters__
-                if gvars is None:
-                    gvars = tvars
-                else:
-                    tvarset = set(tvars)
-                    gvarset = set(gvars)
-                    if not tvarset <= gvarset:
-                        s_vars = ", ".join(str(t) for t in tvars if t not in gvarset)
-                        s_args = ", ".join(str(g) for g in gvars)
-                        cls_name = "Generic" if any(b.__origin__ is typing.Generic
-                                                    for b in bases) else "Protocol"
-                        raise TypeError(f"Some type variables ({s_vars}) are"
-                                        f" not listed in {cls_name}[{s_args}]")
-                    tvars = gvars
-
-            initial_bases = bases
-            if (extra is not None and type(extra) is abc.ABCMeta and
-                    extra not in bases):
-                bases = (extra,) + bases
-            bases = tuple(_gorg(b) if isinstance(b, GenericMeta) else b
-                          for b in bases)
-            if any(isinstance(b, GenericMeta) and b is not typing.Generic for b in bases):
-                bases = tuple(b for b in bases if b is not typing.Generic)
-            namespace.update({'__origin__': origin, '__extra__': extra})
-            self = super(GenericMeta, cls).__new__(cls, name, bases, namespace,
-                                                   _root=True)
-            super(GenericMeta, self).__setattr__('_gorg',
-                                                 self if not origin else
-                                                 _gorg(origin))
-            self.__parameters__ = tvars
-            self.__args__ = tuple(... if a is typing._TypingEllipsis else
-                                  () if a is typing._TypingEmpty else
-                                  a for a in args) if args else None
-            self.__next_in_mro__ = _next_in_mro(self)
-            if orig_bases is None:
-                self.__orig_bases__ = initial_bases
-            elif origin is not None:
-                self._abc_registry = origin._abc_registry
-                self._abc_cache = origin._abc_cache
-            if hasattr(self, '_subs_tree'):
-                self.__tree_hash__ = (hash(self._subs_tree()) if origin else
-                                      super(GenericMeta, self).__hash__())
-            return self
-
-        def __init__(cls, *args, **kwargs):
-            super().__init__(*args, **kwargs)
-            if not cls.__dict__.get('_is_protocol', None):
-                cls._is_protocol = any(b is Protocol or
-                                       isinstance(b, _ProtocolMeta) and
-                                       b.__origin__ is Protocol
-                                       for b in cls.__bases__)
-            if cls._is_protocol:
-                for base in cls.__mro__[1:]:
-                    if not (base in (object, typing.Generic) or
-                            base.__module__ == 'collections.abc' and
-                            base.__name__ in _PROTO_WHITELIST or
-                            isinstance(base, typing.TypingMeta) and base._is_protocol or
-                            isinstance(base, GenericMeta) and
-                            base.__origin__ is typing.Generic):
-                        raise TypeError(f'Protocols can only inherit from other'
-                                        f' protocols, got {repr(base)}')
-
-                cls.__init__ = _no_init
-
-            def _proto_hook(other):
-                if not cls.__dict__.get('_is_protocol', None):
-                    return NotImplemented
-                if not isinstance(other, type):
-                    # Same error as for issubclass(1, int)
-                    raise TypeError('issubclass() arg 1 must be a class')
-                for attr in _get_protocol_attrs(cls):
-                    for base in other.__mro__:
-                        if attr in base.__dict__:
-                            if base.__dict__[attr] is None:
-                                return NotImplemented
-                            break
-                        annotations = getattr(base, '__annotations__', {})
-                        if (isinstance(annotations, typing.Mapping) and
-                                attr in annotations and
-                                isinstance(other, _ProtocolMeta) and
-                                other._is_protocol):
-                            break
-                    else:
-                        return NotImplemented
-                return True
-            if '__subclasshook__' not in cls.__dict__:
-                cls.__subclasshook__ = _proto_hook
-
-        def __instancecheck__(self, instance):
-            # We need this method for situations where attributes are
-            # assigned in __init__.
-            if ((not getattr(self, '_is_protocol', False) or
-                    _is_callable_members_only(self)) and
-                    issubclass(instance.__class__, self)):
-                return True
-            if self._is_protocol:
-                if all(hasattr(instance, attr) and
-                        (not callable(getattr(self, attr, None)) or
-                         getattr(instance, attr) is not None)
-                        for attr in _get_protocol_attrs(self)):
-                    return True
-            return super(GenericMeta, self).__instancecheck__(instance)
-
-        def __subclasscheck__(self, cls):
-            if self.__origin__ is not None:
-                if sys._getframe(1).f_globals['__name__'] not in ['abc', 'functools']:
-                    raise TypeError("Parameterized generics cannot be used with class "
-                                    "or instance checks")
-                return False
-            if (self.__dict__.get('_is_protocol', None) and
-                    not self.__dict__.get('_is_runtime_protocol', None)):
-                if sys._getframe(1).f_globals['__name__'] in ['abc',
-                                                              'functools',
-                                                              'typing']:
-                    return False
-                raise TypeError("Instance and class checks can only be used with"
-                                " @runtime protocols")
-            if (self.__dict__.get('_is_runtime_protocol', None) and
-                    not _is_callable_members_only(self)):
-                if sys._getframe(1).f_globals['__name__'] in ['abc',
-                                                              'functools',
-                                                              'typing']:
-                    return super(GenericMeta, self).__subclasscheck__(cls)
-                raise TypeError("Protocols with non-method members"
-                                " don't support issubclass()")
-            return super(GenericMeta, self).__subclasscheck__(cls)
-
-        @typing._tp_cache
-        def __getitem__(self, params):
-            # We also need to copy this from GenericMeta.__getitem__ to get
-            # special treatment of "Protocol". (Comments removed for brevity.)
-            if not isinstance(params, tuple):
-                params = (params,)
-            if not params and _gorg(self) is not typing.Tuple:
-                raise TypeError(
-                    f"Parameter list to {self.__qualname__}[...] cannot be empty")
-            msg = "Parameters to generic types must be types."
-            params = tuple(_type_check(p, msg) for p in params)
-            if self in (typing.Generic, Protocol):
-                if not all(isinstance(p, typing.TypeVar) for p in params):
-                    raise TypeError(
-                        f"Parameters to {repr(self)}[...] must all be type variables")
-                if len(set(params)) != len(params):
-                    raise TypeError(
-                        f"Parameters to {repr(self)}[...] must all be unique")
-                tvars = params
-                args = params
-            elif self in (typing.Tuple, typing.Callable):
-                tvars = _type_vars(params)
-                args = params
-            elif self.__origin__ in (typing.Generic, Protocol):
-                raise TypeError(f"Cannot subscript already-subscripted {repr(self)}")
-            else:
-                _check_generic(self, params)
-                tvars = _type_vars(params)
-                args = params
-
-            prepend = (self,) if self.__origin__ is None else ()
-            return self.__class__(self.__name__,
-                                  prepend + self.__bases__,
-                                  _no_slots_copy(self.__dict__),
-                                  tvars=tvars,
-                                  args=args,
-                                  origin=self,
-                                  extra=self.__extra__,
-                                  orig_bases=self.__orig_bases__)
-
-    class Protocol(metaclass=_ProtocolMeta):
-        """Base class for protocol classes. Protocol classes are defined as::
-
-          class Proto(Protocol):
-              def meth(self) -> int:
-                  ...
-
-        Such classes are primarily used with static type checkers that recognize
-        structural subtyping (static duck-typing), for example::
-
-          class C:
-              def meth(self) -> int:
-                  return 0
-
-          def func(x: Proto) -> int:
-              return x.meth()
-
-          func(C())  # Passes static type check
-
-        See PEP 544 for details. Protocol classes decorated with
-        @typing_extensions.runtime act as simple-minded runtime protocol that checks
-        only the presence of given attributes, ignoring their type signatures.
-
-        Protocol classes can be generic, they are defined as::
-
-          class GenProto(Protocol[T]):
-              def meth(self) -> T:
-                  ...
-        """
-        __slots__ = ()
-        _is_protocol = True
-
-        def __new__(cls, *args, **kwds):
-            if _gorg(cls) is Protocol:
-                raise TypeError("Type Protocol cannot be instantiated; "
-                                "it can be used only as a base class")
-            return typing._generic_new(cls.__next_in_mro__, cls, *args, **kwds)
-
-
-# 3.8+
-if hasattr(typing, 'runtime_checkable'):
-    runtime_checkable = typing.runtime_checkable
-# 3.6-3.7
-else:
-    def runtime_checkable(cls):
-        """Mark a protocol class as a runtime protocol, so that it
-        can be used with isinstance() and issubclass(). Raise TypeError
-        if applied to a non-protocol class.
-
-        This allows a simple-minded structural check very similar to the
-        one-offs in collections.abc such as Hashable.
-        """
-        if not isinstance(cls, _ProtocolMeta) or not cls._is_protocol:
-            raise TypeError('@runtime_checkable can be only applied to protocol classes,'
-                            f' got {cls!r}')
-        cls._is_runtime_protocol = True
-        return cls
-
-
-# Exists for backwards compatibility.
-runtime = runtime_checkable
-
-
-# 3.8+
-if hasattr(typing, 'SupportsIndex'):
-    SupportsIndex = typing.SupportsIndex
-# 3.6-3.7
-else:
-    @runtime_checkable
-    class SupportsIndex(Protocol):
-        __slots__ = ()
-
-        @abc.abstractmethod
-        def __index__(self) -> int:
-            pass
-
-
-if sys.version_info >= (3, 9, 2):
-    # The standard library TypedDict in Python 3.8 does not store runtime information
-    # about which (if any) keys are optional.  See https://bugs.python.org/issue38834
-    # The standard library TypedDict in Python 3.9.0/1 does not honour the "total"
-    # keyword with old-style TypedDict().  See https://bugs.python.org/issue42059
-    TypedDict = typing.TypedDict
-else:
-    def _check_fails(cls, other):
-        try:
-            if sys._getframe(1).f_globals['__name__'] not in ['abc',
-                                                              'functools',
-                                                              'typing']:
-                # Typed dicts are only for static structural subtyping.
-                raise TypeError('TypedDict does not support instance and class checks')
-        except (AttributeError, ValueError):
-            pass
-        return False
-
-    def _dict_new(*args, **kwargs):
-        if not args:
-            raise TypeError('TypedDict.__new__(): not enough arguments')
-        _, args = args[0], args[1:]  # allow the "cls" keyword be passed
-        return dict(*args, **kwargs)
-
-    _dict_new.__text_signature__ = '($cls, _typename, _fields=None, /, **kwargs)'
-
-    def _typeddict_new(*args, total=True, **kwargs):
-        if not args:
-            raise TypeError('TypedDict.__new__(): not enough arguments')
-        _, args = args[0], args[1:]  # allow the "cls" keyword be passed
-        if args:
-            typename, args = args[0], args[1:]  # allow the "_typename" keyword be passed
-        elif '_typename' in kwargs:
-            typename = kwargs.pop('_typename')
-            import warnings
-            warnings.warn("Passing '_typename' as keyword argument is deprecated",
-                          DeprecationWarning, stacklevel=2)
-        else:
-            raise TypeError("TypedDict.__new__() missing 1 required positional "
-                            "argument: '_typename'")
-        if args:
-            try:
-                fields, = args  # allow the "_fields" keyword be passed
-            except ValueError:
-                raise TypeError('TypedDict.__new__() takes from 2 to 3 '
-                                f'positional arguments but {len(args) + 2} '
-                                'were given')
-        elif '_fields' in kwargs and len(kwargs) == 1:
-            fields = kwargs.pop('_fields')
-            import warnings
-            warnings.warn("Passing '_fields' as keyword argument is deprecated",
-                          DeprecationWarning, stacklevel=2)
-        else:
-            fields = None
-
-        if fields is None:
-            fields = kwargs
-        elif kwargs:
-            raise TypeError("TypedDict takes either a dict or keyword arguments,"
-                            " but not both")
-
-        ns = {'__annotations__': dict(fields)}
-        try:
-            # Setting correct module is necessary to make typed dict classes pickleable.
-            ns['__module__'] = sys._getframe(1).f_globals.get('__name__', '__main__')
-        except (AttributeError, ValueError):
-            pass
-
-        return _TypedDictMeta(typename, (), ns, total=total)
-
-    _typeddict_new.__text_signature__ = ('($cls, _typename, _fields=None,'
-                                         ' /, *, total=True, **kwargs)')
-
-    class _TypedDictMeta(type):
-        def __init__(cls, name, bases, ns, total=True):
-            super().__init__(name, bases, ns)
-
-        def __new__(cls, name, bases, ns, total=True):
-            # Create new typed dict class object.
-            # This method is called directly when TypedDict is subclassed,
-            # or via _typeddict_new when TypedDict is instantiated. This way
-            # TypedDict supports all three syntaxes described in its docstring.
-            # Subclasses and instances of TypedDict return actual dictionaries
-            # via _dict_new.
-            ns['__new__'] = _typeddict_new if name == 'TypedDict' else _dict_new
-            tp_dict = super().__new__(cls, name, (dict,), ns)
-
-            annotations = {}
-            own_annotations = ns.get('__annotations__', {})
-            own_annotation_keys = set(own_annotations.keys())
-            msg = "TypedDict('Name', {f0: t0, f1: t1, ...}); each t must be a type"
-            own_annotations = {
-                n: typing._type_check(tp, msg) for n, tp in own_annotations.items()
-            }
-            required_keys = set()
-            optional_keys = set()
-
-            for base in bases:
-                annotations.update(base.__dict__.get('__annotations__', {}))
-                required_keys.update(base.__dict__.get('__required_keys__', ()))
-                optional_keys.update(base.__dict__.get('__optional_keys__', ()))
-
-            annotations.update(own_annotations)
-            if total:
-                required_keys.update(own_annotation_keys)
-            else:
-                optional_keys.update(own_annotation_keys)
-
-            tp_dict.__annotations__ = annotations
-            tp_dict.__required_keys__ = frozenset(required_keys)
-            tp_dict.__optional_keys__ = frozenset(optional_keys)
-            if not hasattr(tp_dict, '__total__'):
-                tp_dict.__total__ = total
-            return tp_dict
-
-        __instancecheck__ = __subclasscheck__ = _check_fails
-
-    TypedDict = _TypedDictMeta('TypedDict', (dict,), {})
-    TypedDict.__module__ = __name__
-    TypedDict.__doc__ = \
-        """A simple typed name space. At runtime it is equivalent to a plain dict.
-
-        TypedDict creates a dictionary type that expects all of its
-        instances to have a certain set of keys, with each key
-        associated with a value of a consistent type. This expectation
-        is not checked at runtime but is only enforced by type checkers.
-        Usage::
-
-            class Point2D(TypedDict):
-                x: int
-                y: int
-                label: str
-
-            a: Point2D = {'x': 1, 'y': 2, 'label': 'good'}  # OK
-            b: Point2D = {'z': 3, 'label': 'bad'}           # Fails type check
-
-            assert Point2D(x=1, y=2, label='first') == dict(x=1, y=2, label='first')
-
-        The type info can be accessed via the Point2D.__annotations__ dict, and
-        the Point2D.__required_keys__ and Point2D.__optional_keys__ frozensets.
-        TypedDict supports two additional equivalent forms::
-
-            Point2D = TypedDict('Point2D', x=int, y=int, label=str)
-            Point2D = TypedDict('Point2D', {'x': int, 'y': int, 'label': str})
-
-        The class syntax is only supported in Python 3.6+, while two other
-        syntax forms work for Python 2.7 and 3.2+
-        """
-
-
-# Python 3.9+ has PEP 593 (Annotated and modified get_type_hints)
-if hasattr(typing, 'Annotated'):
-    Annotated = typing.Annotated
-    get_type_hints = typing.get_type_hints
-    # Not exported and not a public API, but needed for get_origin() and get_args()
-    # to work.
-    _AnnotatedAlias = typing._AnnotatedAlias
-# 3.7-3.8
-elif PEP_560:
-    class _AnnotatedAlias(typing._GenericAlias, _root=True):
-        """Runtime representation of an annotated type.
-
-        At its core 'Annotated[t, dec1, dec2, ...]' is an alias for the type 't'
-        with extra annotations. The alias behaves like a normal typing alias,
-        instantiating is the same as instantiating the underlying type, binding
-        it to types is also the same.
-        """
-        def __init__(self, origin, metadata):
-            if isinstance(origin, _AnnotatedAlias):
-                metadata = origin.__metadata__ + metadata
-                origin = origin.__origin__
-            super().__init__(origin, origin)
-            self.__metadata__ = metadata
-
-        def copy_with(self, params):
-            assert len(params) == 1
-            new_type = params[0]
-            return _AnnotatedAlias(new_type, self.__metadata__)
-
-        def __repr__(self):
-            return (f"typing_extensions.Annotated[{typing._type_repr(self.__origin__)}, "
-                    f"{', '.join(repr(a) for a in self.__metadata__)}]")
-
-        def __reduce__(self):
-            return operator.getitem, (
-                Annotated, (self.__origin__,) + self.__metadata__
-            )
-
-        def __eq__(self, other):
-            if not isinstance(other, _AnnotatedAlias):
-                return NotImplemented
-            if self.__origin__ != other.__origin__:
-                return False
-            return self.__metadata__ == other.__metadata__
-
-        def __hash__(self):
-            return hash((self.__origin__, self.__metadata__))
-
-    class Annotated:
-        """Add context specific metadata to a type.
-
-        Example: Annotated[int, runtime_check.Unsigned] indicates to the
-        hypothetical runtime_check module that this type is an unsigned int.
-        Every other consumer of this type can ignore this metadata and treat
-        this type as int.
-
-        The first argument to Annotated must be a valid type (and will be in
-        the __origin__ field), the remaining arguments are kept as a tuple in
-        the __extra__ field.
-
-        Details:
-
-        - It's an error to call `Annotated` with less than two arguments.
-        - Nested Annotated are flattened::
-
-            Annotated[Annotated[T, Ann1, Ann2], Ann3] == Annotated[T, Ann1, Ann2, Ann3]
-
-        - Instantiating an annotated type is equivalent to instantiating the
-        underlying type::
-
-            Annotated[C, Ann1](5) == C(5)
-
-        - Annotated can be used as a generic type alias::
-
-            Optimized = Annotated[T, runtime.Optimize()]
-            Optimized[int] == Annotated[int, runtime.Optimize()]
-
-            OptimizedList = Annotated[List[T], runtime.Optimize()]
-            OptimizedList[int] == Annotated[List[int], runtime.Optimize()]
-        """
-
-        __slots__ = ()
-
-        def __new__(cls, *args, **kwargs):
-            raise TypeError("Type Annotated cannot be instantiated.")
-
-        @typing._tp_cache
-        def __class_getitem__(cls, params):
-            if not isinstance(params, tuple) or len(params) < 2:
-                raise TypeError("Annotated[...] should be used "
-                                "with at least two arguments (a type and an "
-                                "annotation).")
-            msg = "Annotated[t, ...]: t must be a type."
-            origin = typing._type_check(params[0], msg)
-            metadata = tuple(params[1:])
-            return _AnnotatedAlias(origin, metadata)
-
-        def __init_subclass__(cls, *args, **kwargs):
-            raise TypeError(
-                f"Cannot subclass {cls.__module__}.Annotated"
-            )
-
-    def _strip_annotations(t):
-        """Strips the annotations from a given type.
-        """
-        if isinstance(t, _AnnotatedAlias):
-            return _strip_annotations(t.__origin__)
-        if isinstance(t, typing._GenericAlias):
-            stripped_args = tuple(_strip_annotations(a) for a in t.__args__)
-            if stripped_args == t.__args__:
-                return t
-            res = t.copy_with(stripped_args)
-            res._special = t._special
-            return res
-        return t
-
-    def get_type_hints(obj, globalns=None, localns=None, include_extras=False):
-        """Return type hints for an object.
-
-        This is often the same as obj.__annotations__, but it handles
-        forward references encoded as string literals, adds Optional[t] if a
-        default value equal to None is set and recursively replaces all
-        'Annotated[T, ...]' with 'T' (unless 'include_extras=True').
-
-        The argument may be a module, class, method, or function. The annotations
-        are returned as a dictionary. For classes, annotations include also
-        inherited members.
-
-        TypeError is raised if the argument is not of a type that can contain
-        annotations, and an empty dictionary is returned if no annotations are
-        present.
-
-        BEWARE -- the behavior of globalns and localns is counterintuitive
-        (unless you are familiar with how eval() and exec() work).  The
-        search order is locals first, then globals.
-
-        - If no dict arguments are passed, an attempt is made to use the
-          globals from obj (or the respective module's globals for classes),
-          and these are also used as the locals.  If the object does not appear
-          to have globals, an empty dictionary is used.
-
-        - If one dict argument is passed, it is used for both globals and
-          locals.
-
-        - If two dict arguments are passed, they specify globals and
-          locals, respectively.
-        """
-        hint = typing.get_type_hints(obj, globalns=globalns, localns=localns)
-        if include_extras:
-            return hint
-        return {k: _strip_annotations(t) for k, t in hint.items()}
-# 3.6
-else:
-
-    def _is_dunder(name):
-        """Returns True if name is a __dunder_variable_name__."""
-        return len(name) > 4 and name.startswith('__') and name.endswith('__')
-
-    # Prior to Python 3.7 types did not have `copy_with`. A lot of the equality
-    # checks, argument expansion etc. are done on the _subs_tre. As a result we
-    # can't provide a get_type_hints function that strips out annotations.
-
-    class AnnotatedMeta(typing.GenericMeta):
-        """Metaclass for Annotated"""
-
-        def __new__(cls, name, bases, namespace, **kwargs):
-            if any(b is not object for b in bases):
-                raise TypeError("Cannot subclass " + str(Annotated))
-            return super().__new__(cls, name, bases, namespace, **kwargs)
-
-        @property
-        def __metadata__(self):
-            return self._subs_tree()[2]
-
-        def _tree_repr(self, tree):
-            cls, origin, metadata = tree
-            if not isinstance(origin, tuple):
-                tp_repr = typing._type_repr(origin)
-            else:
-                tp_repr = origin[0]._tree_repr(origin)
-            metadata_reprs = ", ".join(repr(arg) for arg in metadata)
-            return f'{cls}[{tp_repr}, {metadata_reprs}]'
-
-        def _subs_tree(self, tvars=None, args=None):  # noqa
-            if self is Annotated:
-                return Annotated
-            res = super()._subs_tree(tvars=tvars, args=args)
-            # Flatten nested Annotated
-            if isinstance(res[1], tuple) and res[1][0] is Annotated:
-                sub_tp = res[1][1]
-                sub_annot = res[1][2]
-                return (Annotated, sub_tp, sub_annot + res[2])
-            return res
-
-        def _get_cons(self):
-            """Return the class used to create instance of this type."""
-            if self.__origin__ is None:
-                raise TypeError("Cannot get the underlying type of a "
-                                "non-specialized Annotated type.")
-            tree = self._subs_tree()
-            while isinstance(tree, tuple) and tree[0] is Annotated:
-                tree = tree[1]
-            if isinstance(tree, tuple):
-                return tree[0]
-            else:
-                return tree
-
-        @typing._tp_cache
-        def __getitem__(self, params):
-            if not isinstance(params, tuple):
-                params = (params,)
-            if self.__origin__ is not None:  # specializing an instantiated type
-                return super().__getitem__(params)
-            elif not isinstance(params, tuple) or len(params) < 2:
-                raise TypeError("Annotated[...] should be instantiated "
-                                "with at least two arguments (a type and an "
-                                "annotation).")
-            else:
-                msg = "Annotated[t, ...]: t must be a type."
-                tp = typing._type_check(params[0], msg)
-                metadata = tuple(params[1:])
-            return self.__class__(
-                self.__name__,
-                self.__bases__,
-                _no_slots_copy(self.__dict__),
-                tvars=_type_vars((tp,)),
-                # Metadata is a tuple so it won't be touched by _replace_args et al.
-                args=(tp, metadata),
-                origin=self,
-            )
-
-        def __call__(self, *args, **kwargs):
-            cons = self._get_cons()
-            result = cons(*args, **kwargs)
-            try:
-                result.__orig_class__ = self
-            except AttributeError:
-                pass
-            return result
-
-        def __getattr__(self, attr):
-            # For simplicity we just don't relay all dunder names
-            if self.__origin__ is not None and not _is_dunder(attr):
-                return getattr(self._get_cons(), attr)
-            raise AttributeError(attr)
-
-        def __setattr__(self, attr, value):
-            if _is_dunder(attr) or attr.startswith('_abc_'):
-                super().__setattr__(attr, value)
-            elif self.__origin__ is None:
-                raise AttributeError(attr)
-            else:
-                setattr(self._get_cons(), attr, value)
-
-        def __instancecheck__(self, obj):
-            raise TypeError("Annotated cannot be used with isinstance().")
-
-        def __subclasscheck__(self, cls):
-            raise TypeError("Annotated cannot be used with issubclass().")
-
-    class Annotated(metaclass=AnnotatedMeta):
-        """Add context specific metadata to a type.
-
-        Example: Annotated[int, runtime_check.Unsigned] indicates to the
-        hypothetical runtime_check module that this type is an unsigned int.
-        Every other consumer of this type can ignore this metadata and treat
-        this type as int.
-
-        The first argument to Annotated must be a valid type, the remaining
-        arguments are kept as a tuple in the __metadata__ field.
-
-        Details:
-
-        - It's an error to call `Annotated` with less than two arguments.
-        - Nested Annotated are flattened::
-
-            Annotated[Annotated[T, Ann1, Ann2], Ann3] == Annotated[T, Ann1, Ann2, Ann3]
-
-        - Instantiating an annotated type is equivalent to instantiating the
-        underlying type::
-
-            Annotated[C, Ann1](5) == C(5)
-
-        - Annotated can be used as a generic type alias::
-
-            Optimized = Annotated[T, runtime.Optimize()]
-            Optimized[int] == Annotated[int, runtime.Optimize()]
-
-            OptimizedList = Annotated[List[T], runtime.Optimize()]
-            OptimizedList[int] == Annotated[List[int], runtime.Optimize()]
-        """
-
-# Python 3.8 has get_origin() and get_args() but those implementations aren't
-# Annotated-aware, so we can't use those. Python 3.9's versions don't support
-# ParamSpecArgs and ParamSpecKwargs, so only Python 3.10's versions will do.
-if sys.version_info[:2] >= (3, 10):
-    get_origin = typing.get_origin
-    get_args = typing.get_args
-# 3.7-3.9
-elif PEP_560:
-    try:
-        # 3.9+
-        from typing import _BaseGenericAlias
-    except ImportError:
-        _BaseGenericAlias = typing._GenericAlias
-    try:
-        # 3.9+
-        from typing import GenericAlias
-    except ImportError:
-        GenericAlias = typing._GenericAlias
-
-    def get_origin(tp):
-        """Get the unsubscripted version of a type.
-
-        This supports generic types, Callable, Tuple, Union, Literal, Final, ClassVar
-        and Annotated. Return None for unsupported types. Examples::
-
-            get_origin(Literal[42]) is Literal
-            get_origin(int) is None
-            get_origin(ClassVar[int]) is ClassVar
-            get_origin(Generic) is Generic
-            get_origin(Generic[T]) is Generic
-            get_origin(Union[T, int]) is Union
-            get_origin(List[Tuple[T, T]][int]) == list
-            get_origin(P.args) is P
-        """
-        if isinstance(tp, _AnnotatedAlias):
-            return Annotated
-        if isinstance(tp, (typing._GenericAlias, GenericAlias, _BaseGenericAlias,
-                           ParamSpecArgs, ParamSpecKwargs)):
-            return tp.__origin__
-        if tp is typing.Generic:
-            return typing.Generic
-        return None
-
-    def get_args(tp):
-        """Get type arguments with all substitutions performed.
-
-        For unions, basic simplifications used by Union constructor are performed.
-        Examples::
-            get_args(Dict[str, int]) == (str, int)
-            get_args(int) == ()
-            get_args(Union[int, Union[T, int], str][int]) == (int, str)
-            get_args(Union[int, Tuple[T, int]][str]) == (int, Tuple[str, int])
-            get_args(Callable[[], T][int]) == ([], int)
-        """
-        if isinstance(tp, _AnnotatedAlias):
-            return (tp.__origin__,) + tp.__metadata__
-        if isinstance(tp, (typing._GenericAlias, GenericAlias)):
-            if getattr(tp, "_special", False):
-                return ()
-            res = tp.__args__
-            if get_origin(tp) is collections.abc.Callable and res[0] is not Ellipsis:
-                res = (list(res[:-1]), res[-1])
-            return res
-        return ()
-
-
-# 3.10+
-if hasattr(typing, 'TypeAlias'):
-    TypeAlias = typing.TypeAlias
-# 3.9
-elif sys.version_info[:2] >= (3, 9):
-    class _TypeAliasForm(typing._SpecialForm, _root=True):
-        def __repr__(self):
-            return 'typing_extensions.' + self._name
-
-    @_TypeAliasForm
-    def TypeAlias(self, parameters):
-        """Special marker indicating that an assignment should
-        be recognized as a proper type alias definition by type
-        checkers.
-
-        For example::
-
-            Predicate: TypeAlias = Callable[..., bool]
-
-        It's invalid when used anywhere except as in the example above.
-        """
-        raise TypeError(f"{self} is not subscriptable")
-# 3.7-3.8
-elif sys.version_info[:2] >= (3, 7):
-    class _TypeAliasForm(typing._SpecialForm, _root=True):
-        def __repr__(self):
-            return 'typing_extensions.' + self._name
-
-    TypeAlias = _TypeAliasForm('TypeAlias',
-                               doc="""Special marker indicating that an assignment should
-                               be recognized as a proper type alias definition by type
-                               checkers.
-
-                               For example::
-
-                                   Predicate: TypeAlias = Callable[..., bool]
-
-                               It's invalid when used anywhere except as in the example
-                               above.""")
-# 3.6
-else:
-    class _TypeAliasMeta(typing.TypingMeta):
-        """Metaclass for TypeAlias"""
-
-        def __repr__(self):
-            return 'typing_extensions.TypeAlias'
-
-    class _TypeAliasBase(typing._FinalTypingBase, metaclass=_TypeAliasMeta, _root=True):
-        """Special marker indicating that an assignment should
-        be recognized as a proper type alias definition by type
-        checkers.
-
-        For example::
-
-            Predicate: TypeAlias = Callable[..., bool]
-
-        It's invalid when used anywhere except as in the example above.
-        """
-        __slots__ = ()
-
-        def __instancecheck__(self, obj):
-            raise TypeError("TypeAlias cannot be used with isinstance().")
-
-        def __subclasscheck__(self, cls):
-            raise TypeError("TypeAlias cannot be used with issubclass().")
-
-        def __repr__(self):
-            return 'typing_extensions.TypeAlias'
-
-    TypeAlias = _TypeAliasBase(_root=True)
-
-
-# Python 3.10+ has PEP 612
-if hasattr(typing, 'ParamSpecArgs'):
-    ParamSpecArgs = typing.ParamSpecArgs
-    ParamSpecKwargs = typing.ParamSpecKwargs
-# 3.6-3.9
-else:
-    class _Immutable:
-        """Mixin to indicate that object should not be copied."""
-        __slots__ = ()
-
-        def __copy__(self):
-            return self
-
-        def __deepcopy__(self, memo):
-            return self
-
-    class ParamSpecArgs(_Immutable):
-        """The args for a ParamSpec object.
-
-        Given a ParamSpec object P, P.args is an instance of ParamSpecArgs.
-
-        ParamSpecArgs objects have a reference back to their ParamSpec:
-
-        P.args.__origin__ is P
-
-        This type is meant for runtime introspection and has no special meaning to
-        static type checkers.
-        """
-        def __init__(self, origin):
-            self.__origin__ = origin
-
-        def __repr__(self):
-            return f"{self.__origin__.__name__}.args"
-
-    class ParamSpecKwargs(_Immutable):
-        """The kwargs for a ParamSpec object.
-
-        Given a ParamSpec object P, P.kwargs is an instance of ParamSpecKwargs.
-
-        ParamSpecKwargs objects have a reference back to their ParamSpec:
-
-        P.kwargs.__origin__ is P
-
-        This type is meant for runtime introspection and has no special meaning to
-        static type checkers.
-        """
-        def __init__(self, origin):
-            self.__origin__ = origin
-
-        def __repr__(self):
-            return f"{self.__origin__.__name__}.kwargs"
-
-# 3.10+
-if hasattr(typing, 'ParamSpec'):
-    ParamSpec = typing.ParamSpec
-# 3.6-3.9
-else:
-
-    # Inherits from list as a workaround for Callable checks in Python < 3.9.2.
-    class ParamSpec(list):
-        """Parameter specification variable.
-
-        Usage::
-
-           P = ParamSpec('P')
-
-        Parameter specification variables exist primarily for the benefit of static
-        type checkers.  They are used to forward the parameter types of one
-        callable to another callable, a pattern commonly found in higher order
-        functions and decorators.  They are only valid when used in ``Concatenate``,
-        or s the first argument to ``Callable``. In Python 3.10 and higher,
-        they are also supported in user-defined Generics at runtime.
-        See class Generic for more information on generic types.  An
-        example for annotating a decorator::
-
-           T = TypeVar('T')
-           P = ParamSpec('P')
-
-           def add_logging(f: Callable[P, T]) -> Callable[P, T]:
-               '''A type-safe decorator to add logging to a function.'''
-               def inner(*args: P.args, **kwargs: P.kwargs) -> T:
-                   logging.info(f'{f.__name__} was called')
-                   return f(*args, **kwargs)
-               return inner
-
-           @add_logging
-           def add_two(x: float, y: float) -> float:
-               '''Add two numbers together.'''
-               return x + y
-
-        Parameter specification variables defined with covariant=True or
-        contravariant=True can be used to declare covariant or contravariant
-        generic types.  These keyword arguments are valid, but their actual semantics
-        are yet to be decided.  See PEP 612 for details.
-
-        Parameter specification variables can be introspected. e.g.:
-
-           P.__name__ == 'T'
-           P.__bound__ == None
-           P.__covariant__ == False
-           P.__contravariant__ == False
-
-        Note that only parameter specification variables defined in global scope can
-        be pickled.
-        """
-
-        # Trick Generic __parameters__.
-        __class__ = typing.TypeVar
-
-        @property
-        def args(self):
-            return ParamSpecArgs(self)
-
-        @property
-        def kwargs(self):
-            return ParamSpecKwargs(self)
-
-        def __init__(self, name, *, bound=None, covariant=False, contravariant=False):
-            super().__init__([self])
-            self.__name__ = name
-            self.__covariant__ = bool(covariant)
-            self.__contravariant__ = bool(contravariant)
-            if bound:
-                self.__bound__ = typing._type_check(bound, 'Bound must be a type.')
-            else:
-                self.__bound__ = None
-
-            # for pickling:
-            try:
-                def_mod = sys._getframe(1).f_globals.get('__name__', '__main__')
-            except (AttributeError, ValueError):
-                def_mod = None
-            if def_mod != 'typing_extensions':
-                self.__module__ = def_mod
-
-        def __repr__(self):
-            if self.__covariant__:
-                prefix = '+'
-            elif self.__contravariant__:
-                prefix = '-'
-            else:
-                prefix = '~'
-            return prefix + self.__name__
-
-        def __hash__(self):
-            return object.__hash__(self)
-
-        def __eq__(self, other):
-            return self is other
-
-        def __reduce__(self):
-            return self.__name__
-
-        # Hack to get typing._type_check to pass.
-        def __call__(self, *args, **kwargs):
-            pass
-
-        if not PEP_560:
-            # Only needed in 3.6.
-            def _get_type_vars(self, tvars):
-                if self not in tvars:
-                    tvars.append(self)
-
-
-# 3.6-3.9
-if not hasattr(typing, 'Concatenate'):
-    # Inherits from list as a workaround for Callable checks in Python < 3.9.2.
-    class _ConcatenateGenericAlias(list):
-
-        # Trick Generic into looking into this for __parameters__.
-        if PEP_560:
-            __class__ = typing._GenericAlias
-        else:
-            __class__ = typing._TypingBase
-
-        # Flag in 3.8.
-        _special = False
-        # Attribute in 3.6 and earlier.
-        _gorg = typing.Generic
-
-        def __init__(self, origin, args):
-            super().__init__(args)
-            self.__origin__ = origin
-            self.__args__ = args
-
-        def __repr__(self):
-            _type_repr = typing._type_repr
-            return (f'{_type_repr(self.__origin__)}'
-                    f'[{", ".join(_type_repr(arg) for arg in self.__args__)}]')
-
-        def __hash__(self):
-            return hash((self.__origin__, self.__args__))
-
-        # Hack to get typing._type_check to pass in Generic.
-        def __call__(self, *args, **kwargs):
-            pass
-
-        @property
-        def __parameters__(self):
-            return tuple(
-                tp for tp in self.__args__ if isinstance(tp, (typing.TypeVar, ParamSpec))
-            )
-
-        if not PEP_560:
-            # Only required in 3.6.
-            def _get_type_vars(self, tvars):
-                if self.__origin__ and self.__parameters__:
-                    typing._get_type_vars(self.__parameters__, tvars)
-
-
-# 3.6-3.9
-@typing._tp_cache
-def _concatenate_getitem(self, parameters):
-    if parameters == ():
-        raise TypeError("Cannot take a Concatenate of no types.")
-    if not isinstance(parameters, tuple):
-        parameters = (parameters,)
-    if not isinstance(parameters[-1], ParamSpec):
-        raise TypeError("The last parameter to Concatenate should be a "
-                        "ParamSpec variable.")
-    msg = "Concatenate[arg, ...]: each arg must be a type."
-    parameters = tuple(typing._type_check(p, msg) for p in parameters)
-    return _ConcatenateGenericAlias(self, parameters)
-
-
-# 3.10+
-if hasattr(typing, 'Concatenate'):
-    Concatenate = typing.Concatenate
-    _ConcatenateGenericAlias = typing._ConcatenateGenericAlias # noqa
-# 3.9
-elif sys.version_info[:2] >= (3, 9):
-    @_TypeAliasForm
-    def Concatenate(self, parameters):
-        """Used in conjunction with ``ParamSpec`` and ``Callable`` to represent a
-        higher order function which adds, removes or transforms parameters of a
-        callable.
-
-        For example::
-
-           Callable[Concatenate[int, P], int]
-
-        See PEP 612 for detailed information.
-        """
-        return _concatenate_getitem(self, parameters)
-# 3.7-8
-elif sys.version_info[:2] >= (3, 7):
-    class _ConcatenateForm(typing._SpecialForm, _root=True):
-        def __repr__(self):
-            return 'typing_extensions.' + self._name
-
-        def __getitem__(self, parameters):
-            return _concatenate_getitem(self, parameters)
-
-    Concatenate = _ConcatenateForm(
-        'Concatenate',
-        doc="""Used in conjunction with ``ParamSpec`` and ``Callable`` to represent a
-        higher order function which adds, removes or transforms parameters of a
-        callable.
-
-        For example::
-
-           Callable[Concatenate[int, P], int]
-
-        See PEP 612 for detailed information.
-        """)
-# 3.6
-else:
-    class _ConcatenateAliasMeta(typing.TypingMeta):
-        """Metaclass for Concatenate."""
-
-        def __repr__(self):
-            return 'typing_extensions.Concatenate'
-
-    class _ConcatenateAliasBase(typing._FinalTypingBase,
-                                metaclass=_ConcatenateAliasMeta,
-                                _root=True):
-        """Used in conjunction with ``ParamSpec`` and ``Callable`` to represent a
-        higher order function which adds, removes or transforms parameters of a
-        callable.
-
-        For example::
-
-           Callable[Concatenate[int, P], int]
-
-        See PEP 612 for detailed information.
-        """
-        __slots__ = ()
-
-        def __instancecheck__(self, obj):
-            raise TypeError("Concatenate cannot be used with isinstance().")
-
-        def __subclasscheck__(self, cls):
-            raise TypeError("Concatenate cannot be used with issubclass().")
-
-        def __repr__(self):
-            return 'typing_extensions.Concatenate'
-
-        def __getitem__(self, parameters):
-            return _concatenate_getitem(self, parameters)
-
-    Concatenate = _ConcatenateAliasBase(_root=True)
-
-# 3.10+
-if hasattr(typing, 'TypeGuard'):
-    TypeGuard = typing.TypeGuard
-# 3.9
-elif sys.version_info[:2] >= (3, 9):
-    class _TypeGuardForm(typing._SpecialForm, _root=True):
-        def __repr__(self):
-            return 'typing_extensions.' + self._name
-
-    @_TypeGuardForm
-    def TypeGuard(self, parameters):
-        """Special typing form used to annotate the return type of a user-defined
-        type guard function.  ``TypeGuard`` only accepts a single type argument.
-        At runtime, functions marked this way should return a boolean.
-
-        ``TypeGuard`` aims to benefit *type narrowing* -- a technique used by static
-        type checkers to determine a more precise type of an expression within a
-        program's code flow.  Usually type narrowing is done by analyzing
-        conditional code flow and applying the narrowing to a block of code.  The
-        conditional expression here is sometimes referred to as a "type guard".
-
-        Sometimes it would be convenient to use a user-defined boolean function
-        as a type guard.  Such a function should use ``TypeGuard[...]`` as its
-        return type to alert static type checkers to this intention.
-
-        Using  ``-> TypeGuard`` tells the static type checker that for a given
-        function:
-
-        1. The return value is a boolean.
-        2. If the return value is ``True``, the type of its argument
-        is the type inside ``TypeGuard``.
-
-        For example::
-
-            def is_str(val: Union[str, float]):
-                # "isinstance" type guard
-                if isinstance(val, str):
-                    # Type of ``val`` is narrowed to ``str``
-                    ...
-                else:
-                    # Else, type of ``val`` is narrowed to ``float``.
-                    ...
-
-        Strict type narrowing is not enforced -- ``TypeB`` need not be a narrower
-        form of ``TypeA`` (it can even be a wider form) and this may lead to
-        type-unsafe results.  The main reason is to allow for things like
-        narrowing ``List[object]`` to ``List[str]`` even though the latter is not
-        a subtype of the former, since ``List`` is invariant.  The responsibility of
-        writing type-safe type guards is left to the user.
-
-        ``TypeGuard`` also works with type variables.  For more information, see
-        PEP 647 (User-Defined Type Guards).
-        """
-        item = typing._type_check(parameters, f'{self} accepts only single type.')
-        return typing._GenericAlias(self, (item,))
-# 3.7-3.8
-elif sys.version_info[:2] >= (3, 7):
-    class _TypeGuardForm(typing._SpecialForm, _root=True):
-
-        def __repr__(self):
-            return 'typing_extensions.' + self._name
-
-        def __getitem__(self, parameters):
-            item = typing._type_check(parameters,
-                                      f'{self._name} accepts only a single type')
-            return typing._GenericAlias(self, (item,))
-
-    TypeGuard = _TypeGuardForm(
-        'TypeGuard',
-        doc="""Special typing form used to annotate the return type of a user-defined
-        type guard function.  ``TypeGuard`` only accepts a single type argument.
-        At runtime, functions marked this way should return a boolean.
-
-        ``TypeGuard`` aims to benefit *type narrowing* -- a technique used by static
-        type checkers to determine a more precise type of an expression within a
-        program's code flow.  Usually type narrowing is done by analyzing
-        conditional code flow and applying the narrowing to a block of code.  The
-        conditional expression here is sometimes referred to as a "type guard".
-
-        Sometimes it would be convenient to use a user-defined boolean function
-        as a type guard.  Such a function should use ``TypeGuard[...]`` as its
-        return type to alert static type checkers to this intention.
-
-        Using  ``-> TypeGuard`` tells the static type checker that for a given
-        function:
-
-        1. The return value is a boolean.
-        2. If the return value is ``True``, the type of its argument
-        is the type inside ``TypeGuard``.
-
-        For example::
-
-            def is_str(val: Union[str, float]):
-                # "isinstance" type guard
-                if isinstance(val, str):
-                    # Type of ``val`` is narrowed to ``str``
-                    ...
-                else:
-                    # Else, type of ``val`` is narrowed to ``float``.
-                    ...
-
-        Strict type narrowing is not enforced -- ``TypeB`` need not be a narrower
-        form of ``TypeA`` (it can even be a wider form) and this may lead to
-        type-unsafe results.  The main reason is to allow for things like
-        narrowing ``List[object]`` to ``List[str]`` even though the latter is not
-        a subtype of the former, since ``List`` is invariant.  The responsibility of
-        writing type-safe type guards is left to the user.
-
-        ``TypeGuard`` also works with type variables.  For more information, see
-        PEP 647 (User-Defined Type Guards).
-        """)
-# 3.6
-else:
-    class _TypeGuard(typing._FinalTypingBase, _root=True):
-        """Special typing form used to annotate the return type of a user-defined
-        type guard function.  ``TypeGuard`` only accepts a single type argument.
-        At runtime, functions marked this way should return a boolean.
-
-        ``TypeGuard`` aims to benefit *type narrowing* -- a technique used by static
-        type checkers to determine a more precise type of an expression within a
-        program's code flow.  Usually type narrowing is done by analyzing
-        conditional code flow and applying the narrowing to a block of code.  The
-        conditional expression here is sometimes referred to as a "type guard".
-
-        Sometimes it would be convenient to use a user-defined boolean function
-        as a type guard.  Such a function should use ``TypeGuard[...]`` as its
-        return type to alert static type checkers to this intention.
-
-        Using  ``-> TypeGuard`` tells the static type checker that for a given
-        function:
-
-        1. The return value is a boolean.
-        2. If the return value is ``True``, the type of its argument
-        is the type inside ``TypeGuard``.
-
-        For example::
-
-            def is_str(val: Union[str, float]):
-                # "isinstance" type guard
-                if isinstance(val, str):
-                    # Type of ``val`` is narrowed to ``str``
-                    ...
-                else:
-                    # Else, type of ``val`` is narrowed to ``float``.
-                    ...
-
-        Strict type narrowing is not enforced -- ``TypeB`` need not be a narrower
-        form of ``TypeA`` (it can even be a wider form) and this may lead to
-        type-unsafe results.  The main reason is to allow for things like
-        narrowing ``List[object]`` to ``List[str]`` even though the latter is not
-        a subtype of the former, since ``List`` is invariant.  The responsibility of
-        writing type-safe type guards is left to the user.
-
-        ``TypeGuard`` also works with type variables.  For more information, see
-        PEP 647 (User-Defined Type Guards).
-        """
-
-        __slots__ = ('__type__',)
-
-        def __init__(self, tp=None, **kwds):
-            self.__type__ = tp
-
-        def __getitem__(self, item):
-            cls = type(self)
-            if self.__type__ is None:
-                return cls(typing._type_check(item,
-                           f'{cls.__name__[1:]} accepts only a single type.'),
-                           _root=True)
-            raise TypeError(f'{cls.__name__[1:]} cannot be further subscripted')
-
-        def _eval_type(self, globalns, localns):
-            new_tp = typing._eval_type(self.__type__, globalns, localns)
-            if new_tp == self.__type__:
-                return self
-            return type(self)(new_tp, _root=True)
-
-        def __repr__(self):
-            r = super().__repr__()
-            if self.__type__ is not None:
-                r += f'[{typing._type_repr(self.__type__)}]'
-            return r
-
-        def __hash__(self):
-            return hash((type(self).__name__, self.__type__))
-
-        def __eq__(self, other):
-            if not isinstance(other, _TypeGuard):
-                return NotImplemented
-            if self.__type__ is not None:
-                return self.__type__ == other.__type__
-            return self is other
-
-    TypeGuard = _TypeGuard(_root=True)
-
-if hasattr(typing, "Self"):
-    Self = typing.Self
-elif sys.version_info[:2] >= (3, 7):
-    # Vendored from cpython typing._SpecialFrom
-    class _SpecialForm(typing._Final, _root=True):
-        __slots__ = ('_name', '__doc__', '_getitem')
-
-        def __init__(self, getitem):
-            self._getitem = getitem
-            self._name = getitem.__name__
-            self.__doc__ = getitem.__doc__
-
-        def __getattr__(self, item):
-            if item in {'__name__', '__qualname__'}:
-                return self._name
-
-            raise AttributeError(item)
-
-        def __mro_entries__(self, bases):
-            raise TypeError(f"Cannot subclass {self!r}")
-
-        def __repr__(self):
-            return f'typing_extensions.{self._name}'
-
-        def __reduce__(self):
-            return self._name
-
-        def __call__(self, *args, **kwds):
-            raise TypeError(f"Cannot instantiate {self!r}")
-
-        def __or__(self, other):
-            return typing.Union[self, other]
-
-        def __ror__(self, other):
-            return typing.Union[other, self]
-
-        def __instancecheck__(self, obj):
-            raise TypeError(f"{self} cannot be used with isinstance()")
-
-        def __subclasscheck__(self, cls):
-            raise TypeError(f"{self} cannot be used with issubclass()")
-
-        @typing._tp_cache
-        def __getitem__(self, parameters):
-            return self._getitem(self, parameters)
-
-    @_SpecialForm
-    def Self(self, params):
-        """Used to spell the type of "self" in classes.
-
-        Example::
-
-          from typing import Self
-
-          class ReturnsSelf:
-              def parse(self, data: bytes) -> Self:
-                  ...
-                  return self
-
-        """
-
-        raise TypeError(f"{self} is not subscriptable")
-else:
-    class _Self(typing._FinalTypingBase, _root=True):
-        """Used to spell the type of "self" in classes.
-
-        Example::
-
-          from typing import Self
-
-          class ReturnsSelf:
-              def parse(self, data: bytes) -> Self:
-                  ...
-                  return self
-
-        """
-
-        __slots__ = ()
-
-        def __instancecheck__(self, obj):
-            raise TypeError(f"{self} cannot be used with isinstance().")
-
-        def __subclasscheck__(self, cls):
-            raise TypeError(f"{self} cannot be used with issubclass().")
-
-    Self = _Self(_root=True)
-
-
-if hasattr(typing, 'Required'):
-    Required = typing.Required
-    NotRequired = typing.NotRequired
-elif sys.version_info[:2] >= (3, 9):
-    class _ExtensionsSpecialForm(typing._SpecialForm, _root=True):
-        def __repr__(self):
-            return 'typing_extensions.' + self._name
-
-    @_ExtensionsSpecialForm
-    def Required(self, parameters):
-        """A special typing construct to mark a key of a total=False TypedDict
-        as required. For example:
-
-            class Movie(TypedDict, total=False):
-                title: Required[str]
-                year: int
-
-            m = Movie(
-                title='The Matrix',  # typechecker error if key is omitted
-                year=1999,
-            )
-
-        There is no runtime checking that a required key is actually provided
-        when instantiating a related TypedDict.
-        """
-        item = typing._type_check(parameters, f'{self._name} accepts only single type')
-        return typing._GenericAlias(self, (item,))
-
-    @_ExtensionsSpecialForm
-    def NotRequired(self, parameters):
-        """A special typing construct to mark a key of a TypedDict as
-        potentially missing. For example:
-
-            class Movie(TypedDict):
-                title: str
-                year: NotRequired[int]
-
-            m = Movie(
-                title='The Matrix',  # typechecker error if key is omitted
-                year=1999,
-            )
-        """
-        item = typing._type_check(parameters, f'{self._name} accepts only single type')
-        return typing._GenericAlias(self, (item,))
-
-elif sys.version_info[:2] >= (3, 7):
-    class _RequiredForm(typing._SpecialForm, _root=True):
-        def __repr__(self):
-            return 'typing_extensions.' + self._name
-
-        def __getitem__(self, parameters):
-            item = typing._type_check(parameters,
-                                      '{} accepts only single type'.format(self._name))
-            return typing._GenericAlias(self, (item,))
-
-    Required = _RequiredForm(
-        'Required',
-        doc="""A special typing construct to mark a key of a total=False TypedDict
-        as required. For example:
-
-            class Movie(TypedDict, total=False):
-                title: Required[str]
-                year: int
-
-            m = Movie(
-                title='The Matrix',  # typechecker error if key is omitted
-                year=1999,
-            )
-
-        There is no runtime checking that a required key is actually provided
-        when instantiating a related TypedDict.
-        """)
-    NotRequired = _RequiredForm(
-        'NotRequired',
-        doc="""A special typing construct to mark a key of a TypedDict as
-        potentially missing. For example:
-
-            class Movie(TypedDict):
-                title: str
-                year: NotRequired[int]
-
-            m = Movie(
-                title='The Matrix',  # typechecker error if key is omitted
-                year=1999,
-            )
-        """)
-else:
-    # NOTE: Modeled after _Final's implementation when _FinalTypingBase available
-    class _MaybeRequired(typing._FinalTypingBase, _root=True):
-        __slots__ = ('__type__',)
-
-        def __init__(self, tp=None, **kwds):
-            self.__type__ = tp
-
-        def __getitem__(self, item):
-            cls = type(self)
-            if self.__type__ is None:
-                return cls(typing._type_check(item,
-                           '{} accepts only single type.'.format(cls.__name__[1:])),
-                           _root=True)
-            raise TypeError('{} cannot be further subscripted'
-                            .format(cls.__name__[1:]))
-
-        def _eval_type(self, globalns, localns):
-            new_tp = typing._eval_type(self.__type__, globalns, localns)
-            if new_tp == self.__type__:
-                return self
-            return type(self)(new_tp, _root=True)
-
-        def __repr__(self):
-            r = super().__repr__()
-            if self.__type__ is not None:
-                r += '[{}]'.format(typing._type_repr(self.__type__))
-            return r
-
-        def __hash__(self):
-            return hash((type(self).__name__, self.__type__))
-
-        def __eq__(self, other):
-            if not isinstance(other, type(self)):
-                return NotImplemented
-            if self.__type__ is not None:
-                return self.__type__ == other.__type__
-            return self is other
-
-    class _Required(_MaybeRequired, _root=True):
-        """A special typing construct to mark a key of a total=False TypedDict
-        as required. For example:
-
-            class Movie(TypedDict, total=False):
-                title: Required[str]
-                year: int
-
-            m = Movie(
-                title='The Matrix',  # typechecker error if key is omitted
-                year=1999,
-            )
-
-        There is no runtime checking that a required key is actually provided
-        when instantiating a related TypedDict.
-        """
-
-    class _NotRequired(_MaybeRequired, _root=True):
-        """A special typing construct to mark a key of a TypedDict as
-        potentially missing. For example:
-
-            class Movie(TypedDict):
-                title: str
-                year: NotRequired[int]
-
-            m = Movie(
-                title='The Matrix',  # typechecker error if key is omitted
-                year=1999,
-            )
-        """
-
-    Required = _Required(_root=True)
-    NotRequired = _NotRequired(_root=True)

spaces/Atualli/node-media-server/app.js

@@ -1,18 +0,0 @@
-const NodeMediaServer = require('node-media-server');
-
-const config = {
-  rtmp: {
-    port: 7861,
-    chunk_size: 60000,
-    gop_cache: true,
-    ping: 30,
-    ping_timeout: 60
-  },
-  http: {
-    port: 7860,
-    allow_origin: '*'
-  }
-};
-
-var nms = new NodeMediaServer(config)
-nms.run();

spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/config/instantiate.py

@@ -1,82 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import dataclasses
-import logging
-from collections import abc
-from typing import Any
-
-from detectron2.utils.registry import _convert_target_to_string, locate
-
-__all__ = ["dump_dataclass", "instantiate"]
-
-
-def dump_dataclass(obj: Any):
-    """
-    Dump a dataclass recursively into a dict that can be later instantiated.
-
-    Args:
-        obj: a dataclass object
-
-    Returns:
-        dict
-    """
-    assert dataclasses.is_dataclass(obj) and not isinstance(
-        obj, type
-    ), "dump_dataclass() requires an instance of a dataclass."
-    ret = {"_target_": _convert_target_to_string(type(obj))}
-    for f in dataclasses.fields(obj):
-        v = getattr(obj, f.name)
-        if dataclasses.is_dataclass(v):
-            v = dump_dataclass(v)
-        if isinstance(v, (list, tuple)):
-            v = [dump_dataclass(x) if dataclasses.is_dataclass(x) else x for x in v]
-        ret[f.name] = v
-    return ret
-
-
-def instantiate(cfg):
-    """
-    Recursively instantiate objects defined in dictionaries by
-    "_target_" and arguments.
-
-    Args:
-        cfg: a dict-like object with "_target_" that defines the caller, and
-            other keys that define the arguments
-
-    Returns:
-        object instantiated by cfg
-    """
-    from omegaconf import ListConfig
-
-    if isinstance(cfg, ListConfig):
-        lst = [instantiate(x) for x in cfg]
-        return ListConfig(lst, flags={"allow_objects": True})
-    if isinstance(cfg, list):
-        # Specialize for list, because many classes take
-        # list[objects] as arguments, such as ResNet, DatasetMapper
-        return [instantiate(x) for x in cfg]
-
-    if isinstance(cfg, abc.Mapping) and "_target_" in cfg:
-        # conceptually equivalent to hydra.utils.instantiate(cfg) with _convert_=all,
-        # but faster: https://github.com/facebookresearch/hydra/issues/1200
-        cfg = {k: instantiate(v) for k, v in cfg.items()}
-        cls = cfg.pop("_target_")
-        cls = instantiate(cls)
-
-        if isinstance(cls, str):
-            cls_name = cls
-            cls = locate(cls_name)
-            assert cls is not None, cls_name
-        else:
-            try:
-                cls_name = cls.__module__ + "." + cls.__qualname__
-            except Exception:
-                # target could be anything, so the above could fail
-                cls_name = str(cls)
-        assert callable(cls), f"_target_ {cls} does not define a callable object"
-        try:
-            return cls(**cfg)
-        except TypeError:
-            logger = logging.getLogger(__name__)
-            logger.error(f"Error when instantiating {cls_name}!")
-            raise
-    return cfg  # return as-is if don't know what to do

spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/layers/mask_ops.py

@@ -1,275 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import numpy as np
-from typing import Tuple
-import torch
-from PIL import Image
-from torch.nn import functional as F
-
-__all__ = ["paste_masks_in_image"]
-
-
-BYTES_PER_FLOAT = 4
-# TODO: This memory limit may be too much or too little. It would be better to
-# determine it based on available resources.
-GPU_MEM_LIMIT = 1024 ** 3  # 1 GB memory limit
-
-
-def _do_paste_mask(masks, boxes, img_h: int, img_w: int, skip_empty: bool = True):
-    """
-    Args:
-        masks: N, 1, H, W
-        boxes: N, 4
-        img_h, img_w (int):
-        skip_empty (bool): only paste masks within the region that
-            tightly bound all boxes, and returns the results this region only.
-            An important optimization for CPU.
-
-    Returns:
-        if skip_empty == False, a mask of shape (N, img_h, img_w)
-        if skip_empty == True, a mask of shape (N, h', w'), and the slice
-            object for the corresponding region.
-    """
-    # On GPU, paste all masks together (up to chunk size)
-    # by using the entire image to sample the masks
-    # Compared to pasting them one by one,
-    # this has more operations but is faster on COCO-scale dataset.
-    device = masks.device
-
-    if skip_empty and not torch.jit.is_scripting():
-        x0_int, y0_int = torch.clamp(boxes.min(dim=0).values.floor()[:2] - 1, min=0).to(
-            dtype=torch.int32
-        )
-        x1_int = torch.clamp(boxes[:, 2].max().ceil() + 1, max=img_w).to(dtype=torch.int32)
-        y1_int = torch.clamp(boxes[:, 3].max().ceil() + 1, max=img_h).to(dtype=torch.int32)
-    else:
-        x0_int, y0_int = 0, 0
-        x1_int, y1_int = img_w, img_h
-    x0, y0, x1, y1 = torch.split(boxes, 1, dim=1)  # each is Nx1
-
-    N = masks.shape[0]
-
-    img_y = torch.arange(y0_int, y1_int, device=device, dtype=torch.float32) + 0.5
-    img_x = torch.arange(x0_int, x1_int, device=device, dtype=torch.float32) + 0.5
-    img_y = (img_y - y0) / (y1 - y0) * 2 - 1
-    img_x = (img_x - x0) / (x1 - x0) * 2 - 1
-    # img_x, img_y have shapes (N, w), (N, h)
-
-    gx = img_x[:, None, :].expand(N, img_y.size(1), img_x.size(1))
-    gy = img_y[:, :, None].expand(N, img_y.size(1), img_x.size(1))
-    grid = torch.stack([gx, gy], dim=3)
-
-    if not torch.jit.is_scripting():
-        if not masks.dtype.is_floating_point:
-            masks = masks.float()
-    img_masks = F.grid_sample(masks, grid.to(masks.dtype), align_corners=False)
-
-    if skip_empty and not torch.jit.is_scripting():
-        return img_masks[:, 0], (slice(y0_int, y1_int), slice(x0_int, x1_int))
-    else:
-        return img_masks[:, 0], ()
-
-
-# Annotate boxes as Tensor (but not Boxes) in order to use scripting
-@torch.jit.script_if_tracing
-def paste_masks_in_image(
-    masks: torch.Tensor, boxes: torch.Tensor, image_shape: Tuple[int, int], threshold: float = 0.5
-):
-    """
-    Paste a set of masks that are of a fixed resolution (e.g., 28 x 28) into an image.
-    The location, height, and width for pasting each mask is determined by their
-    corresponding bounding boxes in boxes.
-
-    Note:
-        This is a complicated but more accurate implementation. In actual deployment, it is
-        often enough to use a faster but less accurate implementation.
-        See :func:`paste_mask_in_image_old` in this file for an alternative implementation.
-
-    Args:
-        masks (tensor): Tensor of shape (Bimg, Hmask, Wmask), where Bimg is the number of
-            detected object instances in the image and Hmask, Wmask are the mask width and mask
-            height of the predicted mask (e.g., Hmask = Wmask = 28). Values are in [0, 1].
-        boxes (Boxes or Tensor): A Boxes of length Bimg or Tensor of shape (Bimg, 4).
-            boxes[i] and masks[i] correspond to the same object instance.
-        image_shape (tuple): height, width
-        threshold (float): A threshold in [0, 1] for converting the (soft) masks to
-            binary masks.
-
-    Returns:
-        img_masks (Tensor): A tensor of shape (Bimg, Himage, Wimage), where Bimg is the
-        number of detected object instances and Himage, Wimage are the image width
-        and height. img_masks[i] is a binary mask for object instance i.
-    """
-
-    assert masks.shape[-1] == masks.shape[-2], "Only square mask predictions are supported"
-    N = len(masks)
-    if N == 0:
-        return masks.new_empty((0,) + image_shape, dtype=torch.uint8)
-    if not isinstance(boxes, torch.Tensor):
-        boxes = boxes.tensor
-    device = boxes.device
-    assert len(boxes) == N, boxes.shape
-
-    img_h, img_w = image_shape
-
-    # The actual implementation split the input into chunks,
-    # and paste them chunk by chunk.
-    if device.type == "cpu" or torch.jit.is_scripting():
-        # CPU is most efficient when they are pasted one by one with skip_empty=True
-        # so that it performs minimal number of operations.
-        num_chunks = N
-    else:
-        # GPU benefits from parallelism for larger chunks, but may have memory issue
-        # int(img_h) because shape may be tensors in tracing
-        num_chunks = int(np.ceil(N * int(img_h) * int(img_w) * BYTES_PER_FLOAT / GPU_MEM_LIMIT))
-        assert (
-            num_chunks <= N
-        ), "Default GPU_MEM_LIMIT in mask_ops.py is too small; try increasing it"
-    chunks = torch.chunk(torch.arange(N, device=device), num_chunks)
-
-    img_masks = torch.zeros(
-        N, img_h, img_w, device=device, dtype=torch.bool if threshold >= 0 else torch.uint8
-    )
-    for inds in chunks:
-        masks_chunk, spatial_inds = _do_paste_mask(
-            masks[inds, None, :, :], boxes[inds], img_h, img_w, skip_empty=device.type == "cpu"
-        )
-
-        if threshold >= 0:
-            masks_chunk = (masks_chunk >= threshold).to(dtype=torch.bool)
-        else:
-            # for visualization and debugging
-            masks_chunk = (masks_chunk * 255).to(dtype=torch.uint8)
-
-        if torch.jit.is_scripting():  # Scripting does not use the optimized codepath
-            img_masks[inds] = masks_chunk
-        else:
-            img_masks[(inds,) + spatial_inds] = masks_chunk
-    return img_masks
-
-
-# The below are the original paste function (from Detectron1) which has
-# larger quantization error.
-# It is faster on CPU, while the aligned one is faster on GPU thanks to grid_sample.
-
-
-def paste_mask_in_image_old(mask, box, img_h, img_w, threshold):
-    """
-    Paste a single mask in an image.
-    This is a per-box implementation of :func:`paste_masks_in_image`.
-    This function has larger quantization error due to incorrect pixel
-    modeling and is not used any more.
-
-    Args:
-        mask (Tensor): A tensor of shape (Hmask, Wmask) storing the mask of a single
-            object instance. Values are in [0, 1].
-        box (Tensor): A tensor of shape (4, ) storing the x0, y0, x1, y1 box corners
-            of the object instance.
-        img_h, img_w (int): Image height and width.
-        threshold (float): Mask binarization threshold in [0, 1].
-
-    Returns:
-        im_mask (Tensor):
-            The resized and binarized object mask pasted into the original
-            image plane (a tensor of shape (img_h, img_w)).
-    """
-    # Conversion from continuous box coordinates to discrete pixel coordinates
-    # via truncation (cast to int32). This determines which pixels to paste the
-    # mask onto.
-    box = box.to(dtype=torch.int32)  # Continuous to discrete coordinate conversion
-    # An example (1D) box with continuous coordinates (x0=0.7, x1=4.3) will map to
-    # a discrete coordinates (x0=0, x1=4). Note that box is mapped to 5 = x1 - x0 + 1
-    # pixels (not x1 - x0 pixels).
-    samples_w = box[2] - box[0] + 1  # Number of pixel samples, *not* geometric width
-    samples_h = box[3] - box[1] + 1  # Number of pixel samples, *not* geometric height
-
-    # Resample the mask from it's original grid to the new samples_w x samples_h grid
-    mask = Image.fromarray(mask.cpu().numpy())
-    mask = mask.resize((samples_w, samples_h), resample=Image.BILINEAR)
-    mask = np.array(mask, copy=False)
-
-    if threshold >= 0:
-        mask = np.array(mask > threshold, dtype=np.uint8)
-        mask = torch.from_numpy(mask)
-    else:
-        # for visualization and debugging, we also
-        # allow it to return an unmodified mask
-        mask = torch.from_numpy(mask * 255).to(torch.uint8)
-
-    im_mask = torch.zeros((img_h, img_w), dtype=torch.uint8)
-    x_0 = max(box[0], 0)
-    x_1 = min(box[2] + 1, img_w)
-    y_0 = max(box[1], 0)
-    y_1 = min(box[3] + 1, img_h)
-
-    im_mask[y_0:y_1, x_0:x_1] = mask[
-        (y_0 - box[1]) : (y_1 - box[1]), (x_0 - box[0]) : (x_1 - box[0])
-    ]
-    return im_mask
-
-
-# Our pixel modeling requires extrapolation for any continuous
-# coordinate < 0.5 or > length - 0.5. When sampling pixels on the masks,
-# we would like this extrapolation to be an interpolation between boundary values and zero,
-# instead of using absolute zero or boundary values.
-# Therefore `paste_mask_in_image_old` is often used with zero padding around the masks like this:
-# masks, scale = pad_masks(masks[:, 0, :, :], 1)
-# boxes = scale_boxes(boxes.tensor, scale)
-
-
-def pad_masks(masks, padding):
-    """
-    Args:
-        masks (tensor): A tensor of shape (B, M, M) representing B masks.
-        padding (int): Number of cells to pad on all sides.
-
-    Returns:
-        The padded masks and the scale factor of the padding size / original size.
-    """
-    B = masks.shape[0]
-    M = masks.shape[-1]
-    pad2 = 2 * padding
-    scale = float(M + pad2) / M
-    padded_masks = masks.new_zeros((B, M + pad2, M + pad2))
-    padded_masks[:, padding:-padding, padding:-padding] = masks
-    return padded_masks, scale
-
-
-def scale_boxes(boxes, scale):
-    """
-    Args:
-        boxes (tensor): A tensor of shape (B, 4) representing B boxes with 4
-            coords representing the corners x0, y0, x1, y1,
-        scale (float): The box scaling factor.
-
-    Returns:
-        Scaled boxes.
-    """
-    w_half = (boxes[:, 2] - boxes[:, 0]) * 0.5
-    h_half = (boxes[:, 3] - boxes[:, 1]) * 0.5
-    x_c = (boxes[:, 2] + boxes[:, 0]) * 0.5
-    y_c = (boxes[:, 3] + boxes[:, 1]) * 0.5
-
-    w_half *= scale
-    h_half *= scale
-
-    scaled_boxes = torch.zeros_like(boxes)
-    scaled_boxes[:, 0] = x_c - w_half
-    scaled_boxes[:, 2] = x_c + w_half
-    scaled_boxes[:, 1] = y_c - h_half
-    scaled_boxes[:, 3] = y_c + h_half
-    return scaled_boxes
-
-
-@torch.jit.script_if_tracing
-def _paste_masks_tensor_shape(
-    masks: torch.Tensor,
-    boxes: torch.Tensor,
-    image_shape: Tuple[torch.Tensor, torch.Tensor],
-    threshold: float = 0.5,
-):
-    """
-    A wrapper of paste_masks_in_image where image_shape is Tensor.
-    During tracing, shapes might be tensors instead of ints. The Tensor->int
-    conversion should be scripted rather than traced.
-    """
-    return paste_masks_in_image(masks, boxes, (int(image_shape[0]), int(image_shape[1])), threshold)

spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/tests/modeling/test_model_e2e.py

@@ -1,223 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-
-
-import itertools
-import unittest
-from contextlib import contextmanager
-from copy import deepcopy
-import torch
-
-from detectron2.structures import BitMasks, Boxes, ImageList, Instances
-from detectron2.utils.events import EventStorage
-from detectron2.utils.testing import get_model_no_weights
-
-
-@contextmanager
-def typecheck_hook(model, *, in_dtype=None, out_dtype=None):
-    """
-    Check that the model must be called with the given input/output dtype
-    """
-    if not isinstance(in_dtype, set):
-        in_dtype = {in_dtype}
-    if not isinstance(out_dtype, set):
-        out_dtype = {out_dtype}
-
-    def flatten(x):
-        if isinstance(x, torch.Tensor):
-            return [x]
-        if isinstance(x, (list, tuple)):
-            return list(itertools.chain(*[flatten(t) for t in x]))
-        if isinstance(x, dict):
-            return flatten(list(x.values()))
-        return []
-
-    def hook(module, input, output):
-        if in_dtype is not None:
-            dtypes = {x.dtype for x in flatten(input)}
-            assert (
-                dtypes == in_dtype
-            ), f"Expected input dtype of {type(module)} is {in_dtype}. Got {dtypes} instead!"
-
-        if out_dtype is not None:
-            dtypes = {x.dtype for x in flatten(output)}
-            assert (
-                dtypes == out_dtype
-            ), f"Expected output dtype of {type(module)} is {out_dtype}. Got {dtypes} instead!"
-
-    with model.register_forward_hook(hook):
-        yield
-
-
-def create_model_input(img, inst=None):
-    if inst is not None:
-        return {"image": img, "instances": inst}
-    else:
-        return {"image": img}
-
-
-def get_empty_instance(h, w):
-    inst = Instances((h, w))
-    inst.gt_boxes = Boxes(torch.rand(0, 4))
-    inst.gt_classes = torch.tensor([]).to(dtype=torch.int64)
-    inst.gt_masks = BitMasks(torch.rand(0, h, w))
-    return inst
-
-
-def get_regular_bitmask_instances(h, w):
-    inst = Instances((h, w))
-    inst.gt_boxes = Boxes(torch.rand(3, 4))
-    inst.gt_boxes.tensor[:, 2:] += inst.gt_boxes.tensor[:, :2]
-    inst.gt_classes = torch.tensor([3, 4, 5]).to(dtype=torch.int64)
-    inst.gt_masks = BitMasks((torch.rand(3, h, w) > 0.5))
-    return inst
-
-
-class InstanceModelE2ETest:
-    def setUp(self):
-        torch.manual_seed(43)
-        self.model = get_model_no_weights(self.CONFIG_PATH)
-
-    def _test_eval(self, input_sizes):
-        inputs = [create_model_input(torch.rand(3, s[0], s[1])) for s in input_sizes]
-        self.model.eval()
-        self.model(inputs)
-
-    def _test_train(self, input_sizes, instances):
-        assert len(input_sizes) == len(instances)
-        inputs = [
-            create_model_input(torch.rand(3, s[0], s[1]), inst)
-            for s, inst in zip(input_sizes, instances)
-        ]
-        self.model.train()
-        with EventStorage():
-            losses = self.model(inputs)
-            sum(losses.values()).backward()
-            del losses
-
-    def _inf_tensor(self, *shape):
-        return 1.0 / torch.zeros(*shape, device=self.model.device)
-
-    def _nan_tensor(self, *shape):
-        return torch.zeros(*shape, device=self.model.device).fill_(float("nan"))
-
-    def test_empty_data(self):
-        instances = [get_empty_instance(200, 250), get_empty_instance(200, 249)]
-        self._test_eval([(200, 250), (200, 249)])
-        self._test_train([(200, 250), (200, 249)], instances)
-
-    @unittest.skipIf(not torch.cuda.is_available(), "CUDA unavailable")
-    def test_eval_tocpu(self):
-        model = deepcopy(self.model).cpu()
-        model.eval()
-        input_sizes = [(200, 250), (200, 249)]
-        inputs = [create_model_input(torch.rand(3, s[0], s[1])) for s in input_sizes]
-        model(inputs)
-
-
-class MaskRCNNE2ETest(InstanceModelE2ETest, unittest.TestCase):
-    CONFIG_PATH = "COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml"
-
-    def test_half_empty_data(self):
-        instances = [get_empty_instance(200, 250), get_regular_bitmask_instances(200, 249)]
-        self._test_train([(200, 250), (200, 249)], instances)
-
-    # This test is flaky because in some environment the output features are zero due to relu
-    # def test_rpn_inf_nan_data(self):
-    #     self.model.eval()
-    #     for tensor in [self._inf_tensor, self._nan_tensor]:
-    #         images = ImageList(tensor(1, 3, 512, 512), [(510, 510)])
-    #         features = {
-    #             "p2": tensor(1, 256, 256, 256),
-    #             "p3": tensor(1, 256, 128, 128),
-    #             "p4": tensor(1, 256, 64, 64),
-    #             "p5": tensor(1, 256, 32, 32),
-    #             "p6": tensor(1, 256, 16, 16),
-    #         }
-    #         props, _ = self.model.proposal_generator(images, features)
-    #         self.assertEqual(len(props[0]), 0)
-
-    def test_roiheads_inf_nan_data(self):
-        self.model.eval()
-        for tensor in [self._inf_tensor, self._nan_tensor]:
-            images = ImageList(tensor(1, 3, 512, 512), [(510, 510)])
-            features = {
-                "p2": tensor(1, 256, 256, 256),
-                "p3": tensor(1, 256, 128, 128),
-                "p4": tensor(1, 256, 64, 64),
-                "p5": tensor(1, 256, 32, 32),
-                "p6": tensor(1, 256, 16, 16),
-            }
-            props = [Instances((510, 510))]
-            props[0].proposal_boxes = Boxes([[10, 10, 20, 20]]).to(device=self.model.device)
-            props[0].objectness_logits = torch.tensor([1.0]).reshape(1, 1)
-            det, _ = self.model.roi_heads(images, features, props)
-            self.assertEqual(len(det[0]), 0)
-
-    @unittest.skipIf(not torch.cuda.is_available(), "CUDA not available")
-    def test_autocast(self):
-        from torch.cuda.amp import autocast
-
-        inputs = [{"image": torch.rand(3, 100, 100)}]
-        self.model.eval()
-        with autocast(), typecheck_hook(
-            self.model.backbone, in_dtype=torch.float32, out_dtype=torch.float16
-        ), typecheck_hook(
-            self.model.roi_heads.box_predictor, in_dtype=torch.float16, out_dtype=torch.float16
-        ):
-            out = self.model.inference(inputs, do_postprocess=False)[0]
-            self.assertEqual(out.pred_boxes.tensor.dtype, torch.float32)
-            self.assertEqual(out.pred_masks.dtype, torch.float16)
-            self.assertEqual(out.scores.dtype, torch.float32)  # scores comes from softmax
-
-
-class RetinaNetE2ETest(InstanceModelE2ETest, unittest.TestCase):
-    CONFIG_PATH = "COCO-Detection/retinanet_R_50_FPN_1x.yaml"
-
-    def test_inf_nan_data(self):
-        self.model.eval()
-        self.model.score_threshold = -999999999
-        for tensor in [self._inf_tensor, self._nan_tensor]:
-            images = ImageList(tensor(1, 3, 512, 512), [(510, 510)])
-            features = [
-                tensor(1, 256, 128, 128),
-                tensor(1, 256, 64, 64),
-                tensor(1, 256, 32, 32),
-                tensor(1, 256, 16, 16),
-                tensor(1, 256, 8, 8),
-            ]
-            pred_logits, pred_anchor_deltas = self.model.head(features)
-            pred_logits = [tensor(*x.shape) for x in pred_logits]
-            pred_anchor_deltas = [tensor(*x.shape) for x in pred_anchor_deltas]
-            det = self.model.forward_inference(images, features, [pred_logits, pred_anchor_deltas])
-            # all predictions (if any) are infinite or nan
-            if len(det[0]):
-                self.assertTrue(torch.isfinite(det[0].pred_boxes.tensor).sum() == 0)
-
-    @unittest.skipIf(not torch.cuda.is_available(), "CUDA not available")
-    def test_autocast(self):
-        from torch.cuda.amp import autocast
-
-        inputs = [{"image": torch.rand(3, 100, 100)}]
-        self.model.eval()
-        with autocast(), typecheck_hook(
-            self.model.backbone, in_dtype=torch.float32, out_dtype=torch.float16
-        ), typecheck_hook(self.model.head, in_dtype=torch.float16, out_dtype=torch.float16):
-            out = self.model(inputs)[0]["instances"]
-            self.assertEqual(out.pred_boxes.tensor.dtype, torch.float32)
-            self.assertEqual(out.scores.dtype, torch.float16)
-
-
-class SemSegE2ETest(unittest.TestCase):
-    CONFIG_PATH = "Misc/semantic_R_50_FPN_1x.yaml"
-
-    def setUp(self):
-        torch.manual_seed(43)
-        self.model = get_model_no_weights(self.CONFIG_PATH)
-
-    def _test_eval(self, input_sizes):
-        inputs = [create_model_input(torch.rand(3, s[0], s[1])) for s in input_sizes]
-        self.model.eval()
-        self.model(inputs)
-
-    def test_forward(self):
-        self._test_eval([(200, 250), (200, 249)])

spaces/Bart92/RVC_HF/julius/bands.py

@@ -1,119 +0,0 @@
-# File under the MIT license, see https://github.com/adefossez/julius/LICENSE for details.
-# Author: adefossez, 2020
-"""
-Decomposition of a signal over frequency bands in the waveform domain.
-"""
-from typing import Optional, Sequence
-import torch
-
-from .core import mel_frequencies
-from .lowpass import LowPassFilters
-from .utils import simple_repr
-
-
-class SplitBands(torch.nn.Module):
-    """
-    Decomposes a signal over the given frequency bands in the waveform domain using
-    a cascade of low pass filters as implemented by `julius.lowpass.LowPassFilters`.
-    You can either specify explicitely the frequency cutoffs, or just the number of bands,
-    in which case the frequency cutoffs will be spread out evenly in mel scale.
-
-    Args:
-        sample_rate (float): Sample rate of the input signal in Hz.
-        n_bands (int or None): number of bands, when not giving them explictely with `cutoffs`.
-            In that case, the cutoff frequencies will be evenly spaced in mel-space.
-        cutoffs (list[float] or None): list of frequency cutoffs in Hz.
-        pad (bool): if True, appropriately pad the input with zero over the edge. If `stride=1`,
-            the output will have the same length as the input.
-        zeros (float): Number of zero crossings to keep. See `LowPassFilters` for more informations.
-        fft (bool or None): See `LowPassFilters` for more info.
-
-    ..note::
-        The sum of all the bands will always be the input signal.
-
-    ..warning::
-        Unlike `julius.lowpass.LowPassFilters`, the cutoffs frequencies must be provided in Hz along
-        with the sample rate.
-
-    Shape:
-
-        - Input: `[*, T]`
-        - Output: `[B, *, T']`, with `T'=T` if `pad` is True.
-            If `n_bands` was provided, `B = n_bands` otherwise `B = len(cutoffs) + 1`
-
-    >>> bands = SplitBands(sample_rate=128, n_bands=10)
-    >>> x = torch.randn(6, 4, 1024)
-    >>> list(bands(x).shape)
-    [10, 6, 4, 1024]
-    """
-
-    def __init__(self, sample_rate: float, n_bands: Optional[int] = None,
-                 cutoffs: Optional[Sequence[float]] = None, pad: bool = True,
-                 zeros: float = 8, fft: Optional[bool] = None):
-        super().__init__()
-        if (cutoffs is None) + (n_bands is None) != 1:
-            raise ValueError("You must provide either n_bands, or cutoffs, but not boths.")
-
-        self.sample_rate = sample_rate
-        self.n_bands = n_bands
-        self._cutoffs = list(cutoffs) if cutoffs is not None else None
-        self.pad = pad
-        self.zeros = zeros
-        self.fft = fft
-
-        if cutoffs is None:
-            if n_bands is None:
-                raise ValueError("You must provide one of n_bands or cutoffs.")
-            if not n_bands >= 1:
-                raise ValueError(f"n_bands must be greater than one (got {n_bands})")
-            cutoffs = mel_frequencies(n_bands + 1, 0, sample_rate / 2)[1:-1]
-        else:
-            if max(cutoffs) > 0.5 * sample_rate:
-                raise ValueError("A cutoff above sample_rate/2 does not make sense.")
-        if len(cutoffs) > 0:
-            self.lowpass = LowPassFilters(
-                [c / sample_rate for c in cutoffs], pad=pad, zeros=zeros, fft=fft)
-        else:
-            # Here I cannot make both TorchScript and MyPy happy.
-            # I miss the good old times, before all this madness was created.
-            self.lowpass = None  # type: ignore
-
-    def forward(self, input):
-        if self.lowpass is None:
-            return input[None]
-        lows = self.lowpass(input)
-        low = lows[0]
-        bands = [low]
-        for low_and_band in lows[1:]:
-            # Get a bandpass filter by substracting lowpasses
-            band = low_and_band - low
-            bands.append(band)
-            low = low_and_band
-        # Last band is whatever is left in the signal
-        bands.append(input - low)
-        return torch.stack(bands)
-
-    @property
-    def cutoffs(self):
-        if self._cutoffs is not None:
-            return self._cutoffs
-        elif self.lowpass is not None:
-            return [c * self.sample_rate for c in self.lowpass.cutoffs]
-        else:
-            return []
-
-    def __repr__(self):
-        return simple_repr(self, overrides={"cutoffs": self._cutoffs})
-
-
-def split_bands(signal: torch.Tensor, sample_rate: float, n_bands: Optional[int] = None,
-                cutoffs: Optional[Sequence[float]] = None, pad: bool = True,
-                zeros: float = 8, fft: Optional[bool] = None):
-    """
-    Functional version of `SplitBands`, refer to this class for more information.
-
-    >>> x = torch.randn(6, 4, 1024)
-    >>> list(split_bands(x, sample_rate=64, cutoffs=[12, 24]).shape)
-    [3, 6, 4, 1024]
-    """
-    return SplitBands(sample_rate, n_bands, cutoffs, pad, zeros, fft).to(signal)(signal)

spaces/Bart92/RVC_HF/train/losses.py

@@ -1,59 +0,0 @@
-import torch
-from torch.nn import functional as F
-
-
-def feature_loss(fmap_r, fmap_g):
-    loss = 0
-    for dr, dg in zip(fmap_r, fmap_g):
-        for rl, gl in zip(dr, dg):
-            rl = rl.float().detach()
-            gl = gl.float()
-            loss += torch.mean(torch.abs(rl - gl))
-
-    return loss * 2
-
-
-def discriminator_loss(disc_real_outputs, disc_generated_outputs):
-    loss = 0
-    r_losses = []
-    g_losses = []
-    for dr, dg in zip(disc_real_outputs, disc_generated_outputs):
-        dr = dr.float()
-        dg = dg.float()
-        r_loss = torch.mean((1 - dr) ** 2)
-        g_loss = torch.mean(dg**2)
-        loss += r_loss + g_loss
-        r_losses.append(r_loss.item())
-        g_losses.append(g_loss.item())
-
-    return loss, r_losses, g_losses
-
-
-def generator_loss(disc_outputs):
-    loss = 0
-    gen_losses = []
-    for dg in disc_outputs:
-        dg = dg.float()
-        l = torch.mean((1 - dg) ** 2)
-        gen_losses.append(l)
-        loss += l
-
-    return loss, gen_losses
-
-
-def kl_loss(z_p, logs_q, m_p, logs_p, z_mask):
-    """
-    z_p, logs_q: [b, h, t_t]
-    m_p, logs_p: [b, h, t_t]
-    """
-    z_p = z_p.float()
-    logs_q = logs_q.float()
-    m_p = m_p.float()
-    logs_p = logs_p.float()
-    z_mask = z_mask.float()
-
-    kl = logs_p - logs_q - 0.5
-    kl += 0.5 * ((z_p - m_p) ** 2) * torch.exp(-2.0 * logs_p)
-    kl = torch.sum(kl * z_mask)
-    l = kl / torch.sum(z_mask)
-    return l

spaces/Benson/text-generation/Examples/Descargar Garena Drifters Velocidad.md

@@ -1,73 +0,0 @@
-
-<h1>Descargar Garena AOV Mod dinero ilimitado: Cómo obtener la mejor experiencia MOBA en su dispositivo móvil</h1>
- <p>Si eres un fan de los juegos multijugador online battle arena (MOBA), es posible que hayas oído hablar de Garena AOV, uno de los juegos más populares y emocionantes de este género. Pero ¿sabía usted que puede descargar Garena AOV mod dinero ilimitado y obtener acceso a características premium, contenido y recursos que mejorarán su experiencia de juego? En este artículo, te contaremos todo lo que necesitas saber sobre Garena AOV, por qué deberías descargar su versión mod y cómo hacerlo de forma segura y fácil. </p>
- <h2>¿Qué es Garena AOV? </h2>
- <p>Garena AOV es un nuevo juego 5v5 MOBA que fue desarrollado por Tencent Games y publicado por Garena. También es conocida como Arena del Valor o Reino del Valor en algunas regiones. El juego cuenta con gráficos ultra-HD, jugabilidad suave, héroes equilibrados y varios modos para adaptarse a diferentes preferencias y niveles de habilidad. Puedes elegir entre más de 100 héroes, cada uno con sus propias habilidades, roles y estilos. También puedes hacer equipo con tus amigos u otros jugadores en línea y competir en partidos clasificados, partidos casuales o eventos especiales. El juego es gratis para descargar y jugar, pero también ofrece compras en la aplicación para algunos artículos y servicios. </p>
-<h2>descargar garena drifters velocidad</h2><br /><p><b><b>Download Zip</b> &raquo;&raquo;&raquo; <a href="https://bltlly.com/2v6ICS">https://bltlly.com/2v6ICS</a></b></p><br /><br />
- <h3>Características de Garena AOV</h3>
- <p>Algunas de las características que hacen que Garena AOV se destaque de otros juegos de MOBA son:</p>
- <ul>
-<li>Una diversa lista de héroes, incluyendo personajes originales y con licencia de DC Comics, como Batman, Superman, Wonder Woman, Joker, Harley Quinn y más. </li>
-<li>Un sistema de juego justo y equilibrado que premia la habilidad y el trabajo en equipo, no la mecánica de pago a ganar. </li>
-<li>Una variedad de modos de juego, como Grand Battle (5v5), Valley Skirmish (3v3), Abyssal Clash (5v5 héroes al azar), Solo Battle (1v1), Hook Wars (5v5 con ganchos), Death Match (5v5 con respawns ilimitados), y más. </li>
-
-<li>Una plataforma social e interactiva que te permite chatear con tus amigos, unirte a gremios, ver transmisiones en vivo, compartir aspectos destacados y ganar recompensas. </li>
-</ul>
- <h3>Beneficios de jugar Garena AOV</h3>
- <p>Jugar a Garena AOV puede traerte muchos beneficios, como:</p>
- <ul>
-<li>Mejorar tu pensamiento estratégico, toma de decisiones, comunicación y habilidades de trabajo en equipo. </li>
-<li>Diviértete y diviértete con tus amigos u otros jugadores de todo el mundo. </li>
-<li>Aprender cosas nuevas sobre diferentes culturas, mitos, leyendas e historias a través de los héroes y sus antecedentes. </li>
-<li>Expresar su creatividad y personalidad a través de la personalización de sus héroes, pieles, emblemas, marcos, efectos, etc.</li>
-<li>Obtener recompensas y reconocimiento por tus logros y rendimiento en el juego. </li>
-</ul>
- <h2>¿Por qué descargar Garena AOV mod dinero ilimitado? </h2>
- <p>Como mencionamos anteriormente, Garena AOV es gratis para descargar y jugar, pero también tiene algunas compras en la aplicación que pueden mejorar su experiencia de juego. Por ejemplo, puedes comprar gemas, vales, monedas de oro, cofres de arcanos, cofres de héroes, cofres de piel, etc. Estos artículos pueden ayudarte a desbloquear nuevos héroes, pieles, conjuntos de arcanos, talentos, etc. Sin embargo, estos artículos no son baratos y pueden costar mucho dinero real. No todos pueden permitirse gastar tanto dinero en un juego, especialmente si tienen un presupuesto ajustado o tienen otras prioridades. Es por eso que algunas personas buscan maneras de obtener estos artículos de forma gratuita o a un costo más bajo. Una de las maneras de hacer eso es descargar Garena AOV mod ilimitado dinero. </p>
- <h3>Ventajas de usar Garena AOV mod unlimited money</h3>
- <p>Garena AOV mod unlimited money es una versión modificada del juego original que te da acceso a gemas ilimitadas, vales, monedas de oro y otros recursos. Con este mod, puedes:</p>
- <ul>
-<li>Desbloquea todos los héroes y skins que quieras, sin tener que esperar eventos, misiones o sorteos. </li>
-
-<li>Compre cualquier artículo de la tienda, como emblemas, marcos, efectos, etc., sin tener que preocuparse por quedarse sin joyas o cupones. </li>
-<li>Disfruta del juego sin anuncios ni interrupciones. </li>
-<li>Tener ventaja sobre tus oponentes en el juego, especialmente si están usando la versión normal. </li>
-</ul>
- <h3>Los riesgos de usar Garena AOV mod dinero ilimitado</h3>
- <p>Sin embargo, el uso de Garena AOV mod ilimitado dinero también viene con algunos riesgos y desventajas que usted debe ser consciente de antes de descargarlo. Algunos de ellos son:</p>
- <ul>
-<li>El mod puede no ser compatible con la última versión del juego o con su dispositivo. Esto puede causar fallos, fallos, errores o un rendimiento deficiente. </li>
-<li>El mod puede contener virus, malware, spyware u otros programas dañinos que pueden dañar su dispositivo o robar su información personal. </li>
-<li>El mod puede violar los términos y condiciones del juego y conseguir que se le prohibió jugar. Esto puede resultar en la pérdida de su cuenta, progreso y datos. </li>
-<li>El mod puede arruinar la diversión y el desafío del juego por lo que es demasiado fácil o aburrido. Puede perder interés en el juego o sentirse culpable por hacer trampa. </li>
-<li>El mod puede no funcionar como se anuncia o tener algunos costos ocultos o limitaciones. Usted puede terminar perdiendo su tiempo y recursos en algo que no entrega lo que esperaba. </li>
-</ul>
- <h2>Cómo descargar e instalar Garena AOV mod dinero ilimitado? </h2>
- <p>Si todavía desea descargar e instalar Garena AOV mod dinero ilimitado a pesar de los riesgos, es necesario seguir algunos pasos con cuidado y cautela. Estos son los pasos que debes seguir:</p>
- <h3>Paso 1: Encontrar una fuente confiable para el archivo apk mod</h3>
-
- <h3>Paso 2: Habilitar fuentes desconocidas en la configuración del dispositivo</h3>
- <p>Lo siguiente que debe hacer es habilitar fuentes desconocidas en la configuración del dispositivo. Esto le permitirá instalar aplicaciones desde fuentes distintas de Google Play Store. Para hacer esto, vaya a la configuración del dispositivo > seguridad > fuentes desconocidas > activar. También es posible que tenga que desactivar cualquier software antivirus o firewall que pueda bloquear o interferir con el proceso de instalación. </p>
-<p></p>
- <h3>Paso 3: Descargar e instalar el archivo apk mod</h3>
- <p>La tercera cosa que necesita hacer es descargar e instalar el archivo apk mod en su dispositivo. Para hacer esto, vaya a la página web donde encontró el mod y haga clic en el botón de descarga. Espere a que termine la descarga y luego localice el archivo en el almacenamiento del dispositivo. Toque en el archivo y siga las instrucciones en la pantalla para instalarlo. Es posible que necesite conceder algunos permisos o aceptar algunos términos y condiciones durante el proceso de instalación. </p>
- <h3>Paso 4: Iniciar el juego y disfrutar del dinero ilimitado</h3>
- <p>Lo último que tienes que hacer es lanzar el juego y disfrutar del dinero ilimitado. Para ello, abre el juego desde el cajón de la aplicación o la pantalla de inicio e inicia sesión con tu cuenta. Deberías ver que tienes gemas ilimitadas, vales, monedas de oro y otros recursos en tu cuenta. Ahora puedes usarlos para comprar lo que quieras de la tienda o desbloquear cualquier héroe o piel que te guste. Diviértete jugando Garena AOV con tus amigos u otros jugadores en línea! </p>
- <h2>Conclusión</h2>
-
- <h2>Preguntas frecuentes</h2>
- <p>Aquí hay algunas preguntas frecuentes sobre Garena AOV mod unlimited money:</p>
- <ol>
-<li><b>¿Es Garena AOV dinero ilimitado legal? </b></li>
-<p>No, Garena AOV mod dinero ilimitado no es legal. Es una versión modificada del juego original que viola los términos y condiciones del juego y sus desarrolladores. Usar este mod puede hacer que te prohíban jugar el juego o enfrentar acciones legales de las autoridades. </p>
-<li><b>¿Es seguro el dinero ilimitado Garena AOV mod? </b></li>
-<p>No necesariamente. Garena AOV mod dinero ilimitado puede contener virus, malware, spyware, u otros programas dañinos que pueden dañar su dispositivo o robar su información personal. Siempre debe escanear el archivo apk mod con un software antivirus de buena reputación antes de descargar e instalar. También debe hacer copias de seguridad de sus datos y utilizar una cuenta secundaria para jugar el juego con este mod. </p>
-<li><b>¿Garena AOV mod es dinero ilimitado gratis? </b></li>
-<p>Sí, Garena AOV mod dinero ilimitado es gratis para descargar y usar. Sin embargo, algunos sitios web pueden pedirle que complete encuestas, ofertas o tareas antes de darle el enlace de descarga. Usted debe evitar estos sitios web, ya que pueden ser estafas o intentos de phishing. También debe tener cuidado con los costos ocultos o limitaciones que pueden venir con este mod. </p>
-<li><b>¿Cómo puedo actualizar Garena AOV mod unlimited money? </b></li>
-<p>Puede actualizar Garena AOV mod dinero ilimitado siguiendo los mismos pasos que descargarlo e instalarlo. Sin embargo, siempre debes comprobar si el mod es compatible con la última versión del juego o con tu dispositivo antes de actualizarlo. También debe hacer una copia de seguridad de sus datos y desinstalar la versión anterior del mod antes de instalar el nuevo. </p>
-<li><b>¿Dónde puedo encontrar más información sobre Garena AOV? </b></li>
-
-</ol></p> 64aa2da5cf<br />
-<br />
-<br />

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

@@ -1,883 +0,0 @@
-"""
-    pygments.lexer
-    ~~~~~~~~~~~~~~
-
-    Base lexer classes.
-
-    :copyright: Copyright 2006-2022 by the Pygments team, see AUTHORS.
-    :license: BSD, see LICENSE for details.
-"""
-
-import re
-import sys
-import time
-
-from pip._vendor.pygments.filter import apply_filters, Filter
-from pip._vendor.pygments.filters import get_filter_by_name
-from pip._vendor.pygments.token import Error, Text, Other, Whitespace, _TokenType
-from pip._vendor.pygments.util import get_bool_opt, get_int_opt, get_list_opt, \
-    make_analysator, Future, guess_decode
-from pip._vendor.pygments.regexopt import regex_opt
-
-__all__ = ['Lexer', 'RegexLexer', 'ExtendedRegexLexer', 'DelegatingLexer',
-           'LexerContext', 'include', 'inherit', 'bygroups', 'using', 'this',
-           'default', 'words', 'line_re']
-
-line_re = re.compile('.*?\n')
-
-_encoding_map = [(b'\xef\xbb\xbf', 'utf-8'),
-                 (b'\xff\xfe\0\0', 'utf-32'),
-                 (b'\0\0\xfe\xff', 'utf-32be'),
-                 (b'\xff\xfe', 'utf-16'),
-                 (b'\xfe\xff', 'utf-16be')]
-
-_default_analyse = staticmethod(lambda x: 0.0)
-
-
-class LexerMeta(type):
-    """
-    This metaclass automagically converts ``analyse_text`` methods into
-    static methods which always return float values.
-    """
-
-    def __new__(mcs, name, bases, d):
-        if 'analyse_text' in d:
-            d['analyse_text'] = make_analysator(d['analyse_text'])
-        return type.__new__(mcs, name, bases, d)
-
-
-class Lexer(metaclass=LexerMeta):
-    """
-    Lexer for a specific language.
-
-    Basic options recognized:
-    ``stripnl``
-        Strip leading and trailing newlines from the input (default: True).
-    ``stripall``
-        Strip all leading and trailing whitespace from the input
-        (default: False).
-    ``ensurenl``
-        Make sure that the input ends with a newline (default: True).  This
-        is required for some lexers that consume input linewise.
-
-        .. versionadded:: 1.3
-
-    ``tabsize``
-        If given and greater than 0, expand tabs in the input (default: 0).
-    ``encoding``
-        If given, must be an encoding name. This encoding will be used to
-        convert the input string to Unicode, if it is not already a Unicode
-        string (default: ``'guess'``, which uses a simple UTF-8 / Locale /
-        Latin1 detection.  Can also be ``'chardet'`` to use the chardet
-        library, if it is installed.
-    ``inencoding``
-        Overrides the ``encoding`` if given.
-    """
-
-    #: Name of the lexer
-    name = None
-
-    #: URL of the language specification/definition
-    url = None
-
-    #: Shortcuts for the lexer
-    aliases = []
-
-    #: File name globs
-    filenames = []
-
-    #: Secondary file name globs
-    alias_filenames = []
-
-    #: MIME types
-    mimetypes = []
-
-    #: Priority, should multiple lexers match and no content is provided
-    priority = 0
-
-    def __init__(self, **options):
-        self.options = options
-        self.stripnl = get_bool_opt(options, 'stripnl', True)
-        self.stripall = get_bool_opt(options, 'stripall', False)
-        self.ensurenl = get_bool_opt(options, 'ensurenl', True)
-        self.tabsize = get_int_opt(options, 'tabsize', 0)
-        self.encoding = options.get('encoding', 'guess')
-        self.encoding = options.get('inencoding') or self.encoding
-        self.filters = []
-        for filter_ in get_list_opt(options, 'filters', ()):
-            self.add_filter(filter_)
-
-    def __repr__(self):
-        if self.options:
-            return '<pygments.lexers.%s with %r>' % (self.__class__.__name__,
-                                                     self.options)
-        else:
-            return '<pygments.lexers.%s>' % self.__class__.__name__
-
-    def add_filter(self, filter_, **options):
-        """
-        Add a new stream filter to this lexer.
-        """
-        if not isinstance(filter_, Filter):
-            filter_ = get_filter_by_name(filter_, **options)
-        self.filters.append(filter_)
-
-    def analyse_text(text):
-        """
-        Has to return a float between ``0`` and ``1`` that indicates
-        if a lexer wants to highlight this text. Used by ``guess_lexer``.
-        If this method returns ``0`` it won't highlight it in any case, if
-        it returns ``1`` highlighting with this lexer is guaranteed.
-
-        The `LexerMeta` metaclass automatically wraps this function so
-        that it works like a static method (no ``self`` or ``cls``
-        parameter) and the return value is automatically converted to
-        `float`. If the return value is an object that is boolean `False`
-        it's the same as if the return values was ``0.0``.
-        """
-
-    def get_tokens(self, text, unfiltered=False):
-        """
-        Return an iterable of (tokentype, value) pairs generated from
-        `text`. If `unfiltered` is set to `True`, the filtering mechanism
-        is bypassed even if filters are defined.
-
-        Also preprocess the text, i.e. expand tabs and strip it if
-        wanted and applies registered filters.
-        """
-        if not isinstance(text, str):
-            if self.encoding == 'guess':
-                text, _ = guess_decode(text)
-            elif self.encoding == 'chardet':
-                try:
-                    from pip._vendor import chardet
-                except ImportError as e:
-                    raise ImportError('To enable chardet encoding guessing, '
-                                      'please install the chardet library '
-                                      'from http://chardet.feedparser.org/') from e
-                # check for BOM first
-                decoded = None
-                for bom, encoding in _encoding_map:
-                    if text.startswith(bom):
-                        decoded = text[len(bom):].decode(encoding, 'replace')
-                        break
-                # no BOM found, so use chardet
-                if decoded is None:
-                    enc = chardet.detect(text[:1024])  # Guess using first 1KB
-                    decoded = text.decode(enc.get('encoding') or 'utf-8',
-                                          'replace')
-                text = decoded
-            else:
-                text = text.decode(self.encoding)
-                if text.startswith('\ufeff'):
-                    text = text[len('\ufeff'):]
-        else:
-            if text.startswith('\ufeff'):
-                text = text[len('\ufeff'):]
-
-        # text now *is* a unicode string
-        text = text.replace('\r\n', '\n')
-        text = text.replace('\r', '\n')
-        if self.stripall:
-            text = text.strip()
-        elif self.stripnl:
-            text = text.strip('\n')
-        if self.tabsize > 0:
-            text = text.expandtabs(self.tabsize)
-        if self.ensurenl and not text.endswith('\n'):
-            text += '\n'
-
-        def streamer():
-            for _, t, v in self.get_tokens_unprocessed(text):
-                yield t, v
-        stream = streamer()
-        if not unfiltered:
-            stream = apply_filters(stream, self.filters, self)
-        return stream
-
-    def get_tokens_unprocessed(self, text):
-        """
-        Return an iterable of (index, tokentype, value) pairs where "index"
-        is the starting position of the token within the input text.
-
-        In subclasses, implement this method as a generator to
-        maximize effectiveness.
-        """
-        raise NotImplementedError
-
-
-class DelegatingLexer(Lexer):
-    """
-    This lexer takes two lexer as arguments. A root lexer and
-    a language lexer. First everything is scanned using the language
-    lexer, afterwards all ``Other`` tokens are lexed using the root
-    lexer.
-
-    The lexers from the ``template`` lexer package use this base lexer.
-    """
-
-    def __init__(self, _root_lexer, _language_lexer, _needle=Other, **options):
-        self.root_lexer = _root_lexer(**options)
-        self.language_lexer = _language_lexer(**options)
-        self.needle = _needle
-        Lexer.__init__(self, **options)
-
-    def get_tokens_unprocessed(self, text):
-        buffered = ''
-        insertions = []
-        lng_buffer = []
-        for i, t, v in self.language_lexer.get_tokens_unprocessed(text):
-            if t is self.needle:
-                if lng_buffer:
-                    insertions.append((len(buffered), lng_buffer))
-                    lng_buffer = []
-                buffered += v
-            else:
-                lng_buffer.append((i, t, v))
-        if lng_buffer:
-            insertions.append((len(buffered), lng_buffer))
-        return do_insertions(insertions,
-                             self.root_lexer.get_tokens_unprocessed(buffered))
-
-
-# ------------------------------------------------------------------------------
-# RegexLexer and ExtendedRegexLexer
-#
-
-
-class include(str):  # pylint: disable=invalid-name
-    """
-    Indicates that a state should include rules from another state.
-    """
-    pass
-
-
-class _inherit:
-    """
-    Indicates the a state should inherit from its superclass.
-    """
-    def __repr__(self):
-        return 'inherit'
-
-inherit = _inherit()  # pylint: disable=invalid-name
-
-
-class combined(tuple):  # pylint: disable=invalid-name
-    """
-    Indicates a state combined from multiple states.
-    """
-
-    def __new__(cls, *args):
-        return tuple.__new__(cls, args)
-
-    def __init__(self, *args):
-        # tuple.__init__ doesn't do anything
-        pass
-
-
-class _PseudoMatch:
-    """
-    A pseudo match object constructed from a string.
-    """
-
-    def __init__(self, start, text):
-        self._text = text
-        self._start = start
-
-    def start(self, arg=None):
-        return self._start
-
-    def end(self, arg=None):
-        return self._start + len(self._text)
-
-    def group(self, arg=None):
-        if arg:
-            raise IndexError('No such group')
-        return self._text
-
-    def groups(self):
-        return (self._text,)
-
-    def groupdict(self):
-        return {}
-
-
-def bygroups(*args):
-    """
-    Callback that yields multiple actions for each group in the match.
-    """
-    def callback(lexer, match, ctx=None):
-        for i, action in enumerate(args):
-            if action is None:
-                continue
-            elif type(action) is _TokenType:
-                data = match.group(i + 1)
-                if data:
-                    yield match.start(i + 1), action, data
-            else:
-                data = match.group(i + 1)
-                if data is not None:
-                    if ctx:
-                        ctx.pos = match.start(i + 1)
-                    for item in action(lexer,
-                                       _PseudoMatch(match.start(i + 1), data), ctx):
-                        if item:
-                            yield item
-        if ctx:
-            ctx.pos = match.end()
-    return callback
-
-
-class _This:
-    """
-    Special singleton used for indicating the caller class.
-    Used by ``using``.
-    """
-
-this = _This()
-
-
-def using(_other, **kwargs):
-    """
-    Callback that processes the match with a different lexer.
-
-    The keyword arguments are forwarded to the lexer, except `state` which
-    is handled separately.
-
-    `state` specifies the state that the new lexer will start in, and can
-    be an enumerable such as ('root', 'inline', 'string') or a simple
-    string which is assumed to be on top of the root state.
-
-    Note: For that to work, `_other` must not be an `ExtendedRegexLexer`.
-    """
-    gt_kwargs = {}
-    if 'state' in kwargs:
-        s = kwargs.pop('state')
-        if isinstance(s, (list, tuple)):
-            gt_kwargs['stack'] = s
-        else:
-            gt_kwargs['stack'] = ('root', s)
-
-    if _other is this:
-        def callback(lexer, match, ctx=None):
-            # if keyword arguments are given the callback
-            # function has to create a new lexer instance
-            if kwargs:
-                # XXX: cache that somehow
-                kwargs.update(lexer.options)
-                lx = lexer.__class__(**kwargs)
-            else:
-                lx = lexer
-            s = match.start()
-            for i, t, v in lx.get_tokens_unprocessed(match.group(), **gt_kwargs):
-                yield i + s, t, v
-            if ctx:
-                ctx.pos = match.end()
-    else:
-        def callback(lexer, match, ctx=None):
-            # XXX: cache that somehow
-            kwargs.update(lexer.options)
-            lx = _other(**kwargs)
-
-            s = match.start()
-            for i, t, v in lx.get_tokens_unprocessed(match.group(), **gt_kwargs):
-                yield i + s, t, v
-            if ctx:
-                ctx.pos = match.end()
-    return callback
-
-
-class default:
-    """
-    Indicates a state or state action (e.g. #pop) to apply.
-    For example default('#pop') is equivalent to ('', Token, '#pop')
-    Note that state tuples may be used as well.
-
-    .. versionadded:: 2.0
-    """
-    def __init__(self, state):
-        self.state = state
-
-
-class words(Future):
-    """
-    Indicates a list of literal words that is transformed into an optimized
-    regex that matches any of the words.
-
-    .. versionadded:: 2.0
-    """
-    def __init__(self, words, prefix='', suffix=''):
-        self.words = words
-        self.prefix = prefix
-        self.suffix = suffix
-
-    def get(self):
-        return regex_opt(self.words, prefix=self.prefix, suffix=self.suffix)
-
-
-class RegexLexerMeta(LexerMeta):
-    """
-    Metaclass for RegexLexer, creates the self._tokens attribute from
-    self.tokens on the first instantiation.
-    """
-
-    def _process_regex(cls, regex, rflags, state):
-        """Preprocess the regular expression component of a token definition."""
-        if isinstance(regex, Future):
-            regex = regex.get()
-        return re.compile(regex, rflags).match
-
-    def _process_token(cls, token):
-        """Preprocess the token component of a token definition."""
-        assert type(token) is _TokenType or callable(token), \
-            'token type must be simple type or callable, not %r' % (token,)
-        return token
-
-    def _process_new_state(cls, new_state, unprocessed, processed):
-        """Preprocess the state transition action of a token definition."""
-        if isinstance(new_state, str):
-            # an existing state
-            if new_state == '#pop':
-                return -1
-            elif new_state in unprocessed:
-                return (new_state,)
-            elif new_state == '#push':
-                return new_state
-            elif new_state[:5] == '#pop:':
-                return -int(new_state[5:])
-            else:
-                assert False, 'unknown new state %r' % new_state
-        elif isinstance(new_state, combined):
-            # combine a new state from existing ones
-            tmp_state = '_tmp_%d' % cls._tmpname
-            cls._tmpname += 1
-            itokens = []
-            for istate in new_state:
-                assert istate != new_state, 'circular state ref %r' % istate
-                itokens.extend(cls._process_state(unprocessed,
-                                                  processed, istate))
-            processed[tmp_state] = itokens
-            return (tmp_state,)
-        elif isinstance(new_state, tuple):
-            # push more than one state
-            for istate in new_state:
-                assert (istate in unprocessed or
-                        istate in ('#pop', '#push')), \
-                    'unknown new state ' + istate
-            return new_state
-        else:
-            assert False, 'unknown new state def %r' % new_state
-
-    def _process_state(cls, unprocessed, processed, state):
-        """Preprocess a single state definition."""
-        assert type(state) is str, "wrong state name %r" % state
-        assert state[0] != '#', "invalid state name %r" % state
-        if state in processed:
-            return processed[state]
-        tokens = processed[state] = []
-        rflags = cls.flags
-        for tdef in unprocessed[state]:
-            if isinstance(tdef, include):
-                # it's a state reference
-                assert tdef != state, "circular state reference %r" % state
-                tokens.extend(cls._process_state(unprocessed, processed,
-                                                 str(tdef)))
-                continue
-            if isinstance(tdef, _inherit):
-                # should be processed already, but may not in the case of:
-                # 1. the state has no counterpart in any parent
-                # 2. the state includes more than one 'inherit'
-                continue
-            if isinstance(tdef, default):
-                new_state = cls._process_new_state(tdef.state, unprocessed, processed)
-                tokens.append((re.compile('').match, None, new_state))
-                continue
-
-            assert type(tdef) is tuple, "wrong rule def %r" % tdef
-
-            try:
-                rex = cls._process_regex(tdef[0], rflags, state)
-            except Exception as err:
-                raise ValueError("uncompilable regex %r in state %r of %r: %s" %
-                                 (tdef[0], state, cls, err)) from err
-
-            token = cls._process_token(tdef[1])
-
-            if len(tdef) == 2:
-                new_state = None
-            else:
-                new_state = cls._process_new_state(tdef[2],
-                                                   unprocessed, processed)
-
-            tokens.append((rex, token, new_state))
-        return tokens
-
-    def process_tokendef(cls, name, tokendefs=None):
-        """Preprocess a dictionary of token definitions."""
-        processed = cls._all_tokens[name] = {}
-        tokendefs = tokendefs or cls.tokens[name]
-        for state in list(tokendefs):
-            cls._process_state(tokendefs, processed, state)
-        return processed
-
-    def get_tokendefs(cls):
-        """
-        Merge tokens from superclasses in MRO order, returning a single tokendef
-        dictionary.
-
-        Any state that is not defined by a subclass will be inherited
-        automatically.  States that *are* defined by subclasses will, by
-        default, override that state in the superclass.  If a subclass wishes to
-        inherit definitions from a superclass, it can use the special value
-        "inherit", which will cause the superclass' state definition to be
-        included at that point in the state.
-        """
-        tokens = {}
-        inheritable = {}
-        for c in cls.__mro__:
-            toks = c.__dict__.get('tokens', {})
-
-            for state, items in toks.items():
-                curitems = tokens.get(state)
-                if curitems is None:
-                    # N.b. because this is assigned by reference, sufficiently
-                    # deep hierarchies are processed incrementally (e.g. for
-                    # A(B), B(C), C(RegexLexer), B will be premodified so X(B)
-                    # will not see any inherits in B).
-                    tokens[state] = items
-                    try:
-                        inherit_ndx = items.index(inherit)
-                    except ValueError:
-                        continue
-                    inheritable[state] = inherit_ndx
-                    continue
-
-                inherit_ndx = inheritable.pop(state, None)
-                if inherit_ndx is None:
-                    continue
-
-                # Replace the "inherit" value with the items
-                curitems[inherit_ndx:inherit_ndx+1] = items
-                try:
-                    # N.b. this is the index in items (that is, the superclass
-                    # copy), so offset required when storing below.
-                    new_inh_ndx = items.index(inherit)
-                except ValueError:
-                    pass
-                else:
-                    inheritable[state] = inherit_ndx + new_inh_ndx
-
-        return tokens
-
-    def __call__(cls, *args, **kwds):
-        """Instantiate cls after preprocessing its token definitions."""
-        if '_tokens' not in cls.__dict__:
-            cls._all_tokens = {}
-            cls._tmpname = 0
-            if hasattr(cls, 'token_variants') and cls.token_variants:
-                # don't process yet
-                pass
-            else:
-                cls._tokens = cls.process_tokendef('', cls.get_tokendefs())
-
-        return type.__call__(cls, *args, **kwds)
-
-
-class RegexLexer(Lexer, metaclass=RegexLexerMeta):
-    """
-    Base for simple stateful regular expression-based lexers.
-    Simplifies the lexing process so that you need only
-    provide a list of states and regular expressions.
-    """
-
-    #: Flags for compiling the regular expressions.
-    #: Defaults to MULTILINE.
-    flags = re.MULTILINE
-
-    #: At all time there is a stack of states. Initially, the stack contains
-    #: a single state 'root'. The top of the stack is called "the current state".
-    #:
-    #: Dict of ``{'state': [(regex, tokentype, new_state), ...], ...}``
-    #:
-    #: ``new_state`` can be omitted to signify no state transition.
-    #: If ``new_state`` is a string, it is pushed on the stack. This ensure
-    #: the new current state is ``new_state``.
-    #: If ``new_state`` is a tuple of strings, all of those strings are pushed
-    #: on the stack and the current state will be the last element of the list.
-    #: ``new_state`` can also be ``combined('state1', 'state2', ...)``
-    #: to signify a new, anonymous state combined from the rules of two
-    #: or more existing ones.
-    #: Furthermore, it can be '#pop' to signify going back one step in
-    #: the state stack, or '#push' to push the current state on the stack
-    #: again. Note that if you push while in a combined state, the combined
-    #: state itself is pushed, and not only the state in which the rule is
-    #: defined.
-    #:
-    #: The tuple can also be replaced with ``include('state')``, in which
-    #: case the rules from the state named by the string are included in the
-    #: current one.
-    tokens = {}
-
-    def get_tokens_unprocessed(self, text, stack=('root',)):
-        """
-        Split ``text`` into (tokentype, text) pairs.
-
-        ``stack`` is the initial stack (default: ``['root']``)
-        """
-        pos = 0
-        tokendefs = self._tokens
-        statestack = list(stack)
-        statetokens = tokendefs[statestack[-1]]
-        while 1:
-            for rexmatch, action, new_state in statetokens:
-                m = rexmatch(text, pos)
-                if m:
-                    if action is not None:
-                        if type(action) is _TokenType:
-                            yield pos, action, m.group()
-                        else:
-                            yield from action(self, m)
-                    pos = m.end()
-                    if new_state is not None:
-                        # state transition
-                        if isinstance(new_state, tuple):
-                            for state in new_state:
-                                if state == '#pop':
-                                    if len(statestack) > 1:
-                                        statestack.pop()
-                                elif state == '#push':
-                                    statestack.append(statestack[-1])
-                                else:
-                                    statestack.append(state)
-                        elif isinstance(new_state, int):
-                            # pop, but keep at least one state on the stack
-                            # (random code leading to unexpected pops should
-                            # not allow exceptions)
-                            if abs(new_state) >= len(statestack):
-                                del statestack[1:]
-                            else:
-                                del statestack[new_state:]
-                        elif new_state == '#push':
-                            statestack.append(statestack[-1])
-                        else:
-                            assert False, "wrong state def: %r" % new_state
-                        statetokens = tokendefs[statestack[-1]]
-                    break
-            else:
-                # We are here only if all state tokens have been considered
-                # and there was not a match on any of them.
-                try:
-                    if text[pos] == '\n':
-                        # at EOL, reset state to "root"
-                        statestack = ['root']
-                        statetokens = tokendefs['root']
-                        yield pos, Whitespace, '\n'
-                        pos += 1
-                        continue
-                    yield pos, Error, text[pos]
-                    pos += 1
-                except IndexError:
-                    break
-
-
-class LexerContext:
-    """
-    A helper object that holds lexer position data.
-    """
-
-    def __init__(self, text, pos, stack=None, end=None):
-        self.text = text
-        self.pos = pos
-        self.end = end or len(text)  # end=0 not supported ;-)
-        self.stack = stack or ['root']
-
-    def __repr__(self):
-        return 'LexerContext(%r, %r, %r)' % (
-            self.text, self.pos, self.stack)
-
-
-class ExtendedRegexLexer(RegexLexer):
-    """
-    A RegexLexer that uses a context object to store its state.
-    """
-
-    def get_tokens_unprocessed(self, text=None, context=None):
-        """
-        Split ``text`` into (tokentype, text) pairs.
-        If ``context`` is given, use this lexer context instead.
-        """
-        tokendefs = self._tokens
-        if not context:
-            ctx = LexerContext(text, 0)
-            statetokens = tokendefs['root']
-        else:
-            ctx = context
-            statetokens = tokendefs[ctx.stack[-1]]
-            text = ctx.text
-        while 1:
-            for rexmatch, action, new_state in statetokens:
-                m = rexmatch(text, ctx.pos, ctx.end)
-                if m:
-                    if action is not None:
-                        if type(action) is _TokenType:
-                            yield ctx.pos, action, m.group()
-                            ctx.pos = m.end()
-                        else:
-                            yield from action(self, m, ctx)
-                            if not new_state:
-                                # altered the state stack?
-                                statetokens = tokendefs[ctx.stack[-1]]
-                    # CAUTION: callback must set ctx.pos!
-                    if new_state is not None:
-                        # state transition
-                        if isinstance(new_state, tuple):
-                            for state in new_state:
-                                if state == '#pop':
-                                    if len(ctx.stack) > 1:
-                                        ctx.stack.pop()
-                                elif state == '#push':
-                                    ctx.stack.append(ctx.stack[-1])
-                                else:
-                                    ctx.stack.append(state)
-                        elif isinstance(new_state, int):
-                            # see RegexLexer for why this check is made
-                            if abs(new_state) >= len(ctx.stack):
-                                del ctx.stack[1:]
-                            else:
-                                del ctx.stack[new_state:]
-                        elif new_state == '#push':
-                            ctx.stack.append(ctx.stack[-1])
-                        else:
-                            assert False, "wrong state def: %r" % new_state
-                        statetokens = tokendefs[ctx.stack[-1]]
-                    break
-            else:
-                try:
-                    if ctx.pos >= ctx.end:
-                        break
-                    if text[ctx.pos] == '\n':
-                        # at EOL, reset state to "root"
-                        ctx.stack = ['root']
-                        statetokens = tokendefs['root']
-                        yield ctx.pos, Text, '\n'
-                        ctx.pos += 1
-                        continue
-                    yield ctx.pos, Error, text[ctx.pos]
-                    ctx.pos += 1
-                except IndexError:
-                    break
-
-
-def do_insertions(insertions, tokens):
-    """
-    Helper for lexers which must combine the results of several
-    sublexers.
-
-    ``insertions`` is a list of ``(index, itokens)`` pairs.
-    Each ``itokens`` iterable should be inserted at position
-    ``index`` into the token stream given by the ``tokens``
-    argument.
-
-    The result is a combined token stream.
-
-    TODO: clean up the code here.
-    """
-    insertions = iter(insertions)
-    try:
-        index, itokens = next(insertions)
-    except StopIteration:
-        # no insertions
-        yield from tokens
-        return
-
-    realpos = None
-    insleft = True
-
-    # iterate over the token stream where we want to insert
-    # the tokens from the insertion list.
-    for i, t, v in tokens:
-        # first iteration. store the position of first item
-        if realpos is None:
-            realpos = i
-        oldi = 0
-        while insleft and i + len(v) >= index:
-            tmpval = v[oldi:index - i]
-            if tmpval:
-                yield realpos, t, tmpval
-                realpos += len(tmpval)
-            for it_index, it_token, it_value in itokens:
-                yield realpos, it_token, it_value
-                realpos += len(it_value)
-            oldi = index - i
-            try:
-                index, itokens = next(insertions)
-            except StopIteration:
-                insleft = False
-                break  # not strictly necessary
-        if oldi < len(v):
-            yield realpos, t, v[oldi:]
-            realpos += len(v) - oldi
-
-    # leftover tokens
-    while insleft:
-        # no normal tokens, set realpos to zero
-        realpos = realpos or 0
-        for p, t, v in itokens:
-            yield realpos, t, v
-            realpos += len(v)
-        try:
-            index, itokens = next(insertions)
-        except StopIteration:
-            insleft = False
-            break  # not strictly necessary
-
-
-class ProfilingRegexLexerMeta(RegexLexerMeta):
-    """Metaclass for ProfilingRegexLexer, collects regex timing info."""
-
-    def _process_regex(cls, regex, rflags, state):
-        if isinstance(regex, words):
-            rex = regex_opt(regex.words, prefix=regex.prefix,
-                            suffix=regex.suffix)
-        else:
-            rex = regex
-        compiled = re.compile(rex, rflags)
-
-        def match_func(text, pos, endpos=sys.maxsize):
-            info = cls._prof_data[-1].setdefault((state, rex), [0, 0.0])
-            t0 = time.time()
-            res = compiled.match(text, pos, endpos)
-            t1 = time.time()
-            info[0] += 1
-            info[1] += t1 - t0
-            return res
-        return match_func
-
-
-class ProfilingRegexLexer(RegexLexer, metaclass=ProfilingRegexLexerMeta):
-    """Drop-in replacement for RegexLexer that does profiling of its regexes."""
-
-    _prof_data = []
-    _prof_sort_index = 4  # defaults to time per call
-
-    def get_tokens_unprocessed(self, text, stack=('root',)):
-        # this needs to be a stack, since using(this) will produce nested calls
-        self.__class__._prof_data.append({})
-        yield from RegexLexer.get_tokens_unprocessed(self, text, stack)
-        rawdata = self.__class__._prof_data.pop()
-        data = sorted(((s, repr(r).strip('u\'').replace('\\\\', '\\')[:65],
-                        n, 1000 * t, 1000 * t / n)
-                       for ((s, r), (n, t)) in rawdata.items()),
-                      key=lambda x: x[self._prof_sort_index],
-                      reverse=True)
-        sum_total = sum(x[3] for x in data)
-
-        print()
-        print('Profiling result for %s lexing %d chars in %.3f ms' %
-              (self.__class__.__name__, len(text), sum_total))
-        print('=' * 110)
-        print('%-20s %-64s ncalls  tottime  percall' % ('state', 'regex'))
-        print('-' * 110)
-        for d in data:
-            print('%-20s %-65s %5d %8.4f %8.4f' % d)
-        print('=' * 110)

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

@@ -1,24 +0,0 @@
-"""distutils
-
-The main package for the Python Module Distribution Utilities.  Normally
-used from a setup script as
-
-   from distutils.core import setup
-
-   setup (...)
-"""
-
-import sys
-import importlib
-
-__version__ = sys.version[: sys.version.index(' ')]
-
-
-try:
-    # Allow Debian and pkgsrc (only) to customize system
-    # behavior. Ref pypa/distutils#2 and pypa/distutils#16.
-    # This hook is deprecated and no other environments
-    # should use it.
-    importlib.import_module('_distutils_system_mod')
-except ImportError:
-    pass

spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/build_meta.py

@@ -1,511 +0,0 @@
-"""A PEP 517 interface to setuptools
-
-Previously, when a user or a command line tool (let's call it a "frontend")
-needed to make a request of setuptools to take a certain action, for
-example, generating a list of installation requirements, the frontend would
-would call "setup.py egg_info" or "setup.py bdist_wheel" on the command line.
-
-PEP 517 defines a different method of interfacing with setuptools. Rather
-than calling "setup.py" directly, the frontend should:
-
-  1. Set the current directory to the directory with a setup.py file
-  2. Import this module into a safe python interpreter (one in which
-     setuptools can potentially set global variables or crash hard).
-  3. Call one of the functions defined in PEP 517.
-
-What each function does is defined in PEP 517. However, here is a "casual"
-definition of the functions (this definition should not be relied on for
-bug reports or API stability):
-
-  - `build_wheel`: build a wheel in the folder and return the basename
-  - `get_requires_for_build_wheel`: get the `setup_requires` to build
-  - `prepare_metadata_for_build_wheel`: get the `install_requires`
-  - `build_sdist`: build an sdist in the folder and return the basename
-  - `get_requires_for_build_sdist`: get the `setup_requires` to build
-
-Again, this is not a formal definition! Just a "taste" of the module.
-"""
-
-import io
-import os
-import shlex
-import sys
-import tokenize
-import shutil
-import contextlib
-import tempfile
-import warnings
-from pathlib import Path
-from typing import Dict, Iterator, List, Optional, Union
-
-import setuptools
-import distutils
-from . import errors
-from ._path import same_path
-from ._reqs import parse_strings
-from ._deprecation_warning import SetuptoolsDeprecationWarning
-from distutils.util import strtobool
-
-
-__all__ = ['get_requires_for_build_sdist',
-           'get_requires_for_build_wheel',
-           'prepare_metadata_for_build_wheel',
-           'build_wheel',
-           'build_sdist',
-           'get_requires_for_build_editable',
-           'prepare_metadata_for_build_editable',
-           'build_editable',
-           '__legacy__',
-           'SetupRequirementsError']
-
-SETUPTOOLS_ENABLE_FEATURES = os.getenv("SETUPTOOLS_ENABLE_FEATURES", "").lower()
-LEGACY_EDITABLE = "legacy-editable" in SETUPTOOLS_ENABLE_FEATURES.replace("_", "-")
-
-
-class SetupRequirementsError(BaseException):
-    def __init__(self, specifiers):
-        self.specifiers = specifiers
-
-
-class Distribution(setuptools.dist.Distribution):
-    def fetch_build_eggs(self, specifiers):
-        specifier_list = list(parse_strings(specifiers))
-
-        raise SetupRequirementsError(specifier_list)
-
-    @classmethod
-    @contextlib.contextmanager
-    def patch(cls):
-        """
-        Replace
-        distutils.dist.Distribution with this class
-        for the duration of this context.
-        """
-        orig = distutils.core.Distribution
-        distutils.core.Distribution = cls
-        try:
-            yield
-        finally:
-            distutils.core.Distribution = orig
-
-
-@contextlib.contextmanager
-def no_install_setup_requires():
-    """Temporarily disable installing setup_requires
-
-    Under PEP 517, the backend reports build dependencies to the frontend,
-    and the frontend is responsible for ensuring they're installed.
-    So setuptools (acting as a backend) should not try to install them.
-    """
-    orig = setuptools._install_setup_requires
-    setuptools._install_setup_requires = lambda attrs: None
-    try:
-        yield
-    finally:
-        setuptools._install_setup_requires = orig
-
-
-def _get_immediate_subdirectories(a_dir):
-    return [name for name in os.listdir(a_dir)
-            if os.path.isdir(os.path.join(a_dir, name))]
-
-
-def _file_with_extension(directory, extension):
-    matching = (
-        f for f in os.listdir(directory)
-        if f.endswith(extension)
-    )
-    try:
-        file, = matching
-    except ValueError:
-        raise ValueError(
-            'No distribution was found. Ensure that `setup.py` '
-            'is not empty and that it calls `setup()`.')
-    return file
-
-
-def _open_setup_script(setup_script):
-    if not os.path.exists(setup_script):
-        # Supply a default setup.py
-        return io.StringIO(u"from setuptools import setup; setup()")
-
-    return getattr(tokenize, 'open', open)(setup_script)
-
-
-@contextlib.contextmanager
-def suppress_known_deprecation():
-    with warnings.catch_warnings():
-        warnings.filterwarnings('ignore', 'setup.py install is deprecated')
-        yield
-
-
-_ConfigSettings = Optional[Dict[str, Union[str, List[str], None]]]
-"""
-Currently the user can run::
-
-    pip install -e . --config-settings key=value
-    python -m build -C--key=value -C key=value
-
-- pip will pass both key and value as strings and overwriting repeated keys
-  (pypa/pip#11059).
-- build will accumulate values associated with repeated keys in a list.
-  It will also accept keys with no associated value.
-  This means that an option passed by build can be ``str | list[str] | None``.
-- PEP 517 specifies that ``config_settings`` is an optional dict.
-"""
-
-
-class _ConfigSettingsTranslator:
-    """Translate ``config_settings`` into distutils-style command arguments.
-    Only a limited number of options is currently supported.
-    """
-    # See pypa/setuptools#1928 pypa/setuptools#2491
-
-    def _get_config(self, key: str, config_settings: _ConfigSettings) -> List[str]:
-        """
-        Get the value of a specific key in ``config_settings`` as a list of strings.
-
-        >>> fn = _ConfigSettingsTranslator()._get_config
-        >>> fn("--global-option", None)
-        []
-        >>> fn("--global-option", {})
-        []
-        >>> fn("--global-option", {'--global-option': 'foo'})
-        ['foo']
-        >>> fn("--global-option", {'--global-option': ['foo']})
-        ['foo']
-        >>> fn("--global-option", {'--global-option': 'foo'})
-        ['foo']
-        >>> fn("--global-option", {'--global-option': 'foo bar'})
-        ['foo', 'bar']
-        """
-        cfg = config_settings or {}
-        opts = cfg.get(key) or []
-        return shlex.split(opts) if isinstance(opts, str) else opts
-
-    def _valid_global_options(self):
-        """Global options accepted by setuptools (e.g. quiet or verbose)."""
-        options = (opt[:2] for opt in setuptools.dist.Distribution.global_options)
-        return {flag for long_and_short in options for flag in long_and_short if flag}
-
-    def _global_args(self, config_settings: _ConfigSettings) -> Iterator[str]:
-        """
-        Let the user specify ``verbose`` or ``quiet`` + escape hatch via
-        ``--global-option``.
-        Note: ``-v``, ``-vv``, ``-vvv`` have similar effects in setuptools,
-        so we just have to cover the basic scenario ``-v``.
-
-        >>> fn = _ConfigSettingsTranslator()._global_args
-        >>> list(fn(None))
-        []
-        >>> list(fn({"verbose": "False"}))
-        ['-q']
-        >>> list(fn({"verbose": "1"}))
-        ['-v']
-        >>> list(fn({"--verbose": None}))
-        ['-v']
-        >>> list(fn({"verbose": "true", "--global-option": "-q --no-user-cfg"}))
-        ['-v', '-q', '--no-user-cfg']
-        >>> list(fn({"--quiet": None}))
-        ['-q']
-        """
-        cfg = config_settings or {}
-        falsey = {"false", "no", "0", "off"}
-        if "verbose" in cfg or "--verbose" in cfg:
-            level = str(cfg.get("verbose") or cfg.get("--verbose") or "1")
-            yield ("-q" if level.lower() in falsey else "-v")
-        if "quiet" in cfg or "--quiet" in cfg:
-            level = str(cfg.get("quiet") or cfg.get("--quiet") or "1")
-            yield ("-v" if level.lower() in falsey else "-q")
-
-        valid = self._valid_global_options()
-        args = self._get_config("--global-option", config_settings)
-        yield from (arg for arg in args if arg.strip("-") in valid)
-
-    def __dist_info_args(self, config_settings: _ConfigSettings) -> Iterator[str]:
-        """
-        The ``dist_info`` command accepts ``tag-date`` and ``tag-build``.
-
-        .. warning::
-           We cannot use this yet as it requires the ``sdist`` and ``bdist_wheel``
-           commands run in ``build_sdist`` and ``build_wheel`` to re-use the egg-info
-           directory created in ``prepare_metadata_for_build_wheel``.
-
-        >>> fn = _ConfigSettingsTranslator()._ConfigSettingsTranslator__dist_info_args
-        >>> list(fn(None))
-        []
-        >>> list(fn({"tag-date": "False"}))
-        ['--no-date']
-        >>> list(fn({"tag-date": None}))
-        ['--no-date']
-        >>> list(fn({"tag-date": "true", "tag-build": ".a"}))
-        ['--tag-date', '--tag-build', '.a']
-        """
-        cfg = config_settings or {}
-        if "tag-date" in cfg:
-            val = strtobool(str(cfg["tag-date"] or "false"))
-            yield ("--tag-date" if val else "--no-date")
-        if "tag-build" in cfg:
-            yield from ["--tag-build", str(cfg["tag-build"])]
-
-    def _editable_args(self, config_settings: _ConfigSettings) -> Iterator[str]:
-        """
-        The ``editable_wheel`` command accepts ``editable-mode=strict``.
-
-        >>> fn = _ConfigSettingsTranslator()._editable_args
-        >>> list(fn(None))
-        []
-        >>> list(fn({"editable-mode": "strict"}))
-        ['--mode', 'strict']
-        """
-        cfg = config_settings or {}
-        mode = cfg.get("editable-mode") or cfg.get("editable_mode")
-        if not mode:
-            return
-        yield from ["--mode", str(mode)]
-
-    def _arbitrary_args(self, config_settings: _ConfigSettings) -> Iterator[str]:
-        """
-        Users may expect to pass arbitrary lists of arguments to a command
-        via "--global-option" (example provided in PEP 517 of a "escape hatch").
-
-        >>> fn = _ConfigSettingsTranslator()._arbitrary_args
-        >>> list(fn(None))
-        []
-        >>> list(fn({}))
-        []
-        >>> list(fn({'--build-option': 'foo'}))
-        ['foo']
-        >>> list(fn({'--build-option': ['foo']}))
-        ['foo']
-        >>> list(fn({'--build-option': 'foo'}))
-        ['foo']
-        >>> list(fn({'--build-option': 'foo bar'}))
-        ['foo', 'bar']
-        >>> warnings.simplefilter('error', SetuptoolsDeprecationWarning)
-        >>> list(fn({'--global-option': 'foo'}))  # doctest: +IGNORE_EXCEPTION_DETAIL
-        Traceback (most recent call last):
-        SetuptoolsDeprecationWarning: ...arguments given via `--global-option`...
-        """
-        args = self._get_config("--global-option", config_settings)
-        global_opts = self._valid_global_options()
-        bad_args = []
-
-        for arg in args:
-            if arg.strip("-") not in global_opts:
-                bad_args.append(arg)
-                yield arg
-
-        yield from self._get_config("--build-option", config_settings)
-
-        if bad_args:
-            msg = f"""
-            The arguments {bad_args!r} were given via `--global-option`.
-            Please use `--build-option` instead,
-            `--global-option` is reserved to flags like `--verbose` or `--quiet`.
-            """
-            warnings.warn(msg, SetuptoolsDeprecationWarning)
-
-
-class _BuildMetaBackend(_ConfigSettingsTranslator):
-    def _get_build_requires(self, config_settings, requirements):
-        sys.argv = [
-            *sys.argv[:1],
-            *self._global_args(config_settings),
-            "egg_info",
-            *self._arbitrary_args(config_settings),
-        ]
-        try:
-            with Distribution.patch():
-                self.run_setup()
-        except SetupRequirementsError as e:
-            requirements += e.specifiers
-
-        return requirements
-
-    def run_setup(self, setup_script='setup.py'):
-        # Note that we can reuse our build directory between calls
-        # Correctness comes first, then optimization later
-        __file__ = setup_script
-        __name__ = '__main__'
-
-        with _open_setup_script(__file__) as f:
-            code = f.read().replace(r'\r\n', r'\n')
-
-        exec(code, locals())
-
-    def get_requires_for_build_wheel(self, config_settings=None):
-        return self._get_build_requires(config_settings, requirements=['wheel'])
-
-    def get_requires_for_build_sdist(self, config_settings=None):
-        return self._get_build_requires(config_settings, requirements=[])
-
-    def _bubble_up_info_directory(self, metadata_directory: str, suffix: str) -> str:
-        """
-        PEP 517 requires that the .dist-info directory be placed in the
-        metadata_directory. To comply, we MUST copy the directory to the root.
-
-        Returns the basename of the info directory, e.g. `proj-0.0.0.dist-info`.
-        """
-        info_dir = self._find_info_directory(metadata_directory, suffix)
-        if not same_path(info_dir.parent, metadata_directory):
-            shutil.move(str(info_dir), metadata_directory)
-            # PEP 517 allow other files and dirs to exist in metadata_directory
-        return info_dir.name
-
-    def _find_info_directory(self, metadata_directory: str, suffix: str) -> Path:
-        for parent, dirs, _ in os.walk(metadata_directory):
-            candidates = [f for f in dirs if f.endswith(suffix)]
-
-            if len(candidates) != 0 or len(dirs) != 1:
-                assert len(candidates) == 1, f"Multiple {suffix} directories found"
-                return Path(parent, candidates[0])
-
-        msg = f"No {suffix} directory found in {metadata_directory}"
-        raise errors.InternalError(msg)
-
-    def prepare_metadata_for_build_wheel(self, metadata_directory,
-                                         config_settings=None):
-        sys.argv = [
-            *sys.argv[:1],
-            *self._global_args(config_settings),
-            "dist_info",
-            "--output-dir", metadata_directory,
-            "--keep-egg-info",
-        ]
-        with no_install_setup_requires():
-            self.run_setup()
-
-        self._bubble_up_info_directory(metadata_directory, ".egg-info")
-        return self._bubble_up_info_directory(metadata_directory, ".dist-info")
-
-    def _build_with_temp_dir(self, setup_command, result_extension,
-                             result_directory, config_settings):
-        result_directory = os.path.abspath(result_directory)
-
-        # Build in a temporary directory, then copy to the target.
-        os.makedirs(result_directory, exist_ok=True)
-        with tempfile.TemporaryDirectory(dir=result_directory) as tmp_dist_dir:
-            sys.argv = [
-                *sys.argv[:1],
-                *self._global_args(config_settings),
-                *setup_command,
-                "--dist-dir", tmp_dist_dir,
-                *self._arbitrary_args(config_settings),
-            ]
-            with no_install_setup_requires():
-                self.run_setup()
-
-            result_basename = _file_with_extension(
-                tmp_dist_dir, result_extension)
-            result_path = os.path.join(result_directory, result_basename)
-            if os.path.exists(result_path):
-                # os.rename will fail overwriting on non-Unix.
-                os.remove(result_path)
-            os.rename(os.path.join(tmp_dist_dir, result_basename), result_path)
-
-        return result_basename
-
-    def build_wheel(self, wheel_directory, config_settings=None,
-                    metadata_directory=None):
-        with suppress_known_deprecation():
-            return self._build_with_temp_dir(['bdist_wheel'], '.whl',
-                                             wheel_directory, config_settings)
-
-    def build_sdist(self, sdist_directory, config_settings=None):
-        return self._build_with_temp_dir(['sdist', '--formats', 'gztar'],
-                                         '.tar.gz', sdist_directory,
-                                         config_settings)
-
-    def _get_dist_info_dir(self, metadata_directory: Optional[str]) -> Optional[str]:
-        if not metadata_directory:
-            return None
-        dist_info_candidates = list(Path(metadata_directory).glob("*.dist-info"))
-        assert len(dist_info_candidates) <= 1
-        return str(dist_info_candidates[0]) if dist_info_candidates else None
-
-    if not LEGACY_EDITABLE:
-
-        # PEP660 hooks:
-        # build_editable
-        # get_requires_for_build_editable
-        # prepare_metadata_for_build_editable
-        def build_editable(
-            self, wheel_directory, config_settings=None, metadata_directory=None
-        ):
-            # XXX can or should we hide our editable_wheel command normally?
-            info_dir = self._get_dist_info_dir(metadata_directory)
-            opts = ["--dist-info-dir", info_dir] if info_dir else []
-            cmd = ["editable_wheel", *opts, *self._editable_args(config_settings)]
-            with suppress_known_deprecation():
-                return self._build_with_temp_dir(
-                    cmd, ".whl", wheel_directory, config_settings
-                )
-
-        def get_requires_for_build_editable(self, config_settings=None):
-            return self.get_requires_for_build_wheel(config_settings)
-
-        def prepare_metadata_for_build_editable(self, metadata_directory,
-                                                config_settings=None):
-            return self.prepare_metadata_for_build_wheel(
-                metadata_directory, config_settings
-            )
-
-
-class _BuildMetaLegacyBackend(_BuildMetaBackend):
-    """Compatibility backend for setuptools
-
-    This is a version of setuptools.build_meta that endeavors
-    to maintain backwards
-    compatibility with pre-PEP 517 modes of invocation. It
-    exists as a temporary
-    bridge between the old packaging mechanism and the new
-    packaging mechanism,
-    and will eventually be removed.
-    """
-    def run_setup(self, setup_script='setup.py'):
-        # In order to maintain compatibility with scripts assuming that
-        # the setup.py script is in a directory on the PYTHONPATH, inject
-        # '' into sys.path. (pypa/setuptools#1642)
-        sys_path = list(sys.path)           # Save the original path
-
-        script_dir = os.path.dirname(os.path.abspath(setup_script))
-        if script_dir not in sys.path:
-            sys.path.insert(0, script_dir)
-
-        # Some setup.py scripts (e.g. in pygame and numpy) use sys.argv[0] to
-        # get the directory of the source code. They expect it to refer to the
-        # setup.py script.
-        sys_argv_0 = sys.argv[0]
-        sys.argv[0] = setup_script
-
-        try:
-            super(_BuildMetaLegacyBackend,
-                  self).run_setup(setup_script=setup_script)
-        finally:
-            # While PEP 517 frontends should be calling each hook in a fresh
-            # subprocess according to the standard (and thus it should not be
-            # strictly necessary to restore the old sys.path), we'll restore
-            # the original path so that the path manipulation does not persist
-            # within the hook after run_setup is called.
-            sys.path[:] = sys_path
-            sys.argv[0] = sys_argv_0
-
-
-# The primary backend
-_BACKEND = _BuildMetaBackend()
-
-get_requires_for_build_wheel = _BACKEND.get_requires_for_build_wheel
-get_requires_for_build_sdist = _BACKEND.get_requires_for_build_sdist
-prepare_metadata_for_build_wheel = _BACKEND.prepare_metadata_for_build_wheel
-build_wheel = _BACKEND.build_wheel
-build_sdist = _BACKEND.build_sdist
-
-if not LEGACY_EDITABLE:
-    get_requires_for_build_editable = _BACKEND.get_requires_for_build_editable
-    prepare_metadata_for_build_editable = _BACKEND.prepare_metadata_for_build_editable
-    build_editable = _BACKEND.build_editable
-
-
-# The legacy backend
-__legacy__ = _BuildMetaLegacyBackend()

spaces/Bl1tzie/Jam/README.md

@@ -1,10 +0,0 @@
----
-title: Jam
-emoji: 😻
-colorFrom: green
-colorTo: gray
-sdk: docker
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/Boadiwaa/Recipes/openai/cli.py

@@ -1,1018 +0,0 @@
-import datetime
-import os
-import signal
-import sys
-import warnings
-from functools import partial
-from typing import Optional
-
-import requests
-
-import openai
-import openai.wandb_logger
-from openai.upload_progress import BufferReader
-from openai.validators import (
-    apply_necessary_remediation,
-    apply_validators,
-    get_search_validators,
-    get_validators,
-    read_any_format,
-    write_out_file,
-    write_out_search_file,
-)
-
-
-class bcolors:
-    HEADER = "\033[95m"
-    OKBLUE = "\033[94m"
-    OKGREEN = "\033[92m"
-    WARNING = "\033[93m"
-    FAIL = "\033[91m"
-    ENDC = "\033[0m"
-    BOLD = "\033[1m"
-    UNDERLINE = "\033[4m"
-
-
-def organization_info(obj):
-    organization = getattr(obj, "organization", None)
-    if organization is not None:
-        return "[organization={}] ".format(organization)
-    else:
-        return ""
-
-
-def display(obj):
-    sys.stderr.write(organization_info(obj))
-    sys.stderr.flush()
-    print(obj)
-
-
-def display_error(e):
-    extra = (
-        " (HTTP status code: {})".format(e.http_status)
-        if e.http_status is not None
-        else ""
-    )
-    sys.stderr.write(
-        "{}{}Error:{} {}{}\n".format(
-            organization_info(e), bcolors.FAIL, bcolors.ENDC, e, extra
-        )
-    )
-
-
-class Engine:
-    @classmethod
-    def get(cls, args):
-        engine = openai.Engine.retrieve(id=args.id)
-        display(engine)
-
-    @classmethod
-    def update(cls, args):
-        engine = openai.Engine.modify(args.id, replicas=args.replicas)
-        display(engine)
-
-    @classmethod
-    def generate(cls, args):
-        warnings.warn(
-            "Engine.generate is deprecated, use Completion.create", DeprecationWarning
-        )
-        if args.completions and args.completions > 1 and args.stream:
-            raise ValueError("Can't stream multiple completions with openai CLI")
-
-        kwargs = {}
-        if args.model is not None:
-            kwargs["model"] = args.model
-        resp = openai.Engine(id=args.id).generate(
-            completions=args.completions,
-            context=args.context,
-            length=args.length,
-            stream=args.stream,
-            temperature=args.temperature,
-            top_p=args.top_p,
-            logprobs=args.logprobs,
-            stop=args.stop,
-            **kwargs,
-        )
-        if not args.stream:
-            resp = [resp]
-
-        for part in resp:
-            completions = len(part["data"])
-            for c_idx, c in enumerate(part["data"]):
-                if completions > 1:
-                    sys.stdout.write("===== Completion {} =====\n".format(c_idx))
-                sys.stdout.write("".join(c["text"]))
-                if completions > 1:
-                    sys.stdout.write("\n")
-                sys.stdout.flush()
-
-    @classmethod
-    def search(cls, args):
-        params = {
-            "query": args.query,
-            "max_rerank": args.max_rerank,
-            "return_metadata": args.return_metadata,
-        }
-        if args.documents:
-            params["documents"] = args.documents
-        if args.file:
-            params["file"] = args.file
-
-        if args.version:
-            params["version"] = args.version
-
-        resp = openai.Engine(id=args.id).search(**params)
-        scores = [
-            (search_result["score"], search_result["document"])
-            for search_result in resp["data"]
-        ]
-        scores.sort(reverse=True)
-        dataset = (
-            args.documents if args.documents else [x["text"] for x in resp["data"]]
-        )
-        for score, document_idx in scores:
-            print("=== score {:.3f} ===".format(score))
-            print(dataset[document_idx])
-            if (
-                args.return_metadata
-                and args.file
-                and "metadata" in resp["data"][document_idx]
-            ):
-                print(f"METADATA: {resp['data'][document_idx]['metadata']}")
-
-    @classmethod
-    def list(cls, args):
-        engines = openai.Engine.list()
-        display(engines)
-
-
-class Completion:
-    @classmethod
-    def create(cls, args):
-        if args.n is not None and args.n > 1 and args.stream:
-            raise ValueError("Can't stream completions with n>1 with the current CLI")
-
-        if args.engine and args.model:
-            warnings.warn(
-                "In most cases, you should not be specifying both engine and model."
-            )
-
-        resp = openai.Completion.create(
-            engine=args.engine,
-            model=args.model,
-            n=args.n,
-            max_tokens=args.max_tokens,
-            logprobs=args.logprobs,
-            prompt=args.prompt,
-            stream=args.stream,
-            temperature=args.temperature,
-            top_p=args.top_p,
-            stop=args.stop,
-            echo=True,
-        )
-        if not args.stream:
-            resp = [resp]
-
-        for part in resp:
-            choices = part["choices"]
-            for c_idx, c in enumerate(sorted(choices, key=lambda s: s["index"])):
-                if len(choices) > 1:
-                    sys.stdout.write("===== Completion {} =====\n".format(c_idx))
-                sys.stdout.write(c["text"])
-                if len(choices) > 1:
-                    sys.stdout.write("\n")
-                sys.stdout.flush()
-
-
-class Model:
-    @classmethod
-    def get(cls, args):
-        resp = openai.Model.retrieve(id=args.id)
-        print(resp)
-
-    @classmethod
-    def delete(cls, args):
-        model = openai.Model.delete(args.id)
-        print(model)
-
-    @classmethod
-    def list(cls, args):
-        models = openai.Model.list()
-        print(models)
-
-
-class File:
-    @classmethod
-    def create(cls, args):
-        with open(args.file, "rb") as file_reader:
-            buffer_reader = BufferReader(file_reader.read(), desc="Upload progress")
-        resp = openai.File.create(
-            file=buffer_reader,
-            purpose=args.purpose,
-            model=args.model,
-            user_provided_filename=args.file,
-        )
-        print(resp)
-
-    @classmethod
-    def get(cls, args):
-        resp = openai.File.retrieve(id=args.id)
-        print(resp)
-
-    @classmethod
-    def delete(cls, args):
-        file = openai.File.delete(args.id)
-        print(file)
-
-    @classmethod
-    def list(cls, args):
-        file = openai.File.list()
-        print(file)
-
-
-class Search:
-    @classmethod
-    def prepare_data(cls, args, purpose):
-
-        sys.stdout.write("Analyzing...\n")
-        fname = args.file
-        auto_accept = args.quiet
-
-        optional_fields = ["metadata"]
-
-        if purpose == "classifications":
-            required_fields = ["text", "label"]
-        else:
-            required_fields = ["text"]
-
-        df, remediation = read_any_format(
-            fname, fields=required_fields + optional_fields
-        )
-
-        if "metadata" not in df:
-            df["metadata"] = None
-
-        apply_necessary_remediation(None, remediation)
-        validators = get_search_validators(required_fields, optional_fields)
-
-        write_out_file_func = partial(
-            write_out_search_file,
-            purpose=purpose,
-            fields=required_fields + optional_fields,
-        )
-
-        apply_validators(
-            df, fname, remediation, validators, auto_accept, write_out_file_func
-        )
-
-    @classmethod
-    def create(cls, args):
-        resp = openai.Search.create(
-            query=args.query,
-            documents=args.documents,
-            model=args.model,
-        )
-        print(resp)
-
-
-class FineTune:
-    @classmethod
-    def list(cls, args):
-        resp = openai.FineTune.list()
-        print(resp)
-
-    @classmethod
-    def _is_url(cls, file: str):
-        return file.lower().startswith("http")
-
-    @classmethod
-    def _download_file_from_public_url(cls, url: str) -> Optional[bytes]:
-        resp = requests.get(url)
-        if resp.status_code == 200:
-            return resp.content
-        else:
-            return None
-
-    @classmethod
-    def _maybe_upload_file(
-        cls,
-        file: Optional[str] = None,
-        content: Optional[bytes] = None,
-        user_provided_file: Optional[str] = None,
-        check_if_file_exists: bool = True,
-    ):
-        # Exactly one of `file` or `content` must be provided
-        if (file is None) == (content is None):
-            raise ValueError("Exactly one of `file` or `content` must be provided")
-
-        if content is None:
-            assert file is not None
-            with open(file, "rb") as f:
-                content = f.read()
-
-        if check_if_file_exists:
-            bytes = len(content)
-            matching_files = openai.File.find_matching_files(
-                name=user_provided_file or f.name, bytes=bytes, purpose="fine-tune"
-            )
-            if len(matching_files) > 0:
-                file_ids = [f["id"] for f in matching_files]
-                sys.stdout.write(
-                    "Found potentially duplicated files with name '{name}', purpose 'fine-tune' and size {size} bytes\n".format(
-                        name=os.path.basename(matching_files[0]["filename"]),
-                        size=matching_files[0]["bytes"] if "bytes" in matching_files[0] else matching_files[0]["size"],
-                    )
-                )
-                sys.stdout.write("\n".join(file_ids))
-                while True:
-                    sys.stdout.write(
-                        "\nEnter file ID to reuse an already uploaded file, or an empty string to upload this file anyway: "
-                    )
-                    inp = sys.stdin.readline().strip()
-                    if inp in file_ids:
-                        sys.stdout.write(
-                            "Reusing already uploaded file: {id}\n".format(id=inp)
-                        )
-                        return inp
-                    elif inp == "":
-                        break
-                    else:
-                        sys.stdout.write(
-                            "File id '{id}' is not among the IDs of the potentially duplicated files\n".format(
-                                id=inp
-                            )
-                        )
-
-        buffer_reader = BufferReader(content, desc="Upload progress")
-        resp = openai.File.create(
-            file=buffer_reader,
-            purpose="fine-tune",
-            user_provided_filename=user_provided_file or file,
-        )
-        sys.stdout.write(
-            "Uploaded file from {file}: {id}\n".format(
-                file=user_provided_file or file, id=resp["id"]
-            )
-        )
-        return resp["id"]
-
-    @classmethod
-    def _get_or_upload(cls, file, check_if_file_exists=True):
-        try:
-            # 1. If it's a valid file, use it
-            openai.File.retrieve(file)
-            return file
-        except openai.error.InvalidRequestError:
-            pass
-        if os.path.isfile(file):
-            # 2. If it's a file on the filesystem, upload it
-            return cls._maybe_upload_file(
-                file=file, check_if_file_exists=check_if_file_exists
-            )
-        if cls._is_url(file):
-            # 3. If it's a URL, download it temporarily
-            content = cls._download_file_from_public_url(file)
-            if content is not None:
-                return cls._maybe_upload_file(
-                    content=content,
-                    check_if_file_exists=check_if_file_exists,
-                    user_provided_file=file,
-                )
-        return file
-
-    @classmethod
-    def create(cls, args):
-        create_args = {
-            "training_file": cls._get_or_upload(
-                args.training_file, args.check_if_files_exist
-            ),
-        }
-        if args.validation_file:
-            create_args["validation_file"] = cls._get_or_upload(
-                args.validation_file, args.check_if_files_exist
-            )
-
-        for hparam in (
-            "model",
-            "suffix",
-            "n_epochs",
-            "batch_size",
-            "learning_rate_multiplier",
-            "prompt_loss_weight",
-            "compute_classification_metrics",
-            "classification_n_classes",
-            "classification_positive_class",
-            "classification_betas",
-        ):
-            attr = getattr(args, hparam)
-            if attr is not None:
-                create_args[hparam] = attr
-
-        resp = openai.FineTune.create(**create_args)
-
-        if args.no_follow:
-            print(resp)
-            return
-
-        sys.stdout.write(
-            "Created fine-tune: {job_id}\n"
-            "Streaming events until fine-tuning is complete...\n\n"
-            "(Ctrl-C will interrupt the stream, but not cancel the fine-tune)\n".format(
-                job_id=resp["id"]
-            )
-        )
-        cls._stream_events(resp["id"])
-
-    @classmethod
-    def get(cls, args):
-        resp = openai.FineTune.retrieve(id=args.id)
-        print(resp)
-
-    @classmethod
-    def results(cls, args):
-        fine_tune = openai.FineTune.retrieve(id=args.id)
-        if "result_files" not in fine_tune or len(fine_tune["result_files"]) == 0:
-            raise openai.error.InvalidRequestError(
-                f"No results file available for fine-tune {args.id}", "id"
-            )
-        result_file = openai.FineTune.retrieve(id=args.id)["result_files"][0]
-        resp = openai.File.download(id=result_file["id"])
-        print(resp.decode("utf-8"))
-
-    @classmethod
-    def events(cls, args):
-        if args.stream:
-            raise openai.error.OpenAIError(
-                message=(
-                    "The --stream parameter is deprecated, use fine_tunes.follow "
-                    "instead:\n\n"
-                    "  openai api fine_tunes.follow -i {id}\n".format(id=args.id)
-                ),
-            )
-
-        resp = openai.FineTune.list_events(id=args.id)  # type: ignore
-        print(resp)
-
-    @classmethod
-    def follow(cls, args):
-        cls._stream_events(args.id)
-
-    @classmethod
-    def _stream_events(cls, job_id):
-        def signal_handler(sig, frame):
-            status = openai.FineTune.retrieve(job_id).status
-            sys.stdout.write(
-                "\nStream interrupted. Job is still {status}.\n"
-                "To resume the stream, run:\n\n"
-                "  openai api fine_tunes.follow -i {job_id}\n\n"
-                "To cancel your job, run:\n\n"
-                "  openai api fine_tunes.cancel -i {job_id}\n\n".format(
-                    status=status, job_id=job_id
-                )
-            )
-            sys.exit(0)
-
-        signal.signal(signal.SIGINT, signal_handler)
-
-        events = openai.FineTune.stream_events(job_id)
-        # TODO(rachel): Add a nifty spinner here.
-        try:
-            for event in events:
-                sys.stdout.write(
-                    "[%s] %s"
-                    % (
-                        datetime.datetime.fromtimestamp(event["created_at"]),
-                        event["message"],
-                    )
-                )
-                sys.stdout.write("\n")
-                sys.stdout.flush()
-        except Exception:
-            sys.stdout.write(
-                "\nStream interrupted (client disconnected).\n"
-                "To resume the stream, run:\n\n"
-                "  openai api fine_tunes.follow -i {job_id}\n\n".format(job_id=job_id)
-            )
-            return
-
-        resp = openai.FineTune.retrieve(id=job_id)
-        status = resp["status"]
-        if status == "succeeded":
-            sys.stdout.write("\nJob complete! Status: succeeded 🎉")
-            sys.stdout.write(
-                "\nTry out your fine-tuned model:\n\n"
-                "openai api completions.create -m {model} -p <YOUR_PROMPT>".format(
-                    model=resp["fine_tuned_model"]
-                )
-            )
-        elif status == "failed":
-            sys.stdout.write(
-                "\nJob failed. Please contact [email protected] if you need assistance."
-            )
-        sys.stdout.write("\n")
-
-    @classmethod
-    def cancel(cls, args):
-        resp = openai.FineTune.cancel(id=args.id)
-        print(resp)
-
-    @classmethod
-    def prepare_data(cls, args):
-
-        sys.stdout.write("Analyzing...\n")
-        fname = args.file
-        auto_accept = args.quiet
-        df, remediation = read_any_format(fname)
-        apply_necessary_remediation(None, remediation)
-
-        validators = get_validators()
-
-        apply_validators(
-            df,
-            fname,
-            remediation,
-            validators,
-            auto_accept,
-            write_out_file_func=write_out_file,
-        )
-
-
-class WandbLogger:
-    @classmethod
-    def sync(cls, args):
-        resp = openai.wandb_logger.WandbLogger.sync(
-            id=args.id,
-            n_fine_tunes=args.n_fine_tunes,
-            project=args.project,
-            entity=args.entity,
-            force=args.force,
-        )
-        print(resp)
-
-
-def tools_register(parser):
-    subparsers = parser.add_subparsers(
-        title="Tools", help="Convenience client side tools"
-    )
-
-    def help(args):
-        parser.print_help()
-
-    parser.set_defaults(func=help)
-
-    sub = subparsers.add_parser("fine_tunes.prepare_data")
-    sub.add_argument(
-        "-f",
-        "--file",
-        required=True,
-        help="JSONL, JSON, CSV, TSV, TXT or XLSX file containing prompt-completion examples to be analyzed."
-        "This should be the local file path.",
-    )
-    sub.add_argument(
-        "-q",
-        "--quiet",
-        required=False,
-        action="store_true",
-        help="Auto accepts all suggestions, without asking for user input. To be used within scripts.",
-    )
-    sub.set_defaults(func=FineTune.prepare_data)
-
-    sub = subparsers.add_parser("search.prepare_data")
-    sub.add_argument(
-        "-f",
-        "--file",
-        required=True,
-        help="JSONL, JSON, CSV, TSV, TXT or XLSX file containing text examples to be analyzed."
-        "This should be the local file path.",
-    )
-    sub.add_argument(
-        "-q",
-        "--quiet",
-        required=False,
-        action="store_true",
-        help="Auto accepts all suggestions, without asking for user input. To be used within scripts.",
-    )
-    sub.set_defaults(func=partial(Search.prepare_data, purpose="search"))
-
-    sub = subparsers.add_parser("classifications.prepare_data")
-    sub.add_argument(
-        "-f",
-        "--file",
-        required=True,
-        help="JSONL, JSON, CSV, TSV, TXT or XLSX file containing text-label examples to be analyzed."
-        "This should be the local file path.",
-    )
-    sub.add_argument(
-        "-q",
-        "--quiet",
-        required=False,
-        action="store_true",
-        help="Auto accepts all suggestions, without asking for user input. To be used within scripts.",
-    )
-    sub.set_defaults(func=partial(Search.prepare_data, purpose="classifications"))
-
-    sub = subparsers.add_parser("answers.prepare_data")
-    sub.add_argument(
-        "-f",
-        "--file",
-        required=True,
-        help="JSONL, JSON, CSV, TSV, TXT or XLSX file containing text examples to be analyzed."
-        "This should be the local file path.",
-    )
-    sub.add_argument(
-        "-q",
-        "--quiet",
-        required=False,
-        action="store_true",
-        help="Auto accepts all suggestions, without asking for user input. To be used within scripts.",
-    )
-    sub.set_defaults(func=partial(Search.prepare_data, purpose="answer"))
-
-
-def api_register(parser):
-    # Engine management
-    subparsers = parser.add_subparsers(help="All API subcommands")
-
-    def help(args):
-        parser.print_help()
-
-    parser.set_defaults(func=help)
-
-    sub = subparsers.add_parser("engines.list")
-    sub.set_defaults(func=Engine.list)
-
-    sub = subparsers.add_parser("engines.get")
-    sub.add_argument("-i", "--id", required=True)
-    sub.set_defaults(func=Engine.get)
-
-    sub = subparsers.add_parser("engines.update")
-    sub.add_argument("-i", "--id", required=True)
-    sub.add_argument("-r", "--replicas", type=int)
-    sub.set_defaults(func=Engine.update)
-
-    sub = subparsers.add_parser("engines.generate")
-    sub.add_argument("-i", "--id", required=True)
-    sub.add_argument(
-        "--stream", help="Stream tokens as they're ready.", action="store_true"
-    )
-    sub.add_argument("-c", "--context", help="An optional context to generate from")
-    sub.add_argument("-l", "--length", help="How many tokens to generate", type=int)
-    sub.add_argument(
-        "-t",
-        "--temperature",
-        help="""What sampling temperature to use. Higher values means the model will take more risks. Try 0.9 for more creative applications, and 0 (argmax sampling) for ones with a well-defined answer.
-
-Mutually exclusive with `top_p`.""",
-        type=float,
-    )
-    sub.add_argument(
-        "-p",
-        "--top_p",
-        help="""An alternative to sampling with temperature, called nucleus sampling, where the considers the results of the tokens with top_p probability mass. So 0.1 means only the tokens comprising the top 10%% probability mass are considered.
-
-            Mutually exclusive with `temperature`.""",
-        type=float,
-    )
-    sub.add_argument(
-        "-n",
-        "--completions",
-        help="How many parallel completions to run on this context",
-        type=int,
-    )
-    sub.add_argument(
-        "--logprobs",
-        help="Include the log probabilites on the `logprobs` most likely tokens. So for example, if `logprobs` is 10, the API will return a list of the 10 most likely tokens. If `logprobs` is supplied, the API will always return the logprob of the generated token, so there may be up to `logprobs+1` elements in the response.",
-        type=int,
-    )
-    sub.add_argument(
-        "--stop", help="A stop sequence at which to stop generating tokens."
-    )
-    sub.add_argument(
-        "-m",
-        "--model",
-        required=False,
-        help="A model (most commonly a model ID) to generate from. Defaults to the engine's default model.",
-    )
-    sub.set_defaults(func=Engine.generate)
-
-    sub = subparsers.add_parser("engines.search")
-    sub.add_argument("-i", "--id", required=True)
-    sub.add_argument(
-        "-d",
-        "--documents",
-        action="append",
-        help="List of documents to search over. Only one of `documents` or `file` may be supplied.",
-        required=False,
-    )
-    sub.add_argument(
-        "-f",
-        "--file",
-        help="A file id to search over.  Only one of `documents` or `file` may be supplied.",
-        required=False,
-    )
-    sub.add_argument(
-        "--max_rerank",
-        help="The maximum number of documents to be re-ranked and returned by search. This flag only takes effect when `file` is set.",
-        type=int,
-        default=200,
-    )
-    sub.add_argument(
-        "--return_metadata",
-        help="A special boolean flag for showing metadata. If set `true`, each document entry in the returned json will contain a 'metadata' field. Default to be `false`. This flag only takes effect when `file` is set.",
-        type=bool,
-        default=False,
-    )
-    sub.add_argument(
-        "--version",
-        help="The version of the search routing to use",
-    )
-
-    sub.add_argument("-q", "--query", required=True, help="Search query")
-    sub.set_defaults(func=Engine.search)
-
-    # Completions
-    sub = subparsers.add_parser("completions.create")
-    sub.add_argument(
-        "-e",
-        "--engine",
-        help="The engine to use. See https://beta.openai.com/docs/engines for more about what engines are available.",
-    )
-    sub.add_argument(
-        "-m",
-        "--model",
-        help="The model to use. At most one of `engine` or `model` should be specified.",
-    )
-    sub.add_argument(
-        "--stream", help="Stream tokens as they're ready.", action="store_true"
-    )
-    sub.add_argument("-p", "--prompt", help="An optional prompt to complete from")
-    sub.add_argument(
-        "-M", "--max-tokens", help="The maximum number of tokens to generate", type=int
-    )
-    sub.add_argument(
-        "-t",
-        "--temperature",
-        help="""What sampling temperature to use. Higher values means the model will take more risks. Try 0.9 for more creative applications, and 0 (argmax sampling) for ones with a well-defined answer.
-
-Mutually exclusive with `top_p`.""",
-        type=float,
-    )
-    sub.add_argument(
-        "-P",
-        "--top_p",
-        help="""An alternative to sampling with temperature, called nucleus sampling, where the considers the results of the tokens with top_p probability mass. So 0.1 means only the tokens comprising the top 10%% probability mass are considered.
-
-            Mutually exclusive with `temperature`.""",
-        type=float,
-    )
-    sub.add_argument(
-        "-n",
-        "--n",
-        help="How many sub-completions to generate for each prompt.",
-        type=int,
-    )
-    sub.add_argument(
-        "--logprobs",
-        help="Include the log probabilites on the `logprobs` most likely tokens, as well the chosen tokens. So for example, if `logprobs` is 10, the API will return a list of the 10 most likely tokens. If `logprobs` is 0, only the chosen tokens will have logprobs returned.",
-        type=int,
-    )
-    sub.add_argument(
-        "--stop", help="A stop sequence at which to stop generating tokens."
-    )
-    sub.set_defaults(func=Completion.create)
-
-    # Models
-    sub = subparsers.add_parser("models.list")
-    sub.set_defaults(func=Model.list)
-
-    sub = subparsers.add_parser("models.get")
-    sub.add_argument("-i", "--id", required=True, help="The model ID")
-    sub.set_defaults(func=Model.get)
-
-    sub = subparsers.add_parser("models.delete")
-    sub.add_argument("-i", "--id", required=True, help="The model ID")
-    sub.set_defaults(func=Model.delete)
-
-    # Files
-    sub = subparsers.add_parser("files.create")
-
-    sub.add_argument(
-        "-f",
-        "--file",
-        required=True,
-        help="File to upload",
-    )
-    sub.add_argument(
-        "-p",
-        "--purpose",
-        help="Why are you uploading this file? (see https://beta.openai.com/docs/api-reference/ for purposes)",
-        required=True,
-    )
-    sub.add_argument(
-        "-m",
-        "--model",
-        help="Model for search indexing (e.g. 'ada'). Only meaningful if --purpose is 'search'.",
-    )
-    sub.set_defaults(func=File.create)
-
-    sub = subparsers.add_parser("files.get")
-    sub.add_argument("-i", "--id", required=True, help="The files ID")
-    sub.set_defaults(func=File.get)
-
-    sub = subparsers.add_parser("files.delete")
-    sub.add_argument("-i", "--id", required=True, help="The files ID")
-    sub.set_defaults(func=File.delete)
-
-    sub = subparsers.add_parser("files.list")
-    sub.set_defaults(func=File.list)
-
-    # Search
-    sub = subparsers.add_parser("search.create")
-
-    sub.add_argument(
-        "-d",
-        "--documents",
-        help="Documents to search over",
-        type=str,
-        nargs="+",
-    )
-    sub.add_argument(
-        "-q",
-        "--query",
-        required=True,
-        help="Search query",
-    )
-    sub.add_argument(
-        "-m",
-        "--model",
-        help="The model to search with",
-    )
-    sub.set_defaults(func=Search.create)
-
-    # Finetune
-    sub = subparsers.add_parser("fine_tunes.list")
-    sub.set_defaults(func=FineTune.list)
-
-    sub = subparsers.add_parser("fine_tunes.create")
-    sub.add_argument(
-        "-t",
-        "--training_file",
-        required=True,
-        help="JSONL file containing prompt-completion examples for training. This can "
-        "be the ID of a file uploaded through the OpenAI API (e.g. file-abcde12345), "
-        'a local file path, or a URL that starts with "http".',
-    )
-    sub.add_argument(
-        "-v",
-        "--validation_file",
-        help="JSONL file containing prompt-completion examples for validation. This can "
-        "be the ID of a file uploaded through the OpenAI API (e.g. file-abcde12345), "
-        'a local file path, or a URL that starts with "http".',
-    )
-    sub.add_argument(
-        "--no_check_if_files_exist",
-        dest="check_if_files_exist",
-        action="store_false",
-        help="If this argument is set and training_file or validation_file are file paths, immediately upload them. If this argument is not set, check if they may be duplicates of already uploaded files before uploading, based on file name and file size.",
-    )
-    sub.add_argument(
-        "-m",
-        "--model",
-        help="The model to start fine-tuning from",
-    )
-    sub.add_argument(
-        "--suffix",
-        help="If set, this argument can be used to customize the generated fine-tuned model name."
-        "All punctuation and whitespace in `suffix` will be replaced with a "
-        "single dash, and the string will be lower cased. The max "
-        "length of `suffix` is 40 chars. "
-        "The generated name will match the form `{base_model}:ft-{org-title}:{suffix}-{timestamp}`. "
-        'For example, `openai api fine_tunes.create -t test.jsonl -m ada --suffix "custom model name" '
-        "could generate a model with the name "
-        "ada:ft-your-org:custom-model-name-2022-02-15-04-21-04",
-    )
-    sub.add_argument(
-        "--no_follow",
-        action="store_true",
-        help="If set, returns immediately after creating the job. Otherwise, streams events and waits for the job to complete.",
-    )
-    sub.add_argument(
-        "--n_epochs",
-        type=int,
-        help="The number of epochs to train the model for. An epoch refers to one "
-        "full cycle through the training dataset.",
-    )
-    sub.add_argument(
-        "--batch_size",
-        type=int,
-        help="The batch size to use for training. The batch size is the number of "
-        "training examples used to train a single forward and backward pass.",
-    )
-    sub.add_argument(
-        "--learning_rate_multiplier",
-        type=float,
-        help="The learning rate multiplier to use for training. The fine-tuning "
-        "learning rate is determined by the original learning rate used for "
-        "pretraining multiplied by this value.",
-    )
-    sub.add_argument(
-        "--prompt_loss_weight",
-        type=float,
-        help="The weight to use for the prompt loss. The optimum value here depends "
-        "depends on your use case. This determines how much the model prioritizes "
-        "learning from prompt tokens vs learning from completion tokens.",
-    )
-    sub.add_argument(
-        "--compute_classification_metrics",
-        action="store_true",
-        help="If set, we calculate classification-specific metrics such as accuracy "
-        "and F-1 score using the validation set at the end of every epoch.",
-    )
-    sub.set_defaults(compute_classification_metrics=None)
-    sub.add_argument(
-        "--classification_n_classes",
-        type=int,
-        help="The number of classes in a classification task. This parameter is "
-        "required for multiclass classification.",
-    )
-    sub.add_argument(
-        "--classification_positive_class",
-        help="The positive class in binary classification. This parameter is needed "
-        "to generate precision, recall and F-1 metrics when doing binary "
-        "classification.",
-    )
-    sub.add_argument(
-        "--classification_betas",
-        type=float,
-        nargs="+",
-        help="If this is provided, we calculate F-beta scores at the specified beta "
-        "values. The F-beta score is a generalization of F-1 score. This is only "
-        "used for binary classification.",
-    )
-    sub.set_defaults(func=FineTune.create)
-
-    sub = subparsers.add_parser("fine_tunes.get")
-    sub.add_argument("-i", "--id", required=True, help="The id of the fine-tune job")
-    sub.set_defaults(func=FineTune.get)
-
-    sub = subparsers.add_parser("fine_tunes.results")
-    sub.add_argument("-i", "--id", required=True, help="The id of the fine-tune job")
-    sub.set_defaults(func=FineTune.results)
-
-    sub = subparsers.add_parser("fine_tunes.events")
-    sub.add_argument("-i", "--id", required=True, help="The id of the fine-tune job")
-
-    # TODO(rachel): Remove this in 1.0
-    sub.add_argument(
-        "-s",
-        "--stream",
-        action="store_true",
-        help="[DEPRECATED] If set, events will be streamed until the job is done. Otherwise, "
-        "displays the event history to date.",
-    )
-    sub.set_defaults(func=FineTune.events)
-
-    sub = subparsers.add_parser("fine_tunes.follow")
-    sub.add_argument("-i", "--id", required=True, help="The id of the fine-tune job")
-    sub.set_defaults(func=FineTune.follow)
-
-    sub = subparsers.add_parser("fine_tunes.cancel")
-    sub.add_argument("-i", "--id", required=True, help="The id of the fine-tune job")
-    sub.set_defaults(func=FineTune.cancel)
-
-
-def wandb_register(parser):
-    subparsers = parser.add_subparsers(
-        title="wandb", help="Logging with Weights & Biases"
-    )
-
-    def help(args):
-        parser.print_help()
-
-    parser.set_defaults(func=help)
-
-    sub = subparsers.add_parser("sync")
-    sub.add_argument("-i", "--id", help="The id of the fine-tune job (optional)")
-    sub.add_argument(
-        "-n",
-        "--n_fine_tunes",
-        type=int,
-        default=None,
-        help="Number of most recent fine-tunes to log when an id is not provided. By default, every fine-tune is synced.",
-    )
-    sub.add_argument(
-        "--project",
-        default="GPT-3",
-        help="""Name of the project where you're sending runs. By default, it is "GPT-3".""",
-    )
-    sub.add_argument(
-        "--entity",
-        help="Username or team name where you're sending runs. By default, your default entity is used, which is usually your username.",
-    )
-    sub.add_argument(
-        "--force",
-        action="store_true",
-        help="Forces logging and overwrite existing wandb run of the same fine-tune.",
-    )
-    sub.set_defaults(force=False)
-    sub.set_defaults(func=WandbLogger.sync)

spaces/CVPR/LIVE/pybind11/tests/test_methods_and_attributes.cpp

@@ -1,372 +0,0 @@
-/*
-    tests/test_methods_and_attributes.cpp -- constructors, deconstructors, attribute access,
-    __str__, argument and return value conventions
-
-    Copyright (c) 2016 Wenzel Jakob <[email protected]>
-
-    All rights reserved. Use of this source code is governed by a
-    BSD-style license that can be found in the LICENSE file.
-*/
-
-#include "pybind11_tests.h"
-#include "constructor_stats.h"
-
-#if !defined(PYBIND11_OVERLOAD_CAST)
-template <typename... Args>
-using overload_cast_ = pybind11::detail::overload_cast_impl<Args...>;
-#endif
-
-class ExampleMandA {
-public:
-    ExampleMandA() { print_default_created(this); }
-    ExampleMandA(int value) : value(value) { print_created(this, value); }
-    ExampleMandA(const ExampleMandA &e) : value(e.value) { print_copy_created(this); }
-    ExampleMandA(std::string&&) {}
-    ExampleMandA(ExampleMandA &&e) : value(e.value) { print_move_created(this); }
-    ~ExampleMandA() { print_destroyed(this); }
-
-    std::string toString() {
-        return "ExampleMandA[value=" + std::to_string(value) + "]";
-    }
-
-    void operator=(const ExampleMandA &e) { print_copy_assigned(this); value = e.value; }
-    void operator=(ExampleMandA &&e) { print_move_assigned(this); value = e.value; }
-
-    void add1(ExampleMandA other) { value += other.value; }         // passing by value
-    void add2(ExampleMandA &other) { value += other.value; }        // passing by reference
-    void add3(const ExampleMandA &other) { value += other.value; }  // passing by const reference
-    void add4(ExampleMandA *other) { value += other->value; }       // passing by pointer
-    void add5(const ExampleMandA *other) { value += other->value; } // passing by const pointer
-
-    void add6(int other) { value += other; }                        // passing by value
-    void add7(int &other) { value += other; }                       // passing by reference
-    void add8(const int &other) { value += other; }                 // passing by const reference
-    void add9(int *other) { value += *other; }                      // passing by pointer
-    void add10(const int *other) { value += *other; }               // passing by const pointer
-
-    void consume_str(std::string&&) {}
-
-    ExampleMandA self1() { return *this; }                          // return by value
-    ExampleMandA &self2() { return *this; }                         // return by reference
-    const ExampleMandA &self3() { return *this; }                   // return by const reference
-    ExampleMandA *self4() { return this; }                          // return by pointer
-    const ExampleMandA *self5() { return this; }                    // return by const pointer
-
-    int internal1() { return value; }                               // return by value
-    int &internal2() { return value; }                              // return by reference
-    const int &internal3() { return value; }                        // return by const reference
-    int *internal4() { return &value; }                             // return by pointer
-    const int *internal5() { return &value; }                       // return by const pointer
-
-    py::str overloaded()             { return "()"; }
-    py::str overloaded(int)          { return "(int)"; }
-    py::str overloaded(int, float)   { return "(int, float)"; }
-    py::str overloaded(float, int)   { return "(float, int)"; }
-    py::str overloaded(int, int)     { return "(int, int)"; }
-    py::str overloaded(float, float) { return "(float, float)"; }
-    py::str overloaded(int)          const { return "(int) const"; }
-    py::str overloaded(int, float)   const { return "(int, float) const"; }
-    py::str overloaded(float, int)   const { return "(float, int) const"; }
-    py::str overloaded(int, int)     const { return "(int, int) const"; }
-    py::str overloaded(float, float) const { return "(float, float) const"; }
-
-    static py::str overloaded(float) { return "static float"; }
-
-    int value = 0;
-};
-
-struct TestProperties {
-    int value = 1;
-    static int static_value;
-
-    int get() const { return value; }
-    void set(int v) { value = v; }
-
-    static int static_get() { return static_value; }
-    static void static_set(int v) { static_value = v; }
-};
-int TestProperties::static_value = 1;
-
-struct TestPropertiesOverride : TestProperties {
-    int value = 99;
-    static int static_value;
-};
-int TestPropertiesOverride::static_value = 99;
-
-struct TestPropRVP {
-    UserType v1{1};
-    UserType v2{1};
-    static UserType sv1;
-    static UserType sv2;
-
-    const UserType &get1() const { return v1; }
-    const UserType &get2() const { return v2; }
-    UserType get_rvalue() const { return v2; }
-    void set1(int v) { v1.set(v); }
-    void set2(int v) { v2.set(v); }
-};
-UserType TestPropRVP::sv1(1);
-UserType TestPropRVP::sv2(1);
-
-// Test None-allowed py::arg argument policy
-class NoneTester { public: int answer = 42; };
-int none1(const NoneTester &obj) { return obj.answer; }
-int none2(NoneTester *obj) { return obj ? obj->answer : -1; }
-int none3(std::shared_ptr<NoneTester> &obj) { return obj ? obj->answer : -1; }
-int none4(std::shared_ptr<NoneTester> *obj) { return obj && *obj ? (*obj)->answer : -1; }
-int none5(std::shared_ptr<NoneTester> obj) { return obj ? obj->answer : -1; }
-
-struct StrIssue {
-    int val = -1;
-
-    StrIssue() = default;
-    StrIssue(int i) : val{i} {}
-};
-
-// Issues #854, #910: incompatible function args when member function/pointer is in unregistered base class
-class UnregisteredBase {
-public:
-    void do_nothing() const {}
-    void increase_value() { rw_value++; ro_value += 0.25; }
-    void set_int(int v) { rw_value = v; }
-    int get_int() const { return rw_value; }
-    double get_double() const { return ro_value; }
-    int rw_value = 42;
-    double ro_value = 1.25;
-};
-class RegisteredDerived : public UnregisteredBase {
-public:
-    using UnregisteredBase::UnregisteredBase;
-    double sum() const { return rw_value + ro_value; }
-};
-
-// Test explicit lvalue ref-qualification
-struct RefQualified {
-    int value = 0;
-
-    void refQualified(int other) & { value += other; }
-    int constRefQualified(int other) const & { return value + other; }
-};
-
-TEST_SUBMODULE(methods_and_attributes, m) {
-    // test_methods_and_attributes
-    py::class_<ExampleMandA> emna(m, "ExampleMandA");
-    emna.def(py::init<>())
-        .def(py::init<int>())
-        .def(py::init<std::string&&>())
-        .def(py::init<const ExampleMandA&>())
-        .def("add1", &ExampleMandA::add1)
-        .def("add2", &ExampleMandA::add2)
-        .def("add3", &ExampleMandA::add3)
-        .def("add4", &ExampleMandA::add4)
-        .def("add5", &ExampleMandA::add5)
-        .def("add6", &ExampleMandA::add6)
-        .def("add7", &ExampleMandA::add7)
-        .def("add8", &ExampleMandA::add8)
-        .def("add9", &ExampleMandA::add9)
-        .def("add10", &ExampleMandA::add10)
-        .def("consume_str", &ExampleMandA::consume_str)
-        .def("self1", &ExampleMandA::self1)
-        .def("self2", &ExampleMandA::self2)
-        .def("self3", &ExampleMandA::self3)
-        .def("self4", &ExampleMandA::self4)
-        .def("self5", &ExampleMandA::self5)
-        .def("internal1", &ExampleMandA::internal1)
-        .def("internal2", &ExampleMandA::internal2)
-        .def("internal3", &ExampleMandA::internal3)
-        .def("internal4", &ExampleMandA::internal4)
-        .def("internal5", &ExampleMandA::internal5)
-#if defined(PYBIND11_OVERLOAD_CAST)
-        .def("overloaded", py::overload_cast<>(&ExampleMandA::overloaded))
-        .def("overloaded", py::overload_cast<int>(&ExampleMandA::overloaded))
-        .def("overloaded", py::overload_cast<int,   float>(&ExampleMandA::overloaded))
-        .def("overloaded", py::overload_cast<float,   int>(&ExampleMandA::overloaded))
-        .def("overloaded", py::overload_cast<int,     int>(&ExampleMandA::overloaded))
-        .def("overloaded", py::overload_cast<float, float>(&ExampleMandA::overloaded))
-        .def("overloaded_float", py::overload_cast<float, float>(&ExampleMandA::overloaded))
-        .def("overloaded_const", py::overload_cast<int         >(&ExampleMandA::overloaded, py::const_))
-        .def("overloaded_const", py::overload_cast<int,   float>(&ExampleMandA::overloaded, py::const_))
-        .def("overloaded_const", py::overload_cast<float,   int>(&ExampleMandA::overloaded, py::const_))
-        .def("overloaded_const", py::overload_cast<int,     int>(&ExampleMandA::overloaded, py::const_))
-        .def("overloaded_const", py::overload_cast<float, float>(&ExampleMandA::overloaded, py::const_))
-#else
-        // Use both the traditional static_cast method and the C++11 compatible overload_cast_
-        .def("overloaded", overload_cast_<>()(&ExampleMandA::overloaded))
-        .def("overloaded", overload_cast_<int>()(&ExampleMandA::overloaded))
-        .def("overloaded", overload_cast_<int,   float>()(&ExampleMandA::overloaded))
-        .def("overloaded", static_cast<py::str (ExampleMandA::*)(float,   int)>(&ExampleMandA::overloaded))
-        .def("overloaded", static_cast<py::str (ExampleMandA::*)(int,     int)>(&ExampleMandA::overloaded))
-        .def("overloaded", static_cast<py::str (ExampleMandA::*)(float, float)>(&ExampleMandA::overloaded))
-        .def("overloaded_float", overload_cast_<float, float>()(&ExampleMandA::overloaded))
-        .def("overloaded_const", overload_cast_<int         >()(&ExampleMandA::overloaded, py::const_))
-        .def("overloaded_const", overload_cast_<int,   float>()(&ExampleMandA::overloaded, py::const_))
-        .def("overloaded_const", static_cast<py::str (ExampleMandA::*)(float,   int) const>(&ExampleMandA::overloaded))
-        .def("overloaded_const", static_cast<py::str (ExampleMandA::*)(int,     int) const>(&ExampleMandA::overloaded))
-        .def("overloaded_const", static_cast<py::str (ExampleMandA::*)(float, float) const>(&ExampleMandA::overloaded))
-#endif
-        // test_no_mixed_overloads
-        // Raise error if trying to mix static/non-static overloads on the same name:
-        .def_static("add_mixed_overloads1", []() {
-            auto emna = py::reinterpret_borrow<py::class_<ExampleMandA>>(py::module::import("pybind11_tests.methods_and_attributes").attr("ExampleMandA"));
-            emna.def       ("overload_mixed1", static_cast<py::str (ExampleMandA::*)(int, int)>(&ExampleMandA::overloaded))
-                .def_static("overload_mixed1", static_cast<py::str (              *)(float   )>(&ExampleMandA::overloaded));
-        })
-        .def_static("add_mixed_overloads2", []() {
-            auto emna = py::reinterpret_borrow<py::class_<ExampleMandA>>(py::module::import("pybind11_tests.methods_and_attributes").attr("ExampleMandA"));
-            emna.def_static("overload_mixed2", static_cast<py::str (              *)(float   )>(&ExampleMandA::overloaded))
-                .def       ("overload_mixed2", static_cast<py::str (ExampleMandA::*)(int, int)>(&ExampleMandA::overloaded));
-        })
-        .def("__str__", &ExampleMandA::toString)
-        .def_readwrite("value", &ExampleMandA::value);
-
-    // test_copy_method
-    // Issue #443: can't call copied methods in Python 3
-    emna.attr("add2b") = emna.attr("add2");
-
-    // test_properties, test_static_properties, test_static_cls
-    py::class_<TestProperties>(m, "TestProperties")
-        .def(py::init<>())
-        .def_readonly("def_readonly", &TestProperties::value)
-        .def_readwrite("def_readwrite", &TestProperties::value)
-        .def_property("def_writeonly", nullptr,
-                      [](TestProperties& s,int v) { s.value = v; } )
-        .def_property("def_property_writeonly", nullptr, &TestProperties::set)
-        .def_property_readonly("def_property_readonly", &TestProperties::get)
-        .def_property("def_property", &TestProperties::get, &TestProperties::set)
-        .def_property("def_property_impossible", nullptr, nullptr)
-        .def_readonly_static("def_readonly_static", &TestProperties::static_value)
-        .def_readwrite_static("def_readwrite_static", &TestProperties::static_value)
-        .def_property_static("def_writeonly_static", nullptr,
-                             [](py::object, int v) { TestProperties::static_value = v; })
-        .def_property_readonly_static("def_property_readonly_static",
-                                      [](py::object) { return TestProperties::static_get(); })
-        .def_property_static("def_property_writeonly_static", nullptr,
-                             [](py::object, int v) { return TestProperties::static_set(v); })
-        .def_property_static("def_property_static",
-                             [](py::object) { return TestProperties::static_get(); },
-                             [](py::object, int v) { TestProperties::static_set(v); })
-        .def_property_static("static_cls",
-                             [](py::object cls) { return cls; },
-                             [](py::object cls, py::function f) { f(cls); });
-
-    py::class_<TestPropertiesOverride, TestProperties>(m, "TestPropertiesOverride")
-        .def(py::init<>())
-        .def_readonly("def_readonly", &TestPropertiesOverride::value)
-        .def_readonly_static("def_readonly_static", &TestPropertiesOverride::static_value);
-
-    auto static_get1 = [](py::object) -> const UserType & { return TestPropRVP::sv1; };
-    auto static_get2 = [](py::object) -> const UserType & { return TestPropRVP::sv2; };
-    auto static_set1 = [](py::object, int v) { TestPropRVP::sv1.set(v); };
-    auto static_set2 = [](py::object, int v) { TestPropRVP::sv2.set(v); };
-    auto rvp_copy = py::return_value_policy::copy;
-
-    // test_property_return_value_policies
-    py::class_<TestPropRVP>(m, "TestPropRVP")
-        .def(py::init<>())
-        .def_property_readonly("ro_ref", &TestPropRVP::get1)
-        .def_property_readonly("ro_copy", &TestPropRVP::get2, rvp_copy)
-        .def_property_readonly("ro_func", py::cpp_function(&TestPropRVP::get2, rvp_copy))
-        .def_property("rw_ref", &TestPropRVP::get1, &TestPropRVP::set1)
-        .def_property("rw_copy", &TestPropRVP::get2, &TestPropRVP::set2, rvp_copy)
-        .def_property("rw_func", py::cpp_function(&TestPropRVP::get2, rvp_copy), &TestPropRVP::set2)
-        .def_property_readonly_static("static_ro_ref", static_get1)
-        .def_property_readonly_static("static_ro_copy", static_get2, rvp_copy)
-        .def_property_readonly_static("static_ro_func", py::cpp_function(static_get2, rvp_copy))
-        .def_property_static("static_rw_ref", static_get1, static_set1)
-        .def_property_static("static_rw_copy", static_get2, static_set2, rvp_copy)
-        .def_property_static("static_rw_func", py::cpp_function(static_get2, rvp_copy), static_set2)
-        // test_property_rvalue_policy
-        .def_property_readonly("rvalue", &TestPropRVP::get_rvalue)
-        .def_property_readonly_static("static_rvalue", [](py::object) { return UserType(1); });
-
-    // test_metaclass_override
-    struct MetaclassOverride { };
-    py::class_<MetaclassOverride>(m, "MetaclassOverride", py::metaclass((PyObject *) &PyType_Type))
-        .def_property_readonly_static("readonly", [](py::object) { return 1; });
-
-#if !defined(PYPY_VERSION)
-    // test_dynamic_attributes
-    class DynamicClass {
-    public:
-        DynamicClass() { print_default_created(this); }
-        DynamicClass(const DynamicClass&) = delete;
-        ~DynamicClass() { print_destroyed(this); }
-    };
-    py::class_<DynamicClass>(m, "DynamicClass", py::dynamic_attr())
-        .def(py::init());
-
-    class CppDerivedDynamicClass : public DynamicClass { };
-    py::class_<CppDerivedDynamicClass, DynamicClass>(m, "CppDerivedDynamicClass")
-        .def(py::init());
-#endif
-
-    // test_bad_arg_default
-    // Issue/PR #648: bad arg default debugging output
-#if !defined(NDEBUG)
-    m.attr("debug_enabled") = true;
-#else
-    m.attr("debug_enabled") = false;
-#endif
-    m.def("bad_arg_def_named", []{
-        auto m = py::module::import("pybind11_tests");
-        m.def("should_fail", [](int, UnregisteredType) {}, py::arg(), py::arg("a") = UnregisteredType());
-    });
-    m.def("bad_arg_def_unnamed", []{
-        auto m = py::module::import("pybind11_tests");
-        m.def("should_fail", [](int, UnregisteredType) {}, py::arg(), py::arg() = UnregisteredType());
-    });
-
-    // test_accepts_none
-    py::class_<NoneTester, std::shared_ptr<NoneTester>>(m, "NoneTester")
-        .def(py::init<>());
-    m.def("no_none1", &none1, py::arg().none(false));
-    m.def("no_none2", &none2, py::arg().none(false));
-    m.def("no_none3", &none3, py::arg().none(false));
-    m.def("no_none4", &none4, py::arg().none(false));
-    m.def("no_none5", &none5, py::arg().none(false));
-    m.def("ok_none1", &none1);
-    m.def("ok_none2", &none2, py::arg().none(true));
-    m.def("ok_none3", &none3);
-    m.def("ok_none4", &none4, py::arg().none(true));
-    m.def("ok_none5", &none5);
-
-    // test_str_issue
-    // Issue #283: __str__ called on uninitialized instance when constructor arguments invalid
-    py::class_<StrIssue>(m, "StrIssue")
-        .def(py::init<int>())
-        .def(py::init<>())
-        .def("__str__", [](const StrIssue &si) {
-            return "StrIssue[" + std::to_string(si.val) + "]"; }
-        );
-
-    // test_unregistered_base_implementations
-    //
-    // Issues #854/910: incompatible function args when member function/pointer is in unregistered
-    // base class The methods and member pointers below actually resolve to members/pointers in
-    // UnregisteredBase; before this test/fix they would be registered via lambda with a first
-    // argument of an unregistered type, and thus uncallable.
-    py::class_<RegisteredDerived>(m, "RegisteredDerived")
-        .def(py::init<>())
-        .def("do_nothing", &RegisteredDerived::do_nothing)
-        .def("increase_value", &RegisteredDerived::increase_value)
-        .def_readwrite("rw_value", &RegisteredDerived::rw_value)
-        .def_readonly("ro_value", &RegisteredDerived::ro_value)
-        // These should trigger a static_assert if uncommented
-        //.def_readwrite("fails", &UserType::value) // should trigger a static_assert if uncommented
-        //.def_readonly("fails", &UserType::value) // should trigger a static_assert if uncommented
-        .def_property("rw_value_prop", &RegisteredDerived::get_int, &RegisteredDerived::set_int)
-        .def_property_readonly("ro_value_prop", &RegisteredDerived::get_double)
-        // This one is in the registered class:
-        .def("sum", &RegisteredDerived::sum)
-        ;
-
-    using Adapted = decltype(py::method_adaptor<RegisteredDerived>(&RegisteredDerived::do_nothing));
-    static_assert(std::is_same<Adapted, void (RegisteredDerived::*)() const>::value, "");
-
-    // test_methods_and_attributes
-    py::class_<RefQualified>(m, "RefQualified")
-        .def(py::init<>())
-        .def_readonly("value", &RefQualified::value)
-        .def("refQualified", &RefQualified::refQualified)
-        .def("constRefQualified", &RefQualified::constRefQualified);
-}

spaces/CVPR/LIVE/thrust/thrust/detail/complex/math_private.h

@@ -1,136 +0,0 @@
-/*
- *  Copyright 2008-2013 NVIDIA Corporation
- *  Copyright 2013 Filipe RNC Maia
- *
- *  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.
- */
-
-/*
- * ====================================================
- * Copyright (C) 1993 by Sun Microsystems, Inc. All rights reserved.
- *
- * Developed at SunPro, a Sun Microsystems, Inc. business.
- * Permission to use, copy, modify, and distribute this
- * software is freely granted, provided that this notice
- * is preserved.
- * ====================================================
- */
-
-/* adapted from FreeBSD:
- *    lib/msun/src/math_private.h
- */
-#pragma once
-
-#include <thrust/detail/config.h>
-#include <thrust/complex.h>
-#include <thrust/detail/cstdint.h>
-
-namespace thrust{
-namespace detail{
-namespace complex{
-
-using thrust::complex;
-
-typedef union
-{
-  float value;
-  uint32_t word;
-} ieee_float_shape_type;
-  
-__host__ __device__
-inline void get_float_word(uint32_t & i, float d){
-  ieee_float_shape_type gf_u;
-  gf_u.value = (d);
-  (i) = gf_u.word;
-}
-
-__host__ __device__
-inline void get_float_word(int32_t & i, float d){
-  ieee_float_shape_type gf_u;
-  gf_u.value = (d);
-  (i) = gf_u.word;
-}
-
-__host__ __device__
-inline void set_float_word(float & d, uint32_t i){
-  ieee_float_shape_type sf_u;
-  sf_u.word = (i);
-  (d) = sf_u.value;
-}
-
-// Assumes little endian ordering
-typedef union
-{
-  double value;
-  struct
-  {
-    uint32_t lsw;
-    uint32_t msw;
-  } parts;
-  struct
-  {
-    uint64_t w;
-  } xparts;
-} ieee_double_shape_type;
-  
-__host__ __device__ inline
-void get_high_word(uint32_t & i,double d){
-  ieee_double_shape_type gh_u;
-  gh_u.value = (d);
-  (i) = gh_u.parts.msw;                                   
-}
-  
-/* Set the more significant 32 bits of a double from an int.  */
-__host__ __device__ inline
-void set_high_word(double & d, uint32_t v){
-  ieee_double_shape_type sh_u;
-  sh_u.value = (d);
-  sh_u.parts.msw = (v);
-  (d) = sh_u.value;
-}
-  
-  
-__host__ __device__ inline 
-void  insert_words(double & d, uint32_t ix0, uint32_t ix1){
-  ieee_double_shape_type iw_u;
-  iw_u.parts.msw = (ix0);
-  iw_u.parts.lsw = (ix1);
-  (d) = iw_u.value;
-}
-  
-/* Get two 32 bit ints from a double.  */
-__host__ __device__ inline
-void  extract_words(uint32_t & ix0,uint32_t & ix1, double d){
-  ieee_double_shape_type ew_u;
-  ew_u.value = (d);
-  (ix0) = ew_u.parts.msw;
-  (ix1) = ew_u.parts.lsw;
-}
-  
-/* Get two 32 bit ints from a double.  */
-__host__ __device__ inline
-void  extract_words(int32_t & ix0,int32_t & ix1, double d){
-  ieee_double_shape_type ew_u;
-  ew_u.value = (d);
-  (ix0) = ew_u.parts.msw;
-  (ix1) = ew_u.parts.lsw;
-}
-  
-} // namespace complex
-
-} // namespace detail
-
-} // namespace thrust
-
-
-#include <thrust/detail/complex/c99math.h>

spaces/CVPR/LIVE/thrust/thrust/detail/functional/operators/arithmetic_operators.h

@@ -1,432 +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/detail/functional/actor.h>
-#include <thrust/detail/functional/composite.h>
-#include <thrust/detail/functional/operators/operator_adaptors.h>
-#include <thrust/functional.h>
-
-namespace thrust
-{
-namespace detail
-{
-namespace functional
-{
-
-template<typename Eval>
-__host__ __device__
-actor<
-  composite<
-    transparent_unary_operator<thrust::negate<>>,
-    actor<Eval>
-  >
->
-__host__ __device__
-operator-(const actor<Eval> &_1)
-{
-  return compose(transparent_unary_operator<thrust::negate<>>(), _1);
-} // end operator-()
-
-// there's no standard unary_plus functional, so roll an ad hoc one here
-struct unary_plus
-{
-  using is_transparent = void;
-
-  __thrust_exec_check_disable__
-  template <typename T1>
-  __host__ __device__
-  constexpr auto operator()(T1&& t1) const
-  noexcept(noexcept(+THRUST_FWD(t1))) -> decltype(+THRUST_FWD(t1))
-  {
-    return +THRUST_FWD(t1);
-  }
-};
-
-template<typename Eval>
-__host__ __device__
-actor<
-  composite<
-    transparent_unary_operator<unary_plus>,
-    actor<Eval>
-  >
->
-operator+(const actor<Eval> &_1)
-{
-  return compose(transparent_unary_operator<unary_plus>(), _1);
-} // end operator+()
-
-template<typename T1, typename T2>
-__host__ __device__
-actor<
-  composite<
-    transparent_binary_operator<thrust::plus<>>,
-    actor<T1>,
-    typename as_actor<T2>::type
-  >
->
-operator+(const actor<T1> &_1, const T2 &_2)
-{
-  return compose(transparent_binary_operator<thrust::plus<>>(),
-                 make_actor(_1),
-                 make_actor(_2));
-} // end operator+()
-
-template<typename T1, typename T2>
-__host__ __device__
-actor<
-  composite<
-    transparent_binary_operator<thrust::plus<>>,
-    typename as_actor<T1>::type,
-    actor<T2>
-  >
->
-operator+(const T1 &_1, const actor<T2> &_2)
-{
-  return compose(transparent_binary_operator<thrust::plus<>>(),
-                 make_actor(_1),
-                 make_actor(_2));
-} // end operator+()
-
-template<typename T1, typename T2>
-__host__ __device__
-actor<
-  composite<
-    transparent_binary_operator<thrust::plus<>>,
-    actor<T1>,
-    actor<T2>
-  >
->
-operator+(const actor<T1> &_1, const actor<T2> &_2)
-{
-  return compose(transparent_binary_operator<thrust::plus<>>(),
-                 make_actor(_1),
-                 make_actor(_2));
-} // end operator+()
-
-template<typename T1, typename T2>
-__host__ __device__
-actor<
-  composite<
-    transparent_binary_operator<thrust::minus<>>,
-    typename as_actor<T1>::type,
-    actor<T2>
-  >
->
-operator-(const T1 &_1, const actor<T2> &_2)
-{
-  return compose(transparent_binary_operator<thrust::minus<>>(),
-                 make_actor(_1),
-                 make_actor(_2));
-} // end operator-()
-
-template<typename T1, typename T2>
-__host__ __device__
-actor<
-  composite<
-    transparent_binary_operator<thrust::minus<>>,
-    actor<T1>,
-    typename as_actor<T2>::type
-  >
->
-operator-(const actor<T1> &_1, const T2 &_2)
-{
-  return compose(transparent_binary_operator<thrust::minus<>>(),
-                 make_actor(_1),
-                 make_actor(_2));
-} // end operator-()
-
-template<typename T1, typename T2>
-__host__ __device__
-actor<
-  composite<
-    transparent_binary_operator<thrust::minus<>>,
-    actor<T1>,
-    actor<T2>
-  >
->
-operator-(const actor<T1> &_1, const actor<T2> &_2)
-{
-  return compose(transparent_binary_operator<thrust::minus<>>(),
-                 make_actor(_1),
-                 make_actor(_2));
-} // end operator-()
-
-template<typename T1, typename T2>
-__host__ __device__
-actor<
-  composite<
-    transparent_binary_operator<thrust::multiplies<>>,
-    typename as_actor<T1>::type,
-    actor<T2>
-  >
->
-operator*(const T1 &_1, const actor<T2> &_2)
-{
-  return compose(transparent_binary_operator<thrust::multiplies<>>(),
-                 make_actor(_1),
-                 make_actor(_2));
-} // end operator*()
-
-template<typename T1, typename T2>
-__host__ __device__
-actor<
-  composite<
-    transparent_binary_operator<thrust::multiplies<>>,
-    actor<T1>,
-    typename as_actor<T2>::type
-  >
->
-operator*(const actor<T1> &_1, const T2 &_2)
-{
-  return compose(transparent_binary_operator<thrust::multiplies<>>(),
-                 make_actor(_1),
-                 make_actor(_2));
-} // end operator*()
-
-template<typename T1, typename T2>
-__host__ __device__
-actor<
-  composite<
-    transparent_binary_operator<thrust::multiplies<>>,
-    actor<T1>,
-    actor<T2>
-  >
->
-operator*(const actor<T1> &_1, const actor<T2> &_2)
-{
-  return compose(transparent_binary_operator<thrust::multiplies<>>(),
-                 make_actor(_1),
-                 make_actor(_2));
-} // end operator*()
-
-template<typename T1, typename T2>
-__host__ __device__
-actor<
-  composite<
-    transparent_binary_operator<thrust::divides<>>,
-    actor<T1>,
-    typename as_actor<T2>::type
-  >
->
-operator/(const actor<T1> &_1, const T2 &_2)
-{
-  return compose(transparent_binary_operator<thrust::divides<>>(),
-                 make_actor(_1),
-                 make_actor(_2));
-} // end operator/()
-
-template<typename T1, typename T2>
-__host__ __device__
-actor<
-  composite<
-    transparent_binary_operator<thrust::divides<>>,
-    typename as_actor<T1>::type,
-    actor<T2>
-  >
->
-operator/(const T1 &_1, const actor<T2> &_2)
-{
-  return compose(transparent_binary_operator<thrust::divides<>>(),
-                 make_actor(_1),
-                 make_actor(_2));
-} // end operator/()
-
-template<typename T1, typename T2>
-__host__ __device__
-actor<
-  composite<
-    transparent_binary_operator<thrust::divides<>>,
-    actor<T1>,
-    actor<T2>
-  >
->
-operator/(const actor<T1> &_1, const actor<T2> &_2)
-{
-  return compose(transparent_binary_operator<thrust::divides<>>(),
-                 make_actor(_1),
-                 make_actor(_2));
-} // end operator/()
-
-template<typename T1, typename T2>
-__host__ __device__
-actor<
-  composite<
-    transparent_binary_operator<thrust::modulus<>>,
-    actor<T1>,
-    typename as_actor<T2>::type
-  >
->
-operator%(const actor<T1> &_1, const T2 &_2)
-{
-  return compose(transparent_binary_operator<thrust::modulus<>>(),
-                 make_actor(_1),
-                 make_actor(_2));
-} // end operator%()
-
-template<typename T1, typename T2>
-__host__ __device__
-actor<
-  composite<
-    transparent_binary_operator<thrust::modulus<>>,
-    typename as_actor<T1>::type,
-    actor<T2>
-  >
->
-operator%(const T1 &_1, const actor<T2> &_2)
-{
-  return compose(transparent_binary_operator<thrust::modulus<void>>(),
-                 make_actor(_1),
-                 make_actor(_2));
-} // end operator%()
-
-template<typename T1, typename T2>
-__host__ __device__
-actor<
-  composite<
-    transparent_binary_operator<thrust::modulus<>>,
-    actor<T1>,
-    actor<T2>
-  >
->
-operator%(const actor<T1> &_1, const actor<T2> &_2)
-{
-  return compose(transparent_binary_operator<thrust::modulus<>>(),
-                 make_actor(_1),
-                 make_actor(_2));
-} // end operator%()
-
-// there's no standard prefix_increment functional, so roll an ad hoc one here
-struct prefix_increment
-{
-  using is_transparent = void;
-
-  __thrust_exec_check_disable__
-  template <typename T1>
-  __host__ __device__
-  constexpr auto operator()(T1&& t1) const
-  noexcept(noexcept(++THRUST_FWD(t1))) -> decltype(++THRUST_FWD(t1))
-  {
-    return ++THRUST_FWD(t1);
-  }
-}; // end prefix_increment
-
-template<typename Eval>
-__host__ __device__
-actor<
-  composite<
-    transparent_unary_operator<prefix_increment>,
-    actor<Eval>
-  >
->
-operator++(const actor<Eval> &_1)
-{
-  return compose(transparent_unary_operator<prefix_increment>(), _1);
-} // end operator++()
-
-
-// there's no standard postfix_increment functional, so roll an ad hoc one here
-struct postfix_increment
-{
-  using is_transparent = void;
-
-  __thrust_exec_check_disable__
-  template <typename T1>
-  __host__ __device__
-  constexpr auto operator()(T1&& t1) const
-  noexcept(noexcept(THRUST_FWD(t1)++)) -> decltype(THRUST_FWD(t1)++)
-  {
-    return THRUST_FWD(t1)++;
-  }
-}; // end postfix_increment
-
-template<typename Eval>
-__host__ __device__
-actor<
-  composite<
-    transparent_unary_operator<postfix_increment>,
-    actor<Eval>
-  >
->
-operator++(const actor<Eval> &_1, int)
-{
-  return compose(transparent_unary_operator<postfix_increment>(), _1);
-} // end operator++()
-
-
-// there's no standard prefix_decrement functional, so roll an ad hoc one here
-struct prefix_decrement
-{
-  using is_transparent = void;
-
-  __thrust_exec_check_disable__
-  template <typename T1>
-  __host__ __device__
-  constexpr auto operator()(T1&& t1) const
-  noexcept(noexcept(--THRUST_FWD(t1))) -> decltype(--THRUST_FWD(t1))
-  {
-    return --THRUST_FWD(t1);
-  }
-}; // end prefix_decrement
-
-template<typename Eval>
-__host__ __device__
-actor<
-  composite<
-    transparent_unary_operator<prefix_decrement>,
-    actor<Eval>
-  >
->
-operator--(const actor<Eval> &_1)
-{
-  return compose(transparent_unary_operator<prefix_decrement>(), _1);
-} // end operator--()
-
-
-// there's no standard postfix_decrement functional, so roll an ad hoc one here
-struct postfix_decrement
-{
-  using is_transparent = void;
-
-  __thrust_exec_check_disable__
-  template <typename T1>
-  __host__ __device__
-  constexpr auto operator()(T1&& t1) const
-  noexcept(noexcept(THRUST_FWD(t1)--)) -> decltype(THRUST_FWD(t1)--)
-  {
-    return THRUST_FWD(t1)--;
-  }
-}; // end prefix_increment
-
-template<typename Eval>
-__host__ __device__
-actor<
-  composite<
-    transparent_unary_operator<postfix_decrement>,
-    actor<Eval>
-  >
->
-operator--(const actor<Eval> &_1, int)
-{
-  return compose(transparent_unary_operator<postfix_decrement>(), _1);
-} // end operator--()
-
-} // end functional
-} // end detail
-} // end thrust
-