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spaces/1acneusushi/gradio-2dmoleculeeditor/data/Clip Paint Studio Cost.md

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-<br />
-<h1>How Much Does Clip Paint Studio Cost and Is It Worth It?</h1>
-<p>Clip Paint Studio (also known as Clip Studio Paint or Manga Studio) is a powerful and versatile software for creating digital art, comics, and animation. It offers a wide range of features and tools to help you bring your creative vision to life. But how much does it cost and is it worth the investment? Here are some things to consider before you buy Clip Paint Studio.</p>
-<h2>Clip Paint Studio Pricing Plans</h2>
-<p>Clip Paint Studio has two main versions: Pro and EX. The Pro version is designed for basic illustration and comic creation, while the EX version has more advanced features for professional comic and animation production. You can compare the features of each version <a href="https://www.clipstudio.net/en/purchase/compare">here</a>.</p>
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-<p>The pricing plans for Clip Paint Studio vary depending on the device and the payment method. You can choose to buy a perpetual license or a monthly subscription. Here are the current prices as of May 2023:</p>
-<table>
-<tr>
-<th>Device</th>
-<th>Pro License</th>
-<th>EX License</th>
-<th>Pro Subscription</th>
-<th>EX Subscription</th>
-</tr>
-<tr>
-<td>Windows/Mac</td>
-<td>$49.99 (one-time)</td>
-<td>$219.00 (one-time)</td>
-<td>$4.49/month or $24.99/year</td>
-<td>$8.99/month or $71.99/year</td>
-</tr>
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-<td>iPad/iPhone</td>
-<td>N/A</td>
-<td>N/A</td>
-<td>$4.49/month or $24.99/year</td>
-<td>$8.99/month or $71.99/year</td>
-</tr>
-<tr>
-<td>Android/Galaxy</td>
-<td>N/A</td>
-<td>N/A</td>
-<td>$0.99/month or $9.99/year (first 6 months free)</td>
-<td>$2.49/month or $24.99/year (first 6 months free)</td>
-</tr>
-<tr>
-<td>Chromebook</td>
-<td>N/A</td>
-<td>N/A</td>
-<td>$0.99/month or $9.99/year (first 3 months free)</td>
-<td>$2.49/month or $24.99/year (first 3 months free)</td>
-</tr>
-</table>
-
-<p>Note: These prices are subject to change and may vary by region. You can check the latest prices on the <a href="https://www.clipstudio.net/en/purchase">official website</a>.</p>
-
-<h2>Clip Paint Studio Benefits and Drawbacks</h2>
-
-<p>Clip Paint Studio is a popular choice among artists and creators for many reasons. Here are some of the benefits and drawbacks of using Clip Paint Studio:</p>
-
-<h3>Benefits:</h3>
-
-<ul>
-
-<li>It has a user-friendly interface and customizable workspace.</li>
-
-<li>It supports various file formats and devices.</li>
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-<li>It has a large and active community of users and resources.</li>
-
-<li>It has a rich collection of brushes, pens, textures, materials, and assets.</li>
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-<li>It has powerful tools for drawing, coloring, editing, vectoring, and animating.</li>
-
-<li>It has smart features for comic and manga creation, such as panel layout, perspective rulers, word balloons, and 3D models.</li>
-
-<li>It has frequent updates and improvements based on user feedback.</li>
-
-<li>It offers a free trial and a money-back guarantee.</li>
-
-</ul>
-
-<h3>Drawbacks:</h3>
-
-<ul>
-
-<li>It can be overwhelming for beginners or casual users.</li>
-
-<li>It can be expensive for some users, especially the EX version.</li>
-
-<li>It can have compatibility issues with some devices or software.</li>
-
-<li>It can have bugs or glitches sometimes.</li>
-
-<li>It can have limited support or documentation for some languages or regions.</li>
-
-</ul>
-
-<h2>Is Clip Paint Studio Worth It?</h2>
-
-<p>The answer to this question depends on your needs, preferences, budget, and goals as an artist or creator. Clip Paint Studio is a great software for anyone who wants to create digital art, comics, or animation with high quality and efficiency. However, it may not be the best option for</p>
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spaces/1gistliPinn/ChatGPT4/Examples/CRACK ThunderSoft Folder Password Lock Pro 11.0.0 Multilingual Full Wi BEST.md

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spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Blockudoku A Relaxing and Stimulating Block Puzzle Game for Everyone - Indir and Experience.md

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-<h1>Block Puzzle Indir: How to Download and Play the Best Block Puzzle Games</h1>
- <p>Do you love playing puzzle games that challenge your brain and keep you entertained? If so, you might want to try block puzzle games, which are a popular genre of puzzle games that combine elements of Tetris, Sudoku, and jigsaw puzzles. Block puzzle games are simple yet addictive games that require you to fit different shapes of blocks on a grid, either horizontally or vertically, to clear lines or squares. They are fun, relaxing, and rewarding games that can improve your cognitive skills, mental health, and mood.</p>
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- <p>But how can you download and play block puzzle games? What are the best block puzzle games available? And what are some tips and tricks to master them? In this article, we will answer these questions and more. We will explain what block puzzle games are, how they originated and evolved, what benefits they offer, what features they have, how to download and play them, and how to improve your skills. By the end of this article, you will be ready to enjoy block puzzle games like a champ!</p>
- <h2>What are Block Puzzle Games?</h2>
- <p>Block puzzle games are a type of puzzle games that involve moving and rotating different shapes of blocks on a grid or board. The goal is to fill up the grid with blocks without leaving any gaps or spaces. Depending on the game mode, you may have to clear horizontal or vertical lines, 3x3 squares, or other patterns by placing blocks of the same color or type. You may also have to deal with obstacles, power-ups, timers, or other challenges. The game ends when there is no more space for new blocks or when you reach a certain score or level.</p>
- <h3>The Origin and Evolution of Block Puzzle Games</h3>
- <p>Block puzzle games have a long history that dates back to the 19th century. One of the earliest examples of block puzzle games is Tangram, a Chinese game that consists of seven flat shapes that can be arranged into various figures. Another example is Pentominoes, a game invented by American mathematician Solomon W. Golomb in 1953, which uses 12 shapes made of five squares each.</p>
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- <p>However, the most influential and famous block puzzle game is Tetris, which was created by Soviet engineer Alexey Pajitnov in 1984. Tetris is a game that involves falling blocks of different shapes that can be rotated and moved sideways to fit into a rectangular grid. The game became a worldwide phenomenon and inspired many variations and spin-offs.</p>
- <p>Since then, block puzzle games have evolved and diversified into many subgenres and formats. Some examples of modern block puzzle games are Blockudoku, Blockscapes, Woodoku, Block Blast Adventure Master, Blocks: Block Puzzle Games, and many more. These games offer different features, themes, modes, graphics, sounds, and challenges that appeal to different tastes and preferences.</p>
- <h3>The Benefits of Playing Block Puzzle Games</h3>
- <p>Playing block puzzle games is not only fun but also beneficial for your brain and well-being. Here are some of the benefits of playing block puzzle games:</p>
- <ul>
-<li>They improve your cognitive skills such as memory, attention, concentration, logic, problem-solving, spatial perception, visual analysis, and synthesis.</li>
-<li>They enhance your motor skills such as hand-eye coordination, fine motor control, reaction time, and dexterity.</li>
-<li>They reduce stress and anxiety by providing a relaxing and satisfying activity that distracts you from negative thoughts and emotions.</li>
-<li>They boost your mood and self-esteem by giving you a sense of achievement and reward when you complete a puzzle or beat a high score.</li>
-<li>They stimulate your creativity and imagination by allowing you to create different shapes and patterns with blocks.</li> <h3>The Features of Block Puzzle Games</h3>
- <p>Block puzzle games have various features that make them appealing and enjoyable for players of all ages and backgrounds. Some of the common features of block puzzle games are:</p>
- <ul>
-<li>They have simple and intuitive controls that are easy to learn and use. You can usually move and rotate the blocks with a swipe, a tap, or a drag on your screen.</li>
-<li>They have colorful and attractive graphics that create a pleasant and stimulating visual experience. You can choose from different themes and styles that suit your mood and preference.</li>
-<li>They have soothing and catchy sounds that enhance the gameplay and create a relaxing and immersive atmosphere. You can listen to different music and sound effects that match the theme and mood of the game.</li>
-<li>They have various modes and levels that offer different challenges and goals. You can play classic mode, arcade mode, time mode, endless mode, or other modes that test your skills and strategy. You can also progress through different levels of difficulty and complexity that keep you engaged and motivated.</li>
-<li>They have leaderboards and achievements that allow you to compete with yourself and others. You can track your progress and performance, compare your scores and rankings with other players, and unlock new achievements and rewards.</li>
-</ul>
- <h2>How to Download and Play Block Puzzle Games?</h2>
- <p>If you are interested in playing block puzzle games, you may wonder how to download and play them on your device. Here are some steps that you can follow to enjoy block puzzle games:</p>
- <h3>Choosing the Right Platform and Device</h3>
- <p>The first step is to choose the right platform and device for playing block puzzle games. Block puzzle games are available on various platforms such as web browsers, desktop computers, laptops, tablets, smartphones, consoles, or smart TVs. You can choose the platform that is most convenient and accessible for you.</p>
- <p>However, some platforms may have more options and features than others. For example, web browsers may have limited graphics and sounds, while smartphones may have smaller screens and batteries. Therefore, you should consider the pros and cons of each platform before choosing one.</p>
- <p>The most popular platform for playing block puzzle games is smartphones, as they are portable, versatile, and easy to use. You can download block puzzle games from various app stores such as Google Play Store, Apple App Store, Amazon Appstore, or Samsung Galaxy Store. You can also play block puzzle games online without downloading them by visiting websites such as Block Puzzle Online or Block Puzzle Games.</p>
- <h3>Finding the Best Block Puzzle Games</h3>
- <p>The next step is to find the best block puzzle games that suit your taste and preference. There are hundreds of block puzzle games available on different platforms, so you may feel overwhelmed by the choices. However, you can narrow down your options by using some criteria such as:</p>
- <ul>
-<li>The genre and theme of the game. You can choose from classic block puzzle games, wood block puzzle games, jewel block puzzle games, candy block puzzle games, or other themes that appeal to you.</li>
-<li>The features and modes of the game. You can choose from block puzzle games that have different features such as power-ups, obstacles, timers, hints, or other challenges. You can also choose from block puzzle games that have different modes such as classic mode, arcade mode, time mode, endless mode, or other modes that offer different goals.</li>
-<li>The ratings and reviews of the game. You can check the ratings and reviews of block puzzle games on app stores or websites to see what other players think about them. You can look for block puzzle games that have high ratings, positive reviews, or large numbers of downloads.</li>
-<li>The recommendations and suggestions of the game. You can ask for recommendations and suggestions from your friends, family, or other players who play block puzzle games. You can also look for recommendations and suggestions from online sources such as blogs, forums, social media, or YouTube videos.</li>
-</ul>
- <h3>Installing and Launching the Games</h3>
- <p>The third step is to install and launch the block puzzle games on your device. If you download block puzzle games from app stores or websites, you need to follow the instructions on how to install them on your device. You may need to grant some permissions or accept some terms and conditions before installing them.</p>
- <p>If you play block puzzle games online without downloading them, you need to visit the websites that host them on your web browser. You may need to enable some settings or plugins such as Flash Player or JavaScript before playing them.</p>
- <p>Once you install or access the block puzzle games on your device, you need to launch them by tapping or clicking on their icons or links. You may need to wait for some loading time before the game starts.</p>
- <h3>Learning the Rules and Controls</h3>
- <p>The fourth step is The fourth step is to learn the rules and controls of the block puzzle games. Each block puzzle game may have different rules and controls, so you need to read the instructions or tutorials before playing them. You can usually find the instructions or tutorials on the main menu, the settings, or the help section of the game. You can also look for online guides or videos that explain how to play the game. The basic rules and controls of block puzzle games are: - You need to move and rotate the blocks that appear on the top or the side of the screen to fit them into the grid or board. - You can use your finger, your mouse, or your keyboard to move and rotate the blocks. You can swipe, tap, drag, click, or press the arrow keys to control the blocks. - You need to fill up the grid with blocks without leaving any gaps or spaces. You can place the blocks horizontally or vertically, depending on the game mode. - You need to clear lines or squares by placing blocks of the same color or type. You can clear horizontal or vertical lines, 3x3 squares, or other patterns, depending on the game mode. - You need to avoid filling up the grid with blocks that cannot be cleared. If there is no more space for new blocks, the game is over. - You need to score points by clearing lines or squares. The more lines or squares you clear at once, the more points you get. You may also get bonus points for clearing special blocks, using power-ups, or completing achievements. - You need to reach a certain score or level to win the game or advance to the next stage. You may also have a time limit or a move limit to complete the game or stage. <h3>Applying Some Tips and Tricks</h3>
- <p>The fifth and final step is to apply some tips and tricks to improve your skills and enjoyment of block puzzle games. Here are some tips and tricks that you can use:</p>
- <ul>
-<li>Plan ahead and think strategically. Before placing a block, look at the grid and see where it would fit best. Try to create as many lines or squares as possible with each block. Avoid placing blocks randomly or impulsively.</li>
-<li>Use power-ups wisely. Some block puzzle games have power-ups that can help you clear more blocks, change the shape or color of blocks, remove obstacles, or extend time. Use them when you are stuck or when you want to boost your score.</li>
-<li>Practice regularly and challenge yourself. The more you play block puzzle games, the better you will get at them. Practice different modes and levels to improve your speed, accuracy, and strategy. Challenge yourself by playing harder levels, setting higher goals, or competing with other players.</li>
-<li>Have fun and relax. Block puzzle games are meant to be fun and relaxing, not stressful or frustrating. Don't worry too much about your score or performance. Enjoy the process of solving puzzles and creating shapes with blocks. Take breaks when you feel tired or bored.</li>
-</ul>
- <h2>Conclusion</h2>
- <p>Block puzzle games are a great way to spend your free time and exercise your brain. They are simple yet addictive games that require you to fit different shapes of blocks on a grid, either horizontally or vertically, to clear lines or squares. They have various benefits, features, modes, and challenges that make them appealing and enjoyable for players of all ages and backgrounds.</p>
- <h3>Summary of the Main Points</h3>
- <p>In this article, we have covered:</p>
- <ul>
-<li>What are block puzzle games and how they originated and evolved.</li>
-<li>What benefits they offer for your cognitive skills, motor skills, stress relief, mood enhancement, and creativity.</li>
-<li>What features they have such as graphics, sounds, modes, levels, leaderboards, and achievements.</li>
-<li>How to download and play them on different platforms and devices.</li>
-<li>How to learn the rules and controls of different block puzzle games.</li>
-<li>How to apply some tips and tricks to improve your skills and enjoyment of block puzzle games.</li>
-</ul>
- <h3>Call to Action</h3>
- <p>If you are interested in playing block puzzle games, don't hesitate to download them from app stores or websites today. You can also play them online without downloading them by visiting websites such as Block Puzzle Online or Block Puzzle Games. You will find a wide range of block puzzle games that suit your taste and preference.</p>
- <p>Block puzzle games are fun, relaxing, and rewarding games that can improve your brain and well-being. They are easy to learn and play but hard to master and put down. They are perfect for killing time, relieving stress, boosting mood, stimulating creativity, and challenging yourself.</p>
- <p>So what are you waiting for? Download block puzzle indir now and start playing!</p>
- <h3>FAQs</h3>
- <p <p>Here are some frequently asked questions (FAQs) about block puzzle games:</p>
- <table>
-<tr>
-<th>Question</th>
-<th>Answer</th>
-</tr>
-<tr>
-<td>What is the difference between block puzzle games and Tetris?</td>
-<td>Tetris is a specific block puzzle game that involves falling blocks of four squares each that can be rotated and moved sideways to fit into a rectangular grid. Block puzzle games are a broader genre of puzzle games that involve different shapes of blocks that can be moved and rotated to fit into various grids or boards.</td>
-</tr>
-<tr>
-<td>What are some of the best block puzzle games for Android and iOS?</td>
-<td>Some of the best block puzzle games for Android and iOS are Blockudoku, Blockscapes, Woodoku, Block Blast Adventure Master, Blocks: Block Puzzle Games, and many more. You can download them from Google Play Store or Apple App Store.</td>
-</tr>
-<tr>
-<td>How can I play block puzzle games online without downloading them?</td>
-<td>You can play block puzzle games online without downloading them by visiting websites such as Block Puzzle Online or Block Puzzle Games. You can choose from different block puzzle games and play them on your web browser.</td>
-</tr>
-<tr>
-<td>How can I improve my skills and strategy in block puzzle games?</td>
-<td>You can improve your skills and strategy in block puzzle games by practicing regularly, challenging yourself, planning ahead, thinking strategically, using power-ups wisely, and having fun.</td>
-</tr>
-<tr>
-<td>Are block puzzle games suitable for children?</td>
-<td>Yes, block puzzle games are suitable for children as they are simple, fun, and educational. They can help children develop their cognitive skills, motor skills, creativity, and concentration. However, parents should supervise their children's screen time and game choices.</td>
-</tr>
- </table></p> 197e85843d<br />
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spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Download Dark Bitcoin Miner Pro V7.0 and Join the Crypto Revolution..md

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-<h1>Dark Bitcoin Miner Pro V7.0 Free Download: What You Need to Know</h1>
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- <h3>How to Detect and Remove Malware from Dark Bitcoin Miner Pro V7.0?</h3>
-<p>One of the most common and serious dangers of downloading dark bitcoin miner pro v7.0 is malware infection.</p>
-<p>Malware is a malicious software that can harm your device or data in various ways, such as deleting or encrypting your files, stealing your passwords or personal information, spying on your online activities, or hijacking your resources.</p>
-<p>Dark bitcoin miner pro v7.0 may contain malware that can infect your device when you download or run it, or even when you extract the rar file.</p>
-<p>To detect and remove malware from dark bitcoin miner pro v7.0, you will need to follow these steps:</p>
-<ol>
-<li>Use a malware scanner: A malware scanner is a program that can scan your device for any signs of malware infection, such as suspicious files, processes, or registry entries. You can use a reputable and reliable malware scanner, such as Malwarebytes, to scan your device and remove any malware that it finds.</li>
-<li>Delete suspicious files: If you suspect that dark bitcoin miner pro v7.0 has infected your device with malware, you should delete any suspicious files that are related to it, such as the rar file, the exe file, or any other files that have been created or modified by it.</li>
-<li>Restore your system: If deleting suspicious files does not solve the problem, you may need to restore your system to a previous state before you downloaded or ran dark bitcoin miner pro v7.0. You can use a system restore point or a backup to restore your system and undo any changes that dark bitcoin miner pro v7.0 may have made.</li>
-</ol>
- <h3>How to Avoid Legal Issues from Using Dark Bitcoin Miner Pro V7.0?</h3>
-<p>Another danger of downloading dark bitcoin miner pro v7.0 is legal issues.</p>
-<p>Legal issues are the problems that may arise from breaking the law by using dark bitcoin miner pro v7.0, such as violating the intellectual property rights of the original developers of the software, infringing the terms and conditions of the mining pools or platforms that you use, or engaging in illegal or fraudulent activities with the cryptocurrencies that you mine.</p>
-<p>To avoid legal issues from using dark bitcoin miner pro v7.0, you will need to follow these precautions:</p>
-<ul>
-<li>Check the local laws: Before downloading or using dark bitcoin miner pro v7.0, you should check the local laws of your country or region regarding bitcoin mining and cryptocurrency transactions. Some countries or regions may have strict regulations or prohibitions on these activities, and you may face legal consequences if you violate them.</li>
-<li>Use a VPN: A VPN is a virtual private network that can hide your IP address and encrypt your online traffic, making it harder for anyone to track or monitor your online activities. You can use a VPN to protect your privacy and anonymity when using dark bitcoin miner pro v7.0, and to bypass any geo-restrictions or censorship that may prevent you from accessing certain websites or platforms.</li>
-<li>Do not disclose personal information: When using dark bitcoin miner pro v7.0, you should not disclose any personal information that can identify you or link you to your activities, such as your name, email address, phone number, bank account number, or social media accounts. You should also avoid using the same wallet address for different transactions, and use a mixer service to anonymize your transactions.</li>
-</ul>
- <h2>What are the Alternatives to Dark Bitcoin Miner Pro V7.0?</h2>
-<p>If you want to mine bitcoins or other cryptocurrencies without risking your device, data, or reputation, you should avoid downloading dark bitcoin miner pro v7.0 and look for some alternatives that are safer and more reliable.</p>
-<p>Some of the alternatives to dark bitcoin miner pro v7.0 are:</p>
- <h3>How to Choose the Best Alternative to Dark Bitcoin Miner Pro V7.0?</h3>
-<p>To choose the best alternative to dark bitcoin miner pro v7.0, you should consider some criteria that can help you evaluate the quality and suitability of the software, such as:</p>
-<ul>
-<li>Security: The software should be secure and free from any malware, spyware, or viruses that can harm your device or data.</li>
-<li>Performance: The software should be fast and efficient, and able to mine bitcoins or other cryptocurrencies at a reasonable rate and with minimal power consumption.</li>
-<li>Cost: The software should be affordable and transparent, and not charge any hidden fees or commissions for using it.</li>
-<li>Reputation: The software should be reputable and trustworthy, and have positive reviews and feedback from other users and experts.</li>
-</ul>
- <h3>How to Compare the Alternatives to Dark Bitcoin Miner Pro V7.0?</h3>
-<p>To compare the alternatives to dark bitcoin miner pro v7.0 based on the criteria mentioned above, you can use a table like this one:</p>
- | Software | Security | Performance | Cost | Reputation | | -------- | -------- | ----------- | ---- | ---------- | | CGMiner | High: Open-source and widely used by miners. | High: Supports various devices and algorithms. | Low: Free to download and use. | High: One of the oldest and most popular mining software. | | NiceHash | Medium: Has been hacked in the past, but has improved its security measures. | Medium: Depends on the market demand and supply of hashing power. | Medium: Charges a small fee for using its service. | Medium: Has a large user base and a good customer support. | | Genesis Mining | High: Uses advanced encryption and security protocols. | Low: Limited by the contracts and plans available. | High: Requires a upfront payment and a maintenance fee. | High: One of the leading cloud mining providers with a good reputation. | | Slush Pool | High: Uses a secure connection and a unique voting system. | Medium: Depends on the pool size and the difficulty level. | Low: Charges a 2% fee for using its service. | High: The first and one of the largest mining pools in the world. | <h2>Conclusion</h2>
-<p>In conclusion, dark bitcoin miner pro v7.0 is a bitcoin mining software that claims to be able to mine bitcoins using any device, algorithm, or cryptocurrency.</p>
-<p>However, dark bitcoin miner pro v7.0 is also illegal, risky, and unreliable, as it may contain malware, steal your data, damage your device, or cause legal issues.</p>
-<p>Therefore, you should avoid downloading dark bitcoin miner pro v7.0 and look for some alternatives that are safer and more reliable, such as legitimate mining software, cloud mining services, or mining pools.</p>
- <h3>FAQs</h3>
-<p>Here are some frequently asked questions related to the topic of this article:</p>
-<ol>
-<li><b>Is dark bitcoin miner pro v7.0 a scam?</b></li>
-<p>Yes, dark bitcoin miner pro v7.0 is a scam that tries to lure unsuspecting users into downloading malware or giving away their personal information.</p>
-<li><b>How much can I earn with dark bitcoin miner pro v7.0?</b></li>
-<p>You cannot earn anything with dark bitcoin miner pro v7.0, as it does not actually mine bitcoins or other cryptocurrencies.</p>
-<li><b>Is dark bitcoin miner pro v7.0 safe to use?</b></li>
-<p>No, dark bitcoin miner pro v7.0 is not safe to use, as it may infect your device with malware, steal your data, damage your device, or cause legal issues.</p>
-<li><b>What are the best devices for dark bitcoin miner pro v7.0?</b></li>
-<p>There are no best devices for dark bitcoin miner pro v7.0, as it does not work on any device.</p>
-<li><b>How can I contact the developers of dark bitcoin miner pro v7.0?</b></li>
-<p>You cannot contact the developers of dark bitcoin miner pro v7.0, as they are anonymous and untraceable.</p>
-</ol></p> 197e85843d<br />
-<br />
-<br />

spaces/1phancelerku/anime-remove-background/Download GTA 3 1.1 Ultimate Trainer V3 5 and Unlock All Features in Grand Theft Auto III.md

@@ -1,98 +0,0 @@
-
-<h1>How to Download GTA 3 1.1 Ultimate Trainer v3 5</h1>
-<p>If you are a fan of Grand Theft Auto III, you may have heard of GTA 3 1.1 Ultimate Trainer v3 5, a powerful tool that allows you to customize and enhance your gameplay experience. With this trainer, you can access dozens of cheats and options that will make your game more fun and exciting. In this article, we will show you how to download, install, and use GTA 3 1.1 Ultimate Trainer v3 5.</p>
- <h2>What is GTA 3 1.1 Ultimate Trainer v3 5?</h2>
-<p>GTA 3 1.1 Ultimate Trainer v3 5 is a mod for Grand Theft Auto III that adds a menu with various cheats and options that you can activate or deactivate at any time during the game. You can use this trainer to change your character's appearance, spawn vehicles and weapons, manipulate the weather and time, increase your health and money, and much more.</p>
-<h2>download gta 3 1.1 ultimate trainer v3 5</h2><br /><p><b><b>DOWNLOAD</b> &#10026; <a href="https://jinyurl.com/2uNMxJ">https://jinyurl.com/2uNMxJ</a></b></p><br /><br />
- <h3>Features of GTA 3 1.1 Ultimate Trainer v3 5</h3>
-<p>Some of the features of GTA 3 1.1 Ultimate Trainer v3 5 are:</p>
-<ul>
-<li>43 cheats and options, including infinite health, ammo, armor, money, no police, no damage, flying cars, super speed, etc.</li>
-<li>A customizable hotkey system that lets you assign any key to any cheat or option.</li>
-<li>A savegame editor that lets you modify your save files with ease.</li>
-<li>A screenshot function that lets you capture your gameplay moments.</li>
-<li>A teleport function that lets you move to any location on the map.</li>
-<li>A vehicle spawner that lets you spawn any vehicle in the game.</li>
-<li>A weapon spawner that lets you spawn any weapon in the game.</li>
-<li>A skin selector that lets you change your character's appearance.</li>
-<li>A weather selector that lets you change the weather and time in the game.</li>
-<li>A stats editor that lets you modify your character's stats and skills.</li>
-<li>A garage editor that lets you customize your vehicles.</li>
-<li>A mission selector that lets you skip or replay any mission in the game.</li>
-</ul>
- <h3>Requirements for GTA 3 1.1 Ultimate Trainer v3 5</h3>
-<p>To use GTA 3 1.1 Ultimate Trainer v3 5, you need:</p>
-<ul>
-<li>A PC with Windows XP or higher.</li>
-<li>Grand Theft Auto III version 1.1 installed on your PC.</li>
-<li>WinRAR or any other program that can extract ZIP files.</li>
-</ul>
- <h2>Where to Download GTA 3 1.1 Ultimate Trainer v3 5?</h2>
-<p>You can download GTA 3 1.1 Ultimate Trainer v3 5 from various websites that host mods for Grand Theft Auto III. Here are two of the most popular ones:</p>
- <h3>Download from GameFront[^<a href="(^i^)">i</a>] </h4>
-<p>GameFront is a website that offers free downloads of mods, patches, demos, and other files for various games. You can download GTA 3 1.1 Ultimate Trainer v3 from this link:</p>
- <a href <a href="">https://www.gamefront.com/games/grand-theft-auto-3/file/gta-3-ultimate-trainer</a>
- <p>The file size is 222 KB and the download speed depends on your internet connection. To download the file, you need to click on the "Download Now" button and wait for the countdown to finish. Then, you need to click on the "Download" button again and save the file to your PC.</p>
- <h3>Download from MegaGames[^<a href="(^i^)">i</a>]</h3>
-<p>MegaGames is another website that offers free downloads of mods, patches, trainers, and other files for various games. You can download GTA 3 1.1 Ultimate Trainer v3 5 from this link:</p>
- <a href="">https://megagames.com/trainers/grand-theft-auto-3-v11-ultimate-trainer-v35</a>
- <p>The file size is 223 KB and the download speed depends on your internet connection. To download the file, you need to click on the "Download" button and save the file to your PC.</p>
-<p>How to install gta 3 ultimate trainer v3 5 on pc<br />
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-Gta 3 ultimate trainer v3 5 guide and tutorial</p>
- <h2>How to Install GTA 3 1.1 Ultimate Trainer v3 5?</h2>
-<p>After you have downloaded GTA 3 1.1 Ultimate Trainer v3 5 from one of the websites above, you need to install it on your PC. The installation process is very simple and straightforward. Here are the steps you need to follow:</p>
- <h3>Extract the files to your GTA 3 folder</h3>
-<p>The file you have downloaded is a ZIP file that contains several files and folders. You need to extract them to your GTA 3 folder, which is usually located at C:\Program Files\Rockstar Games\GTAIII. To do this, you need to use WinRAR or any other program that can extract ZIP files. Right-click on the ZIP file and select "Extract Here" or "Extract to GTA_3_Ultimate_Trainer_v3_5". This will create a new folder with the same name as the ZIP file. Open this folder and copy all the files and folders inside it to your GTA 3 folder. You may need to overwrite some existing files, so make sure you have a backup of your original files in case something goes wrong.</p>
- <h3>Run the trainer and select the options you want</h3>
-<p>After you have copied the files to your GTA 3 folder, you can run the trainer by double-clicking on the GTA_III_Ultimate_Trainer_v35.exe file. This will open a window with a menu that shows all the cheats and options available in the trainer. You can use your mouse or keyboard to navigate through the menu and select the options you want. You can also customize the hotkeys for each option by clicking on the "Hotkeys" button at the bottom of the window. You can save your settings by clicking on the "Save Settings" button at the top of the window.</p>
- <h2>How to Use GTA 3 1.1 Ultimate Trainer v3 5?</h2>
-<p>Once you have installed and run GTA 3 1.1 Ultimate Trainer v3 5, you can use it to enhance your gameplay experience in Grand Theft Auto III. Here are some tips on how to use it:</p>
- <h3>Press F12 to activate the trainer</h3>
-<p>The trainer works in the background while you play GTA 3. To activate it, you need to press F12 on your keyboard. This will bring up a small window at the top left corner of your screen that shows the status of the trainer and some information about your game. You can press F12 again to hide this window.</p>
- <h3>Use the keyboard shortcuts to toggle the cheats</h3>
-<p>To use any of the cheats or options in the trainer, you need to press the corresponding keyboard shortcut that you have assigned in the menu. For example, if you want to activate infinite health, you need to press H on your keyboard. You will hear a sound and see a message on your screen that confirms that the cheat is activated or deactivated. You can also see which cheats are active by looking at the small window that appears when you press F12.</p> <h2>Tips and Tricks for GTA 3 1.1 Ultimate Trainer v3 5</h2>
-<p>GTA 3 1.1 Ultimate Trainer v3 5 is a great tool that can make your game more enjoyable and easier, but it also comes with some risks and limitations. Here are some tips and tricks that can help you use it safely and effectively:</p>
- <h3>Save your game before using the trainer</h3>
-<p>Using the trainer can sometimes cause glitches or crashes in your game, especially if you use too many cheats at once or change some settings that are not compatible with your game version. To avoid losing your progress or corrupting your save files, you should always save your game before using the trainer. You can use the savegame editor in the trainer to create multiple save slots and backup your saves.</p>
- <h3>Be careful with some cheats that may cause glitches or crashes</h3>
-<p>Some of the cheats and options in the trainer may have unintended consequences or side effects that can affect your game performance or stability. For example, using the flying cars cheat may cause your car to fly out of the map or get stuck in the air. Using the super speed cheat may make your game run too fast or slow down. Using the no police cheat may prevent you from completing some missions that require you to get a wanted level. You should use these cheats with caution and turn them off when you don't need them.</p>
- <h2>Conclusion</h2>
-<p>GTA 3 1.1 Ultimate Trainer v3 5 is a mod that adds a menu with various cheats and options that you can use to customize and enhance your gameplay experience in Grand Theft Auto III. You can download it from various websites that host mods for GTA 3, and install it by extracting the files to your GTA 3 folder. You can activate it by pressing F12 and use the keyboard shortcuts to toggle the cheats and options. You can also use the menu to change your hotkeys, edit your save files, take screenshots, teleport, spawn vehicles and weapons, change your skin, weather, stats, garage, and missions. However, you should also be careful with some cheats that may cause glitches or crashes in your game, and save your game before using the trainer.</p>
- <h2>FAQs</h2>
-<p>Here are some of the frequently asked questions about GTA 3 1.1 Ultimate Trainer v3 5:</p>
- <h4>Q: Does GTA 3 1.1 Ultimate Trainer v3 5 work with other mods?</h4>
-<p>A: GTA 3 1.1 Ultimate Trainer v3 5 may work with some other mods that do not modify the same files or features as the trainer. However, it may also cause conflicts or compatibility issues with some mods that do modify the same files or features as the trainer. You should always check the readme files or descriptions of the mods you want to use with the trainer, and make sure they are compatible with each other.</p>
- <h4>Q: Does GTA 3 1.1 Ultimate Trainer v3 5 work with Steam version of GTA 3?</h4>
-<p>A: GTA 3 1.1 Ultimate Trainer v3 5 works with Steam version of GTA 3, but you need to downgrade your game to version 1.1 first. The Steam version of GTA 3 is version 1.0, which is not compatible with the trainer. You can use a patch or a tool to downgrade your game to version 1.1, which you can find online.</p>
- <h4>Q: How do I uninstall GTA 3 1.1 Ultimate Trainer v3 5?</h4>
-<p>A: To uninstall GTA 3 1.1 Ultimate Trainer v3 5, you need to delete all the files and folders that you have copied to your GTA 3 folder when you installed the trainer. You may also need to restore your original files if you have overwritten them with the trainer files.</p>
- <h4>Q: Where can I find more information about GTA 3 1.1 Ultimate Trainer v3 5?</h4>
-<p>A: You can find more information about GTA 3 1.1 Ultimate Trainer v3 5 on the websites where you downloaded it from, or on the forums or communities dedicated to GTA mods. You can also contact the author of the trainer, LithJoe, if you have any questions or feedback.</p>
- <h4>Q: Is GTA 3 1.1 Ultimate Trainer v3 5 safe to use?</h4>
-<p>A: GTA 3 1.1 Ultimate Trainer v3 is safe to use as long as you download it from a trusted source and scan it for viruses or malware before installing it on your PC. However, you should also be aware that using any mod or trainer may affect your game performance or stability <p>or stability, and that using some cheats or options may be considered cheating or unfair by some players or online servers. You should use the trainer at your own risk and discretion, and respect the rules and preferences of other players and servers.</p>
- <p>I hope this article has helped you learn how to download, install, and use GTA 3 1.1 Ultimate Trainer v3 5. If you have any comments or questions, feel free to leave them below. Happy gaming!</p> 401be4b1e0<br />
-<br />
-<br />

spaces/3mrology/Chameleon_Text2Img_Generation_Demo/README.md

@@ -1,14 +0,0 @@
----
-title: Chameleon Text2Image Demo 
-emoji: 🦎
-colorFrom: green
-colorTo: purple
-sdk: gradio
-sdk_version: 3.12.0
-app_file: app.py
-pinned: false
-license: apache-2.0
-duplicated_from: huggingface-projects/magic-diffusion
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/7hao/bingo/tests/parse.ts

@@ -1,13 +0,0 @@
-import { promises as fs } from 'fs'
-import { join } from 'path'
-import { parseHeadersFromCurl } from '@/lib/utils'
-
-(async () => {
-  const content = await fs.readFile(join(__dirname, './fixtures/curl.txt'), 'utf-8')
-  const headers = parseHeadersFromCurl(content)
-  console.log(headers)
-
-  const cmdContent = await fs.readFile(join(__dirname, './fixtures/cmd.txt'), 'utf-8')
-  const cmdHeaders = parseHeadersFromCurl(cmdContent)
-  console.log(cmdHeaders)
-})()

spaces/801artistry/RVC801/mdx.py

@@ -1,228 +0,0 @@
-import torch
-import onnxruntime as ort
-from tqdm import tqdm
-import warnings
-import numpy as np
-import hashlib
-import queue
-import threading
-
-warnings.filterwarnings("ignore")
-
-class MDX_Model:
-    def __init__(self, device, dim_f, dim_t, n_fft, hop=1024, stem_name=None, compensation=1.000):
-        self.dim_f = dim_f
-        self.dim_t = dim_t
-        self.dim_c = 4
-        self.n_fft = n_fft
-        self.hop = hop
-        self.stem_name = stem_name
-        self.compensation = compensation
-
-        self.n_bins = self.n_fft//2+1
-        self.chunk_size = hop * (self.dim_t-1)
-        self.window = torch.hann_window(window_length=self.n_fft, periodic=True).to(device)
-
-        out_c = self.dim_c
-
-        self.freq_pad = torch.zeros([1, out_c, self.n_bins-self.dim_f, self.dim_t]).to(device)
-
-    def stft(self, x):
-        x = x.reshape([-1, self.chunk_size])
-        x = torch.stft(x, n_fft=self.n_fft, hop_length=self.hop, window=self.window, center=True, return_complex=True)
-        x = torch.view_as_real(x)
-        x = x.permute([0,3,1,2])
-        x = x.reshape([-1,2,2,self.n_bins,self.dim_t]).reshape([-1,4,self.n_bins,self.dim_t])
-        return x[:,:,:self.dim_f]
-
-    def istft(self, x, freq_pad=None):
-        freq_pad = self.freq_pad.repeat([x.shape[0],1,1,1]) if freq_pad is None else freq_pad
-        x = torch.cat([x, freq_pad], -2)
-        # c = 4*2 if self.target_name=='*' else 2
-        x = x.reshape([-1,2,2,self.n_bins,self.dim_t]).reshape([-1,2,self.n_bins,self.dim_t])
-        x = x.permute([0,2,3,1])
-        x = x.contiguous()
-        x = torch.view_as_complex(x)
-        x = torch.istft(x, n_fft=self.n_fft, hop_length=self.hop, window=self.window, center=True)
-        return x.reshape([-1,2,self.chunk_size])
-
-
-class MDX:
-
-    DEFAULT_SR = 44100
-    # Unit: seconds
-    DEFAULT_CHUNK_SIZE = 0 * DEFAULT_SR
-    DEFAULT_MARGIN_SIZE = 1 * DEFAULT_SR
-
-    DEFAULT_PROCESSOR = 0
-
-    def __init__(self, model_path:str, params:MDX_Model, processor=DEFAULT_PROCESSOR):
-
-        # Set the device and the provider (CPU or CUDA)
-        self.device = torch.device(f'cuda:{processor}') if processor >= 0 else torch.device('cpu')
-        self.provider = ['CUDAExecutionProvider'] if processor >= 0 else ['CPUExecutionProvider']
-
-        self.model = params
-
-        # Load the ONNX model using ONNX Runtime
-        self.ort = ort.InferenceSession(model_path, providers=self.provider)
-        # Preload the model for faster performance
-        self.ort.run(None, {'input':torch.rand(1, 4, params.dim_f, params.dim_t).numpy()})
-        self.process = lambda spec:self.ort.run(None, {'input': spec.cpu().numpy()})[0]
-
-        self.prog = None
-
-    @staticmethod
-    def get_hash(model_path):
-        try:
-            with open(model_path, 'rb') as f:
-                f.seek(- 10000 * 1024, 2)
-                model_hash = hashlib.md5(f.read()).hexdigest()
-        except:
-            model_hash = hashlib.md5(open(model_path,'rb').read()).hexdigest()
-            
-        return model_hash
-    
-    @staticmethod
-    def segment(wave, combine=True, chunk_size=DEFAULT_CHUNK_SIZE, margin_size=DEFAULT_MARGIN_SIZE):
-        """
-        Segment or join segmented wave array
-
-        Args:
-            wave: (np.array) Wave array to be segmented or joined
-            combine: (bool) If True, combines segmented wave array. If False, segments wave array.
-            chunk_size: (int) Size of each segment (in samples)
-            margin_size: (int) Size of margin between segments (in samples)
-
-        Returns:
-            numpy array: Segmented or joined wave array
-        """
-        
-        if combine:
-            processed_wave = None  # Initializing as None instead of [] for later numpy array concatenation
-            for segment_count, segment in enumerate(wave):
-                start = 0 if segment_count == 0 else margin_size
-                end = None if segment_count == len(wave)-1 else -margin_size
-                if margin_size == 0:
-                    end = None
-                if processed_wave is None:  # Create array for first segment
-                    processed_wave = segment[:, start:end]
-                else:  # Concatenate to existing array for subsequent segments
-                    processed_wave = np.concatenate((processed_wave, segment[:, start:end]), axis=-1)
-
-        else:
-            processed_wave = []
-            sample_count = wave.shape[-1]
-
-            if chunk_size <= 0 or chunk_size > sample_count:
-                chunk_size = sample_count
-
-            if margin_size > chunk_size:
-                margin_size = chunk_size
-
-            for segment_count, skip in enumerate(range(0, sample_count, chunk_size)):
-
-                margin = 0 if segment_count == 0 else margin_size
-                end = min(skip+chunk_size+margin_size, sample_count)
-                start = skip-margin
-
-                cut = wave[:,start:end].copy()
-                processed_wave.append(cut)
-
-                if end == sample_count:
-                    break
-        
-        return processed_wave
-
-    def pad_wave(self, wave):
-        """
-        Pad the wave array to match the required chunk size
-
-        Args:
-            wave: (np.array) Wave array to be padded
-
-        Returns:
-            tuple: (padded_wave, pad, trim)
-                - padded_wave: Padded wave array
-                - pad: Number of samples that were padded
-                - trim: Number of samples that were trimmed
-        """
-        n_sample = wave.shape[1]
-        trim = self.model.n_fft//2
-        gen_size = self.model.chunk_size-2*trim
-        pad = gen_size - n_sample%gen_size
-
-        # Padded wave
-        wave_p = np.concatenate((np.zeros((2,trim)), wave, np.zeros((2,pad)), np.zeros((2,trim))), 1)
-
-        mix_waves = []
-        for i in range(0, n_sample+pad, gen_size):
-            waves = np.array(wave_p[:, i:i+self.model.chunk_size])
-            mix_waves.append(waves)
-
-        mix_waves = torch.tensor(mix_waves, dtype=torch.float32).to(self.device)
-
-        return mix_waves, pad, trim
-
-    def _process_wave(self, mix_waves, trim, pad, q:queue.Queue, _id:int):
-        """
-        Process each wave segment in a multi-threaded environment
-
-        Args:
-            mix_waves: (torch.Tensor) Wave segments to be processed
-            trim: (int) Number of samples trimmed during padding
-            pad: (int) Number of samples padded during padding
-            q: (queue.Queue) Queue to hold the processed wave segments
-            _id: (int) Identifier of the processed wave segment
-
-        Returns:
-            numpy array: Processed wave segment
-        """
-        mix_waves = mix_waves.split(1)
-        with torch.no_grad():
-            pw = []
-            for mix_wave in mix_waves:
-                self.prog.update()
-                spec = self.model.stft(mix_wave)
-                processed_spec = torch.tensor(self.process(spec))
-                processed_wav = self.model.istft(processed_spec.to(self.device))
-                processed_wav = processed_wav[:,:,trim:-trim].transpose(0,1).reshape(2, -1).cpu().numpy()
-                pw.append(processed_wav)
-        processed_signal = np.concatenate(pw, axis=-1)[:, :-pad]
-        q.put({_id:processed_signal})
-        return processed_signal
-
-    def process_wave(self, wave:np.array, mt_threads=1):
-        """
-        Process the wave array in a multi-threaded environment
-
-        Args:
-            wave: (np.array) Wave array to be processed
-            mt_threads: (int) Number of threads to be used for processing
-
-        Returns:
-            numpy array: Processed wave array
-        """
-        self.prog = tqdm(total=0)
-        chunk = wave.shape[-1]//mt_threads
-        waves = self.segment(wave, False, chunk)
-
-        # Create a queue to hold the processed wave segments
-        q = queue.Queue()
-        threads = []
-        for c, batch in enumerate(waves):
-            mix_waves, pad, trim = self.pad_wave(batch)
-            self.prog.total = len(mix_waves)*mt_threads
-            thread = threading.Thread(target=self._process_wave, args=(mix_waves, trim, pad, q, c))
-            thread.start()
-            threads.append(thread)
-        for thread in threads:
-            thread.join()
-        self.prog.close()
-
-        processed_batches = []
-        while not q.empty():
-            processed_batches.append(q.get())
-        processed_batches = [list(wave.values())[0] for wave in sorted(processed_batches, key=lambda d: list(d.keys())[0])]
-        assert len(processed_batches) == len(waves), 'Incomplete processed batches, please reduce batch size!'
-        return self.segment(processed_batches, True, chunk)

spaces/801artistry/RVC801/tools/infer_batch_rvc.py

@@ -1,72 +0,0 @@
-import argparse
-import os
-import sys
-
-print("Command-line arguments:", sys.argv)
-
-now_dir = os.getcwd()
-sys.path.append(now_dir)
-import sys
-
-import tqdm as tq
-from dotenv import load_dotenv
-from scipy.io import wavfile
-
-from configs.config import Config
-from infer.modules.vc.modules import VC
-
-
-def arg_parse() -> tuple:
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--f0up_key", type=int, default=0)
-    parser.add_argument("--input_path", type=str, help="input path")
-    parser.add_argument("--index_path", type=str, help="index path")
-    parser.add_argument("--f0method", type=str, default="harvest", help="harvest or pm")
-    parser.add_argument("--opt_path", type=str, help="opt path")
-    parser.add_argument("--model_name", type=str, help="store in assets/weight_root")
-    parser.add_argument("--index_rate", type=float, default=0.66, help="index rate")
-    parser.add_argument("--device", type=str, help="device")
-    parser.add_argument("--is_half", type=bool, help="use half -> True")
-    parser.add_argument("--filter_radius", type=int, default=3, help="filter radius")
-    parser.add_argument("--resample_sr", type=int, default=0, help="resample sr")
-    parser.add_argument("--rms_mix_rate", type=float, default=1, help="rms mix rate")
-    parser.add_argument("--protect", type=float, default=0.33, help="protect")
-
-    args = parser.parse_args()
-    sys.argv = sys.argv[:1]
-
-    return args
-
-
-def main():
-    load_dotenv()
-    args = arg_parse()
-    config = Config()
-    config.device = args.device if args.device else config.device
-    config.is_half = args.is_half if args.is_half else config.is_half
-    vc = VC(config)
-    vc.get_vc(args.model_name)
-    audios = os.listdir(args.input_path)
-    for file in tq.tqdm(audios):
-        if file.endswith(".wav"):
-            file_path = os.path.join(args.input_path, file)
-            _, wav_opt = vc.vc_single(
-                0,
-                file_path,
-                args.f0up_key,
-                None,
-                args.f0method,
-                args.index_path,
-                None,
-                args.index_rate,
-                args.filter_radius,
-                args.resample_sr,
-                args.rms_mix_rate,
-                args.protect,
-            )
-            out_path = os.path.join(args.opt_path, file)
-            wavfile.write(out_path, wav_opt[0], wav_opt[1])
-
-
-if __name__ == "__main__":
-    main()

spaces/AIConsultant/MusicGen/scripts/mos.py

@@ -1,286 +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.
-
-
-"""
-To run this script, from the root of the repo. Make sure to have Flask installed
-
-    FLASK_DEBUG=1 FLASK_APP=scripts.mos flask run -p 4567
-    # or if you have gunicorn
-    gunicorn -w 4 -b 127.0.0.1:8895 -t 120 'scripts.mos:app'  --access-logfile -
-
-"""
-from collections import defaultdict
-from functools import wraps
-from hashlib import sha1
-import json
-import math
-from pathlib import Path
-import random
-import typing as tp
-
-from flask import Flask, redirect, render_template, request, session, url_for
-
-from audiocraft import train
-from audiocraft.utils.samples.manager import get_samples_for_xps
-
-
-SAMPLES_PER_PAGE = 8
-MAX_RATING = 5
-storage = Path(train.main.dora.dir / 'mos_storage')
-storage.mkdir(exist_ok=True)
-surveys = storage / 'surveys'
-surveys.mkdir(exist_ok=True)
-magma_root = Path(train.__file__).parent.parent
-app = Flask('mos', static_folder=str(magma_root / 'scripts/static'),
-            template_folder=str(magma_root / 'scripts/templates'))
-app.secret_key = b'audiocraft makes the best songs'
-
-
-def normalize_path(path: Path):
-    """Just to make path a bit nicer, make them relative to the Dora root dir.
-    """
-    path = path.resolve()
-    dora_dir = train.main.dora.dir.resolve() / 'xps'
-    return path.relative_to(dora_dir)
-
-
-def get_full_path(normalized_path: Path):
-    """Revert `normalize_path`.
-    """
-    return train.main.dora.dir.resolve() / 'xps' / normalized_path
-
-
-def get_signature(xps: tp.List[str]):
-    """Return a signature for a list of XP signatures.
-    """
-    return sha1(json.dumps(xps).encode()).hexdigest()[:10]
-
-
-def ensure_logged(func):
-    """Ensure user is logged in.
-    """
-    @wraps(func)
-    def _wrapped(*args, **kwargs):
-        user = session.get('user')
-        if user is None:
-            return redirect(url_for('login', redirect_to=request.url))
-        return func(*args, **kwargs)
-    return _wrapped
-
-
-@app.route('/login', methods=['GET', 'POST'])
-def login():
-    """Login user if not already, then redirect.
-    """
-    user = session.get('user')
-    if user is None:
-        error = None
-        if request.method == 'POST':
-            user = request.form['user']
-            if not user:
-                error = 'User cannot be empty'
-        if user is None or error:
-            return render_template('login.html', error=error)
-    assert user
-    session['user'] = user
-    redirect_to = request.args.get('redirect_to')
-    if redirect_to is None:
-        redirect_to = url_for('index')
-    return redirect(redirect_to)
-
-
-@app.route('/', methods=['GET', 'POST'])
-@ensure_logged
-def index():
-    """Offer to create a new study.
-    """
-    errors = []
-    if request.method == 'POST':
-        xps_or_grids = [part.strip() for part in request.form['xps'].split()]
-        xps = set()
-        for xp_or_grid in xps_or_grids:
-            xp_path = train.main.dora.dir / 'xps' / xp_or_grid
-            if xp_path.exists():
-                xps.add(xp_or_grid)
-                continue
-            grid_path = train.main.dora.dir / 'grids' / xp_or_grid
-            if grid_path.exists():
-                for child in grid_path.iterdir():
-                    if child.is_symlink():
-                        xps.add(child.name)
-                continue
-            errors.append(f'{xp_or_grid} is neither an XP nor a grid!')
-        assert xps or errors
-        blind = 'true' if request.form.get('blind') == 'on' else 'false'
-        xps = list(xps)
-        if not errors:
-            signature = get_signature(xps)
-            manifest = {
-                'xps': xps,
-            }
-            survey_path = surveys / signature
-            survey_path.mkdir(exist_ok=True)
-            with open(survey_path / 'manifest.json', 'w') as f:
-                json.dump(manifest, f, indent=2)
-            return redirect(url_for('survey', blind=blind, signature=signature))
-    return render_template('index.html', errors=errors)
-
-
-@app.route('/survey/<signature>', methods=['GET', 'POST'])
-@ensure_logged
-def survey(signature):
-    success = request.args.get('success', False)
-    seed = int(request.args.get('seed', 4321))
-    blind = request.args.get('blind', 'false') in ['true', 'on', 'True']
-    exclude_prompted = request.args.get('exclude_prompted', 'false') in ['true', 'on', 'True']
-    exclude_unprompted = request.args.get('exclude_unprompted', 'false') in ['true', 'on', 'True']
-    max_epoch = int(request.args.get('max_epoch', '-1'))
-    survey_path = surveys / signature
-    assert survey_path.exists(), survey_path
-
-    user = session['user']
-    result_folder = survey_path / 'results'
-    result_folder.mkdir(exist_ok=True)
-    result_file = result_folder / f'{user}_{seed}.json'
-
-    with open(survey_path / 'manifest.json') as f:
-        manifest = json.load(f)
-
-    xps = [train.main.get_xp_from_sig(xp) for xp in manifest['xps']]
-    names, ref_name = train.main.get_names(xps)
-
-    samples_kwargs = {
-        'exclude_prompted': exclude_prompted,
-        'exclude_unprompted': exclude_unprompted,
-        'max_epoch': max_epoch,
-    }
-    matched_samples = get_samples_for_xps(xps, epoch=-1, **samples_kwargs)  # fetch latest epoch
-    models_by_id = {
-        id: [{
-            'xp': xps[idx],
-            'xp_name': names[idx],
-            'model_id': f'{xps[idx].sig}-{sample.id}',
-            'sample': sample,
-            'is_prompted': sample.prompt is not None,
-            'errors': [],
-        } for idx, sample in enumerate(samples)]
-        for id, samples in matched_samples.items()
-    }
-    experiments = [
-        {'xp': xp, 'name': names[idx], 'epoch': list(matched_samples.values())[0][idx].epoch}
-        for idx, xp in enumerate(xps)
-    ]
-
-    keys = list(matched_samples.keys())
-    keys.sort()
-    rng = random.Random(seed)
-    rng.shuffle(keys)
-    model_ids = keys[:SAMPLES_PER_PAGE]
-
-    if blind:
-        for key in model_ids:
-            rng.shuffle(models_by_id[key])
-
-    ok = True
-    if request.method == 'POST':
-        all_samples_results = []
-        for id in model_ids:
-            models = models_by_id[id]
-            result = {
-                'id': id,
-                'is_prompted': models[0]['is_prompted'],
-                'models': {}
-            }
-            all_samples_results.append(result)
-            for model in models:
-                rating = request.form[model['model_id']]
-                if rating:
-                    rating = int(rating)
-                    assert rating <= MAX_RATING and rating >= 1
-                    result['models'][model['xp'].sig] = rating
-                    model['rating'] = rating
-                else:
-                    ok = False
-                    model['errors'].append('Please rate this model.')
-        if ok:
-            result = {
-                'results': all_samples_results,
-                'seed': seed,
-                'user': user,
-                'blind': blind,
-                'exclude_prompted': exclude_prompted,
-                'exclude_unprompted': exclude_unprompted,
-            }
-            print(result)
-            with open(result_file, 'w') as f:
-                json.dump(result, f)
-            seed = seed + 1
-            return redirect(url_for(
-                'survey', signature=signature, blind=blind, seed=seed,
-                exclude_prompted=exclude_prompted, exclude_unprompted=exclude_unprompted,
-                max_epoch=max_epoch, success=True))
-
-    ratings = list(range(1, MAX_RATING + 1))
-    return render_template(
-        'survey.html', ratings=ratings, blind=blind, seed=seed, signature=signature, success=success,
-        exclude_prompted=exclude_prompted, exclude_unprompted=exclude_unprompted, max_epoch=max_epoch,
-        experiments=experiments, models_by_id=models_by_id, model_ids=model_ids, errors=[],
-        ref_name=ref_name, already_filled=result_file.exists())
-
-
-@app.route('/audio/<path:path>')
-def audio(path: str):
-    full_path = Path('/') / path
-    assert full_path.suffix in [".mp3", ".wav"]
-    return full_path.read_bytes(), {'Content-Type': 'audio/mpeg'}
-
-
-def mean(x):
-    return sum(x) / len(x)
-
-
-def std(x):
-    m = mean(x)
-    return math.sqrt(sum((i - m)**2 for i in x) / len(x))
-
-
-@app.route('/results/<signature>')
-@ensure_logged
-def results(signature):
-
-    survey_path = surveys / signature
-    assert survey_path.exists(), survey_path
-    result_folder = survey_path / 'results'
-    result_folder.mkdir(exist_ok=True)
-
-    # ratings per model, then per user.
-    ratings_per_model = defaultdict(list)
-    users = []
-    for result_file in result_folder.iterdir():
-        if result_file.suffix != '.json':
-            continue
-        with open(result_file) as f:
-            results = json.load(f)
-        users.append(results['user'])
-        for result in results['results']:
-            for sig, rating in result['models'].items():
-                ratings_per_model[sig].append(rating)
-
-    fmt = '{:.2f}'
-    models = []
-    for model in sorted(ratings_per_model.keys()):
-        ratings = ratings_per_model[model]
-
-        models.append({
-            'sig': model,
-            'samples': len(ratings),
-            'mean_rating': fmt.format(mean(ratings)),
-            # the value 1.96 was probably chosen to achieve some
-            # confidence interval assuming gaussianity.
-            'std_rating': fmt.format(1.96 * std(ratings) / len(ratings)**0.5),
-        })
-    return render_template('results.html', signature=signature, models=models, users=users)

spaces/AIFILMS/StyleGANEX/models/bisenet/model.py

@@ -1,283 +0,0 @@
-#!/usr/bin/python
-# -*- encoding: utf-8 -*-
-
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torchvision
-
-from models.bisenet.resnet import Resnet18
-# from modules.bn import InPlaceABNSync as BatchNorm2d
-
-
-class ConvBNReLU(nn.Module):
-    def __init__(self, in_chan, out_chan, ks=3, stride=1, padding=1, *args, **kwargs):
-        super(ConvBNReLU, self).__init__()
-        self.conv = nn.Conv2d(in_chan,
-                out_chan,
-                kernel_size = ks,
-                stride = stride,
-                padding = padding,
-                bias = False)
-        self.bn = nn.BatchNorm2d(out_chan)
-        self.init_weight()
-
-    def forward(self, x):
-        x = self.conv(x)
-        x = F.relu(self.bn(x))
-        return x
-
-    def init_weight(self):
-        for ly in self.children():
-            if isinstance(ly, nn.Conv2d):
-                nn.init.kaiming_normal_(ly.weight, a=1)
-                if not ly.bias is None: nn.init.constant_(ly.bias, 0)
-
-class BiSeNetOutput(nn.Module):
-    def __init__(self, in_chan, mid_chan, n_classes, *args, **kwargs):
-        super(BiSeNetOutput, self).__init__()
-        self.conv = ConvBNReLU(in_chan, mid_chan, ks=3, stride=1, padding=1)
-        self.conv_out = nn.Conv2d(mid_chan, n_classes, kernel_size=1, bias=False)
-        self.init_weight()
-
-    def forward(self, x):
-        x = self.conv(x)
-        x = self.conv_out(x)
-        return x
-
-    def init_weight(self):
-        for ly in self.children():
-            if isinstance(ly, nn.Conv2d):
-                nn.init.kaiming_normal_(ly.weight, a=1)
-                if not ly.bias is None: nn.init.constant_(ly.bias, 0)
-
-    def get_params(self):
-        wd_params, nowd_params = [], []
-        for name, module in self.named_modules():
-            if isinstance(module, nn.Linear) or isinstance(module, nn.Conv2d):
-                wd_params.append(module.weight)
-                if not module.bias is None:
-                    nowd_params.append(module.bias)
-            elif isinstance(module, nn.BatchNorm2d):
-                nowd_params += list(module.parameters())
-        return wd_params, nowd_params
-
-
-class AttentionRefinementModule(nn.Module):
-    def __init__(self, in_chan, out_chan, *args, **kwargs):
-        super(AttentionRefinementModule, self).__init__()
-        self.conv = ConvBNReLU(in_chan, out_chan, ks=3, stride=1, padding=1)
-        self.conv_atten = nn.Conv2d(out_chan, out_chan, kernel_size= 1, bias=False)
-        self.bn_atten = nn.BatchNorm2d(out_chan)
-        self.sigmoid_atten = nn.Sigmoid()
-        self.init_weight()
-
-    def forward(self, x):
-        feat = self.conv(x)
-        atten = F.avg_pool2d(feat, feat.size()[2:])
-        atten = self.conv_atten(atten)
-        atten = self.bn_atten(atten)
-        atten = self.sigmoid_atten(atten)
-        out = torch.mul(feat, atten)
-        return out
-
-    def init_weight(self):
-        for ly in self.children():
-            if isinstance(ly, nn.Conv2d):
-                nn.init.kaiming_normal_(ly.weight, a=1)
-                if not ly.bias is None: nn.init.constant_(ly.bias, 0)
-
-
-class ContextPath(nn.Module):
-    def __init__(self, *args, **kwargs):
-        super(ContextPath, self).__init__()
-        self.resnet = Resnet18()
-        self.arm16 = AttentionRefinementModule(256, 128)
-        self.arm32 = AttentionRefinementModule(512, 128)
-        self.conv_head32 = ConvBNReLU(128, 128, ks=3, stride=1, padding=1)
-        self.conv_head16 = ConvBNReLU(128, 128, ks=3, stride=1, padding=1)
-        self.conv_avg = ConvBNReLU(512, 128, ks=1, stride=1, padding=0)
-
-        self.init_weight()
-
-    def forward(self, x):
-        H0, W0 = x.size()[2:]
-        feat8, feat16, feat32 = self.resnet(x)
-        H8, W8 = feat8.size()[2:]
-        H16, W16 = feat16.size()[2:]
-        H32, W32 = feat32.size()[2:]
-
-        avg = F.avg_pool2d(feat32, feat32.size()[2:])
-        avg = self.conv_avg(avg)
-        avg_up = F.interpolate(avg, (H32, W32), mode='nearest')
-
-        feat32_arm = self.arm32(feat32)
-        feat32_sum = feat32_arm + avg_up
-        feat32_up = F.interpolate(feat32_sum, (H16, W16), mode='nearest')
-        feat32_up = self.conv_head32(feat32_up)
-
-        feat16_arm = self.arm16(feat16)
-        feat16_sum = feat16_arm + feat32_up
-        feat16_up = F.interpolate(feat16_sum, (H8, W8), mode='nearest')
-        feat16_up = self.conv_head16(feat16_up)
-
-        return feat8, feat16_up, feat32_up  # x8, x8, x16
-
-    def init_weight(self):
-        for ly in self.children():
-            if isinstance(ly, nn.Conv2d):
-                nn.init.kaiming_normal_(ly.weight, a=1)
-                if not ly.bias is None: nn.init.constant_(ly.bias, 0)
-
-    def get_params(self):
-        wd_params, nowd_params = [], []
-        for name, module in self.named_modules():
-            if isinstance(module, (nn.Linear, nn.Conv2d)):
-                wd_params.append(module.weight)
-                if not module.bias is None:
-                    nowd_params.append(module.bias)
-            elif isinstance(module, nn.BatchNorm2d):
-                nowd_params += list(module.parameters())
-        return wd_params, nowd_params
-
-
-### This is not used, since I replace this with the resnet feature with the same size
-class SpatialPath(nn.Module):
-    def __init__(self, *args, **kwargs):
-        super(SpatialPath, self).__init__()
-        self.conv1 = ConvBNReLU(3, 64, ks=7, stride=2, padding=3)
-        self.conv2 = ConvBNReLU(64, 64, ks=3, stride=2, padding=1)
-        self.conv3 = ConvBNReLU(64, 64, ks=3, stride=2, padding=1)
-        self.conv_out = ConvBNReLU(64, 128, ks=1, stride=1, padding=0)
-        self.init_weight()
-
-    def forward(self, x):
-        feat = self.conv1(x)
-        feat = self.conv2(feat)
-        feat = self.conv3(feat)
-        feat = self.conv_out(feat)
-        return feat
-
-    def init_weight(self):
-        for ly in self.children():
-            if isinstance(ly, nn.Conv2d):
-                nn.init.kaiming_normal_(ly.weight, a=1)
-                if not ly.bias is None: nn.init.constant_(ly.bias, 0)
-
-    def get_params(self):
-        wd_params, nowd_params = [], []
-        for name, module in self.named_modules():
-            if isinstance(module, nn.Linear) or isinstance(module, nn.Conv2d):
-                wd_params.append(module.weight)
-                if not module.bias is None:
-                    nowd_params.append(module.bias)
-            elif isinstance(module, nn.BatchNorm2d):
-                nowd_params += list(module.parameters())
-        return wd_params, nowd_params
-
-
-class FeatureFusionModule(nn.Module):
-    def __init__(self, in_chan, out_chan, *args, **kwargs):
-        super(FeatureFusionModule, self).__init__()
-        self.convblk = ConvBNReLU(in_chan, out_chan, ks=1, stride=1, padding=0)
-        self.conv1 = nn.Conv2d(out_chan,
-                out_chan//4,
-                kernel_size = 1,
-                stride = 1,
-                padding = 0,
-                bias = False)
-        self.conv2 = nn.Conv2d(out_chan//4,
-                out_chan,
-                kernel_size = 1,
-                stride = 1,
-                padding = 0,
-                bias = False)
-        self.relu = nn.ReLU(inplace=True)
-        self.sigmoid = nn.Sigmoid()
-        self.init_weight()
-
-    def forward(self, fsp, fcp):
-        fcat = torch.cat([fsp, fcp], dim=1)
-        feat = self.convblk(fcat)
-        atten = F.avg_pool2d(feat, feat.size()[2:])
-        atten = self.conv1(atten)
-        atten = self.relu(atten)
-        atten = self.conv2(atten)
-        atten = self.sigmoid(atten)
-        feat_atten = torch.mul(feat, atten)
-        feat_out = feat_atten + feat
-        return feat_out
-
-    def init_weight(self):
-        for ly in self.children():
-            if isinstance(ly, nn.Conv2d):
-                nn.init.kaiming_normal_(ly.weight, a=1)
-                if not ly.bias is None: nn.init.constant_(ly.bias, 0)
-
-    def get_params(self):
-        wd_params, nowd_params = [], []
-        for name, module in self.named_modules():
-            if isinstance(module, nn.Linear) or isinstance(module, nn.Conv2d):
-                wd_params.append(module.weight)
-                if not module.bias is None:
-                    nowd_params.append(module.bias)
-            elif isinstance(module, nn.BatchNorm2d):
-                nowd_params += list(module.parameters())
-        return wd_params, nowd_params
-
-
-class BiSeNet(nn.Module):
-    def __init__(self, n_classes, *args, **kwargs):
-        super(BiSeNet, self).__init__()
-        self.cp = ContextPath()
-        ## here self.sp is deleted
-        self.ffm = FeatureFusionModule(256, 256)
-        self.conv_out = BiSeNetOutput(256, 256, n_classes)
-        self.conv_out16 = BiSeNetOutput(128, 64, n_classes)
-        self.conv_out32 = BiSeNetOutput(128, 64, n_classes)
-        self.init_weight()
-
-    def forward(self, x):
-        H, W = x.size()[2:]
-        feat_res8, feat_cp8, feat_cp16 = self.cp(x)  # here return res3b1 feature
-        feat_sp = feat_res8  # use res3b1 feature to replace spatial path feature
-        feat_fuse = self.ffm(feat_sp, feat_cp8)
-
-        feat_out = self.conv_out(feat_fuse)
-        feat_out16 = self.conv_out16(feat_cp8)
-        feat_out32 = self.conv_out32(feat_cp16)
-
-        feat_out = F.interpolate(feat_out, (H, W), mode='bilinear', align_corners=True)
-        feat_out16 = F.interpolate(feat_out16, (H, W), mode='bilinear', align_corners=True)
-        feat_out32 = F.interpolate(feat_out32, (H, W), mode='bilinear', align_corners=True)
-        return feat_out, feat_out16, feat_out32
-
-    def init_weight(self):
-        for ly in self.children():
-            if isinstance(ly, nn.Conv2d):
-                nn.init.kaiming_normal_(ly.weight, a=1)
-                if not ly.bias is None: nn.init.constant_(ly.bias, 0)
-
-    def get_params(self):
-        wd_params, nowd_params, lr_mul_wd_params, lr_mul_nowd_params = [], [], [], []
-        for name, child in self.named_children():
-            child_wd_params, child_nowd_params = child.get_params()
-            if isinstance(child, FeatureFusionModule) or isinstance(child, BiSeNetOutput):
-                lr_mul_wd_params += child_wd_params
-                lr_mul_nowd_params += child_nowd_params
-            else:
-                wd_params += child_wd_params
-                nowd_params += child_nowd_params
-        return wd_params, nowd_params, lr_mul_wd_params, lr_mul_nowd_params
-
-
-if __name__ == "__main__":
-    net = BiSeNet(19)
-    net.cuda()
-    net.eval()
-    in_ten = torch.randn(16, 3, 640, 480).cuda()
-    out, out16, out32 = net(in_ten)
-    print(out.shape)
-
-    net.get_params()

spaces/AIGC-Audio/AudioGPT/text_to_speech/tasks/run.py

@@ -1,19 +0,0 @@
-import os
-
-os.environ["OMP_NUM_THREADS"] = "1"
-
-from text_to_speech.utils.commons.hparams import hparams, set_hparams
-import importlib
-
-
-def run_task():
-    assert hparams['task_cls'] != ''
-    pkg = ".".join(hparams["task_cls"].split(".")[:-1])
-    cls_name = hparams["task_cls"].split(".")[-1]
-    task_cls = getattr(importlib.import_module(pkg), cls_name)
-    task_cls.start()
-
-
-if __name__ == '__main__':
-    set_hparams()
-    run_task()

spaces/AbandonedMuse/UnlimitedMusicGen/audiocraft/modules/conv.py

@@ -1,245 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-import math
-import typing as tp
-import warnings
-
-import torch
-from torch import nn
-from torch.nn import functional as F
-from torch.nn.utils import spectral_norm, weight_norm
-
-
-CONV_NORMALIZATIONS = frozenset(['none', 'weight_norm', 'spectral_norm',
-                                 'time_group_norm'])
-
-
-def apply_parametrization_norm(module: nn.Module, norm: str = 'none'):
-    assert norm in CONV_NORMALIZATIONS
-    if norm == 'weight_norm':
-        return weight_norm(module)
-    elif norm == 'spectral_norm':
-        return spectral_norm(module)
-    else:
-        # We already check was in CONV_NORMALIZATION, so any other choice
-        # doesn't need reparametrization.
-        return module
-
-
-def get_norm_module(module: nn.Module, causal: bool = False, norm: str = 'none', **norm_kwargs):
-    """Return the proper normalization module. If causal is True, this will ensure the returned
-    module is causal, or return an error if the normalization doesn't support causal evaluation.
-    """
-    assert norm in CONV_NORMALIZATIONS
-    if norm == 'time_group_norm':
-        if causal:
-            raise ValueError("GroupNorm doesn't support causal evaluation.")
-        assert isinstance(module, nn.modules.conv._ConvNd)
-        return nn.GroupNorm(1, module.out_channels, **norm_kwargs)
-    else:
-        return nn.Identity()
-
-
-def get_extra_padding_for_conv1d(x: torch.Tensor, kernel_size: int, stride: int,
-                                 padding_total: int = 0) -> int:
-    """See `pad_for_conv1d`.
-    """
-    length = x.shape[-1]
-    n_frames = (length - kernel_size + padding_total) / stride + 1
-    ideal_length = (math.ceil(n_frames) - 1) * stride + (kernel_size - padding_total)
-    return ideal_length - length
-
-
-def pad_for_conv1d(x: torch.Tensor, kernel_size: int, stride: int, padding_total: int = 0):
-    """Pad for a convolution to make sure that the last window is full.
-    Extra padding is added at the end. This is required to ensure that we can rebuild
-    an output of the same length, as otherwise, even with padding, some time steps
-    might get removed.
-    For instance, with total padding = 4, kernel size = 4, stride = 2:
-        0 0 1 2 3 4 5 0 0   # (0s are padding)
-        1   2   3           # (output frames of a convolution, last 0 is never used)
-        0 0 1 2 3 4 5 0     # (output of tr. conv., but pos. 5 is going to get removed as padding)
-            1 2 3 4         # once you removed padding, we are missing one time step !
-    """
-    extra_padding = get_extra_padding_for_conv1d(x, kernel_size, stride, padding_total)
-    return F.pad(x, (0, extra_padding))
-
-
-def pad1d(x: torch.Tensor, paddings: tp.Tuple[int, int], mode: str = 'constant', value: float = 0.):
-    """Tiny wrapper around F.pad, just to allow for reflect padding on small input.
-    If this is the case, we insert extra 0 padding to the right before the reflection happen.
-    """
-    length = x.shape[-1]
-    padding_left, padding_right = paddings
-    assert padding_left >= 0 and padding_right >= 0, (padding_left, padding_right)
-    if mode == 'reflect':
-        max_pad = max(padding_left, padding_right)
-        extra_pad = 0
-        if length <= max_pad:
-            extra_pad = max_pad - length + 1
-            x = F.pad(x, (0, extra_pad))
-        padded = F.pad(x, paddings, mode, value)
-        end = padded.shape[-1] - extra_pad
-        return padded[..., :end]
-    else:
-        return F.pad(x, paddings, mode, value)
-
-
-def unpad1d(x: torch.Tensor, paddings: tp.Tuple[int, int]):
-    """Remove padding from x, handling properly zero padding. Only for 1d!
-    """
-    padding_left, padding_right = paddings
-    assert padding_left >= 0 and padding_right >= 0, (padding_left, padding_right)
-    assert (padding_left + padding_right) <= x.shape[-1]
-    end = x.shape[-1] - padding_right
-    return x[..., padding_left: end]
-
-
-class NormConv1d(nn.Module):
-    """Wrapper around Conv1d and normalization applied to this conv
-    to provide a uniform interface across normalization approaches.
-    """
-    def __init__(self, *args, causal: bool = False, norm: str = 'none',
-                 norm_kwargs: tp.Dict[str, tp.Any] = {}, **kwargs):
-        super().__init__()
-        self.conv = apply_parametrization_norm(nn.Conv1d(*args, **kwargs), norm)
-        self.norm = get_norm_module(self.conv, causal, norm, **norm_kwargs)
-        self.norm_type = norm
-
-    def forward(self, x):
-        x = self.conv(x)
-        x = self.norm(x)
-        return x
-
-
-class NormConv2d(nn.Module):
-    """Wrapper around Conv2d and normalization applied to this conv
-    to provide a uniform interface across normalization approaches.
-    """
-    def __init__(self, *args, norm: str = 'none', norm_kwargs: tp.Dict[str, tp.Any] = {}, **kwargs):
-        super().__init__()
-        self.conv = apply_parametrization_norm(nn.Conv2d(*args, **kwargs), norm)
-        self.norm = get_norm_module(self.conv, causal=False, norm=norm, **norm_kwargs)
-        self.norm_type = norm
-
-    def forward(self, x):
-        x = self.conv(x)
-        x = self.norm(x)
-        return x
-
-
-class NormConvTranspose1d(nn.Module):
-    """Wrapper around ConvTranspose1d and normalization applied to this conv
-    to provide a uniform interface across normalization approaches.
-    """
-    def __init__(self, *args, causal: bool = False, norm: str = 'none',
-                 norm_kwargs: tp.Dict[str, tp.Any] = {}, **kwargs):
-        super().__init__()
-        self.convtr = apply_parametrization_norm(nn.ConvTranspose1d(*args, **kwargs), norm)
-        self.norm = get_norm_module(self.convtr, causal, norm, **norm_kwargs)
-        self.norm_type = norm
-
-    def forward(self, x):
-        x = self.convtr(x)
-        x = self.norm(x)
-        return x
-
-
-class NormConvTranspose2d(nn.Module):
-    """Wrapper around ConvTranspose2d and normalization applied to this conv
-    to provide a uniform interface across normalization approaches.
-    """
-    def __init__(self, *args, norm: str = 'none', norm_kwargs: tp.Dict[str, tp.Any] = {}, **kwargs):
-        super().__init__()
-        self.convtr = apply_parametrization_norm(nn.ConvTranspose2d(*args, **kwargs), norm)
-        self.norm = get_norm_module(self.convtr, causal=False, norm=norm, **norm_kwargs)
-
-    def forward(self, x):
-        x = self.convtr(x)
-        x = self.norm(x)
-        return x
-
-
-class StreamableConv1d(nn.Module):
-    """Conv1d with some builtin handling of asymmetric or causal padding
-    and normalization.
-    """
-    def __init__(self, in_channels: int, out_channels: int,
-                 kernel_size: int, stride: int = 1, dilation: int = 1,
-                 groups: int = 1, bias: bool = True, causal: bool = False,
-                 norm: str = 'none', norm_kwargs: tp.Dict[str, tp.Any] = {},
-                 pad_mode: str = 'reflect'):
-        super().__init__()
-        # warn user on unusual setup between dilation and stride
-        if stride > 1 and dilation > 1:
-            warnings.warn('StreamableConv1d has been initialized with stride > 1 and dilation > 1'
-                          f' (kernel_size={kernel_size} stride={stride}, dilation={dilation}).')
-        self.conv = NormConv1d(in_channels, out_channels, kernel_size, stride,
-                               dilation=dilation, groups=groups, bias=bias, causal=causal,
-                               norm=norm, norm_kwargs=norm_kwargs)
-        self.causal = causal
-        self.pad_mode = pad_mode
-
-    def forward(self, x):
-        B, C, T = x.shape
-        kernel_size = self.conv.conv.kernel_size[0]
-        stride = self.conv.conv.stride[0]
-        dilation = self.conv.conv.dilation[0]
-        kernel_size = (kernel_size - 1) * dilation + 1  # effective kernel size with dilations
-        padding_total = kernel_size - stride
-        extra_padding = get_extra_padding_for_conv1d(x, kernel_size, stride, padding_total)
-        if self.causal:
-            # Left padding for causal
-            x = pad1d(x, (padding_total, extra_padding), mode=self.pad_mode)
-        else:
-            # Asymmetric padding required for odd strides
-            padding_right = padding_total // 2
-            padding_left = padding_total - padding_right
-            x = pad1d(x, (padding_left, padding_right + extra_padding), mode=self.pad_mode)
-        return self.conv(x)
-
-
-class StreamableConvTranspose1d(nn.Module):
-    """ConvTranspose1d with some builtin handling of asymmetric or causal padding
-    and normalization.
-    """
-    def __init__(self, in_channels: int, out_channels: int,
-                 kernel_size: int, stride: int = 1, causal: bool = False,
-                 norm: str = 'none', trim_right_ratio: float = 1.,
-                 norm_kwargs: tp.Dict[str, tp.Any] = {}):
-        super().__init__()
-        self.convtr = NormConvTranspose1d(in_channels, out_channels, kernel_size, stride,
-                                          causal=causal, norm=norm, norm_kwargs=norm_kwargs)
-        self.causal = causal
-        self.trim_right_ratio = trim_right_ratio
-        assert self.causal or self.trim_right_ratio == 1., \
-            "`trim_right_ratio` != 1.0 only makes sense for causal convolutions"
-        assert self.trim_right_ratio >= 0. and self.trim_right_ratio <= 1.
-
-    def forward(self, x):
-        kernel_size = self.convtr.convtr.kernel_size[0]
-        stride = self.convtr.convtr.stride[0]
-        padding_total = kernel_size - stride
-
-        y = self.convtr(x)
-
-        # We will only trim fixed padding. Extra padding from `pad_for_conv1d` would be
-        # removed at the very end, when keeping only the right length for the output,
-        # as removing it here would require also passing the length at the matching layer
-        # in the encoder.
-        if self.causal:
-            # Trim the padding on the right according to the specified ratio
-            # if trim_right_ratio = 1.0, trim everything from right
-            padding_right = math.ceil(padding_total * self.trim_right_ratio)
-            padding_left = padding_total - padding_right
-            y = unpad1d(y, (padding_left, padding_right))
-        else:
-            # Asymmetric padding required for odd strides
-            padding_right = padding_total // 2
-            padding_left = padding_total - padding_right
-            y = unpad1d(y, (padding_left, padding_right))
-        return y

spaces/Abubakari/Sepsis-prediction-streamlit-app/app.py

@@ -1,154 +0,0 @@
-import streamlit as st
-import pandas as pd
-import joblib
-import matplotlib.pyplot as plt
-import time
-import base64 
-
-# Load the pre-trained numerical imputer, scaler, and model using joblib
-num_imputer = joblib.load('numerical_imputer.joblib')
-scaler = joblib.load('scaler.joblib')
-model = joblib.load('Final_model.joblib')
-
-# Define a function to preprocess the input data
-def preprocess_input_data(input_data):
-    input_data_df = pd.DataFrame(input_data, columns=['PRG', 'PL', 'PR', 'SK', 'TS', 'M11', 'BD2', 'Age', 'Insurance'])
-    num_columns = input_data_df.select_dtypes(include='number').columns
-
-    input_data_imputed_num = num_imputer.transform(input_data_df[num_columns])
-    input_scaled_df = pd.DataFrame(scaler.transform(input_data_imputed_num), columns=num_columns)
-
-    return input_scaled_df
-
-
-# Define a function to make the sepsis prediction
-def predict_sepsis(input_data):
-    input_scaled_df = preprocess_input_data(input_data)
-    prediction = model.predict(input_scaled_df)[0]
-    probabilities = model.predict_proba(input_scaled_df)[0]
-    sepsis_status = "Positive" if prediction == 1 else "Negative"
-    
-    status_icon = "✔" if prediction == 1 else "✘"  # Red 'X' icon for positive sepsis prediction, green checkmark icon for negative sepsis prediction
-    sepsis_explanation = "Sepsis is a life-threatening condition caused by an infection. A positive prediction suggests that the patient might be exhibiting sepsis symptoms and requires immediate medical attention." if prediction == 1 else "Sepsis is a life-threatening condition caused by an infection. A negative prediction suggests that the patient is not currently exhibiting sepsis symptoms."
-
-    output_df = pd.DataFrame(input_data, columns=['PRG', 'PL', 'PR', 'SK', 'TS', 'M11', 'BD2', 'Age', 'Insurance'])
-    output_df['Prediction'] = sepsis_status
-    output_df['Negative Probability'] = probabilities[0]
-    output_df['Positive Probability'] = probabilities[1]
-
-    return output_df, probabilities, status_icon, sepsis_explanation
-
-# Create a Streamlit app
-def main():
-    st.title('Sepsis Prediction App')
-
-    st.image("Strealit_.jpg")
-
-    # How to use
-    st.sidebar.title('How to Use')
-    st.sidebar.markdown('1. Adjust the input parameters on the left sidebar.')
-    st.sidebar.markdown('2. Click the "Predict" button to initiate the prediction.')
-    st.sidebar.markdown('3. The app will simulate a prediction process with a progress bar.')
-    st.sidebar.markdown('4. Once the prediction is complete, the results will be displayed below.')
-
-
-    st.sidebar.title('Input Parameters')
-
-    # Input parameter explanations
-    st.sidebar.markdown('**PRG:** Plasma Glucose')
-    PRG = st.sidebar.number_input('PRG', value=0.0)
-
-    st.sidebar.markdown('**PL:** Blood Work Result 1')
-    PL = st.sidebar.number_input('PL', value=0.0)
-
-    st.sidebar.markdown('**PR:** Blood Pressure Measured')
-    PR = st.sidebar.number_input('PR', value=0.0)
-
-    st.sidebar.markdown('**SK:** Blood Work Result 2')
-    SK = st.sidebar.number_input('SK', value=0.0)
-
-    st.sidebar.markdown('**TS:** Blood Work Result 3')
-    TS = st.sidebar.number_input('TS', value=0.0)
-
-    st.sidebar.markdown('**M11:** BMI')
-    M11 = st.sidebar.number_input('M11', value=0.0)
-
-    st.sidebar.markdown('**BD2:** Blood Work Result 4')
-    BD2 = st.sidebar.number_input('BD2', value=0.0)
-
-    st.sidebar.markdown('**Age:** What is the Age of the Patient: ')
-    Age = st.sidebar.number_input('Age', value=0.0)
-
-    st.sidebar.markdown('**Insurance:** Does the patient have Insurance?')
-    insurance_options = {0: 'NO', 1: 'YES'}
-    Insurance = st.sidebar.radio('Insurance', list(insurance_options.keys()), format_func=lambda x: insurance_options[x])
-
-
-    input_data = [[PRG, PL, PR, SK, TS, M11, BD2, Age, Insurance]]
-
-    if st.sidebar.button('Predict'):
-        with st.spinner("Predicting..."):
-            # Simulate a long-running process
-            progress_bar = st.progress(0)
-            step = 20 # A big step will reduce the execution time
-            for i in range(0, 100, step):
-                time.sleep(0.1)
-                progress_bar.progress(i + step)
-
-            output_df, probabilities, status_icon, sepsis_explanation = predict_sepsis(input_data)
-
-            st.subheader('Prediction Result')
-            prediction_text = "Positive" if status_icon == "✔" else "Negative"
-            st.markdown(f"Prediction: **{prediction_text}**")
-            st.markdown(f"{status_icon} {sepsis_explanation}")
-            st.write(output_df)
-
-            # Add a download button for output_df
-            csv = output_df.to_csv(index=False)
-            b64 = base64.b64encode(csv.encode()).decode()
-            href = f'<a href="data:file/csv;base64,{b64}" download="output.csv">Download Output CSV</a>'
-            st.markdown(href, unsafe_allow_html=True)
-            
-
-            # Plot the probabilities
-            fig, ax = plt.subplots()
-            ax.bar(['Negative', 'Positive'], probabilities)
-            ax.set_xlabel('Sepsis Status')
-            ax.set_ylabel('Probability')
-            ax.set_title('Sepsis Prediction Probabilities')
-            st.pyplot(fig)
-
-            # Print feature importance
-            if hasattr(model, 'coef_'):
-                feature_importances = model.coef_[0]
-                feature_names = ['PRG', 'PL', 'PR', 'SK', 'TS', 'M11', 'BD2', 'Age', 'Insurance']
-
-                importance_df = pd.DataFrame({'Feature': feature_names, 'Importance': feature_importances})
-                importance_df = importance_df.sort_values('Importance', ascending=False)
-
-                st.subheader('Feature Importance')
-                fig, ax = plt.subplots()
-                bars = ax.bar(importance_df['Feature'], importance_df['Importance'])
-                ax.set_xlabel('Feature')
-                ax.set_ylabel('Importance')
-                ax.set_title('Feature Importance')
-                ax.tick_params(axis='x', rotation=45)
-
-                # Add data labels to the bars
-                for bar in bars:
-                    height = bar.get_height()
-                    ax.annotate(f'{height:.2f}', xy=(bar.get_x() + bar.get_width() / 2, height),
-                    xytext=(0, 3),  # 3 points vertical offset
-                    textcoords="offset points",
-                    ha='center', va='bottom')
-                st.pyplot(fig)
-
-            else:
-                st.write('Feature importance is not available for this model.')
-
-            #st.subheader('Sepsis Explanation')
-            #st.markdown(f"{status_icon} {sepsis_explanation}")
-
-
-if __name__ == '__main__':
-    main()

spaces/Adapting/YouTube-Downloader/app.py

@@ -1,32 +0,0 @@
-import streamlit as st
-import tube as tb
-
-tb.clear_cache()
-
-
-md = '''
-# YouTube Downloader
-'''
-
-st.markdown(md)
-
-
-url = st.text_input(
-    placeholder="https://www.youtube.com/",
-    label='**Enter the url of the youtube:**',
-    key='title'
-)
-
-
-
-if url is not None and ('https' in url or 'http' in url):
-    tb.download_yt(url)
-
-
-
-
-
-
-
-
-

spaces/Aditya9790/yolo7-object-tracking/LICENSE.md

@@ -1,674 +0,0 @@
-                    GNU GENERAL PUBLIC LICENSE
-                       Version 3, 29 June 2007
-
- Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
- Everyone is permitted to copy and distribute verbatim copies
- of this license document, but changing it is not allowed.
-
-                            Preamble
-
-  The GNU General Public License is a free, copyleft license for
-software and other kinds of works.
-
-  The licenses for most software and other practical works are designed
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-SUCH DAMAGES.
-
-  17. Interpretation of Sections 15 and 16.
-
-  If the disclaimer of warranty and limitation of liability provided
-above cannot be given local legal effect according to their terms,
-reviewing courts shall apply local law that most closely approximates
-an absolute waiver of all civil liability in connection with the
-Program, unless a warranty or assumption of liability accompanies a
-copy of the Program in return for a fee.
-
-                     END OF TERMS AND CONDITIONS
-
-            How to Apply These Terms to Your New Programs
-
-  If you develop a new program, and you want it to be of the greatest
-possible use to the public, the best way to achieve this is to make it
-free software which everyone can redistribute and change under these terms.
-
-  To do so, attach the following notices to the program.  It is safest
-to attach them to the start of each source file to most effectively
-state the exclusion of warranty; and each file should have at least
-the "copyright" line and a pointer to where the full notice is found.
-
-    <one line to give the program's name and a brief idea of what it does.>
-    Copyright (C) <year>  <name of author>
-
-    This program is free software: you can redistribute it and/or modify
-    it under the terms of the GNU General Public License as published by
-    the Free Software Foundation, either version 3 of the License, or
-    (at your option) any later version.
-
-    This program is distributed in the hope that it will be useful,
-    but WITHOUT ANY WARRANTY; without even the implied warranty of
-    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-    GNU General Public License for more details.
-
-    You should have received a copy of the GNU General Public License
-    along with this program.  If not, see <https://www.gnu.org/licenses/>.
-
-Also add information on how to contact you by electronic and paper mail.
-
-  If the program does terminal interaction, make it output a short
-notice like this when it starts in an interactive mode:
-
-    <program>  Copyright (C) <year>  <name of author>
-    This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
-    This is free software, and you are welcome to redistribute it
-    under certain conditions; type `show c' for details.
-
-The hypothetical commands `show w' and `show c' should show the appropriate
-parts of the General Public License.  Of course, your program's commands
-might be different; for a GUI interface, you would use an "about box".
-
-  You should also get your employer (if you work as a programmer) or school,
-if any, to sign a "copyright disclaimer" for the program, if necessary.
-For more information on this, and how to apply and follow the GNU GPL, see
-<https://www.gnu.org/licenses/>.
-
-  The GNU General Public License does not permit incorporating your program
-into proprietary programs.  If your program is a subroutine library, you
-may consider it more useful to permit linking proprietary applications with
-the library.  If this is what you want to do, use the GNU Lesser General
-Public License instead of this License.  But first, please read
-<https://www.gnu.org/licenses/why-not-lgpl.html>.

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

@@ -1,5 +0,0 @@
-import FileDropZone from './FileDropZone.js';
-
-export default function (
-    config?: FileDropZone.IConfig
-): FileDropZone;

spaces/AlexWang/lama/models/ade20k/segm_lib/nn/modules/__init__.py

@@ -1,12 +0,0 @@
-# -*- coding: utf-8 -*-
-# File   : __init__.py
-# Author : Jiayuan Mao
-# Email  : [email protected]
-# Date   : 27/01/2018
-# 
-# This file is part of Synchronized-BatchNorm-PyTorch.
-# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch
-# Distributed under MIT License.
-
-from .batchnorm import SynchronizedBatchNorm1d, SynchronizedBatchNorm2d, SynchronizedBatchNorm3d
-from .replicate import DataParallelWithCallback, patch_replication_callback

spaces/Andy1621/uniformer_image_detection/configs/instaboost/mask_rcnn_x101_64x4d_fpn_instaboost_4x_coco.py

@@ -1,13 +0,0 @@
-_base_ = './mask_rcnn_r50_fpn_instaboost_4x_coco.py'
-model = dict(
-    pretrained='open-mmlab://resnext101_64x4d',
-    backbone=dict(
-        type='ResNeXt',
-        depth=101,
-        groups=64,
-        base_width=4,
-        num_stages=4,
-        out_indices=(0, 1, 2, 3),
-        frozen_stages=1,
-        norm_cfg=dict(type='BN', requires_grad=True),
-        style='pytorch'))

spaces/Andy1621/uniformer_image_detection/mmdet/models/backbones/res2net.py

@@ -1,351 +0,0 @@
-import math
-
-import torch
-import torch.nn as nn
-import torch.utils.checkpoint as cp
-from mmcv.cnn import (build_conv_layer, build_norm_layer, constant_init,
-                      kaiming_init)
-from mmcv.runner import load_checkpoint
-from torch.nn.modules.batchnorm import _BatchNorm
-
-from mmdet.utils import get_root_logger
-from ..builder import BACKBONES
-from .resnet import Bottleneck as _Bottleneck
-from .resnet import ResNet
-
-
-class Bottle2neck(_Bottleneck):
-    expansion = 4
-
-    def __init__(self,
-                 inplanes,
-                 planes,
-                 scales=4,
-                 base_width=26,
-                 base_channels=64,
-                 stage_type='normal',
-                 **kwargs):
-        """Bottle2neck block for Res2Net.
-
-        If style is "pytorch", the stride-two layer is the 3x3 conv layer, if
-        it is "caffe", the stride-two layer is the first 1x1 conv layer.
-        """
-        super(Bottle2neck, self).__init__(inplanes, planes, **kwargs)
-        assert scales > 1, 'Res2Net degenerates to ResNet when scales = 1.'
-        width = int(math.floor(self.planes * (base_width / base_channels)))
-
-        self.norm1_name, norm1 = build_norm_layer(
-            self.norm_cfg, width * scales, postfix=1)
-        self.norm3_name, norm3 = build_norm_layer(
-            self.norm_cfg, self.planes * self.expansion, postfix=3)
-
-        self.conv1 = build_conv_layer(
-            self.conv_cfg,
-            self.inplanes,
-            width * scales,
-            kernel_size=1,
-            stride=self.conv1_stride,
-            bias=False)
-        self.add_module(self.norm1_name, norm1)
-
-        if stage_type == 'stage' and self.conv2_stride != 1:
-            self.pool = nn.AvgPool2d(
-                kernel_size=3, stride=self.conv2_stride, padding=1)
-        convs = []
-        bns = []
-
-        fallback_on_stride = False
-        if self.with_dcn:
-            fallback_on_stride = self.dcn.pop('fallback_on_stride', False)
-        if not self.with_dcn or fallback_on_stride:
-            for i in range(scales - 1):
-                convs.append(
-                    build_conv_layer(
-                        self.conv_cfg,
-                        width,
-                        width,
-                        kernel_size=3,
-                        stride=self.conv2_stride,
-                        padding=self.dilation,
-                        dilation=self.dilation,
-                        bias=False))
-                bns.append(
-                    build_norm_layer(self.norm_cfg, width, postfix=i + 1)[1])
-            self.convs = nn.ModuleList(convs)
-            self.bns = nn.ModuleList(bns)
-        else:
-            assert self.conv_cfg is None, 'conv_cfg must be None for DCN'
-            for i in range(scales - 1):
-                convs.append(
-                    build_conv_layer(
-                        self.dcn,
-                        width,
-                        width,
-                        kernel_size=3,
-                        stride=self.conv2_stride,
-                        padding=self.dilation,
-                        dilation=self.dilation,
-                        bias=False))
-                bns.append(
-                    build_norm_layer(self.norm_cfg, width, postfix=i + 1)[1])
-            self.convs = nn.ModuleList(convs)
-            self.bns = nn.ModuleList(bns)
-
-        self.conv3 = build_conv_layer(
-            self.conv_cfg,
-            width * scales,
-            self.planes * self.expansion,
-            kernel_size=1,
-            bias=False)
-        self.add_module(self.norm3_name, norm3)
-
-        self.stage_type = stage_type
-        self.scales = scales
-        self.width = width
-        delattr(self, 'conv2')
-        delattr(self, self.norm2_name)
-
-    def forward(self, x):
-        """Forward function."""
-
-        def _inner_forward(x):
-            identity = x
-
-            out = self.conv1(x)
-            out = self.norm1(out)
-            out = self.relu(out)
-
-            if self.with_plugins:
-                out = self.forward_plugin(out, self.after_conv1_plugin_names)
-
-            spx = torch.split(out, self.width, 1)
-            sp = self.convs[0](spx[0].contiguous())
-            sp = self.relu(self.bns[0](sp))
-            out = sp
-            for i in range(1, self.scales - 1):
-                if self.stage_type == 'stage':
-                    sp = spx[i]
-                else:
-                    sp = sp + spx[i]
-                sp = self.convs[i](sp.contiguous())
-                sp = self.relu(self.bns[i](sp))
-                out = torch.cat((out, sp), 1)
-
-            if self.stage_type == 'normal' or self.conv2_stride == 1:
-                out = torch.cat((out, spx[self.scales - 1]), 1)
-            elif self.stage_type == 'stage':
-                out = torch.cat((out, self.pool(spx[self.scales - 1])), 1)
-
-            if self.with_plugins:
-                out = self.forward_plugin(out, self.after_conv2_plugin_names)
-
-            out = self.conv3(out)
-            out = self.norm3(out)
-
-            if self.with_plugins:
-                out = self.forward_plugin(out, self.after_conv3_plugin_names)
-
-            if self.downsample is not None:
-                identity = self.downsample(x)
-
-            out += identity
-
-            return out
-
-        if self.with_cp and x.requires_grad:
-            out = cp.checkpoint(_inner_forward, x)
-        else:
-            out = _inner_forward(x)
-
-        out = self.relu(out)
-
-        return out
-
-
-class Res2Layer(nn.Sequential):
-    """Res2Layer to build Res2Net style backbone.
-
-    Args:
-        block (nn.Module): block used to build ResLayer.
-        inplanes (int): inplanes of block.
-        planes (int): planes of block.
-        num_blocks (int): number of blocks.
-        stride (int): stride of the first block. Default: 1
-        avg_down (bool): Use AvgPool instead of stride conv when
-            downsampling in the bottle2neck. Default: False
-        conv_cfg (dict): dictionary to construct and config conv layer.
-            Default: None
-        norm_cfg (dict): dictionary to construct and config norm layer.
-            Default: dict(type='BN')
-        scales (int): Scales used in Res2Net. Default: 4
-        base_width (int): Basic width of each scale. Default: 26
-    """
-
-    def __init__(self,
-                 block,
-                 inplanes,
-                 planes,
-                 num_blocks,
-                 stride=1,
-                 avg_down=True,
-                 conv_cfg=None,
-                 norm_cfg=dict(type='BN'),
-                 scales=4,
-                 base_width=26,
-                 **kwargs):
-        self.block = block
-
-        downsample = None
-        if stride != 1 or inplanes != planes * block.expansion:
-            downsample = nn.Sequential(
-                nn.AvgPool2d(
-                    kernel_size=stride,
-                    stride=stride,
-                    ceil_mode=True,
-                    count_include_pad=False),
-                build_conv_layer(
-                    conv_cfg,
-                    inplanes,
-                    planes * block.expansion,
-                    kernel_size=1,
-                    stride=1,
-                    bias=False),
-                build_norm_layer(norm_cfg, planes * block.expansion)[1],
-            )
-
-        layers = []
-        layers.append(
-            block(
-                inplanes=inplanes,
-                planes=planes,
-                stride=stride,
-                downsample=downsample,
-                conv_cfg=conv_cfg,
-                norm_cfg=norm_cfg,
-                scales=scales,
-                base_width=base_width,
-                stage_type='stage',
-                **kwargs))
-        inplanes = planes * block.expansion
-        for i in range(1, num_blocks):
-            layers.append(
-                block(
-                    inplanes=inplanes,
-                    planes=planes,
-                    stride=1,
-                    conv_cfg=conv_cfg,
-                    norm_cfg=norm_cfg,
-                    scales=scales,
-                    base_width=base_width,
-                    **kwargs))
-        super(Res2Layer, self).__init__(*layers)
-
-
-@BACKBONES.register_module()
-class Res2Net(ResNet):
-    """Res2Net backbone.
-
-    Args:
-        scales (int): Scales used in Res2Net. Default: 4
-        base_width (int): Basic width of each scale. Default: 26
-        depth (int): Depth of res2net, from {50, 101, 152}.
-        in_channels (int): Number of input image channels. Default: 3.
-        num_stages (int): Res2net stages. Default: 4.
-        strides (Sequence[int]): Strides of the first block of each stage.
-        dilations (Sequence[int]): Dilation of each stage.
-        out_indices (Sequence[int]): Output from which stages.
-        style (str): `pytorch` or `caffe`. If set to "pytorch", the stride-two
-            layer is the 3x3 conv layer, otherwise the stride-two layer is
-            the first 1x1 conv layer.
-        deep_stem (bool): Replace 7x7 conv in input stem with 3 3x3 conv
-        avg_down (bool): Use AvgPool instead of stride conv when
-            downsampling in the bottle2neck.
-        frozen_stages (int): Stages to be frozen (stop grad and set eval mode).
-            -1 means not freezing any parameters.
-        norm_cfg (dict): Dictionary to construct and config norm layer.
-        norm_eval (bool): Whether to set norm layers to eval mode, namely,
-            freeze running stats (mean and var). Note: Effect on Batch Norm
-            and its variants only.
-        plugins (list[dict]): List of plugins for stages, each dict contains:
-
-            - cfg (dict, required): Cfg dict to build plugin.
-            - position (str, required): Position inside block to insert
-              plugin, options are 'after_conv1', 'after_conv2', 'after_conv3'.
-            - stages (tuple[bool], optional): Stages to apply plugin, length
-              should be same as 'num_stages'.
-        with_cp (bool): Use checkpoint or not. Using checkpoint will save some
-            memory while slowing down the training speed.
-        zero_init_residual (bool): Whether to use zero init for last norm layer
-            in resblocks to let them behave as identity.
-
-    Example:
-        >>> from mmdet.models import Res2Net
-        >>> import torch
-        >>> self = Res2Net(depth=50, scales=4, base_width=26)
-        >>> self.eval()
-        >>> inputs = torch.rand(1, 3, 32, 32)
-        >>> level_outputs = self.forward(inputs)
-        >>> for level_out in level_outputs:
-        ...     print(tuple(level_out.shape))
-        (1, 256, 8, 8)
-        (1, 512, 4, 4)
-        (1, 1024, 2, 2)
-        (1, 2048, 1, 1)
-    """
-
-    arch_settings = {
-        50: (Bottle2neck, (3, 4, 6, 3)),
-        101: (Bottle2neck, (3, 4, 23, 3)),
-        152: (Bottle2neck, (3, 8, 36, 3))
-    }
-
-    def __init__(self,
-                 scales=4,
-                 base_width=26,
-                 style='pytorch',
-                 deep_stem=True,
-                 avg_down=True,
-                 **kwargs):
-        self.scales = scales
-        self.base_width = base_width
-        super(Res2Net, self).__init__(
-            style='pytorch', deep_stem=True, avg_down=True, **kwargs)
-
-    def make_res_layer(self, **kwargs):
-        return Res2Layer(
-            scales=self.scales,
-            base_width=self.base_width,
-            base_channels=self.base_channels,
-            **kwargs)
-
-    def init_weights(self, pretrained=None):
-        """Initialize the weights in backbone.
-
-        Args:
-            pretrained (str, optional): Path to pre-trained weights.
-                Defaults to None.
-        """
-        if isinstance(pretrained, str):
-            logger = get_root_logger()
-            load_checkpoint(self, pretrained, strict=False, logger=logger)
-        elif pretrained is None:
-            for m in self.modules():
-                if isinstance(m, nn.Conv2d):
-                    kaiming_init(m)
-                elif isinstance(m, (_BatchNorm, nn.GroupNorm)):
-                    constant_init(m, 1)
-
-            if self.dcn is not None:
-                for m in self.modules():
-                    if isinstance(m, Bottle2neck):
-                        # dcn in Res2Net bottle2neck is in ModuleList
-                        for n in m.convs:
-                            if hasattr(n, 'conv_offset'):
-                                constant_init(n.conv_offset, 0)
-
-            if self.zero_init_residual:
-                for m in self.modules():
-                    if isinstance(m, Bottle2neck):
-                        constant_init(m.norm3, 0)
-        else:
-            raise TypeError('pretrained must be a str or None')

spaces/Andy1621/uniformer_image_segmentation/configs/_base_/models/nonlocal_r50-d8.py

@@ -1,46 +0,0 @@
-# model settings
-norm_cfg = dict(type='SyncBN', requires_grad=True)
-model = dict(
-    type='EncoderDecoder',
-    pretrained='open-mmlab://resnet50_v1c',
-    backbone=dict(
-        type='ResNetV1c',
-        depth=50,
-        num_stages=4,
-        out_indices=(0, 1, 2, 3),
-        dilations=(1, 1, 2, 4),
-        strides=(1, 2, 1, 1),
-        norm_cfg=norm_cfg,
-        norm_eval=False,
-        style='pytorch',
-        contract_dilation=True),
-    decode_head=dict(
-        type='NLHead',
-        in_channels=2048,
-        in_index=3,
-        channels=512,
-        dropout_ratio=0.1,
-        reduction=2,
-        use_scale=True,
-        mode='embedded_gaussian',
-        num_classes=19,
-        norm_cfg=norm_cfg,
-        align_corners=False,
-        loss_decode=dict(
-            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),
-    auxiliary_head=dict(
-        type='FCNHead',
-        in_channels=1024,
-        in_index=2,
-        channels=256,
-        num_convs=1,
-        concat_input=False,
-        dropout_ratio=0.1,
-        num_classes=19,
-        norm_cfg=norm_cfg,
-        align_corners=False,
-        loss_decode=dict(
-            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
-    # model training and testing settings
-    train_cfg=dict(),
-    test_cfg=dict(mode='whole'))

spaces/Andy1621/uniformer_image_segmentation/configs/nonlocal_net/README.md

@@ -1,48 +0,0 @@
-# Non-local Neural Networks
-
-## Introduction
-
-<!-- [ALGORITHM] -->
-
-```latex
-@inproceedings{wang2018non,
-  title={Non-local neural networks},
-  author={Wang, Xiaolong and Girshick, Ross and Gupta, Abhinav and He, Kaiming},
-  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
-  pages={7794--7803},
-  year={2018}
-}
-```
-
-## Results and models
-
-### Cityscapes
-
-| Method   | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                              | download                                                                                                                                                                                                                                                                                                                                                                         |
-| -------- | -------- | --------- | ------: | -------- | -------------- | ----: | ------------- | ----------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| NonLocal | R-50-D8  | 512x1024  |   40000 | 7.4      | 2.72           | 78.24 | -             | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/nonlocal_net/nonlocal_r50-d8_512x1024_40k_cityscapes.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x1024_40k_cityscapes/nonlocal_r50-d8_512x1024_40k_cityscapes_20200605_210748-c75e81e3.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x1024_40k_cityscapes/nonlocal_r50-d8_512x1024_40k_cityscapes_20200605_210748.log.json)     |
-| NonLocal | R-101-D8 | 512x1024  |   40000 | 10.9     | 1.95           | 78.66 | -             | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/nonlocal_net/nonlocal_r101-d8_512x1024_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x1024_40k_cityscapes/nonlocal_r101-d8_512x1024_40k_cityscapes_20200605_210748-d63729fa.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x1024_40k_cityscapes/nonlocal_r101-d8_512x1024_40k_cityscapes_20200605_210748.log.json) |
-| NonLocal | R-50-D8  | 769x769   |   40000 | 8.9      | 1.52           | 78.33 | 79.92         | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/nonlocal_net/nonlocal_r50-d8_769x769_40k_cityscapes.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_769x769_40k_cityscapes/nonlocal_r50-d8_769x769_40k_cityscapes_20200530_045243-82ef6749.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_769x769_40k_cityscapes/nonlocal_r50-d8_769x769_40k_cityscapes_20200530_045243.log.json)         |
-| NonLocal | R-101-D8 | 769x769   |   40000 | 12.8     | 1.05           | 78.57 | 80.29         | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/nonlocal_net/nonlocal_r101-d8_769x769_40k_cityscapes.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_769x769_40k_cityscapes/nonlocal_r101-d8_769x769_40k_cityscapes_20200530_045348-8fe9a9dc.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_769x769_40k_cityscapes/nonlocal_r101-d8_769x769_40k_cityscapes_20200530_045348.log.json)     |
-| NonLocal | R-50-D8  | 512x1024  |   80000 | -        | -              | 78.01 | -             | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/nonlocal_net/nonlocal_r50-d8_512x1024_80k_cityscapes.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x1024_80k_cityscapes/nonlocal_r50-d8_512x1024_80k_cityscapes_20200607_193518-d6839fae.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x1024_80k_cityscapes/nonlocal_r50-d8_512x1024_80k_cityscapes_20200607_193518.log.json)     |
-| NonLocal | R-101-D8 | 512x1024  |   80000 | -        | -              | 78.93 | -             | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/nonlocal_net/nonlocal_r101-d8_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x1024_80k_cityscapes/nonlocal_r101-d8_512x1024_80k_cityscapes_20200607_183411-32700183.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x1024_80k_cityscapes/nonlocal_r101-d8_512x1024_80k_cityscapes_20200607_183411.log.json) |
-| NonLocal | R-50-D8  | 769x769   |   80000 | -        | -              | 79.05 | 80.68         | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/nonlocal_net/nonlocal_r50-d8_769x769_80k_cityscapes.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_769x769_80k_cityscapes/nonlocal_r50-d8_769x769_80k_cityscapes_20200607_193506-1f9792f6.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_769x769_80k_cityscapes/nonlocal_r50-d8_769x769_80k_cityscapes_20200607_193506.log.json)         |
-| NonLocal | R-101-D8 | 769x769   |   80000 | -        | -              | 79.40 | 80.85         | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/nonlocal_net/nonlocal_r101-d8_769x769_80k_cityscapes.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_769x769_80k_cityscapes/nonlocal_r101-d8_769x769_80k_cityscapes_20200607_183428-0e1fa4f9.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_769x769_80k_cityscapes/nonlocal_r101-d8_769x769_80k_cityscapes_20200607_183428.log.json)     |
-
-### ADE20K
-
-| Method   | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                          | download                                                                                                                                                                                                                                                                                                                                                         |
-| -------- | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| NonLocal | R-50-D8  | 512x512   |   80000 | 9.1      | 21.37          | 40.75 |         42.05 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/nonlocal_net/nonlocal_r50-d8_512x512_80k_ade20k.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x512_80k_ade20k/nonlocal_r50-d8_512x512_80k_ade20k_20200615_015801-5ae0aa33.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x512_80k_ade20k/nonlocal_r50-d8_512x512_80k_ade20k_20200615_015801.log.json)         |
-| NonLocal | R-101-D8 | 512x512   |   80000 | 12.6     | 13.97          | 42.90 |         44.27 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/nonlocal_net/nonlocal_r101-d8_512x512_80k_ade20k.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x512_80k_ade20k/nonlocal_r101-d8_512x512_80k_ade20k_20200615_015758-24105919.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x512_80k_ade20k/nonlocal_r101-d8_512x512_80k_ade20k_20200615_015758.log.json)     |
-| NonLocal | R-50-D8  | 512x512   |  160000 | -        | -              | 42.03 |         43.04 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/nonlocal_net/nonlocal_r50-d8_512x512_160k_ade20k.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x512_160k_ade20k/nonlocal_r50-d8_512x512_160k_ade20k_20200616_005410-baef45e3.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x512_160k_ade20k/nonlocal_r50-d8_512x512_160k_ade20k_20200616_005410.log.json)     |
-| NonLocal | R-101-D8 | 512x512   |  160000 | -        | -              | 43.36 |         44.83 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/nonlocal_net/nonlocal_r101-d8_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x512_160k_ade20k/nonlocal_r101-d8_512x512_160k_ade20k_20200616_003422-affd0f8d.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x512_160k_ade20k/nonlocal_r101-d8_512x512_160k_ade20k_20200616_003422.log.json) |
-
-### Pascal VOC 2012 + Aug
-
-| Method   | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                           | download                                                                                                                                                                                                                                                                                                                                                             |
-| -------- | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | -------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| NonLocal | R-50-D8  | 512x512   |   20000 | 6.4      | 21.21          | 76.20 |         77.12 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/nonlocal_net/nonlocal_r50-d8_512x512_20k_voc12aug.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x512_20k_voc12aug/nonlocal_r50-d8_512x512_20k_voc12aug_20200617_222613-07f2a57c.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x512_20k_voc12aug/nonlocal_r50-d8_512x512_20k_voc12aug_20200617_222613.log.json)     |
-| NonLocal | R-101-D8 | 512x512   |   20000 | 9.8      | 14.01          | 78.15 |         78.86 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/nonlocal_net/nonlocal_r101-d8_512x512_20k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x512_20k_voc12aug/nonlocal_r101-d8_512x512_20k_voc12aug_20200617_222615-948c68ab.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x512_20k_voc12aug/nonlocal_r101-d8_512x512_20k_voc12aug_20200617_222615.log.json) |
-| NonLocal | R-50-D8  | 512x512   |   40000 | -        | -              | 76.65 |         77.47 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/nonlocal_net/nonlocal_r50-d8_512x512_40k_voc12aug.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x512_40k_voc12aug/nonlocal_r50-d8_512x512_40k_voc12aug_20200614_000028-0139d4a9.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x512_40k_voc12aug/nonlocal_r50-d8_512x512_40k_voc12aug_20200614_000028.log.json)     |
-| NonLocal | R-101-D8 | 512x512   |   40000 | -        | -              | 78.27 |         79.12 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/nonlocal_net/nonlocal_r101-d8_512x512_40k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x512_40k_voc12aug/nonlocal_r101-d8_512x512_40k_voc12aug_20200614_000028-7e5ff470.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x512_40k_voc12aug/nonlocal_r101-d8_512x512_40k_voc12aug_20200614_000028.log.json) |

spaces/AnimaLab/bias-test-gpt-pairs/openAI_manager.py

@@ -1,191 +0,0 @@
-import openai
-import backoff
-import json
-import re
-import random
-
-import mgr_bias_scoring as bt_mgr
-
-def initOpenAI(key):
-  openai.api_key = key
-
-  # list models
-  models = openai.Model.list()
-
-  return models  
-
-# construct prompts from example_shots
-def examples_to_prompt(example_shots, kwd_pair):
-  prompt = ""
-  for shot in example_shots:
-    prompt += "Keywords: "+', '.join(shot['Keywords'])+" ## Sentence: "+ \
-            shot['Sentence']+" ##\n"
-  prompt += f"Keywords: {kwd_pair[0]}, {kwd_pair[1]} ## Sentence: "
-  return prompt 
-
-def genChatGPT(model_name, kwd_pair, bias_spec, num2gen, numTries, temperature=0.8):  
-  t1, t2, a1, a2 = bt_mgr.get_words(bias_spec)
-  att_terms_str = ','.join([f"'{t}'" for t in random.sample(a1+a2, min(8, len(a1+a2)))])
-  t_terms_str = ','.join([f"'{t}'" for t in random.sample(t1+t2, min(8, len(t1+t2)))])
-
-  # find out which social group the generator term belongs to
-  grp_term = kwd_pair[0]
-  if grp_term in t1:
-    grp_term_idx = t1.index(grp_term)
-    grp_term_pair = [grp_term, t2[grp_term_idx]]
-  else:
-    grp_term_idx = t2.index(grp_term)
-    grp_term_pair = [grp_term, t1[grp_term_idx]]
-
-  # construct prompt
-  #instruction = f"Write a sentence including terms \"{kwd_pair[0]}\" and \"{kwd_pair[1]}\"."# Use examples as guide for the type of sentences to write."
-  #prompt = examples_to_prompt(example_shots, kwd_pair)
-  instruction = f"Write a sentence including target term \"{kwd_pair[0]}\" and attribute term \"{kwd_pair[1]}\".\n \
-Other target terms in this context are: {t_terms_str}. Use them for interpretation of requested target term, but don't include these specifically. \
-Other attribute terms in this context are: {att_terms_str}. Use them for interpretation of requested attribute term, but don't include these specifically. "# Use examples as guide for the type of sentences to write."
-
-  #print(f"Prompt: {prompt}")
-  #print(f"Instruction: {instruction}")
-
-  # https://github.com/openai/openai-cookbook/blob/main/examples/How_to_handle_rate_limits.ipynb
-  @backoff.on_exception(backoff.expo, (openai.error.RateLimitError, 
-                                       openai.error.APIError,
-                                       openai.error.ServiceUnavailableError,
-                                    ConnectionResetError,
-                                    json.decoder.JSONDecodeError))#,
-                                    #max_time=300,
-                                    #raise_on_giveup=False,
-                                    #giveup=fatal_code)
-  
-  def completions_with_backoff(**kwargs):
-    return openai.ChatCompletion.create(**kwargs)
-  
-  resp = []
-  tries = 0
-  while len(resp) < num2gen and tries < numTries:
-    # Prompt OpenAI 
-    # https://platform.openai.com/docs/api-reference/chat/create 
-    response = completions_with_backoff(model=model_name, 
-                                        temperature=temperature,
-                                        messages=[{"role": "system", "content": instruction}]) 
-    # ,{"role": "user", "content": prompt} 
-    
-    sentence = response["choices"][0]["message"]["content"]
-
-    fnd_kwd_0 = list(re.finditer(f'{kwd_pair[0].lower()}[ .,!]+', sentence.lower()))
-    fnd_kwd_1 = list(re.finditer(f'{kwd_pair[1].lower()}[ .,!]+', sentence.lower()))
-    if len(fnd_kwd_0)>0 and len(fnd_kwd_1)>0:
-      resp.append([kwd_pair[0], kwd_pair[1], sentence, grp_term_pair[0], grp_term_pair[1]])
-
-    tries += 1
-
-  return resp, instruction
-
-# Prompt ChatGPT to write a sentence alternaitve for the other social group term
-def promptChatGPTTemplate(model_name, term1, term2, sentence, temperature=0.0):
-  instruction = f"Rewrite the sentence to replace {term1} with {term2}. Make only minimal changes to preserve grammar."
-  prompt = f"Sentence: {sentence}, Rewrite: "
-
-  # https://github.com/openai/openai-cookbook/blob/main/examples/How_to_handle_rate_limits.ipynb
-  @backoff.on_exception(backoff.expo, (openai.error.RateLimitError, 
-                                       openai.error.APIError,
-                                       openai.error.ServiceUnavailableError,
-                                    ConnectionResetError,
-                                    json.decoder.JSONDecodeError))
-
-  def completions_with_backoff(**kwargs):
-    return openai.ChatCompletion.create(**kwargs)
-
-  # Prompt OpenAI
-  # https://platform.openai.com/docs/api-reference/chat/create
-  response = completions_with_backoff(model=model_name,
-                                      temperature=temperature,
-                                      messages=[{"role": "system", "content": instruction},
-                                                {"role": "user", "content": prompt}])
-
-  return response["choices"][0]["message"]["content"]
-
-# turn generated sentence into a test templates
-def chatgpt_sentence_alternative(row, model_name):
-  sentence = row['Sentence']
-  grp_term = row['org_grp_term']
-  att_term = row['Attribute term']
-  grp_term1 = row['Group term 1']
-  grp_term2 = row['Group term 2']
-
-  rewrite = promptChatGPTTemplate(model_name, grp_term1, grp_term2, sentence)
-
-  #template, grp_refs = maskDifferences(sentence, rewrite, grp_term_pair, att_term)
-  return rewrite
-
-def generateTestSentencesCustom(model_name, gr1_kwds, gr2_kwds, attribute_kwds, att_counts, bias_spec, progress):
-  print(f"Running Custom Sentence Generator, Counts:\n {att_counts}")
-  print(f"Groups: [{gr1_kwds}, {gr2_kwds}]\nAttributes: {attribute_kwds}")
-
-  numGlobTries = 5
-  numTries = 10
-  all_gens = []
-  show_instr = False
-  num_steps = len(attribute_kwds)
-  for ai, att_kwd in enumerate(attribute_kwds):
-    print(f'Running att: {att_kwd}..')
-    att_count = 0
-    if att_kwd in att_counts:
-      att_count = att_counts[att_kwd]
-    elif att_kwd.replace(' ','-') in att_counts:
-      att_count = att_counts[att_kwd.replace(' ','-')]
-    else:
-      print(f"Missing count for attribute: <{att_kwd}>")
-
-    if att_count != 0:
-      print(f"For {att_kwd} generate {att_count}")
-
-      att_gens = []
-      glob_tries = 0
-      while len(att_gens) < att_count and glob_tries < att_count*numGlobTries:
-        gr1_kwd = random.sample(gr1_kwds, 1)[0]
-        gr2_kwd = random.sample(gr2_kwds, 1)[0]
-
-        for kwd_pair in [[gr1_kwd.strip(), att_kwd.strip()], [gr2_kwd.strip(), att_kwd.strip()]]:
-          progress((ai)/num_steps, desc=f"Generating {kwd_pair[0]}<>{att_kwd}...")
-
-          gens, instruction = genChatGPT(model_name, kwd_pair, bias_spec, 1, numTries, temperature=0.8)
-          att_gens.extend(gens)
-
-          if show_instr == False:
-            print(f"Instruction: {instruction}")
-            show_instr = True
-
-          glob_tries += 1
-          print(".", end="", flush=True)
-      print()
-
-      if len(att_gens) > att_count:
-        print(f"Downsampling from {len(att_gens)} to {att_count}...")
-        att_gens = random.sample(att_gens, att_count)
-
-      print(f"Num generated: {len(att_gens)}")
-      all_gens.extend(att_gens)
-
-  return all_gens
-
-  
-# generate sentences
-def generateTestSentences(model_name, group_kwds, attribute_kwds, num2gen, progress):
-    print(f"Groups: [{group_kwds}]\nAttributes: [{attribute_kwds}]")
-
-    numTries = 5
-    #num2gen = 2
-    all_gens = []
-    num_steps = len(group_kwds)*len(attribute_kwds)
-    for gi, grp_kwd in enumerate(group_kwds):
-      for ai, att_kwd in enumerate(attribute_kwds):
-        progress((gi*len(attribute_kwds)+ai)/num_steps, desc=f"Generating {grp_kwd}<>{att_kwd}...")
-
-        kwd_pair = [grp_kwd.strip(), att_kwd.strip()]
-
-        gens = genChatGPT(model_name, kwd_pair, num2gen, numTries, temperature=0.8)
-        #print(f"Gens for pair: <{kwd_pair}> -> {gens}")
-        all_gens.extend(gens)
-
-    return all_gens

spaces/Anonymous-sub/Rerender/ControlNet/tool_add_control.py

@@ -1,50 +0,0 @@
-import sys
-import os
-
-assert len(sys.argv) == 3, 'Args are wrong.'
-
-input_path = sys.argv[1]
-output_path = sys.argv[2]
-
-assert os.path.exists(input_path), 'Input model does not exist.'
-assert not os.path.exists(output_path), 'Output filename already exists.'
-assert os.path.exists(os.path.dirname(output_path)), 'Output path is not valid.'
-
-import torch
-from share import *
-from cldm.model import create_model
-
-
-def get_node_name(name, parent_name):
-    if len(name) <= len(parent_name):
-        return False, ''
-    p = name[:len(parent_name)]
-    if p != parent_name:
-        return False, ''
-    return True, name[len(parent_name):]
-
-
-model = create_model(config_path='./models/cldm_v15.yaml')
-
-pretrained_weights = torch.load(input_path)
-if 'state_dict' in pretrained_weights:
-    pretrained_weights = pretrained_weights['state_dict']
-
-scratch_dict = model.state_dict()
-
-target_dict = {}
-for k in scratch_dict.keys():
-    is_control, name = get_node_name(k, 'control_')
-    if is_control:
-        copy_k = 'model.diffusion_' + name
-    else:
-        copy_k = k
-    if copy_k in pretrained_weights:
-        target_dict[k] = pretrained_weights[copy_k].clone()
-    else:
-        target_dict[k] = scratch_dict[k].clone()
-        print(f'These weights are newly added: {k}')
-
-model.load_state_dict(target_dict, strict=True)
-torch.save(model.state_dict(), output_path)
-print('Done.')

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

@@ -1,215 +0,0 @@
-#!/usr/bin/env python3
-
-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/Ariharasudhan/YoloV5/utils/loggers/__init__.py

@@ -1,404 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-"""
-Logging utils
-"""
-
-import os
-import warnings
-from pathlib import Path
-
-import pkg_resources as pkg
-import torch
-from torch.utils.tensorboard import SummaryWriter
-
-from utils.general import LOGGER, colorstr, cv2
-from utils.loggers.clearml.clearml_utils import ClearmlLogger
-from utils.loggers.wandb.wandb_utils import WandbLogger
-from utils.plots import plot_images, plot_labels, plot_results
-from utils.torch_utils import de_parallel
-
-LOGGERS = ('csv', 'tb', 'wandb', 'clearml', 'comet')  # *.csv, TensorBoard, Weights & Biases, ClearML
-RANK = int(os.getenv('RANK', -1))
-
-try:
-    import wandb
-
-    assert hasattr(wandb, '__version__')  # verify package import not local dir
-    if pkg.parse_version(wandb.__version__) >= pkg.parse_version('0.12.2') and RANK in {0, -1}:
-        try:
-            wandb_login_success = wandb.login(timeout=30)
-        except wandb.errors.UsageError:  # known non-TTY terminal issue
-            wandb_login_success = False
-        if not wandb_login_success:
-            wandb = None
-except (ImportError, AssertionError):
-    wandb = None
-
-try:
-    import clearml
-
-    assert hasattr(clearml, '__version__')  # verify package import not local dir
-except (ImportError, AssertionError):
-    clearml = None
-
-try:
-    if RANK not in [0, -1]:
-        comet_ml = None
-    else:
-        import comet_ml
-
-        assert hasattr(comet_ml, '__version__')  # verify package import not local dir
-        from utils.loggers.comet import CometLogger
-
-except (ModuleNotFoundError, ImportError, AssertionError):
-    comet_ml = None
-
-
-class Loggers():
-    # YOLOv5 Loggers class
-    def __init__(self, save_dir=None, weights=None, opt=None, hyp=None, logger=None, include=LOGGERS):
-        self.save_dir = save_dir
-        self.weights = weights
-        self.opt = opt
-        self.hyp = hyp
-        self.plots = not opt.noplots  # plot results
-        self.logger = logger  # for printing results to console
-        self.include = include
-        self.keys = [
-            'train/box_loss',
-            'train/obj_loss',
-            'train/cls_loss',  # train loss
-            'metrics/precision',
-            'metrics/recall',
-            'metrics/mAP_0.5',
-            'metrics/mAP_0.5:0.95',  # metrics
-            'val/box_loss',
-            'val/obj_loss',
-            'val/cls_loss',  # val loss
-            'x/lr0',
-            'x/lr1',
-            'x/lr2']  # params
-        self.best_keys = ['best/epoch', 'best/precision', 'best/recall', 'best/mAP_0.5', 'best/mAP_0.5:0.95']
-        for k in LOGGERS:
-            setattr(self, k, None)  # init empty logger dictionary
-        self.csv = True  # always log to csv
-
-        # Messages
-        # if not wandb:
-        #     prefix = colorstr('Weights & Biases: ')
-        #     s = f"{prefix}run 'pip install wandb' to automatically track and visualize YOLOv5 🚀 runs in Weights & Biases"
-        #     self.logger.info(s)
-        if not clearml:
-            prefix = colorstr('ClearML: ')
-            s = f"{prefix}run 'pip install clearml' to automatically track, visualize and remotely train YOLOv5 🚀 in ClearML"
-            self.logger.info(s)
-        if not comet_ml:
-            prefix = colorstr('Comet: ')
-            s = f"{prefix}run 'pip install comet_ml' to automatically track and visualize YOLOv5 🚀 runs in Comet"
-            self.logger.info(s)
-        # TensorBoard
-        s = self.save_dir
-        if 'tb' in self.include and not self.opt.evolve:
-            prefix = colorstr('TensorBoard: ')
-            self.logger.info(f"{prefix}Start with 'tensorboard --logdir {s.parent}', view at http://localhost:6006/")
-            self.tb = SummaryWriter(str(s))
-
-        # W&B
-        if wandb and 'wandb' in self.include:
-            wandb_artifact_resume = isinstance(self.opt.resume, str) and self.opt.resume.startswith('wandb-artifact://')
-            run_id = torch.load(self.weights).get('wandb_id') if self.opt.resume and not wandb_artifact_resume else None
-            self.opt.hyp = self.hyp  # add hyperparameters
-            self.wandb = WandbLogger(self.opt, run_id)
-            # temp warn. because nested artifacts not supported after 0.12.10
-            # if pkg.parse_version(wandb.__version__) >= pkg.parse_version('0.12.11'):
-            #    s = "YOLOv5 temporarily requires wandb version 0.12.10 or below. Some features may not work as expected."
-            #    self.logger.warning(s)
-        else:
-            self.wandb = None
-
-        # ClearML
-        if clearml and 'clearml' in self.include:
-            self.clearml = ClearmlLogger(self.opt, self.hyp)
-        else:
-            self.clearml = None
-
-        # Comet
-        if comet_ml and 'comet' in self.include:
-            if isinstance(self.opt.resume, str) and self.opt.resume.startswith("comet://"):
-                run_id = self.opt.resume.split("/")[-1]
-                self.comet_logger = CometLogger(self.opt, self.hyp, run_id=run_id)
-
-            else:
-                self.comet_logger = CometLogger(self.opt, self.hyp)
-
-        else:
-            self.comet_logger = None
-
-    @property
-    def remote_dataset(self):
-        # Get data_dict if custom dataset artifact link is provided
-        data_dict = None
-        if self.clearml:
-            data_dict = self.clearml.data_dict
-        if self.wandb:
-            data_dict = self.wandb.data_dict
-        if self.comet_logger:
-            data_dict = self.comet_logger.data_dict
-
-        return data_dict
-
-    def on_train_start(self):
-        if self.comet_logger:
-            self.comet_logger.on_train_start()
-
-    def on_pretrain_routine_start(self):
-        if self.comet_logger:
-            self.comet_logger.on_pretrain_routine_start()
-
-    def on_pretrain_routine_end(self, labels, names):
-        # Callback runs on pre-train routine end
-        if self.plots:
-            plot_labels(labels, names, self.save_dir)
-            paths = self.save_dir.glob('*labels*.jpg')  # training labels
-            if self.wandb:
-                self.wandb.log({"Labels": [wandb.Image(str(x), caption=x.name) for x in paths]})
-            # if self.clearml:
-            #    pass  # ClearML saves these images automatically using hooks
-            if self.comet_logger:
-                self.comet_logger.on_pretrain_routine_end(paths)
-
-    def on_train_batch_end(self, model, ni, imgs, targets, paths, vals):
-        log_dict = dict(zip(self.keys[0:3], vals))
-        # Callback runs on train batch end
-        # ni: number integrated batches (since train start)
-        if self.plots:
-            if ni < 3:
-                f = self.save_dir / f'train_batch{ni}.jpg'  # filename
-                plot_images(imgs, targets, paths, f)
-                if ni == 0 and self.tb and not self.opt.sync_bn:
-                    log_tensorboard_graph(self.tb, model, imgsz=(self.opt.imgsz, self.opt.imgsz))
-            if ni == 10 and (self.wandb or self.clearml):
-                files = sorted(self.save_dir.glob('train*.jpg'))
-                if self.wandb:
-                    self.wandb.log({'Mosaics': [wandb.Image(str(f), caption=f.name) for f in files if f.exists()]})
-                if self.clearml:
-                    self.clearml.log_debug_samples(files, title='Mosaics')
-
-        if self.comet_logger:
-            self.comet_logger.on_train_batch_end(log_dict, step=ni)
-
-    def on_train_epoch_end(self, epoch):
-        # Callback runs on train epoch end
-        if self.wandb:
-            self.wandb.current_epoch = epoch + 1
-
-        if self.comet_logger:
-            self.comet_logger.on_train_epoch_end(epoch)
-
-    def on_val_start(self):
-        if self.comet_logger:
-            self.comet_logger.on_val_start()
-
-    def on_val_image_end(self, pred, predn, path, names, im):
-        # Callback runs on val image end
-        if self.wandb:
-            self.wandb.val_one_image(pred, predn, path, names, im)
-        if self.clearml:
-            self.clearml.log_image_with_boxes(path, pred, names, im)
-
-    def on_val_batch_end(self, batch_i, im, targets, paths, shapes, out):
-        if self.comet_logger:
-            self.comet_logger.on_val_batch_end(batch_i, im, targets, paths, shapes, out)
-
-    def on_val_end(self, nt, tp, fp, p, r, f1, ap, ap50, ap_class, confusion_matrix):
-        # Callback runs on val end
-        if self.wandb or self.clearml:
-            files = sorted(self.save_dir.glob('val*.jpg'))
-            if self.wandb:
-                self.wandb.log({"Validation": [wandb.Image(str(f), caption=f.name) for f in files]})
-            if self.clearml:
-                self.clearml.log_debug_samples(files, title='Validation')
-
-        if self.comet_logger:
-            self.comet_logger.on_val_end(nt, tp, fp, p, r, f1, ap, ap50, ap_class, confusion_matrix)
-
-    def on_fit_epoch_end(self, vals, epoch, best_fitness, fi):
-        # Callback runs at the end of each fit (train+val) epoch
-        x = dict(zip(self.keys, vals))
-        if self.csv:
-            file = self.save_dir / 'results.csv'
-            n = len(x) + 1  # number of cols
-            s = '' if file.exists() else (('%20s,' * n % tuple(['epoch'] + self.keys)).rstrip(',') + '\n')  # add header
-            with open(file, 'a') as f:
-                f.write(s + ('%20.5g,' * n % tuple([epoch] + vals)).rstrip(',') + '\n')
-
-        if self.tb:
-            for k, v in x.items():
-                self.tb.add_scalar(k, v, epoch)
-        elif self.clearml:  # log to ClearML if TensorBoard not used
-            for k, v in x.items():
-                title, series = k.split('/')
-                self.clearml.task.get_logger().report_scalar(title, series, v, epoch)
-
-        if self.wandb:
-            if best_fitness == fi:
-                best_results = [epoch] + vals[3:7]
-                for i, name in enumerate(self.best_keys):
-                    self.wandb.wandb_run.summary[name] = best_results[i]  # log best results in the summary
-            self.wandb.log(x)
-            self.wandb.end_epoch(best_result=best_fitness == fi)
-
-        if self.clearml:
-            self.clearml.current_epoch_logged_images = set()  # reset epoch image limit
-            self.clearml.current_epoch += 1
-
-        if self.comet_logger:
-            self.comet_logger.on_fit_epoch_end(x, epoch=epoch)
-
-    def on_model_save(self, last, epoch, final_epoch, best_fitness, fi):
-        # Callback runs on model save event
-        if (epoch + 1) % self.opt.save_period == 0 and not final_epoch and self.opt.save_period != -1:
-            if self.wandb:
-                self.wandb.log_model(last.parent, self.opt, epoch, fi, best_model=best_fitness == fi)
-            if self.clearml:
-                self.clearml.task.update_output_model(model_path=str(last),
-                                                      model_name='Latest Model',
-                                                      auto_delete_file=False)
-
-        if self.comet_logger:
-            self.comet_logger.on_model_save(last, epoch, final_epoch, best_fitness, fi)
-
-    def on_train_end(self, last, best, epoch, results):
-        # Callback runs on training end, i.e. saving best model
-        if self.plots:
-            plot_results(file=self.save_dir / 'results.csv')  # save results.png
-        files = ['results.png', 'confusion_matrix.png', *(f'{x}_curve.png' for x in ('F1', 'PR', 'P', 'R'))]
-        files = [(self.save_dir / f) for f in files if (self.save_dir / f).exists()]  # filter
-        self.logger.info(f"Results saved to {colorstr('bold', self.save_dir)}")
-
-        if self.tb and not self.clearml:  # These images are already captured by ClearML by now, we don't want doubles
-            for f in files:
-                self.tb.add_image(f.stem, cv2.imread(str(f))[..., ::-1], epoch, dataformats='HWC')
-
-        if self.wandb:
-            self.wandb.log(dict(zip(self.keys[3:10], results)))
-            self.wandb.log({"Results": [wandb.Image(str(f), caption=f.name) for f in files]})
-            # Calling wandb.log. TODO: Refactor this into WandbLogger.log_model
-            if not self.opt.evolve:
-                wandb.log_artifact(str(best if best.exists() else last),
-                                   type='model',
-                                   name=f'run_{self.wandb.wandb_run.id}_model',
-                                   aliases=['latest', 'best', 'stripped'])
-            self.wandb.finish_run()
-
-        if self.clearml and not self.opt.evolve:
-            self.clearml.task.update_output_model(model_path=str(best if best.exists() else last),
-                                                  name='Best Model',
-                                                  auto_delete_file=False)
-
-        if self.comet_logger:
-            final_results = dict(zip(self.keys[3:10], results))
-            self.comet_logger.on_train_end(files, self.save_dir, last, best, epoch, final_results)
-
-    def on_params_update(self, params: dict):
-        # Update hyperparams or configs of the experiment
-        if self.wandb:
-            self.wandb.wandb_run.config.update(params, allow_val_change=True)
-        if self.comet_logger:
-            self.comet_logger.on_params_update(params)
-
-
-class GenericLogger:
-    """
-    YOLOv5 General purpose logger for non-task specific logging
-    Usage: from utils.loggers import GenericLogger; logger = GenericLogger(...)
-    Arguments
-        opt:             Run arguments
-        console_logger:  Console logger
-        include:         loggers to include
-    """
-
-    def __init__(self, opt, console_logger, include=('tb', 'wandb')):
-        # init default loggers
-        self.save_dir = Path(opt.save_dir)
-        self.include = include
-        self.console_logger = console_logger
-        self.csv = self.save_dir / 'results.csv'  # CSV logger
-        if 'tb' in self.include:
-            prefix = colorstr('TensorBoard: ')
-            self.console_logger.info(
-                f"{prefix}Start with 'tensorboard --logdir {self.save_dir.parent}', view at http://localhost:6006/")
-            self.tb = SummaryWriter(str(self.save_dir))
-
-        if wandb and 'wandb' in self.include:
-            self.wandb = wandb.init(project=web_project_name(str(opt.project)),
-                                    name=None if opt.name == "exp" else opt.name,
-                                    config=opt)
-        else:
-            self.wandb = None
-
-    def log_metrics(self, metrics, epoch):
-        # Log metrics dictionary to all loggers
-        if self.csv:
-            keys, vals = list(metrics.keys()), list(metrics.values())
-            n = len(metrics) + 1  # number of cols
-            s = '' if self.csv.exists() else (('%23s,' * n % tuple(['epoch'] + keys)).rstrip(',') + '\n')  # header
-            with open(self.csv, 'a') as f:
-                f.write(s + ('%23.5g,' * n % tuple([epoch] + vals)).rstrip(',') + '\n')
-
-        if self.tb:
-            for k, v in metrics.items():
-                self.tb.add_scalar(k, v, epoch)
-
-        if self.wandb:
-            self.wandb.log(metrics, step=epoch)
-
-    def log_images(self, files, name='Images', epoch=0):
-        # Log images to all loggers
-        files = [Path(f) for f in (files if isinstance(files, (tuple, list)) else [files])]  # to Path
-        files = [f for f in files if f.exists()]  # filter by exists
-
-        if self.tb:
-            for f in files:
-                self.tb.add_image(f.stem, cv2.imread(str(f))[..., ::-1], epoch, dataformats='HWC')
-
-        if self.wandb:
-            self.wandb.log({name: [wandb.Image(str(f), caption=f.name) for f in files]}, step=epoch)
-
-    def log_graph(self, model, imgsz=(640, 640)):
-        # Log model graph to all loggers
-        if self.tb:
-            log_tensorboard_graph(self.tb, model, imgsz)
-
-    def log_model(self, model_path, epoch=0, metadata={}):
-        # Log model to all loggers
-        if self.wandb:
-            art = wandb.Artifact(name=f"run_{wandb.run.id}_model", type="model", metadata=metadata)
-            art.add_file(str(model_path))
-            wandb.log_artifact(art)
-
-    def update_params(self, params):
-        # Update the paramters logged
-        if self.wandb:
-            wandb.run.config.update(params, allow_val_change=True)
-
-
-def log_tensorboard_graph(tb, model, imgsz=(640, 640)):
-    # Log model graph to TensorBoard
-    try:
-        p = next(model.parameters())  # for device, type
-        imgsz = (imgsz, imgsz) if isinstance(imgsz, int) else imgsz  # expand
-        im = torch.zeros((1, 3, *imgsz)).to(p.device).type_as(p)  # input image (WARNING: must be zeros, not empty)
-        with warnings.catch_warnings():
-            warnings.simplefilter('ignore')  # suppress jit trace warning
-            tb.add_graph(torch.jit.trace(de_parallel(model), im, strict=False), [])
-    except Exception as e:
-        LOGGER.warning(f'WARNING ⚠️ TensorBoard graph visualization failure {e}')
-
-
-def web_project_name(project):
-    # Convert local project name to web project name
-    if not project.startswith('runs/train'):
-        return project
-    suffix = '-Classify' if project.endswith('-cls') else '-Segment' if project.endswith('-seg') else ''
-    return f'YOLOv5{suffix}'

spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_internal/network/download.py

@@ -1,186 +0,0 @@
-"""Download files with progress indicators.
-"""
-import email.message
-import logging
-import mimetypes
-import os
-from typing import Iterable, Optional, Tuple
-
-from pip._vendor.requests.models import CONTENT_CHUNK_SIZE, Response
-
-from pip._internal.cli.progress_bars import get_download_progress_renderer
-from pip._internal.exceptions import NetworkConnectionError
-from pip._internal.models.index import PyPI
-from pip._internal.models.link import Link
-from pip._internal.network.cache import is_from_cache
-from pip._internal.network.session import PipSession
-from pip._internal.network.utils import HEADERS, raise_for_status, response_chunks
-from pip._internal.utils.misc import format_size, redact_auth_from_url, splitext
-
-logger = logging.getLogger(__name__)
-
-
-def _get_http_response_size(resp: Response) -> Optional[int]:
-    try:
-        return int(resp.headers["content-length"])
-    except (ValueError, KeyError, TypeError):
-        return None
-
-
-def _prepare_download(
-    resp: Response,
-    link: Link,
-    progress_bar: str,
-) -> Iterable[bytes]:
-    total_length = _get_http_response_size(resp)
-
-    if link.netloc == PyPI.file_storage_domain:
-        url = link.show_url
-    else:
-        url = link.url_without_fragment
-
-    logged_url = redact_auth_from_url(url)
-
-    if total_length:
-        logged_url = "{} ({})".format(logged_url, format_size(total_length))
-
-    if is_from_cache(resp):
-        logger.info("Using cached %s", logged_url)
-    else:
-        logger.info("Downloading %s", logged_url)
-
-    if logger.getEffectiveLevel() > logging.INFO:
-        show_progress = False
-    elif is_from_cache(resp):
-        show_progress = False
-    elif not total_length:
-        show_progress = True
-    elif total_length > (40 * 1000):
-        show_progress = True
-    else:
-        show_progress = False
-
-    chunks = response_chunks(resp, CONTENT_CHUNK_SIZE)
-
-    if not show_progress:
-        return chunks
-
-    renderer = get_download_progress_renderer(bar_type=progress_bar, size=total_length)
-    return renderer(chunks)
-
-
-def sanitize_content_filename(filename: str) -> str:
-    """
-    Sanitize the "filename" value from a Content-Disposition header.
-    """
-    return os.path.basename(filename)
-
-
-def parse_content_disposition(content_disposition: str, default_filename: str) -> str:
-    """
-    Parse the "filename" value from a Content-Disposition header, and
-    return the default filename if the result is empty.
-    """
-    m = email.message.Message()
-    m["content-type"] = content_disposition
-    filename = m.get_param("filename")
-    if filename:
-        # We need to sanitize the filename to prevent directory traversal
-        # in case the filename contains ".." path parts.
-        filename = sanitize_content_filename(str(filename))
-    return filename or default_filename
-
-
-def _get_http_response_filename(resp: Response, link: Link) -> str:
-    """Get an ideal filename from the given HTTP response, falling back to
-    the link filename if not provided.
-    """
-    filename = link.filename  # fallback
-    # Have a look at the Content-Disposition header for a better guess
-    content_disposition = resp.headers.get("content-disposition")
-    if content_disposition:
-        filename = parse_content_disposition(content_disposition, filename)
-    ext: Optional[str] = splitext(filename)[1]
-    if not ext:
-        ext = mimetypes.guess_extension(resp.headers.get("content-type", ""))
-        if ext:
-            filename += ext
-    if not ext and link.url != resp.url:
-        ext = os.path.splitext(resp.url)[1]
-        if ext:
-            filename += ext
-    return filename
-
-
-def _http_get_download(session: PipSession, link: Link) -> Response:
-    target_url = link.url.split("#", 1)[0]
-    resp = session.get(target_url, headers=HEADERS, stream=True)
-    raise_for_status(resp)
-    return resp
-
-
-class Downloader:
-    def __init__(
-        self,
-        session: PipSession,
-        progress_bar: str,
-    ) -> None:
-        self._session = session
-        self._progress_bar = progress_bar
-
-    def __call__(self, link: Link, location: str) -> Tuple[str, str]:
-        """Download the file given by link into location."""
-        try:
-            resp = _http_get_download(self._session, link)
-        except NetworkConnectionError as e:
-            assert e.response is not None
-            logger.critical(
-                "HTTP error %s while getting %s", e.response.status_code, link
-            )
-            raise
-
-        filename = _get_http_response_filename(resp, link)
-        filepath = os.path.join(location, filename)
-
-        chunks = _prepare_download(resp, link, self._progress_bar)
-        with open(filepath, "wb") as content_file:
-            for chunk in chunks:
-                content_file.write(chunk)
-        content_type = resp.headers.get("Content-Type", "")
-        return filepath, content_type
-
-
-class BatchDownloader:
-    def __init__(
-        self,
-        session: PipSession,
-        progress_bar: str,
-    ) -> None:
-        self._session = session
-        self._progress_bar = progress_bar
-
-    def __call__(
-        self, links: Iterable[Link], location: str
-    ) -> Iterable[Tuple[Link, Tuple[str, str]]]:
-        """Download the files given by links into location."""
-        for link in links:
-            try:
-                resp = _http_get_download(self._session, link)
-            except NetworkConnectionError as e:
-                assert e.response is not None
-                logger.critical(
-                    "HTTP error %s while getting %s",
-                    e.response.status_code,
-                    link,
-                )
-                raise
-
-            filename = _get_http_response_filename(resp, link)
-            filepath = os.path.join(location, filename)
-
-            chunks = _prepare_download(resp, link, self._progress_bar)
-            with open(filepath, "wb") as content_file:
-                for chunk in chunks:
-                    content_file.write(chunk)
-            content_type = resp.headers.get("Content-Type", "")
-            yield link, (filepath, content_type)

spaces/AutoLLM/AutoAgents/setup.py

@@ -1,7 +0,0 @@
-from setuptools import setup, find_packages
-
-setup(
-    name='autoagents',
-    version='0.1.0',
-    packages=find_packages(include=['autoagents', 'autoagents.*'])
-)

spaces/AzumaSeren100/XuanShen-Bert-VITS2/text/english.py

@@ -1,138 +0,0 @@
-import pickle
-import os
-import re
-from g2p_en import G2p
-from string import punctuation
-
-from text import symbols
-
-current_file_path = os.path.dirname(__file__)
-CMU_DICT_PATH = os.path.join(current_file_path, 'cmudict.rep')
-CACHE_PATH = os.path.join(current_file_path, 'cmudict_cache.pickle')
-_g2p = G2p()
-
-arpa = {'AH0', 'S', 'AH1', 'EY2', 'AE2', 'EH0', 'OW2', 'UH0', 'NG', 'B', 'G', 'AY0', 'M', 'AA0', 'F', 'AO0', 'ER2', 'UH1', 'IY1', 'AH2', 'DH', 'IY0', 'EY1', 'IH0', 'K', 'N', 'W', 'IY2', 'T', 'AA1', 'ER1', 'EH2', 'OY0', 'UH2', 'UW1', 'Z', 'AW2', 'AW1', 'V', 'UW2', 'AA2', 'ER', 'AW0', 'UW0', 'R', 'OW1', 'EH1', 'ZH', 'AE0', 'IH2', 'IH', 'Y', 'JH', 'P', 'AY1', 'EY0', 'OY2', 'TH', 'HH', 'D', 'ER0', 'CH', 'AO1', 'AE1', 'AO2', 'OY1', 'AY2', 'IH1', 'OW0', 'L', 'SH'}
-
-
-def post_replace_ph(ph):
-    rep_map = {
-        '：': ',',
-        '；': ',',
-        '，': ',',
-        '。': '.',
-        '！': '!',
-        '？': '?',
-        '\n': '.',
-        "·": ",",
-        '、': ",",
-        '...': '…',
-        'v': "V"
-    }
-    if ph in rep_map.keys():
-        ph = rep_map[ph]
-    if ph in symbols:
-        return ph
-    if ph not in symbols:
-        ph = 'UNK'
-    return ph
-
-def read_dict():
-    g2p_dict = {}
-    start_line = 49
-    with open(CMU_DICT_PATH) as f:
-        line = f.readline()
-        line_index = 1
-        while line:
-            if line_index >= start_line:
-                line = line.strip()
-                word_split = line.split('  ')
-                word = word_split[0]
-
-                syllable_split = word_split[1].split(' - ')
-                g2p_dict[word] = []
-                for syllable in syllable_split:
-                    phone_split = syllable.split(' ')
-                    g2p_dict[word].append(phone_split)
-
-            line_index = line_index + 1
-            line = f.readline()
-
-    return g2p_dict
-
-
-def cache_dict(g2p_dict, file_path):
-    with open(file_path, 'wb') as pickle_file:
-        pickle.dump(g2p_dict, pickle_file)
-
-
-def get_dict():
-    if os.path.exists(CACHE_PATH):
-        with open(CACHE_PATH, 'rb') as pickle_file:
-            g2p_dict = pickle.load(pickle_file)
-    else:
-        g2p_dict = read_dict()
-        cache_dict(g2p_dict, CACHE_PATH)
-
-    return g2p_dict
-
-eng_dict = get_dict()
-
-def refine_ph(phn):
-    tone = 0
-    if re.search(r'\d$', phn):
-        tone = int(phn[-1]) + 1
-        phn = phn[:-1]
-    return phn.lower(), tone
-
-def refine_syllables(syllables):
-    tones = []
-    phonemes = []
-    for phn_list in syllables:
-        for i in range(len(phn_list)):
-            phn = phn_list[i]
-            phn, tone = refine_ph(phn)
-            phonemes.append(phn)
-            tones.append(tone)
-    return phonemes, tones
-
-
-def text_normalize(text):
-    # todo: eng text normalize
-    return text
-
-def g2p(text):
-
-    phones = []
-    tones = []
-    words = re.split(r"([,;.\-\?\!\s+])", text)
-    for w in words:
-        if w.upper() in eng_dict:
-            phns, tns = refine_syllables(eng_dict[w.upper()])
-            phones += phns
-            tones += tns
-        else:
-            phone_list = list(filter(lambda p: p != " ", _g2p(w)))
-            for ph in phone_list:
-                if ph in arpa:
-                    ph, tn = refine_ph(ph)
-                    phones.append(ph)
-                    tones.append(tn)
-                else:
-                    phones.append(ph)
-                    tones.append(0)
-    # todo: implement word2ph
-    word2ph = [1 for i in phones]
-
-    phones = [post_replace_ph(i) for i in phones]
-    return phones, tones, word2ph
-
-if __name__ == "__main__":
-    # print(get_dict())
-    # print(eng_word_to_phoneme("hello"))
-    print(g2p("In this paper, we propose 1 DSPGAN, a GAN-based universal vocoder."))
-    # all_phones = set()
-    # for k, syllables in eng_dict.items():
-    #     for group in syllables:
-    #         for ph in group:
-    #             all_phones.add(ph)
-    # print(all_phones)

spaces/BAAI/vid2vid-zero/gradio_demo/runner.py

@@ -1,137 +0,0 @@
-from __future__ import annotations
-
-import datetime
-import os
-import pathlib
-import shlex
-import shutil
-import subprocess
-import sys
-
-import gradio as gr
-import slugify
-import torch
-import huggingface_hub
-from huggingface_hub import HfApi
-from omegaconf import OmegaConf
-
-
-ORIGINAL_SPACE_ID = 'BAAI/vid2vid-zero'
-SPACE_ID = os.getenv('SPACE_ID', ORIGINAL_SPACE_ID)
-
-
-class Runner:
-    def __init__(self, hf_token: str | None = None):
-        self.hf_token = hf_token
-
-        self.checkpoint_dir = pathlib.Path('checkpoints')
-        self.checkpoint_dir.mkdir(exist_ok=True)
-
-    def download_base_model(self, base_model_id: str, token=None) -> str:
-        model_dir = self.checkpoint_dir / base_model_id
-        org_name = base_model_id.split('/')[0]
-        org_dir = self.checkpoint_dir / org_name
-        if not model_dir.exists():
-            org_dir.mkdir(exist_ok=True)
-        print(f'https://huggingface.co/{base_model_id}')
-        if token == None:
-            subprocess.run(shlex.split(f'git lfs install'), cwd=org_dir)
-            subprocess.run(shlex.split(
-                f'git lfs clone https://huggingface.co/{base_model_id}'),
-                            cwd=org_dir)
-            return model_dir.as_posix()
-        else:
-            temp_path = huggingface_hub.snapshot_download(base_model_id, use_auth_token=token)
-            print(temp_path, org_dir)
-            # subprocess.run(shlex.split(f'mv {temp_path} {model_dir.as_posix()}'))
-            # return model_dir.as_posix()
-            return temp_path
-
-    def join_model_library_org(self, token: str) -> None:
-        subprocess.run(
-            shlex.split(
-                f'curl -X POST -H "Authorization: Bearer {token}" -H "Content-Type: application/json" {URL_TO_JOIN_MODEL_LIBRARY_ORG}'
-            ))
-
-    def run_vid2vid_zero(
-        self,
-        model_path: str,
-        input_video: str,
-        prompt: str,
-        n_sample_frames: int,
-        sample_start_idx: int,
-        sample_frame_rate: int,
-        validation_prompt: str,
-        guidance_scale: float,
-        resolution: str,
-        seed: int,
-        remove_gpu_after_running: bool,
-        input_token: str = None,
-    ) -> str:
-
-        if not torch.cuda.is_available():
-            raise gr.Error('CUDA is not available.')
-        if input_video is None:
-            raise gr.Error('You need to upload a video.')
-        if not prompt:
-            raise gr.Error('The input prompt is missing.')
-        if not validation_prompt:
-            raise gr.Error('The validation prompt is missing.')
-
-        resolution = int(resolution)
-        n_sample_frames = int(n_sample_frames)
-        sample_start_idx = int(sample_start_idx)
-        sample_frame_rate = int(sample_frame_rate)
-
-        repo_dir = pathlib.Path(__file__).parent
-        prompt_path = prompt.replace(' ', '_')
-        output_dir = repo_dir / 'outputs' / prompt_path
-        output_dir.mkdir(parents=True, exist_ok=True)
-
-        config = OmegaConf.load('configs/black-swan.yaml')
-        config.pretrained_model_path = self.download_base_model(model_path, token=input_token)
-
-        # we remove null-inversion & use fp16 for fast inference on web demo
-        config.mixed_precision = "fp16"
-        config.validation_data.use_null_inv = False
-
-        config.output_dir = output_dir.as_posix()
-        config.input_data.video_path = input_video.name  # type: ignore
-        config.input_data.prompt = prompt
-        config.input_data.n_sample_frames = n_sample_frames
-        config.input_data.width = resolution
-        config.input_data.height = resolution
-        config.input_data.sample_start_idx = sample_start_idx
-        config.input_data.sample_frame_rate = sample_frame_rate
-
-        config.validation_data.prompts = [validation_prompt]
-        config.validation_data.video_length = 8
-        config.validation_data.width = resolution
-        config.validation_data.height = resolution
-        config.validation_data.num_inference_steps = 50
-        config.validation_data.guidance_scale = guidance_scale
-
-        config.input_batch_size = 1
-        config.seed = seed
-
-        config_path = output_dir / 'config.yaml'
-        with open(config_path, 'w') as f:
-            OmegaConf.save(config, f)
-
-        command = f'accelerate launch test_vid2vid_zero.py --config {config_path}'
-        subprocess.run(shlex.split(command))
-
-        output_video_path = os.path.join(output_dir, "sample-all.mp4")
-        print(f"video path for gradio: {output_video_path}")
-        message = 'Running completed!'
-        print(message)
-
-        if remove_gpu_after_running:
-            space_id = os.getenv('SPACE_ID')
-            if space_id:
-                api = HfApi(
-                    token=self.hf_token if self.hf_token else input_token)
-                api.request_space_hardware(repo_id=space_id,
-                                           hardware='cpu-basic')
-
-        return output_video_path

spaces/BasToTheMax/22h-vintedois-diffusion-v0-1/app.py

@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/22h/vintedois-diffusion-v0-1").launch()

spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_internal/operations/build/build_tracker.py

@@ -1,124 +0,0 @@
-import contextlib
-import hashlib
-import logging
-import os
-from types import TracebackType
-from typing import Dict, Generator, Optional, Set, Type, Union
-
-from pip._internal.models.link import Link
-from pip._internal.req.req_install import InstallRequirement
-from pip._internal.utils.temp_dir import TempDirectory
-
-logger = logging.getLogger(__name__)
-
-
-@contextlib.contextmanager
-def update_env_context_manager(**changes: str) -> Generator[None, None, None]:
-    target = os.environ
-
-    # Save values from the target and change them.
-    non_existent_marker = object()
-    saved_values: Dict[str, Union[object, str]] = {}
-    for name, new_value in changes.items():
-        try:
-            saved_values[name] = target[name]
-        except KeyError:
-            saved_values[name] = non_existent_marker
-        target[name] = new_value
-
-    try:
-        yield
-    finally:
-        # Restore original values in the target.
-        for name, original_value in saved_values.items():
-            if original_value is non_existent_marker:
-                del target[name]
-            else:
-                assert isinstance(original_value, str)  # for mypy
-                target[name] = original_value
-
-
-@contextlib.contextmanager
-def get_build_tracker() -> Generator["BuildTracker", None, None]:
-    root = os.environ.get("PIP_BUILD_TRACKER")
-    with contextlib.ExitStack() as ctx:
-        if root is None:
-            root = ctx.enter_context(TempDirectory(kind="build-tracker")).path
-            ctx.enter_context(update_env_context_manager(PIP_BUILD_TRACKER=root))
-            logger.debug("Initialized build tracking at %s", root)
-
-        with BuildTracker(root) as tracker:
-            yield tracker
-
-
-class BuildTracker:
-    def __init__(self, root: str) -> None:
-        self._root = root
-        self._entries: Set[InstallRequirement] = set()
-        logger.debug("Created build tracker: %s", self._root)
-
-    def __enter__(self) -> "BuildTracker":
-        logger.debug("Entered build tracker: %s", self._root)
-        return self
-
-    def __exit__(
-        self,
-        exc_type: Optional[Type[BaseException]],
-        exc_val: Optional[BaseException],
-        exc_tb: Optional[TracebackType],
-    ) -> None:
-        self.cleanup()
-
-    def _entry_path(self, link: Link) -> str:
-        hashed = hashlib.sha224(link.url_without_fragment.encode()).hexdigest()
-        return os.path.join(self._root, hashed)
-
-    def add(self, req: InstallRequirement) -> None:
-        """Add an InstallRequirement to build tracking."""
-
-        assert req.link
-        # Get the file to write information about this requirement.
-        entry_path = self._entry_path(req.link)
-
-        # Try reading from the file. If it exists and can be read from, a build
-        # is already in progress, so a LookupError is raised.
-        try:
-            with open(entry_path) as fp:
-                contents = fp.read()
-        except FileNotFoundError:
-            pass
-        else:
-            message = "{} is already being built: {}".format(req.link, contents)
-            raise LookupError(message)
-
-        # If we're here, req should really not be building already.
-        assert req not in self._entries
-
-        # Start tracking this requirement.
-        with open(entry_path, "w", encoding="utf-8") as fp:
-            fp.write(str(req))
-        self._entries.add(req)
-
-        logger.debug("Added %s to build tracker %r", req, self._root)
-
-    def remove(self, req: InstallRequirement) -> None:
-        """Remove an InstallRequirement from build tracking."""
-
-        assert req.link
-        # Delete the created file and the corresponding entries.
-        os.unlink(self._entry_path(req.link))
-        self._entries.remove(req)
-
-        logger.debug("Removed %s from build tracker %r", req, self._root)
-
-    def cleanup(self) -> None:
-        for req in set(self._entries):
-            self.remove(req)
-
-        logger.debug("Removed build tracker: %r", self._root)
-
-    @contextlib.contextmanager
-    def track(self, req: InstallRequirement) -> Generator[None, None, None]:
-        self.add(req)
-        yield
-        self.remove(req)

spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_internal/utils/inject_securetransport.py

@@ -1,35 +0,0 @@
-"""A helper module that injects SecureTransport, on import.
-
-The import should be done as early as possible, to ensure all requests and
-sessions (or whatever) are created after injecting SecureTransport.
-
-Note that we only do the injection on macOS, when the linked OpenSSL is too
-old to handle TLSv1.2.
-"""
-
-import sys
-
-
-def inject_securetransport() -> None:
-    # Only relevant on macOS
-    if sys.platform != "darwin":
-        return
-
-    try:
-        import ssl
-    except ImportError:
-        return
-
-    # Checks for OpenSSL 1.0.1
-    if ssl.OPENSSL_VERSION_NUMBER >= 0x1000100F:
-        return
-
-    try:
-        from pip._vendor.urllib3.contrib import securetransport
-    except (ImportError, OSError):
-        return
-
-    securetransport.inject_into_urllib3()
-
-
-inject_securetransport()

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

@@ -1,210 +0,0 @@
-from distutils import log
-import distutils.command.sdist as orig
-import os
-import sys
-import io
-import contextlib
-from itertools import chain
-
-from .py36compat import sdist_add_defaults
-
-from .._importlib import metadata
-from .build import _ORIGINAL_SUBCOMMANDS
-
-_default_revctrl = list
-
-
-def walk_revctrl(dirname=''):
-    """Find all files under revision control"""
-    for ep in metadata.entry_points(group='setuptools.file_finders'):
-        for item in ep.load()(dirname):
-            yield item
-
-
-class sdist(sdist_add_defaults, orig.sdist):
-    """Smart sdist that finds anything supported by revision control"""
-
-    user_options = [
-        ('formats=', None,
-         "formats for source distribution (comma-separated list)"),
-        ('keep-temp', 'k',
-         "keep the distribution tree around after creating " +
-         "archive file(s)"),
-        ('dist-dir=', 'd',
-         "directory to put the source distribution archive(s) in "
-         "[default: dist]"),
-        ('owner=', 'u',
-         "Owner name used when creating a tar file [default: current user]"),
-        ('group=', 'g',
-         "Group name used when creating a tar file [default: current group]"),
-    ]
-
-    negative_opt = {}
-
-    README_EXTENSIONS = ['', '.rst', '.txt', '.md']
-    READMES = tuple('README{0}'.format(ext) for ext in README_EXTENSIONS)
-
-    def run(self):
-        self.run_command('egg_info')
-        ei_cmd = self.get_finalized_command('egg_info')
-        self.filelist = ei_cmd.filelist
-        self.filelist.append(os.path.join(ei_cmd.egg_info, 'SOURCES.txt'))
-        self.check_readme()
-
-        # Run sub commands
-        for cmd_name in self.get_sub_commands():
-            self.run_command(cmd_name)
-
-        self.make_distribution()
-
-        dist_files = getattr(self.distribution, 'dist_files', [])
-        for file in self.archive_files:
-            data = ('sdist', '', file)
-            if data not in dist_files:
-                dist_files.append(data)
-
-    def initialize_options(self):
-        orig.sdist.initialize_options(self)
-
-        self._default_to_gztar()
-
-    def _default_to_gztar(self):
-        # only needed on Python prior to 3.6.
-        if sys.version_info >= (3, 6, 0, 'beta', 1):
-            return
-        self.formats = ['gztar']
-
-    def make_distribution(self):
-        """
-        Workaround for #516
-        """
-        with self._remove_os_link():
-            orig.sdist.make_distribution(self)
-
-    @staticmethod
-    @contextlib.contextmanager
-    def _remove_os_link():
-        """
-        In a context, remove and restore os.link if it exists
-        """
-
-        class NoValue:
-            pass
-
-        orig_val = getattr(os, 'link', NoValue)
-        try:
-            del os.link
-        except Exception:
-            pass
-        try:
-            yield
-        finally:
-            if orig_val is not NoValue:
-                setattr(os, 'link', orig_val)
-
-    def add_defaults(self):
-        super().add_defaults()
-        self._add_defaults_build_sub_commands()
-
-    def _add_defaults_optional(self):
-        super()._add_defaults_optional()
-        if os.path.isfile('pyproject.toml'):
-            self.filelist.append('pyproject.toml')
-
-    def _add_defaults_python(self):
-        """getting python files"""
-        if self.distribution.has_pure_modules():
-            build_py = self.get_finalized_command('build_py')
-            self.filelist.extend(build_py.get_source_files())
-            self._add_data_files(self._safe_data_files(build_py))
-
-    def _add_defaults_build_sub_commands(self):
-        build = self.get_finalized_command("build")
-        missing_cmds = set(build.get_sub_commands()) - _ORIGINAL_SUBCOMMANDS
-        # ^-- the original built-in sub-commands are already handled by default.
-        cmds = (self.get_finalized_command(c) for c in missing_cmds)
-        files = (c.get_source_files() for c in cmds if hasattr(c, "get_source_files"))
-        self.filelist.extend(chain.from_iterable(files))
-
-    def _safe_data_files(self, build_py):
-        """
-        Since the ``sdist`` class is also used to compute the MANIFEST
-        (via :obj:`setuptools.command.egg_info.manifest_maker`),
-        there might be recursion problems when trying to obtain the list of
-        data_files and ``include_package_data=True`` (which in turn depends on
-        the files included in the MANIFEST).
-
-        To avoid that, ``manifest_maker`` should be able to overwrite this
-        method and avoid recursive attempts to build/analyze the MANIFEST.
-        """
-        return build_py.data_files
-
-    def _add_data_files(self, data_files):
-        """
-        Add data files as found in build_py.data_files.
-        """
-        self.filelist.extend(
-            os.path.join(src_dir, name)
-            for _, src_dir, _, filenames in data_files
-            for name in filenames
-        )
-
-    def _add_defaults_data_files(self):
-        try:
-            super()._add_defaults_data_files()
-        except TypeError:
-            log.warn("data_files contains unexpected objects")
-
-    def check_readme(self):
-        for f in self.READMES:
-            if os.path.exists(f):
-                return
-        else:
-            self.warn(
-                "standard file not found: should have one of " +
-                ', '.join(self.READMES)
-            )
-
-    def make_release_tree(self, base_dir, files):
-        orig.sdist.make_release_tree(self, base_dir, files)
-
-        # Save any egg_info command line options used to create this sdist
-        dest = os.path.join(base_dir, 'setup.cfg')
-        if hasattr(os, 'link') and os.path.exists(dest):
-            # unlink and re-copy, since it might be hard-linked, and
-            # we don't want to change the source version
-            os.unlink(dest)
-            self.copy_file('setup.cfg', dest)
-
-        self.get_finalized_command('egg_info').save_version_info(dest)
-
-    def _manifest_is_not_generated(self):
-        # check for special comment used in 2.7.1 and higher
-        if not os.path.isfile(self.manifest):
-            return False
-
-        with io.open(self.manifest, 'rb') as fp:
-            first_line = fp.readline()
-        return (first_line !=
-                '# file GENERATED by distutils, do NOT edit\n'.encode())
-
-    def read_manifest(self):
-        """Read the manifest file (named by 'self.manifest') and use it to
-        fill in 'self.filelist', the list of files to include in the source
-        distribution.
-        """
-        log.info("reading manifest file '%s'", self.manifest)
-        manifest = open(self.manifest, 'rb')
-        for line in manifest:
-            # The manifest must contain UTF-8. See #303.
-            try:
-                line = line.decode('UTF-8')
-            except UnicodeDecodeError:
-                log.warn("%r not UTF-8 decodable -- skipping" % line)
-                continue
-            # ignore comments and blank lines
-            line = line.strip()
-            if line.startswith('#') or not line:
-                continue
-            self.filelist.append(line)
-        manifest.close()

spaces/BongoCaat/ArtGenerator/README.md

@@ -1,13 +0,0 @@
----
-title: ArtGenerator
-emoji: 🏃
-colorFrom: red
-colorTo: red
-sdk: gradio
-sdk_version: 3.29.0
-app_file: app.py
-pinned: false
-license: gpl-3.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/detectron2/modeling/proposal_generator/rpn.py

@@ -1,185 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-from typing import Dict, List
-import torch
-import torch.nn.functional as F
-from torch import nn
-
-from detectron2.layers import ShapeSpec
-from detectron2.utils.registry import Registry
-
-from ..anchor_generator import build_anchor_generator
-from ..box_regression import Box2BoxTransform
-from ..matcher import Matcher
-from .build import PROPOSAL_GENERATOR_REGISTRY
-from .rpn_outputs import RPNOutputs, find_top_rpn_proposals
-
-RPN_HEAD_REGISTRY = Registry("RPN_HEAD")
-RPN_HEAD_REGISTRY.__doc__ = """
-Registry for RPN heads, which take feature maps and perform
-objectness classification and bounding box regression for anchors.
-
-The registered object will be called with `obj(cfg, input_shape)`.
-The call should return a `nn.Module` object.
-"""
-
-
-def build_rpn_head(cfg, input_shape):
-    """
-    Build an RPN head defined by `cfg.MODEL.RPN.HEAD_NAME`.
-    """
-    name = cfg.MODEL.RPN.HEAD_NAME
-    return RPN_HEAD_REGISTRY.get(name)(cfg, input_shape)
-
-
-@RPN_HEAD_REGISTRY.register()
-class StandardRPNHead(nn.Module):
-    """
-    RPN classification and regression heads. Uses a 3x3 conv to produce a shared
-    hidden state from which one 1x1 conv predicts objectness logits for each anchor
-    and a second 1x1 conv predicts bounding-box deltas specifying how to deform
-    each anchor into an object proposal.
-    """
-
-    def __init__(self, cfg, input_shape: List[ShapeSpec]):
-        super().__init__()
-
-        # Standard RPN is shared across levels:
-        in_channels = [s.channels for s in input_shape]
-        assert len(set(in_channels)) == 1, "Each level must have the same channel!"
-        in_channels = in_channels[0]
-
-        # RPNHead should take the same input as anchor generator
-        # NOTE: it assumes that creating an anchor generator does not have unwanted side effect.
-        anchor_generator = build_anchor_generator(cfg, input_shape)
-        num_cell_anchors = anchor_generator.num_cell_anchors
-        box_dim = anchor_generator.box_dim
-        assert (
-            len(set(num_cell_anchors)) == 1
-        ), "Each level must have the same number of cell anchors"
-        num_cell_anchors = num_cell_anchors[0]
-
-        # 3x3 conv for the hidden representation
-        self.conv = nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1)
-        # 1x1 conv for predicting objectness logits
-        self.objectness_logits = nn.Conv2d(in_channels, num_cell_anchors, kernel_size=1, stride=1)
-        # 1x1 conv for predicting box2box transform deltas
-        self.anchor_deltas = nn.Conv2d(
-            in_channels, num_cell_anchors * box_dim, kernel_size=1, stride=1
-        )
-
-        for l in [self.conv, self.objectness_logits, self.anchor_deltas]:
-            nn.init.normal_(l.weight, std=0.01)
-            nn.init.constant_(l.bias, 0)
-
-    def forward(self, features):
-        """
-        Args:
-            features (list[Tensor]): list of feature maps
-        """
-        pred_objectness_logits = []
-        pred_anchor_deltas = []
-        for x in features:
-            t = F.relu(self.conv(x))
-            pred_objectness_logits.append(self.objectness_logits(t))
-            pred_anchor_deltas.append(self.anchor_deltas(t))
-        return pred_objectness_logits, pred_anchor_deltas
-
-
-@PROPOSAL_GENERATOR_REGISTRY.register()
-class RPN(nn.Module):
-    """
-    Region Proposal Network, introduced by the Faster R-CNN paper.
-    """
-
-    def __init__(self, cfg, input_shape: Dict[str, ShapeSpec]):
-        super().__init__()
-
-        # fmt: off
-        self.min_box_side_len        = cfg.MODEL.PROPOSAL_GENERATOR.MIN_SIZE
-        self.in_features             = cfg.MODEL.RPN.IN_FEATURES
-        self.nms_thresh              = cfg.MODEL.RPN.NMS_THRESH
-        self.batch_size_per_image    = cfg.MODEL.RPN.BATCH_SIZE_PER_IMAGE
-        self.positive_fraction       = cfg.MODEL.RPN.POSITIVE_FRACTION
-        self.smooth_l1_beta          = cfg.MODEL.RPN.SMOOTH_L1_BETA
-        self.loss_weight             = cfg.MODEL.RPN.LOSS_WEIGHT
-        # fmt: on
-
-        # Map from self.training state to train/test settings
-        self.pre_nms_topk = {
-            True: cfg.MODEL.RPN.PRE_NMS_TOPK_TRAIN,
-            False: cfg.MODEL.RPN.PRE_NMS_TOPK_TEST,
-        }
-        self.post_nms_topk = {
-            True: cfg.MODEL.RPN.POST_NMS_TOPK_TRAIN,
-            False: cfg.MODEL.RPN.POST_NMS_TOPK_TEST,
-        }
-        self.boundary_threshold = cfg.MODEL.RPN.BOUNDARY_THRESH
-
-        self.anchor_generator = build_anchor_generator(
-            cfg, [input_shape[f] for f in self.in_features]
-        )
-        self.box2box_transform = Box2BoxTransform(weights=cfg.MODEL.RPN.BBOX_REG_WEIGHTS)
-        self.anchor_matcher = Matcher(
-            cfg.MODEL.RPN.IOU_THRESHOLDS, cfg.MODEL.RPN.IOU_LABELS, allow_low_quality_matches=True
-        )
-        self.rpn_head = build_rpn_head(cfg, [input_shape[f] for f in self.in_features])
-
-    def forward(self, images, features, gt_instances=None):
-        """
-        Args:
-            images (ImageList): input images of length `N`
-            features (dict[str: Tensor]): input data as a mapping from feature
-                map name to tensor. Axis 0 represents the number of images `N` in
-                the input data; axes 1-3 are channels, height, and width, which may
-                vary between feature maps (e.g., if a feature pyramid is used).
-            gt_instances (list[Instances], optional): a length `N` list of `Instances`s.
-                Each `Instances` stores ground-truth instances for the corresponding image.
-
-        Returns:
-            proposals: list[Instances]: contains fields "proposal_boxes", "objectness_logits"
-            loss: dict[Tensor] or None
-        """
-        gt_boxes = [x.gt_boxes for x in gt_instances] if gt_instances is not None else None
-        del gt_instances
-        features = [features[f] for f in self.in_features]
-        pred_objectness_logits, pred_anchor_deltas = self.rpn_head(features)
-        anchors = self.anchor_generator(features)
-        # TODO: The anchors only depend on the feature map shape; there's probably
-        # an opportunity for some optimizations (e.g., caching anchors).
-        outputs = RPNOutputs(
-            self.box2box_transform,
-            self.anchor_matcher,
-            self.batch_size_per_image,
-            self.positive_fraction,
-            images,
-            pred_objectness_logits,
-            pred_anchor_deltas,
-            anchors,
-            self.boundary_threshold,
-            gt_boxes,
-            self.smooth_l1_beta,
-        )
-
-        if self.training:
-            losses = {k: v * self.loss_weight for k, v in outputs.losses().items()}
-        else:
-            losses = {}
-
-        with torch.no_grad():
-            # Find the top proposals by applying NMS and removing boxes that
-            # are too small. The proposals are treated as fixed for approximate
-            # joint training with roi heads. This approach ignores the derivative
-            # w.r.t. the proposal boxes’ coordinates that are also network
-            # responses, so is approximate.
-            proposals = find_top_rpn_proposals(
-                outputs.predict_proposals(),
-                outputs.predict_objectness_logits(),
-                images,
-                self.nms_thresh,
-                self.pre_nms_topk[self.training],
-                self.post_nms_topk[self.training],
-                self.min_box_side_len,
-                self.training,
-            )
-
-        return proposals, losses

spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/projects/DensePose/densepose/__init__.py

@@ -1,10 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-from . import dataset  # just to register data
-from .config import add_densepose_config
-from .dataset_mapper import DatasetMapper
-from .densepose_head import ROI_DENSEPOSE_HEAD_REGISTRY
-from .evaluator import DensePoseCOCOEvaluator
-from .roi_head import DensePoseROIHeads
-from .structures import DensePoseDataRelative, DensePoseList, DensePoseTransformData
-from .modeling.test_time_augmentation import DensePoseGeneralizedRCNNWithTTA
-from .utils.transform import load_from_cfg

spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/projects/DensePose/tests/test_setup.py

@@ -1,20 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
-
-import unittest
-
-from .common import get_config_files, get_quick_schedules_config_files, setup
-
-
-class TestSetup(unittest.TestCase):
-    def _test_setup(self, config_file):
-        setup(config_file)
-
-    def test_setup_configs(self):
-        config_files = get_config_files()
-        for config_file in config_files:
-            self._test_setup(config_file)
-
-    def test_setup_quick_schedules_configs(self):
-        config_files = get_quick_schedules_config_files()
-        for config_file in config_files:
-            self._test_setup(config_file)

spaces/CVPR/LIVE/pybind11/tests/test_stl_binders.py

@@ -1,285 +0,0 @@
-# -*- coding: utf-8 -*-
-import pytest
-
-import env  # noqa: F401
-
-from pybind11_tests import stl_binders as m
-
-
-def test_vector_int():
-    v_int = m.VectorInt([0, 0])
-    assert len(v_int) == 2
-    assert bool(v_int) is True
-
-    # test construction from a generator
-    v_int1 = m.VectorInt(x for x in range(5))
-    assert v_int1 == m.VectorInt([0, 1, 2, 3, 4])
-
-    v_int2 = m.VectorInt([0, 0])
-    assert v_int == v_int2
-    v_int2[1] = 1
-    assert v_int != v_int2
-
-    v_int2.append(2)
-    v_int2.insert(0, 1)
-    v_int2.insert(0, 2)
-    v_int2.insert(0, 3)
-    v_int2.insert(6, 3)
-    assert str(v_int2) == "VectorInt[3, 2, 1, 0, 1, 2, 3]"
-    with pytest.raises(IndexError):
-        v_int2.insert(8, 4)
-
-    v_int.append(99)
-    v_int2[2:-2] = v_int
-    assert v_int2 == m.VectorInt([3, 2, 0, 0, 99, 2, 3])
-    del v_int2[1:3]
-    assert v_int2 == m.VectorInt([3, 0, 99, 2, 3])
-    del v_int2[0]
-    assert v_int2 == m.VectorInt([0, 99, 2, 3])
-
-    v_int2.extend(m.VectorInt([4, 5]))
-    assert v_int2 == m.VectorInt([0, 99, 2, 3, 4, 5])
-
-    v_int2.extend([6, 7])
-    assert v_int2 == m.VectorInt([0, 99, 2, 3, 4, 5, 6, 7])
-
-    # test error handling, and that the vector is unchanged
-    with pytest.raises(RuntimeError):
-        v_int2.extend([8, 'a'])
-
-    assert v_int2 == m.VectorInt([0, 99, 2, 3, 4, 5, 6, 7])
-
-    # test extending from a generator
-    v_int2.extend(x for x in range(5))
-    assert v_int2 == m.VectorInt([0, 99, 2, 3, 4, 5, 6, 7, 0, 1, 2, 3, 4])
-
-    # test negative indexing
-    assert v_int2[-1] == 4
-
-    # insert with negative index
-    v_int2.insert(-1, 88)
-    assert v_int2 == m.VectorInt([0, 99, 2, 3, 4, 5, 6, 7, 0, 1, 2, 3, 88, 4])
-
-    # delete negative index
-    del v_int2[-1]
-    assert v_int2 == m.VectorInt([0, 99, 2, 3, 4, 5, 6, 7, 0, 1, 2, 3, 88])
-
-    v_int2.clear()
-    assert len(v_int2) == 0
-
-
-# Older PyPy's failed here, related to the PyPy's buffer protocol.
-def test_vector_buffer():
-    b = bytearray([1, 2, 3, 4])
-    v = m.VectorUChar(b)
-    assert v[1] == 2
-    v[2] = 5
-    mv = memoryview(v)  # We expose the buffer interface
-    if not env.PY2:
-        assert mv[2] == 5
-        mv[2] = 6
-    else:
-        assert mv[2] == '\x05'
-        mv[2] = '\x06'
-    assert v[2] == 6
-
-    if not env.PY2:
-        mv = memoryview(b)
-        v = m.VectorUChar(mv[::2])
-        assert v[1] == 3
-
-    with pytest.raises(RuntimeError) as excinfo:
-        m.create_undeclstruct()  # Undeclared struct contents, no buffer interface
-    assert "NumPy type info missing for " in str(excinfo.value)
-
-
-def test_vector_buffer_numpy():
-    np = pytest.importorskip("numpy")
-    a = np.array([1, 2, 3, 4], dtype=np.int32)
-    with pytest.raises(TypeError):
-        m.VectorInt(a)
-
-    a = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]], dtype=np.uintc)
-    v = m.VectorInt(a[0, :])
-    assert len(v) == 4
-    assert v[2] == 3
-    ma = np.asarray(v)
-    ma[2] = 5
-    assert v[2] == 5
-
-    v = m.VectorInt(a[:, 1])
-    assert len(v) == 3
-    assert v[2] == 10
-
-    v = m.get_vectorstruct()
-    assert v[0].x == 5
-    ma = np.asarray(v)
-    ma[1]['x'] = 99
-    assert v[1].x == 99
-
-    v = m.VectorStruct(np.zeros(3, dtype=np.dtype([('w', 'bool'), ('x', 'I'),
-                                                   ('y', 'float64'), ('z', 'bool')], align=True)))
-    assert len(v) == 3
-
-    b = np.array([1, 2, 3, 4], dtype=np.uint8)
-    v = m.VectorUChar(b[::2])
-    assert v[1] == 3
-
-
-def test_vector_bool():
-    import pybind11_cross_module_tests as cm
-
-    vv_c = cm.VectorBool()
-    for i in range(10):
-        vv_c.append(i % 2 == 0)
-    for i in range(10):
-        assert vv_c[i] == (i % 2 == 0)
-    assert str(vv_c) == "VectorBool[1, 0, 1, 0, 1, 0, 1, 0, 1, 0]"
-
-
-def test_vector_custom():
-    v_a = m.VectorEl()
-    v_a.append(m.El(1))
-    v_a.append(m.El(2))
-    assert str(v_a) == "VectorEl[El{1}, El{2}]"
-
-    vv_a = m.VectorVectorEl()
-    vv_a.append(v_a)
-    vv_b = vv_a[0]
-    assert str(vv_b) == "VectorEl[El{1}, El{2}]"
-
-
-def test_map_string_double():
-    mm = m.MapStringDouble()
-    mm['a'] = 1
-    mm['b'] = 2.5
-
-    assert list(mm) == ['a', 'b']
-    assert list(mm.items()) == [('a', 1), ('b', 2.5)]
-    assert str(mm) == "MapStringDouble{a: 1, b: 2.5}"
-
-    um = m.UnorderedMapStringDouble()
-    um['ua'] = 1.1
-    um['ub'] = 2.6
-
-    assert sorted(list(um)) == ['ua', 'ub']
-    assert sorted(list(um.items())) == [('ua', 1.1), ('ub', 2.6)]
-    assert "UnorderedMapStringDouble" in str(um)
-
-
-def test_map_string_double_const():
-    mc = m.MapStringDoubleConst()
-    mc['a'] = 10
-    mc['b'] = 20.5
-    assert str(mc) == "MapStringDoubleConst{a: 10, b: 20.5}"
-
-    umc = m.UnorderedMapStringDoubleConst()
-    umc['a'] = 11
-    umc['b'] = 21.5
-
-    str(umc)
-
-
-def test_noncopyable_containers():
-    # std::vector
-    vnc = m.get_vnc(5)
-    for i in range(0, 5):
-        assert vnc[i].value == i + 1
-
-    for i, j in enumerate(vnc, start=1):
-        assert j.value == i
-
-    # std::deque
-    dnc = m.get_dnc(5)
-    for i in range(0, 5):
-        assert dnc[i].value == i + 1
-
-    i = 1
-    for j in dnc:
-        assert(j.value == i)
-        i += 1
-
-    # std::map
-    mnc = m.get_mnc(5)
-    for i in range(1, 6):
-        assert mnc[i].value == 10 * i
-
-    vsum = 0
-    for k, v in mnc.items():
-        assert v.value == 10 * k
-        vsum += v.value
-
-    assert vsum == 150
-
-    # std::unordered_map
-    mnc = m.get_umnc(5)
-    for i in range(1, 6):
-        assert mnc[i].value == 10 * i
-
-    vsum = 0
-    for k, v in mnc.items():
-        assert v.value == 10 * k
-        vsum += v.value
-
-    assert vsum == 150
-
-    # nested std::map<std::vector>
-    nvnc = m.get_nvnc(5)
-    for i in range(1, 6):
-        for j in range(0, 5):
-            assert nvnc[i][j].value == j + 1
-
-    # Note: maps do not have .values()
-    for _, v in nvnc.items():
-        for i, j in enumerate(v, start=1):
-            assert j.value == i
-
-    # nested std::map<std::map>
-    nmnc = m.get_nmnc(5)
-    for i in range(1, 6):
-        for j in range(10, 60, 10):
-            assert nmnc[i][j].value == 10 * j
-
-    vsum = 0
-    for _, v_o in nmnc.items():
-        for k_i, v_i in v_o.items():
-            assert v_i.value == 10 * k_i
-            vsum += v_i.value
-
-    assert vsum == 7500
-
-    # nested std::unordered_map<std::unordered_map>
-    numnc = m.get_numnc(5)
-    for i in range(1, 6):
-        for j in range(10, 60, 10):
-            assert numnc[i][j].value == 10 * j
-
-    vsum = 0
-    for _, v_o in numnc.items():
-        for k_i, v_i in v_o.items():
-            assert v_i.value == 10 * k_i
-            vsum += v_i.value
-
-    assert vsum == 7500
-
-
-def test_map_delitem():
-    mm = m.MapStringDouble()
-    mm['a'] = 1
-    mm['b'] = 2.5
-
-    assert list(mm) == ['a', 'b']
-    assert list(mm.items()) == [('a', 1), ('b', 2.5)]
-    del mm['a']
-    assert list(mm) == ['b']
-    assert list(mm.items()) == [('b', 2.5)]
-
-    um = m.UnorderedMapStringDouble()
-    um['ua'] = 1.1
-    um['ub'] = 2.6
-
-    assert sorted(list(um)) == ['ua', 'ub']
-    assert sorted(list(um.items())) == [('ua', 1.1), ('ub', 2.6)]
-    del um['ua']
-    assert sorted(list(um)) == ['ub']
-    assert sorted(list(um.items())) == [('ub', 2.6)]

spaces/CVPR/LIVE/thrust/thrust/system/omp/detail/logical.h

@@ -1,23 +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>
-
-// this system inherits logical
-#include <thrust/system/cpp/detail/logical.h>
-

spaces/CVPR/transfiner/configs/common/models/keypoint_rcnn_fpn.py

@@ -1,33 +0,0 @@
-from detectron2.config import LazyCall as L
-from detectron2.layers import ShapeSpec
-from detectron2.modeling.poolers import ROIPooler
-from detectron2.modeling.roi_heads import KRCNNConvDeconvUpsampleHead
-
-from .mask_rcnn_fpn import model
-
-[model.roi_heads.pop(x) for x in ["mask_in_features", "mask_pooler", "mask_head"]]
-
-model.roi_heads.update(
-    num_classes=1,
-    keypoint_in_features=["p2", "p3", "p4", "p5"],
-    keypoint_pooler=L(ROIPooler)(
-        output_size=14,
-        scales=(1.0 / 4, 1.0 / 8, 1.0 / 16, 1.0 / 32),
-        sampling_ratio=0,
-        pooler_type="ROIAlignV2",
-    ),
-    keypoint_head=L(KRCNNConvDeconvUpsampleHead)(
-        input_shape=ShapeSpec(channels=256, width=14, height=14),
-        num_keypoints=17,
-        conv_dims=[512] * 8,
-        loss_normalizer="visible",
-    ),
-)
-
-# Detectron1 uses 2000 proposals per-batch, but this option is per-image in detectron2.
-# 1000 proposals per-image is found to hurt box AP.
-# Therefore we increase it to 1500 per-image.
-model.proposal_generator.post_nms_topk = (1500, 1000)
-
-# Keypoint AP degrades (though box AP improves) when using plain L1 loss
-model.roi_heads.box_predictor.smooth_l1_beta = 0.5

spaces/ChandraMohanNayal/AutoGPT/autogpt/json_utils/json_fix_general.py

@@ -1,124 +0,0 @@
-"""This module contains functions to fix JSON strings using general programmatic approaches, suitable for addressing
-common JSON formatting issues."""
-from __future__ import annotations
-
-import contextlib
-import json
-import re
-from typing import Optional
-
-from autogpt.config import Config
-from autogpt.json_utils.utilities import extract_char_position
-
-CFG = Config()
-
-
-def fix_invalid_escape(json_to_load: str, error_message: str) -> str:
-    """Fix invalid escape sequences in JSON strings.
-
-    Args:
-        json_to_load (str): The JSON string.
-        error_message (str): The error message from the JSONDecodeError
-          exception.
-
-    Returns:
-        str: The JSON string with invalid escape sequences fixed.
-    """
-    while error_message.startswith("Invalid \\escape"):
-        bad_escape_location = extract_char_position(error_message)
-        json_to_load = (
-            json_to_load[:bad_escape_location] + json_to_load[bad_escape_location + 1 :]
-        )
-        try:
-            json.loads(json_to_load)
-            return json_to_load
-        except json.JSONDecodeError as e:
-            if CFG.debug_mode:
-                print("json loads error - fix invalid escape", e)
-            error_message = str(e)
-    return json_to_load
-
-
-def balance_braces(json_string: str) -> Optional[str]:
-    """
-    Balance the braces in a JSON string.
-
-    Args:
-        json_string (str): The JSON string.
-
-    Returns:
-        str: The JSON string with braces balanced.
-    """
-
-    open_braces_count = json_string.count("{")
-    close_braces_count = json_string.count("}")
-
-    while open_braces_count > close_braces_count:
-        json_string += "}"
-        close_braces_count += 1
-
-    while close_braces_count > open_braces_count:
-        json_string = json_string.rstrip("}")
-        close_braces_count -= 1
-
-    with contextlib.suppress(json.JSONDecodeError):
-        json.loads(json_string)
-        return json_string
-
-
-def add_quotes_to_property_names(json_string: str) -> str:
-    """
-    Add quotes to property names in a JSON string.
-
-    Args:
-        json_string (str): The JSON string.
-
-    Returns:
-        str: The JSON string with quotes added to property names.
-    """
-
-    def replace_func(match: re.Match) -> str:
-        return f'"{match[1]}":'
-
-    property_name_pattern = re.compile(r"(\w+):")
-    corrected_json_string = property_name_pattern.sub(replace_func, json_string)
-
-    try:
-        json.loads(corrected_json_string)
-        return corrected_json_string
-    except json.JSONDecodeError as e:
-        raise e
-
-
-def correct_json(json_to_load: str) -> str:
-    """
-    Correct common JSON errors.
-    Args:
-        json_to_load (str): The JSON string.
-    """
-
-    try:
-        if CFG.debug_mode:
-            print("json", json_to_load)
-        json.loads(json_to_load)
-        return json_to_load
-    except json.JSONDecodeError as e:
-        if CFG.debug_mode:
-            print("json loads error", e)
-        error_message = str(e)
-        if error_message.startswith("Invalid \\escape"):
-            json_to_load = fix_invalid_escape(json_to_load, error_message)
-        if error_message.startswith(
-            "Expecting property name enclosed in double quotes"
-        ):
-            json_to_load = add_quotes_to_property_names(json_to_load)
-            try:
-                json.loads(json_to_load)
-                return json_to_load
-            except json.JSONDecodeError as e:
-                if CFG.debug_mode:
-                    print("json loads error - add quotes", e)
-                error_message = str(e)
-        if balanced_str := balance_braces(json_to_load):
-            return balanced_str
-    return json_to_load

spaces/ChrisPreston/diff-svc_minato_aqua/infer_tools/trans_key.py

@@ -1,67 +0,0 @@
-import os
-head_list = ["C", "C#", "D", "D#", "E", "F", "F#", "G", "G#", "A", "A#", "B"]
-
-
-def trans_f0_seq(feature_pit, transform):
-    feature_pit = feature_pit * 2 ** (transform / 12)
-    return round(feature_pit, 1)
-
-
-def move_key(raw_data, mv_key):
-    head = raw_data[:-1]
-    body = int(raw_data[-1])
-    new_head_index = head_list.index(head) + mv_key
-    while new_head_index < 0:
-        body -= 1
-        new_head_index += 12
-    while new_head_index > 11:
-        body += 1
-        new_head_index -= 12
-    result_data = head_list[new_head_index] + str(body)
-    return result_data
-
-
-def trans_key(raw_data, key):
-    for i in raw_data:
-        note_seq_list = i["note_seq"].split(" ")
-        new_note_seq_list = []
-        for note_seq in note_seq_list:
-            if note_seq != "rest":
-                new_note_seq = move_key(note_seq, key)
-                new_note_seq_list.append(new_note_seq)
-            else:
-                new_note_seq_list.append(note_seq)
-        i["note_seq"] = " ".join(new_note_seq_list)
-
-        f0_seq_list = i["f0_seq"].split(" ")
-        f0_seq_list = [float(x) for x in f0_seq_list]
-        new_f0_seq_list = []
-        for f0_seq in f0_seq_list:
-            new_f0_seq = trans_f0_seq(f0_seq, key)
-            new_f0_seq_list.append(str(new_f0_seq))
-        i["f0_seq"] = " ".join(new_f0_seq_list)
-    return raw_data
-
-
-def trans_opencpop(raw_txt, res_txt, key):
-    if os.path.exists(raw_txt):
-        f_w = open(res_txt, "w", encoding='utf-8')
-        with open(raw_txt, "r", encoding='utf-8') as f:
-            raw_data = f.readlines()
-            for raw in raw_data:
-                raw_list = raw.split("|")
-                new_note_seq_list = []
-                for note_seq in raw_list[3].split(" "):
-                    if note_seq != "rest":
-                        note_seq = note_seq.split("/")[0] if "/" in note_seq else note_seq
-                        new_note_seq = move_key(note_seq, key)
-                        new_note_seq_list.append(new_note_seq)
-                    else:
-                        new_note_seq_list.append(note_seq)
-                raw_list[3] = " ".join(new_note_seq_list)
-                f_w.write("|".join(raw_list))
-        f_w.close()
-        print("opencpop标注文件转换完毕")
-    else:
-        print("未发现opencpop标注文件，请检查路径")
-

spaces/Codecooker/rvcapi/src/webui.py

@@ -1,309 +0,0 @@
-import json
-import os
-os.system("pip install torchcrepe")
-os.system("pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu")
-import shutil
-import urllib.request
-import zipfile
-from argparse import ArgumentParser
-
-import gradio as gr
-
-from main import song_cover_pipeline
-
-BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
-
-mdxnet_models_dir = os.path.join(BASE_DIR, 'mdxnet_models')
-rvc_models_dir = os.path.join(BASE_DIR, 'rvc_models')
-output_dir = os.path.join(BASE_DIR, 'song_output')
-
-
-def get_current_models(models_dir):
-    models_list = os.listdir(models_dir)
-    items_to_remove = ['hubert_base.pt', 'MODELS.txt', 'public_models.json', 'rmvpe.pt']
-    return [item for item in models_list if item not in items_to_remove]
-
-
-def update_models_list():
-    models_l = get_current_models(rvc_models_dir)
-    return gr.Dropdown.update(choices=models_l)
-
-
-def load_public_models():
-    models_table = []
-    for model in public_models['voice_models']:
-        if not model['name'] in voice_models:
-            model = [model['name'], model['description'], model['credit'], model['url'], ', '.join(model['tags'])]
-            models_table.append(model)
-
-    tags = list(public_models['tags'].keys())
-    return gr.DataFrame.update(value=models_table), gr.CheckboxGroup.update(choices=tags)
-
-
-def extract_zip(extraction_folder, zip_name):
-    os.makedirs(extraction_folder)
-    with zipfile.ZipFile(zip_name, 'r') as zip_ref:
-        zip_ref.extractall(extraction_folder)
-    os.remove(zip_name)
-
-    index_filepath, model_filepath = None, None
-    for root, dirs, files in os.walk(extraction_folder):
-        for name in files:
-            if name.endswith('.index'):
-                index_filepath = os.path.join(root, name)
-
-            if name.endswith('.pth'):
-                model_filepath = os.path.join(root, name)
-
-    if not model_filepath:
-        raise gr.Error(f'No .pth model file was found in the extracted zip. Please check {extraction_folder}.')
-
-    # move model and index file to extraction folder
-    os.rename(model_filepath, os.path.join(extraction_folder, os.path.basename(model_filepath)))
-    if index_filepath:
-        os.rename(index_filepath, os.path.join(extraction_folder, os.path.basename(index_filepath)))
-
-    # remove any unnecessary nested folders
-    for filepath in os.listdir(extraction_folder):
-        if os.path.isdir(os.path.join(extraction_folder, filepath)):
-            shutil.rmtree(os.path.join(extraction_folder, filepath))
-
-
-def download_online_model(url, dir_name, progress=gr.Progress()):
-    try:
-        progress(0, desc=f'[~] Downloading voice model with name {dir_name}...')
-        zip_name = url.split('/')[-1]
-        extraction_folder = os.path.join(rvc_models_dir, dir_name)
-        if os.path.exists(extraction_folder):
-            raise gr.Error(f'Voice model directory {dir_name} already exists! Choose a different name for your voice model.')
-
-        if 'pixeldrain.com' in url:
-            url = f'https://pixeldrain.com/api/file/{zip_name}'
-
-        urllib.request.urlretrieve(url, zip_name)
-
-        progress(0.5, desc='[~] Extracting zip...')
-        extract_zip(extraction_folder, zip_name)
-        return f'[+] {dir_name} Model successfully downloaded!'
-
-    except Exception as e:
-        raise gr.Error(str(e))
-
-
-def upload_local_model(zip_path, dir_name, progress=gr.Progress()):
-    try:
-        extraction_folder = os.path.join(rvc_models_dir, dir_name)
-        if os.path.exists(extraction_folder):
-            raise gr.Error(f'Voice model directory {dir_name} already exists! Choose a different name for your voice model.')
-
-        zip_name = zip_path.name
-        progress(0.5, desc='[~] Extracting zip...')
-        extract_zip(extraction_folder, zip_name)
-        return f'[+] {dir_name} Model successfully uploaded!'
-
-    except Exception as e:
-        raise gr.Error(str(e))
-
-
-def filter_models(tags, query):
-    models_table = []
-
-    # no filter
-    if len(tags) == 0 and len(query) == 0:
-        for model in public_models['voice_models']:
-            models_table.append([model['name'], model['description'], model['credit'], model['url'], model['tags']])
-
-    # filter based on tags and query
-    elif len(tags) > 0 and len(query) > 0:
-        for model in public_models['voice_models']:
-            if all(tag in model['tags'] for tag in tags):
-                model_attributes = f"{model['name']} {model['description']} {model['credit']} {' '.join(model['tags'])}".lower()
-                if query.lower() in model_attributes:
-                    models_table.append([model['name'], model['description'], model['credit'], model['url'], model['tags']])
-
-    # filter based on only tags
-    elif len(tags) > 0:
-        for model in public_models['voice_models']:
-            if all(tag in model['tags'] for tag in tags):
-                models_table.append([model['name'], model['description'], model['credit'], model['url'], model['tags']])
-
-    # filter based on only query
-    else:
-        for model in public_models['voice_models']:
-            model_attributes = f"{model['name']} {model['description']} {model['credit']} {' '.join(model['tags'])}".lower()
-            if query.lower() in model_attributes:
-                models_table.append([model['name'], model['description'], model['credit'], model['url'], model['tags']])
-
-    return gr.DataFrame.update(value=models_table)
-
-
-def pub_dl_autofill(pub_models, event: gr.SelectData):
-    return gr.Text.update(value=pub_models.loc[event.index[0], 'URL']), gr.Text.update(value=pub_models.loc[event.index[0], 'Model Name'])
-
-
-def swap_visibility():
-    return gr.update(visible=True), gr.update(visible=False), gr.update(value=''), gr.update(value=None)
-
-
-def process_file_upload(file):
-    return file.name, gr.update(value=file.name)
-
-
-if __name__ == '__main__':
-    os.system("pip install torchcrepe")
-    os.system("pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu")
-    parser = ArgumentParser(description='Generate a AI cover song in the song_output/id directory.', add_help=True)
-    parser.add_argument("--share", action="store_true", dest="share_enabled", default=False, help="Enable sharing")
-    parser.add_argument("--listen", action="store_true", default=False, help="Make the WebUI reachable from your local network.")
-    parser.add_argument('--listen-host', type=str, help='The hostname that the server will use.')
-    parser.add_argument('--listen-port', type=int, help='The listening port that the server will use.')
-    args = parser.parse_args()
-
-    voice_models = get_current_models(rvc_models_dir)
-    with open(os.path.join(rvc_models_dir, 'public_models.json'), encoding='utf8') as infile:
-        public_models = json.load(infile)
-
-    with gr.Blocks(title='AICoverGenWebUI') as app:
-
-        gr.Label('AICoverGen WebUI created with ❤️', show_label=False)
-
-        # main tab
-        with gr.Tab("Generate"):
-
-            with gr.Accordion('Main Options'):
-                with gr.Row():
-                    with gr.Column():
-                        rvc_model = gr.Dropdown(voice_models, label='Voice Models', info='Models folder "AICoverGen --> rvc_models". After new models are added into this folder, click the refresh button')
-                        ref_btn = gr.Button('Refresh Models 🔁', variant='primary')
-
-                    with gr.Column() as yt_link_col:
-                        song_input = gr.Text(label='Song input', info='Link to a song on YouTube or full path to a local file. For file upload, click the button below.')
-                        show_file_upload_button = gr.Button('Upload file instead')
-
-                    with gr.Column(visible=False) as file_upload_col:
-                        local_file = gr.File(label='Audio file')
-                        song_input_file = gr.UploadButton('Upload 📂', file_types=['audio'], variant='primary')
-                        show_yt_link_button = gr.Button('Paste YouTube link/Path to local file instead')
-                        song_input_file.upload(process_file_upload, inputs=[song_input_file], outputs=[local_file, song_input])
-
-                    pitch = gr.Slider(-24, 24, value=0, step=1, label='Pitch Change', info='Pitch Change should be set to either -12, 0, or 12 (multiples of 12) to ensure the vocals are not out of tune')
-                    show_file_upload_button.click(swap_visibility, outputs=[file_upload_col, yt_link_col, song_input, local_file])
-                    show_yt_link_button.click(swap_visibility, outputs=[yt_link_col, file_upload_col, song_input, local_file])
-
-            with gr.Accordion('Voice conversion options', open=False):
-                with gr.Row():
-                    index_rate = gr.Slider(0, 1, value=0.5, label='Index Rate', info="Controls how much of the AI voice's accent to keep in the vocals")
-                    filter_radius = gr.Slider(0, 7, value=3, step=1, label='Filter radius', info='If >=3: apply median filtering median filtering to the harvested pitch results. Can reduce breathiness')
-                    rms_mix_rate = gr.Slider(0, 1, value=0.25, label='RMS mix rate', info="Control how much to use the original vocal's loudness (0) or a fixed loudness (1)")
-                    protect = gr.Slider(0, 0.5, value=0.33, label='Protect rate', info='Protect voiceless consonants and breath sounds. Set to 0.5 to disable.')
-                keep_files = gr.Checkbox(label='Keep intermediate files',
-                                         info='Keep all audio files generated in the song_output/id directory, e.g. Isolated Vocals/Instrumentals. Leave unchecked to save space')
-
-            with gr.Accordion('Audio mixing options', open=False):
-                gr.Markdown('### Volume Change (decibels)')
-                with gr.Row():
-                    main_gain = gr.Slider(-20, 20, value=0, step=1, label='Main Vocals')
-                    backup_gain = gr.Slider(-20, 20, value=0, step=1, label='Backup Vocals')
-                    inst_gain = gr.Slider(-20, 20, value=0, step=1, label='Music')
-
-                gr.Markdown('### Reverb Control on AI Vocals')
-                with gr.Row():
-                    reverb_rm_size = gr.Slider(0, 1, value=0.15, label='Room size', info='The larger the room, the longer the reverb time')
-                    reverb_wet = gr.Slider(0, 1, value=0.2, label='Wetness level', info='Level of AI vocals with reverb')
-                    reverb_dry = gr.Slider(0, 1, value=0.8, label='Dryness level', info='Level of AI vocals without reverb')
-                    reverb_damping = gr.Slider(0, 1, value=0.7, label='Damping level', info='Absorption of high frequencies in the reverb')
-
-            with gr.Row():
-                clear_btn = gr.ClearButton(value='Clear', components=[song_input, rvc_model, keep_files, local_file])
-                generate_btn = gr.Button("Generate", variant='primary')
-                ai_cover = gr.Audio(label='AI Cover', show_share_button=False)
-
-            ref_btn.click(update_models_list, None, outputs=rvc_model)
-            is_webui = gr.Number(value=1, visible=False)
-            generate_btn.click(song_cover_pipeline,
-                               inputs=[song_input, rvc_model, pitch, keep_files, is_webui, main_gain, backup_gain,
-                                       inst_gain, index_rate, filter_radius, rms_mix_rate, protect, reverb_rm_size,
-                                       reverb_wet, reverb_dry, reverb_damping],
-                               outputs=[ai_cover])
-            clear_btn.click(lambda: [0, 0, 0, 0, 0.5, 3, 0.25, 0.33, 0.15, 0.2, 0.8, 0.7, None],
-                            outputs=[pitch, main_gain, backup_gain, inst_gain, index_rate, filter_radius, rms_mix_rate,
-                                     protect, reverb_rm_size, reverb_wet, reverb_dry, reverb_damping, ai_cover])
-
-        # Download tab
-        with gr.Tab('Download model'):
-
-            with gr.Tab('From HuggingFace/Pixeldrain URL'):
-                with gr.Row():
-                    model_zip_link = gr.Text(label='Download link to model', info='Should be a zip file containing a .pth model file and an optional .index file.')
-                    model_name = gr.Text(label='Name your model', info='Give your new model a unique name from your other voice models.')
-
-                with gr.Row():
-                    download_btn = gr.Button('Download 🌐', variant='primary', scale=19)
-                    dl_output_message = gr.Text(label='Output Message', interactive=False, scale=20)
-
-                download_btn.click(download_online_model, inputs=[model_zip_link, model_name], outputs=dl_output_message)
-
-                gr.Markdown('## Input Examples')
-                gr.Examples(
-                    [
-                        ['https://huggingface.co/phant0m4r/LiSA/resolve/main/LiSA.zip', 'Lisa'],
-                        ['https://pixeldrain.com/u/3tJmABXA', 'Gura'],
-                        ['https://huggingface.co/Kit-Lemonfoot/kitlemonfoot_rvc_models/resolve/main/AZKi%20(Hybrid).zip', 'Azki']
-                    ],
-                    [model_zip_link, model_name],
-                    [],
-                    download_online_model,
-                )
-
-            with gr.Tab('From Public Index'):
-
-                gr.Markdown('## How to use')
-                gr.Markdown('- Click Initialize public models table')
-                gr.Markdown('- Filter models using tags or search bar')
-                gr.Markdown('- Select a row to autofill the download link and model name')
-                gr.Markdown('- Click Download')
-
-                with gr.Row():
-                    pub_zip_link = gr.Text(label='Download link to model')
-                    pub_model_name = gr.Text(label='Model name')
-
-                with gr.Row():
-                    download_pub_btn = gr.Button('Download 🌐', variant='primary', scale=19)
-                    pub_dl_output_message = gr.Text(label='Output Message', interactive=False, scale=20)
-
-                filter_tags = gr.CheckboxGroup(value=[], label='Show voice models with tags', choices=[])
-                search_query = gr.Text(label='Search')
-                load_public_models_button = gr.Button(value='Initialize public models table', variant='primary')
-
-                public_models_table = gr.DataFrame(value=[], headers=['Model Name', 'Description', 'Credit', 'URL', 'Tags'], label='Available Public Models', interactive=False)
-                public_models_table.select(pub_dl_autofill, inputs=[public_models_table], outputs=[pub_zip_link, pub_model_name])
-                load_public_models_button.click(load_public_models, outputs=[public_models_table, filter_tags])
-                search_query.change(filter_models, inputs=[filter_tags, search_query], outputs=public_models_table)
-                filter_tags.change(filter_models, inputs=[filter_tags, search_query], outputs=public_models_table)
-                download_pub_btn.click(download_online_model, inputs=[pub_zip_link, pub_model_name], outputs=pub_dl_output_message)
-
-        # Upload tab
-        with gr.Tab('Upload model'):
-            gr.Markdown('## Upload locally trained RVC v2 model and index file')
-            gr.Markdown('- Find model file (weights folder) and optional index file (logs/[name] folder)')
-            gr.Markdown('- Compress files into zip file')
-            gr.Markdown('- Upload zip file and give unique name for voice')
-            gr.Markdown('- Click Upload model')
-
-            with gr.Row():
-                with gr.Column():
-                    zip_file = gr.File(label='Zip file')
-
-                local_model_name = gr.Text(label='Model name')
-
-            with gr.Row():
-                model_upload_button = gr.Button('Upload model', variant='primary', scale=19)
-                local_upload_output_message = gr.Text(label='Output Message', interactive=False, scale=20)
-                model_upload_button.click(upload_local_model, inputs=[zip_file, local_model_name], outputs=local_upload_output_message)
-
-    app.launch(
-        share=args.share_enabled,
-        enable_queue=True,
-        server_name=None if not args.listen else (args.listen_host or '0.0.0.0'),
-        server_port=args.listen_port,
-    )

spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/cu2qu/__init__.py

@@ -1,15 +0,0 @@
-# Copyright 2016 Google Inc. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from .cu2qu import *

spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/feaLib/builder.py

@@ -1,1706 +0,0 @@
-from fontTools.misc import sstruct
-from fontTools.misc.textTools import Tag, tostr, binary2num, safeEval
-from fontTools.feaLib.error import FeatureLibError
-from fontTools.feaLib.lookupDebugInfo import (
-    LookupDebugInfo,
-    LOOKUP_DEBUG_INFO_KEY,
-    LOOKUP_DEBUG_ENV_VAR,
-)
-from fontTools.feaLib.parser import Parser
-from fontTools.feaLib.ast import FeatureFile
-from fontTools.feaLib.variableScalar import VariableScalar
-from fontTools.otlLib import builder as otl
-from fontTools.otlLib.maxContextCalc import maxCtxFont
-from fontTools.ttLib import newTable, getTableModule
-from fontTools.ttLib.tables import otBase, otTables
-from fontTools.otlLib.builder import (
-    AlternateSubstBuilder,
-    ChainContextPosBuilder,
-    ChainContextSubstBuilder,
-    LigatureSubstBuilder,
-    MultipleSubstBuilder,
-    CursivePosBuilder,
-    MarkBasePosBuilder,
-    MarkLigPosBuilder,
-    MarkMarkPosBuilder,
-    ReverseChainSingleSubstBuilder,
-    SingleSubstBuilder,
-    ClassPairPosSubtableBuilder,
-    PairPosBuilder,
-    SinglePosBuilder,
-    ChainContextualRule,
-)
-from fontTools.otlLib.error import OpenTypeLibError
-from fontTools.varLib.varStore import OnlineVarStoreBuilder
-from fontTools.varLib.builder import buildVarDevTable
-from fontTools.varLib.featureVars import addFeatureVariationsRaw
-from fontTools.varLib.models import normalizeValue, piecewiseLinearMap
-from collections import defaultdict
-import itertools
-from io import StringIO
-import logging
-import warnings
-import os
-
-
-log = logging.getLogger(__name__)
-
-
-def addOpenTypeFeatures(font, featurefile, tables=None, debug=False):
-    """Add features from a file to a font. Note that this replaces any features
-    currently present.
-
-    Args:
-        font (feaLib.ttLib.TTFont): The font object.
-        featurefile: Either a path or file object (in which case we
-            parse it into an AST), or a pre-parsed AST instance.
-        tables: If passed, restrict the set of affected tables to those in the
-            list.
-        debug: Whether to add source debugging information to the font in the
-            ``Debg`` table
-
-    """
-    builder = Builder(font, featurefile)
-    builder.build(tables=tables, debug=debug)
-
-
-def addOpenTypeFeaturesFromString(
-    font, features, filename=None, tables=None, debug=False
-):
-    """Add features from a string to a font. Note that this replaces any
-    features currently present.
-
-    Args:
-        font (feaLib.ttLib.TTFont): The font object.
-        features: A string containing feature code.
-        filename: The directory containing ``filename`` is used as the root of
-            relative ``include()`` paths; if ``None`` is provided, the current
-            directory is assumed.
-        tables: If passed, restrict the set of affected tables to those in the
-            list.
-        debug: Whether to add source debugging information to the font in the
-            ``Debg`` table
-
-    """
-
-    featurefile = StringIO(tostr(features))
-    if filename:
-        featurefile.name = filename
-    addOpenTypeFeatures(font, featurefile, tables=tables, debug=debug)
-
-
-class Builder(object):
-    supportedTables = frozenset(
-        Tag(tag)
-        for tag in [
-            "BASE",
-            "GDEF",
-            "GPOS",
-            "GSUB",
-            "OS/2",
-            "head",
-            "hhea",
-            "name",
-            "vhea",
-            "STAT",
-        ]
-    )
-
-    def __init__(self, font, featurefile):
-        self.font = font
-        # 'featurefile' can be either a path or file object (in which case we
-        # parse it into an AST), or a pre-parsed AST instance
-        if isinstance(featurefile, FeatureFile):
-            self.parseTree, self.file = featurefile, None
-        else:
-            self.parseTree, self.file = None, featurefile
-        self.glyphMap = font.getReverseGlyphMap()
-        self.varstorebuilder = None
-        if "fvar" in font:
-            self.axes = font["fvar"].axes
-            self.varstorebuilder = OnlineVarStoreBuilder(
-                [ax.axisTag for ax in self.axes]
-            )
-        self.default_language_systems_ = set()
-        self.script_ = None
-        self.lookupflag_ = 0
-        self.lookupflag_markFilterSet_ = None
-        self.language_systems = set()
-        self.seen_non_DFLT_script_ = False
-        self.named_lookups_ = {}
-        self.cur_lookup_ = None
-        self.cur_lookup_name_ = None
-        self.cur_feature_name_ = None
-        self.lookups_ = []
-        self.lookup_locations = {"GSUB": {}, "GPOS": {}}
-        self.features_ = {}  # ('latn', 'DEU ', 'smcp') --> [LookupBuilder*]
-        self.required_features_ = {}  # ('latn', 'DEU ') --> 'scmp'
-        self.feature_variations_ = {}
-        # for feature 'aalt'
-        self.aalt_features_ = []  # [(location, featureName)*], for 'aalt'
-        self.aalt_location_ = None
-        self.aalt_alternates_ = {}
-        # for 'featureNames'
-        self.featureNames_ = set()
-        self.featureNames_ids_ = {}
-        # for 'cvParameters'
-        self.cv_parameters_ = set()
-        self.cv_parameters_ids_ = {}
-        self.cv_num_named_params_ = {}
-        self.cv_characters_ = defaultdict(list)
-        # for feature 'size'
-        self.size_parameters_ = None
-        # for table 'head'
-        self.fontRevision_ = None  # 2.71
-        # for table 'name'
-        self.names_ = []
-        # for table 'BASE'
-        self.base_horiz_axis_ = None
-        self.base_vert_axis_ = None
-        # for table 'GDEF'
-        self.attachPoints_ = {}  # "a" --> {3, 7}
-        self.ligCaretCoords_ = {}  # "f_f_i" --> {300, 600}
-        self.ligCaretPoints_ = {}  # "f_f_i" --> {3, 7}
-        self.glyphClassDefs_ = {}  # "fi" --> (2, (file, line, column))
-        self.markAttach_ = {}  # "acute" --> (4, (file, line, column))
-        self.markAttachClassID_ = {}  # frozenset({"acute", "grave"}) --> 4
-        self.markFilterSets_ = {}  # frozenset({"acute", "grave"}) --> 4
-        # for table 'OS/2'
-        self.os2_ = {}
-        # for table 'hhea'
-        self.hhea_ = {}
-        # for table 'vhea'
-        self.vhea_ = {}
-        # for table 'STAT'
-        self.stat_ = {}
-        # for conditionsets
-        self.conditionsets_ = {}
-        # We will often use exactly the same locations (i.e. the font's masters)
-        # for a large number of variable scalars. Instead of creating a model
-        # for each, let's share the models.
-        self.model_cache = {}
-
-    def build(self, tables=None, debug=False):
-        if self.parseTree is None:
-            self.parseTree = Parser(self.file, self.glyphMap).parse()
-        self.parseTree.build(self)
-        # by default, build all the supported tables
-        if tables is None:
-            tables = self.supportedTables
-        else:
-            tables = frozenset(tables)
-            unsupported = tables - self.supportedTables
-            if unsupported:
-                unsupported_string = ", ".join(sorted(unsupported))
-                raise NotImplementedError(
-                    "The following tables were requested but are unsupported: "
-                    f"{unsupported_string}."
-                )
-        if "GSUB" in tables:
-            self.build_feature_aalt_()
-        if "head" in tables:
-            self.build_head()
-        if "hhea" in tables:
-            self.build_hhea()
-        if "vhea" in tables:
-            self.build_vhea()
-        if "name" in tables:
-            self.build_name()
-        if "OS/2" in tables:
-            self.build_OS_2()
-        if "STAT" in tables:
-            self.build_STAT()
-        for tag in ("GPOS", "GSUB"):
-            if tag not in tables:
-                continue
-            table = self.makeTable(tag)
-            if self.feature_variations_:
-                self.makeFeatureVariations(table, tag)
-            if (
-                table.ScriptList.ScriptCount > 0
-                or table.FeatureList.FeatureCount > 0
-                or table.LookupList.LookupCount > 0
-            ):
-                fontTable = self.font[tag] = newTable(tag)
-                fontTable.table = table
-            elif tag in self.font:
-                del self.font[tag]
-        if any(tag in self.font for tag in ("GPOS", "GSUB")) and "OS/2" in self.font:
-            self.font["OS/2"].usMaxContext = maxCtxFont(self.font)
-        if "GDEF" in tables:
-            gdef = self.buildGDEF()
-            if gdef:
-                self.font["GDEF"] = gdef
-            elif "GDEF" in self.font:
-                del self.font["GDEF"]
-        if "BASE" in tables:
-            base = self.buildBASE()
-            if base:
-                self.font["BASE"] = base
-            elif "BASE" in self.font:
-                del self.font["BASE"]
-        if debug or os.environ.get(LOOKUP_DEBUG_ENV_VAR):
-            self.buildDebg()
-
-    def get_chained_lookup_(self, location, builder_class):
-        result = builder_class(self.font, location)
-        result.lookupflag = self.lookupflag_
-        result.markFilterSet = self.lookupflag_markFilterSet_
-        self.lookups_.append(result)
-        return result
-
-    def add_lookup_to_feature_(self, lookup, feature_name):
-        for script, lang in self.language_systems:
-            key = (script, lang, feature_name)
-            self.features_.setdefault(key, []).append(lookup)
-
-    def get_lookup_(self, location, builder_class):
-        if (
-            self.cur_lookup_
-            and type(self.cur_lookup_) == builder_class
-            and self.cur_lookup_.lookupflag == self.lookupflag_
-            and self.cur_lookup_.markFilterSet == self.lookupflag_markFilterSet_
-        ):
-            return self.cur_lookup_
-        if self.cur_lookup_name_ and self.cur_lookup_:
-            raise FeatureLibError(
-                "Within a named lookup block, all rules must be of "
-                "the same lookup type and flag",
-                location,
-            )
-        self.cur_lookup_ = builder_class(self.font, location)
-        self.cur_lookup_.lookupflag = self.lookupflag_
-        self.cur_lookup_.markFilterSet = self.lookupflag_markFilterSet_
-        self.lookups_.append(self.cur_lookup_)
-        if self.cur_lookup_name_:
-            # We are starting a lookup rule inside a named lookup block.
-            self.named_lookups_[self.cur_lookup_name_] = self.cur_lookup_
-        if self.cur_feature_name_:
-            # We are starting a lookup rule inside a feature. This includes
-            # lookup rules inside named lookups inside features.
-            self.add_lookup_to_feature_(self.cur_lookup_, self.cur_feature_name_)
-        return self.cur_lookup_
-
-    def build_feature_aalt_(self):
-        if not self.aalt_features_ and not self.aalt_alternates_:
-            return
-        alternates = {g: set(a) for g, a in self.aalt_alternates_.items()}
-        for location, name in self.aalt_features_ + [(None, "aalt")]:
-            feature = [
-                (script, lang, feature, lookups)
-                for (script, lang, feature), lookups in self.features_.items()
-                if feature == name
-            ]
-            # "aalt" does not have to specify its own lookups, but it might.
-            if not feature and name != "aalt":
-                warnings.warn("%s: Feature %s has not been defined" % (location, name))
-                continue
-            for script, lang, feature, lookups in feature:
-                for lookuplist in lookups:
-                    if not isinstance(lookuplist, list):
-                        lookuplist = [lookuplist]
-                    for lookup in lookuplist:
-                        for glyph, alts in lookup.getAlternateGlyphs().items():
-                            alternates.setdefault(glyph, set()).update(alts)
-        single = {
-            glyph: list(repl)[0] for glyph, repl in alternates.items() if len(repl) == 1
-        }
-        # TODO: Figure out the glyph alternate ordering used by makeotf.
-        # https://github.com/fonttools/fonttools/issues/836
-        multi = {
-            glyph: sorted(repl, key=self.font.getGlyphID)
-            for glyph, repl in alternates.items()
-            if len(repl) > 1
-        }
-        if not single and not multi:
-            return
-        self.features_ = {
-            (script, lang, feature): lookups
-            for (script, lang, feature), lookups in self.features_.items()
-            if feature != "aalt"
-        }
-        old_lookups = self.lookups_
-        self.lookups_ = []
-        self.start_feature(self.aalt_location_, "aalt")
-        if single:
-            single_lookup = self.get_lookup_(location, SingleSubstBuilder)
-            single_lookup.mapping = single
-        if multi:
-            multi_lookup = self.get_lookup_(location, AlternateSubstBuilder)
-            multi_lookup.alternates = multi
-        self.end_feature()
-        self.lookups_.extend(old_lookups)
-
-    def build_head(self):
-        if not self.fontRevision_:
-            return
-        table = self.font.get("head")
-        if not table:  # this only happens for unit tests
-            table = self.font["head"] = newTable("head")
-            table.decompile(b"\0" * 54, self.font)
-            table.tableVersion = 1.0
-            table.created = table.modified = 3406620153  # 2011-12-13 11:22:33
-        table.fontRevision = self.fontRevision_
-
-    def build_hhea(self):
-        if not self.hhea_:
-            return
-        table = self.font.get("hhea")
-        if not table:  # this only happens for unit tests
-            table = self.font["hhea"] = newTable("hhea")
-            table.decompile(b"\0" * 36, self.font)
-            table.tableVersion = 0x00010000
-        if "caretoffset" in self.hhea_:
-            table.caretOffset = self.hhea_["caretoffset"]
-        if "ascender" in self.hhea_:
-            table.ascent = self.hhea_["ascender"]
-        if "descender" in self.hhea_:
-            table.descent = self.hhea_["descender"]
-        if "linegap" in self.hhea_:
-            table.lineGap = self.hhea_["linegap"]
-
-    def build_vhea(self):
-        if not self.vhea_:
-            return
-        table = self.font.get("vhea")
-        if not table:  # this only happens for unit tests
-            table = self.font["vhea"] = newTable("vhea")
-            table.decompile(b"\0" * 36, self.font)
-            table.tableVersion = 0x00011000
-        if "verttypoascender" in self.vhea_:
-            table.ascent = self.vhea_["verttypoascender"]
-        if "verttypodescender" in self.vhea_:
-            table.descent = self.vhea_["verttypodescender"]
-        if "verttypolinegap" in self.vhea_:
-            table.lineGap = self.vhea_["verttypolinegap"]
-
-    def get_user_name_id(self, table):
-        # Try to find first unused font-specific name id
-        nameIDs = [name.nameID for name in table.names]
-        for user_name_id in range(256, 32767):
-            if user_name_id not in nameIDs:
-                return user_name_id
-
-    def buildFeatureParams(self, tag):
-        params = None
-        if tag == "size":
-            params = otTables.FeatureParamsSize()
-            (
-                params.DesignSize,
-                params.SubfamilyID,
-                params.RangeStart,
-                params.RangeEnd,
-            ) = self.size_parameters_
-            if tag in self.featureNames_ids_:
-                params.SubfamilyNameID = self.featureNames_ids_[tag]
-            else:
-                params.SubfamilyNameID = 0
-        elif tag in self.featureNames_:
-            if not self.featureNames_ids_:
-                # name table wasn't selected among the tables to build; skip
-                pass
-            else:
-                assert tag in self.featureNames_ids_
-                params = otTables.FeatureParamsStylisticSet()
-                params.Version = 0
-                params.UINameID = self.featureNames_ids_[tag]
-        elif tag in self.cv_parameters_:
-            params = otTables.FeatureParamsCharacterVariants()
-            params.Format = 0
-            params.FeatUILabelNameID = self.cv_parameters_ids_.get(
-                (tag, "FeatUILabelNameID"), 0
-            )
-            params.FeatUITooltipTextNameID = self.cv_parameters_ids_.get(
-                (tag, "FeatUITooltipTextNameID"), 0
-            )
-            params.SampleTextNameID = self.cv_parameters_ids_.get(
-                (tag, "SampleTextNameID"), 0
-            )
-            params.NumNamedParameters = self.cv_num_named_params_.get(tag, 0)
-            params.FirstParamUILabelNameID = self.cv_parameters_ids_.get(
-                (tag, "ParamUILabelNameID_0"), 0
-            )
-            params.CharCount = len(self.cv_characters_[tag])
-            params.Character = self.cv_characters_[tag]
-        return params
-
-    def build_name(self):
-        if not self.names_:
-            return
-        table = self.font.get("name")
-        if not table:  # this only happens for unit tests
-            table = self.font["name"] = newTable("name")
-            table.names = []
-        for name in self.names_:
-            nameID, platformID, platEncID, langID, string = name
-            # For featureNames block, nameID is 'feature tag'
-            # For cvParameters blocks, nameID is ('feature tag', 'block name')
-            if not isinstance(nameID, int):
-                tag = nameID
-                if tag in self.featureNames_:
-                    if tag not in self.featureNames_ids_:
-                        self.featureNames_ids_[tag] = self.get_user_name_id(table)
-                        assert self.featureNames_ids_[tag] is not None
-                    nameID = self.featureNames_ids_[tag]
-                elif tag[0] in self.cv_parameters_:
-                    if tag not in self.cv_parameters_ids_:
-                        self.cv_parameters_ids_[tag] = self.get_user_name_id(table)
-                        assert self.cv_parameters_ids_[tag] is not None
-                    nameID = self.cv_parameters_ids_[tag]
-            table.setName(string, nameID, platformID, platEncID, langID)
-        table.names.sort()
-
-    def build_OS_2(self):
-        if not self.os2_:
-            return
-        table = self.font.get("OS/2")
-        if not table:  # this only happens for unit tests
-            table = self.font["OS/2"] = newTable("OS/2")
-            data = b"\0" * sstruct.calcsize(getTableModule("OS/2").OS2_format_0)
-            table.decompile(data, self.font)
-        version = 0
-        if "fstype" in self.os2_:
-            table.fsType = self.os2_["fstype"]
-        if "panose" in self.os2_:
-            panose = getTableModule("OS/2").Panose()
-            (
-                panose.bFamilyType,
-                panose.bSerifStyle,
-                panose.bWeight,
-                panose.bProportion,
-                panose.bContrast,
-                panose.bStrokeVariation,
-                panose.bArmStyle,
-                panose.bLetterForm,
-                panose.bMidline,
-                panose.bXHeight,
-            ) = self.os2_["panose"]
-            table.panose = panose
-        if "typoascender" in self.os2_:
-            table.sTypoAscender = self.os2_["typoascender"]
-        if "typodescender" in self.os2_:
-            table.sTypoDescender = self.os2_["typodescender"]
-        if "typolinegap" in self.os2_:
-            table.sTypoLineGap = self.os2_["typolinegap"]
-        if "winascent" in self.os2_:
-            table.usWinAscent = self.os2_["winascent"]
-        if "windescent" in self.os2_:
-            table.usWinDescent = self.os2_["windescent"]
-        if "vendor" in self.os2_:
-            table.achVendID = safeEval("'''" + self.os2_["vendor"] + "'''")
-        if "weightclass" in self.os2_:
-            table.usWeightClass = self.os2_["weightclass"]
-        if "widthclass" in self.os2_:
-            table.usWidthClass = self.os2_["widthclass"]
-        if "unicoderange" in self.os2_:
-            table.setUnicodeRanges(self.os2_["unicoderange"])
-        if "codepagerange" in self.os2_:
-            pages = self.build_codepages_(self.os2_["codepagerange"])
-            table.ulCodePageRange1, table.ulCodePageRange2 = pages
-            version = 1
-        if "xheight" in self.os2_:
-            table.sxHeight = self.os2_["xheight"]
-            version = 2
-        if "capheight" in self.os2_:
-            table.sCapHeight = self.os2_["capheight"]
-            version = 2
-        if "loweropsize" in self.os2_:
-            table.usLowerOpticalPointSize = self.os2_["loweropsize"]
-            version = 5
-        if "upperopsize" in self.os2_:
-            table.usUpperOpticalPointSize = self.os2_["upperopsize"]
-            version = 5
-
-        def checkattr(table, attrs):
-            for attr in attrs:
-                if not hasattr(table, attr):
-                    setattr(table, attr, 0)
-
-        table.version = max(version, table.version)
-        # this only happens for unit tests
-        if version >= 1:
-            checkattr(table, ("ulCodePageRange1", "ulCodePageRange2"))
-        if version >= 2:
-            checkattr(
-                table,
-                (
-                    "sxHeight",
-                    "sCapHeight",
-                    "usDefaultChar",
-                    "usBreakChar",
-                    "usMaxContext",
-                ),
-            )
-        if version >= 5:
-            checkattr(table, ("usLowerOpticalPointSize", "usUpperOpticalPointSize"))
-
-    def setElidedFallbackName(self, value, location):
-        # ElidedFallbackName is a convenience method for setting
-        # ElidedFallbackNameID so only one can be allowed
-        for token in ("ElidedFallbackName", "ElidedFallbackNameID"):
-            if token in self.stat_:
-                raise FeatureLibError(
-                    f"{token} is already set.",
-                    location,
-                )
-        if isinstance(value, int):
-            self.stat_["ElidedFallbackNameID"] = value
-        elif isinstance(value, list):
-            self.stat_["ElidedFallbackName"] = value
-        else:
-            raise AssertionError(value)
-
-    def addDesignAxis(self, designAxis, location):
-        if "DesignAxes" not in self.stat_:
-            self.stat_["DesignAxes"] = []
-        if designAxis.tag in (r.tag for r in self.stat_["DesignAxes"]):
-            raise FeatureLibError(
-                f'DesignAxis already defined for tag "{designAxis.tag}".',
-                location,
-            )
-        if designAxis.axisOrder in (r.axisOrder for r in self.stat_["DesignAxes"]):
-            raise FeatureLibError(
-                f"DesignAxis already defined for axis number {designAxis.axisOrder}.",
-                location,
-            )
-        self.stat_["DesignAxes"].append(designAxis)
-
-    def addAxisValueRecord(self, axisValueRecord, location):
-        if "AxisValueRecords" not in self.stat_:
-            self.stat_["AxisValueRecords"] = []
-        # Check for duplicate AxisValueRecords
-        for record_ in self.stat_["AxisValueRecords"]:
-            if (
-                {n.asFea() for n in record_.names}
-                == {n.asFea() for n in axisValueRecord.names}
-                and {n.asFea() for n in record_.locations}
-                == {n.asFea() for n in axisValueRecord.locations}
-                and record_.flags == axisValueRecord.flags
-            ):
-                raise FeatureLibError(
-                    "An AxisValueRecord with these values is already defined.",
-                    location,
-                )
-        self.stat_["AxisValueRecords"].append(axisValueRecord)
-
-    def build_STAT(self):
-        if not self.stat_:
-            return
-
-        axes = self.stat_.get("DesignAxes")
-        if not axes:
-            raise FeatureLibError("DesignAxes not defined", None)
-        axisValueRecords = self.stat_.get("AxisValueRecords")
-        axisValues = {}
-        format4_locations = []
-        for tag in axes:
-            axisValues[tag.tag] = []
-        if axisValueRecords is not None:
-            for avr in axisValueRecords:
-                valuesDict = {}
-                if avr.flags > 0:
-                    valuesDict["flags"] = avr.flags
-                if len(avr.locations) == 1:
-                    location = avr.locations[0]
-                    values = location.values
-                    if len(values) == 1:  # format1
-                        valuesDict.update({"value": values[0], "name": avr.names})
-                    if len(values) == 2:  # format3
-                        valuesDict.update(
-                            {
-                                "value": values[0],
-                                "linkedValue": values[1],
-                                "name": avr.names,
-                            }
-                        )
-                    if len(values) == 3:  # format2
-                        nominal, minVal, maxVal = values
-                        valuesDict.update(
-                            {
-                                "nominalValue": nominal,
-                                "rangeMinValue": minVal,
-                                "rangeMaxValue": maxVal,
-                                "name": avr.names,
-                            }
-                        )
-                    axisValues[location.tag].append(valuesDict)
-                else:
-                    valuesDict.update(
-                        {
-                            "location": {i.tag: i.values[0] for i in avr.locations},
-                            "name": avr.names,
-                        }
-                    )
-                    format4_locations.append(valuesDict)
-
-        designAxes = [
-            {
-                "ordering": a.axisOrder,
-                "tag": a.tag,
-                "name": a.names,
-                "values": axisValues[a.tag],
-            }
-            for a in axes
-        ]
-
-        nameTable = self.font.get("name")
-        if not nameTable:  # this only happens for unit tests
-            nameTable = self.font["name"] = newTable("name")
-            nameTable.names = []
-
-        if "ElidedFallbackNameID" in self.stat_:
-            nameID = self.stat_["ElidedFallbackNameID"]
-            name = nameTable.getDebugName(nameID)
-            if not name:
-                raise FeatureLibError(
-                    f"ElidedFallbackNameID {nameID} points "
-                    "to a nameID that does not exist in the "
-                    '"name" table',
-                    None,
-                )
-        elif "ElidedFallbackName" in self.stat_:
-            nameID = self.stat_["ElidedFallbackName"]
-
-        otl.buildStatTable(
-            self.font,
-            designAxes,
-            locations=format4_locations,
-            elidedFallbackName=nameID,
-        )
-
-    def build_codepages_(self, pages):
-        pages2bits = {
-            1252: 0,
-            1250: 1,
-            1251: 2,
-            1253: 3,
-            1254: 4,
-            1255: 5,
-            1256: 6,
-            1257: 7,
-            1258: 8,
-            874: 16,
-            932: 17,
-            936: 18,
-            949: 19,
-            950: 20,
-            1361: 21,
-            869: 48,
-            866: 49,
-            865: 50,
-            864: 51,
-            863: 52,
-            862: 53,
-            861: 54,
-            860: 55,
-            857: 56,
-            855: 57,
-            852: 58,
-            775: 59,
-            737: 60,
-            708: 61,
-            850: 62,
-            437: 63,
-        }
-        bits = [pages2bits[p] for p in pages if p in pages2bits]
-        pages = []
-        for i in range(2):
-            pages.append("")
-            for j in range(i * 32, (i + 1) * 32):
-                if j in bits:
-                    pages[i] += "1"
-                else:
-                    pages[i] += "0"
-        return [binary2num(p[::-1]) for p in pages]
-
-    def buildBASE(self):
-        if not self.base_horiz_axis_ and not self.base_vert_axis_:
-            return None
-        base = otTables.BASE()
-        base.Version = 0x00010000
-        base.HorizAxis = self.buildBASEAxis(self.base_horiz_axis_)
-        base.VertAxis = self.buildBASEAxis(self.base_vert_axis_)
-
-        result = newTable("BASE")
-        result.table = base
-        return result
-
-    def buildBASEAxis(self, axis):
-        if not axis:
-            return
-        bases, scripts = axis
-        axis = otTables.Axis()
-        axis.BaseTagList = otTables.BaseTagList()
-        axis.BaseTagList.BaselineTag = bases
-        axis.BaseTagList.BaseTagCount = len(bases)
-        axis.BaseScriptList = otTables.BaseScriptList()
-        axis.BaseScriptList.BaseScriptRecord = []
-        axis.BaseScriptList.BaseScriptCount = len(scripts)
-        for script in sorted(scripts):
-            record = otTables.BaseScriptRecord()
-            record.BaseScriptTag = script[0]
-            record.BaseScript = otTables.BaseScript()
-            record.BaseScript.BaseLangSysCount = 0
-            record.BaseScript.BaseValues = otTables.BaseValues()
-            record.BaseScript.BaseValues.DefaultIndex = bases.index(script[1])
-            record.BaseScript.BaseValues.BaseCoord = []
-            record.BaseScript.BaseValues.BaseCoordCount = len(script[2])
-            for c in script[2]:
-                coord = otTables.BaseCoord()
-                coord.Format = 1
-                coord.Coordinate = c
-                record.BaseScript.BaseValues.BaseCoord.append(coord)
-            axis.BaseScriptList.BaseScriptRecord.append(record)
-        return axis
-
-    def buildGDEF(self):
-        gdef = otTables.GDEF()
-        gdef.GlyphClassDef = self.buildGDEFGlyphClassDef_()
-        gdef.AttachList = otl.buildAttachList(self.attachPoints_, self.glyphMap)
-        gdef.LigCaretList = otl.buildLigCaretList(
-            self.ligCaretCoords_, self.ligCaretPoints_, self.glyphMap
-        )
-        gdef.MarkAttachClassDef = self.buildGDEFMarkAttachClassDef_()
-        gdef.MarkGlyphSetsDef = self.buildGDEFMarkGlyphSetsDef_()
-        gdef.Version = 0x00010002 if gdef.MarkGlyphSetsDef else 0x00010000
-        if self.varstorebuilder:
-            store = self.varstorebuilder.finish()
-            if store:
-                gdef.Version = 0x00010003
-                gdef.VarStore = store
-                varidx_map = store.optimize()
-
-                gdef.remap_device_varidxes(varidx_map)
-                if "GPOS" in self.font:
-                    self.font["GPOS"].table.remap_device_varidxes(varidx_map)
-            self.model_cache.clear()
-        if any(
-            (
-                gdef.GlyphClassDef,
-                gdef.AttachList,
-                gdef.LigCaretList,
-                gdef.MarkAttachClassDef,
-                gdef.MarkGlyphSetsDef,
-            )
-        ) or hasattr(gdef, "VarStore"):
-            result = newTable("GDEF")
-            result.table = gdef
-            return result
-        else:
-            return None
-
-    def buildGDEFGlyphClassDef_(self):
-        if self.glyphClassDefs_:
-            classes = {g: c for (g, (c, _)) in self.glyphClassDefs_.items()}
-        else:
-            classes = {}
-            for lookup in self.lookups_:
-                classes.update(lookup.inferGlyphClasses())
-            for markClass in self.parseTree.markClasses.values():
-                for markClassDef in markClass.definitions:
-                    for glyph in markClassDef.glyphSet():
-                        classes[glyph] = 3
-        if classes:
-            result = otTables.GlyphClassDef()
-            result.classDefs = classes
-            return result
-        else:
-            return None
-
-    def buildGDEFMarkAttachClassDef_(self):
-        classDefs = {g: c for g, (c, _) in self.markAttach_.items()}
-        if not classDefs:
-            return None
-        result = otTables.MarkAttachClassDef()
-        result.classDefs = classDefs
-        return result
-
-    def buildGDEFMarkGlyphSetsDef_(self):
-        sets = []
-        for glyphs, id_ in sorted(
-            self.markFilterSets_.items(), key=lambda item: item[1]
-        ):
-            sets.append(glyphs)
-        return otl.buildMarkGlyphSetsDef(sets, self.glyphMap)
-
-    def buildDebg(self):
-        if "Debg" not in self.font:
-            self.font["Debg"] = newTable("Debg")
-            self.font["Debg"].data = {}
-        self.font["Debg"].data[LOOKUP_DEBUG_INFO_KEY] = self.lookup_locations
-
-    def buildLookups_(self, tag):
-        assert tag in ("GPOS", "GSUB"), tag
-        for lookup in self.lookups_:
-            lookup.lookup_index = None
-        lookups = []
-        for lookup in self.lookups_:
-            if lookup.table != tag:
-                continue
-            lookup.lookup_index = len(lookups)
-            self.lookup_locations[tag][str(lookup.lookup_index)] = LookupDebugInfo(
-                location=str(lookup.location),
-                name=self.get_lookup_name_(lookup),
-                feature=None,
-            )
-            lookups.append(lookup)
-        try:
-            otLookups = [l.build() for l in lookups]
-        except OpenTypeLibError as e:
-            raise FeatureLibError(str(e), e.location) from e
-        return otLookups
-
-    def makeTable(self, tag):
-        table = getattr(otTables, tag, None)()
-        table.Version = 0x00010000
-        table.ScriptList = otTables.ScriptList()
-        table.ScriptList.ScriptRecord = []
-        table.FeatureList = otTables.FeatureList()
-        table.FeatureList.FeatureRecord = []
-        table.LookupList = otTables.LookupList()
-        table.LookupList.Lookup = self.buildLookups_(tag)
-
-        # Build a table for mapping (tag, lookup_indices) to feature_index.
-        # For example, ('liga', (2,3,7)) --> 23.
-        feature_indices = {}
-        required_feature_indices = {}  # ('latn', 'DEU') --> 23
-        scripts = {}  # 'latn' --> {'DEU': [23, 24]} for feature #23,24
-        # Sort the feature table by feature tag:
-        # https://github.com/fonttools/fonttools/issues/568
-        sortFeatureTag = lambda f: (f[0][2], f[0][1], f[0][0], f[1])
-        for key, lookups in sorted(self.features_.items(), key=sortFeatureTag):
-            script, lang, feature_tag = key
-            # l.lookup_index will be None when a lookup is not needed
-            # for the table under construction. For example, substitution
-            # rules will have no lookup_index while building GPOS tables.
-            lookup_indices = tuple(
-                [l.lookup_index for l in lookups if l.lookup_index is not None]
-            )
-
-            size_feature = tag == "GPOS" and feature_tag == "size"
-            force_feature = self.any_feature_variations(feature_tag, tag)
-            if len(lookup_indices) == 0 and not size_feature and not force_feature:
-                continue
-
-            for ix in lookup_indices:
-                try:
-                    self.lookup_locations[tag][str(ix)] = self.lookup_locations[tag][
-                        str(ix)
-                    ]._replace(feature=key)
-                except KeyError:
-                    warnings.warn(
-                        "feaLib.Builder subclass needs upgrading to "
-                        "stash debug information. See fonttools#2065."
-                    )
-
-            feature_key = (feature_tag, lookup_indices)
-            feature_index = feature_indices.get(feature_key)
-            if feature_index is None:
-                feature_index = len(table.FeatureList.FeatureRecord)
-                frec = otTables.FeatureRecord()
-                frec.FeatureTag = feature_tag
-                frec.Feature = otTables.Feature()
-                frec.Feature.FeatureParams = self.buildFeatureParams(feature_tag)
-                frec.Feature.LookupListIndex = list(lookup_indices)
-                frec.Feature.LookupCount = len(lookup_indices)
-                table.FeatureList.FeatureRecord.append(frec)
-                feature_indices[feature_key] = feature_index
-            scripts.setdefault(script, {}).setdefault(lang, []).append(feature_index)
-            if self.required_features_.get((script, lang)) == feature_tag:
-                required_feature_indices[(script, lang)] = feature_index
-
-        # Build ScriptList.
-        for script, lang_features in sorted(scripts.items()):
-            srec = otTables.ScriptRecord()
-            srec.ScriptTag = script
-            srec.Script = otTables.Script()
-            srec.Script.DefaultLangSys = None
-            srec.Script.LangSysRecord = []
-            for lang, feature_indices in sorted(lang_features.items()):
-                langrec = otTables.LangSysRecord()
-                langrec.LangSys = otTables.LangSys()
-                langrec.LangSys.LookupOrder = None
-
-                req_feature_index = required_feature_indices.get((script, lang))
-                if req_feature_index is None:
-                    langrec.LangSys.ReqFeatureIndex = 0xFFFF
-                else:
-                    langrec.LangSys.ReqFeatureIndex = req_feature_index
-
-                langrec.LangSys.FeatureIndex = [
-                    i for i in feature_indices if i != req_feature_index
-                ]
-                langrec.LangSys.FeatureCount = len(langrec.LangSys.FeatureIndex)
-
-                if lang == "dflt":
-                    srec.Script.DefaultLangSys = langrec.LangSys
-                else:
-                    langrec.LangSysTag = lang
-                    srec.Script.LangSysRecord.append(langrec)
-            srec.Script.LangSysCount = len(srec.Script.LangSysRecord)
-            table.ScriptList.ScriptRecord.append(srec)
-
-        table.ScriptList.ScriptCount = len(table.ScriptList.ScriptRecord)
-        table.FeatureList.FeatureCount = len(table.FeatureList.FeatureRecord)
-        table.LookupList.LookupCount = len(table.LookupList.Lookup)
-        return table
-
-    def makeFeatureVariations(self, table, table_tag):
-        feature_vars = {}
-        has_any_variations = False
-        # Sort out which lookups to build, gather their indices
-        for (_, _, feature_tag), variations in self.feature_variations_.items():
-            feature_vars[feature_tag] = []
-            for conditionset, builders in variations.items():
-                raw_conditionset = self.conditionsets_[conditionset]
-                indices = []
-                for b in builders:
-                    if b.table != table_tag:
-                        continue
-                    assert b.lookup_index is not None
-                    indices.append(b.lookup_index)
-                    has_any_variations = True
-                feature_vars[feature_tag].append((raw_conditionset, indices))
-
-        if has_any_variations:
-            for feature_tag, conditions_and_lookups in feature_vars.items():
-                addFeatureVariationsRaw(
-                    self.font, table, conditions_and_lookups, feature_tag
-                )
-
-    def any_feature_variations(self, feature_tag, table_tag):
-        for (_, _, feature), variations in self.feature_variations_.items():
-            if feature != feature_tag:
-                continue
-            for conditionset, builders in variations.items():
-                if any(b.table == table_tag for b in builders):
-                    return True
-        return False
-
-    def get_lookup_name_(self, lookup):
-        rev = {v: k for k, v in self.named_lookups_.items()}
-        if lookup in rev:
-            return rev[lookup]
-        return None
-
-    def add_language_system(self, location, script, language):
-        # OpenType Feature File Specification, section 4.b.i
-        if script == "DFLT" and language == "dflt" and self.default_language_systems_:
-            raise FeatureLibError(
-                'If "languagesystem DFLT dflt" is present, it must be '
-                "the first of the languagesystem statements",
-                location,
-            )
-        if script == "DFLT":
-            if self.seen_non_DFLT_script_:
-                raise FeatureLibError(
-                    'languagesystems using the "DFLT" script tag must '
-                    "precede all other languagesystems",
-                    location,
-                )
-        else:
-            self.seen_non_DFLT_script_ = True
-        if (script, language) in self.default_language_systems_:
-            raise FeatureLibError(
-                '"languagesystem %s %s" has already been specified'
-                % (script.strip(), language.strip()),
-                location,
-            )
-        self.default_language_systems_.add((script, language))
-
-    def get_default_language_systems_(self):
-        # OpenType Feature File specification, 4.b.i. languagesystem:
-        # If no "languagesystem" statement is present, then the
-        # implementation must behave exactly as though the following
-        # statement were present at the beginning of the feature file:
-        # languagesystem DFLT dflt;
-        if self.default_language_systems_:
-            return frozenset(self.default_language_systems_)
-        else:
-            return frozenset({("DFLT", "dflt")})
-
-    def start_feature(self, location, name):
-        self.language_systems = self.get_default_language_systems_()
-        self.script_ = "DFLT"
-        self.cur_lookup_ = None
-        self.cur_feature_name_ = name
-        self.lookupflag_ = 0
-        self.lookupflag_markFilterSet_ = None
-        if name == "aalt":
-            self.aalt_location_ = location
-
-    def end_feature(self):
-        assert self.cur_feature_name_ is not None
-        self.cur_feature_name_ = None
-        self.language_systems = None
-        self.cur_lookup_ = None
-        self.lookupflag_ = 0
-        self.lookupflag_markFilterSet_ = None
-
-    def start_lookup_block(self, location, name):
-        if name in self.named_lookups_:
-            raise FeatureLibError(
-                'Lookup "%s" has already been defined' % name, location
-            )
-        if self.cur_feature_name_ == "aalt":
-            raise FeatureLibError(
-                "Lookup blocks cannot be placed inside 'aalt' features; "
-                "move it out, and then refer to it with a lookup statement",
-                location,
-            )
-        self.cur_lookup_name_ = name
-        self.named_lookups_[name] = None
-        self.cur_lookup_ = None
-        if self.cur_feature_name_ is None:
-            self.lookupflag_ = 0
-            self.lookupflag_markFilterSet_ = None
-
-    def end_lookup_block(self):
-        assert self.cur_lookup_name_ is not None
-        self.cur_lookup_name_ = None
-        self.cur_lookup_ = None
-        if self.cur_feature_name_ is None:
-            self.lookupflag_ = 0
-            self.lookupflag_markFilterSet_ = None
-
-    def add_lookup_call(self, lookup_name):
-        assert lookup_name in self.named_lookups_, lookup_name
-        self.cur_lookup_ = None
-        lookup = self.named_lookups_[lookup_name]
-        if lookup is not None:  # skip empty named lookup
-            self.add_lookup_to_feature_(lookup, self.cur_feature_name_)
-
-    def set_font_revision(self, location, revision):
-        self.fontRevision_ = revision
-
-    def set_language(self, location, language, include_default, required):
-        assert len(language) == 4
-        if self.cur_feature_name_ in ("aalt", "size"):
-            raise FeatureLibError(
-                "Language statements are not allowed "
-                'within "feature %s"' % self.cur_feature_name_,
-                location,
-            )
-        if self.cur_feature_name_ is None:
-            raise FeatureLibError(
-                "Language statements are not allowed "
-                "within standalone lookup blocks",
-                location,
-            )
-        self.cur_lookup_ = None
-
-        key = (self.script_, language, self.cur_feature_name_)
-        lookups = self.features_.get((key[0], "dflt", key[2]))
-        if (language == "dflt" or include_default) and lookups:
-            self.features_[key] = lookups[:]
-        else:
-            self.features_[key] = []
-        self.language_systems = frozenset([(self.script_, language)])
-
-        if required:
-            key = (self.script_, language)
-            if key in self.required_features_:
-                raise FeatureLibError(
-                    "Language %s (script %s) has already "
-                    "specified feature %s as its required feature"
-                    % (
-                        language.strip(),
-                        self.script_.strip(),
-                        self.required_features_[key].strip(),
-                    ),
-                    location,
-                )
-            self.required_features_[key] = self.cur_feature_name_
-
-    def getMarkAttachClass_(self, location, glyphs):
-        glyphs = frozenset(glyphs)
-        id_ = self.markAttachClassID_.get(glyphs)
-        if id_ is not None:
-            return id_
-        id_ = len(self.markAttachClassID_) + 1
-        self.markAttachClassID_[glyphs] = id_
-        for glyph in glyphs:
-            if glyph in self.markAttach_:
-                _, loc = self.markAttach_[glyph]
-                raise FeatureLibError(
-                    "Glyph %s already has been assigned "
-                    "a MarkAttachmentType at %s" % (glyph, loc),
-                    location,
-                )
-            self.markAttach_[glyph] = (id_, location)
-        return id_
-
-    def getMarkFilterSet_(self, location, glyphs):
-        glyphs = frozenset(glyphs)
-        id_ = self.markFilterSets_.get(glyphs)
-        if id_ is not None:
-            return id_
-        id_ = len(self.markFilterSets_)
-        self.markFilterSets_[glyphs] = id_
-        return id_
-
-    def set_lookup_flag(self, location, value, markAttach, markFilter):
-        value = value & 0xFF
-        if markAttach:
-            markAttachClass = self.getMarkAttachClass_(location, markAttach)
-            value = value | (markAttachClass << 8)
-        if markFilter:
-            markFilterSet = self.getMarkFilterSet_(location, markFilter)
-            value = value | 0x10
-            self.lookupflag_markFilterSet_ = markFilterSet
-        else:
-            self.lookupflag_markFilterSet_ = None
-        self.lookupflag_ = value
-
-    def set_script(self, location, script):
-        if self.cur_feature_name_ in ("aalt", "size"):
-            raise FeatureLibError(
-                "Script statements are not allowed "
-                'within "feature %s"' % self.cur_feature_name_,
-                location,
-            )
-        if self.cur_feature_name_ is None:
-            raise FeatureLibError(
-                "Script statements are not allowed " "within standalone lookup blocks",
-                location,
-            )
-        if self.language_systems == {(script, "dflt")}:
-            # Nothing to do.
-            return
-        self.cur_lookup_ = None
-        self.script_ = script
-        self.lookupflag_ = 0
-        self.lookupflag_markFilterSet_ = None
-        self.set_language(location, "dflt", include_default=True, required=False)
-
-    def find_lookup_builders_(self, lookups):
-        """Helper for building chain contextual substitutions
-
-        Given a list of lookup names, finds the LookupBuilder for each name.
-        If an input name is None, it gets mapped to a None LookupBuilder.
-        """
-        lookup_builders = []
-        for lookuplist in lookups:
-            if lookuplist is not None:
-                lookup_builders.append(
-                    [self.named_lookups_.get(l.name) for l in lookuplist]
-                )
-            else:
-                lookup_builders.append(None)
-        return lookup_builders
-
-    def add_attach_points(self, location, glyphs, contourPoints):
-        for glyph in glyphs:
-            self.attachPoints_.setdefault(glyph, set()).update(contourPoints)
-
-    def add_feature_reference(self, location, featureName):
-        if self.cur_feature_name_ != "aalt":
-            raise FeatureLibError(
-                'Feature references are only allowed inside "feature aalt"', location
-            )
-        self.aalt_features_.append((location, featureName))
-
-    def add_featureName(self, tag):
-        self.featureNames_.add(tag)
-
-    def add_cv_parameter(self, tag):
-        self.cv_parameters_.add(tag)
-
-    def add_to_cv_num_named_params(self, tag):
-        """Adds new items to ``self.cv_num_named_params_``
-        or increments the count of existing items."""
-        if tag in self.cv_num_named_params_:
-            self.cv_num_named_params_[tag] += 1
-        else:
-            self.cv_num_named_params_[tag] = 1
-
-    def add_cv_character(self, character, tag):
-        self.cv_characters_[tag].append(character)
-
-    def set_base_axis(self, bases, scripts, vertical):
-        if vertical:
-            self.base_vert_axis_ = (bases, scripts)
-        else:
-            self.base_horiz_axis_ = (bases, scripts)
-
-    def set_size_parameters(
-        self, location, DesignSize, SubfamilyID, RangeStart, RangeEnd
-    ):
-        if self.cur_feature_name_ != "size":
-            raise FeatureLibError(
-                "Parameters statements are not allowed "
-                'within "feature %s"' % self.cur_feature_name_,
-                location,
-            )
-        self.size_parameters_ = [DesignSize, SubfamilyID, RangeStart, RangeEnd]
-        for script, lang in self.language_systems:
-            key = (script, lang, self.cur_feature_name_)
-            self.features_.setdefault(key, [])
-
-    # GSUB rules
-
-    # GSUB 1
-    def add_single_subst(self, location, prefix, suffix, mapping, forceChain):
-        if self.cur_feature_name_ == "aalt":
-            for from_glyph, to_glyph in mapping.items():
-                alts = self.aalt_alternates_.setdefault(from_glyph, set())
-                alts.add(to_glyph)
-            return
-        if prefix or suffix or forceChain:
-            self.add_single_subst_chained_(location, prefix, suffix, mapping)
-            return
-        lookup = self.get_lookup_(location, SingleSubstBuilder)
-        for from_glyph, to_glyph in mapping.items():
-            if from_glyph in lookup.mapping:
-                if to_glyph == lookup.mapping[from_glyph]:
-                    log.info(
-                        "Removing duplicate single substitution from glyph"
-                        ' "%s" to "%s" at %s',
-                        from_glyph,
-                        to_glyph,
-                        location,
-                    )
-                else:
-                    raise FeatureLibError(
-                        'Already defined rule for replacing glyph "%s" by "%s"'
-                        % (from_glyph, lookup.mapping[from_glyph]),
-                        location,
-                    )
-            lookup.mapping[from_glyph] = to_glyph
-
-    # GSUB 2
-    def add_multiple_subst(
-        self, location, prefix, glyph, suffix, replacements, forceChain=False
-    ):
-        if prefix or suffix or forceChain:
-            chain = self.get_lookup_(location, ChainContextSubstBuilder)
-            sub = self.get_chained_lookup_(location, MultipleSubstBuilder)
-            sub.mapping[glyph] = replacements
-            chain.rules.append(ChainContextualRule(prefix, [{glyph}], suffix, [sub]))
-            return
-        lookup = self.get_lookup_(location, MultipleSubstBuilder)
-        if glyph in lookup.mapping:
-            if replacements == lookup.mapping[glyph]:
-                log.info(
-                    "Removing duplicate multiple substitution from glyph"
-                    ' "%s" to %s%s',
-                    glyph,
-                    replacements,
-                    f" at {location}" if location else "",
-                )
-            else:
-                raise FeatureLibError(
-                    'Already defined substitution for glyph "%s"' % glyph, location
-                )
-        lookup.mapping[glyph] = replacements
-
-    # GSUB 3
-    def add_alternate_subst(self, location, prefix, glyph, suffix, replacement):
-        if self.cur_feature_name_ == "aalt":
-            alts = self.aalt_alternates_.setdefault(glyph, set())
-            alts.update(replacement)
-            return
-        if prefix or suffix:
-            chain = self.get_lookup_(location, ChainContextSubstBuilder)
-            lookup = self.get_chained_lookup_(location, AlternateSubstBuilder)
-            chain.rules.append(ChainContextualRule(prefix, [{glyph}], suffix, [lookup]))
-        else:
-            lookup = self.get_lookup_(location, AlternateSubstBuilder)
-        if glyph in lookup.alternates:
-            raise FeatureLibError(
-                'Already defined alternates for glyph "%s"' % glyph, location
-            )
-        # We allow empty replacement glyphs here.
-        lookup.alternates[glyph] = replacement
-
-    # GSUB 4
-    def add_ligature_subst(
-        self, location, prefix, glyphs, suffix, replacement, forceChain
-    ):
-        if prefix or suffix or forceChain:
-            chain = self.get_lookup_(location, ChainContextSubstBuilder)
-            lookup = self.get_chained_lookup_(location, LigatureSubstBuilder)
-            chain.rules.append(ChainContextualRule(prefix, glyphs, suffix, [lookup]))
-        else:
-            lookup = self.get_lookup_(location, LigatureSubstBuilder)
-
-        if not all(glyphs):
-            raise FeatureLibError("Empty glyph class in substitution", location)
-
-        # OpenType feature file syntax, section 5.d, "Ligature substitution":
-        # "Since the OpenType specification does not allow ligature
-        # substitutions to be specified on target sequences that contain
-        # glyph classes, the implementation software will enumerate
-        # all specific glyph sequences if glyph classes are detected"
-        for g in sorted(itertools.product(*glyphs)):
-            lookup.ligatures[g] = replacement
-
-    # GSUB 5/6
-    def add_chain_context_subst(self, location, prefix, glyphs, suffix, lookups):
-        if not all(glyphs) or not all(prefix) or not all(suffix):
-            raise FeatureLibError(
-                "Empty glyph class in contextual substitution", location
-            )
-        lookup = self.get_lookup_(location, ChainContextSubstBuilder)
-        lookup.rules.append(
-            ChainContextualRule(
-                prefix, glyphs, suffix, self.find_lookup_builders_(lookups)
-            )
-        )
-
-    def add_single_subst_chained_(self, location, prefix, suffix, mapping):
-        if not mapping or not all(prefix) or not all(suffix):
-            raise FeatureLibError(
-                "Empty glyph class in contextual substitution", location
-            )
-        # https://github.com/fonttools/fonttools/issues/512
-        # https://github.com/fonttools/fonttools/issues/2150
-        chain = self.get_lookup_(location, ChainContextSubstBuilder)
-        sub = chain.find_chainable_single_subst(mapping)
-        if sub is None:
-            sub = self.get_chained_lookup_(location, SingleSubstBuilder)
-        sub.mapping.update(mapping)
-        chain.rules.append(
-            ChainContextualRule(prefix, [list(mapping.keys())], suffix, [sub])
-        )
-
-    # GSUB 8
-    def add_reverse_chain_single_subst(self, location, old_prefix, old_suffix, mapping):
-        if not mapping:
-            raise FeatureLibError("Empty glyph class in substitution", location)
-        lookup = self.get_lookup_(location, ReverseChainSingleSubstBuilder)
-        lookup.rules.append((old_prefix, old_suffix, mapping))
-
-    # GPOS rules
-
-    # GPOS 1
-    def add_single_pos(self, location, prefix, suffix, pos, forceChain):
-        if prefix or suffix or forceChain:
-            self.add_single_pos_chained_(location, prefix, suffix, pos)
-        else:
-            lookup = self.get_lookup_(location, SinglePosBuilder)
-            for glyphs, value in pos:
-                if not glyphs:
-                    raise FeatureLibError(
-                        "Empty glyph class in positioning rule", location
-                    )
-                otValueRecord = self.makeOpenTypeValueRecord(
-                    location, value, pairPosContext=False
-                )
-                for glyph in glyphs:
-                    try:
-                        lookup.add_pos(location, glyph, otValueRecord)
-                    except OpenTypeLibError as e:
-                        raise FeatureLibError(str(e), e.location) from e
-
-    # GPOS 2
-    def add_class_pair_pos(self, location, glyphclass1, value1, glyphclass2, value2):
-        if not glyphclass1 or not glyphclass2:
-            raise FeatureLibError("Empty glyph class in positioning rule", location)
-        lookup = self.get_lookup_(location, PairPosBuilder)
-        v1 = self.makeOpenTypeValueRecord(location, value1, pairPosContext=True)
-        v2 = self.makeOpenTypeValueRecord(location, value2, pairPosContext=True)
-        lookup.addClassPair(location, glyphclass1, v1, glyphclass2, v2)
-
-    def add_specific_pair_pos(self, location, glyph1, value1, glyph2, value2):
-        if not glyph1 or not glyph2:
-            raise FeatureLibError("Empty glyph class in positioning rule", location)
-        lookup = self.get_lookup_(location, PairPosBuilder)
-        v1 = self.makeOpenTypeValueRecord(location, value1, pairPosContext=True)
-        v2 = self.makeOpenTypeValueRecord(location, value2, pairPosContext=True)
-        lookup.addGlyphPair(location, glyph1, v1, glyph2, v2)
-
-    # GPOS 3
-    def add_cursive_pos(self, location, glyphclass, entryAnchor, exitAnchor):
-        if not glyphclass:
-            raise FeatureLibError("Empty glyph class in positioning rule", location)
-        lookup = self.get_lookup_(location, CursivePosBuilder)
-        lookup.add_attachment(
-            location,
-            glyphclass,
-            self.makeOpenTypeAnchor(location, entryAnchor),
-            self.makeOpenTypeAnchor(location, exitAnchor),
-        )
-
-    # GPOS 4
-    def add_mark_base_pos(self, location, bases, marks):
-        builder = self.get_lookup_(location, MarkBasePosBuilder)
-        self.add_marks_(location, builder, marks)
-        if not bases:
-            raise FeatureLibError("Empty glyph class in positioning rule", location)
-        for baseAnchor, markClass in marks:
-            otBaseAnchor = self.makeOpenTypeAnchor(location, baseAnchor)
-            for base in bases:
-                builder.bases.setdefault(base, {})[markClass.name] = otBaseAnchor
-
-    # GPOS 5
-    def add_mark_lig_pos(self, location, ligatures, components):
-        builder = self.get_lookup_(location, MarkLigPosBuilder)
-        componentAnchors = []
-        if not ligatures:
-            raise FeatureLibError("Empty glyph class in positioning rule", location)
-        for marks in components:
-            anchors = {}
-            self.add_marks_(location, builder, marks)
-            for ligAnchor, markClass in marks:
-                anchors[markClass.name] = self.makeOpenTypeAnchor(location, ligAnchor)
-            componentAnchors.append(anchors)
-        for glyph in ligatures:
-            builder.ligatures[glyph] = componentAnchors
-
-    # GPOS 6
-    def add_mark_mark_pos(self, location, baseMarks, marks):
-        builder = self.get_lookup_(location, MarkMarkPosBuilder)
-        self.add_marks_(location, builder, marks)
-        if not baseMarks:
-            raise FeatureLibError("Empty glyph class in positioning rule", location)
-        for baseAnchor, markClass in marks:
-            otBaseAnchor = self.makeOpenTypeAnchor(location, baseAnchor)
-            for baseMark in baseMarks:
-                builder.baseMarks.setdefault(baseMark, {})[
-                    markClass.name
-                ] = otBaseAnchor
-
-    # GPOS 7/8
-    def add_chain_context_pos(self, location, prefix, glyphs, suffix, lookups):
-        if not all(glyphs) or not all(prefix) or not all(suffix):
-            raise FeatureLibError(
-                "Empty glyph class in contextual positioning rule", location
-            )
-        lookup = self.get_lookup_(location, ChainContextPosBuilder)
-        lookup.rules.append(
-            ChainContextualRule(
-                prefix, glyphs, suffix, self.find_lookup_builders_(lookups)
-            )
-        )
-
-    def add_single_pos_chained_(self, location, prefix, suffix, pos):
-        if not pos or not all(prefix) or not all(suffix):
-            raise FeatureLibError(
-                "Empty glyph class in contextual positioning rule", location
-            )
-        # https://github.com/fonttools/fonttools/issues/514
-        chain = self.get_lookup_(location, ChainContextPosBuilder)
-        targets = []
-        for _, _, _, lookups in chain.rules:
-            targets.extend(lookups)
-        subs = []
-        for glyphs, value in pos:
-            if value is None:
-                subs.append(None)
-                continue
-            otValue = self.makeOpenTypeValueRecord(
-                location, value, pairPosContext=False
-            )
-            sub = chain.find_chainable_single_pos(targets, glyphs, otValue)
-            if sub is None:
-                sub = self.get_chained_lookup_(location, SinglePosBuilder)
-                targets.append(sub)
-            for glyph in glyphs:
-                sub.add_pos(location, glyph, otValue)
-            subs.append(sub)
-        assert len(pos) == len(subs), (pos, subs)
-        chain.rules.append(
-            ChainContextualRule(prefix, [g for g, v in pos], suffix, subs)
-        )
-
-    def add_marks_(self, location, lookupBuilder, marks):
-        """Helper for add_mark_{base,liga,mark}_pos."""
-        for _, markClass in marks:
-            for markClassDef in markClass.definitions:
-                for mark in markClassDef.glyphs.glyphSet():
-                    if mark not in lookupBuilder.marks:
-                        otMarkAnchor = self.makeOpenTypeAnchor(
-                            location, markClassDef.anchor
-                        )
-                        lookupBuilder.marks[mark] = (markClass.name, otMarkAnchor)
-                    else:
-                        existingMarkClass = lookupBuilder.marks[mark][0]
-                        if markClass.name != existingMarkClass:
-                            raise FeatureLibError(
-                                "Glyph %s cannot be in both @%s and @%s"
-                                % (mark, existingMarkClass, markClass.name),
-                                location,
-                            )
-
-    def add_subtable_break(self, location):
-        self.cur_lookup_.add_subtable_break(location)
-
-    def setGlyphClass_(self, location, glyph, glyphClass):
-        oldClass, oldLocation = self.glyphClassDefs_.get(glyph, (None, None))
-        if oldClass and oldClass != glyphClass:
-            raise FeatureLibError(
-                "Glyph %s was assigned to a different class at %s"
-                % (glyph, oldLocation),
-                location,
-            )
-        self.glyphClassDefs_[glyph] = (glyphClass, location)
-
-    def add_glyphClassDef(
-        self, location, baseGlyphs, ligatureGlyphs, markGlyphs, componentGlyphs
-    ):
-        for glyph in baseGlyphs:
-            self.setGlyphClass_(location, glyph, 1)
-        for glyph in ligatureGlyphs:
-            self.setGlyphClass_(location, glyph, 2)
-        for glyph in markGlyphs:
-            self.setGlyphClass_(location, glyph, 3)
-        for glyph in componentGlyphs:
-            self.setGlyphClass_(location, glyph, 4)
-
-    def add_ligatureCaretByIndex_(self, location, glyphs, carets):
-        for glyph in glyphs:
-            if glyph not in self.ligCaretPoints_:
-                self.ligCaretPoints_[glyph] = carets
-
-    def makeLigCaret(self, location, caret):
-        if not isinstance(caret, VariableScalar):
-            return caret
-        default, device = self.makeVariablePos(location, caret)
-        if device is not None:
-            return (default, device)
-        return default
-
-    def add_ligatureCaretByPos_(self, location, glyphs, carets):
-        carets = [self.makeLigCaret(location, caret) for caret in carets]
-        for glyph in glyphs:
-            if glyph not in self.ligCaretCoords_:
-                self.ligCaretCoords_[glyph] = carets
-
-    def add_name_record(self, location, nameID, platformID, platEncID, langID, string):
-        self.names_.append([nameID, platformID, platEncID, langID, string])
-
-    def add_os2_field(self, key, value):
-        self.os2_[key] = value
-
-    def add_hhea_field(self, key, value):
-        self.hhea_[key] = value
-
-    def add_vhea_field(self, key, value):
-        self.vhea_[key] = value
-
-    def add_conditionset(self, location, key, value):
-        if "fvar" not in self.font:
-            raise FeatureLibError(
-                "Cannot add feature variations to a font without an 'fvar' table",
-                location,
-            )
-
-        # Normalize
-        axisMap = {
-            axis.axisTag: (axis.minValue, axis.defaultValue, axis.maxValue)
-            for axis in self.axes
-        }
-
-        value = {
-            tag: (
-                normalizeValue(bottom, axisMap[tag]),
-                normalizeValue(top, axisMap[tag]),
-            )
-            for tag, (bottom, top) in value.items()
-        }
-
-        # NOTE: This might result in rounding errors (off-by-ones) compared to
-        # rules in Designspace files, since we're working with what's in the
-        # `avar` table rather than the original values.
-        if "avar" in self.font:
-            mapping = self.font["avar"].segments
-            value = {
-                axis: tuple(
-                    piecewiseLinearMap(v, mapping[axis]) if axis in mapping else v
-                    for v in condition_range
-                )
-                for axis, condition_range in value.items()
-            }
-
-        self.conditionsets_[key] = value
-
-    def makeVariablePos(self, location, varscalar):
-        if not self.varstorebuilder:
-            raise FeatureLibError(
-                "Can't define a variable scalar in a non-variable font", location
-            )
-
-        varscalar.axes = self.axes
-        if not varscalar.does_vary:
-            return varscalar.default, None
-
-        default, index = varscalar.add_to_variation_store(
-            self.varstorebuilder, self.model_cache, self.font.get("avar")
-        )
-
-        device = None
-        if index is not None and index != 0xFFFFFFFF:
-            device = buildVarDevTable(index)
-
-        return default, device
-
-    def makeOpenTypeAnchor(self, location, anchor):
-        """ast.Anchor --> otTables.Anchor"""
-        if anchor is None:
-            return None
-        variable = False
-        deviceX, deviceY = None, None
-        if anchor.xDeviceTable is not None:
-            deviceX = otl.buildDevice(dict(anchor.xDeviceTable))
-        if anchor.yDeviceTable is not None:
-            deviceY = otl.buildDevice(dict(anchor.yDeviceTable))
-        for dim in ("x", "y"):
-            varscalar = getattr(anchor, dim)
-            if not isinstance(varscalar, VariableScalar):
-                continue
-            if getattr(anchor, dim + "DeviceTable") is not None:
-                raise FeatureLibError(
-                    "Can't define a device coordinate and variable scalar", location
-                )
-            default, device = self.makeVariablePos(location, varscalar)
-            setattr(anchor, dim, default)
-            if device is not None:
-                if dim == "x":
-                    deviceX = device
-                else:
-                    deviceY = device
-                variable = True
-
-        otlanchor = otl.buildAnchor(
-            anchor.x, anchor.y, anchor.contourpoint, deviceX, deviceY
-        )
-        if variable:
-            otlanchor.Format = 3
-        return otlanchor
-
-    _VALUEREC_ATTRS = {
-        name[0].lower() + name[1:]: (name, isDevice)
-        for _, name, isDevice, _ in otBase.valueRecordFormat
-        if not name.startswith("Reserved")
-    }
-
-    def makeOpenTypeValueRecord(self, location, v, pairPosContext):
-        """ast.ValueRecord --> otBase.ValueRecord"""
-        if not v:
-            return None
-
-        vr = {}
-        for astName, (otName, isDevice) in self._VALUEREC_ATTRS.items():
-            val = getattr(v, astName, None)
-            if not val:
-                continue
-            if isDevice:
-                vr[otName] = otl.buildDevice(dict(val))
-            elif isinstance(val, VariableScalar):
-                otDeviceName = otName[0:4] + "Device"
-                feaDeviceName = otDeviceName[0].lower() + otDeviceName[1:]
-                if getattr(v, feaDeviceName):
-                    raise FeatureLibError(
-                        "Can't define a device coordinate and variable scalar", location
-                    )
-                vr[otName], device = self.makeVariablePos(location, val)
-                if device is not None:
-                    vr[otDeviceName] = device
-            else:
-                vr[otName] = val
-
-        if pairPosContext and not vr:
-            vr = {"YAdvance": 0} if v.vertical else {"XAdvance": 0}
-        valRec = otl.buildValue(vr)
-        return valRec

spaces/DaleChen/AutoGPT/autogpt/commands/file_operations.py

@@ -1,267 +0,0 @@
-"""File operations for AutoGPT"""
-from __future__ import annotations
-
-import os
-import os.path
-from typing import Generator
-
-import requests
-from colorama import Back, Fore
-from requests.adapters import HTTPAdapter, Retry
-
-from autogpt.spinner import Spinner
-from autogpt.utils import readable_file_size
-from autogpt.workspace import WORKSPACE_PATH, path_in_workspace
-
-LOG_FILE = "file_logger.txt"
-LOG_FILE_PATH = WORKSPACE_PATH / LOG_FILE
-
-
-def check_duplicate_operation(operation: str, filename: str) -> bool:
-    """Check if the operation has already been performed on the given file
-
-    Args:
-        operation (str): The operation to check for
-        filename (str): The name of the file to check for
-
-    Returns:
-        bool: True if the operation has already been performed on the file
-    """
-    log_content = read_file(LOG_FILE)
-    log_entry = f"{operation}: {filename}\n"
-    return log_entry in log_content
-
-
-def log_operation(operation: str, filename: str) -> None:
-    """Log the file operation to the file_logger.txt
-
-    Args:
-        operation (str): The operation to log
-        filename (str): The name of the file the operation was performed on
-    """
-    log_entry = f"{operation}: {filename}\n"
-
-    # Create the log file if it doesn't exist
-    if not os.path.exists(LOG_FILE_PATH):
-        with open(LOG_FILE_PATH, "w", encoding="utf-8") as f:
-            f.write("File Operation Logger ")
-
-    append_to_file(LOG_FILE, log_entry, shouldLog=False)
-
-
-def split_file(
-    content: str, max_length: int = 4000, overlap: int = 0
-) -> Generator[str, None, None]:
-    """
-    Split text into chunks of a specified maximum length with a specified overlap
-    between chunks.
-
-    :param content: The input text to be split into chunks
-    :param max_length: The maximum length of each chunk,
-        default is 4000 (about 1k token)
-    :param overlap: The number of overlapping characters between chunks,
-        default is no overlap
-    :return: A generator yielding chunks of text
-    """
-    start = 0
-    content_length = len(content)
-
-    while start < content_length:
-        end = start + max_length
-        if end + overlap < content_length:
-            chunk = content[start : end + overlap - 1]
-        else:
-            chunk = content[start:content_length]
-
-            # Account for the case where the last chunk is shorter than the overlap, so it has already been consumed
-            if len(chunk) <= overlap:
-                break
-
-        yield chunk
-        start += max_length - overlap
-
-
-def read_file(filename: str) -> str:
-    """Read a file and return the contents
-
-    Args:
-        filename (str): The name of the file to read
-
-    Returns:
-        str: The contents of the file
-    """
-    try:
-        filepath = path_in_workspace(filename)
-        with open(filepath, "r", encoding="utf-8") as f:
-            content = f.read()
-        return content
-    except Exception as e:
-        return f"Error: {str(e)}"
-
-
-def ingest_file(
-    filename: str, memory, max_length: int = 4000, overlap: int = 200
-) -> None:
-    """
-    Ingest a file by reading its content, splitting it into chunks with a specified
-    maximum length and overlap, and adding the chunks to the memory storage.
-
-    :param filename: The name of the file to ingest
-    :param memory: An object with an add() method to store the chunks in memory
-    :param max_length: The maximum length of each chunk, default is 4000
-    :param overlap: The number of overlapping characters between chunks, default is 200
-    """
-    try:
-        print(f"Working with file {filename}")
-        content = read_file(filename)
-        content_length = len(content)
-        print(f"File length: {content_length} characters")
-
-        chunks = list(split_file(content, max_length=max_length, overlap=overlap))
-
-        num_chunks = len(chunks)
-        for i, chunk in enumerate(chunks):
-            print(f"Ingesting chunk {i + 1} / {num_chunks} into memory")
-            memory_to_add = (
-                f"Filename: {filename}\n" f"Content part#{i + 1}/{num_chunks}: {chunk}"
-            )
-
-            memory.add(memory_to_add)
-
-        print(f"Done ingesting {num_chunks} chunks from {filename}.")
-    except Exception as e:
-        print(f"Error while ingesting file '{filename}': {str(e)}")
-
-
-def write_to_file(filename: str, text: str) -> str:
-    """Write text to a file
-
-    Args:
-        filename (str): The name of the file to write to
-        text (str): The text to write to the file
-
-    Returns:
-        str: A message indicating success or failure
-    """
-    if check_duplicate_operation("write", filename):
-        return "Error: File has already been updated."
-    try:
-        filepath = path_in_workspace(filename)
-        directory = os.path.dirname(filepath)
-        if not os.path.exists(directory):
-            os.makedirs(directory)
-        with open(filepath, "w", encoding="utf-8") as f:
-            f.write(text)
-        log_operation("write", filename)
-        return "File written to successfully."
-    except Exception as e:
-        return f"Error: {str(e)}"
-
-
-def append_to_file(filename: str, text: str, shouldLog: bool = True) -> str:
-    """Append text to a file
-
-    Args:
-        filename (str): The name of the file to append to
-        text (str): The text to append to the file
-
-    Returns:
-        str: A message indicating success or failure
-    """
-    try:
-        filepath = path_in_workspace(filename)
-        with open(filepath, "a") as f:
-            f.write(text)
-
-        if shouldLog:
-            log_operation("append", filename)
-
-        return "Text appended successfully."
-    except Exception as e:
-        return f"Error: {str(e)}"
-
-
-def delete_file(filename: str) -> str:
-    """Delete a file
-
-    Args:
-        filename (str): The name of the file to delete
-
-    Returns:
-        str: A message indicating success or failure
-    """
-    if check_duplicate_operation("delete", filename):
-        return "Error: File has already been deleted."
-    try:
-        filepath = path_in_workspace(filename)
-        os.remove(filepath)
-        log_operation("delete", filename)
-        return "File deleted successfully."
-    except Exception as e:
-        return f"Error: {str(e)}"
-
-
-def search_files(directory: str) -> list[str]:
-    """Search for files in a directory
-
-    Args:
-        directory (str): The directory to search in
-
-    Returns:
-        list[str]: A list of files found in the directory
-    """
-    found_files = []
-
-    if directory in {"", "/"}:
-        search_directory = WORKSPACE_PATH
-    else:
-        search_directory = path_in_workspace(directory)
-
-    for root, _, files in os.walk(search_directory):
-        for file in files:
-            if file.startswith("."):
-                continue
-            relative_path = os.path.relpath(os.path.join(root, file), WORKSPACE_PATH)
-            found_files.append(relative_path)
-
-    return found_files
-
-
-def download_file(url, filename):
-    """Downloads a file
-    Args:
-        url (str): URL of the file to download
-        filename (str): Filename to save the file as
-    """
-    safe_filename = path_in_workspace(filename)
-    try:
-        message = f"{Fore.YELLOW}Downloading file from {Back.LIGHTBLUE_EX}{url}{Back.RESET}{Fore.RESET}"
-        with Spinner(message) as spinner:
-            session = requests.Session()
-            retry = Retry(total=3, backoff_factor=1, status_forcelist=[502, 503, 504])
-            adapter = HTTPAdapter(max_retries=retry)
-            session.mount("http://", adapter)
-            session.mount("https://", adapter)
-
-            total_size = 0
-            downloaded_size = 0
-
-            with session.get(url, allow_redirects=True, stream=True) as r:
-                r.raise_for_status()
-                total_size = int(r.headers.get("Content-Length", 0))
-                downloaded_size = 0
-
-                with open(safe_filename, "wb") as f:
-                    for chunk in r.iter_content(chunk_size=8192):
-                        f.write(chunk)
-                        downloaded_size += len(chunk)
-
-                        # Update the progress message
-                        progress = f"{readable_file_size(downloaded_size)} / {readable_file_size(total_size)}"
-                        spinner.update_message(f"{message} {progress}")
-
-            return f'Successfully downloaded and locally stored file: "{filename}"! (Size: {readable_file_size(total_size)})'
-    except requests.HTTPError as e:
-        return f"Got an HTTP Error whilst trying to download file: {e}"
-    except Exception as e:
-        return "Error: " + str(e)

spaces/Datasculptor/DescriptionGPT/tools/merge_lvis_coco.py

@@ -1,202 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-from collections import defaultdict
-import torch
-import sys
-import json
-import numpy as np
-
-from detectron2.structures import Boxes, pairwise_iou
-COCO_PATH = 'datasets/coco/annotations/instances_train2017.json'
-IMG_PATH = 'datasets/coco/train2017/'
-LVIS_PATH = 'datasets/lvis/lvis_v1_train.json'
-NO_SEG = False
-if NO_SEG:
-    SAVE_PATH = 'datasets/lvis/lvis_v1_train+coco_box.json'
-else:
-    SAVE_PATH = 'datasets/lvis/lvis_v1_train+coco_mask.json'
-THRESH = 0.7
-DEBUG = False
-
-# This mapping is extracted from the official LVIS mapping:
-# https://github.com/lvis-dataset/lvis-api/blob/master/data/coco_to_synset.json
-COCO_SYNSET_CATEGORIES = [
-    {"synset": "person.n.01", "coco_cat_id": 1},
-    {"synset": "bicycle.n.01", "coco_cat_id": 2},
-    {"synset": "car.n.01", "coco_cat_id": 3},
-    {"synset": "motorcycle.n.01", "coco_cat_id": 4},
-    {"synset": "airplane.n.01", "coco_cat_id": 5},
-    {"synset": "bus.n.01", "coco_cat_id": 6},
-    {"synset": "train.n.01", "coco_cat_id": 7},
-    {"synset": "truck.n.01", "coco_cat_id": 8},
-    {"synset": "boat.n.01", "coco_cat_id": 9},
-    {"synset": "traffic_light.n.01", "coco_cat_id": 10},
-    {"synset": "fireplug.n.01", "coco_cat_id": 11},
-    {"synset": "stop_sign.n.01", "coco_cat_id": 13},
-    {"synset": "parking_meter.n.01", "coco_cat_id": 14},
-    {"synset": "bench.n.01", "coco_cat_id": 15},
-    {"synset": "bird.n.01", "coco_cat_id": 16},
-    {"synset": "cat.n.01", "coco_cat_id": 17},
-    {"synset": "dog.n.01", "coco_cat_id": 18},
-    {"synset": "horse.n.01", "coco_cat_id": 19},
-    {"synset": "sheep.n.01", "coco_cat_id": 20},
-    {"synset": "beef.n.01", "coco_cat_id": 21},
-    {"synset": "elephant.n.01", "coco_cat_id": 22},
-    {"synset": "bear.n.01", "coco_cat_id": 23},
-    {"synset": "zebra.n.01", "coco_cat_id": 24},
-    {"synset": "giraffe.n.01", "coco_cat_id": 25},
-    {"synset": "backpack.n.01", "coco_cat_id": 27},
-    {"synset": "umbrella.n.01", "coco_cat_id": 28},
-    {"synset": "bag.n.04", "coco_cat_id": 31},
-    {"synset": "necktie.n.01", "coco_cat_id": 32},
-    {"synset": "bag.n.06", "coco_cat_id": 33},
-    {"synset": "frisbee.n.01", "coco_cat_id": 34},
-    {"synset": "ski.n.01", "coco_cat_id": 35},
-    {"synset": "snowboard.n.01", "coco_cat_id": 36},
-    {"synset": "ball.n.06", "coco_cat_id": 37},
-    {"synset": "kite.n.03", "coco_cat_id": 38},
-    {"synset": "baseball_bat.n.01", "coco_cat_id": 39},
-    {"synset": "baseball_glove.n.01", "coco_cat_id": 40},
-    {"synset": "skateboard.n.01", "coco_cat_id": 41},
-    {"synset": "surfboard.n.01", "coco_cat_id": 42},
-    {"synset": "tennis_racket.n.01", "coco_cat_id": 43},
-    {"synset": "bottle.n.01", "coco_cat_id": 44},
-    {"synset": "wineglass.n.01", "coco_cat_id": 46},
-    {"synset": "cup.n.01", "coco_cat_id": 47},
-    {"synset": "fork.n.01", "coco_cat_id": 48},
-    {"synset": "knife.n.01", "coco_cat_id": 49},
-    {"synset": "spoon.n.01", "coco_cat_id": 50},
-    {"synset": "bowl.n.03", "coco_cat_id": 51},
-    {"synset": "banana.n.02", "coco_cat_id": 52},
-    {"synset": "apple.n.01", "coco_cat_id": 53},
-    {"synset": "sandwich.n.01", "coco_cat_id": 54},
-    {"synset": "orange.n.01", "coco_cat_id": 55},
-    {"synset": "broccoli.n.01", "coco_cat_id": 56},
-    {"synset": "carrot.n.01", "coco_cat_id": 57},
-    # {"synset": "frank.n.02", "coco_cat_id": 58},
-    {"synset": "sausage.n.01", "coco_cat_id": 58},
-    {"synset": "pizza.n.01", "coco_cat_id": 59},
-    {"synset": "doughnut.n.02", "coco_cat_id": 60},
-    {"synset": "cake.n.03", "coco_cat_id": 61},
-    {"synset": "chair.n.01", "coco_cat_id": 62},
-    {"synset": "sofa.n.01", "coco_cat_id": 63},
-    {"synset": "pot.n.04", "coco_cat_id": 64},
-    {"synset": "bed.n.01", "coco_cat_id": 65},
-    {"synset": "dining_table.n.01", "coco_cat_id": 67},
-    {"synset": "toilet.n.02", "coco_cat_id": 70},
-    {"synset": "television_receiver.n.01", "coco_cat_id": 72},
-    {"synset": "laptop.n.01", "coco_cat_id": 73},
-    {"synset": "mouse.n.04", "coco_cat_id": 74},
-    {"synset": "remote_control.n.01", "coco_cat_id": 75},
-    {"synset": "computer_keyboard.n.01", "coco_cat_id": 76},
-    {"synset": "cellular_telephone.n.01", "coco_cat_id": 77},
-    {"synset": "microwave.n.02", "coco_cat_id": 78},
-    {"synset": "oven.n.01", "coco_cat_id": 79},
-    {"synset": "toaster.n.02", "coco_cat_id": 80},
-    {"synset": "sink.n.01", "coco_cat_id": 81},
-    {"synset": "electric_refrigerator.n.01", "coco_cat_id": 82},
-    {"synset": "book.n.01", "coco_cat_id": 84},
-    {"synset": "clock.n.01", "coco_cat_id": 85},
-    {"synset": "vase.n.01", "coco_cat_id": 86},
-    {"synset": "scissors.n.01", "coco_cat_id": 87},
-    {"synset": "teddy.n.01", "coco_cat_id": 88},
-    {"synset": "hand_blower.n.01", "coco_cat_id": 89},
-    {"synset": "toothbrush.n.01", "coco_cat_id": 90},
-]
-
-
-def get_bbox(ann):
-    bbox = ann['bbox']
-    return [bbox[0], bbox[1], bbox[0] + bbox[2], bbox[1] + bbox[3]]
-
-
-if __name__ == '__main__':
-    file_name_key = 'file_name' if 'v0.5' in LVIS_PATH else 'coco_url'
-    coco_data = json.load(open(COCO_PATH, 'r'))
-    lvis_data = json.load(open(LVIS_PATH, 'r'))
-
-    coco_cats = coco_data['categories']
-    lvis_cats = lvis_data['categories']
-
-    num_find = 0
-    num_not_find = 0
-    num_twice = 0
-    coco2lviscats = {}
-    synset2lvisid = {x['synset']: x['id'] for x in lvis_cats}
-    # cocoid2synset = {x['coco_cat_id']: x['synset'] for x in COCO_SYNSET_CATEGORIES}
-    coco2lviscats = {x['coco_cat_id']: synset2lvisid[x['synset']] \
-        for x in COCO_SYNSET_CATEGORIES if x['synset'] in synset2lvisid}
-    print(len(coco2lviscats))
-    
-    lvis_file2id = {x[file_name_key][-16:]: x['id'] for x in lvis_data['images']}
-    lvis_id2img = {x['id']: x for x in lvis_data['images']}
-    lvis_catid2name = {x['id']: x['name'] for x in lvis_data['categories']}
-
-    coco_file2anns = {}
-    coco_id2img = {x['id']: x for x in coco_data['images']}
-    coco_img2anns = defaultdict(list)
-    for ann in coco_data['annotations']:
-        coco_img = coco_id2img[ann['image_id']]
-        file_name = coco_img['file_name'][-16:]
-        if ann['category_id'] in coco2lviscats and \
-            file_name in lvis_file2id:
-            lvis_image_id = lvis_file2id[file_name]
-            lvis_image = lvis_id2img[lvis_image_id]
-            lvis_cat_id = coco2lviscats[ann['category_id']]
-            if lvis_cat_id in lvis_image['neg_category_ids']:
-                continue
-                if DEBUG:
-                    import cv2
-                    img_path = IMG_PATH + file_name
-                    img = cv2.imread(img_path)
-                    print(lvis_catid2name[lvis_cat_id])
-                    print('neg', [lvis_catid2name[x] for x in lvis_image['neg_category_ids']])
-                    cv2.imshow('img', img)
-                    cv2.waitKey()
-            ann['category_id'] = lvis_cat_id
-            ann['image_id'] = lvis_image_id
-            coco_img2anns[file_name].append(ann)
-    
-    lvis_img2anns = defaultdict(list)
-    for ann in lvis_data['annotations']:
-        lvis_img = lvis_id2img[ann['image_id']]
-        file_name = lvis_img[file_name_key][-16:]
-        lvis_img2anns[file_name].append(ann)
-
-    ann_id_count = 0
-    anns = []
-    for file_name in lvis_img2anns:
-        coco_anns = coco_img2anns[file_name]
-        lvis_anns = lvis_img2anns[file_name]
-        ious = pairwise_iou(
-            Boxes(torch.tensor([get_bbox(x) for x in coco_anns])), 
-            Boxes(torch.tensor([get_bbox(x) for x in lvis_anns]))
-        )
-
-        for ann in lvis_anns:
-            ann_id_count = ann_id_count + 1
-            ann['id'] = ann_id_count
-            anns.append(ann)
-
-        for i, ann in enumerate(coco_anns):
-            if len(ious[i]) == 0 or ious[i].max() < THRESH:
-                ann_id_count = ann_id_count + 1
-                ann['id'] = ann_id_count
-                anns.append(ann)
-            else:
-                duplicated = False
-                for j in range(len(ious[i])):
-                    if ious[i, j] >= THRESH and \
-                        coco_anns[i]['category_id'] == lvis_anns[j]['category_id']:
-                        duplicated = True
-                if not duplicated:
-                    ann_id_count = ann_id_count + 1
-                    ann['id'] = ann_id_count
-                    anns.append(ann)
-    if NO_SEG:
-        for ann in anns:
-            del ann['segmentation']
-    lvis_data['annotations'] = anns
-    
-    print('# Images', len(lvis_data['images']))
-    print('# Anns', len(lvis_data['annotations']))
-    json.dump(lvis_data, open(SAVE_PATH, 'w'))

spaces/Detomo/ai-comic-generation/src/lib/replaceNonWhiteWithTransparent.ts

@@ -1,46 +0,0 @@
-export function replaceNonWhiteWithTransparent(imageBase64: string): Promise<string> {
-    return new Promise((resolve, reject) => {
-        const img = new Image();
-        img.onload = () => {
-            const canvas = document.createElement('canvas');
-            const ctx = canvas.getContext('2d');
-            if (!ctx) {
-                reject('Unable to get canvas context');
-                return;
-            }
-
-            const ratio = window.devicePixelRatio || 1;
-            canvas.width = img.width * ratio;
-            canvas.height = img.height * ratio;
-            ctx.scale(ratio, ratio);
-
-            ctx.drawImage(img, 0, 0);
-
-            const imageData = ctx.getImageData(0, 0, img.width, img.height);
-            const data = imageData.data;
-            console.log("ok")
-
-            for (let i = 0; i < data.length; i += 4) {
-                if (data[i] === 255 && data[i + 1] === 255 && data[i + 2] === 255) {
-                    // Change white (also shades of grays) pixels to black
-                    data[i] = 0;
-                    data[i + 1] = 0;
-                    data[i + 2] = 0;
-                } else {
-                    // Change all other pixels to transparent
-                    data[i + 3] = 0;
-                }
-            }
-
-            ctx.putImageData(imageData, 0, 0);
-
-            resolve(canvas.toDataURL());
-        };
-
-        img.onerror = (err) => {
-            reject(err);
-        };
-
-        img.src = imageBase64;
-    });
-}